CONSERVATION IN SOUTHEASTERN PERUVIAN

AMAZON: TWO APPROACHES

RENZO GIUDICE

Thesis submitted for the degree of MSc by Research

University of East Anglia

School of Biological Sciences

September 2009

This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognize that its copyright rests with the author and that no quotation from the thesis, nor any information derived therefrom, may be published without the author's prior, written consent. !

II

SUMMARY

Human population growth, immigration, and the development of infrastructure

threaten the integrity of southeastern Peruvian Amazon and its forest ecosystems

services. The present work presents two studies that explore two different

approaches to support ongoing conservation efforts in the region. First, using the

theory of common pool resources I analyzed the functioning of a common

property regime that allows a group of individuals to use and govern a forestry

resource in a sustainable, efficient, and equitable manner. Key attributes of the

resource and its units, users, and the governance system, as well as of the social

and economic contexts, were identified to facilitate this functioning. As results

indicate, the strengthening of common property regimes for managing natural

resources could prove useful for resolving people-park conflicts and maximizing

benefits from the flow of natural resources out of protected areas into buffer

zones. Second, I developed a spatially explicit model based on (1) the effect of

population growth and secondary roads on deforestation rates and (2)

DINAMICA, a stochastic cellular automata model that simulates deforestation

based on a set of spatial variables. The model successfully allowed defining a

baseline projection of the amount and location of expected deforestation. This

baseline is necessary for establishing RED projects (Reducing Emissions from

Deforestation) and negotiating the corresponding carbon credits. Considering the

relatively high potential revenues (up to US$1612.4M) that would be eventually

obtained, a regional RED project could compensate the opportunity costs of

preserving large tracks of forests within the region.

III

ACKNOWLEDGEMENTS

First of all, to Dr Douglas W. Yu, my competent supervisor, for his constant

support and patient dedication to teach me how to correct and improve my work.

Some of his advices were not only utterly helpful for designing my research,

analyzing results, and writing the thesis, but also for life.

To my friend and colleague Chris Kirkby, for the long hours of important

discussions on the development of the deforestation model and the use of

DINAMICA.

To Dr Britaldo Soares-Filho, who kindly welcomed me at the Centro de

Sensoramiento Remoto at the Universidade Federal de Minas Gerais where I

developed part of the deforestation model and to his team, especially to Rafaella

Almeida Silvestrini and Hermann Rodrigues, who patiently taught me how to

improve my use and understanding of DINAMICA.

To Dr Rob Williams from the Frankfurt Zoological Society, for his motivation

and invaluable help provided to undertake the research at Boca Manu.

To my family, for their unconditional and infinite support in all conceivable

aspects. For patiently dealing with my stressful and irritable days. Not a single

piece of this work would have been possible without their encouraging

motivation.

To Mickelly, my partner in love, life, and friendship, without doubt this work is

product of her effort too.

IV

To Andrea Santy and the Russell E. Train Education for Nature Program at

World Wildlife Fund, for providing the Fellowship to attend University of East

Anglia.

To the University of East Anglia, for providing an International Scholarship to

reduce tuition fees.

V

Table of Contents!

Chapter 1:

Tropical biodiversity protection from a ‘harbor gang:’ a case study of

the riverine tree capture system in Manu National Park, Peru...............1

SUMMARY ..................................................................................................1

Introduction ..................................................................................................3

Methods.........................................................................................................9 Study site and brief history ................................................................................ 9 Interviews & Questionnaires ........................................................................... 12 Total trees captured.......................................................................................... 14 Costs, revenues, and profits ............................................................................. 15

The effect of assigning single sales categories to appropriators .................... 18

Results .........................................................................................................19 The history of the tree capture activity........................................................... 19

Users and the appropriation of floating trees ................................................. 19 Organization of the tree capture activity ........................................................ 22 Population growth and its consequences ........................................................ 25 A new institutional setting.............................................................................. 29 Evolution of the rotation system..................................................................... 31 Perceived benefits and costs of the rotation system ....................................... 32 Rule breaking, sanctions, and monitoring ...................................................... 33

Financial Benefits.............................................................................................. 35 The number and volume of captured trees between 2005 and 2007 .............. 35 The value of logs, lumber, and boats for 2006-2007 season .......................... 37

Discussion....................................................................................................38 Common property regimes and the Boca Manu system ............................... 39 Factors favoring the emergence of a CPR regime ......................................... 43 Monitoring and sanctioning ............................................................................. 48 A demographic challenge to the future of the Boca Manu CPR .................. 50

Conclusions .................................................................................................50

Acknowledgements.....................................................................................52

References ...................................................................................................54

Figures and Tables .....................................................................................59

Appendix 1 ..................................................................................................74

Chapter 2:

Modeling the effect of population growth and secondary road expansion

along the new Interoceanica Sur highway of southeastern Peruvian

Amazon........................................................................................................85

SUMMARY ................................................................................................85

Introduction ................................................................................................88

Methods.......................................................................................................93 Study area and context ..................................................................................... 93

VI

Model development........................................................................................... 95 General approach............................................................................................ 95 Relationship between population, population growth and deforestation rates........................................................................................................................ 96 Deforestation allocation................................................................................ 102

Results .......................................................................................................126 Total deforestation .......................................................................................... 127 Deforestation within PAs................................................................................ 128

Tambopata National Reserve (TNR)............................................................ 129 Bahuaja Sonene National Park (BSNP) ....................................................... 130 Amarakaeri Communal Reserve (ACR)....................................................... 130 Manu National Park (MNP) ......................................................................... 131

Deforestation within FCs................................................................................ 131

Discussion..................................................................................................132

Acknowledments ......................................................................................138

References .................................................................................................139

Figures and Tables ...................................................................................147

Appendix 1 ................................................................................................193

CHAPTER 1

TROPICAL BIODIVERSITY PROTECTION FROM A ‘HARBOR

GANG:’ A CASE STUDY OF THE RIVERINE TREE CAPTURE

SYSTEM IN MANU NATIONAL PARK, PERU

RENZO GIUDICE1, DOUGLAS W. YU1,2

1 School of Biological Sciences, University of East Anglia, Norwich, Norfolk NR47TJ, UK

2 State Key Laboratory of Genetic Resources and Evolution; Ecology, Conservation, and

Environment Center (ECEC), Kunming Institute of Zoology, Chinese Academy of Science,

Kunming, Yunnan, 650223, China

SUMMARY

Human population growth, immigration, and the development of

infrastructure threaten the integrity of natural protected areas in tropical

regions. We reconstructed the history and functioning of a communally

regulated riverine tree-capture system on the border of Manu National Park,

Peru, and analyzed it using the theory of common pool resources. Our thesis

is that the 'roving bandits' of the pre-park era have developed into a 'harbor-

gang', with potential protective benefits for the park itself. A shared past and

successful history in managing a common resource, local arenas for

harvesting and conflict resolution, state-level recognition, a small number of

participants, the existence of mutual monitoring and sanctioning, and a set

of rules that limit access and govern behavior have all facilitated the

emergence and evolution of a common management regime and increased

efficiency and equity. We suggest that the strengthening of common

2

property regimes for managing natural resources could prove useful for

resolving people-park conflicts and maximizing benefits from the flow of

natural resources out of a protected area into buffer zones.

Keywords: Amazon, common property management regimes, common pool

resources, people-park conflicts, Boca Manu

3

INTRODUCTION

To social scientists, an institution is a set of rules and norms that organizes

activities and interactions among individuals by affecting the type of

information they can access and the kinds of incentives they face (Ferris &

Tang 1993; Smith 2002; Dietz et al. 2003). Rules are understood as

prescriptions that require, forbid, or permit specific actions and are

commonly known and used by a group of individuals to achieve order and

predictability within particular situations (Ostrom 1986), whereas shared

individuals’ perceptions of what actions are proper or not are defined as

norms (Crawford & Ostrom 1995; Smith 2002). Among institutions,

common pool resource (CPR) institutions deal with the issue of how

individuals organize their activities so as to avoid or mitigate the negative

outcomes (such as resource depletion and rent dissipation) of independent

action, when trying to maximize their own private benefits. CPRs are

natural or man-made resources that are sufficiently large so that it is costly

to exclude many potential users, and where one individual’s use of the

resource subtracts from its use by others (Ostrom 1990).

The process and consequences of organizing these institutions are

understood as collective actions, in which a group of individuals decides to

coordinate behavior to achieve a collective benefit (Ostrom 1990; Smith

2002). In the past, however, it was assumed that individuals were unable to

establish CPR institutions and therefore avoid the consequences of self-

interested behavior (Hardin 1968). Under this assumption, the only viable

solutions to CPR problems were thought to be nationalization or

privatization (Ostrom 1990; Quinn et al. 2007). The theoretical argument

4

that the lack of either state or private ownership always implies open access

(i.e. no limits on the use of resources) and leads inexorably to the ‘tragedy

of the commons’ (Hardin 1968), contributed to this assumption (Ostrom

1990).

However, much empirical as well as experimental research has proven the

contrary. Groups of individuals do engage in collective action to restrict

access to the commons by establishing rules for appropriation, monitoring,

and punishment activities, rules that apply to both the resource and the

institution itself (Ostrom 1990; Berkes 1992; Ostrom et al. 1994; White and

Runge 1995; Agrawal 2002; McCay 2002; Cardenas 2004; Quinn et al.

2007; Bowles 2008) (see http://dlc.dlib.indiana.edu/: accessed: February 15,

2009). Much progress has been made in identifying the types of rules,

resources, and resource users that are associated with successful collective

actions (Ostrom 1990; Ostrom et al. 1994; Agrawal 2002), but the debate

continues about the characteristics of the resource, its users, and the context

that influence the likelihood of success in collective management (Agrawal

2002) and about why and how collective action institutions initially emerge

and survive across time (White & Runge 1995; McCay 2002).

Much of the work trying to answer the question of how individuals engage

in creating and evolving collective actions has been based on the rational

choice theory of human behavior (Ostrom 1990; White & Runge 1995).

Each individual is assumed to have an internally consistent value system, to

be able to calculate the consequences of their choices, and to choose what is

best for their own interest (Dixit & Skeath 2004). If this is so, then each

5

individual should always weigh his or her private costs and benefits and

choose accordingly whether to cooperate or defect in a CPR situation

(Ostrom 1990). Ostrom (1990) uses a broad conception of rational action to

describe changes in human behavior that will lead to coordinated actions

and bases her individual internal choice model on four variables: expected

benefits, expected costs, internal norms, and discount rates. The norms are

affected by the shared norms held by others regarding specific types of

situations (Ostrom 1990; Crawford & Ostrom 1995). Similarly, the context

around any particular situation affects individual’s internal discount rates

(Ostrom 1990). Ostrom (1990) adds however that not all situations admit

the assumption of purely rational choice; in complex situations, individuals

are engaged in a trial and error process to improve the understanding of how

their actions affect costs and benefits.

The emergence of a collective action is also affected by the interdependence

of individuals who share a CPR (Ostrom 1990), thus forcing individuals to

act strategically. If individuals instead act independently, scarcity will likely

be the result, causing total net benefits to be less than those they would have

achieved had they coordinated their actions (Ostrom 1990). The use of

contingent strategies and reciprocity has therefore been recognized as

factors that favor the evolution and survival of cooperation. It is known that

when individuals learn that others are willing to collaborate for the good of

all, they also act cooperatively (Ostrom 1990). Individuals are also known to

undertake costly actions consistent with social norms, expecting that

someday these ‘banked favors’ will be reciprocated (White & Runge 1995).

However, a theoretical explanation about the likelihood of success in

6

collective actions lacks (Ostrom 1990). As an interim step to filling this

theoretical gap, Ostrom (1990) proposed a list of design principles to

describe the core conditions facilitating the achievement of institutional

robustness in CPR regimes. These principles, although probably not

sufficient in themselves to determine success (Morrow & Hull 1996;

Agrawal 2002), have been used to describe and analyze the rules and

relative performance of many CPR regimes (Morrow & Hull 1996; Quinn et

al. 2007).

Another interesting approach is used by White and Runge (1995), who

present a conceptual framework to explain the emergence of collective

action based on individual choices that are strongly embedded in particular

socio-cultural and physical systems. Under this approach, existing

interactions, such as conflicting claims over the resource and an unequal

distribution of benefits among individuals, define the status quo, which,

together with the socio-physical context, determine the factors affecting

three phases in the emergence of collective action. These three phases are:

(1) the challenge to the status quo and proposal of a collective action, (2)

individual choices to either defect or cooperate, and (3) the emergence and

evolution of action. A challenge to the status quo emerges because a current

situation is perceived to be inefficient, unfair, or both, and it involves an act

of either an endogenous or exogenous political leadership, which favors the

creation or redistribution of rights and duties (Guttman 1982). In the second

phase, individuals effectively cast votes to determine their cooperation or

defection, and in the third phase, collective action can emerge, conditional

on the voting result (White & Runge 1995). These three phases are recursive

7

and iterative, determining whether individuals will cooperate or defect and

determining the consequences of these individual choices. Similar to

Ostrom (1990), cooperation is contingent, in that individuals will probably

not undertake a coordinated action unless they believe that others will also

engage in the collective action (White & Runge 1995).

Important remarks about the emergence of CPR institutions are also made

by McCay (2002), who states that institutions for managing commons tend

to arise in situations where conflicting claims to CPR exist and where it is

perceived that there is a risk that access will be lost or that the resource will

deteriorate. He also argues that when trying to understand different scale

linkages among social groups, more attention should be given to the fact

that external forces could play an important and positive role in institutional

changes, such as those provided by governmental and non-governmental

organizations (NGOs) (Ostrom 1990; Morrow & Hull 1996). Finally,

McCay (2002) points out that in order to reduce the negative effects of free

riders, which reduce the number of cooperators and increase their costs,

institutional changes are best taken incrementally, starting small and cheap.

Using the above framework of collective actions for managing common

pool resources, I present and analyze the emergence and evolution of a

common-pool resource management regime in southeastern Amazonian

Peru. The forests lining the Manu watershed rivers in Manu National Park in

Peru provide floating trees of valuable timber species to a group of

individuals who collect and sell these trees. The trees fall into the rivers

because of riverine erosion, and the floating trees constitute the common

8

pool resource units of interest. Tree-capture activity began 45 years ago,

before the establishment of Manu Park in 1973, and at its inception,

behaved as a classic, open-access, free-for-all commons. Over the

intervening years though, many institutional changes have taken place, and

today, there is a closed list of authorized users who take turns to capture

trees, thus increasing profits and equitability.

I describe here how these changes came about, with reference to previous

work on the theory of the commons (Ostrom 1990; Ostrom 1992; Schlager

& Ostrom 1992; Ostrom et al. 1994; White & Runge 1995; Agrawal 2002;

Ostrom et al. 2007), and I present cost and benefit data from the 2006-2007

season regarding the number, volume, and species of trees captured as well

as three final products, raw logs, lumber, and boats, that are produced with

the captured trees. With this information, we derive some insight into how

the capture system has over time changed the efficiency and equity of

benefits distribution among users.

We believe that a theoretical analysis of how collective actions for

managing CPRs emerge and survive in the long run, as presented through

this case study, is of interest for practical conservation action, at least for the

Manu Park administration. Our thesis is that CPR institutions are useful for

managing and maintaining a flow of benefits from protected areas to

neighboring local human populations. Recognizing the conditions and

preconditions for successful CPR institutions could guide the decisions of

protected area managers who wish to avoid undermining such collective

actions, as well as suggesting new arrangements that could improve their

9

functioning, especially in the face of increasing human population growth

rates at the edges of protected areas (Wittemyer et al. 2008).

Immigration towards protected area edges has been suggested as being the

cause of this trend (as opposed to endogenous population growth), driven in

part by an increasing scarcity of ecosystem services far from protected areas

(Wittemyer et al. 2008). If this is true in the Peruvian Amazon, then we

should expect further immigration on the borders of Manu Park, since

deforestation, and, thus, deterioration of ecosystem services, is increasing

outside of protected areas in the region (Oliveira et al. 2007). As has been

shown, human populations bordering protected areas frequently have

negative impacts on biodiversity (Luck 2007). The state alone is unlikely to

be able to prevent immigration to and incursion of protected areas, but we

will suggest here that established common pool resource management

regimes will be able to, especially if they are supported by state (or even in

opposition to the state, see Ascue & Paricahua 2009). CPR regimes by

definition enforce limits on access to public resources, thus benefiting the

members of the CPR and also resulting in protective benefits for the

resource, in this case, protected areas. In the Peruvian Amazon at least, we

suggest that such a set of institutions could forge stewards in local

populations who will look after environmental and economic sustainability

at the edges of protected areas.

METHODS

Study site and brief history

The study area is located on the eastern border of the 1.7M Ha Manu

10

National Park (hereafter, MNP), located in southeastern Amazonian Peru, in

the Department (province) of Madre de Dios (Fig. 1). MNP covers the

watershed of the Manu River, which constitutes the core area of a United

Nations Educational, Scientific and Cultural Organization (UNESCO)

Biosphere Reserve, and is a World Heritage Site. Rainfall is seasonal (ca.

2100-2600 mm rain/yr), and the region is characterized by extensive tropical

rainforest, with a pronounced dry season from July to September (see also,

MacQuarrie 1992).

Two settlements, Boca Manu (12.266º S; 70.912º W) and the Isla de los

Valles Native Community (12.263º S; 70.917º W), are located just outside

MNP, at the mouth of the Manu River (Fig. 1). A Dominican Christian

mission originally established Boca Manu as San Luis del Manu in 1908

(MacQuarrie 1992) in the middle of the rubber boom era (ca. 1895-1917),

followed by depopulation after the collapse of rubber prices in 1921. In the

1950s, Boca Manu was resettled by timber and animal pelt traders. Sawmills

were set up on the lower Manu River to exploit cedro (tropical cedar,

Cedrela odorata L.) and caoba (tropical mahogany, Swietenia

macrophylla King) (Fig. 1).

The subsequent establishment of MNP in 1973 resulted in the expulsion of

hunters and sawmills from Manu, although Boca Manu was not completely

abandoned, and a group of Piro-speaking Amerindians, or Yines as they call

themselves, who had lived within Manu during the Rubber Boom and later

worked for hunters and timber traders, moved downstream and established

at both settlements. At this time, inhabitants of the area, including

11

colonos (Andean settlers), ribereños (settlers from other Amazon regions

within Peru), and Amerindians started to sell timber from naturally fallen

trees that were captured as they floated down the Manu River. Some

inhabitants of Boca Manu learned to build outboard motor canoes from the

timber, eventually supplying boats to a diverse set of buyers, from

researchers and tourism operators to regional gold miners and timber

traders. More recently, shops, gas stations, and a couple of hotels have been

established at Boca Manu to service the local ecotourism industry, and a

local airstrip, previously used for timber shipments and oil-exploration

activities, is now used to receive tourists. There are also several public

services, such as the primary and secondary schools, a healthcare post, a

police office, and government offices.

There are profound cultural and economic differences between the two

settlements. Most of inhabitants of Boca Manu (ca. 33 families, 160

persons) are mestizo colonists, people of mixed Amerindian, Andean and

ribereño descent, while the inhabitants of Isla de los Valles (23 families

embodied in three large groups, 85 persons) are primarily a group of Piro-

speaking people of mixed descent (mainly Yine), plus some Matsigenka

(Machiguenga) Amerindians and Andean colonists (the Rojas family).

Inhabitants of Boca Manu are mostly engaged in commerce and tourism

while the inhabitants of Isla de los Valles concentrate on subsistence

farming, hunting, fishing, and the collection of non-timber forest products.

Inhabitants of both settlements engage in selective timber extraction from

local standing forests. Asset poverty, in the form of chainsaws, boats, and

boat motors is common in Isla de los Valles, while in Boca Manu it is

12

difficult to find a household lacking either of those.

Interviews and Questionnaires

Open-ended interviews and structured questionnaires (Appendix 1) were

used to acquire background information on the individuals who engage in

the capture of floating trees (‘appropriators’), the financial costs and

benefits of capturing, transporting, and transforming the trees into final

products, and the organization of the tree-capture system. The interviews

sought qualitative information about the history and structure of the

organization: when and how it was established, the current appropriation

and sanctioning rules, and how the system evolved from a first-come-first-

serve to a rotation system for capturing trees. The interviews also tried to

define attributes of the resource, users, and the context that have contributed

with the emergence, evolution, and survival of the common property

regime.

Permission for the research project was granted in March 2007, during RG’s

first visit, by the Junta Directiva (board of directors) of the Asociación de

Artesanos Recolectores de Troncas Ecológicas de Boca Manu e Isla de los

Valles (Association of Craftsmen Gatherers of Ecological Trees in Boca

Manu and Isla de los Valles), which is the legal entity embodying its 46

members, hereafter referred to as the Association. Between March and

September 2007, I explained my research project individually to most of the

members of the Association, and I conducted open-ended interviews prior to

the questionnaires in order to gain confidence with the interviewees. Most

interviews took between forty minutes and an hour and questionnaires took

13

thirty minutes with each member. With some interviewees, volunteered

information caused interviews to take longer. In total, 15 subjects from Boca

Manu (13 males, 2 females, mean age 47.3, SD = 15.4), and 12 subjects

from Isla (10 males, 2 females, mean age 40.0, SD = 13.51) were

interviewed, out of a total of 46 Association-registered members (R. Catpo

& A. de la Cruz, unpublished). Nine members were absent during the study,

and the rest declined to be interviewed. I also conducted informal

conversations with two retired appropriators and with park guards at the

Limonal guard post, where the tree-capture activity takes place (Fig. 1). The

names of these 27 interviewees have been anonymized.

A second visit to the field was necessary in March 2009 to gather more data

on the evolution of the management system and to acquire information

directly from the previous Minutes Books where the Association keeps a

register of their meetings and internal and external agreements, mainly those

with the MNP administration.

Finally, fifteen other persons who have been involved with or aware of the

management system in one way or another were interviewed either to

confirm statements or to acquire more information about the past and

present functioning of the system. These included the 2007 administrative

head of MNP (hereafter referred to as the park chief, after the Spanish term

jefe del parque), previous MNP chiefs, other MNP administration members

and park guards, anthropologists who have conducted research in the area,

the staff of NGOs, and one previous member of the Agriculture Ministry

Office.

14

Total trees captured

The number, volume, and species of captured trees were obtained from a

register at Limonal guard post. Park guards, usually assisted by

appropriators, record this information after capture and once the flooding

event has passed so as to make this task easier and safer, and in the

meantime, the appropriators usually secure their trees to the riverbanks,

carving their initials for later identification. Park guards measure the trees

for maximum and minimum diameters and length and apply the Tabla

Oficial de Cubicación de Madera Rolliza (Official Table for Measuring

Round Wood) (INRENA, unpublished) to determine round and board feet

volumes (1m3 of round wood = 220 board feet of sawn wood, 52% yield).

Identification of the commercial tree species is determined from bark color,

texture, and wood color. Many of the park guards and appropriators have

worked as loggers somewhere else, so this task is considered

straightforward.

As might be expected, the records are incomplete. Sometimes, appropriators

remove trees without recording the capture, which potentially allows those

appropriators to re-enter the rotation system immediately (see Results).

Thus, the total volume recorded is always an underestimate. Secondly, only

in the 2006-2007 season was the record system expanded to include dates,

volumes, and appropriator names. These data are necessary in order to

calculate the variable capture costs and to estimate the distribution of

revenues. Thus, we present results from October 2006 through April 2007

on the number, volume, and species of trees registered as captured by 41

15

users during the 2006-2007 season. We also present total volumes captured

from January 2005 through April 2007. Unfortunately, given the informal

nature of the economy at Boca Manu, there are no statistics on tree capture

rates, costs, or financial returns before 2006, which precludes us from

comparing current estimates of profitability and distributional equity with

the situation that obtained when tree capture was an open-access system.

However, our interviews allow us to make some qualitative historical

comparisons.

Costs, revenues, and profits

Appropriators do not keep individual records of the final-product fates of

their captured trees: raw logs, boards, and boats. However, based on

interviews, we assigned each appropriator (n = 27) to the final-product class

that constituted ! 50% of their stated sales in previous seasons. We then

calculated the costs, revenues, and profits for each appropriator for each

product class, which means that we assume that each appropriator sold all

his/her captured trees as only one class of product. At the time we

conducted the research, not all the wood had been sold, so real sales could

not be obtained for the whole 2006-2007 season. We therefore assumed

remaining sales and prices were the same as the previous sales. For those

present at the study area but who could not be interviewed (n = 14),

classification was based on direct observations of their activities and verbal

information gathered from other appropriators, park guards, and villagers.

To estimate the costs of production, appropriators (n = 27), together with

two shop owners, provided a list of inputs and tools, together with prices

16

and depreciation lifetimes, for each activity (e.g., chainsaws) (Table 2).

When there was more than one price for each item I averaged these. All

prices were converted from Peruvian Nuevos Soles to US Dollars by using

the mean of the exchange rates of the last days of all months from October

2006 to April 2007 (US$ 1.00 = S/3.199), available on a Peruvian

government website (SUNAT 2009) http://www.sunat.gob.pe/cl-at-

ittipcam/tcS01Alias, accessed: 15 February 2009).

In addition, appropriators provided the required quantities of tools, inputs,

and labor regularly used to produce each final product class from captured

trees (e.g., chainsaw removal of branches and roots to produce raw logs).

We averaged the input estimates across appropriators and multiplied by

each input’s average price. In addition, some appropriators provided total

cost estimates for specific production stages, such as sawing 1000 board

feet, which were used when not enough information for the whole

production stage’s costs were available. When unpaid labor was employed,

such as with kin relations, we used 2007 local wages (US$6.25/day).

For the case of boats, a detailed list of tools and inputs is presented in Table

2 and is based on two days of direct observation and interviews with one

boat-seller while he was building two 15-m long boats. However, boats vary

in size (9 to 20 m) and in design, depending on the intended cargo (e.g.

timber or tourists), which causes production costs to vary. Furthermore,

some boat producers stated that prices could vary for the same boat design,

depending on the buyer’s perceived wealth (e.g. boats for tourism are more

expensive than those for gold miners). Boat producers did not record the

17

sizes and designs of their boat sales, so we applied the costs and prices of

the most frequently used design and size (15 m) to all boats produced. A

further complication is that each boat hull (the casco) is made from a fourth

of a hollowed-out trunk of the tree species Hura crepitans (Eurphorbiaceae),

known locally as catahua. Boat producers together captured only

seven catahuas (totaling 10,661 board feet (b.f.)) during the 2006-2007

season, which was not enough to use all the cedro they captured (58 trees;

69,400 b.f.) to build boats. This means that the rest would have to have been

purchased from other appropriators or from nearby villages, where one

could go and buy a standing catahua tree. The cost of a hull is therefore

different depending on whether boat builders used their own

captured catahuas or purchased them. If captured, the hull’s cost is based on

capture plus transformation costs. The cost of the captured catahua volume

for each individual is an average cost of all his/her captured board feet

during the season. This is calculated by dividing all 2006-2007 season’s

captured costs by each individual’s own total captured volume. On the other

hand, a purchased catahua's average cost is US$ 138.06 (SD = 40.05, n =

6), which is then divided by 4 to get the cost of the necessary volume

(~1,272 b.f.) to construct a hull.

Finally, for producers of boats (n = 9), raw logs (n = 15), and lumber (n =

17), individual costs are determined by the number of days spent waiting at

Limonal for floating trees. We calculated a mean visit of 3.00 (SD = 0.83, n

= 27) days, based on the interviews, which was used to calculate an

opportunity cost of wage labor. Revenues are a function of the number of

captured trees and their volumes, assuming that no cavities were found

18

within the trunks. The number of captured trees by each appropriator is a

function of the number of trees that float down the river (which in turn is a

function of riparian forest dynamics) and of the system for assigning those

trees to appropriators. Total revenues were derived by multiplying the

number of final products by their local average prices (raw logs: US$

0.22/b.f. (SD = 0.07, n = 14), boards: US$ 0.39/b.f: (SD = 0.03, n = 14), and

boats US$ 1,354.59 for 15m boats, (SD = 90.24, n = 3)). Total and per

capita profits were calculated by subtracting total costs from total revenues.

For all these calculations, we only include first-order transactions in order to

avoid double counting. That is why we did not take into account the revenue

that any board seller made by selling boards that were sawn from a log

purchased from one of the raw-log sellers. This simplification is equivalent

to assuming that all the production was sold to outsiders or to other villagers

but not to another appropriator (a zero multiplier).

The effect of assigning single sales categories to appropriators

As explained above, the individual revenues that we estimated are the

potential revenues assuming appropriators sold all their wood as one class of

final product. For example, based on interview results, we identified nine

boat-building specialists in Boca Manu. For these individuals, we assumed

that all trees captured were used to build and sell boats. This is a reasonable

assumption because boats add the most profit value to the wood, so we are

assuming that profit is maximized and that other inputs are not limiting

(e.g., nails, labor, etc.). Also, the identified lumber specialists do not have

the skills to build boats, and we observed that sellers of raw logs lack

19

chainsaws and other materials.

There are, nonetheless, other possible uses for the captured trees, for

example, as furniture or house building material or as informal currency in

exchange for rides to Limonal or as payment for chainsaw use. These uses

are not recorded in any detail by the appropriators, nor were formal records

kept of sales in the 2006-2007 season. Thus, our revenue and profit

estimates should be treated with caution.

RESULTS

The history of the tree capture activity

In this section we present the important events and context that led current

appropriators to develop a collective institution for managing the tree

capture activity, as well as how the MNP administration has been involved.

We also provide a qualitative and a quantitative analysis of the effects of the

current tree-capture system. Lastly, we describe how sanctioning and

monitoring are undertaken by the appropriators to regulate the system.

Users and the appropriation of floating trees

In the early 1960’s, the forests lining the Manu River constituted an open-

access resource for loggers and hunters, whose exploitation levels were

likely locally unsustainable (MacQuarrie 1992, see Methods). During these

years, settlers and natives, who had been working for sawmill owners or pelt

traders, also collected floating catahua, cedro, and caoba trees, an activity

that required the ability to identify the valuable trees out of the myriad that

floated down and paddle a dugout canoe alongside and pull the trees to the

20

riverbank, where a handsaw was used to trim the roots and branches (C.

Román, personal communication, May 22, 2007). Sales of these captured

trees supplemented the incomes of both groups and provided the raw

material for dugout canoes. In the same decade, some of the collectors

learned to build up the sides of dugout canoes with planks to build boats that

could take outboard motors (Fig. 2).

Despite this income source, by the late 1960’s most of the Yine had

nonetheless moved away from the mouth of the Manu River, establishing

themselves upstream on the Alto Madre de Dios River (Fig. 1) in what is

now called the Native Community of Diamante (Federación Nativa del Río

Madre de Dios y sus Afluentes (FENAMAD), unpublished data 1998). The

Yine moved because of constant flooding of their farms and because of

territorial conflicts with the colonos. Thus, by virtue of their proximity to

the mouth of the Manu River (Figs. 1 & 3), the settlers, who founded the

village of Boca Manu and a single, extended Yine family, the Valles, gained

privileged access to the floating trees. In addition, inhabitants of Boca Manu

invested in boat motors (16 hp, two-stroke engines known locally as 'peke-

pekes'), which increased their advantage in capturing trees, whereas the

inhabitants of Diamante did not (A. Smith, pers. comm., April 3, 2009). The

loss of access to the floating trees led to disputes and fights between the

inhabitants of the two villages, and, eventually, to the theft of secured trees,

by both sides (A. Castillo, pers. comm., February 12, 2009; A. Smith, pers.

comm., April 3, 2009). This state of affairs continued for many years.

In 1973, Manu National Park was established (Decreto Supremo - D.S. Nº

21

0644-73-AG-DGFF), and the forest upstream of the Panagua tributary on

the Manu River (Figs. 1) became closed to loggers and hunters. In 1980, the

lower Manu River was designated as a Reserved Zone (Resolución Suprema

- R.S.- Nº 0151-80-AA-DGFF) (Figs. 1 & 3) and also closed to settlement,

logging, and hunting. A Reserved Zone is a temporary land-use designation

under Peruvian Protected Areas Law that is designed to restrict settlement

and habitat conversion while decisions are made on a parcel of land's

permanent designation, such as national park status (Article 59, D.S. 038-

2001-AG). Floating-tree capture continued in the Reserved Zone.

In the early 1980s, park authorities began to require participants to register

their activities by applying for an annual tree-capture permit which, at the

time, was issued free of charge by the Agriculture Ministry Office (Agencia

Agraria) in the town of Salvación, 70 km from Boca Manu (Fig. 1), and to

pay a harvesting fee (Pago por derecho de aprovechamiento) that was based

on the tree species and volumes captured. The permits allowed the users to

legally transport and sell their wood outside Boca Manu (Guía de

Transporte Forestal, D.S. Nº 161-77-AG, Article 130; C. Román, pers.

comm., May 22, 2007). This was in the interest of the users, as all sawmills

in Manu had been evicted, and trees had to be transported downstream 160

km to the town of Laberinto or 245 km to the city of Puerto Maldonado to

be sold (Fig. 1) (L. Kalinowski, pers. comm., July 17, 2007).

In the late 1980’s, in response to ongoing conflict between the inhabitants of

Boca Manu and Diamante, park authorities finally assigned different tree-

capture zones to each group (A. García, pers. comm., March 17, 2009; J.C.

22

Flores, pers. comm., March 23, 2009; A. Smith, pers. comm., April 3,

2009), based on the proximity of the settlements to the rivers (M. Challco,

pers. comm., April 14, 2009). Thus, the Valles and the settlers, who lived

closer to MNP, were given exclusive permission to capture trees inside the

Reserved Zone, from the mouth of the Pinquén Tributary to the mouth of

the Manu River, and the ‘Diamantinos’ were given permission to capture

trees downstream of this, starting from the mouth of the Manu River (A.

García, pers. comm., March 17, 2009). ‘Diamantinos’ have peacefully

complained to park authorities about this unequal zoning ever since, but

park authorities have never re-granted park access rights to the

‘Diamantinos’, arguing that they have sufficient natural resources in the

forests bordering the Diamante community (M. Challco pers. comm., April

14, 2009). This argument appears to be correct, as the ‘Diamantinos’ have

never escalated their grievance to a major conflict.

Organization of the tree capture activity

Since they won exclusive permission to enter the Reserved Zone, the

inhabitants of Boca Manu and the Isla de los Valles have engaged in various

costly activities to restrict and regulate the appropriation of floating trees

within their tree-capture zone, so as to secure the benefits for themselves

and their descendants, and to increase the fairness in the distribution of

trees.

Over the 1980’s and early 1990’s, this group of families formed an

unofficial association whose main objective was “to capture and sell

floating trees for the benefit of their families and support of their children”

23

(C. Román, pers. comm., May 22, 2007). The group periodically met to

coordinate tree capture activity, as well as to elect a president and vice-

president, who were responsible for requesting permits and dealing with

park authorities when necessary. It was during this era that the group began

to restrict the number of people who were allowed to capture trees. In 1987,

for example, the association voted that only settlers who had lived in Boca

Manu or the Isla for at least three years could join the association, or face

eviction from the tree capture zone by members of the group (M. Blanco,

pers. comm., March 6, 2009; C. Román, pers. comm., March 7, 2009; R.

Rivera & L. Meza, pers. comm., March 8, 2009). Children of association

members were given the automatic right to join once they reached eighteen

years of age (C. Román, pers. comm., March 7, 2009).

In 1993, prompted by the chief of MNP, the appropriators took the decision

to formalize their existence by registering as a Peruvian legal entity known

as a Private Association (Asociación Privada, Superintendencia Nacional de

Registros Públicos - SUNARP, Acta de Constitución, unpublished data

April 19, 1993), under the name of Asociación de Artesanos y Moradores de

“Boca Manu” de Madre de Dios (Tomo 3, Folio 181, Nº01, Registros

Públicos - Madre de Dios, Puerto Maldonado, 29 April 1993). To pay the

registration fee (US$ 9.36) and travel costs of the president and secretary to

the provincial capitol, Puerto Maldonado, the Association members

collectively captured and sold a cedro tree.

Although the relevant forestry law (D.L. 21147 and D.S. 161-77-AG) did

not require that users form a Private Association to be allowed to capture

24

floating trees, the group's motive for this act was derived from their

perception that by forming a Private Association, the MNP's administration

would be legally bound to continue granting them exclusive access rights to

the tree capture zone (J. García, pers. comm., March 6, 2009; B. Lau, pers.

comm., May 12, 2009). As a former Association president put it: "once we

got registered there was no longer any chance of us being blocked by

anyone to continue capturing trees" (J. García, pers. comm., March 6,

2009). This was an overly simplistic view, although not entirely incorrect.

In the Peruvian Civil Code, a Private Association enables a group of

individuals to enter into a contract as a collective unit instead of as

individuals entering into multiple contracts, and the Association is (and all

its members are) then subject to legal prosecution in the event that any

individual member breaches the contract terms. The benefit to the

Association for taking on collective responsibility is that it reduces

complexity, allows collective bargaining, and makes some contracts more

likely to be agreed. For example, in 1997, the Association signed a contract

to exclusively sell all their wood to a single intermediary (J. García, pers.

comm., March 6, 2009). More importantly, the state’s Agencia Agraria was

able to issue only one permit to the Association, rather than individual

permits (B. Lau, pers. comm., May 12, 2009), and the MNP administration

could then rely on the members of the Association to design its own systems

for monitoring and sanctioning rule breaking rather than having to monitor

many individuals (A. Castillo, pers. comm., February 12, 2009). This shifts

some of the burden of proof and monitoring to the Association. For

example, if illegal logging occurs where the Association operates as part of

25

its activities, and if preventing illegal logging is part of the contract signed

with the park, the park administration can hold the Association responsible

rather than having to try to determine individual guilt. Association members

are of course more likely to be able to find violators, as they have more

information about the activities of their members. As a result, contracts

between the park and the Association are made more robust.

It is worth mentioning that a settler-Yine-mixed family, the Rojas-Valles,

opposed the registration of the Association, arguing that the family’s

ancestral use of resources and territory, considering its Yine descent, should

have given them exclusive appropriation rights. The head of the family, Mr.

Rojas, threatened to form a separate association (J. García & E. Salas, pers.

comm., March 6, 2009), but the MNP rejected this proposal on the grounds

that two associations would engage in conflict (J. García, pers. comm., May

16, 2007). In subsequent years, Mr. Rojas has led the Valles to boycott the

Association (R. Rivera, pers. comm., March 6, 2009) and repeatedly

violated the park’s restrictions, for which he was once sent to prison for

some months, after he entered the park and logged over 20 cedar trees in the

mid 1990’s (A. Castillo, pers. comm., June 15, 2007).

Population growth and its consequences

During the 1990s, the number of appropriators increased from around

nineteen to 41. Fourteen Rojas and Valles family members reached eighteen

years old, and eight immigrants claimed appropriation rights after

completing three years of residence. This increase in numbers resulted in

disputes and fights during tree capture, as observed by the MNP's chief at

26

this time (A. Castillo, pers. comm., February 12, 2009).

Two problems arose from the increased number of appropriators:

inefficiency and inequity. First, more members naturally decreased per

capita captures and profits, not only because of the effectively fixed

resource size (as noted by eight interviewees) but also because capture costs

increased (as twelve interviewees stated). Although the number of floating

trees predictably peaks at every flood surge (Fig. 4), only a few of the trees

that float out of the park belong to one of the valuable timber species. Tree

size is also variable, and appropriators have only scant minutes to scan the

bark and a few centimeters of exposed wood to determine species identity,

often in the dark and rain. Thus, as two appropriators (J. García and E.

Campos) recalled, during this era, there was a high degree of uncertainty

over whether an investment of time and petrol to go capture trees would be

repaid, since tree capture was conducted on a first-come-first-serve system.

There was also a non-trivial risk of injury. Thus, as one member recalled, on

more than half of the times he went to Limonal in that era, he did not

capture any trees because too many other users had gotten there first (T.

Ruesta, pers. comm., September 9, 2007). As a result, as the number of

members increased, the number of visits that resulted in no captured trees

also increased, each exacting a minimum cost. In addition, the more intense

competition provoked more violent confrontations. The result of this

increased cost and risk was to dissuade many Association members from

participating, leaving the trees to a small group of members (~10) who lived

closer to the mouth of the Manu River (the Rojas) or who were risk-takers

(as fifteen interviewees stated). Finally, the skewed distribution of tree

27

captures was exacerbated by the unequal distribution of peke-peke motors

amongst the members.

A key intervention from the MNP administration was therefore necessary

during the mid-1990s, as an increasing number of unsettled and

disorganized users threatened the MNP's integrity; with reduced financial

returns from tree capture, some users might have found it worthwhile to

breach the capture-zone limit for collecting trees, or even to log, hunt, or

fish within the park. Initially, in 1994, appropriators were required to

register at the guard post and queue, so that appropriation would follow the

order of arrival, and each appropriator was limited to one tree per turn (J.

García, pers. comm., March 3, 2009; Reglamento para el Manejo de

Troncas en la Zona Reservada del Manu, Jefatura del Parque Nacional del

Manu, unpublished data 1999). Secondly, MNP authorities relocated the

tree capture zone downstream twice, finally establishing it at the new

Limonal guard post in 1996 (Fig. 3), where the appropriators could be

monitored more easily and transport costs minimized.

Although this measure reduced priority disputes, queuing by itself did not

resolve the equity problem, as the same few members dominated the queue

(J. Campos, pers. comm., June 4, 2007 & March 8, 2009). Finally, at an

Association meeting on 29 April 2004, a key step was taken when the

Association’s president, J. Campos, proposed that all Association members

follow a fixed order of turns (Second Minutes Book, page 6, unpublished

data, April 29, 2004). This way, risk would be reduced by reducing physical

interference and unnecessary visits (J. Campos, pers. comm., March 8,

28

2009). When at the top of the list, a member is given right of first refusal for

all floating trees, and if he or she rejects a tree, the right to that tree is

transferred down the queue. Enforcement would be undertaken by the

President of the Association and a designated fiscal (an elected member)

(Article 28 Association's Statute, unpublished), and ‘list-jumpers’ would be

suspended or evicted, depending on the frequency of violations, while park

guards would be tasked with maintaining order at the access point and

continuing to prevent outsiders from entering into the park.

In his interviews (June 4, 2007 & March 8, 2009), the Association's then-

president, J. Campos, stated that he was motivated by his belief that it was

unfair that only a few users were capturing all the trees. Another member

separately pointed out that Campos lives far from the mouth of the Manu

River (about 10 km downstream on the Madre de Dios River) and was

disadvantaged by the first-come-first-serve system, since he usually arrived

later than the others (M. Valdés, pers. comm., July 5, 2007). At the same

meeting, another member (R. Rivera, 29 April, 2004) proposed that no

additional members be allowed in the Association, except for descendants at

the age of eighteen (Second Minutes Book, page 7, April 29, 2004). Both

changes to the Association's statutes were unanimously approved by the

attendees (33 out of a total of 40 registered members, Second Minutes

Book, page 8), and the changes were registered in the Public Registers

Office, paying a fee of 32.00 Nuevos Soles (US$ 9.70, December 3, 2004).

Not surprisingly, this decision was protested by the Rojas-Valles family

(Second Minutes Book, page 28), which would as a result capture fewer

29

trees (J. Campos, pers. comm., March 8, 2009). During the following

months and again in 2007, they threatened to form their own independent

association, arguing for exclusive rights to the tree resource, given their

ancestral use of the territory (Second Minutes Book, page 31; A. Osorio,

pers. comm., June 22, 2007; J. García, pers. comm., March 6, 2009; H.

Morales, pers. comm., July 7, 2007). These initiatives have naturally been

opposed by both Boca Manu and many residents of the Isla de los Valles (J.

García, pers. comm., March 6, 2009; M. Valdés, pers. comm., July 7, 2007),

and, more importantly, the MNP administration has stated that it will not

allow two associations to operate in the park, threatening to allow a private

company to undertake collection if the Association splits (A. Osorio, pers.

comm., June 22, 2007).

A new institutional setting

In parallel, two important institutional changes occurred in the early 2000s.

Firstly, in 2001 a new Protected Areas Law (Reglamento de la Ley de Áreas

Naturales Protegidas, D.S. Nº 038-2001-AG) transferred responsibility for

forestry products within natural protected areas from the Agencia Agraria to

a new institutional body, the Dirección General de Áreas Naturales

Protegidas (DGANP) (later called the Intendencia de Áreas Naturales

Protegidas - IANP). Secondly, the Reserved Zone was finally incorporated

into the MNP proper in 2002 (D.S. Nº 045-2002-AG). As a result, the tree

capture activity became subject to a new series of requirements applicable to

national parks (Article 106, D.S. Nº 038-2001-AG and Procedimiento 113,

D.S. Nº 013-2002-AG, Texto Único de Procedimientos Administrativos del

Instituto Nacional de Recursos Naturales - TUPA-INRENA).

30

Under Peruvian law, appropriators of natural resources from protected areas

(as opposed to Reserved Zones) must formally request capture

authorization, pay a harvesting fee based on the volume and species to be

captured (e.g. ~US$8.6/m3 for cedar), and pay an annual tax equivalent to

US$ 62.00 to the MNP administration (values current as of 2004). In

addition, a management plan must regulate the activity (Plan de Manejo de

Aprovechamiento) (Procedimiento 113, D.S. Nº 013-2002-AG), which must

take place within a Special Use Zone (a management status within the

protected area’s Plan Maestro, which allows the sustainable use of

resources within an otherwise strictly protected area (Articles 102, 103, 105,

106, D.S. Nº 038-2001-AG). The capture authorization would result in a

certificate from the MNP attesting to the legal and sustainable origin of the

wood, which would allow appropriators to sell their trees on the legal

market at a higher price than on the black market, where they had been

forced to sell their trees after the Agencia Agraria stopped issuing permits in

2001.

In 2004, the Association and the MNP administration discussed the

possibility of granting an exclusive, 20-year capture permit (Second

Minutes Book, pages 16 & 18), and it was within this context that the

Association instituted the rotation system and the limits on new members. It

appears that the incentive of a long-term agreement prompted appropriators

and their leaders to incur the costs of reorganizing the activity (J. Campos,

pers. comm., March 8, 2009).

31

A management plan draft was eventually produced in 2006 (Plan de Manejo

Forestal para el Aprovechamiento de árboles arrastrados por el río Manu,

Catpo & de la Cruz, unpublished 2006) with the aim of providing general

guidelines for the management of the activity. However, as of this writing,

the MNP administration has not yet created the Special Use Zone, and the

management plan has not yet been approved by the IANP (which itself has

in 2009 been placed under the new Ministry of the Environment and

renamed as the Servicio Nacional de Áreas Naturales Protegidas -

SERNANP).

Evolution of the rotation system

Despite the lack of progress on the legal front, the Association's 47 members

(as of 2009) largely follow the rotation system set up in 2004, with the

innovation that the original list has evolved into two simultaneous lists, one

for small trees (<1,000 board feet = 4.53m3 of round wood, 52% yield) and

one for all larger trees, rotating in opposite order. The idea of two lists was

suggested by the MNP administration (A. Oroz, pers. comm., February 11,

2009) and approved by the majority of present members (18 out of 25) in an

Association meeting on January 25th, 2006 (Second Minutes Book, page

57).

If an appropriator does not appear for his turn, then that is understood as a

refusal and the appropriator gives up the turn. As a result, on days when few

appropriators are present in the queue and multiple commercial trees appear,

individual appropriators can capture multiple trees in a single flooding

episode (between one and three days). Exceptions can be made for illness or

32

other justified absence, and with the approval of the Board of Directors of

the Association, a lost turn can be recovered. In addition, if an appropriator

exercises his large-tree turn but subsequently discovers that the tree is less

than 1,000 board feet (e.g. due to internal cavities), the Board of Directors

can decide to give the appropriator a new large-tree turn.

It appears that the two-list system simplifies mutual monitoring. The

appropriators who wish to concentrate their effort on large trees follow that

list, and appropriators who wish also to capture small trees can often capture

several, using the turns that are given up. As a result, the capture of small

trees tends to be quite disorganized, as the list order is not followed. There

is also strategic behavior and risk-taking behavior. Appropriators regularly

reject trees over 1,000 board feet, sometimes even trees over 2,000 board

feet (Limonal Guard Post Chief, pers. comm., September 9, 2007), waiting

for a chance to capture a bigger tree. In part, rejecting large trees can be

strategic if the next person is kin and is present for his small-tree turn, in

which case, the rejected tree can be claimed by the kin (E. Campos, pers.

comm., May 17, 2007). It is worth noting that error is also important, as at

night there is uncertainty about the real volume of the tree (R. Rivera, pers.

comm., May 25, 2007).

Perceived benefits and costs of the rotation system

Thirteen appropriators (Boca Manu (7) and Isla de los Valles (6)) out of

fifteen who freely commented on the functioning of the system, volunteered

in interviews that the rotation system reduced costs by reducing uncertainty

and the possibility of violent confrontations. One of the appropriators noted

33

that before the introduction of the rotation system, he captured nothing in

more than half his visits, but with the new system, he only goes when his

turn is near, avoiding unnecessary waiting (T. Ruesta, pers. comm.,

September 9, 2007). It is, however, difficult to determine whether the

rotation system has increased distributional equity among members; only

twelve out of 22 interviewees stated that they perceived an increase in

equity (see Fig. 5). However, eight of the ten appropriators who did not

agree that equity has been increased are from the Valdés family, who also

complain that they lack boats and motors to go to Limonal. One of the

Valles appropriators (pers. comm., May 18, 2007) also noted that he lacks a

chainsaw and that ropes for tying the captured trees are expensive in the

Boca Manu shop. This lack of appropriate infrastructure is reflected in the

lower capture rates during the 2006-7 season; Valles family members

captured on average significantly fewer trees than members from Boca

Manu and the Rojas family (Fig. 6). Nonetheless, all participants captured at

least one tree during the 2006-2007 season (Fig. 5), which, according to five

interviewees, did not occur before the establishment of the rotation system

in 2004.

Rule breaking, sanctions, and monitoring

Nine appropriators volunteered that the tree-capture zone limit (the Limonal

guard post) is often trespassed by list members, especially at night when

park guards are absent. Also, appropriators sometimes hide captured trees to

pick up later, so that when appropriators arrive for their turns, the previous

appropriators can claim that they are still waiting to exercise their turns (E.

Campos, pers. comm., May 17, 2007). It was not possible to estimate

34

quantitatively the degree to which appropriators cheat in these two ways,

especially since the rotation lists are not strictly followed.

Appropriators who are caught violating the Association rules face graduated

sanctions ranging from verbal warnings to eviction from the Association

(Association statutes, Articles 10 & 15). However, interviews revealed

many complaints about the capacity of the Association's executives to

enforce the rules. Twenty appropriators out of 27 stated that sanctions are

not applied. As a response to the perceived lack of rule enforcement,

thirteen appropriators freely commented that they believe that the park

guards should get involved in the monitoring and sanctioning of the activity

more profoundly. Some of them (5) stated that park guards were often not

present during the capture events, which facilitates opportunistic behavior.

However, Association records (Second Minutes Book, page 83) and

interviews reveal five instances in which appropriators were sanctioned in

the recent past (2004-2006), two of whom were even expelled after

repeatedly committing serious infractions, such as stealing others' captured

trees. The other three were suspended for times periods between three flood

surges to an entire rainy season for having transgressed the capture zone

limit. It is worth noting that most interviewees (21 out of 27) answered that

they are informed when other appropriators break rules, which suggests that

there is a great deal of mutual monitoring and gossip. Nonetheless, almost

half of the interviewees (13) stated that they themselves never accuse

cheaters. One appropriator explained that this would cause social conflict

among them, which is not desired among members because other day-to-day

interactions would be affected and therefore they avoid this situation (R.

35

Rivera, pers. comm., March 8, 2009).

The Association Board of Director's president and fiscal (prosecutor), who

are elected by all members every two years, are responsible for monitoring

and sanctioning member's behavior according to the Association's Statute

(Articles 23b, 28b, & 28c), but they are not required to be present at

Limonal for every flooding episode. Nonetheless, mutual monitoring during

the capture activity is facilitated by the fact that it occurs in a small area (ca.

152 ha) (Fig. 3), where tree capture is made easier by a slower current and

the presence of low-flow areas where trees can be manipulated and secured

(A. Castillo, pers. comm., February 12, 2009). Thus, a member can be

accused by another member of having committed a fault, and the Board of

Directors will present the case to all members who vote on a sanction.

Financial Benefits

In this section, we present the total number of trees, volume, and species

captured between 2005-2007. We then calculate the financial benefits from

sales of these trees as raw logs, lumber, or boats during the 2006-2007

season.

The number and volume of captured trees between 2005 and 2007

The total number of cedro and catahua trees captured between 2005-2007

and their total volume are presented in Table 3. Most of these were captured

after the beginning of the rainy seasons, which typically starts in November

(Fig. 4). The short timeline precludes us from concluding that volumes

recorded here are consistent across years, bearing in mind that the 2005 data

36

does not include the whole season. For instance, twelve appropriators

believe the river carries fewer trees in recent years than during the 1990s,

though seven others suggested that this appearance of a reduction is due to

the increased number of appropriators.

Regardless, it is still interesting to note that the 2006-2007 total cedro

volume captured (190,222 b.f. or 448.64 m3 of sawn wood) is equivalent to

8.6% of the 2007 cedro production out of all terrestrial forestry concessions

in Madre de Dios (5,215.9 m3 of sawn wood, taken from 1’270,468 ha of

concessions, or 14.89% of Madre de Dios area) (see INRENA 2008). One

reason for the high productivity out of MNP is that cedro is present at high

density in successional zones on the Manu River (ten trees per ha along the

Manu River from the mouth to 64 km upstream) (see Flores & Lombardi

1990), whereas cedro has been extirpated along rivers outside of protected

areas, and densities in primary forest are lower. Profitability at Boca Manu

is probably also higher, due to considerably lower transport and search

costs.

As mentioned above, although all appropriators captured at least one tree

during the 2006-2007 season, an unequal distribution of captures still

remains (Fig. 5). The top 10% of members (4) captured 53 trees, or 26.9%

of the total captured, whereas the bottom 10% captured only 2% of all trees

(n = 4), representing an estimated Gini coefficient of 0.42 (higher values

indicate more inequality).

37

The value of logs, lumber, and boats for 2006-2007 season

Estimated total revenues and profits gained from transforming and selling

the trees captured during the 2006-2007 season were US$ 126,377 and US$

74,751, respectively. Regarding the possibility of a 20-year contract

between the Association and MNP administration (see A new institutional

setting) we calculated the profit net present value (NPV) for 20 years using

a discount rate of 10%, and obtained a NPV of US$ 636,397.40.

Per capita revenues and profits gained from transforming and selling the

trees by each class of producers are shown in Figures 7 & 8. The boat

producers gained by far the largest revenues and profits, followed by the

lumber producers first and then by the raw logs producers. Estimated

annual, per capita worked days for each class of producers were: 139, 16,

and 7 days, respectively, resulting in a daily salary of US$ 40.92, US$

53.38, and 86.53 for boats, raw logs, and lumber producers, respectively. As

a rough comparison the nominal daily salary of workers in Puerto

Maldonado in 2007 was only US$ 8.25 (based on the monthly salary of S/.

792.1, see Webb & Fernández 2008). This amount could be used as a proxy

for the opportunity cost of the tree capture activity and thus reflect its

relative financial importance. Nonetheless, the estimated Gini coefficient of

0.62 for the total profits represents yet an even larger unequal distribution

among all producers than that estimated for the distribution of captured

trees. This fact can be explained by the higher profit margins of boats,

versus lumber and raw logs. Thus, even though the seventeen lumber sellers

captured most of the trees in 2006-2007 (73, compared to 65 and 60 for the

fifteen boat and the nineteen raw logs producers, respectively), it was the

38

boat builder 'guild' that by far earned the largest total profit (Fig. 9).

Moreover, per capita profits of boat builders were more evenly distributed

(Gini coefficient = 0.28) than those of the other classes of producers (lumber

producers’ Gini coefficient = 0.40 and raw logs producers’ Gini coefficient

= 0.63). We reiterate that these estimates are underestimates, as not all trees

are captured and that there is additional error since not all trees had been

sold at the time of our study (see METHODS).

DISCUSSION

Many events and contextual factors influenced the transition of the Boca

Manu tree capture system from an open access situation into a common

property regime, benefiting the appropriators and potentially the MNP. The

establishment of the MNP and the consequent expulsion of loggers and

hunters in 1973 made inhabitants of nearby settlements dependent on

riverine tree-capture as one of their main economic activities. Appropriators

from Boca Manu and the Isla de los Valles then restricted access to the

resource in 1987 by gaining exclusive rights to capture trees in the best

capture zone. In 1993, the group became a Private Association to facilitate

the signing and enforcement of agreements between the Association and the

park administration, regarding the appropriation of trees within the Manu

River. By late 1990s, the Association’s members had increased from

nineteen to 41, resulting in conflicting claims over the trees and reduced per

capita captures and profits. As a solution, the Association devised in 2004 a

prearranged list of turns to capture trees and further restricted the access to

the floating trees by allowing only members’ descendants to enter the

39

Association. The rotation system appears to have reduced costs and allowed

all appropriators to capture at least one tree during a season, thus increasing

efficiency and equity in the distribution of trees. Mutual monitoring of

members’ behavior and the occasional imposition of sanctions appear to

contribute to the maintenance of the rotation system and access rights. The

value of the trees captured is considerable: during the 2006-2007 season the

Association captured 190,222 b.f. of cedro, and per capita revenues and

profits gained from transforming and selling the trees were: (1) US$ 873.24

and US$ 628.77, respectively, for raw logs producers; (2) US$ 1,643.53 and

US$ 828.99, for lumber producers; and (3) US$ 9,482.12 and US$ 5,691.92,

for boats producers. Estimated per capita worked days for each class of

producers were: 7, 15, and 139 days, respectively, resulting on a daily wage

of US$ 86.53, US$ 53.38, and US$ 40.92 for raw logs, lumber, and boats

producers, respectively. In contrast, the 2007 Puerto Maldonado’s nominal

daily salary for workers was only US$ 8.25.

I now examine the Boca Manu system using the theory of collective action

for governing common pool resources (Ostrom 1990; Ostrom 1992;

Schlager & Ostrom 1992; Ostrom et al. 1994; White & Runge 1995;

Agrawal 2002; Ostrom et al. 2007).

Common property regimes and the Boca Manu system

We start by differentiating between a renewable CPR system (MNP's

riparian forest) and the CPR units themselves (the trees). The former is a

stock that is capable, under favorable conditions, of producing a flow of

resource units without harming the stock, as long as the withdrawal rate is

40

less than the natural rate of replenishment (Ostrom 1990). It is also

important to distinguish between the nature of the resource itself, as

determined by its exclusion and subtractability attributes (i.e. commons are

‘costly excludable,’ and ‘rival:’ one individual’s appropriation subtracts

from what is left to others), and the property regime, the kind of

arrangements created by humans to regulate the use and tenure of the

resource, such as private or common property. Overlooking this difference

has previously caused common pool resources to be confounded with

common property resources, a concept which in turn was confused with

open access conditions: the absence of rules to regulate its use (Dietz et al.

2002). Thus, in the Boca Manu system a group of individuals has

established a common property regime to use the resource units of a

common pool resource.

Having said so, riparian tree capture in itself is as sustainable an activity as

can be imagined, since the stock is untouched by humans. Thus, our

theoretical interest in this system lies less in how the appropriators have

limited their extraction rate to sustainable levels, since that is enforced by

the existence of the MNP, and more in (1) how the appropriators have

organized to limit access to the capture zone and to distribute benefits and in

(2) the evolution of the relationship between the MNP and the appropriators.

As such, our observations are most applicable to situations where

conservationists are interested in people-park conflicts and in managing the

flow of benefits from a protected area to bordering human populations. Our

thesis is that the 'roving bandits' of the pre-MNP era have developed into a

'harbor-gang' (sensu Acheson 1975; Berkes et al. 2006), with potential

41

protective benefits for the MNP itself.

Collectively managing the distribution of benefits of a heterogeneously

distributed (temporally or spatially) resource, known as an assignment

problem (Ostrom et al. 1994), is not a simple task to solve (see Berkes

1992). In fact, designing mechanisms to allocate a seasonal and sporadic

flow of trees among users has been the fundamental management problem

that has faced the appropriators.

McCay (2002) has suggested that the emergence of collective action for

managing a commons is, at least initially, driven more by limiting access

than by limiting extraction rates and that "indigenous conservation" can be

thought of as "indigenous conflict management." This appears to be the case

at Boca Manu, since conflicting claims over the floating trees, especially

those between the ‘Diamantinos’ and the Boca Manu group during the early

years, and, later, among the Association's members, appear to have provided

the major incentive behind the formulation of operational rules to limit

access of outsiders and to regulate behavior among members. In both cases,

users perceived a risk of losing access to a valuable resource to competitors

(McCay 2002). This system therefore resembles previous research on sea-

tenure institutions in fisheries where the zero-sum nature of fisheries

sustains management regimes that limit access to fishing grounds (Berkes

1992). It is important to recognize that in the Boca Manu system, actions to

limit access were carried out not only by the resource users but also by the

MNP administration, so as to thwart possible threats to its integrity. In this

way, the MNP has represented an external force that has contributed

42

important institutional changes, as has been observed elsewhere (see Ostrom

1990; Morrow & Hull 1996).

The MNP administration also contributed importantly to the introduction of

prearranged rotation rules, such as the rotation list, which appears to have

reduced some of the inequality among appropriators (see also Berkes 1992;

Ostrom et al. 1994). Thus, although it has been recognized that central

governments should not undermine local authority to devise their own CPR

institutions (Ostrom 1990), it is important to recognize the potential positive

interventions that protected areas administrations can undertake. In fact, we

believe that the MNP's imposition of queuing in the 1990s might have

facilitated the evolution of prearranged rotation rules and thus to mitigate

the assignment problem of unequal distribution of trees. Thus, it is useful

not to dismiss the political leadership that government authorities possess to

exert positive changes and to support CPR regimes (see McCay 2002),

especially in rural areas of developing countries where access to assistance

programs for managing natural resources is difficult or the management

itself has been dominated by government property regimes alone. Therefore,

the gate for an exogenous intervention that could lead to improvements in

traditional management systems must be left open and exercised,

considering the fact that successful interventions could increase social

benefits and reduce threats to common pool resources. In the future, finally

ratifying the management plan and therefore changing the status of the

park’s tree-capture zone into a special use zone would increase the value of

floating trees, as the trees would be able to gain legal status, allowing

appropriators to sell them at a better price (almost three times the current

43

price). Higher profits could imply a higher incentive for protecting the stock

from which CPR users benefit. Moreover, legitimatization of the common

property regime would reduce perceived risks, which in turn could

potentially reduce appropriators' discount rates so as to enforce long-term

measures to manage and continue managing the CPR.

Factors favoring the emergence of a CPR regime

At this point it will be useful to consider and relate two frameworks for

analyzing the emergence and evolution of the Boca Manu common property

regime: (1) White and Runge’s (1995) three-phases framework (referred to

as W-R framework hereafter) (see INTRODUCTION), and (2) Ostrom’s

(1990, p. 90) eight ‘design principles’ which she derived from analyzing a

set of long-enduring CPR institutions and were proposed as conditions

helping to account for their success.

The W-R framework makes clear the importance of local leaders in

challenging the status quo, proposing new strategies and agreements to

implement them. An important example is Campos's proposal of the

rotation system. Campos's motives, as well as those of the members who

supported his measure, appear to have been rational, because the cost of

organization (e.g. travel to Puerto Maldonado and registration in SUNARP)

appear to have been less than the benefits. (However, we cannot quantify

what we might call the 'social costs' of gathering community consensus and

possibly dealing with rivals). Ensuring that the net benefit of collective

action remains positive will be an important consideration when fees for

collections and permits are reactivated in the future. Maintaining an elected

44

Board of Directors is fundamental for allowing local leaders to design and

propose potential improvements to the CPR regime.

The subsequent phase in W-R framework determines whether participants

will cooperate or defect. Such a decision is in turn affected by individual

expected costs and benefits (Ostrom 1990; White & Runge 1995). For

example, Association members must decide whether to follow the list order

or not. Complying increases total benefits as it reduces risk (especially from

fights) and collection costs, whereas defecting offers the potential for higher

private benefits but could lead to suspension or expulsion by the Association

and possibly by the MNP administration itself. The process of weighing

these is facilitated by having had previous financial experience (Morrow &

Hull 1996). We suggest that the experience of engaging in a market

economy since the era of timber and pelt extraction has facilitated the

process of balancing the potential costs and benefits of following

Association rules. Moreover, past experience of successful collective action,

such as obtaining exclusive withdrawal rights and the registration process, is

also likely to have promoted the acceptance of new rules Baland and

Platteau (1996).

Finally, in W-R framework’s third phase, collective actions are expected to

emerge and survive only when a 'critical mass' of users understands the

potential gains from action. Thus, although the Rojas family was against the

rotation system, the majority of members, regardless of whether from Boca

Manu or from the Isla de los Valles community, perceived the new system

as fair and accepted it.

45

White and Runge (1995) state that each phase is affected by socio-cultural

and biophysical contexts. Ostrom’s design principles help to identify those

contexts. Ostrom’s design principles are: (1) clearly defined boundaries;

(2) congruence between appropriation and provision rules and local

conditions; (3) collective-choice arrangements; (4) monitoring; (5)

graduated sanctions; (6) conflict-resolution mechanisms; (7) minimal

recognition of rights to organize; and (8) nested enterprises.

Principle (1), clearly defined boundaries, is present in the Boca Manu

system in two ways. The tree-capture zone (Fig 3) is clearly delimited by

the MNP, and the membership of the Association is known to all. This

factor has facilitated the establishment of new rules, and thus the evolution

of action (W-R framework’s third phase), because the appropriators can

identify the beneficiaries of coordinated actions (Ostrom 1990). In addition

to this condition, however, users must be capable of enforcing internal and

external rules and their exclusive access rights to secure resource tenure

(Acheson 1975; Morrow & Hull 1996). The Association now has a record of

sanctioning internal rule breakers, and it is evident from their previous

competitive interactions with the community of Diamante that they have

been able to evict non-members from the tree capture zone. This aspect is

relevant to that of protecting not only the resource units from intruders but

also the stock itself, i.e., the park. Therefore we propose that the Association

has the potential to become a steward of the MNP, protecting the park’s

main entrance from incursions such as miners and loggers.

46

Design principle (2) is present regarding the congruence between rules and

local conditions. Ostrom (1990, p. 92) refers to the local conditions as “the

specific attributes of the particular resource”. In Boca Manu, the turn and

two-list systems try to cope with the temporal variability in the provision of

the resource. Sporadically flooding episodes occur only during the rainy

season (5-6 months a year) and tree sizes varies considerably (see Table 3).

As such, with the rules in use each appropriator has a high degree of

certainty about when to go to the tree capture zone to capture at least one

tree. More over, the two lists copes with tree volume variability giving the

chance to each appropriator to capture one big and one small tree. These

attributes help the appropriators decide when to go to the capturing zone and

thus decide whether to cooperate or defect (W-R framework’s second

phase). On the other hand however, the congruence between appropriation

and provision rules is not present. Provision of trees, that is, the resource

flow from the stock, is not dependent on appropriators’ behavior but on

environmental variability and on the MNP administration (i.e. monitoring

and protecting the stock), thus the Association lacks provision rules.

Design principles (3), (6), and (7) are also present in the Boca Manu system

and have affected all three phases of W-R framework. The opportunity to

conduct regular meetings in which all appropriators have the right to discuss

the functioning of the capture activity and can participate in modifying the

rules in use (design principle (3)) played a major role in the Boca Manu

system as it allowed the proposal, acceptance, and evolution of operational

rules (all W-R framework’s three phases). The fact that individuals who

face repeated CPR dilemmas can communicate with each other has been

47

recognized, in both empirical and experimental settings, as an important

factor allowing resource users “...(1) to calculate coordinated yield

improving strategies, (2) to devise verbal agreements to implement these

strategies, and (3) to deal with non-conforming players....” (Ostrom et al.

1994, pp. 167; see also Bowles 2008). Since the Association’s members are

repeated game players, as they live in the same extended community and are

dependent on the resource for many seasons into the future, they have had

the incentive to gather and discuss problems to be able to find viable

solutions. As we have related above, these solutions have been implemented

in the form of operational rules.

Low-cost local arenas, design principle (6), such as the Association's regular

meetings and those called by the MNP administration, facilitated the

creation of a consensus in the face of conflicting claims, such as that

presented by the Rojas, and thus affecting W-R framework’s third phase. It

appears that the MNP administration can continue to provide more help in

this, such as by sponsoring information-sharing workshops, so as to

continue to improve the Boca Manu CPR regime (see Ostrom 1990). We

believe this should be a priority in park administration's activities as well as

in those of conservationists NGOs, in addition to focusing on resource use

restrictions, at least for the case of long term established bordering human

populations.

Design principle (7) was also present. The Association has the right to

devise its own internal rules, and this right has not been challenged by the

park administration or any other governmental authority.

48

Finally, design principle (8) is present in Boca Manu too. Ostrom (1990, p.

90) states that “for CPR institutions that are part of larger systems,

appropriation, provision, monitoring, enforcement, conflict resolution, and

governance activities are organized in multiple layers of nested enterprises”.

The Association is nested within the larger system of the Peruvian protected

areas and forestry legislations. As such, the institutional changes that took

place in the early 2000s (see A new institutional setting) prevent the

Association from further obtaining the tree capture authorization, forcing

appropriators to sell their products in the black market. There appears to be

a contradiction in this situation since at the Association-MNP administration

level the Association is recognized and authorized to capture trees but

considering the Peruvian legislation they do not comply with all

requirements and, thus, are not allowed to legally transport and sell captured

trees. This situation produces an incomplete system (Ostrom 1990) and we

speculate that this situation could be affecting the likelihood of cooperation

(W-R framework’s second phase), as low black market earnings motivates

appropriators to capture more than one tree during their turn so as to fulfill

their income requirements.

Monitoring and sanctioning

Monitoring and sanctioning, design principles (4) and (5), have been

previously termed "the crux of the problem" (Ostrom 1990, p. 94), because

they determine the expected costs and benefits that individuals face and

therefore determine whether individuals will follow the rules of the CPR

49

(W-R framework’s second phase) (Ostrom 1990, Ostrom et al. 1994). What

factors facilitate monitoring and sanctioning in Boca Manu, given "the

normal presumption [...] that participants themselves will not undertake

mutual monitoring and enforcement because such actions involve relatively

high personal costs and produce public goods available to everyone"

(Ostrom 1990, pp. 95)? One of the most important factors appears to be the

small area in which the trees are captured (see Agrawal 2002), which allows

mutual monitoring. In addition, all the users live in the same extended

community, which allows quick dissemination of rule-breaking behavior,

and thus legitimizes sanctions. The number of users is also small, but the

importance of this factor to the functioning of CPR regimes is contested

(Varughese & Ostrom 2001).

In essence, the clear delimitation of the tree capture zone has allowed the

Association to act like a 'harbor gang' (Acheson 1975) and protect its

resource units and the resource stock from 'roving bandits' (Berkes et al.

2006) who might trespass MNP boundaries. This possibility is becoming

more likely now that road construction in Madre de Dios is increasing

immigration by reducing transport costs and accelerating the rate of land use

change by harvesting activities such as illegal logging and gold-wash

mining (Dourojeanni 2006; Mendoza et al. 2007). Elsewhere, we have

described such potential protective externalities from local resource users as

‘conservation wagers:’ known, small, but growing biodiversity costs that

are paid for the possibility of a much larger conservation benefit some

undefined time in the future (Shepard et al. 2009). Here, the biodiversity

cost is effectively zero, given that the resource units are floating tree trunks.

50

A demographic challenge to the future of the Boca Manu CPR

For Boca Manu, as elsewhere (Wittemyer et al. 2008), population growth is

occurring at the edges of protected areas, and the sustainability of the

current CPR would appear threatened as more inhabitants of Boca Manu

reach eighteen years of age. Although it is not clear that a larger number of

resource users tends to be less likely to successfully manage a CPR

(Varughese & Ostrom 2001), we believe that in the Boca Manu system,

population growth poses a threat because of the decreasing per-capita

benefits. Examining how the local and MNP institutions adapt to this

change will be of real research interest.

CONCLUSIONS

The Boca Manu system exemplifies a particular common pool resource

regime in which, although the stock is not affected by the harvesting of

resource units, an institution has emerged and developed to act as a harbor

gang (Acheson 1975) that appears to reduce intra-group conflict, distribute

benefits, and increase profits. Moreover, the Boca Manu CPR potentially

can lead to increased protection of the park itself, since the Association is

already set up to exclude outsiders from the tree-capture area, which

happens to be the only riverine entrance to the park.

The perception that access to the common property, floating trees, was at

risk appears to have been an important reason for why collective action

emerged and progressed in Boca Manu, as has been previously suggested

for other systems (McCay 2002). In addition, the past common history of

51

initial appropriators, during the pre-MNP era, has likely allowed natives and

settlers to communicate among themselves to pursue the common good.

This attribute has survived until today and has been reinforced by the park

administration, which, without challenging the right of appropriators to

devise their own rules, has repeatedly participated in meetings with the

Association's members seeking to contribute with the improvement of the

system's functioning. This type of joint participation, one in which a harbor

gang is allowed to decide how to internally regulate its actions and is

officially recognized, can act as a model for negotiations between protected

area administrations and adjacent populations. However, at least some level

of shared monitoring must remain.

Nevertheless much remains to be done and we believe there is potential for

improvement. First, as we have seen, a skewed distribution of benefits still

remains, though this is apparently not so much because of cheating going on

in the system but because as a consequence of the asset poverty among the

Valles family and a much higher value of boats compared to the other final

products. Causes for the former observation remain to be studied but if the

process of complying with the official rules to capture floating trees within

protected areas is completed, all the users will be allowed to sell their

captured trees and lumber at a legal, higher price, which would at least

improve welfare generally.

Finally the potential endogenous threat from an increased number of users

should be urgently treated as this could deteriorate the functioning of the

system. As a possible solution we propose that the Association should avoid

52

increasing the capture effort and seek to promote the employment of

otherwise new eighteen years old members in trades such as carpentry using

not only commonly utilized tree parts but also, and especially, wood debris

such as roots and branches, which are otherwise almost always discarded.

Difficulties in the development of such a project abound, such as the capital

constraint and high transportation costs from and to Boca Manu for the

connection to markets. Nevertheless such an initiative should take

advantage of the already existing institutions to build upon them.

ACKNOWLEDGEMENTS

We are grateful to the Asociación de Artesanos Recolectores de Troncas

Ecológicas de Boca Manu e Isla de los Valles members and its 2007 and

2009 Board of Directors for having voluntarily allowed one of us (RG) to

conduct research in their settlements and for having shared information in

interviews and questionnaires, as well as for having granted access to the

Association’s Minutes Books. We especially would like to thank Wilson

Valles, Wilfredo Valles, Ricardo Guerra, Jorge Sarmiento, Manuel Moreno,

Eugenia Soto, Albertina Chura and Juan de Dios Carpio for hospitality and

cooperation. We also thank all persons and researchers who had previously

worked in the area and contributed with valuable information. Research

funding was provided by the Russell E. Train Education for Nature Program

and the Frankfurt Zoological Society, which additionally provided logistical

support. We thank INRENA and the Manu National Park administration,

especially Amilcar Osorio, Angela Oroz and Carlos Nieto, for research

permissions, sharing information, and logistical support. We also thank

53

CREES Foundation, Blanquillo Lodge and SAS Travel for logistical

support.

54

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collective action: Some evidence from community forestry in Nepal. World

Development 29(5): 747-765.

White, T. A. & Runge, C. F. (1995) The emergence and evolution of

collective action: lessons from watershed management in Haití. World

Development 23(10): 1683-1698.

Wittemyer, G., Elsen, P., Bean, W. T., Burton, A. C. O. & Brashares, J. S.

(2008) Accelerated human population growth at protected area edges.

Science 321(5885): 123-126.

59

FIGURES AND TABLES

Figure 1. Map of Manu National Park and location of (1) Limonal guard post, (2) Boca Manu, (3) Isla de los Valles and (4) Diamante. See also Figure 3.

60

a)

b)

c)

Figure 2. Photos showing the current production of boats by building up the sides of dugout canoes with planks (a, b). Sits and a roof are also built (c). An outboard or peke-peke motor is usually attached to the rear of the boat.

61

Figure 3. Location of the tree capture zone and the Limonal guard post within the MNP. (Modified from Catpo & de la Cruz 2006).

62

Figure 3. Monthly total volume (board feet) of cedro (Cedrela odorata) and catahua (Hura crepitans) reported to have been captured at the tree capture zone between January 2005 and April 2007. Data corresponds to the register at Limonal guard post, Manu National Park (1 m3 of sawn wood = 424 board feet). A precipitation line trend reported from the Cocha Cashu Biological Station is also presented. (Provided by CCBS staff).

63

Figure 5. Bars showing total number of trees (198) captured and registered at Limonal guard post by each Association’s member (41) between October 2006 and April 2007.

64

Figure 6. Means and 95% confidence intervals bars are of mean number of trees captured by the appropriators from Boca Manu, the Valles families, and the Rojas families during the 2006-07 season (ANOVA: F2,38 = 5.169, p = 0.01). Bars sharing a superscript are not significantly different (p > 0.05) using the LSD post-hoc test.

65

Figure 7. Means and 95% confidence intervals bars of per capita revenues earned by each class of producers. (Kruskal-Wallis: !2

= 22.428, df = 2, p < 0.001).

Medians are US$ 5,532.87, US$ 378.32, and US$ 645.18, respectively.

66

Figure 8. Means and 95% confidence intervals of per capita profits earned by each class of producers. (Kruskal-Wallis: !2

= 20.849, df = 2, p < 0.001). Medians

are US$ 9,482.12, US$ 288.92, and US$ 1,298.45, respectively.

67

Figure 9. Estimated total revenues (white) and profits (black) generated with cedro trees captured between October 2006 and April 2007 for each class of product: raw logs, lumber and boats. Estimates are calculated after assigning each of the 41 producers (15 raw logs; 17 lumber; and 9 boat producers) to the product class that made up the majority of their revenues. Prices: Raw Logs, US$ 0.22/board feet; lumber, US$ 0.39/ b.f.; and 15m boats, US$ 1406.69/each.

68

Table 1. Tools, inputs, and labor used to capture trees and saw lumber, and their 2007 prices and costs at Boca Manu. All prices and costs are

presented in US Dollars after converting Nuevos Soles into US Dollars using a rate of 3.199 Nuevos Soles per one US Dollar. 1 m3 of sawn

wood is equivalent to 424 board feet (b.f.) of sawn wood.

Activity

Capturing trees

Tools

5 Kg. of rope 1 Machete 1 Hammer 1 Flashlight 1 Boat 1 Motor (peke peke) Inputs

5 gl. of gasoline 1/6gl. of oil 4 Eyebolts

Unit price

2.19/Kg. 4.69/unit 4.69/unit 9.38/unit 312.60/unit 468.90/unit Unit price

4.02/gl. 6.25/gl. 1.56/unit

Depreciation

1 Season 1 Season 1 Season 1 Season 1825 days 1825 days Expenditure unit

Visit to Limonal Visit to Limonal Captured tree

Season/Daily cost

10.95 4.69 4.69 9.38 0.17 0.26 Cost per visit/tree

20.1 1.04 6.24

69

Table 1. Continue

Sawing trees

Labor and others

2 persons/day

Trimming roots and branches

Transport outside MNP

Inputs

3.5 gl. of gasoline 1gl. of used oil Labor

1 person/day

Sawing the wood

Total

Unit price

6.25/person

9.38/log

10.27/log

Unit price

4.02/gl.

1.88/gl.

6.25/person

53.14/650 b.f.*

Expenditure unit

Person

Captured tree

Captured tree

Cost per 650 b.f.

14.07

1.88

12.5

53.14

81.59

Cost per capture

12.5

9.38 10.27

Cost per board foot

0.12

* This relationship was obtained from six lumber producers during the interviews.

70

Table 2. Tools, inputs and labor used to build one 15-meter boat and their 2007 local prices (Boca Manu). Items and prices were provided by

one boat producer and corroborated by another two and two local shop owners. All prices and costs are presented in US Dollars (US$ 1.00 = S/.

3.199). (1 m3 of sawn wood = 424 board feet of sawn wood).

Tools

Hand adze

Big adze

Saw

Big plane

Small plane

Square

Spokeshave

Chisel

Hammer

Level

Price

2.19

4.69

4.06

11.88

9.38

2.50

2.50

2.19

4.69

4.69

Lifetime (days)

365

365

365

365

365

365

365

365

365

365

Daily cost

0.006

0.013

0.011

0.033

0.026

0.007

0.007

0.006

0.013

0.013

Used days

15

19

15

19

15

19

15

15

15

15

Final cost

0.09

0.25

0.17

0.63

0.39

0.13

0.11

0.09

0.20

0.20

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Table 2. Continue

Chainsaw

Chainsaw file

Board puller

Total

Inputs

Cedro boards

Catahua

Tar

3’’ Nails

4’’ Nails

1’’ Nails

Rope

Jute

1,563.00

2.5

109.41

Unit

Board foot

Board foot

Block (~7 Kg.)

Kg.

Kg.

Kg.

Kg.

Meter

1825

365

1825

Quantity

1021

1272

2.5

7

7

10

2

5

0.86

0.007

0.06

Unit price

Variable

Variable

15.63

2.19

2.19

3.13

0.63

1.56

19

19

1

16.34

0.13

0.06

18.79

Final cost

Variable

Variable

39.08

15.33

15.33

31.30

1.26

7.80

72

Table 2. Continue

Gasoline

Used oil

Total

Labor

Sawing 2,293 b.f.

Building the boat

Total

Gallon

Gallon

Days

7

11

19

5

Daily wage

6.25

6.25

5.94

1.56

Workers

2

2

112.86

7.80

Variable

Final cost

87.50

137.50

225.00

73

Table 3. Total number, total volume, mean tree volume, and volume ranges of cedro and catahua trees captured between 2005 and 2007.

Season

2005

(Jan – Apr)

2005 – 2006

(Nov – Mar)

2006 – 2007

(Oct – Apr)

Total

Species

Cedro

Catahua

Cedro

Catahua

Cedro

Catahua

Cedro

Catahua

Total trees

89

4

159

4

187

11

435

19

Total volume (b.f.)

94,537

19,409

183,779

12,029

190,222

22,610

468,538

54,048

Mean tree volume (b.f.)

1,062.21 (SD = 1,024.47)

4,852.25 (SD = 4,057.31)

1,160.12 (SD = 889.35)

3,007.25 (SD = 1,748.71)

1,026.86 (SD = 800.79)

2,055.45 (SD = 1,206.76)

Volume Range (b.f.)

[Max – Min]

[4,545 – 128]

[10,367 – 1,800]

[4,886 – 210]

[5,175 – 918]

[4,293 – 12]

[5,007 – 1,016]

APPENDIX 1

QUESTIONNAIRE

Questionnaire number: Date:

I. Appropriator identification

1. Has ID: Yes No

2. Place of birth:

3. Age:

4. House is at: Boca or Isla de los Valles

5. Number of family members:

6. Family members within the members list

75

7. How many family members participate and collaborate during your capture

turn:

8. Since when do you participate in the capture activity:

9. Main use you give to the capture wood:

Build and sell boats:.............1 Furniture production:.........2

House building:....................3 Handcrafts production:......4

Sell swan wood (lumber):....................................................................5

Sell raw logs (timber):.........................................................................6

Others:

II. Economic activity:

II.1 Benefits

10. What are the price of one board foot (b.f.) and its most profitable use (raw

logs, lumber, boats, others)?

Species Price (S/.

Nuevos Soles)

Most profitable use

Caoba (Kao)

Cedro (Ce)

Catahua (Kt)

Others

11. How many floating trees (and volume) did you capture in the last flooding

event, and in previous ones?

1(#/V) 2 (#/V) 3(#/V) 4(#/V)

Kao

Ce

Kt

Other

76

11.1 How many did you use to capture before the turn system was established?

(More) (Less) (Same)

12. How many boats did you build and sell with the trees you captured in the

last flooding event and previous ones (only for I.10: 1)?

Before the turn system: (more boats) (less boats)

(same)

13. From that wood (volume) that you captured in the last flooding event and

previous ones, how much did you saw and sell (only for I. 10: 5)?

Before the turn system: (More volume) (Less volume) (No

change)

Approximated volume (b.f.) per year before the turn system:

14. From that sawn wood did you keep any (volume) for self use (only for I.10:

5)?

1(#/price) 2 (#/price) 3(#/price) 4(#/price)

1(V/price) 2 (V/price) 3(V/price) 4(V/price)

Kao

Ce

Kt

Other

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15. How many raw logs did you sell from those you captured in the last flooding

event and previous ones (only for I.10: 6)?

Before the turn system: (More) (Less) (No change)

15.1 How many did you approximately sell per month before the turn system?

16. Did you sell all the captured trees from the last flooding event or did you

keep some (how many)?

(All) (Half) (Less than half) (None)

II. Costs

17. Do you share the necessary materials to capture floating trees with any other

member (with how many)?

Yes No Number:

18. Do you own a boat for capturing trees?

Own Lend Hired (price):

19. Do you own a chainsaw?

Own Lend Hired (price):

1(V) 2 (V) 3(V) 4(V)

Kao

Ce

Kt

Other

1(V/price) 2 (V/price) 3(V/price) 4(V/price)

Kao

Ce

Kt

Other

78

20. Do you provide receipts when you sell boats?

Yes No

21. How many days, petrol and oil (lubricant) did you spend to capture trees

during the last flooding event and previous ones:

22. Do you buy petrol and oil at Boca Manu? (Yes) (No)

23. Before the turn system did you spend: More days: (Less days) (Same

days)

24. How many days, petrol and oil (lubricant) did you spend to transport the

captured trees from the last flooding event and previous ones:

Before the turn system you spent: (More days) (Less days)

(Same)

25. How many days, petrol and oil (lubricant) did you spend building boats with

the captured trees from last flooding event and previous ones?

1 2 3 4

Days

Petrol

Oil

1 2 3 4

Days

Petrol

Oil

79

Before the turn system did you spend (More) (Less) (Same) number of

days?

26. How many days, petrol and oil (lubricant) did you spend sawing the wood

you capture in the last flooding event and previous ones for selling it?

Before the turn system did you spend: (More) (Less) (Same) number of days?

27. Have you bought captured wood (volume) by other appropriator during the

last flooding event and previous ones?

r: round logs; a: sawn wood

What do you use it for?

(Building boats) (Furniture)

(Building house) (Handcrafts)

(Selling lumber)

1 2 3 4

Days

Petrol

Oil

1 2 3 4

Days

Petrol

Oil

1(V/price) 2 (V/price) 3(V/price) 4(V/price)

Kao

Ce

Kt

Other

80

28. What other economic activities do you undertake when you are not engaged

in any log capture and/or processing activities:

(Mining) (Agriculture) (Timber) (Fishing and

hunting)

(Tourism) (Commerce) (Handcrafts) (Others)

III. Organization

Access rights

29. Who are the persons allowed to capture floating trees?

List members Others Do not know

30. Do you agree with allowing new persons into the list?

Yes No Do not know

Appropriation rules

31. Do you agree with the turn system for capturing floating trees?

Yes No Do not know

32. Do you agree with the amount of floating trees each appropriator is allowed

to capture in his/her turn?

Yes No Do not know

Collective choice arrangements

33. Do you participate in the Association meetings?

Always Few times Never

81

34. Last time you had a complaint or accusation, did you communicated it to the

Board of Directors?

Yes No Notes:

35. Have any of these complaints or accusations been resolved either by all list

members voting to sanction it or other method?

Yes No Notes:

Monitoring

36. (Without providing names) Have you ever witnessed another user

committing a fault such as trespassing the Limonal guard post limit for

capturing the floating trees?

Yes No

37. Have you ever accused another user for committing a fault?

Always Rarely Never

38. Have you ever helped the park guards to control the capture activity?

Always Rarely Never

39. Have you ever helped the park guards to measure the captured trees to

estimate their volume?

Always Rarely Never

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40. Have you ever accompanied the park guards during their patrolling and

monitoring of riparian forests to audit their condition?

Frequently Rarely Never

Graduated sanctions

41. Have you ever been informed when another user committed a fault?

Always Rarely Never

42. Do you agree to forbid one user from further capturing if he/she has

committed the same faults again and again?

Yes No

43. Are there any temporally suspensions for committing a fault?

Yes No Period of time:

INTERVIEW

1. How was the Association formed? Who organized it; who developed the idea

of the turn system; what is your opinion about this system? Do you think that

the rules in use (turns, amounts, etc.) that have been established are fair,

why?

2. What are the most serious problems or constraints that reduce your benefits;

are these problems related to the MNP administration or to other

83

appropriators or both? Which are the agreements that are not respected or

enforced. Which are the most common faults that have been committed by

appropriators in the past (provide examples)?

3. What is your opinion on the case when Mr. Rojas and sons entered the MNP

and log several standing trees; were they sanctioned; were those trees seized,

has this kind of event happened again ever since? How are faults, disputes,

offences, etc. usually resolved?

4. Are there more or less trees floating down the river than in previous years?

Do you consider that floating tree numbers are enough for the amount of

boats and lumber that are demanded? If not, what measures would you

suggest to improve this situation? Do you think that the fact that MNP

protects the forests assures the provision of floating trees that you later

capture?

5. Is the tree capturing activity, and later transformation and selling, your main

income source; do you think that this activity is profitable? What other

economic activities do you undertake?

6. If you were told that you could no longer capture the floating trees within

MNP or these trees were suddenly disappeared, what other economic activity

would you do?

84

7. How do you see the capture activity in the next 10 years; do you think your

children will continue with this? Do you think new people will enter into the

list or would you totally prohibit new members? How would you enforce

this?

8. What is your opinion on the proposal to extend the road from Shintuya to

Boca Manu and then to Colorado? Which will be the advantages and

disadvantages for you?

CHAPTER 2

MODELING THE EFFECT OF POPULATION GROWTH AND

SECONDARY ROAD EXPANSION ALONG THE NEW

INTEROCEANICA SUR HIGHWAY OF SOUTHEASTERN PERUVIAN

AMAZON

RENZO GIUDICE1, CHRIS KIRKBY1, DOUGLAS W. YU1,2, RAFAELLA

SILVESTRINI3, BRITALDO SOARES-FILHO3, HERMANN RODRIGUES3

1 School of Biological Sciences, University of East Anglia, Norwich, Norfolk NR47TJ, UK

2 State Key Laboratory of Genetic Resources and Evolution; Ecology, Conservation, and

Environment Center (ECEC), Kunming Institute of Zoology, Chinese Academy of Science,

Kunming, Yunnan, 650223, China

3Centro de Sensoramiento Remoto, Universidade Federal de Minas Gerais, Av. Antônio Carlos

6627, Belo Horizonte 31270-900, MG, Brazil

SUMMARY

Deforestation rates in southeastern Peruvian Amazon have been historically low

due to its relative remoteness and isolation from major roads. This situation is

changing in the face of the current construction and paving of the Interoceanica

Sur highway, which extends Brazil’s Trans-Amazon highway (BR-230) into

Peruvian territory, passing through one of the most diverse and rich ecosystems

in the world and is regarded as the major driver of current deforestation in the

region. As a means to contribute with ongoing efforts to offset the negative

effects deforestation has on ecosystem services and in the face of a post-Kyoto

agreement on reducing emissions from deforestation (RED), we developed a

spatially explicit deforestation model to simulate the pattern and extent of

86

deforestation in the region between 2000 and 2035. The simulation process is

comprised of two steps. The first generates five different deforestation rates

growth trends which represent five scenarios: low population

growth/construction or extension of secondary roads, low population

growth/construction and extension of secondary roads, high population

growth/no construction or extension of secondary roads, and high population

growth/construction and extension of secondary roads, plus a control scenario in

which, on the contrary to the first four scenarios, the deforestation rate remains

constant at historical levels. These scenarios seek to provide the first set of

deforestation baselines for the region. The second step involves a geo-referenced

stochastic cellular automata model, DINAMICA EGO, which simulates

deforestation based on the scenarios’ rates and on the distribution of spatial

variables that independently affect the deforestation risk across the landscape.

We identified that a maximum of 1,056,521 ha (11.4% reduction) of primary

forest could be lost due to the effect of the high population growth/construction

and extension of secondary roads scenario. If a regional RED project is

implemented to reduce the effect of secondary roads, RED credits could generate

a NPV of up to US$1,597.4M. The Tambopata National Reserve, in turn, is the

protected area within the study area mostly affected, and could loose up to

14,006 ha (5.3% reduction), whereas forestry concessions could loose up to

134,841 ha (9.97% reduction). The model demonstrates how data on, human

settlements, historical population growth, land-use legislation, and a set of spatial

variables can be used to evaluate the effect different scenarios could have on the

landscape dynamics and as such provide a useful information tool for decision-

making processes, and governments and civil society.

87

Keywords: Amazon, modeling deforestation scenarios, Interoceanica Sur highway,

IIRSA, RED, DINAMICA EGO, Peru.

88

INTRODUCTION

Deforestation has profound ecological and socio-economic effects on tropical

forests and upon their inhabitants. It enhances aridity and desertification,

modifies the water cycle and regional climate, and produces fragmentation and

loss of habitat and species, among others (Millenium Ecosystem Services 2005;

Magrin et al. 2007).

As a consequence of current increased rates of deforestation (Magrin et al.

2007), the ability of forests to deliver benefits, such as clean air, drinking water,

food, timber, and non-timber products, is being reduced and destroyed

(Millenium Ecosystem Services 2005). Such benefits sustain or enhance human

welfare (Fisher et al. 2008) and are not only critical for current and future

sustainable local and regional livelihoods but also for the global community

(Balmford & Whitten 2003).

In particular, the ability of Amazon rainforests to store carbon is viewed as a

crucial means to mitigate global warming by reducing and limiting the

concentration of CO2 in the atmosphere (Santilli et al. 2005; Killeen 2007;

Magrin et al. 2007; Strassburg et al. 2009). In fact, the estimated amount of

carbon stored in Amazon trees, 119 ± 28 Pg (Petagrams) (Houghton et al. 2001),

represents 1.5 decades of current global carbon emissions (Soares-Filho et al.

2006). This large carbon stock coupled with historical and current rates of

emissions (Strassburg et al. 2009) supports the initiative to implement, under a

post-Kyoto agreement, a scheme to Reduce Emissions from Deforestation and

Degradation (REDD) (UNFCCC 2006; Nepstad et al. 2007; Parker et al. 2008;

89

Pedroni et al. 2009). Although implementation methodologies are currently still

being debated (Pedroni et al. 2009; Strassburg et al. 2009), (REDD is not only

viewed as a relatively low cost means to mitigate global warming through the

reduction of carbon emissions (Angelsen 2008; Strassburg et al. 2009) but also

as a means to reduce poverty in developing countries through payments to local

people to avoid deforestation (UNFCCC 2006; Peskett et al. 2008).

Therefore, the Peruvian Amazon, the second largest portion of the Amazon basin

(68.7 M ha) containing an estimated 8,763 M tC (Strassburg et al. 2009) and

widely considered as one of the most biodiverse and still well preserved primary

forest regions in the world (Myers et al. 2000), has a major conservation value.

Historical deforestation rates in Peru have been relatively low –annual rate

between 1990-2000 was only 0.1–, compared to those of some of its Amazonian

neighbors such as Brazil or Ecuador (0.6 and 1.7, respectively) totaling almost

3M ha by 2000 (FAO 2009). More recently, the estimated annual net rate

between 1999 and 2005 was 64,700 ha, from which, only a small fraction (1-2%)

occurred within protected areas (Oliveira et al. 2007). It has been argued that the

land-use policy, specially that considering the governmental establishment and

extension of natural protected areas, indigenous reserves, and forestry

concessions, together with remoteness were effectively protecting the Peruvian

Amazon (Oliveira et al. 2007).

Nonetheless, the deforestation process takes place at different time and space

scales, responding to the local and regional socioeconomic and biophysical

90

characteristics and contexts, as well as to each area’s interrelation with distant

factors (e.g. global markets) (Geist & Lambin 2001; Wood 2002).

As such, the Southeastern region of the Amazonian Peru is witnessing a

relatively more accelerated increase in forest damage compared to other regions

(Instituto Nacional de Recursos Naturales (INRENA), et al. 2005, 2006; Centro

de Datos para la Conservación – Universidad Nacional Agraria La Molina

(CDC-UNALM) et al. 2007; Oliveira et al. 2007).

The current construction and paving of the Interoceanica highway, one of the

main IIRSA (Initiative for the Integration of the Regional Infrastructure of South

America) projects, extends Brazil’s Trans-Amazon highway (BR-230) into

Peruvian territory and is regarded as the major driver of current deforestation in

the region (Dourojeanni 2006; Soares-Filho et al. 2006; Killeen 2007; Mendoza

et al. 2007; Oliveira et al. 2007).

As it has been described before, opening access through the construction of roads

to previously isolated areas such as that of the southeastern Amazonian Peru is

one of the primary determinants in forest conversion (Chomitz & Gray 1996;

Kaimowitz & Angelsen 1998; Geist & Lambin 2001; Alves 2002; Soares-Filho

et al. 2004, 2006; Dourojeanni 2006; Perz et al. 2007)

Moreover, the construction of an official road promotes the expansion of

secondary roads, which are constructed by non-state, 'unofficial' actors and thus

are more difficult to monitor and control in relation to environmental impacts

91

(Perz et al. 2007). These roads further connect remote areas with the official

road, thus increasing the pressure on the forests to be logged or converted into

cropland (Chomitz & Gray 1996; Perz et al. 2007).

In addition, the construction of major roads such as the Interoceanica highway,

promotes immigration into newly accessible areas (Laurance et al. 2001; Alves

2002; Soares-Filho et al. 2004; Dourojeanni 2006). In fact, the Interoceanica has

already promoted human immigration to the area, mostly from Andean peoples

(Dourojeanni 2006; Oliveira et al. 2007), who are engaging themselves in

unsustainable activities such as the removal and destruction of large tracks of

forests adjacent to rivers so as to extract mineral gold from alluvial sediments, as

well as in illegal logging and contributing with the expansion of the agricultural

frontier (Dourojeanni 2006; Killeen 2007).

Thus, we use a spatially explicit deforestation model to forecast the pattern and

extent of deforestation in the southeastern Peruvian Amazon from 2006 to 2035,

with special attention to deforestation inside natural protected areas, indigenous

territories, and forestry concessions.

The model we present incorporates spatial variables, both biophysical (e.g.

swamps) and political (i.e. land tenure), and uses DINAMICA EGO, a stochastic

cellular automata model previously used to simulate deforestation in the Amazon

basin (Soares-Filho et al. 2002, 2004, 2006), to project the future allocation of

deforestation.

92

Future deforestation rates are projected as a function of two different human-

population growth trends (high and low) and two functional relationships

between human population sizes and deforestation rates (exponential and

logistic), so as to define four different deforestation scenarios, plus a control

scenario (historical trend), altogether representing the range of effects that the

eventual expansion of the secondary road network could have on the

deforestation process.

Although the effect of local human population growth relative to other drivers of

deforestation is widely debated, its effect on tropical forest cover reduction is

undisputed (Meyer & Turner 1992; Geist & Lambin 2001), and, thus, we assume

a positive correlation between them.

It is our aim to contribute with the efforts to offset the negative effects that

deforestation will have on ecosystem services by providing stakeholders and

politicians with a visual tool to guide their decisions when balancing

environmental conservation and development.

In particular, this tool should help in the process of establishing RED projects

(deforestation only, since we do not measure degradation) and negotiating the

corresponding carbon credits, which require the definition of a baseline

projection of the amount and location of expected deforestation (UNFCCC 2006;

Angelsen 2008). Based on the concept of additionality and current expectations

(Angelsen 2008; Strassburg et al. 2009) it is likely that RED credits (and REDD

credits in general) will be paid to preserve only forest cover that would have

93

been lost in the absence of RED projects. Thus, if the baseline projection were

based only on Peru's overall, historical deforestation rates, which are low, Peru

would be unlikely to receive enough RED funding to compensate the opportunity

costs of future forest conversion. However, historical rates do not take the effect

of the Interoceanica highway and secondary roads into account, so we provide

these four scenarios as the first set of baseline projections for the region.

METHODS

Study area and context

The study area is located in southeastern Amazonian Peru, within the Tropical

Andes biodiversity hotspot (Myers et al. 2000), and extends over a 10.8M ha

area and over an altitudinal range of 130-5500 m, incorporating the Department

of Madre de Dios and portions of the Departments of Puno and Cusco (Fig. 1).

The study area covers all or part of six state-protected areas (from North to

South: Alto Purús National Park (37%), Manu National Park (100%), Megantoni

National Sanctuary (65%), Amarakaeri Communal Reserve (100%), Tambopata

National Reserve (100%), and Bahuaja Sonene National Park (75%)), two

reserves for isolated indigenous peoples, hereafter referred to as Territorial

Reserves (Kugapakori Territorial Reserve (20%), and Madre de Dios Territorial

Reserve (100%)), and one large state-leased conservation concession (Los

Amigos Conservation Concession (100%)). In turn, these areas lie within two of

the three protected-area complexes (Vilcabamba-Manu and Tambopata-Pilón

Lajas) that make up the Vilcabamba-Amboró Conservation Corridor (VACC)

(Critical Ecosystem Partnership Fund - CEPF 2005). The VACC stretches from

the Vilcabamba mountain range in southern Peru to Amboró National Park in

central Bolivia (Fig. 2), covering 30M ha of one of the biologically richest and

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most diverse habitats on the planet (Myers et al. 2000; CEPF 2005). The VACC,

a bi-national trans-boundary conservation strategy with financing for

conservation and development projects provided by bilateral and multilateral

donors, seeks to maintain and enhance the connectivity within and between the

protected area complexes and to protect part of the southern half of the Tropical

Andes hotspot from the negative impacts that human activities, such as gold

mining, uncontrolled logging, road and dam construction, and population growth

are imposing on biodiversity in the region (CEPF 2005; Dourojeanni 2006;

Killeen 2007).

Bisecting the VACC between the two protected area complexes, and traversing

the study area, are sections 2, 3, and 4 of the Interoceanica Sur highway (IOS), a

westerly extension of the Trans-Amazon highway (Brazil’s BR-230) that will

connect major Brazilian cities and industrial centers with Pacific Ocean ports in

Peru, thus reducing transportation costs of Brazil’s agricultural and manufactured

exports on route to the Far East, particularly China and Japan. These sections are

expected to be the major driver of deforestation and concomitant biodiversity

loss, as well as social degradation in the region (Dourojeanni 2006). The IOS is

one of the principal projects of the Initiative for the Integration of the Regional

Infrastructure of South America (IIRSA) (see Dourojeanni 2006; Killeen 2007), a

consortium of twelve South American countries that promotes the development

of transport, energy and telecommunications infrastructure

(http://www.iirsa.org//Institucional_ENG.asp?CodIdioma=ENG; accessed July 9,

2009). The three sections are currently being paved and are scheduled for

completion in 2011 at a cost of at least US$ 892M (see Dourojeanni 2006; Bank

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Information Center 2009 (http://www.bicusa.org/es/Article.11327.aspx, accessed

July 22, 2009); Diario Gestión, June 2009 (http://gestion.pe/noticia/280423/IOS-

sur-entregada-primer-trimestre-2011, accessed July 23, 2009)).

Model development

General approach

The simulation process of cumulative annual deforestation across the landscape

is comprised of two main steps. The first involves a scenario-generating model

that calculates annual deforestation rates and deforested area (in hectares) based

on human population growth levels in the study area. It estimates the expected

annual deforestation rate and deforested area for each of the 30 years (2005-

2034, inclusive) of each scenario, based on projections of historical deforestation

rates from the five years between 2000 and 2004 and associating these rates to

population growth rates. By altering population growth rates upwards, an

expected result of paving the IOS, we generated four distinct scenarios over the

35-year study period by crossing two deforestation rates (Low and High) with

two functional relationships between population growth and deforestation rates,

exponential and logistic. The first one represents no further expansion of the

secondary road network, whereas the second considers the expansion. Thus our

five scenarios are defined as follows: low population growth/no construction or

extension of secondary roads (ScenarioLow,No2ndaryRoads), low population

growth/construction and extension of secondary roads (ScenarioLow,Yes2ndaryRoads),

high population growth/no construction or extension of secondary roads

(ScenarioHigh,No2ndaryRoads), and high population growth/construction and extension

of secondary roads (ScenarioHigh,Yes2ndaryRoads), plus a control scenario

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(ScenarioCtrl) in which, on the contrary to the first four scenarios, the

deforestation rate remains constant throughout the simulation process. We

assumed no re-growth of forest following deforestation as we are only interested

in primary forest, which takes considerably longer than 35 years to regenerate.

The second step involves passing each year’s deforestation to DINAMICA EGO

version 1.2.3 (hereafter DINAMICA), a geo-referenced stochastic cellular

automata model that simulates land-cover change, in this case deforestation

(from forest to deforested) based on the spatial distribution of static variables

(such as land tenure categories and biophysical attributes) and dynamic variables

(such as distance to deforested) that independently affect the deforestation

probability (risk) at every point across a landscape (Soares-Filho et al. 2002,

2004, 2006; Almeida 2003; Silvestrini 2008).

Relationship between population, population growth and deforestation rates

Human population growth

We calculated human population growth trajectories for the study area between

2005 and 2035. The study area contains 536 population centers, of which 310

(57%) are in Madre de Dios, 119 (38%) in Cusco, and 27 (5%) in Puno

(Appendix 1). These centers represent 100%, 2.73% and 0.27% of all centers to

be found in the Departments of Madre de Dios, Cusco and Puno, respectively

(INEI 1994). The centers that correspond to Cusco are those associated with

twelve districts: Yanatile, Quellouno, Challabamba, Paucartambo, Kosñipata,

Camanti, Marcapata, Cusco, San Jeronimo, San Sebastian, Santiago, and

Wanchaq districts, while those in Puno correspond to only two districts:

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Ayabaca and San Gabán. The population centers in Madre de Dios are

distributed across all of its eleven districts: Tambopata, Inambari, Las Piedras,

Laberinto, Manu, Fitzcarrald, Madre de Dios, Huepetuhe, Iñapari, Iberia, and

Tahuamanu. We included parts of Cusco and Puno (and their respective

population centers) in the study area because (1) sections 2 and 4 of the IOS pass

through them (Fig. 1), (2) they contain tropical forest, (3) we wanted to include

the city of Cusco, as it exerts a deforestation pressure in all three departments via

the demand from its population for agricultural and timber products, and (4)

because we wanted to include all of the Manu National Park (MNP, located in

both Cusco and Madre de Dios) and Bahuaja Sonene National Park (BSNP,

located in both Puno and Madre de Dios). Most of the BSNP (821,233 out of

1,091,416 ha, 75.2%) lies within 50 km of the IOS, regarded as the area of

influence of major highways in the Amazon basin (Laurance et al. 2001;

Dourojeanni 2006).

Data on the number of inhabitants in each populations centre in 2005 was

obtained from a GIS shape-file constructed by the Peruvian government’s

Ministerio de Educación (Ministry of Education) and kindly provided to us by

the Centro para la Sostenibilidad Ambiental – CSA (Center for Environmental

Sustainability) at the Universidad Cayetano Heredia, Lima, Peru.

There were six population centers in Puno that had no data for 2005. For these,

we estimated their populations by growing their 1993 census counts (Npop ctr,1993)

as follows:

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(Npop ctr,1993) (! district)12 = Npop ctr,2005

where ! is the annualized population growth rate between 1993 and 2005 of the

district in which each center is located, and was calculated as:

!district = (Ndistrict,2005 / Ndistrict,1993)1/12

Both population center and district population data were acquired from INEI’s

webpage (1993 Population Census:

http://iinei.inei.gob.pe/iinei/RedatamCpv1993.asp?ori=C; 2005 Population

Census: http://iinei.inei.gob.pe/iinei/RedatamCpv2005.asp?ori=C; both accessed

June 17, 2008). After calculating these six population estimates and adding them

to the previously acquired ones, we obtained a total population estimate of

3,401people in 2005 for the Puno portion of the study site.

Similarly, there were 84 population centers in Cusco and 77 in Madre de Dios

that were missing population data for 2005. We used the same procedure

outlined above to estimate their respective 2005 population sizes, giving a total

of 342,789 and 92,024 people for Cusco and Madre de Dios, respectively.

Thereafter, we grew each Dept.’s 2005 population until 2035, using Puno’s and

Cusco’s mean, district-level annualized population growth rates, which were

calculated using only the districts within the study area, and using the Dept.-wide

growth rate for Madre de Dios for the 1993-2005 inter-census period (see Table

1). 1993 and 2005 populations for the Madre de Dios department were also

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acquired from the INEI website (http://www.inei.gob.pe/; accessed June 17,

2008).

Finally, we summed the calculated populations in our study area for each year

between 2005 (438,214 inhabitants) and 2035 (795,345 inhabitants). These

numbers were used in our two low-deforestation scenarios

(ScenarioLow,No2ndaryRoads) and (ScenarioLow,Yes2ndaryRoads), and reflect a situation in

which immigration is kept at a minimum, even after the IOS has been paved.

In contrast, our high-deforestation scenarios (ScenarioHigh,No2ndaryRoads)

(ScenarioHigh,Yes2ndaryRoads), used the mean, district-level annualized population

growth rates observed between the 1981 and 1993 censuses (Table 1), which

were acquired following the same procedures as above. For the case of Madre de

Dios, we used its total population growth rate during the same era. Population

data from the 1981 census were obtained from

http://iinei.inei.gob.pe/iinei/RedatamCpv1981.asp?ori=C (accessed June 19,

2008). The 2035 total estimated population for the study area is 1,338,877

inhabitants.

We created the two high-deforestation scenarios because from 1985 to 1990, the

administration of then-President Garcia instituted a series of agricultural

subsidies in the form of land titles and easy credit that promoted immigration and

caused a rapid expansion of the agricultural frontier in the Tambopata Province

of Madre de Dios (Alvarez & Naughton-Treves 2003). In fact, averaged

population growth rates in all three Departments were higher in 1981-93 than in

1993-05 (Table 1). The new IOS can be seen as another subsidy for the

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agricultural sector of Madre de Dios, which in turn should increase current

population growth and deforestation rates.

Deforestation rate scenarios

We then calculated deforestation rates as two functions of the estimated human

population growth rates to define our scenarios. Firstly, the historical

deforestation rate, used in the initial simulation between 2000 and 2005, was

calculated by using DINAMICA to compare the 2000 and 2005 land-cover raster

images of our study area (100m x 100m resolution), which were originally

produced and classified from Landsat TM+7 and CBERS satellite images (see

Model calibration and parameterization below). The annualized rate is "2000-05 =

0.001512 (Step 1 below). We then generated two functional relationships

between human population size and deforestation to project growth in the

deforestation rate from 2005 to 2034.

Exponential: The historical deforestation rate (") was increased annually, from

2005 to 2034, by the population growth rate (!) of the previous year i: "i+1 = "i

!i, where !i = (Ni+1 /Ni). Because the population size (N) is growing

exponentially (Fig. 3), this kind of relationship also produces exponential growth

in the deforestation rate for both (ScenarioLow,No2ndaryRoads) and

(ScenarioHigh,No2ndaryRoads), (Fig. 4 & 5). (ScenarioLow,No2ndaryRoads) produces a

deforestation rate of 0.002743 for the last year (2034), equivalent to an 81%

increase in the historical deforestation rate (0.001512) (Fig. 4), generating a net

forest decline of 630,006 ha by 2035, or 6.8% out of the initial total forest cover

of (9’295,926 ha) in 2000 (Fig. 6). In contrast, (ScenarioHigh,No2ndaryRoads)

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produces a final deforestation rate (2034) of 0.004319, a 186% increase on the

historical rate (Fig. 5) and a net forest decline of 780,888 ha (8.4%) (Fig. 7).

Logistic: An upper asymptote was set at the previously calculated final

deforestation rate (2034) in both low and high-deforestation situations, defining

(ScenarioLow,Yes2ndaryRoads) and (ScenarioHigh,Yes2ndaryRoads), respectively (Fig. 4 &

5). The deforestation rate approaches the asymptote logistically, following:

"i = 0.0027/[1+(0.813 e -0.25 * i)], for (ScenarioLow,Yes2ndaryRoads), and

"i = 0.0432/[1+(1.857 e -0.25 * i)], for (ScenarioHigh,Yes2ndaryRoads).

The deforestation rates therefore increase more quickly early in the projection,

relative to the exponential function, which is meant to reflect a rapid increase in

deforestation after paving of the IOS and the extension of secondary roads

(Chomitz & Gray 1996; Perz et al. 2007). We obtained a net forest decline of

735,203 ha (7.9%) for (ScenarioLow,Yes2ndaryRoads) and 1’056,521 ha (11.4%) for

(ScenarioHigh,Yes2ndaryRoads) (Fig. 6 & 7).

Constant: In (ScenarioCtrl), the 2000-2005 historical deforestation rate remains

constant from 2000 to 2035 to compare with the other scenarios. This trend

produces a net deforestation of 479,468 ha (5.2%) by 2035 (Fig. 6 & 7).

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Deforestation allocation

The scenario-generating model was linked to DINAMICA by passing the

generated deforestation rates and taking into account the initial landscape

distribution of land-cover classes to run the model from 2000 to 2035. First,

however, DINAMICA’s algorithms must be calibrated and parameterized using

the period of time between the real landscapes, that is, 2000-2005.

DINAMICA simulates cell state transitions (e.g. from forest to deforested)

determined by discrete-step-generated transition probability maps (Soares-Filho

et al. 2002, 2004). These maps are produced based on (1) a set of spatial

variables, by calculating their weights of evidence – a Bayesian method for

modeling spatial data –, corresponding to the transition of interest (Goodacre et

al. 1991; Almeida 2003; Soares-Filho et al. 2004; Silvestrini 2008) and (2) a map

of changes between an initial and final real landscapes (Fig. 8). As such,

DINAMICA was used to simulate the allocation of the deforestation in our study

area, based on a set of land use and biophysical variables, such as protected

areas, slope, and distance to rivers. Each time step corresponds to one year.

Model calibration and parameterization (2000-2005)

An initial simulation process was undertaken to calibrate the model over the

2000-2005 period in order to obtain a 2005 simulated landscape as similar to the

real 2005 landscape as possible and to parameterize DINAMICA’s algorithms

for our study area. We used fifteen spatial variables, which we describe below.

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Real landscapes

First, we produced two raster-format, real landscapes from 2000 and 2005 (initial

and final), of 100m x 100m resolution, 4087 columns, and 2932 rows

(119,830.84 Km2). There are five land-cover classes, (1) forest, (2) deforested,

(3) water bodies, and (4) non-forest (including built infrastructure and mountain

ecosystems). These landscapes were clipped, reclassified, and rescaled from the

classified 2000 and 2005 land-cover raster images (30m x 30m pixel resolution)

that were originally produced and classified from Landsat TM+7 and CBERS

satellite images by the Instituto Nacional de Recursos Naturales (INRENA), the

Frankfurt Zoological Society (FZS), and the Centro de Datos para la

Conservación – Universidad Nacional Agraria La Molina (CDC-UNALM)

(INRENA et al. 2005, 2006; CDC-UNALM et al. 2007) and provided to us by

the CDC-UNALM.

The original landscapes had a set of nineteen different land-use and land-cover

classes, which were grouped and reclassified using ArcGIS 9.2’s spatial analyst

function into our five classes (Fig 9). All anthropogenic classes (fallow fields,

cattle pasture, burnt ground, agriculture, agropecuaria (areas in which it was not

possible to differentiate cattle pasture from agriculture and involved different

proportions of both), mining, patio de trozas (forestry areas in which logs are

hauled to and stored until transported to sawmills), and secondary forests) were

reclassified as deforested land cover. All infrastructure classes, (roads, urban

areas, and landing strips) were reclassified as infrastructure. Rivers, riverbanks,

and lakes were reclassified as bodies of water. Highland pastures and mountains

were reclassified as non-forest. Forested land remains as such. Finally, we

104

reclassified most clouds and their shadows as forested, since most clouds were

surrounded by primary forest. Clouds above other classes were reclassified into

these other classes.

Both initial and final landscapes obtained were modified to present the same

number of cells per class in the 2000 and 2005 images for all classes except, as

expected, forested and deforested lands. This modification is needed to avoid

DINAMICA generating impossible or irrelevant transition rates. For example,

the original CDC’s images presented non-forest classes growing and reducing in

extent in different areas. Since we do not expect mountains to displace forests,

we attribute the differences between the 2000 and 2005 original images to errors

in classification. Similarly, 2000 and 2005 infrastructure was unified in extent

and location because we were not interested in projecting the growth of the road

network in this way. Instead, we generated scenarios of road growth

independently (see below).

Spatial static and dynamic variables

Fifteen spatial variables, grouped into three broad classes, (1) biophysical, (2)

infrastructure, and (3) land tenure were considered for the simulation process

(Table 2). CDC, CSA, the Sociedad Peruana de Derecho Ambiental (SPDA),

and the Amazon Conservation Association (ACA) provided vectorized land

covers for these classes, which were then transformed into raster format with 1-

ha resolution, using ArcGIS 9.2 and stored within a raster cube dataset, called the

static variables map (Fig 8). This process is necessary because DINAMICA only

supports raster maps for spatial data (Soares-Fihlo et al. 2008). In addition, all

105

raster maps must have the same number of cells, that is, the same number of

rows and columns, and must be tied to the same coordinate space and registration

point (Fig. 10) (Soares-Fihlo et al. 2008). Lastly, spatial extent and resolution

must coincide with those of the real landscapes.

All three broad classes were chosen on the basis of (1) previous research about

the effect that they have on deforestation processes (Kaimowitz & Angelsen

1998; Geist & Lambin 2001; Soares-Filho et al. 2004, 2006) and (2) current

influence on the study area’s deforestation process (INRENA et al. 2005, 2006;

Dourojeanni 2006; CDC-UNALM et al. 2007), and (3) availability. All fifteen

variables but one are static variables (Soares-Fihlo et al. 2008) because their

attributes remain unchanged through this initial process. That other variable,

‘distance to deforested’, is dynamic because DINAMICA updates it for each cell

in each time step, according to the evolving land-cover simulation. This layer

map represents the frontage Euclidean distance between a pixel and the closest

deforested one (Soares-Fihlo et al. 2008).

In addition, nine of the original land covers provided had to be transformed. Four

of these (rivers, IOS, secondary roads, and population centers) were transformed

into continuous distance to feature maps (i.e. distance to rivers, distance to IOS,

distance to secondary roads, and distance to population centers) using

DINAMICA’s algorithms for calculating ‘distance to feature’ map (Soares-Filho

et al. 2008). This algorithm calculates a map representing the Euclidean distance

in meters between a cell and the closest cell representing a feature.

106

Conservation and tourism concessions were merged into one layer map. The

slope layer map was derived from a CDC Digital Elevation Model (DEM) and

converted into a slope using ArcGIS 9.2 spatial analyst. Four forest types

(INRENA’s Forestry Map:

http://www.inrena.gob.pe/biblioteca/data_de_biblioteca/docs/mapas_

peru_ambiental/biblidigital_0107.htm; accessed June 13, 2008) were merged and

re-categorized into two new types based on each type’s likelihood of being

flooded (Phillips et al. 1994). Thus the new types were defined as follows:

flooded forest type is composed by lower floodplain humid forest and meander

plain forest (llanura meándrica); terra firme forest is composed by upper and

middle floodplain humid forests. The rest of the forest types, lower slope humid

forest, upper slope humid forest, mountain humid forest, and bamboo (Guadua

spp.) forests remained unchanged.

Finally, we used the population centers’ spatial distribution layer map and each

center’s estimated 2000 population to derive a 2000 population attraction map,

which is an ‘interaction potential’ map. This map represents a gravitational

model between non-null cells, whose values (the centers’ populations) represent

the gravitational masses and null cells (the rest of the map). For each null cell (i),

the interaction potential (pi) is the sum of each center’s population (j) divided by

the distance to it (dist (i,j)):

!

pi =valuei

dist(i, j)j=1

n

"

107

Thus, the urban attraction map constitutes a fuzzy representation of population

density across the landscape, providing a mechanism by which deforestation is

made to gravitate towards population centres based on their population size, on

the assumption that larger centres exert a greater deforestation pressure on

neighbouring forest than do smaller ones.

The 2000 population size for each population center (Npop ctr, 2000) is estimated

using each center’s 1993-2005 population growth rates (! pop ctr) or each district’s

1993-2005 growth rate (! district) where the missing data centers were located (see

Human Population Growth above).

Following, we interpolated all estimated Cusco, Puno, and Madre de Dios 1993-

2005 centers’ or district’s population growth rates to calculate each center’s year

2000 population using:

(Npop ctr,1993) (! pop ctr/district)7 = Npop ctr,2000

Building the model

The calibration and parameterization process comprises six steps, which are

based on the ten steps of the land use and land cover change simulation model of

Soares-Filho et al. (2008). We used these steps to calibrate, run, and validate our

calibration and parameterization process. Each step is executed by an

independent model, which were constructed using DINAMICA’s interface and

algorithms.

108

DINAMICA considers as initial inputs the map for 2000 (the initial landscape)

and the 2005 map (the final landscape). DINAMICA does not handle class

names such as forest or deforested. Thus, all land cover classes form both maps

have the same number identifier.

Identifier Land cover class

-99 Null Value

0 Bodies of water

1 Deforested

2 Forest

3 Non-forest

First step: the transition matrix

By comparing the initial (2000) and final (2005) landscapes, DINAMICA

calculates the historical multiple-step transition matrix. The matrix describes a

system that changes over discrete time increments (e.g. a year), in which the

value of any variable (such as the deforested area) in yearn is the sum of the

variable’s value in yearn-1 plus its value multiplied by the transition rate (Soares-

Filho et al. 2004).

In our case, the transition matrix presented the forested-to-deforested (2 # 1)

transition, which is just the deforestation rate, since no other transition was

modeled. The multiple-step matrix calculates deforestation rates for each year

between 2000 and 2004 and is calculated as follows:

109

where n2,2005 is the final landscape’s forest area (9,225,880 ha) and n2,2000 the

initial landscape’s forest area (9,295,926 ha). Thus, the annualized deforestation

rate is 0.001512 or 0.15%.

Second step: categorization of continuous variables

In this step, DINAMICA calculates ranges to categorize continuous spatial

variables, such as ‘distance to rivers’, ‘slope’, etc. DINAMICA requires all

variables to be presented as categorical maps in order to determine the

deforestation probability maps (see Soares-Filho et al. 2008) and calculates

ranges according to each spatial variable data structure, which we describe

below.

First, the minimum increment (I) in the graphical interface of each variable (e.g.

100 meters for the distance to feature maps, one degree for the slope map) is

specified and input into DINAMICA’s Determine Weights of Evidence Ranges

algorithm (Fig 11). The increment is used to build n incremental buffers

comprising intervals from xminimum to xminimum + nI (e.g. 0-100, 0-200, etc. for the

distance to feature maps). Thus, each n defines a threshold, dividing the layer

map into two classes, b (one buffer) and

!

b (the rest of the map).

Second, the number (n) of pixels classified in each land-cover class, denoted by

the variable A, and the number of deforested pixels within each buffer (b) are

!

Rate2"1 =

n2,2005

n2,2000

#1

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counted and used to calculate each buffer’s weight of evidence (Wb+) (Goodacre

et al. 1991; Almeida 2003; Soares-Filho et al. 2004, 2008), given by:

where Wb+ is the weight of evidence coefficient for buffer b of one variable (A),

stands for the number of variable A’s pixels within

buffer b that overlap with deforested pixels and , stands

for the number of variable A’s pixels within buffer b that overlap with non-

deforested pixels.

The weight of evidence coefficient represents the tendency of finding one

deforested pixel given the presence of the evidence A (e.g. protected areas) also

termed the explanatory variable (Almeida 2003). Higher positive coefficient

values denote a stronger positive association between the explanatory variable

and the presence of deforested pixels.

Third, a sequence of An values are plotted against An*exp(W+) (Fig. 12).

Breaking points for this graph will be determined by applying a “line-

generalizing algorithm” (see Soares-Filho et al. 2008). This algorithm contains

three parameters: (1) minimum distance (mindx) interval along the x axis,

minimum delta in Figure 11, (2) maximum distance (maxdx) interval along the x

axis, maximum delta in Figure 11, and (3) tolerance angle. A new breaking point

is placed whenever the distance between two points on the x axis $ mindx or

when the angle between the two arrows (v and v’) linking the current point to the

!

Wb

+= ln

n(Ab |DeforestedPixel

n(Ab |DeforestedPixel

"

# $

%

& '

!

n(Ab |DeforestedPixel)

!

n(Ab |DeforestedPixel)

111

last one and the last one to the previous one, respectively (Fig. 12), exceeds the

tolerance angle (Soares-Filho et al. 2008). Thus, fewer points will be determined

as both the tolerance angle and the mindx are increased, and vice versa.

Finally the number of categories, that is, the range intervals, is defined by linking

the breaking points with straight lines (Fig 12).

Categories comprise a lower inclusive and a higher exclusive boundaries,

denoted as, for example, [0-100) meters.

Third step: calculation of weights of evidence coefficients

Now DINAMICA calculates the weights of evidence coefficients for each

variable’s category (k) based on:

.

In addition, for those categorical variables presenting a binary map, B, defining

the presence or absence of one land tenure or biophysical attribute, such as

protected areas or palm swamps, DINAMICA calculates the weights of evidence

coefficient for that whole particular binary pattern, as follows:

!

Wk

+= ln

n Ak |DeforestedPixel( )n Ak |DeforestedPixel( )

"

#

$ $

%

&

' '

!

W+ = ln

n B |DeforestedPixel( )n B |DeforestedPixel( )

"

#

$ $

%

&

' '

112

and determines whether there is a significant association between these and the

deforested areas. If no significant association is found, the variable has to be

removed.

The spatial association between the binary pattern and the deforested pixels is

measured by the contrast, C, given by C = W+ – W-, where W- is given by:

!

W" = ln

n B |DeforestedPixel( )n B |DeforestedPixel( )

#

$

% %

&

'

( (

and stands for the absence of the binary pattern.

Thus, for those cases when the deforested pixels overlap with the presence of the

binary pattern more often than would be expected by chance, W+ will be positive

and W- will be negative (see Goodacre et al. 1991). In other words, W+ is

positive when the number of deforested pixels overlapping with the presence of

the binary pattern is larger than that of non-deforested pixels with the pattern’s

presence. And W- is negative when the number of non-deforested pixels

overlapping with the absence of the binary pattern is larger than that of

deforested pixels with the pattern’s absence. Thus, the larger the value of C, the

stronger the influence a significant variable will have on a deforested pixel’s

location.

DINAMICA determines whether the magnitude of C is large enough to be

statistically significant by estimating the variance of the contrast given by:

!

B

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.

As explained in Goodacre et al. (1991), “if |C | is normally distributed around

zero, then the null hypothesis that there is a lack of spatial association can be

rejected if |C | > 1.96 sI with 95% probability”. (See Goodacre et al. 1991 for

further details)

DINAMICA applies this protocol to the above-categorized variables as well, by

treating each category at a time as B and combining the areas of the rest of

categories to treat them as . In this case, if one category turns out to be non-

significant it has to be removed.

The way in which non-significant categories can be removed is by reducing the

initial number of categories DINAMICA produces for each continuous variable

(see step 2), which tend to reduce the overall number of non-significant

categories. For example, when we increased the tolerance angle from five to

seven for the ‘distance to the IOS’ variable, the number of categories was

reduced from 195 to 77, while the number of non-significant ones was reduced

from 67 to fourteen. These are finally deleted by joining their both upper and

lower adjacent significant categories.

For example, if the significant [100-200) meters category is followed by a non-

significant [200-500) category, which in turn is followed by a significant [500-

!

s2C( ) =

1

area B"D( )+

1

area B"D( )+

1

area B"D( )+

1

area B"D( )

!

B

114

1000) one, the second one is deleted and only two categories, [100-500) and

[500-1000), remain. Then, the original coefficient of the [100-200) category is

assigned to the new built category, that is, to the [100-500).

Finally, although some categories will be statistically significant, they might not

represent the observed trends that relate one given variable with the location of

deforested pixels.

As an example of how to solve this inconsistency, the following protocol,

applied to the ‘distance to population centers’, is presented.

The weights of evidence coefficients (W+) obtained for this variable’s categories

presented a clear tendency denoting higher coefficients for categories

representing closer distances to centers, as might be expected (Fig. 13a).

Nevertheless, two categories, [23.4-27.2) and [100.4-121.5) (in km) represented

exceptions to the observed trend.

Using ArcGIS 9.2, we reclassified the map’s distance categories to visually

represent the significant categories DINAMICA calculated and laid the map of

changes, that is, the deforestation occurred between 2000 and 2005, on top of

this.

When we scrutinized these maps (Fig 14), we observed that these two categories,

although farther away from centers, presented relatively more deforested pixels

115

than some categories located closer to centers and, thus, obtained higher

(although still negative) weight of evidence coefficients than the latter.

Clearly, the observed deforested patches must be product of other factors rather

than to the distance to centers variable. These factors might be distant seasonal

grazing areas observed to the south of the Megantoni National Sanctuary to the

west of our study area (INRENA et al. 2006), natural forest clearings, or even

errors in the original classification.

Therefore, we manually modified the trend by assigning new lower coefficients

to each of these two categories (Fig 13b). These were calculated as the average

between both the upper and lower adjacent categories’ coefficients.

The same protocol was used for the rest of variables presenting similar

inconsistencies to obtain their final weights of evidence coefficients (Fig. 15).

This analysis identified the variables ‘distance to deforested areas’, ‘distance to

the Interoceanica’, distance to secondary roads’, and ‘distance to population

centers’ to be the strongest predictors of deforestation and demonstrated the

importance of protected areas and territorial reserves on deterring deforestation

(Fig 13 and 15).

Fourth step: correlation test

As we have explained, DINAMICA calculates the weight of evidence

coefficients for each explanatory variable and assumes these are independent

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before integrating their effect into one deforestation probability map. As such, in

this step, DINAMICA determines the correlation between variables using a set of

statistical tests (see Soares-Filho et al. 2008) from which we apply the Joint

Information Uncertainty test (Almeida 2003). The test determines the association

between two maps based on a 0 to 1 scale, in which higher values denote a

higher correlation.

We decided to use a value of 0.5 as a threshold (exclusive) for determining

independence, since it has been stated (Almeida 2003) that such a threshold

value suggests less association between two variable maps. We found that none

of the used variables was correlated (Table 3), and thus we retained all variables

within the analysis for building the deforestation probability map.

Fifth step: running the simulation

In this step DINAMICA runs a deforestation simulation (see Soares-Filho et al.

2008) using the inputs and algorithms presented in Figure 16. The output from

one time step constitutes the input for the subsequent. Similarly, iteration sub-

products are used as inputs during the same iteration to obtain final outputs.

The model uses the real initial landscape (2000), the spatial variables (stored in

the static variables map) and their weights of evidence coefficients, and the

transition matrix, as inputs to run and iterate five times (i.e. five years; defined

within the ‘repeat’ box, Fig. 16) to produce (1) ‘distance to deforested’ maps (the

dynamic variable), (2) transition probability maps, and (3) simulated landscape

maps, one for each time step.

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During each iteration, the first and second outputs constitute sub-products that

will be used as inputs to obtain each of the simulated landscape maps, which in

turn constitute the initial landscape maps for the second iteration and subsequent

ones (this is allowed by the ‘Mux categorical map’ algorithm, by creating a loop,

Fig. 16).

The ‘distance to deforested’ maps are updated according to the evolving

distribution of deforested pixels in each step, starting with that of the 2000 real

landscape and subsequently using the following simulated landscapes produced.

In turn, transition probability maps are determined as a function of each

explanatory variable’s (static and dynamic) influence on the spatial probability of

occurring a deforested pixel. Therefore, given a set of spatial variables (A, B,

C,…,N), the probability of one pixel at location (x,y) being deforested is

determined by:

where Wk+

(x,y) is the weight of evidence coefficient for category k of one variable

A, in the case of categorized variables (e.g. ‘distance to rivers’), or simply the

coefficient for the binary pattern of categorical variable A (e.g. ‘protected areas’),

at location (x,y) and is given by:

!

P DeforestedPixel | A" B"C" ..."N( )(x,y )

=e

Wk+( x,y )

k=1

n

#

1$ eWk

+( x,y )

k=1

n

#

118

and stored within the ‘weights of evidence coefficients’ input file. Therefore, as

DINAMICA iterates, the ‘distance to deforested’ maps will allow the model to

automatically update the probability maps.

In turn, the transition matrix is used to determine the net number of pixels to be

deforested during each step. It is important to note that during the calibration and

parameterization process the transition matrix remains constant, whereas during

the next simulation process it is dynamic, based on the corresponding scenarios.

Using the ‘calc change matrix’ algorithm, DINAMICA transforms the historical

annual deforestation rate, stored in the transition matrix, into the number of

pixels to be deforested by multiplying the deforestation rate by the total number

of possible changes, that is, the remaining forest.

The total number of cells to be deforested is then divided into two fractions by

the ‘modulate change matrix’ algorithm, which is set to determine the percentage

of the total number of changes that will be executed by the ‘expander’ and

‘patcher’ algorithms. Both are concerned with the landscape change dynamics,

though the first one determines only the expansion of previous patches of

deforestation, whereas the second generates new deforestation patches alone (see

Soares-Filho et al. 2002 for further details). The idea of splitting the total number

of executed changes between both algorithms and into varying proportions

!

Wk

+(x,y ) = ln

P Ak |DeforestedPixel( )P Ak |DeforestedPixel( )

"

#

$ $

%

&

' '

119

allows us to approximate and calibrate the simulated landscapes to the real

structure of a landscape.

The size of expansion fringes and new patches follow a lognormal probability

distribution (Soares-Filho et al. 2002). Thus, both algorithms require specifying

the parameters of this distribution by a mean and variance patch size. Higher

values of mean patch size imply a less fragmented landscape and higher values

of variance patch size imply a more heterogeneous landscape (the opposite

applies). In addition, a ‘patch isometry’ number must be defined. This parameter

varies from 0 to 2 and patches assume a more isometric (‘circular’) form with

higher values and a more linear form otherwise (Soares-Filho et al. 2002).

Once all parameters are set, DINAMICA is run to produce the five simulated

landscapes (2001-2005) and their associated probability maps.

Sixth step: validation

In this step we validate the model by comparing the 2005 simulated landscape

with the real 2005 one. The method we apply is the fuzzy similarity analysis,

which compares two maps of changes (in our case, maps of deforested pixels

alone) (Silvestrini 2008; Soares-Filho et al. 2008). The first map represents the

observed changes between the 2000 and 2005 real landscapes and the second,

those between the 2000 real landscape and the 2005 simulated one.

The method compares the number of deforested pixels within the first map with

that of the second, that fall within a central cell neighborhood. This

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neighborhood is defined by a set of cell window sizes of 1x1, 3x3, 5x5, etc. We

decided to use a range of window sizes from 1x1 to 11x11 cells, representing

areas of one to eleven hectares (or 0.01 to 1.21 km2), respectively. Using a

constant decay function, if a deforested pixel is found within the window,

regardless of whether it is located exactly in the central cell of the window (for

those larger than 1x1), that is, in the same x,y coordinates of both maps, a

similarity fit of 1 is assigned. On the other hand, if no deforested pixel is found

within the window, a zero fit is assigned. Once each window size has convoluted

over the whole map of changes, a mean similarity index for each window size is

calculated as the sum of ones divided by the number of deforested pixels. Thus,

the closer this quotient is to one the higher the fuzzy similarity between the real

and the simulated landscape is. As would be expected, larger windows relax the

comparison, increasing the goodness of fit between the two maps. Finally, this

method applies a comparison in two ways, that is, it analyses the difference in

the location of pixels in the first map relative to that in the second and vice versa,

ultimately choosing the lowest calculated mean index fit for each window (see

Soares-Filho et al. 2008).

To define the mean and variance size of new expansion fringes and patches for

the expander and patcher algorithms, respectively, we first calculated the size of

each new deforested patch (including both expansions and patches) observed

between the real landscapes of 2000 and 2005. Following, we divided each patch

size by five, so as to obtain a proxy for the yearly expansion of new patches. We

obtained a mean and variance patch size of 1 and 8 ha, respectively. We then ran

the model several times so as to obtain the best possible model fitness after

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changing the isometry and the ‘modulate change matrix’ parameters, finally

setting these at 1.5 and 0.8, respectively.

We obtained the fuzzy similarity presented in Figure 17, which achieves an 80%

similarity at a window size of 11x11. We considered this similarity to be

satisfactory based on previous results obtained using DINAMICA (see Soares-

Filho et al. 2006) and thus, used the set parameters in the next simulation

process.

Simulation of future deforestation (2000-2035)

A second process was undertaken to simulate the cumulative annual

deforestation across the landscape between years 2001 and 2035. This process

starts again with the real 2000 landscape as the initial landscape and iterates five

times until the 2005 simulated landscape is produced but then deviates from the

previously set parameters at time step 2005-2006 to introduce the effect of each

of the five deforestation scenarios (Low,High/No,Yes2ndaryRoads and control)

and carries on until 2034-2035. Each of the first four scenarios includes a

different set of new dynamic variables, transition matrices, and weights of

evidence coefficients from time step 2005-2006 onwards.

Similarly as in the calibration and parameterization process, the ‘distance to

deforested’ dynamic variable, probability maps, and simulated landscapes for

each time step are produced, where the latter represent the input for each

subsequent time step. Each model was set to iterate 35 times in total, that is, from

2000 to 2035.

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New dynamic variables

‘Population attraction’ and ‘distance to secondary roads’ were turned into

partially dynamic variables as their layer maps are updated for every five-year

period, from time step 2005-2006 onwards, in order to replace those of each

previous five-year period during the whole simulation process. In other words,

both variables remained unchanged within the five years of each five-year period

but were then updated from one period to the other. Reasons for updating these

two variables as well as the way in which each is constructed are explained

below.

Population attraction

As we expect center’s population to grow or decrease in time, we also expect that

they will exert different deforestation pressures on neighborhood forests in the

future.

Thus, to model this effect, we created six new ‘population attraction’ layer maps

based on the projection of each centre’s population size. These six maps

correspond to the first year of every five-year period between 2005-2010 and

2030-2035 (i.e. 2005, 2010,…,2030).

Projected population sizes were estimated for each of these years (Npop ctr, (5)

year), between 2010 and 2030 (inclusive) (we already had their 2005 population

sizes, see Human Population Growth) based on:

(Npop ctr,2005) (! pop ctr/district)5 = Npop ctr,2010

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(Npop ctr,2010) (! pop ctr/district)5 = Npop ctr,2015

and so on, where (! pop ctr/district) stands for each center’s 1993-2005

population growth rate or each district’s 1993-2005 growth rate where the

missing data centers are located (see Human Population Growth).

Projected populations were used to construct the new ‘population attraction’

layer maps following the same protocol as before (see Spatial static and synamic

variables).

Distance to secondary roads

The construction of an “official road” (Perz et al. 2007) such as the IOS,

promotes the development of a secondary road network to link timber,

agriculture, and mining activities, among others, to the main road and then to

regional markets (Perz et al. 2007). In fact, the IOS has already promoted the

extension of roads into previously isolated areas (Kirkby et al. in manuscript).

Therefore, using ArcGIS 9.2, we manually generated new secondary roads and

extend existing ones to create six new ‘distance to secondary roads’ layer maps,

each corresponding to the first year of every five-year period between 2005-2010

and 2030-2035 (i.e. 2005, 2010,…,2030).

We based the secondary road extensions on (1) our own knowledge about the

most plausible paths and directions (mainly to gradually connect population

centers) and (2) two road projects that have been proposed by the Regional

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Governments of Madre de Dios and Cusco and are currently being evaluated by

the Peruvian Economy Ministry (Ministerio de Economía) for approval (road

Nuevo Edén-Boca Manu-Boca Colorado and road Patria-Quincemil,

respectively, Fig. 18) (see

http://ofi.mef.gob.pe/bp/ConsultarPIP/frmConsultarPIP.asp?accion=consultar&tx

tCodigo=95220, accessed July 14, 2009;

http://ofi.mef.gob.pe/bp/ConsultarPIP/frmConsultarPIP.asp?accion=consultar&tx

tCodigo=107575, accessed July 14, 2009). These two roads, if finally approved,

will be due on 2011 and 2013, respectively, and have as their main objective to

connect some of the two Dept’s most isolated areas to the IOS.

New dynamic trasition matrices

Beginning in time step 2005-2006, the historical deforestation rate was increased

according to the functional relationships (exponential or logistic) previously

established for each scenario (see Deforestation rate scenarios) and projected

until time step 2034-2035 (Table 4).

As such, four dynamic transition matrices area assigned to each of the four

scenarios and one matrix, whose rates remain constant throughout the simulation

process, corresponds to the control (Table 4).

New weights of evidence coefficients

In addition to the scenario-generating model, one further manipulation of

parameters was used to reflect the effect of the paving of the IOS in the

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allocation of deforestation, specifically regarding the extension of the secondary

road network.

The weights of evidence coefficients obtained during the calibration and

parameterization process (see Third step) were manually changed for

(ScenarioLow,Yes2ndaryRoads) and (ScenarioHigh,Yes2ndaryRoads), so as to

reflect a greater deforestation pressure in the vicinity of newly created roads. For

(ScenarioLow,Yes2ndaryRoads), we increased both coefficients for the ‘distance

to secondary roads’ categories of [0-800) and [200-2800) meters from 2.64 and

1.88, respectively, to 6 and to 8 for (ScenarioHigh,Yes2ndaryRoads).

We changed the coefficients of these two scenarios because, as might be

recalled, it is the logistic relationship that represents the extension of secondary

roads (see Deforestation rate scenarios). On the other hand, the rest variables’

coefficients were left unchanged. For the land tenure variables this effectively

implies that we assumed a similar level of governance into the future, especially

regarding the conservation status of protected areas and territorial reserves.

Building the models

Models were built maintaining the same structure as our Fifth step model, though

in this process they were set to iterate 35 times, one new algorithm (‘For’) was

introduced to upload the new static variable maps for every five time steps, one

for each five-year period between 2005-2010 and 2030-2035, and the new

transition matrices were included (Fig. 19).

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In addition to all the previously used static variables, each new static variable

map contains now one of the new six ‘population attraction’ layer maps, which

were introduced in each static variable map according to the corresponding

periods. For example, the 2005 ‘population attraction’ map is introduced into the

static variable map used in period 2005-2010 (i.e. the year of the ‘population

attraction’ map must coincide with the first year of the current five-year period).

On top of this inclusion, the models executing (ScenarioLow,Yes2ndaryRoads)

and (ScenarioHigh,Yes2ndaryRoads), additionally receive the six new ‘distance

to secondary roads’ layer maps. Each ‘distance to secondary roads’ was assigned

to each static variables map in the same way as before. Furthermore, these two

models receive the new weights of evidence coefficients.

Finally, each of the four models executing (ScenarioLow,No2ndaryRoads),

(ScenarioLow,Yes2ndaryRoads), (ScenarioHigh,No2ndaryRoads), and

(ScenarioHigh,Yes2ndaryRoads), receive its corresponding new dynamic

transition matrices, while (ScenarioCtrl), receives the constant trend. Each set of

matrices is stored in a ‘lookup table’ algorithm, which replaces the transition

matrix table in Figure 16.

RESULTS

The model was run for five scenarios, (1) low population growth/no further

construction or extension of secondary roads (ScenarioLow,No2ndaryRoads), (2) low

population growth/construction and extension of secondary roads

(ScenarioLow,Yes2ndaryRoads), (3) high population growth/no construction or

127

extension of secondary roads (ScenarioHigh,No2ndaryRoads), and (4) high population

growth/construction and extension of secondary roads (ScenarioHigh,Yes2ndaryRoads),

(5) plus a control scenario (ScenarioCtrl) in which, unlike the first four scenarios,

the deforestation rate remains constant at the 2000-2005 historical level

throughout the simulation process.

The projected ‘new’ (added between 2000-2035) and total (new + deforestation

before 2000) deforestation for the five scenarios are presented in Table 5. Figures

20-24 present the simulated landscapes for years 2020 and 2035, depicting the

distribution of the projected total deforestation for each scenario. Tables 6-12

summarize the projected new and total deforestation inside the protected areas

(PAs) and forestry concessions (FCs), for each scenario.

Total deforestation

We project that after 35 years (2000-2035) total forest cover in the region will

decline from 9,295,926 to 8,816,458 ha (a 5.2% reduction) for ScenarioCtrl, and

to 8,665,920 (6.8% reduction), 8,560,723 (7.9% reduction), 8,515,038 (8.4%

reduction), and 8,239,405 ha (11.4% reduction) for ScenarioLow,No2ndaryRoads,

ScenarioLow,Yes2ndaryRoads, ScenarioHigh,No2ndaryRoads, and ScenarioHigh,Yes2ndaryRoads,

respectively.

In the DINAMICA runs, future deforestation was concentrated near previously

deforested areas (<1.6 km from each previous time-step’s total deforested

pixels), the Interoceánica Sur highway (IOS) (<17 km), existing and new

secondary roads (<10.5 km), and population centers (<6.7 km) with large

128

populations (e.g. Puerto Maldonado). As a result, by 2035 the connectivity of the

Vilcabamba-Amboró Conservation Corridor (VACC) will be seriously

compromised in all five scenarios, since most of the new deforestation is

concentrated near the IOS, thus, further bisecting the VACC. On the other hand,

deforestation was lower inside PAs and indigenous territorial reserves (TRs).

This result is a direct consequence of the weights of evidence coefficients

obtained during the calibration process (2000-2005) and as modified for the

secondary road scenarios. Recall that using the same coefficients through to 2035

means that we have assumed a similar level of governance into the future,

especially regarding the conservation status of PAs and TRs.

The main differences among the simulated landscapes is how far the new

deforestation extends across the study area. As such, ScenarioLow,No2ndaryRoads and

ScenarioHigh,No2ndaryRoads produced a much more localized deforestation pattern,

mainly near the IOS and population centers, than ScenarioLow,Yes2ndaryRoads and

ScenarioHigh,Yes2ndaryRoads, which, by design, spread new deforestation across the

landscape, following the path of extended secondary roads.

ScenarioLow,Yes2ndaryRoads and ScenarioHigh,Yes2ndaryRoads therefore resulted in more

deforestation near and, in some cases, inside PAs and FCs, as we describe below.

Deforestation within PAs

Overall, we do not expect much deforestation to occur inside Madre de Dios’

protected areas. No forest decline was produced inside Alto Purus National Park,

Kugapakori Territorial Reserve, Megantoni National Sanctuary, and Madre de

Dios Territorial Reserve, whose total deforestation remained constant at the

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trivial levels of 12, 21, 20, and 7 ha, respectively, in all scenarios. Overall, the

model estimated that after 35 years, the total area of PAs (5,059,143 ha) loses

6,181 ha from its initial 4,896,970 ha forest cover (a 0.13% reduction) in

ScenarioCtrl, and 8,111 (0.17% reduction), 14,066 (0.29%), 10,885 (0.22%), and

23,577 ha (0.13%) in ScenarioLow,No2ndaryRoads, ScenarioLow,Yes2ndaryRoads,

ScenarioHigh,No2ndaryRoads, and ScenarioHigh,Yes2ndaryRoads, respectively. Tables 6-10

present the projected total and new deforestation in 2035 inside Tambopata

National Reserve, Bahuaja Sonene National Park, Amarakaeri Communal

Reserve, and Manu National Park.

Tambopata National Reserve (TNR)

The TNR is closest to the IOS, and a large number of population centers

surrounds it (30), including Puerto Maldonado (Fig. 1). Thus, this PA suffers the

largest amount of deforestation (Table 6).

As expected, the largest total and new deforestation within the TNR was

produced by ScenarioHigh,Yes2ndaryRoads (Table 6), concentrated in two areas, to the

north of the reserve, near Puerto Maldonado (Fig. 25), and near new and

expanded secondary roads on the Malinowski river, a zone devoted to mining

(Fig 26). It is interesting to note that although ScenarioHigh,No2ndaryRoads generates

more total deforestation than does ScenarioLow,Yes2ndaryRoads, for the whole study

area (Table 5), ScenarioLow,Yes2ndaryRoads produced more deforestation inside the

TNR, especially near the Malinowski River. This is a consequence of the

secondary roads (Fig. 27).

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Bahuaja Sonene National Park (BSNP)

BSNP suffers less total and new deforestation compared to the TNR (Table 7).

Only ScenarioLow,Yes2ndaryRoads and ScenarioHigh,Yes2ndaryRoads substantially increased

new deforestation within the BSNP, although it remains low in both cases (228

and 214 ha, respectively). Only the western tip of the park, near the IOS, is

where deforestation invades the BSNP (Fig 28).

This result represents the fact that most of the BSNP inside the study area

remains relatively isolated from the IOS and secondary roads.

Amarakaeri Communal Reserve (ACR)

ScenarioHigh,Yes2ndaryRoads generated the largest new deforestation (2,767 ha) inside

the ACR as a consequence of its higher deforestation rates, compared to the other

scenarios, and because of the construction of the Patria-Quincemil road (Table

9). This road generated most of the new deforestation near and inside its

southeastern boundary, north of Quincemil town and the Interoceanica (Fig 29).

Although the new road bisects the ACR, new deforestation occurs only within

the southeastern boundary and not all along the road itself, as might be expected.

This is because the internal area of the ACR is largely uninhabited and distant

from the IOS, population centers, and mining concessions. Therefore,

probabilities inside the ACR remained low, even after the road effect is taken

into account, as is shown in the 2035 deforestation probability map (Fig 30).

131

In contrast, although the Nuevo Edén-Boca Manu-Boca Colorado road is not

inside the reserve, it would seriously threaten the connectivity of the ACR with

other areas, such as Manu National Park, as this road allocates new deforestation

between these two PAs (Fig 29).

Manu National Park (MNP)

All five scenarios produced a relatively similar forest decline within the MNP (<

0.5%) (Table 10). ScenarioHigh,Yes2ndaryRoads produced a slightly larger net

deforestation (6,194 ha) in 2035, compared to the other scenarios (Table 10), and

allocated new deforestation inside the MNP near the town of Patria and within

the park’s southern tip (Fig. 31). In addition, ScenarioLow,Yes2ndaryRoads and

ScenarioHigh,Yes2ndaryRoads, both representing the construction of the road Nuevo

Edén-Boca Manu-Boca Colorado, allocated much more new deforestation to the

southeastern end of the park than did either ScenarioLow,No2ndaryRoads or

ScenarioHigh,No2ndaryRoads (Fig. 31 & 32). The effect of the road is the same as in

the case of the ACR, as the connectivity between the park and the ACR is

reduced.

Deforestation within FCs

After the 35-year period, FCs (1,374,552 ha) lose more forest than do PAs (Table

12). ScenarioCtrl generated a 21,181 ha forest decline (1.57% reduction) from the

initial 1,352,896 ha forest cover. ScenarioLow,No2ndaryRoads,

ScenarioLow,Yes2ndaryRoads, ScenarioHigh,No2ndaryRoads, and ScenarioHigh,Yes2ndaryRoads

respectively generate 42,209 (3.12% reduction), 79,000 (5.84%), 57,606

(4.26%), and 134,841 ha (9.97%) in new deforestation.

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Concessions located north and south of the Inambari River were the most

strongly affected by the allocation of new deforestation. Without secondary

roads, deforestation was concentrated south of the Inambari, nearer the IOS (Fig

33), and with secondary roads, deforestation spread to the north of the Inambari

(Fig. 34).

DISCUSSION

Deforestation in southeastern Amazonian Peru is and has been driven by state-

sponsored incentives, such as easy access to agricultural credit, and, more

recently, by road construction and market-based incentives like high gold prices

(Alvarez & Naughton-Treves 2003; Dourojeanni 2006; Killeen 2007). These

incentives promote immigration of Andean people towards the eastern lowlands

of the Madre de Dios department, most of whom come from the neighboring

Cusco and Puno departments (Dourojeanni 2006). Traditionally, immigration has

prompted forest clearance for the production of crops and cattle, and more

recently, for lucrative, alluvial gold mining (Dourojeanni 2006; Killeen 2007).

The paving of the Interoceanica Sur highway (IOS) is now and will further

promote these changes when completed in 2011, as the IOS reduces costs of

transportation and encourages the creation of new secondary roads (Kaimowitz

& Angelsen 1998; Dourojeanni 2006; INRENA 2006; Killeen 2007; Oliveira et

al. 2007).

The effects that increased population size and the extension of secondary roads

will have on deforestation rates was considered by the scenario-generating

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model, which sought to represent our current knowledge of the region’s

dynamics. We necessarily made several assumptions, which we discuss here.

First, we assumed that population growth (immigration and organic growth) and

deforestation rates are positively correlated throughout the simulation process,

thus, implying that population growth is the main cause of deforestation in our

model. Population growth is considered a fundamental driver of deforestation

(Geist & Lambin 2001, Killeen 2007), for example, by increasing the demand for

agricultural and forest products and by increasing the number of gold miners

(Geist & Lambin 2001, Perz 2002, Killeen 2007).

Second, we assumed that the population within the study area maintains a

positive and increasing growth rate throughout the simulation process, that is,

from 2000 to 2035. We based this assumption on the fact that (1) the Madre de

Dios department contains the lowest population density of all Peruvian

departments (1.3 ind./km2 as of 2007, INEI 2007) and, thus, could potentially

support a sustained population growth rate and (2) future development projects

sponsored by the IIRSA or governmental initiatives, such as highways, hydrovias

(river dredging projects to allow large boats to pass), and energy projects, will

further increase immigration rates (see Killeen 2007).

Third, we assumed that the higher population growth rate used to develop the

high deforestation scenarios is the same as the one observed between 1981 and

1993. We used this rate because from 1985 to 1990, the administration of then-

President Garcia instituted a series of agricultural subsidies in the form of land

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titles and easy credit that promoted immigration and caused a rapid expansion of

the agricultural frontier in Madre de Dios (Alvarez & Naughton-Treves 2003).

Thus, we expect a similar effect by the construction of the IOS, which could be

seen as another subsidy for the agricultural sector and others.

Fourth, we represented the effect that new secondary roads have on deforestation

rates by a logistic trend in the deforestation rate growth. A logistic trend

produces a rapid increase in deforestation rates early in time, a consequence that

has been reported after new roads are laid (Killeen 2007).

The DINAMICA software package provided a useful tool to represent the effect

different scenarios would have on the patterns of deforestation. As observed in

the results, DINAMICA allocated more deforestation in the simulated landscapes

of high deforestation scenarios than the low deforestation ones and more

deforestation near secondary roads in scenarios contemplating the extension of

roads. This was possible because DINAMICA allowed us to introduce new

dynamic deforestation rates and modified weights of evidence coefficients for

the ‘distance to secondary roads’ variable.

It is important to note, however, that we assumed that the rest of variable’s

coefficients remain constant into the future. Nevertheless, coefficients related to

specific variables such as PAs may vary with changes in policy. In fact, such

considerations could be taken into account when developing new scenarios.

135

Similarly, the mean size and variance of new expansion fringes and patches, as

well as the isometry parameter, were assumed to be equal for both expander and

patcher algorithms and were calculated on the assumption that the deforestation

pattern was homogenously distributed per year (we divided the mean patch size

between 2000 and 2005, one step, by five). These parameters were based on the

calibration process results, and we obtained a relatively good model fitness

(~80%). Nevertheless, we recommend that in the future, the size of new patch

expansions of deforestation should be empirically determined, on a yearly basis.

The scenarios developed here aim to provide a set of projected deforestation

paths to support the establishment of RED projects in southeastern Peruvian

Amazon. Projected paths are necessary in order to answer the counterfactual

question of what would deforestation be without the RED project, and thus, set

the reference level to which the additionality performance of the project can be

measured (Angelsen 2008) and to set the level at which the RED project is

expected to reduce deforestation rates. Most RED proposals to the United

Nations Frameworks Convention on Climate Change (UNFCCC) have chosen

the historical deforestation rates as their reference level, also referred to as the

business as usual (BAU) baseline (Angelsen 2008, Parker et al 2008). However,

past deforestation is not always an accurate predictor of future deforestation

(Angelsen 2008) and thus, the historical deforestation rate could underestimate

the expected BAU baseline.

Southeastern Peruvian Amazon is a region with a historically low deforestation

rate (0.1% between 2000-2005) and a high percentage of land under forest cover

136

(~95%) (Soares-Filho et al. 2006, Oliveira et al. 2007). This suggests that the

region’s forest cover dynamics is situated at an early stage of the forest transition

(Angelsen 2007) and thus, is expected to have accelerating deforestation rates in

the future (Angelsen 2008). Regions such as this should consider higher rates of

deforestation when setting their BAU scenarios (Angelsen 2008).

Our results clearly indicate that ScenarioHigh,Yes2ndaryRoads generates the highest

deforestation rates. Thus, if we considered this scenario’s projected deforestation

path as the regional BAU baseline and that of ScenarioHigh,No2ndaryRoads as the

realized path of a regional RED project that is designed to mitigate the effect of

secondary roads, then the difference between these two paths (the area below the

latter scenario’s projected deforestation path minus the former’s, see Figure 7)

would be the avoided deforestation eligible for RED credits.

Considering these two scenarios, total avoided deforestation between 2012 (the

year in which a post-Kyoto agreement will come into force) and 2035 would sum

223,071 ha. Setting the average amount of carbon stored in the above biomass at

172 tC ha-1, based on a carbon storage study undertaken at the Los Amigos

Conservation Concession (LACC) of Madre de Dios (Winrock 2006) and two

possible carbon prices, one set fix at US$5.63/tCO2 (see Strassburg et al. 2009)

and one variable price, evolving as a function of time: US$21/tCO2 in year

2012, rising to US$30/tCO2 in 2020-2029, and to US$49/tCO2 by 2030-2035

(Environmental Defense Fund 2008) we estimated the present value of revenue

(PVR"=10%,23-years) to be US$380.6M and US$1612.4M, respectively. Both results

are much greater than the PVR"=10%,23-years derived from the average revenues per

137

used area of agriculture (PVR = US$41.8M), cattle and poultry (PVR =

US$22.0M), and timber (PVR = US$13.4M), in the region (Kirkby et al. in

manuscript). This indicates a relatively low opportunity cost to implement a

regional RED project, and thus, would make it economically efficient, though

transaction costs remain to be considered.

We present these estimates as a source of initial argumentation and further

research motivation for building the case of RED credits in the region. Several

conservation non-governmental organizations (NGOs) and the Madre de Dios

Regional Government are already initiating the process of developing RED

initiatives. A such, their efforts require a simulation system that can help them to

set the projected deforestation trends to support their eventual RED credits

claims.

Moreover, our model could help these institutions to understand and visualize

deforestation patterns and their relation with threats such as mining, population

growth, and the expansion of the agriculture frontier, so that they can plan their

future conservation interventions.

Finally, we would like to add that DINAMICA analyses are an 'ongoing process'

of gathering and organizing information so as to allow improvements on

designing and parameterizing the models. As such the underlying purpose of this

study has been to gather the bulk of the original data for demography, human

settlements, historical population growth, and a set of spatial variables to

138

introduce on a first DINAMICA analysis. We expect to continue improving the

model as new variables and parameters will be added.

ACKNOWLEDMENTS

We are grateful to the Centro de Datos para la Conservación – Universidad

Nacional Agraria La Molina (CDC-UNALM) for kindly providing most of the

spatial data used in this study, especially the classified satellite images of 2000

and 2005, which are product of their previous and ongoing work in the region.

Without its contribution this study could not have been undertaken. We would

also like to thank the Centro para la Sostenibilidad Ambiental – Universidad

Peruana Cayetano Heredia (CSA-UPCH) for providing population centers data.

Other important spatial data was provided by the Amazon Conservation

Association (ACA) and its Peruvian Office Asociación para la Conservación de

la Cuenca Amazónica (ACCA), Sociedad Peruana de Derecho Ambiental

(SPDA), Eddy Mendoza (Conservation International Peru), and Gary Geller

(JPL-NASA, for access to ASTER and LANDSAT imagery), whose contribution

is gratefully acknowledge. The Rufford Small Grants for Conservation provided

the necessary funds for covering the expenses of one visit to the Universidade

Federal de Minas Gerais and to buy a computer, which was used to run the

models.

139

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FIGURES AND TABLES

Figure 1. Study area showing protected areas, territorial reserves, Los Amigos Conservation Concession, and sections 2, 3, and 4 of the Interoceanica highway (IOS).

148

Figure 2. Vilcabamba Amaboró Conservation Corridor and the study area (black square) (Modified from Critical Ecosystem Partnership Fund 2005).

149

Figure 3. Low and high population growth rates for the study area between 2005 and 2035.

0

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150

Figure 4. Deforestation rate growth trends between 2000 and 2034 for ScenarioLow,No2ndaryRoads (exponential), ScenarioLow,Yes2ndaryRoads (logistical), and ScenarioCtrl (constant).

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151

Figure 5. Deforestation rate growth trends between 2000 and 2034 for ScenarioHigh,No2ndaryRoads (exponential), ScenarioHigh,Yes2ndaryRoads (logistical), and ScenarioCtrl (constant).

152

Figure 6. Forest cover area (ha) for ScenarioLow,No2ndaryRoads (exponential), ScenarioLow,Yes2ndaryRoads (logistical), and ScenarioCtrl (constant).

153

Figure 7. Forest cover area (ha) for ScenarioHigh,No2ndaryRoads (exponential), ScenarioHigh,Yes2ndaryRoads (logistical), and ScenarioCtrl (constant).

154

Figure 8. Schematic view of the weights of evidence method to produce a transition probability map.

155

Figure 9. 2000 real landscape showing the four categories: bodies of water, deforested, forest, and non-forest (includes non-forest ecosystems, the Interoceanica Sur highway, and secondary roads).

156

Figure 10. A cube raster data set. (Modified from Soares-Filho et al. 2008)

157

Figure 11. DINAMICA’s algorithm “determine the weights of evidence ranges” and required parameters.

158

Figure 12. Plot of An against An*exp(W+) showing how the tolerance angle (ta) is conceived (Modified from Soares Filho et al. 2008).

159

a)

b)

Figure 13. Weight of evidence coefficients (W+) for the variable ‘distance to population centers’. a) All significant categories (24) are presented. b) Categories that did not follow the observed trend ([23.4-27.2) and [100.4-121.5), in km) were assigned new coefficients.

160

Figure 14. ‘Distance to population centers’ significant categories map, overlapped with the map of deforestation occurred between 2000 and 2005 (black).

161

Figure 15. Weights of evidence (W+) graphs for the variables: (1) distance to the Interoceanica highway; (2) population attraction (interaction potential); (3) distance to secondary roads; (4) distance to rivers; (5) slope; (6) distance to 2000’s deforested land; (7) forest type: HF-lh – Lower hills humid forest, FF – Flooding forest, TF – Terra firme, HF-hh – Upper hills humid forest, HF-m – Mountain Humid forest, and BF – Bamboo forest; and (8) Biophysical and Land tenure: TR – Territorial Reserves, PS – Palm swamps, PA – Protected areas, BN – Brazil nut concessions, CT – Conservation and Tourism concessions, F – Forestry concessions, M – Mining concessions, and NC – Native communities.

162

Figure 16. DINAMICA’s model used to run the simulation of deforestation between 2000 and 2035.

163

Figure 17. Model fitness based on the fuzzy similarity method for 2005.

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MA DRE DE DIO SMA DRE DE DIO S

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VIA EVITAMIENTO URCOS

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73°0'0"W

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72°0'0"W

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16°0

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!

E C U A D O RE C U A D O R

C O L O M B I AC O L O M B I A

B R A S I LB R A S I L

BOLIVIA

BOLIVIA

C H I L EC H I L E

O C E A N O

P A C I F I C O

75 0 7537,5Km

08

PROYECTO 2009

CUSCO

Oficina de Estadística - OGPP-MTC/mschl Abril 2009

PROYECTOS 2009

TRANSPORTES:

AsfaltadaSin Asfaltar

En Proyecto

Línea Férrea

Provincial

Departamental

Límites

Superficie de Rodadura

Polígono Urbano

SÍMBOLOS CONVENCIONALES

!/ Capital Provincial#· Capital Distrital

#S Puebloú Puenteú PontonÎ PuertoÌ MinaN Abra

l Aeródromo

RíosLagunas

Capital Departamental!

PROVIAS NACIONAL

RUTINARIO

PERFIL,PREFACTIBILIDAD,FACTIBILIDAD

CONSTRUCCIÓN,MEJORAMIENTO,REHABILITACIÓN

!)

!̀

#*

!)

PUENTE

CEBAF

PUENTE

VIA EVITAMIENTO

ESTUDIOS

OBRAS

MANTENIMIENTOPOR NIVELES DE SERVICIO

PERIÓDICO

!) PUENTE

!̀ VIA EVITAMIENTO

PROVIAS DESCENTRALIZADO

CONSTRUCCIÓN,MEJORAMIENTO,REHABILITACIÓN

OBRAS

VIAS DEPARTAMENTALES

Y VECINALES

PROGRAMA DE TRANSPORTE RURAL DESCENTRALIZADO

!) PUENTE

CONSTRUCCIÓN,MEJORAMIENTO,REHABILITACIÓN

!) PUENTE

CONCESIONES

OTORGADAl

l PROCESOCONCESIONES

Î

Î

COMUNICACIONES:

575

116

!(

!(

BANDA ANCHA PARA LOCALIDADES AISLADAS :

INTERNET RURAL : EN

EJECUCIÓN

Años deEjecución

Número deLocalidades

2009 - 2010

2009

EN PROCESO DE

ADJUDICACIÓN(PROINVERSIÓN)

!( CAMISEA - LURÍN :

Núm. de Localidades

181

39

45

!( BANDA ANCHA RURAL II:

!(BANDA ANCHA PARA EL DESARROLLO DEL VRAE :

Figure 18. Peruvian Ministry of Transportation and Communication (Ministerio

de Transportes y Comunicaciones) maps showing the proposed roads Nuevo

Edén-Boca Manu-Boca Colorado (top) and Patria-Quincemil (bottom).

165

Figure 19. DINAMICA’s model used to run the simulation calibration and parameterization process of deforestation between 2000 and 2005.

166

Figure 20. Simulated landscapes for years 2020 and 2035 based on

ScenarioLow,No2ndaryRoads.

2020

Puerto Maldonado

2035

Puerto Maldonado

167

Figure 21. Simulated landscapes for years 2020 and 2035 based on

ScenarioLow,Yes2ndaryRoads.

2020

Puerto Maldonado

2035

Puerto Maldonado

168

Figure 22. Simulated landscapes for years 2020 and 2035 based on

ScenarioHigh,No2ndaryRoads.

2020

Puerto Maldonado

2035

Puerto Maldonado

169

Figure 23. Simulated landscapes for years 2020 and 2035 based on

ScenarioHigh,Yes2ndaryRoads.

2020

Puerto Maldonado

2035

Puerto Maldonado

170

Figure 24. Simulated landscapes for years 2020 and 2035 based on ScenarioCtrl.

2020

Puerto Maldonado

2035

Puerto Maldonado

171

Figure 25. Simulated landscape for year 2035 presenting the effect of

ScenarioHigh,Yes2ndaryRoads within the north area of the Tambopata National Reserve

near Puerto Maldonado (PEM).

172

Figure 26. Simulated landscape for year 2035 presenting the effect of

ScenarioHigh,Yes2ndaryRoads within the north area of the Tambopata National Reserve

near the Malinowski River.

173

Figure 27. Simulated landscapes for 2035 presenting the effect of

ScenarioLow,Yes2ndaryRoads (top) and ScenarioHigh,No2ndaryRoads (bottom) inside the

Tambopata National Reserve. Note that more deforestation is located inside the

reserve near the Malinowski River in ScenarioLow,Yes2ndaryRoads than in

ScenarioHigh,No2ndaryRoads.

174

Figure 28. Simulated landscapes for year 2035 presenting the effect of

ScenarioLow,Yes2ndaryRoads (top) and ScenarioHigh,Yes2ndaryRoads (bottom) on the most

western boundary of the Bahuaja Sonene National Park near the Interoceanica

highway.

175

Figure 29. Deforestation produced by ScenarioHigh,Yes2ndaryRoads in 2035 around

and inside the Amarakaeri Communal Reserve (ACR). Note how deforestation

invades the ACR from south to northwest, as it starts to follow the path of the

would-be constructed road Patria-Quincemil.

176

Figure 30. Deforestation probability map for the last model iteration (2034-

2035). Areas depicted by their original cover classes (bodies of water,

deforested, forest, and non-forest) do not have a probability as their

corresponding weights of evidence coefficients were all negative.

177

Figure 31. Simulated landscapes for year 2035 presenting the effect of

ScenarioHigh,No2ndaryRoads (top) and ScenarioHigh,Yes2ndaryRoads (bottom). Note the

difference in the amount of total deforestation located between Nuevo Edén and

Boca Manu towns.

178

Figure 32. Simulated landscapes for year 2035 presenting the effect of

ScenarioLow,No2ndaryRoads (top) and ScenarioLow,Yes2ndaryRoads (bottom). Note the

difference in the amount of total deforestation located between Nuevo Edén and

Boca Manu towns.

179

Figure 33. Simulated landscapes for year 2035 presenting the effect of

ScenarioLow,No2ndaryRoads (top) and ScenarioHigh,No2ndaryRoads (bottom) on the forestry

concessions to the north and south of the Inambari River and near the

Interoceanica highway.

180

Figure 34. Simulated landscapes for year 2035 presenting the effect of

ScenarioLow,Yes2ndaryRoads (top) and ScenarioHigh,Yes2ndaryRoads (bottom) on the

forestry concessions to the north and south of the Inambari River and near the

Interoceanica highway.

181

Table 1. District total population and population growth rates between 1981-

1993 and 1993-2005 for the study area’s districts. District-level average growth

rates (*) are given for Cusco and Puno Departments. Total Dept. population

growth rate is given for Madre de Dios.

Total Population Population

growth rate (!)

Department District 1981 1993 2005 81-93 93-05

Cusco Quellouno - 11,197 16,469 - 0.033

Yanatile - 8,158 9,520 - 0.013

Challabamba 5,663 8,621 9,600 0.036 0.009

Kosñipata 2,947 3,873 4,610 0.023 0.015

Paucartambo 8,832 11,028 14,168 0.019 0.021

Camanti 1,513 2,175 1,700 0.031 -0.02

Marcapata 4,481 4,805 5,141 0.006 0.006

Cusco 91,042 93,187 103,836 0.002 0.009

San Jerónimo 9,093 15,166 28,855 0.044 0.055

San Sebastián 15,978 32,134 85,472 0.06 0.085

San Tiago 51,901 73,129 66,277 0.029 -0.008

Wanchaq 35,803 51,584 54,524 0.031 0.005

Total 227,253 315,057 400,172 0.028* 0.018*

Puno Ayapata 3,403 4,864 6,820 0.030 0.029

San Gabán 2,100 3,554 4,243 0.045 0.015

Total 5,503 8,418 11,063 0.038* 0.022*

Madre de Dios Tambopata 20,341 34,329 51,384 0.045 0.042

Inambari 1,716 3,909 4,888 0.071 0.019

Las Piedras 2,526 4,514 6,072 0.050 0.025

Laberinto - 3,986 4,954 - 0.018

Manu 1,467 1,559 2,500 0.005 0.040

Fizcarrald 139 458 1,062 0.104 0.073

Madre de

Dios

1,890 8,999 5,605 0.139 -0.039

Huepetuhe - 2,811 8,130 - 0.093

Iñapari 812 841 791 0.003 -0.005

Iberia 3,013 3,858 4,868 0.021 0.020

Tahuamanu 1,103 1,744 1,770 0.039 0.001

Total 33,007 67,008 92,024 0.061 0.027

182

Table 2. List of used spatial variables and sources.

Class Variable Source

Biophysical Type of forest CDC

Palm swamps CDC and digitalized

by us

Distance to rivers CDC/modified

(distance to feature)

Slope CDC/modified

(from DEM)

Distance to

deforested

Internally modeled

Infrastructure Distance to Inter-

oceanica Sur (IOS)

highway

CDC/modified

(distance to feature)

Distance to

secondary roads

CDC/ACA/modified

(distance to feature)

Distance to

population centers

CSA/modified

(distance to feature)

Population

attraction

Built by us

Land tenure Protected areas CDC

Territorial reserves CDC

Conservation and

tourism state leased

concessions

SPDA/modified

(merged)

Brazil nut

concessions

CDC

Forestry

concessions

CDC

Mining

concessions

CDC

183

Table 3. Weights of evidence correlation between selected variables. Values

below 0.5 indicate non-correlation.

Weights of evidence correlation

First variable Second variable

Joint Information

Uncertainty

Distance to

deforested areas Palm swamps 0.00472543

Protected areas 0.0565405

Brazil nut concessions 0.0172925

Conservation and tourism state

leased concessions 0.00612854

Forestry concessions 0.014214

Mining concessions 0.0169147

Native communities 0.0171398

Distance to Interoceanica Sur

(IOS) highway 0.188054

Distance to secondary roads 0.200927

Distance to population centers 0.284913

Distance to rivers 0.0787644

Population attraction 0.153539

Slope 0.0280562

Territorial reserves 0.0230479

Forest type 0.0972614

Palm swamps Protected areas 0.00254837

Brazil nut concessions 0.00308716

Conservation and tourism state

leased concessions 0.0103458

Forestry concessions 0.00003409

Mining concessions 0.0200761

Native communities 0.0107076

Distance to IOS 0.00473998

Distance to secondary roads 0.00517067

Distance to population centers 0.00374398

Distance to rivers 0.000668577

Population attraction 0.00297491

Slope 0.00397451

Territorial reserves 0.00384616

Forest type 0.0121914

Protected areas Brazil nut concessions 0.0277034

Conservation and tourism state

leased concessions 0.0187988

Forestry concessions 0.104698

Mining concessions 0.0317904

Native communities 0.00847395

Distance to Interoceanica

highway 0.136166

Distance to secondary roads 0.121351

Distance to population centers 0.0643427

Distance to rivers 0.0165442

Population attraction 0.0231282

Slope 0.0108901

Territorial reserves 0.0726453

Forest type 0.112044

184

Brazil nut

concessions

Conservation and tourism state

leased concessions 0.00210212

Forestry concessions 0.0246246

Mining concessions 0.00297434

Native communities 0.0100887

Distance to Interoceanica

highway 0.0355496

Distance to secondary roads 0.0249492

Distance to population centers 0.0265256

Distance to rivers 0.00866169

Population attraction 0.0231623

Slope 0.0265332

Territorial reserves 0.0210322

Forest type 0.0592534

Conservation and

tourism state leased

concessions Forestry concessions 0.00998791

Mining concessions 0.000100237

Native communities 0.00206442

Distance to Interoceanica

highway 0.00885476

Distance to secondary roads 0.00595784

Distance to population centers 0.00478807

Distance to rivers 0.00135641

Population attraction 0.0126993

Slope 0.00413541

Territorial reserves 0.00873868

Forest type 0.00908538

Forestry

concessions Mining concessions 0.0027809

Native communities 0.0109765

Distance to Interoceanica

highway 0.043994

Distance to secondary roads 0.0389101

Distance to population centers 0.0133934

Distance to rivers 0.0115526

Population attraction 0.0253859

Slope 0.0290724

Territorial reserves 0.0307643

Forest type 0.0254409

Mining concessions Native communities 0.00271211

Distance to Interoceanica

highway 0.0270089

Distance to secondary roads 0.0171244

Distance to population centers 0.0242998

Distance to rivers 0.00338408

Population attraction 0.0237914

Slope 0.00112657

Territorial reserves 0.0121274

Forest type 0.0189239

Native communities

Distance to Interoceanica

highway 0.0124747

Distance to secondary roads 0.0175026

Distance to population centers 0.0160917

Distance to rivers 0.00368005

Population attraction 0.0213602

185

Slope 0.00232386

Territorial reserves 0.0120074

Forest type 0.0212814

Distance to

Interoceanica

highway Distance to secondary roads 0.314009

Distance to population centers 0.229994

Distance to rivers 0.0998564

Population attraction 0.173516

Slope 0.0432058

Territorial reserves 0.0515385

Forest type 0.149466

Distance to

secondary roads Distance to population centers 0.224567

Distance to rivers 0.100577

Population attraction 0.162667

Slope 0.051018

Territorial reserves 0.0581368

Forest type 0.133197

Distance to

population centers Distance to rivers 0.100008

Population attraction 0.205297

Slope 0.0379071

Territorial reserves 0.0429492

Forest type 0.119848

Distance to rivers Population attraction 0.0769434

Slope 0.0247381

Territorial reserves 0.00430606

Forest type 0.0581421

Population

attraction Slope 0.112092

Territorial reserves 0.0393911

Forest type 0.217188

Slope Territorial reserves 0.018503

Forest type 0.173833

Territorial reserves Forest type 0.0362614

186

Table 4. Deforestation rates growth trends for each scenario.

Deforestation rates (")

Year

ScenarioLow,

No2ndaryRoads

ScenarioLow,Y

es2ndaryRoads

ScenarioHigh,N

o2ndaryRoads

ScenarioHigh,Y

es2ndaryRoads ScenarioCtrl

2000 0.001512 0.001512 0.001512 0.001512 0.001512

2001 0.001512 0.001512 0.001512 0.001512 0.001512

2002 0.001512 0.001512 0.001512 0.001512 0.001512

2003 0.001512 0.001512 0.001512 0.001512 0.001512

2004 0.001512 0.001512 0.001512 0.001512 0.001512

2005 0.001541 0.001646 0.001560 0.001766 0.001512

2006 0.001572 0.001774 0.001610 0.002032 0.001512

2007 0.001603 0.001896 0.001662 0.002301 0.001512

2008 0.001635 0.002008 0.001716 0.002567 0.001512

2009 0.001668 0.002110 0.001772 0.002820 0.001512

2010 0.001701 0.002202 0.001830 0.003054 0.001512

2011 0.001735 0.002284 0.001891 0.003266 0.001512

2012 0.001770 0.002355 0.001955 0.003452 0.001512

2013 0.001805 0.002417 0.002021 0.003613 0.001512

2014 0.001841 0.002470 0.002089 0.003749 0.001512

2015 0.001878 0.002515 0.002161 0.003862 0.001512

2016 0.001916 0.002554 0.002236 0.003954 0.001512

2017 0.001954 0.002586 0.002313 0.004030 0.001512

2018 0.001993 0.002613 0.002395 0.004091 0.001512

2019 0.002033 0.002635 0.002479 0.004139 0.001512

2020 0.002074 0.002654 0.002568 0.004178 0.001512

2021 0.002116 0.002670 0.002660 0.004209 0.001512

2022 0.002158 0.002682 0.002756 0.004233 0.001512

2023 0.002202 0.002693 0.002857 0.004252 0.001512

2024 0.002246 0.002702 0.002962 0.004267 0.001512

2025 0.002291 0.002709 0.003071 0.004278 0.001512

2026 0.002337 0.002715 0.003186 0.004287 0.001512

2027 0.002384 0.002720 0.003306 0.004295 0.001512

2028 0.002432 0.002724 0.003432 0.004300 0.001512

2029 0.002482 0.002727 0.003563 0.004305 0.001512

2030 0.002532 0.002730 0.003701 0.004308 0.001512

2031 0.002583 0.002732 0.003845 0.004311 0.001512

2032 0.002635 0.002734 0.003995 0.004313 0.001512

2033 0.002689 0.002735 0.004153 0.004314 0.001512

2034 0.002743 0.002736 0.004319 0.004316 0.001512

187

Table 5. Projected total and net deforestation within the study area for the five

Scenarios between 2000 and 2035.

Study area Land cover (ha)

Net

change

(ha)

Net

change

(%)

Scenario 2000 2035 2035 2035

ScenarioLow,No2ndaryRoads Forest 9295926 8665920 -630006 6.8

Deforested 246834 876840

ScenarioLow,Yes2ndaryRoads Forest 9295926 8560723 -735203 7.9

Deforested 246834 982037

ScenarioHigh,No2ndaryRoads Forest 9295926 8515038 -780888 8.4

Deforested 246834 1027722

ScenarioHigh,Yes2ndaryRoads Forest 9295926 8239405 -1056521 11.4

Deforested 246834 1303355

ScenarioCtrl Forest 9295926 8816458 -479468 5.2

Deforested 246834 726302

188

Table 6. Projected and net deforestation inside Tambopata National Reserve for

the five scenarios.

Tambopata National Reserve (277727 ha) Land cover (ha)

Net change

(ha)

Net change

(%)

Scenario 2000 2035 2035 2035

ScenarioLow,No2ndaryRoads Forest 266728 261803 -4925 1.8

Deforested 950 5875

ScenarioLow,Yes2ndaryRoads Forest 266728 256826 -9902 3.7

Deforested 950 10852

ScenarioHigh,No2ndaryRoads Forest 266728 259941 -6787 2.5

Deforested 950 7737

ScenarioHigh,Yes2ndaryRoads Forest 266728 252722 -14006 5.3

Deforested 950 14956

ScenarioCtrl Forest 266728 263072 -3656 1.4

Deforested 950 4606

189

Table 7. Projected and net deforestation inside Bahuaja Sonene National Park

for the five scenarios.

Bahuaja Sonene National Park (817824

ha) Land cover (ha)

Net change

(ha)

Net change

(%)

Scenario 2000 2035 2035 2035

ScenarioLow,No2ndaryRoads Forest 786202 786181 -21 0.003

Deforested 3 24

ScenarioLow,Yes2ndaryRoads Forest 786202 785974 -228 0.029

Deforested 3 231

ScenarioHigh,No2ndaryRoads Forest 786202 786166 -36 0.005

Deforested 3 39

ScenarioHigh,Yes2ndaryRoads Forest 786202 785988 -214 0.027

Deforested 3 217

ScenarioCtrl Forest 786202 786190 -12 0.002

Deforested 3 15

190

Table 8. Projected and net deforestation inside Amarakaeri Communal Reserve

for the five scenarios.

Amarakaeri Communal Reserve (402486

ha) Land cover (ha)

Net change

(ha)

Net change

(%)

Scenario 2000 2035 2035 2035

ScenarioLow,No2ndaryRoads Forest 391385 391300 -85 0.022

Deforested 76 161

ScenarioLow,Yes2ndaryRoads Forest 391385 391139 -246 0.063

Deforested 76 322

ScenarioHigh,No2ndaryRoads Forest 391385 391156 -229 0.059

Deforested 76 305

ScenarioHigh,Yes2ndaryRoads Forest 391385 388618 -2767 0.707

Deforested 76 2843

ScenarioCtrl Forest 391385 391367 -18 0.005

Deforested 76 94

191

Table 9. Projected and net deforestation inside Manu National Park for the five

scenarios.

Manu National Park (1696435 ha) Land cover (ha)

Net change

(ha)

Net change

(%)

Scenario 2000 2035 2035 2035

ScenarioLow,No2ndaryRoads Forest 1632318 1629242 -3076 0.19

Deforested 3228 6304

ScenarioLow,Yes2ndaryRoads Forest 1632318 1628645 -3673 0.23

Deforested 3228 6901

ScenarioHigh,No2ndaryRoads Forest 1632318 1628505 -3813 0.23

Deforested 3228 7041

ScenarioHigh,Yes2ndaryRoads Forest 1632318 1625732 -6586 0.40

Deforested 3228 9814

ScenarioCtrl Forest 1632318 1629830 -2488 0.15

Deforested 3228 5716

192

Table 10. Projected and net deforestation inside Forestry Concessions for the

five scenarios.

Forestry concessions

(1374552 ha) Land cover (ha)

Net change

(ha)

Net change

(%)

Scenario 2000 2035 2035 2035

ScenarioLow,No2ndaryRoads Forest 1352896 1310687 -42209 3.12

Deforested 2912 45121

ScenarioLow,Yes2ndaryRoads Forest 1352896 1273896 -79000 5.84

Deforested 2912 81912

ScenarioHigh,No2ndaryRoads Forest 1352896 1295290 -57606 4.26

Deforested 2912 60518

ScenarioHigh,Yes2ndaryRoads Forest 1352896 1218055 -134841 9.97

Deforested 2912 137753

ScenarioCtrl Forest 1352896 1331715 -21181 1.57

Deforested 2912 24093

193

APPENDIX 1

Code Easting Northing Department Province District Population centre

Population

2000

Population

2005

1 346254 8506363 Puno Carabaya San Gaban Arica 39 37

2 358549 8530321 Puno Carabaya San Gaban Carmen 123 174

3 354796 8522944 Puno Carabaya San Gaban Challhuamayo 102 159

4 349309 8515558 Puno Carabaya San Gaban Chaquimayo 44 48

5 353666 8537868 Puno Carabaya San Gaban Chaspa Alto 58 61

6 350961 8540824 Puno Carabaya San Gaban Chaspa Bajo 84 113

7 352898 8515262 Puno Carabaya San Gaban Cuchillune 3 3

8 358297 8527601 Puno Carabaya San Gaban Cuesta Blanca 95 84

9 346165 8508024 Puno Carabaya San Gaban Esperanza 31 33

10 346580 8510279 Puno Carabaya San Gaban Lanlacuni 16 8

11 347203 8510308 Puno Carabaya San Gaban Lanlacuni Bajo 872 883

12 358230 8532076 Puno Carabaya San Gaban Lechemayo Chico 292 320

13 355948 8533991 Puno Carabaya San Gaban Lechemayo Grande 45 74

194

14 350515 8542541 Puno Carabaya San Gaban Loromayo 46 163

15 365208 8514105 Puno Carabaya San Gaban Mancayoc 5 4

16 344029 8502596 Puno Carabaya San Gaban Mayhuanto 39 42

17 346046 8505711 Puno Carabaya San Gaban Paqui Llusi 8 8

18 352071 8540118 Puno Carabaya San Gaban Puerto Leguia 24 15

19 359097 8518050 Puno Carabaya San Gaban Puerto Manoa 393 549

20 359603 8532076 Puno Carabaya San Gaban Salimayo 88 114

21 349546 8514550 Puno Carabaya San Gaban San Gaban 136 140

22 350406 8515470 Puno Carabaya San Gaban San Juan Bajo 58 44

23 360907 8511880 Puno Carabaya San Gaban San Trifon 7 7

24 344682 8503872 Puno Carabaya San Gaban Sangari 14 8

25 356665 8525851 Puno Carabaya San Gaban Tantamayo 78 155

26 360136 8529322 Puno Carabaya San Gaban Yahuarmayo 72 106

27 343466 8500935 Puno Carabaya Ayapata Quilla Bamba 41 48

28 167445 8592776 Cusco Calca Yanatile Aguaypille 17 18

29 163574 8600518 Cusco Calca Yanatile Ccochachayoc 15 16

195

30 172496 8594023 Cusco Calca Yanatile Ccorihuairachina 3 3

31 179674 8579621 Cusco Calca Yanatile Ccorimayo 115 66

32 173540 8593162 Cusco Calca Yanatile Cedropata 4 5

33 164727 8596123 Cusco Calca Yanatile Chaquimayoc 18 20

34 171618 8590389 Cusco Calca Yanatile Chaupiurca 14 11

35 164699 8594698 Cusco Calca Yanatile Chintapata 17 18

36 185501 8568713 Cusco Calca Yanatile Chullo 60 64

37 163601 8598778 Cusco Calca Yanatile Chunchusmayo 8 3

38 191129 8568770 Cusco Calca Yanatile Churuyoc 12 10

39 173732 8584059 Cusco Calca Yanatile Estrella 6 10

40 179758 8576733 Cusco Calca Yanatile Floridayoc 48 34

41 178026 8578265 Cusco Calca Yanatile Hualla 390 416

42 162723 8596348 Cusco Calca Yanatile Huaynapata 90 68

43 167445 8593967 Cusco Calca Yanatile Inca Andenniyoc 18 19

44 173814 8583183 Cusco Calca Yanatile Killapata 8 8

45 172661 8592581 Cusco Calca Yanatile La Merced 39 31

196

46 175317 8585424 Cusco Calca Yanatile Lacco 2 2

47 155941 8601589 Cusco Calca Yanatile Lechemayo 2 2

48 166209 8593613 Cusco Calca Yanatile Llactapata 18 15

49 162228 8607359 Cusco Calca Yanatile Llactapata Baja 10 5

50 179720 8572659 Cusco Calca Yanatile Matipata 17 12

51 181204 8584213 Cusco Calca Yanatile Mendosayoc 130 139

52 185291 8573154 Cusco Calca Yanatile Mesapata 26 14

53 180872 8579256 Cusco Calca Yanatile Mesapata 3 44 47

54 170520 8606403 Cusco Calca Yanatile Miraflores 116 125

55 161844 8601861 Cusco Calca Yanatile Naranjayoc 18 14

56 161213 8597166 Cusco Calca Yanatile Pacchac 1 21 22

57 175473 8582549 Cusco Calca Yanatile Pacchac 2 50 53

58 170986 8592363 Cusco Calca Yanatile Pallar 20 21

59 181997 8576819 Cusco Calca Yanatile Pucara 37 68

60 176230 8575905 Cusco Calca Yanatile Quellomayo 63 54

61 163519 8597803 Cusco Calca Yanatile Rataratayoc 9 13

197

62 166676 8594179 Cusco Calca Yanatile Retiro del Carmen 2 44 47

63 182508 8575547 Cusco Calca Yanatile San Antonio 80 58

64 170437 8595336 Cusco Calca Yanatile San Jose 26 28

65 164370 8597186 Cusco Calca Yanatile San Miguel 3 3

66 169394 8593755 Cusco Calca Yanatile Sarahuasi 14 12

67 179285 8578555 Cusco Calca Yanatile Suyo 254 271

68 181165 8572063 Cusco Calca Yanatile Torocmayo 24 23

69 164288 8594206 Cusco Calca Yanatile Villoc Pampa 51 54

70 180447 8576115 Cusco Calca Yanatile Vista Florida 53 42

71 129366 8624755 Cusco

La

Convención Quellouno Amancaes 131 128

72 149105 8613599 Cusco

La

Convención Quellouno Bellavista 247 327

73 132770 8623612 Cusco

La

Convención Quellouno Calangato 97 113

74 150451 8608266 Cusco

La

Convención Quellouno Chaupichullo 9 10

75 158714 8604389 Cusco La

Quellouno Chunchusmayo 14 17

198

Convención

76 139249 8618325 Cusco

La

Convención Quellouno Esmeralda 75 82

77 152592 8606530 Cusco

La

Convención Quellouno Huaynapata 85 94

78 155447 8604242 Cusco

La

Convención Quellouno Kcarun 14 16

79 153663 8608288 Cusco

La

Convención Quellouno Lacco 1 10 12

80 149188 8612041 Cusco

La

Convención Quellouno Mesapata 1 103 121

81 149901 8611552 Cusco

La

Convención Quellouno Monte Cirialo 30 35

82 129915 8624200 Cusco

La

Convención Quellouno Pampa Blanca 137 125

83 154773 8608329 Cusco

La

Convención Quellouno Quellomayo 36 49

84 142928 8614965 Cusco

La

Convención Quellouno Quellouno 64 56

85 145838 8614086 Cusco La

Quellouno Rosario 36 41

199

Convención

86 146003 8619047 Cusco

La

Convención Quellouno Sacramento 85 100

87 134335 8619687 Cusco

La

Convención Quellouno Victoria 50 85

88 191422 8562917 Cusco Paucartambo Challabamba Bombon 267 258

89 194507 8565079 Cusco Paucartambo Challabamba Chilcayoc 55 57

90 192773 8562784 Cusco Paucartambo Challabamba Chimor 331 321

91 193448 8566142 Cusco Paucartambo Challabamba Churuyoc 44 61

92 197501 8559099 Cusco Paucartambo Challabamba Jesus Maria 39 14

93 193937 8563386 Cusco Paucartambo Challabamba Lali 120 126

94 185383 8558613 Cusco Paucartambo Challabamba Pachamachay 214 123

95 193940 8561509 Cusco Paucartambo Challabamba Pipobamba 31 37

96 196204 8566136 Cusco Paucartambo Challabamba Pucara 33 23

97 193642 8560511 Cusco Paucartambo Challabamba Solan 189 200

98 195919 8564736 Cusco Paucartambo Challabamba Televan 155 148

99 195851 8564261 Cusco Paucartambo Challabamba Utucany 243 254

200

100 194507 8565079 Cusco Paucartambo Challabamba Yuracmayoc 8 8

101 190905 8557525 Cusco Paucartambo Challabamba Yuractoruyoc 112 77

102 215407 8539068 Cusco Paucartambo Kosñipata Acjanaco 9 10

103 235279 8560290 Cusco Paucartambo Kosñipata Agua Santa 97 104

104 235930 8560691 Cusco Paucartambo Kosñipata Asuncion 93 86

105 244088 8573663 Cusco Paucartambo Kosñipata Atalaya 103 180

106 244866 8555821 Cusco Paucartambo Kosñipata Bajo Quero 26 28

107 233640 8570440 Cusco Paucartambo Kosñipata Bienvenida 11 10

108 219456 8544436 Cusco Paucartambo Kosñipata Buenos Aires 3 4

109 195208 8579119 Cusco Paucartambo Kosñipata Callanga 186 200

110 233919 8567897 Cusco Paucartambo Kosñipata Castilla (Tono Bajo) 89 77

111 232156 8558906 Cusco Paucartambo Kosñipata Chontachaca 99 85

112 246316 8572365 Cusco Paucartambo Kosñipata Coloradito 40 31

113 230837 8558608 Cusco Paucartambo Kosñipata Consuelo 6 5

114 217718 8541654 Cusco Paucartambo Kosñipata Esperanza 0 0

115 232590 8559169 Cusco Paucartambo Kosñipata Fortaleza 43 49

201

116 238692 8562571 Cusco Paucartambo Kosñipata Lastenia 18 19

117 239787 8570901 Cusco Paucartambo Kosñipata Maria 3 4

118 248570 8569921 Cusco Paucartambo Kosñipata Mirador 6 6

119 239378 8561261 Cusco Paucartambo Kosñipata Mistiana 30 41

120 236120 8561763 Cusco Paucartambo Kosñipata Montanesa 19 57

121 237177 8564898 Cusco Paucartambo Kosñipata Patria 1019 1240

122 244854 8573229 Cusco Paucartambo Kosñipata Pelayo 52 49

123 217841 8543503 Cusco Paucartambo Kosñipata Pillahuata 2 1

124 239145 8571781 Cusco Paucartambo Kosñipata Pillcopata 1361 1463

125 235210 8566749 Cusco Paucartambo Kosñipata Primavera 15 14

126 239429 8552926 Cusco Paucartambo Kosñipata Progreso 7 7

127 245547 8565176 Cusco Paucartambo Kosñipata Queros (Huachipaire) 34 32

128 246803 8563462 Cusco Paucartambo Kosñipata Rio Blanco 22 22

129 249057 8568524 Cusco Paucartambo Kosñipata Rio Carbon 56 64

130 246530 8567035 Cusco Paucartambo Kosñipata Sabaluyoc 94 76

131 225662 8565609 Cusco Paucartambo Kosñipata San Miguel 28 38

202

132 221555 8550269 Cusco Paucartambo Kosñipata San Pedro 2 2

133 238027 8567812 Cusco Paucartambo Kosñipata Santa Alicia 37 44

134 222598 8553891 Cusco Paucartambo Kosñipata Santa Isabel 6 6

135 237659 8561722 Cusco Paucartambo Kosñipata Santa Rosa 5 7

136 234456 8574242 Cusco Paucartambo Kosñipata Santa Rosa de Huacaria 117 113

137 233930 8560015 Cusco Paucartambo Kosñipata Sector Eva 44 43

138 221098 8550186 Cusco Paucartambo Kosñipata Suiza 4 5

139 226755 8565782 Cusco Paucartambo Kosñipata Tono Alto 67 72

140 241125 8554616 Cusco Paucartambo Kosñipata Trabajo 17 18

141 242129 8567185 Cusco Paucartambo Kosñipata Tupac Amaru (Ubaldina) 298 277

142 239027 8564745 Cusco Paucartambo Kosñipata Ubaldina 15 17

143 239120 8573336 Cusco Paucartambo Kosñipata Villa Carmen 13 10

144 233457 8561727 Cusco Paucartambo Kosñipata Yupurqui 35 29

145 218988 8526209 Cusco Paucartambo Paucartambo Paucartambo 3282 4234

146 340349 8541985 Cusco Quispicanchis Camanti Asnamayo Chico 10 9

147 325303 8540844 Cusco Quispicanchis Camanti Balceadero 10 0

203

148 345371 8548570 Cusco Quispicanchis Camanti Boca de Kitari 6 5

149 298195 8524628 Cusco Quispicanchis Camanti Cadena 8 12

150 317406 8538022 Cusco Quispicanchis Camanti Ccapacmayo 10 9

151 300743 8527358 Cusco Quispicanchis Camanti Ccollamayo 4 4

152 300798 8526418 Cusco Quispicanchis Camanti Chonta Puncu 2 1

153 300012 8524637 Cusco Quispicanchis Camanti Choque Llusca 12 13

154 328956 8537155 Cusco Quispicanchis Camanti Chunchusmayo 15 10

155 323614 8542003 Cusco Quispicanchis Camanti Collpamayo 3 2

156 329842 8538413 Cusco Quispicanchis Camanti Comandante 2 3

157 305025 8528454 Cusco Quispicanchis Camanti Coperma 24 20

158 307444 8532654 Cusco Quispicanchis Camanti Cruz Pata 6 5

159 331649 8540451 Cusco Quispicanchis Camanti Esperanza 16 14

160 329950 8541419 Cusco Quispicanchis Camanti Fortaleza 10 5

161 345052 8542268 Cusco Quispicanchis Camanti Garrafon Chico 5 4

162 348220 8541364 Cusco Quispicanchis Camanti Garrafon Grande 20 21

163 322875 8541830 Cusco Quispicanchis Camanti Huacyumbre 79 68

204

164 304541 8527157 Cusco Quispicanchis Camanti Huaropascay 3 3

165 295493 8522382 Cusco Quispicanchis Camanti Huaynapata 10 8

166 305381 8537524 Cusco Quispicanchis Camanti Huinchomayo 10 9

167 349415 8541257 Cusco Quispicanchis Camanti Inambari 33 30

168 342546 8538171 Cusco Quispicanchis Camanti Jujununta Choquetamura 1 1

169 307493 8543587 Cusco Quispicanchis Camanti Kitare 10 7

170 324445 8541245 Cusco Quispicanchis Camanti Limonchayoc 82 129

171 294242 8520638 Cusco Quispicanchis Camanti Mandor 17 26

172 305901 8530015 Cusco Quispicanchis Camanti Maniri 2 1

173 325778 8541994 Cusco Quispicanchis Camanti Media Luna 5 4

174 296534 8523031 Cusco Quispicanchis Camanti Moroto 3 1

175 319464 8543939 Cusco Quispicanchis Camanti Munaypampa 3 2

176 344138 8531488 Cusco Quispicanchis Camanti Nujununta 1 1

177 344174 8543756 Cusco Quispicanchis Camanti Oro Mayo 3 3

178 307006 8534032 Cusco Quispicanchis Camanti Oroya 9 11

179 348558 8541309 Cusco Quispicanchis Camanti Otorongo Chico 5 4

205

180 348521 8541556 Cusco Quispicanchis Camanti Otorongo Grande 7 5

181 318300 8540077 Cusco Quispicanchis Camanti Palcamayo 12 15

182 343581 8534284 Cusco Quispicanchis Camanti Palmira 1 1

183 310567 8535229 Cusco Quispicanchis Camanti Pan de Azucar 5 5

184 348932 8541574 Cusco Quispicanchis Camanti Pinhalchayoc 31 28

185 311224 8539444 Cusco Quispicanchis Camanti Pipitayoc 10 9

186 306085 8547382 Cusco Quispicanchis Camanti Pobre Mayo 5 4

187 295380 8514748 Cusco Quispicanchis Camanti Poyonco 1 1

188 346978 8541227 Cusco Quispicanchis Camanti Puente Golondrina 7 5

189 318407 8544330 Cusco Quispicanchis Camanti Puerta Falsa 19 30

190 325346 8539681 Cusco Quispicanchis Camanti Quebrada Seca 5 4

191 309773 8536735 Cusco Quispicanchis Camanti Quincemil 949 920

192 308321 8535795 Cusco Quispicanchis Camanti Sacracumbre 9 8

193 348750 8541464 Cusco Quispicanchis Camanti San Agustin 7 5

194 347764 8541264 Cusco Quispicanchis Camanti San Jose 6 3

195 297611 8523441 Cusco Quispicanchis Camanti San Jose 10 12

206

196 333566 8539839 Cusco Quispicanchis Camanti San Lorenzo 141 150

197 294810 8514486 Cusco Quispicanchis Camanti San Melchor 5 5

198 294251 8516968 Cusco Quispicanchis Camanti San Miguel 75 99

199 293619 8514989 Cusco Quispicanchis Camanti San Pedro 31 28

200 302596 8527331 Cusco Quispicanchis Camanti Saniaca 4 3

201 332078 8539702 Cusco Quispicanchis Camanti Santa Elena 18 14

202 308627 8547655 Cusco Quispicanchis Camanti Santa Isidora 13 10

203 318848 8539237 Cusco Quispicanchis Camanti Santa Marta 10 9

204 299757 8524628 Cusco Quispicanchis Camanti Sausipata 5 3

205 324408 8541948 Cusco Quispicanchis Camanti Tigrimayo 6 5

206 311279 8537356 Cusco Quispicanchis Camanti Tocoro Cumbre 5 6

207 309097 8545973 Cusco Quispicanchis Camanti Tunquimayo 15 17

208 345400 8551718 Cusco Quispicanchis Camanti Villa Alegría 3 3

209 328124 8541830 Cusco Quispicanchis Camanti Villanubia 9 6

210 303975 8527751 Cusco Quispicanchis Camanti Vitobamba 12 15

211 307189 8531887 Cusco Quispicanchis Camanti Yanamayo Chico (Caserio) 9 4

207

212 305071 8530846 Cusco Quispicanchis Camanti Yanamayo Grande (Caserío) 4 1

213 309087 8540348 Cusco Quispicanchis Camanti Yanamayo Grande (Minero) 42 38

214 306280 8544252 Cusco Quispicanchis Camanti Yanaurco 4 4

215 293926 8513558 Cusco Quispicanchis Marcapata Capire 61 45

216 294423 8510176 Cusco Quispicanchis Marcapata Chaupichaca 36 26

217 292987 8500420 Cusco Quispicanchis Marcapata Chiari 7 2

218 295239 8501156 Cusco Quispicanchis Marcapata Chilechile 45 36

219 294057 8507174 Cusco Quispicanchis Marcapata Culebrayoc 23 19

220 294102 8506572 Cusco Quispicanchis Marcapata Iscaybamba 14 13

221 294258 8500754 Cusco Quispicanchis Marcapata Limac Punco 101 151

222 293904 8512046 Cusco Quispicanchis Marcapata Mamabamba 59 64

223 295261 8506572 Cusco Quispicanchis Marcapata Mancara 18 10

224 294526 8500130 Cusco Quispicanchis Marcapata Raqchipata 65 63

225 294971 8503964 Cusco Quispicanchis Marcapata Ttio 70 73

226 177515 8503803 Cusco Cusco Cusco Cusco 289939 326405

227 309191 8635956 Madre de Dios Manu Fitzcarrald Barraca (Puerto Azul) 39 75

208

228 292155 8643182 Madre de Dios Manu Fitzcarrald Boca Manu 124 201

229 287534 8636045 Madre de Dios Manu Fitzcarrald Diamante 248 232

230 292166 8643471 Madre de Dios Manu Fitzcarrald Isla de los Valles 60 62

231 231009 8691514 Madre de Dios Manu Fitzcarrald Maizal 37 52

232 265039 8614357 Madre de Dios Manu Fitzcarrald Nuevo Eden 50 67

233 211476 8702071 Madre de Dios Manu Fitzcarrald Tayacome 130 142

234 186761 8698288 Madre de Dios Manu Fitzcarrald Yomibato 182 231

235 332355 8578806 Madre de Dios Manu Huepetuhe Alto Pukiri 67 105

236 320134 8561447 Madre de Dios Manu Huepetuhe Bamberme 85 132

237 349078 8556608 Madre de Dios Manu Huepetuhe Boca Punkiri 188 178

238 335982 8582075 Madre de Dios Manu Huepetuhe Boca Toacabe 66 103

239 347964 8557883 Madre de Dios Manu Huepetuhe Caychihue Barraca 291 619

240 340734 8559431 Madre de Dios Manu Huepetuhe Caychiwe 663 901

241 323955 8565463 Madre de Dios Manu Huepetuhe Choque 447 530

242 333611 8562683 Madre de Dios Manu Huepetuhe Huaypetuhe 2777 3998

243 346640 8551462 Madre de Dios Manu Huepetuhe Kimbiri 49 46

209

244 344583 8552805 Madre de Dios Manu Huepetuhe Kimiri 209 199

245 345976 8549290 Madre de Dios Manu Huepetuhe Israel 33 51

246 325748 8565351 Madre de Dios Manu Huepetuhe Libertad 350 652

247 349523 8542919 Madre de Dios Manu Huepetuhe Machiche 42 77

248 349750 8541671 Madre de Dios Manu Huepetuhe Puente Inambari 226 135

249 355979 8571715 Madre de Dios Manu Huepetuhe Puquiri 100 143

250 346999 8550236 Madre de Dios Manu Huepetuhe Sachabacayoc 7 30

251 335979 8559697 Madre de Dios Manu Huepetuhe Santa Ines 117 76

252 345723 8547091 Madre de Dios Manu Huepetuhe Tazon Chico 16 26

253 347222 8545349 Madre de Dios Manu Huepetuhe Tazon Grande 16 26

254 327815 8571006 Madre de Dios Manu Huepetuhe Tranquera (Barranco Chico) 148 376

255 340629 8601698 Madre de Dios Manu

Madre de

Dios Bajo Colorado (Playa oculta) 97 121

256 337886 8596859 Madre de Dios Manu

Madre de

Dios Bajo Pukiri (Delta 3) 89 65

257 314328 8626104 Madre de Dios Manu

Madre de

Dios Blanquillo 23 16

210

258 381863 8606886 Madre de Dios Manu

Madre de

Dios Boca Amigo 124 96

259 349059 8604910 Madre de Dios Manu

Madre de

Dios Boca Colorado 841 1075

260 338997 8598155 Madre de Dios Manu

Madre de

Dios Boca Pukiri 107 88

261 335387 8590793 Madre de Dios Manu

Madre de

Dios

Centro Pukiri (Comunidad

Pukiri) 132 108

262 334825 8587283 Madre de Dios Manu

Madre de

Dios Delta 1 1484 1218

263 334201 8584614 Madre de Dios Manu

Madre de

Dios Delta 2 112 92

264 335531 8591426 Madre de Dios Manu

Madre de

Dios Delta 4 123 101

265 361047 8604926 Madre de Dios Manu

Madre de

Dios Guacamayo 118 54

266 310985 8570695 Madre de Dios Manu

Madre de

Dios Huasoroquito 110 69

267 294726 8587686 Madre de Dios Manu

Madre de

Dios Ishiriwe 58 80

211

268 378421 8605056 Madre de Dios Manu

Madre de

Dios Malvinas 120 109

269 343022 8608368 Madre de Dios Manu

Madre de

Dios Mirador Chico 7 6

270 343022 8608368 Madre de Dios Manu

Madre de

Dios Mirador Grande 20 53

271 367104 8603949 Madre de Dios Manu

Madre de

Dios Nuevo San Juan 4 3

272 359190 8605687 Madre de Dios Manu

Madre de

Dios Pacal Guacamayo 146 114

273 342989 8611543 Madre de Dios Manu

Madre de

Dios Palometayoc 12 10

274 326699 8590373 Madre de Dios Manu

Madre de

Dios Puerto Luz 344 455

275 320970 8560854 Madre de Dios Manu

Madre de

Dios Punkiri Chico 227 547

276 351043 8568417 Madre de Dios Manu

Madre de

Dios Puquiri 973 1492

277 334859 8596908 Madre de Dios Manu

Madre de

Dios San Jose de Kerene 197 238

212

278 374250 8611355 Madre de Dios Manu

Madre de

Dios San Juan Chico 42 0

279 369161 8608417 Madre de Dios Manu

Madre de

Dios San Juan Grande 629 609

280 320280 8566436 Madre de Dios Manu

Madre de

Dios Setapo 123 204

281 351521 8604097 Madre de Dios Manu

Madre de

Dios Viejo Aeropuerto 3 0

282 242486 8586280 Madre de Dios Manu Manu Adan Rayo 35 24

283 248053 8572616 Madre de Dios Manu Manu Alto Carbon 43 51

284 240733 8579566 Madre de Dios Manu Manu Amazonia 2 2

285 257314 8607600 Madre de Dios Manu Manu Bonanza 15 11

286 242407 8588760 Madre de Dios Manu Manu Cabo de Hornos 1 14 17

287 247437 8588221 Madre de Dios Manu Manu Cabo de Hornos 2 4 5

288 240668 8581884 Madre de Dios Manu Manu Erika 5 8

289 244945 8576025 Madre de Dios Manu Manu Gamitana 135 135

290 257955 8602766 Madre de Dios Manu Manu Itahuania 154 179

291 243299 8594636 Madre de Dios Manu Manu Jose Olaya 5 6

213

292 243136 8597172 Madre de Dios Manu Manu Llactapampa (Palotoa) 156 230

293 242147 8584348 Madre de Dios Manu Manu Los Aguanos 78 78

294 258831 8609094 Madre de Dios Manu Manu Mamajapac 34 42

295 193925 8608117 Madre de Dios Manu Manu Mameria 90 110

296 244682 8589387 Madre de Dios Manu Manu Mansilla I (M.) 132 145

297 243442 8589367 Madre de Dios Manu Manu Mansilla II (Nueva M.) 76 93

298 240633 8586588 Madre de Dios Manu Manu Mascoitania 1 1

299 249368 8578922 Madre de Dios Manu Manu Pacasmayo 20 24

300 243586 8578056 Madre de Dios Manu Manu Pampa Arizona 11 13

301 243723 8579972 Madre de Dios Manu Manu Salvacion 547 786

302 245500 8598345 Madre de Dios Manu Manu Santa Cruz 112 129

303 241762 8590522 Madre de Dios Manu Manu Santa Elena 7 6

304 250485 8598346 Madre de Dios Manu Manu Shintuya 228 245

305 269909 8626602 Madre de Dios Manu Manu Shipetiari 86 120

306 243553 8597246 Madre de Dios Manu Manu Teparo Grande (CCNN) 87 101

307 241843 8577572 Madre de Dios Manu Manu Tropical I 21 11

214

308 236697 8576858 Madre de Dios Manu Manu Tropical II 7 8

309 243916 8582418 Madre de Dios Manu Manu Yunguyo 43 30

310 440571 8740491 Madre de Dios Tahuamanu Iberia Iberia 3621 3915

311 423438 8752015 Madre de Dios Tahuamanu Iberia Arrozal 24 12

312 445803 8740004 Madre de Dios Tahuamanu Iberia Bello Horizonte 14 7

313 446950 8750183 Madre de Dios Tahuamanu Iberia Carachamayo 5 5

314 442688 8750948 Madre de Dios Tahuamanu Iberia Chilina Vieja 45 34

315 430771 8755890 Madre de Dios Tahuamanu Iberia Flor de Acre 62 72

316 421429 8739555 Madre de Dios Tahuamanu Iberia Grupo ocho 12 13

317 449596 8748774 Madre de Dios Tahuamanu Iberia La Republica 47 52

318 442363 8740709 Madre de Dios Tahuamanu Iberia Maria Cristina 12 6

319 453189 8736107 Madre de Dios Tahuamanu Iberia Miraflores 17 15

320 425475 8745227 Madre de Dios Tahuamanu Iberia Nueva Alianza 91 100

321 441102 8739372 Madre de Dios Tahuamanu Iberia Oceania 13 15

322 423438 8752014 Madre de Dios Tahuamanu Iberia Pacahuara 183 496

323 445883 8747106 Madre de Dios Tahuamanu Iberia Ponalillo 10 0

215

324 422258 8715770 Madre de Dios Tahuamanu Iberia Portillo 75 52

325 444421 8746023 Madre de Dios Tahuamanu Iberia San Antonio Abad 23 16

326 438277 8738254 Madre de Dios Tahuamanu Iberia San Francisco de Asis 55 14

327 447690 8745318 Madre de Dios Tahuamanu Iberia Tropezon 20 28

328 418756 8787598 Madre de Dios Tahuamanu Inhapari Alto Belgica 17 17

329 420732 8787325 Madre de Dios Tahuamanu Inhapari Belgica 64 61

330 436870 8790126 Madre de Dios Tahuamanu Inhapari Inhapari 444 533

331 438404 8770714 Madre de Dios Tahuamanu Inhapari Noaya 24 24

332 434176 8782227 Madre de Dios Tahuamanu Inhapari Nueva Esperanza 66 55

333 440898 8759456 Madre de Dios Tahuamanu Inhapari San Isidro de Chilina 78 50

334 437530 8777514 Madre de Dios Tahuamanu Inhapari Villa Primavera 74 44

335 461473 8738534 Madre de Dios Tahuamanu Tahuamanu Abeja 43 45

336 474304 8711799 Madre de Dios Tahuamanu Tahuamanu Alerta 598 611

337 473886 8711368 Madre de Dios Tahuamanu Tahuamanu Alerta 2 2

338 471732 8733423 Madre de Dios Tahuamanu Tahuamanu Alto Peru 5 0

339 451662 8743356 Madre de Dios Tahuamanu Tahuamanu La Merced 23 14

216

340 466770 8698192 Madre de Dios Tahuamanu Tahuamanu La Novia 207 233

341 468787 8722502 Madre de Dios Tahuamanu Tahuamanu Maranguape 7 12

342 478564 8696221 Madre de Dios Tahuamanu Tahuamanu Nuevo Pacaran 86 101

343 464920 8733683 Madre de Dios Tahuamanu Tahuamanu San Lorenzo 179 158

344 464916 8733997 Madre de Dios Tahuamanu Tahuamanu San Lorenzo 17 17

345 477397 8708360 Madre de Dios Tahuamanu Tahuamanu San Pedro 81 122

346 424199 8727351 Madre de Dios Tahuamanu Tahuamanu Santa Maria 76 85

347 490866 8684265 Madre de Dios Tahuamanu Tahuamanu Shiringayoc 257 263

348 472231 8705712 Madre de Dios Tahuamanu Tahuamanu Villa Rocio 83 107

349 351584 8551691 Madre de Dios Tambopata Inambari Mazuko 1712 1920

350 355661 8554053 Madre de Dios Tambopata Inambari Alto Dos de Mayo 77 59

351 396686 8574078 Madre de Dios Tambopata Inambari Alto Libertad 125 110

352 401135 8560263 Madre de Dios Tambopata Inambari Azul 66 72

353 364600 8576270 Madre de Dios Tambopata Inambari Bello Porvenir 45 32

354 352396 8554053 Madre de Dios Tambopata Inambari Dos de Mayo 120 124

355 408156 8577137 Madre de Dios Tambopata Inambari El Progreso 31 34

217

356 394441 8585598 Madre de Dios Tambopata Inambari Jayave 163 177

357 384065 8561093 Madre de Dios Tambopata Inambari Kotsimba 84 111

358 387053 8576153 Madre de Dios Tambopata Inambari La Distancia 22 24

359 392229 8561110 Madre de Dios Tambopata Inambari Malinosqui 254 176

360 387582 8561091 Madre de Dios Tambopata Inambari Manuani Malinosqui 45 66

361 357727 8572698 Madre de Dios Tambopata Inambari Nueva Esperanza 25 27

362 370408 8576433 Madre de Dios Tambopata Inambari Nueva Generacion 22 24

363 391448 8574521 Madre de Dios Tambopata Inambari Nueva Arequipa 77 60

364 422156 8576322 Madre de Dios Tambopata Inambari Padre Hermogenes 11 12

365 350109 8549674 Madre de Dios Tambopata Inambari Palmera 76 60

366 368716 8585188 Madre de Dios Tambopata Inambari Ponal 124 136

367 375752 8572009 Madre de Dios Tambopata Inambari Primavera Alta 101 96

368 379519 8572511 Madre de Dios Tambopata Inambari Primavera Baja 84 80

369 352405 8572712 Madre de Dios Tambopata Inambari Puerto Carlos 17 12

370 349487 8552687 Madre de Dios Tambopata Inambari Puerto Mazuko 185 206

371 354191 8551560 Madre de Dios Tambopata Inambari Quebrada Seca 7 8

218

372 364325 8571381 Madre de Dios Tambopata Inambari Santa Rita Alta 98 82

373 370227 8572009 Madre de Dios Tambopata Inambari Santa Rita Baja 129 107

374 358448 8570970 Madre de Dios Tambopata Inambari Santa Rosa 309 411

375 385258 8584323 Madre de Dios Tambopata Inambari Sarayacu 207 248

376 402498 8573893 Madre de Dios Tambopata Inambari Sol Naciente 26 29

377 349965 8549237 Madre de Dios Tambopata Inambari Tazon 15 16

378 410158 8575776 Madre de Dios Tambopata Inambari Union Progreso 129 166

379 353727 8560949 Madre de Dios Tambopata Inambari Villa Santiago (Arazaire) 119 117

380 387053 8576153 Madre de Dios Tambopata Inambari Virgen de la Candelaria 117 86

381 442294 8587873 Madre de Dios Tambopata Laberinto Aguas Blancas 2 2

382 419100 8593178 Madre de Dios Tambopata Laberinto Amaracaire 51 77

383 426297 8595137 Madre de Dios Tambopata Laberinto Boca Union 187 225

384 432516 8593506 Madre de Dios Tambopata Laberinto Catarata 5 6

385 419553 8593399 Madre de Dios Tambopata Laberinto CCNN Boca Inambari 150 165

386 395325 8601127 Madre de Dios Tambopata Laberinto Cinco Islas 40 60

387 441535 8602415 Madre de Dios Tambopata Laberinto Copamanu 33 54

219

388 433585 8586516 Madre de Dios Tambopata Laberinto Florida Alta 201 177

389 439993 8590263 Madre de Dios Tambopata Laberinto Florida Baja 86 75

390 430943 8595853 Madre de Dios Tambopata Laberinto Fortuna Alto Laberinto 151 178

391 413461 8598277 Madre de Dios Tambopata Laberinto Horacio Cevallos 50 149

392 413005 8588772 Madre de Dios Tambopata Laberinto Huacamayo Chico 38 117

393 435550 8586534 Madre de Dios Tambopata Laberinto Huantupa 14 15

394 390113 8604868 Madre de Dios Tambopata Laberinto Huitoto 33 30

395 409784 8601177 Madre de Dios Tambopata Laberinto Lagarto (Base Naval) 93 115

396 413745 8593334 Madre de Dios Tambopata Laberinto Lago Inambarillo 259 154

397 451330 8595783 Madre de Dios Tambopata Laberinto Las Mercedes 157 139

398 449457 8590755 Madre de Dios Tambopata Laberinto Los Cedros 20 16

399 441449 8588177 Madre de Dios Tambopata Laberinto Manantiales 21 12

400 413495 8600592 Madre de Dios Tambopata Laberinto Nueva Alianza 104 114

401 441023 8606635 Madre de Dios Tambopata Laberinto Pastora Grande 183 233

402 445449 8590339 Madre de Dios Tambopata Laberinto Progreso Verde 25 27

403 428415 8593319 Madre de Dios Tambopata Laberinto Puerto Aguila 26 30

220

404 435937 8594034 Madre de Dios Tambopata Laberinto Puerto Rosario de Laberinto 1805 2069

405 436749 8585932 Madre de Dios Tambopata Laberinto Residentes Cusqueños 25 27

406 427978 8581617 Madre de Dios Tambopata Laberinto San Juan 147 169

407 441763 8599703 Madre de Dios Tambopata Laberinto Santa Rosa 75 63

408 438317 8589080 Madre de Dios Tambopata Laberinto Santo Domingo 152 166

409 402384 8602873 Madre de Dios Tambopata Laberinto

Shiringayoc Vuelta

Grande 70 67

410 443542 8592431 Madre de Dios Tambopata Laberinto Tahuantinsuyo 112 84

411 411072 8599481 Madre de Dios Tambopata Laberinto Tumi 137 149

412 410158 8575776 Madre de Dios Tambopata Laberinto Union Progreso 130 170

413 427966 8582278 Madre de Dios Tambopata Laberinto Virgenes del Sol 132 128

414 446992 8593910 Madre de Dios Tambopata Laberinto VRH de la Torre 95 92

415 483907 8646304 Madre de Dios Tambopata Las Piedras 1 de Mayo 50 50

416 471248 8642525 Madre de Dios Tambopata Las Piedras Aguajal 2 1

417 491107 8614664 Madre de Dios Tambopata Las Piedras Aguajalito 15 17

418 452148 8601105 Madre de Dios Tambopata Las Piedras Aguas Negras 11 11

221

419 486887 8660084 Madre de Dios Tambopata Las Piedras Alegria 649 732

420 481387 8622839 Madre de Dios Tambopata Las Piedras Alto Loboyoc 105 76

421 476609 8608761 Madre de Dios Tambopata Las Piedras Andres A Caceres 150 174

422 494692 8669857 Madre de Dios Tambopata Las Piedras Bajo Alegria 118 53

423 485300 8607277 Madre de Dios Tambopata Las Piedras Bajo Madre de Dios 100 93

424 475940 8618019 Madre de Dios Tambopata Las Piedras Bajo Piedras 86 120

425 484941 8616631 Madre de Dios Tambopata Las Piedras Bello Horizonte 207 80

426 501901 8617471 Madre de Dios Tambopata Las Piedras Boca Gamitana 75 87

427 489768 8643143 Madre de Dios Tambopata Las Piedras Botijon 22 25

428 481171 8616608 Madre de Dios Tambopata Las Piedras Cachuela Margen Izquierda 67 74

429 481907 8616521 Madre de Dios Tambopata Las Piedras Cachuela Oviedo 72 64

430 483179 8677846 Madre de Dios Tambopata Las Piedras Cafetal 75 59

431 479006 8660362 Madre de Dios Tambopata Las Piedras Carmen Rosa 13 15

432 472487 8683803 Madre de Dios Tambopata Las Piedras Colpac 43 49

433 473126 8638202 Madre de Dios Tambopata Las Piedras Colpayoc 93 98

434 463995 8662625 Madre de Dios Tambopata Las Piedras Filadelfia 22 25

222

435 485568 8670855 Madre de Dios Tambopata Las Piedras Fray Martin de Porras 143 141

436 489673 8626197 Madre de Dios Tambopata Las Piedras Gamitana 13 13

437 469894 8647496 Madre de Dios Tambopata Las Piedras La Florida 13 22

438 521420 8628610 Madre de Dios Tambopata Las Piedras Lago Valencia 152 178

439 485712 8623265 Madre de Dios Tambopata Las Piedras Loboyoc 35 76

440 446862 8666135 Madre de Dios Tambopata Las Piedras Loreto 32 67

441 487252 8632106 Madre de Dios Tambopata Las Piedras Los Angeles 34 18

442 451472 8661384 Madre de Dios Tambopata Las Piedras Lucerna 48 43

443 488335 8614265 Madre de Dios Tambopata Las Piedras Madama 30 66

444 486643 8680977 Madre de Dios Tambopata Las Piedras Mavila 205 920

445 492536 8610077 Madre de Dios Tambopata Las Piedras Micaela Bastidas I 9 13

446 490187 8614625 Madre de Dios Tambopata Las Piedras Micaela Bastidas II 143 162

447 463436 8683402 Madre de Dios Tambopata Las Piedras Miraflores 63 71

448 486009 8651053 Madre de Dios Tambopata Las Piedras Monterrey 108 154

449 486638 8627277 Madre de Dios Tambopata Las Piedras Nueva Alianza 40 33

450 477399 8681265 Madre de Dios Tambopata Las Piedras Nueva Esperanza 26 22

223

451 473358 8678132 Madre de Dios Tambopata Las Piedras Nueva Visita 60 74

452 498678 8673451 Madre de Dios Tambopata Las Piedras Nuevo San Juan 76 67

453 484231 8646354 Madre de Dios Tambopata Las Piedras Pampa Hermosa 7 8

454 491979 8668854 Madre de Dios Tambopata Las Piedras Pinhal 47 36

455 483291 8642673 Madre de Dios Tambopata Las Piedras Planchon 556 638

456 478731 8619644 Madre de Dios Tambopata Las Piedras Puerto Arturo 148 101

457 481906 8608453 Madre de Dios Tambopata Las Piedras Rimac o Parque del Triunfo 625 821

458 458268 8685840 Madre de Dios Tambopata Las Piedras San Antonio 9 5

459 466593 8660403 Madre de Dios Tambopata Las Piedras San Carlos 31 35

460 482531 8635401 Madre de Dios Tambopata Las Piedras San Francisco de Asis 108 103

461 485758 8615202 Madre de Dios Tambopata Las Piedras San Isidro 17 30

462 473060 8622340 Madre de Dios Tambopata Las Piedras San Jose de Centro Piedras 11 58

463 502530 8682545 Madre de Dios Tambopata Las Piedras San Juan de Aposento 43 40

464 500856 8671360 Madre de Dios Tambopata Las Piedras Santa Julia 13 12

465 492536 8610077 Madre de Dios Tambopata Las Piedras Santa Rosa 43 28

466 484530 8614162 Madre de Dios Tambopata Las Piedras Santa Teresa 79 73

224

467 484226 8631446 Madre de Dios Tambopata Las Piedras Sudadero 239 290

468 470620 8646160 Madre de Dios Tambopata Las Piedras Tipishca 7 16

469 508708 8660752 Madre de Dios Tambopata Las Piedras Triunfo 46 33

470 469718 8656398 Madre de Dios Tambopata Las Piedras Varsovia 59 80

471 456530 8686462 Madre de Dios Tambopata Las Piedras Venecia 2 1

472 484324 8619459 Madre de Dios Tambopata Las Piedras Victoria 72 81

473 501272 8660084 Madre de Dios Tambopata Las Piedras Virgen del Carmen 45 24

474 479918 8607218 Madre de Dios Tambopata Tambopata Puerto Maldonado 39820 56026

475 472586 8603907 Madre de Dios Tambopata Tambopata Aguajal 285 349

476 453427 8600172 Madre de Dios Tambopata Tambopata Aguas Negras 32 18

477 480518 8619254 Madre de Dios Tambopata Tambopata Alta Cachuela 78 74

478 473463 8610254 Madre de Dios Tambopata Tambopata Alta Pastora 55 69

479 456869 8611722 Madre de Dios Tambopata Tambopata Alto Chorrillos 59 52

480 482579 8602486 Madre de Dios Tambopata Tambopata Alto Loero 105 18

481 485694 8606646 Madre de Dios Tambopata Tambopata

Bajo Madre de Dios

Izquierda 107 77

225

482 476073 8601501 Madre de Dios Tambopata Tambopata Bajo Tambopata 68 64

483 451546 8581489 Madre de Dios Tambopata Tambopata Baltimori 52 33

484 469871 8626814 Madre de Dios Tambopata Tambopata Boca Pariamanu 51 79

485 472489 8617924 Madre de Dios Tambopata Tambopata Boca Piedras 25 21

486 419107 8655527 Madre de Dios Tambopata Tambopata Cachuela Trigoso 5 6

487 479232 8614316 Madre de Dios Tambopata Tambopata Centro Cachuela 151 143

488 475683 8608144 Madre de Dios Tambopata Tambopata Centro Pastora 57 53

489 475284 8599470 Madre de Dios Tambopata Tambopata Chonta 62 48

490 462469 8609123 Madre de Dios Tambopata Tambopata Chorrillos 47 20

491 454717 8577373 Madre de Dios Tambopata Tambopata Condenado 14 9

492 471538 8603176 Madre de Dios Tambopata Tambopata El Castanhal 63 40

493 473163 8610407 Madre de Dios Tambopata Tambopata El Pilar 88 66

494 479078 8613545 Madre de Dios Tambopata Tambopata El Prado 141 165

495 462173 8599332 Madre de Dios Tambopata Tambopata Fitzcarrald 93 52

496 489414 8606087 Madre de Dios Tambopata Tambopata Fundo Concepcion 20 12

497 441241 8649015 Madre de Dios Tambopata Tambopata Huascar 7 9

226

498 475186 8592530 Madre de Dios Tambopata Tambopata Infierno 321 319

499 495198 8612344 Madre de Dios Tambopata Tambopata Isla Rolin 65 53

500 479780 8603571 Madre de Dios Tambopata Tambopata Izuyama 104 124

501 487607 8600515 Madre de Dios Tambopata Tambopata Jorge Chavez 104 88

502 496381 8615105 Madre de Dios Tambopata Tambopata Juan Velasco 2 3

503 477355 8606824 Madre de Dios Tambopata Tambopata La Joya 872 1221

504 478460 8609276 Madre de Dios Tambopata Tambopata La Pastora 436 473

505 466925 8582356 Madre de Dios Tambopata Tambopata La Torre 52 32

506 494994 8605775 Madre de Dios Tambopata Tambopata Lago Sandoval 16 9

507 482817 8600030 Madre de Dios Tambopata Tambopata Loero 184 170

508 471992 8602747 Madre de Dios Tambopata Tambopata Lomas 431 529

509 379042 8679698 Madre de Dios Tambopata Tambopata Monte Salvado 78 138

510 464046 8594009 Madre de Dios Tambopata Tambopata Monte Sinai 40 28

511 480000 8605884 Madre de Dios Tambopata Tambopata Nuevo Sol Naciente 37 45

512 476917 8614522 Madre de Dios Tambopata Tambopata Otilia 162 149

513 524180 8616485 Madre de Dios Tambopata Tambopata Palma Real 221 260

227

514 458892 8617597 Madre de Dios Tambopata Tambopata Palmichal 107 131

515 459838 8618634 Madre de Dios Tambopata Tambopata Playa Alta 9 47

516 392447 8668230 Madre de Dios Tambopata Tambopata Puerto Nuevo 29 35

517 536858 8617455 Madre de Dios Tambopata Tambopata Puerto Pardo 44 48

518 462523 8613809 Madre de Dios Tambopata Tambopata Puerto Union 53 40

519 471946 8603458 Madre de Dios Tambopata Tambopata Quinhones 122 150

520 477877 8609930 Madre de Dios Tambopata Tambopata Rompeolas 114 112

521 460258 8648586 Madre de Dios Tambopata Tambopata Sabaluyoc 104 125

522 460850 8579628 Madre de Dios Tambopata Tambopata Sachavacayoc 57 36

523 456554 8597459 Madre de Dios Tambopata Tambopata San Bernardo 223 230

524 445554 8602678 Madre de Dios Tambopata Tambopata San Jacinto 366 476

525 534827 8615696 Madre de Dios Tambopata Tambopata Sonene 87 93

526 460990 8602585 Madre de Dios Tambopata Tambopata Tnte. Alejandro Acevedo 105 136

527 466291 8597166 Madre de Dios Tambopata Tambopata Tres Estrellas 24 14

528 456450 8615275 Madre de Dios Tambopata Tambopata Tres Islas 220 218

529 461595 8611649 Madre de Dios Tambopata Tambopata Tupac Amaru 84 64

228

530 411062 8668650 Madre de Dios Tambopata Tambopata Zapayal 2 2

531 482164 8614625 Madre de Dios Tambopata Tambopata Cachuela 75 72

532 481752 8612002 Madre de Dios Tambopata Tambopata Cachuela Baja 100 96

533 477019 8595842 Madre de Dios Tambopata Tambopata Cascajal 200 192

534 485301 8616168 Madre de Dios Tambopata Tambopata Km 11 45 43

535 475580 8609636 Madre de Dios Tambopata Tambopata Pastora Baja 150 144

536 481751 8601284 Madre de Dios Tambopata Tambopata Tambopata 100 96