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Implementing REDD+ in Papua New Guinea: Canbiodiversity indicators be effectively integrated inPNG's National Forest Inventory?G. Grussuab, F. Attorrea, D. Molliconeb, P. Darguschc, A. Guilletd & M. Marchettiea Department of Environmental Biology, Sapienza University of Rome, P. le A. Moro 5, Rome00185, Italyb Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla,Rome 00153, Italyc School of Geography Planning and Environmental Management, University of Queensland,St Lucia Campus, Brisbane QLD 4072, Australiad Directorate General for Development Cooperation, Ministry of Foreign Affairs, via S.Contarini 25, Rome 00194, Italye Forest Ecology and Geomatics Laboratory, University of Molise, Contrada Fonte Lappone,86090 Pesche, ISItalyAccepted author version posted online: 05 Mar 2014.Published online: 15 Apr 2014.

To cite this article: G. Grussu, F. Attorre, D. Mollicone, P. Dargusch, A. Guillet & M. Marchetti (2014) Implementing REDD+ inPapua New Guinea: Can biodiversity indicators be effectively integrated in PNG's National Forest Inventory?, Plant Biosystems- An International Journal Dealing with all Aspects of Plant Biology: Official Journal of the Societa Botanica Italiana, 148:3,519-528, DOI: 10.1080/11263504.2014.900131

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PLANT ECOLOGY AND CONSERVATION IN INTERNATIONAL COOPERATION:

APPROACHES AND METHODOLOGIES

Implementing REDD1 in Papua New Guinea: Can biodiversityindicators be effectively integrated in PNG’s National Forest Inventory?

G. GRUSSU1,2, F. ATTORRE1, D. MOLLICONE2, P. DARGUSCH3, A. GUILLET4, &

M. MARCHETTI5

1Department of Environmental Biology, Sapienza University of Rome, P. le A. Moro 5, Rome 00185, Italy; 2Food and

Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, Rome 00153, Italy; 3School of Geography

Planning and Environmental Management, University of Queensland, St Lucia Campus, Brisbane QLD 4072, Australia;4Directorate General for Development Cooperation, Ministry of Foreign Affairs, via S. Contarini 25, Rome 00194, Italy and5Forest Ecology and Geomatics Laboratory, University of Molise, Contrada Fonte Lappone, 86090 Pesche, IS, Italy

AbstractUNFCCC’s “Cancun safeguards” (COP 16, 2010) provide a strong call for comprehensive steps to prevent harm tobiodiversity from Reducing Emissions from Deforestation and forest Degradation (REDDþ) activities and to support itsconservation. However, as non-binding “principles” and due to their general wording, they are not operational in the presentform. Additionally, the scientific literature on biodiversity monitoring for REDDþ is still very limited, particularly when itcomes to REDDþ in tropical forests and at the national scale. Whereas some authors suggest that biodiversity integration canbe achieved bymeans of standardised protocols and techniques, others consider that an effective monitoring of biodiversity intropical forests at the national scale may be an impossible task to achieve in a cost-effective way. However, recent researchoffers some functional approaches to tackle the many challenges involved. This paper explores the perspectives and limits ofdeveloping and effectively incorporating appropriate biodiversity objectives and indicators in Papua New Guinea’smultipurpose National Forest Inventory (PNG’s NFI). The PNG’s NFI is currently being designed under the UN-REDDprogramme as a key component of the National Forest Monitoring System that PNG is required to establish in order toparticipate in a future REDDþ mechanism. We conclude that the challenge cannot be effectively tackled only at the designstage of the NFI, as it needs to address a number of issues related to different stages of the REDDþ preparedness process:

a. If biodiversity integration is carried out directly at the NFI stage, it will need to rely on proxies derived from indicators designed tomonitor carbon stock change;

b. At the planning stage, a carbon–biodiversity overlay map analysis would allow for a preliminary selection of areas of high biodiversity thatcould be threatened by REDDþ activities either directly or indirectly through “leakage”;

c. During the implementation stage, the selection could be refined by identifying a sub-sample of sites where forests are undergoing thegreatest changes;

d. A comprehensive biodiversity monitoring programme involving field measurements of key species could only be designed once thepriority areas have been clearly defined and limited in both number and size.

Keywords: REDDþ, UN-REDD, biodiversity integration, National Forest Inventory, remote sensing

Introduction

In 2005, when the negotiations on a post-Kyoto

regime began in Montreal at the UNFCCCCOP 11,

a proposal was put on the agenda to develop a

mechanism that would create positive incentives for

countries that succeed in avoiding deforestation and

thus contribute to the mitigation of climate change

and the net reduction in global greenhouse gas

emissions (GHG) (Pistorius et al. 2011). This

q 2014 Societa Botanica Italiana

Correspondence: Giorgio Grussu, Department of Environmental Biology, Sapienza University of Rome, Piazzale Aldo Moro 5, I-00185 Rome, Italy. Tel: þ39

334 7913664. Email: giorgio.grussu@uniroma1.it

Plant Biosystems, 2014Vol. 148, No. 3, 519–528, http://dx.doi.org/10.1080/11263504.2014.900131

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proposal, which eventually led to the concept of

Reducing Emissions from Deforestation and forest

Degradation (REDDþ), was introduced as a formal

topic of discussion by a group of countries led by

Papua New Guinea (PNG) and Costa Rica and

opened a dialogue aimed at developing scientific,

technical, policy and capacity responses to address

such emissions resulting from tropical deforestation

(Thompson et al. 2011).

At the beginning of the academic and political

debate, it was widely assumed that the proposed

mechanism, which was intended to avoid deforesta-

tion, would be generally beneficial for biodiversity

(Pistorius et al. 2011). Yet, what was originally

conceived as a “simple mechanism”, over the years

expanded considerably its focus and ambitions,

proving to be much more complex than expected. On

the onehand, as pointedout byKohl et al. (2009), little

attention had been paid to the impact of uncertainties

associatedwith the estimationof forest area andcarbon

stock changes on accountable carbon credits, so that

even small assessment errors may outweigh successful

efforts to reducedeforestation anddegradation.On the

other hand, criticism was being raised that a

mechanism focusing on biomass merely from the

quantitative perspectiveof carbon storagewouldpose a

series of risks to biodiversity (Pistorius et al. 2011). It

could provide incentives for a conversion of primary

forests and degraded forests into commercial tree

plantations or it could lead to “inter-ecosystem

leakage”, e.g. induced shift of land-use activities,

such as agriculture, to non-forest and low carbon forest

ecosystems with high relevance for biodiversity, which

would increase pressure on such ecosystems (Pistorius

et al. 2011; Tyrrell & Alcorn 2011).

Since 2009, the UNFCCC negotiations have

increasingly taken up the concerns regarding potential

negative effects of such a mechanism, with consensus

reached regarding the need to include biodiversity

safeguards and to enable additional benefits (FCCC/

CP/2010/7 2010; Pistorius et al. 2011). The most

recent developments of REDDþ in light of the so

called “Cancun safeguards” reflect the need to go

beyond deforestation and forest degradation by

mainstreaming conservation, sustainablemanagement

of forests and enhancement of forest carbon stocks into

the original REDD programme (UN-REDD 2011).

Even in the case that REDDþ is not adopted as

anticipated, the numerous preparedness activities

already launched worldwide bear the potential to

shape comprehensive integrated land-use plans that

can serve the sustainable development of a country

(Pistorius et al. 2011).

In the current scenario, whereas at policy level a

strong call there exists for integrating biodiversity

concerns into the REDDþ design (Epple et al.

2011), at scientific level only few specific papers

examine biodiversity integration in tropical forests at

national scale (Dickson & Kapos 2012) with

divergent conclusions. In particular:

. Waldon et al. (2011) suggest that biodiversity can

be integrated using standard approaches by

monitoring abundance of target species;. Rennolls and Reynolds (2007) point out that the

techniques for identification of keystone species

in tropical forests are problematic, that the

measurement of biodiversity is impractical in a

routine inventory context and that detection of

endangered species seems to be practically

impossible;. Venter et al. (2009) suggest that the trade-off

between protecting biodiversity and reducing

emissions is highly nonlinear, so that a careful

targeting of REDD funds could aspire to

maximizing both objectives simultaneously;. Pistorius et al. (2011) say that there exist

structured approaches for setting and monitoring

biodiversity objectives in different contexts that

can be modified to integrate biodiversity issues

into REDDþ strategies and projects;. Gardner et al. (2012) and Dickson and Kapos

(2012) have offered a series of indications on

biodiversity integration in national REDDþ,

building upon IPCC’s guidelines for assessing

carbon emissions.

The UN-REDD programme in Papua

New Guinea

PNG is chosen as the case country for its enormous

biodiversity richness and the increasing threat of

extinction posed by human activities on its remaining

ecosystems.

PNG contains an estimated 5–7% of the world’s

terrestrial biodiversity in less than 1% of the land

area. However, there are large gaps in its scientific

knowledge and a species or ecosystem database is not

available for the country, which would help

determine their conservation status and trends

(PNG’s 4th National Report to the CBD 2010).

The terrestrial biodiversity of PNG ismostly linked

to its forests, with particular reference to montane

forests that contain a disproportionately large percen-

tage of the country’s entire biodiversity (Shearman

et al. 2008). Additionally, forests represent in PNG a

vital resource for about 80% of its population (UN-

REDDPNG2011).Overall, the islandofNewGuinea

(consisting of PNG and West Papua) hosts the third

largest expanse of tropical rainforest on the planet after

the Amazon and Congo forests (PNG’s 4th National

Report to the CBD 2010).

Shearman et al. (2008) estimated that almost one

quarter of PNG’s intact forests, which covered 82%

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of the country’s land area in 1972, had been either

destroyed (15%) or degraded (8.8%) by 2002, the

main drivers being logging (48.2%) and expansion of

subsistence agriculture (45.6%). Rainforests cover

around 28million hectares and comprise 80% of the

country’s forest estate. The rest of the forest estate

comprises dry evergreen forest, swamp forest and

mangroves (PNG’s 4th National Report to the CBD

2010).

The recognized global importance of PNG’s

forests and the threat posed by unsustainable

pressures to their biodiversity have contributed to

lead the country towards becoming one of the first

proponents of REDDþ at the international level in

2005. A few years later, PNG would become one of

the original “pilot” countries of the UN-REDD

Programme, a collaborative partnership launched in

2008 between FAO, UNDP and UNEP to support

countries to develop capacity to implement a future

REDDþ mechanism (Sheyvens 2012).

AMultipurpose National Forest Inventory (NFI)

for PNG is currently being designed with the support

of FAO and other international Agencies (JICA,

AUSAID, etc.) and will collect the forest data to feed

the National Forest Monitoring System that PNG is

required to establish in order to participate in a

future REDDþ mechanism (UN-REDD PNG

2011).

Whereas traditional forest inventories are largely

centred on the appraisal of timber resources, current

inventories are evolving towards multipurpose

resource surveys by broadening their scope in several

directions, aiming to incorporate the multiple values

(products and services) that forests provide, includ-

ing those related to biodiversity (Corona et al. 2010;

Marchetti et al. 2012; Keenan & Read 2012). PNG’s

NFI project aims to combine inventory activities for

carbon and GHG measurement (see Dean et al.

2012) with other significant features such as

biodiversity and cultural features in addition to

timber volume (UN-REDD PNG 2011).

Selected approaches

While practical REDDþ experience worldwide is

still limited to sub-national pilot initiatives, carried

out as tests for the implementation at the national

scale (Pistorius et al. 2011), there are very few

scientific papers that look specifically at biodiversity

monitoring for REDDþ (Dickson & Kapos 2012).

Two recent studies in particular, by Dickson &

Kapos (2012) and Gardner et al. (2012), have

provided a series of useful indications on how to

integrate biodiversity concerns into REDDþ at the

national scale.

Building upon these indications, this paper

explores the perspectives of effectively incorporating

appropriate biodiversity objectives and indicators in

the design of PNG’s multipurpose National Forest

Inventory.

Cultural and socio-economic benefits through

REDDþ are not assessed within the scope of this

article, even though it is acknowledged that they are

essential to a successful implementation of REDDþ.

In Gardner et al. (2012), the overall REDDþprocess encompasses two major phases: the strategic

planning and the assessment. While the planning

process is meant to guide the selection of priority

regions and types of REDDþ activities to

implement, the subsequent assessment process

focuses on how to measure the changes in

biodiversity following REDDþ implementation. In

terms of data requirements:

(1) The planning process could be achieved through

spatial carbon–biodiversity overlay analysis

based on the best available information, without

embarking in new field surveys;

(2) The assessment process is divided in three stages

(tiers), each one depending on the scale at which

biodiversity data are derived (global, national or

project) and the degree of accuracy of the

available data. More specifically, the assessment

approach developed by IPCC (IPCC 2006) –

which only referred to carbon – was expanded

by Gardner et al. (2012) so as to embrace

biodiversity without altering its general scheme.

In terms of data requirements:

(2.1) Proposed tier 1 is based on coarse-scale

data on forest type/area changes

coupled with globally available biodi-

versity distribution and response data;

(2.2) Proposed tier 2 focuses on remote-

sensing derived indicators of landscape

structure and forest structural degra-

dation;

(2.3) Proposed tier 3 relies on field collection

of new biodiversity data.

Dickson and Kapos (2012) have integrated the

framework described by Gardner et al. (2012) by

highlighting additional challenges and approaches.

The challenges of: (a) choosing what aspects of

biodiversity to monitor; (b) taking into account the

cost-efficiency of such monitoring and (c) under-

standing which changes are consequences of

REDDþ activities could be approached by: (i)

drawing on existing policy objectives and targets to

help identify priorities for monitoring, (ii) making

use of existing monitoring efforts to provide data with

limited additional investment and (iii) developing

clear conceptual models and theories of change to

help identify likely impacts and target monitoring

accordingly. As for drawing on other existing policies,

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Tyrrell and Alcorn (2011) have indicated the benefit

of taking advantage of the processes related to

monitoring under the Rio Conventions, with

particular reference to the data derived from their

implementation and the respective systems of

indicators.

Breakdown of the issues arising from the case

study

This paper attempts an analysis of the perspectives

and limits of effectively developing and incorporating

appropriate biodiversity objectives and indicators in

the present stage of PNG’s REDDþ preparedness

programme, so as to align it with UNFCCC’s

Cancun biodiversity safeguards.

The main boundaries and challenges of our

research are linked to three specific aspects of the

case study:

. the scale at which REDDþ is being implemented

in PNG, which is the national scale;. the current stage of REDDþ preparedness effort

in PNG, which focuses on providing the country

with a multipurpose NFI and. the enormous biodiversity richness of PNG’s

tropical forests, most of which are still unknown.

As a tool derived using data collected from

ground-based forest survey and repeated measure-

ments, an NFI is – according to IPCC’s guidelines

for national greenhouse gas inventories – the basis

for the tier 3 carbon assessment, which is the final stage

in the REDDþ preparedness process.

In Gardner et al. (2012), the tier 3 carbon

assessment was modified into a tier 3 carbon–

biodiversity assessment. The key challenge of this

(final) stage is that of choosing what aspects of

biodiversity to monitor, that is, a set of “SMART”

indicators (specific, measurable, attainable, relevant

and time bound) (Angelsen et al. 2012) that also

need to be simple and not overburden the limited

capacity available in the country.

However, such an attempt faces a series of

obstacles in PNG’s case study at the current stage.

First of all, PNG does not have a National

REDDþ strategy yet, which is necessary as a

reference for implementing biodiversity safeguards

and additional benefits. Such a policy document, as

part of the planning stage that aims to define where

REDDþ investments will occur and for which

activities, would also define PNG’s particular

national biodiversity objectives and targets, which

are fundamental to identify priorities for monitoring

(Pistorius et al. 2011). In an attempt to gather such

information from other national policy documents,

we concluded that PNG only has identified a series of

general objectives in its National Biodiversity

Strategy and Action Plan that do not provide

sufficient guidance in this pursuit. The most

stringent objective, goal 4, which aimed to ensure

that protected areas for terrestrial species would be

increased to 10% by 2010 (Government of PNG

2007) was not achieved. In 2010, PNG’s protected

areas covered approximately 4% of terrestrial areas,

the majority of which have minimal or no manage-

ment structure in place (PNG’s 4th National Report

to the CBD 2010).

Moreover, PNG does not have a comprehensive

species or ecosystem database and there are large

gaps in the scientific knowledge of its biodiversity.

Enormous areas of the country have yet to be

systematically surveyed, and the total number of

different plants and animals, whereas not accurately

known, almost certainly exceeds 200,000 species, far

higher than the 26,318 species reported by IUCN.

Scientists estimate that more than half the plants and

animals found in PNG have yet to be scientifically

named and that PNG has the highest number of

endemic mammals globally (PNG’s 4th National

Report to the CBD 2010).

In such scenarios, we concur with Gardner et al.

that it would be unrealistic to plan for a biodiversity

monitoring at a national scale at all expected

REDDþ sites or in a large number of sample sites

within each forest type across the nation. A number

of issues would make the effort ineffective, if not

impractical:

. the very limited knowledge of the country’s

biodiversity would make it impossible to choose

which species to monitor;. the cost to put in place such a national

biodiversity monitoring system could exceed any

expected financial benefit of a future REDDþmechanism and

. the time needed to develop the local capacities

and to complete the monitoring could be too

long, if we consider that Shearman et al. (2008)

estimated PNG’s intact forests to be nearly all

cleared or degraded by 2021 if the current

deforestation trends continue.

Waldon et al. (2011) have suggested a model

protocol for ground-based biodiversity monitoring of

individual REDDþ projects based on two tech-

niques to monitor changes and trends in populations

of the key indicator taxa: camera trapping for large

mammals and acoustic monitoring for bats. Harrison

et al. (2012) in reviewing such proposals have

suggested that a standardized protocol would be

unrealistic, owing to the huge differences among the

world’s forests in terms of structure, species

composition, ecological interactions and ecosystem

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services provided. Instead, they suggest a standard

approach in which ecological monitoring research is:

(i) designed to reflect a project’s biodiversity

conservation goals; (ii) based upon scientifically-

tractable, policy-relevant questions regarding the

impacts of management interventions on the

ecosystem; (iii) founded on detailed knowledge of

the habitat type in question; (iv) includes monitoring

of a number of indicators, as appropriate to the

project and (v) defines appropriate reference/baseline

conditions against which progress can be assessed.

None of these options seems to be suitable for

application to our case study. A country such as

PNG, where rainforests cover almost 30million

hectares and the estimated number of unknown

species is extremely high, even the model protocol

suggested by Waldon et al. (2011) could be neither

cost-effective nor realistically time-bound.

In our case study, a new NFI is being designed

building upon PNG’s existing national spatial

datasets. In terms of feasibility and cost-effective-

ness, an attempt to tackle the challenge of

incorporating biodiversity indicators directly at the

NFI level would need to consider the option of using

proxies. Because the new NFI is already expected to

monitor carbon stock change, it would be advisable

to take full advantage of indicators designed for such

purpose, some of which could also serve as proxies

for biodiversity. According to Newton & Kapos

(2002), such biodiversity indicators can be referred

to the following groups: (i) forest area by forest type

and successional stage and phases relative to land

area; (ii) protected forest area by type, successional

stage and phases and protection category relative to

total forest area; (iii) degree of fragmentation of

forest types; (iv) rate of conversion of forest cover (by

type) to other uses; (v) area and percentage of forests

affected by anthropogenic and natural disturbance;

(vi) complexity and heterogeneity of forest structure;

(vii) number of forest-dependent species and (viii)

conservation status of forest dependent species.

Another study by Kapos and Jenkins (2002)

indicated, however, that existing forest inventories,

surveys or networks of permanent plots are often

inadequate to provide a representative assessment of

forest biodiversity.

According to Gardner et al (2012), the key task

would consist of identifying easily quantifiable

threatening processes (e.g. unsustainable logging,

fragmentation, overgrazing, etc.) that can be linked

to key functional elements of a forest ecosystem (e.g.

tree density and target species abundance), together

with predictable (if not fully understandable)

relationships that can be mapped onto estimates of

forest degradation with a minimal amount of ground-

truthing data.

Corona et al. (2011) have pointed out that one of

the strengths of large-scale forest inventories in their

potential contribute to biodiversity monitoring is that

they provide statistically sound periodic assessments

of key baseline variables over large areas. On the

other hand, a number of weaknesses has to be

considered: (a) a low statistical precision for small

area estimates, (b) probabilistic sampling schemes

that are often not appropriate to quantify changes in

the abundance of rare species that are typically of

interest for biological conservation and (c) the fact

that most biodiversity indicators in NFIs are based

on tree-related variables, which do neither constitute

the potential best set of indicators for monitoring

forest biodiversity nor the set required to completely

satisfy international reporting commitments. More-

over, Chirici et al. (2012), while acknowledging the

role of National Forest Inventories as potential

important tools to assess and monitor status and

trends in forest biodiversity, have called attention to

their limits, mainly related to the need of improving

the harmonization of field protocols and to enhance

the collection of non-tree forest information.

Building upon these constraints, a realistic

approach to biodiversity monitoring that goes

beyond proxy indicators would require to drastically

restrict the field of action in terms of size and number

of areas to monitor, that is, to prioritize the areas for

biodiversity monitoring. Such endeavour, as

described below, starts with the planning stage but

can only be completed at the tier 3 assessment phase.

Planning

As suggested by Gardner et al. (2012), the first

planning stage entails identifying where REDDþinvestments will occur and for which activities. Such

decisions are expected to become part of the national

REDDþ strategy, which PNG does not have yet and

which is supposed to be developed through a

consultation process with all stakeholders.

Nevertheless, we can gather some useful infor-

mation on which REDDþ activities PNG will be

likely to implement, from other national policy and

technical documents, such as:

. The Interim Action Plan for Climate Compatible

Development (Government of PNG 2010), which

establishes that REDDþ policies in PNG are

intended to reduce the impact of logging, improve

the management of secondary forests, reduce

deforestation for commercial and subsistence

agriculture and improve fire management.. The National REDDþ project guidelines (Dus

2012), which describe a Government supported

REDDþ project as any activity that specifically

Papua New Guinea 523

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aims to develop, test and try mechanisms that

substantially and measurably reduce GHG emis-

sions by utilising one or more of these strategies:

(a) avoiding or limiting deforestation (i.e. through

protection of forest areas that would otherwise be

converted to other land use), (b) enhancing forest

carbon stocks (i.e. afforestation and/or reforesta-

tion) and (c) avoiding or limiting forest degra-

dation (i.e. implementing sustainable forest

management techniques, such as reduced impact

logging, alternative agricultural practices etc).. The UN-REDD National Programme Document

(UN-REDD PNG 2011), which clarifies that

the aim of the Government of PNG is not that

of generating the maximum potential abate-

ment, but to achieve considerable reductions

while preserving economic growth. To this end,

abatement options such as stopping subsistence

agriculture altogether or stopping logging

altogether are clearly considered as not feasible

options.

Potential risks to biodiversity from REDDþactivities could arise, in particular, if promoting

conversion of natural forests into plantations, or

timber harvesting in old growth forests, as well as

from leakage, e.g. from displacing deforestation

elsewhere or from leading to agricultural intensifica-

tion in other areas of high biodiversity importance

(Pistorius et al. 2011).

The concept of old growth forests is highly

debated in the literature because of its implications

from close-to-nature forest management to carbon

balance. Additionally, the description of forest

structure requires a vast amount of information

about several biometric attributes, making its

classification very time- and resource-consuming

(Chiavetta et al. 2012; Lombardi et al. 2012).

Nevertheless, it is acknowledged that to meet

biodiversity conservation objectives, the manage-

ment focus is shifting from assessing and protecting

old growth forests to providing for forests across the

landscape with old growth attributes. New manage-

ment systems that are capable of maintaining such

attributes within harvested stands are being investi-

gated by employing a variety of approaches for

managing spatial and temporal structural complexity

(Barbati et al. 2012; Keenan & Read 2012).

In the absence of a national REDDþ strategy, the

current available information suggests to take into

account any potential scenario. Therefore, as a

precautionary approach we will have to consider any

REDDþ activity as a potential threat to biodiversity.

With such an assumption, when it comes to where

REDDþ activities will be implemented, our atten-

tion will go to the areas that are biodiversity-relevant,

as they would be our priority locations for

biodiversity monitoring. A preliminary analysis of

this kind was proposed by Kapos et al. (2008) in their

carbon–biodiversity demonstration Atlas. A car-

bon–biodiversity overlay map analysis was carried

out by using existing data on biodiversity and carbon.

Building upon a preliminary classification of areas

according to the estimated carbon stocks and the

biodiversity significance, the analysis resulted in a

map of areas of potential REDDþ investment as well

as areas of potential risk for biodiversity. Such overlay

could effectively guide the identification of areas of

both high “opportunity” (where there is a strong

positive correlation in carbon and biodiversity

values) and of high “risk” (areas that are low in

carbon but high in biodiversity) (Parrotta et al.

2012).

Whereas direct threats to biodiversity from

REDDþ activities could occur in areas of “high

opportunity” (which are the first targets for REDDþinvestments), indirect threats such as “leakage”

could arise from areas of “high risk” as an indirect

consequence of successful avoided deforestation in

other areas. Therefore, our biodiversity monitoring

will need to consider both categories as a priority.

The usefulness of a carbon–biodiversity spatial

analysis such as that demonstrated by Kapos et al.

(2008) could also be enhanced by integrating the

overlay with updated biodiversity maps for PNG.

In particular, in Kapos et al. (2008), the prioritiza-

tion schemes in PNG’s assessment included: (1)

Conservation International’s Hotspots; (2) WWF

Global 200 ecoregions; (3) Birdlife International

Endemic Bird Areas; (4) WWF/IUCN Centres of

Plant Diversity; (5) Amphibian Diversity Areas and

(6) Alliance for Zero Extinction sites. Other available

biodiversity information includes, for instance: (i)

dataset on plants of PNG provided by the Global

Biodiversity Information Facility (gbif.org) and (ii)

the map of restricted range endemic species such as

that included in PNG’s report to the CBD,

representing the best estimates for the current

distribution of species with the narrowest geographic

and climatic ranges (PNG’s 4th National Report to

the CBD 2010).

In addition to data on the spatial distribution of

biodiversity, prioritising REDDþ investments

should ideally consider an estimate of species

responses to different forms of land-use change

when available (Gardner et al. 2012). However, this

would imply knowing exactly what type of REDDþactivity is being implemented in a given area, which is

only available for the existing pilot project sites.

Other types of parameters could also be included

if they provide clear information allowing to reduce

the size of our areas of interest, that is, parameters

that describe specific circumstances in which the

threat to biodiversity is considerably reduced.

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It could be the case, for instance, of forested areas

below 50,000 hectares, which are considered too

small to support commercial logging operations or

large areas of forest in PNG that are relatively

inaccessible to commercial forestry due to the rugged

and mountainous terrain (Shearman et al. 2009).

In such cases, some areas might not need to be

included in the priority subset earmarked for

biodiversity monitoring.

To sum up, the analysis described above would

allow for a preliminary selection of areas that are

potential targets for REDDþ activities and, at the

same time, have high biodiversity significance. Such

areas would be our primary target for a subsequent

biodiversity monitoring. Understandably, only when

REDDþ activities are actually being implemented,

will we be in a condition to better delineate the areas

of higher risk of biodiversity depletion, as different

types of (REDDþ) activities will have different levels

of expected impacts; therefore, some of our

previously selected areas could be discarded while

others could be included.

Assessment

As described above, while the planning process

culminates in a national REDDþ strategy which

delineates priority regions (the where) and types of

REDDþ activities (the which) that the Government

intends to implement, the subsequent assessment

process will occur in the areas where REDDþactivities are being actually implemented and will

focus on measuring, over the years, the changes in

biodiversity as triggered by the actual implemen-

tation of the very REDDþ activities.

Considering that PNG is currently in the

planning process – with REDDþ activities only

being implemented in five pilot project sites (FCPF

UN-REDD 2013) – we can only make hypotheses

on how the assessment process could develop when

the REDDþ strategy will be in place and REDDþactivities will be chosen and implemented at the

national scale.

According to Gardner et al. (2012), the assess-

ment process will build upon the planning process by

using much of the same biodiversity information, as

described below.

Tier 1 assessment. A tier 1 carbon–biodiversity

assessment, according to Gardner et al. (2012), would

focus on highlighting possible threats to biodiversity

as a consequence of REDDþ activities, e.g. an

increase in the clearance or degradation of rare forest

types that are low in carbon but ecologically distinct.

Such assessment would cover forest areas within and

between regions of known ecological distinctiveness

and would rely upon coarse-scale estimates of forest

type and levels of disturbance. It would track changes

in forest type and area and would be coupled with

globally available biodiversity distribution and

response data to REDDþ activities as used during

the planning stage.

In our case study, by the time the national

REDDþstrategy is in place and activities are being

implemented, the NFI would have been already

designed and tested, this possibly making the tier 1

analysis redundant.

Tier 2 assessment. The tier 2 carbon–biodiversity

assessment, as defined by Gardner et al. (2012),

would help refine the selection process by identifying

a sub-sample of sites where forests are undergoing

the greatest changes (whether through clearance,

degradation or restoration) so that monitoring data

collected at tier 3 will then help improve estimates of

biodiversity responses to REDDþ activities.

An assessment at tier 2 involves an analysis of

remote-sensing derived indicators of landscape

structure (e.g. fragmentation indices such as average

area of forest patches and total forest edge) and forest

structural degradation (satellite-based indicators of

logging scars and forest fires) (Gardner et al. 2012)

covering all REDDþ sites.

Remote sensing is likely to become the major tool

for setting reference levels and monitoring trends in

carbon dynamics in the national monitoring of

carbon. There is great potential to broaden the scope

of these remote-sensing activities that will be carried

out for carbon in any case (principally to estimate

change in forest extent) in order to include

biodiversity issues (Pistorius et al. 2011). Remote

sensing can deliver biodiversity-relevant information

if forest changes are quantified for particular forest or

ecosystem types (e.g. ecological zones, ecoregions or

regional or national classifications) (Blasi & Fron-

doni 2011; Dickson & Kapos 2012).

The erosion of biodiversity is strongly related to

changes of habitat because of land-use change,

especially in tropical forests. Changes in habitat are a

function of changes in forest structure, which is more

correlated with changes in biomass than with forest

cover through forest degradation and deforestation

(ESA 2012).

Forest fragmentation information obtained from

remote sensing data can be used both as an indicator

of potential forest degradation and in relation to

known impacts on biodiversity (Dickson & Kapos

2012). Habitat fragmentation affects the ecology of

tropical forests by changing the composition and

configuration, leading to genetic isolation of plants

and animal species, reducing genetic biodiversity of

species. For instance, reduced dense canopy cover in

tropical forests may usually result in greater mortality

of drought-sensitive plants. When forests are lost or

degraded, ecosystem-based benefits, such as the

forest biological diversity they deliver, would

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disappear with the carbon they contain (Lu et al.

2012).

There is considerable existing knowledge that can

be used to identify relationships between particular

types of structural and compositional changes in the

tree community and likely changes in other groups.

These relationships can be used to infer biodiversity

change, and to identify priorities for repeated

inventory, for promoting additional data collection

in conjunction with inventory programmes and for

targeting more intense monitoring efforts (Dickson

& Kapos 2012).

At this stage of the assessment, the problem is

represented by what information can be extrapolated

from the various technologies available.

The report released in 2008 by the remote

sensing Centre of the University of PNG (Shearman

et al. 2008) mapped the changes in forest cover at a

national scale between 1972 and 2002 using multi-

band imagery acquired from Landsat, SPOT 4 and

SPOT 5 sensors. Landsat and SPOT images are

widely available and relatively cheap and can be used

to monitor large areas, providing such information as

the coverage of different forest ecosystems and the

extension of (monoculture) plantations (Pistorius

et al. 2011).

In “Modelling Forest Fragmentation and Carbon

Emissions for REDDþ”, Lu et al. (2012) mapped

forest fragmentation in Indonesia using Landsat

imagery acquiring a number of landscape forest

fragmentation metrics describing forest composition

(number, proportional frequency and diversity of

landscape elements within the landscape) and

configuration (spatial position and distribution of

the elements within the landscape). Calculation of

forest fragmentation metrics was undertaken using

Fragstats (McGarigal et al. 2012).

Higher resolution remote sensing has an improv-

ing capacity to detect changes in composition and

forest structure, which are key aspects of biodiversity

likely to be affected by REDDþ actions (Dickson &

Kapos 2012). Images with high spatial or spectral

resolution (Quickbird or Ikonos and HYDICE,

respectively) are costlier but might be applied in

some areas of special interest complementary to the

large-scale coverage of Landsat and SPOT images.

They can be used, e.g. to monitor particular tree

species. However, some ground-based assessment is

likely to be necessary to better understand impacts at

project scales (Pistorius et al. 2011; Dickson &

Kapos 2012).

Airborne waveform LIDAR can generate highly

accurate information on canopy heights, biomass

measurement and leaf area (UNEP/CBD/

SBSTTA/16/8 2012). However, there is no space-

borne lidar optimised for vegetation (ESA 2012) and

the extent to which it will be able to inform REDDþ

monitoring remains unclear as the technology

is still in the experimental phase (UNEP/CBD/

SBSTTA/16/8 2012). Synthetic Aperture Radar

(SAR) systems (airborne or spaceborne) obser-

vations are particularly interesting for tropical forests

as they are not hampered by cloud cover. They were

used in 2006 to map PNG, allowing for an accurate

land-cover classification at high spatial resolution of

more than 20 classes. SAR data may be used to

recover estimates of forest cover and forest cover

change at regular intervals (Fox et al. 2011).

While deforestation monitoring has received

considerable attention, forest degradation is not yet

as well understood or addressed, and many technical

challenges to assess and address forest degradation

remain. Forest degradation guidelines for assessing

and monitoring are being developed under the

Collaborative Partnership on Forests led by the FAO.

This process began in 2009 and has resulted in a

FAO Forest Resources Assessment Working Paper

177: Assessing Forest Degradation” (UNEP/CBD/

SBSTTA/16/8 2012).

RapidEye satellite optical technology, by using

multi-spectral imagery, can provide information on a

wider range of indicators than through classical

remote sensing data including degradation (Teobal-

delli et al. 2010). The availability of five spectral

bands also allows for a more accurate representation

of plant communities. The red band, in particular,

which is correlated with photosynthetic activity while

being insensitive to soil background and atmospheric

noise, can be used to add more detail to the analysis

of the spectral features of the vegetation classes

(Malatesta et al. 2013). With a constellation of five

satellites and a pixel resolution of 5m, RapidEye

offers daily images for any area on Earth at a

temporal resolution of 5.5 days at nadir, therefore

reducing the risk of cloud-covered areas (Rapideye.

com 2012).

In 2014, PNG’s Forest Authority is expected to

make available a beta version of the new land-cover

map obtained with this technology.

Tier 3 assessment. A biodiversity monitoring

programme in which field measurements of key

species replace proxy indicators could be effectively

designed only when priority areas are clearly defined

and have been drastically reduced in both number

and size throughout the planning process and the

remote-sensing assessment.

Only at that stage, it would be reasonable to

attempt to select specific indicators for each area

while aiming at a cost-effective monitoring that takes

into consideration the expected financial benefits

resulting from a future REDDþ mechanism.

Additionally, ground-based measurements for moni-

toring carbon stock change, which could be useful in

the context of monitoring biodiversity, are also

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needed to calibrate remote sensing estimates

(Teobaldelli et al. 2010; Doswald et al. 2010).

Conclusions

In the framework of IPCC’s three-tiered approach to

REDDþ preparedness activities, an NFI is an

assessment activity at tier 3, which is the final and

most complex stage in the process, not only from the

analytical point of view but also for its data

requirements.

In our case study, REDDþ is being implemented

at the national scale in a country – PNG –

characterized by tropical forests of the highest

biodiversity, most of which are still unknown. The

challenge of incorporating appropriate biodiversity

objectives and indicators in such NFIs cannot be

effectively tackled only at the NFI stage, as it needs to

address a number of issues related to different phases

of the preparedness process.

When attempting an integration of such indi-

cators directly in the NFI structure, the lack of

information concerning PNG’s biodiversity would

render the effort either inefficient or non-cost-

effective or non-realistically time-bound, unless

considering only proxies for biodiversity derived

from indicators that are expected to be already

included in the NFI to monitor carbon stock change.

A biodiversity monitoring that goes beyond proxies

and aims at defining direct indicators needs to tackle

a series of issues at different stages, from the planning

to the assessment and requires reducing the size of

intervention by prioritizing the areas to be

monitored.

At the planning stage, a carbon–biodiversity

overlay map analysis can help limit the choice by

identifying areas of high biodiversity that could be

threatened by future REDDþ activities either

directly (areas with high carbon stocks) or indirectly

due to “leakage” (areas with low carbon stocks). The

information obtained with such analysis can also

become the basis for the stakeholders’ consultation

that will lead to a REDDþ strategy, which will

formalize what types of REDDþ activities will

eventually take place and where.

During the implementation phase, the identifi-

cation of a sub-sample of sites where forests are

undergoing the greatest changes can help refine the

selection process. This can be achieved by taking

advantage of the remote sensing technologies that are

already being put in place for carbon monitoring

purposes. In the case of PNG, the available imagery

includes Landsat, SPOT 4 and 5, and the latest

RapidEye land-cover maps, which, in combination,

could provide a number of landscape indicators on

deforestation, forest degradation and habitat frag-

mentation, which can also be used in relation to

known impacts on biodiversity.

A biodiversity monitoring programme involving

field measurements of key species could be effectively

designed only during the implementation phase of

REDDþ, having the priority areas already been

clearly defined and limited in both number and size.

Acknowledgements

This study was supported by FAO-Mountain

Partnership Secretariat and the Italian Development

Cooperation (DGCS).

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