Biodiversity Implications of Forest Disturbance and Biodiversity Implications of Forest Disturbance and Related Landscape Dynamics in the Brazilian Related Landscape Dynamics in the Brazilian

AmazonAmazon

Mark A. CochraneMark A. Cochrane1,21,2, David P. Roy, David P. Roy11, Carlos Souza Jr., Carlos Souza Jr.22, Jos Barlow, Jos Barlow33, , Eugenio ArimaEugenio Arima44, Izaya Numata, Izaya Numata11, Christopher P. Barber, Christopher P. Barber11, Luiz Mestre, Luiz Mestre11, ,

Rafael AndradeRafael Andrade11, and Sanath Kumar, and Sanath Kumar11

1 Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD USA2 IMAZON, Instituto do Homem e Meio Ambiente da Amazônia, Belém, PA Brazil

3 Lancaster University, Lancaster United Kingdom4 Hobart and William Smith Colleges, Geneva NY USA

What is the project?What is the project? The fundamental hypothesis underlying this project

is that the biodiversity levels of Amazonian forests are strongly related to two competing factors: forest disturbance and time since last disturbance

The Brazilian AmazonThe Brazilian Amazon

Amazon humid tropical forest biomes: ~ 6.4 km2

Portion in Brazil: ~ 4 km2

Source: WWF

Human Access to ForestHuman Access to Forest

Source: IBGE, IMAZON

85% of deforestation within 50 km of main roads

73,000 km of official roads in region

240,000 km of unofficial roads

Expansion rates > 40 km / 10,000 km2 / year

Forest wildfires: Interact with ongoing threats to the Amazon

How do we propose to test this?

Our approach is straightforward… 1) Determine recent forest disturbance history

across the Brazilian Amazon (2000-2009); 2) Conduct extensive field studies of indicator

taxa, stratified by disturbance history, to determine biodiversity responses;

3) Model the determinants of fire ignition and fire spread;

4) Predict the current and future levels of biodiversity similarity in disturbed forests spatially across the Brazilian Amazon.

Phase 1. Imagery Acquisition and Processinga) Paragominas,Pará State - 223/62

Soil

GV NDFI

NPV

Image Processing Steps

Shade

Soil

NPV

GV

Image RegistrationRadiance Conversion

CorrectHaze?

AtmosphericCorrection(ACORN)

Yes

No

Estimate Visibilityand water vapor

Apply Carlotto’s

Technique

(1) PRE-PROCESSING

Landsat

ReflectanceSpace

Pixel PurityIndex - (PPI)

VisualizationScatter matrix

Spectral curves

40 million pixels

(2) Build Spectral Library

Generic Image Endmembers

SVDC

(3) SMA

Landsat

NDFI

(4) Enhance and Detect Canopy Damage

ExtractPatios

CCA

CanopyDamage Soil ≥ 10%

1 pixel ≤ Area ≤ 4 pixels

NDFI ≤ 0.75

GV + NPV + Soil + Shade = 1

Souza Jr. et al. (2005), RSE

Haze Correction

Contaminated Image Corrected Image

Ji-Parana, 231/67 – R3, G2, B1

Normalized Difference Fraction Index

SoilNPVGV

Soil)(NPVGVNDFI

Shade

Shade

Shade100

GVGVShade

-1 ≤ NDFI ≤1

NDFI low to moderate

NDFI near 1

High GVLow NPV and Soil

Low to moderate GVModerate to high NPV and Soil

Souza Jr. et al. (2005), RSE

NDFI

226/68 - 2001 (Sinop - MT)

Roads

Logged

Forest

NDFI

226/68 - 2000 (Sinop - MT)

NDFI

226/68 - 2001 (Sinop - MT)

NDFI

226/68 - 2003 (Sinop - MT)

Mapping Forest Damage History

Characterizing Forest FragmentationCharacterizing Forest Fragmentation

Age map

Derived from Landsat time series Used for calculation of fragmentation

features

Forest

Pasture

S.G.Forest

Time series Land cover map Age map

>22 ys

1 y

2 ys

3 ys

4 ys

5 ys

6 ys

7 ys

8 ys

9 ys

10 ys

11 ys

12 ys

13 ys

14 ys

15 ys

16 ys

17 ys

18 ys

19 ys

20 ys

21 ys

1975

2005

Persistence of Forest Edge (Ariquemes)

y = 90.919x-0.522

R² = 0.9771

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20

Rem

aini

ng e

dge

(%)

Edge age

Rem

ain

ing

edge

%

Edge length classification using ISO data

0

10

20

30

40

50

60

70

80

90

100

1975 1980 1985 1990 1995 2000 2005

Fore

st e

dge

leng

th (k

m)

Years

Different patterns of forest edge dynamicsClass 1Class 2Class 3Class 4Class 5

1975198419861988199019921994199619982000200220042005

Class 1

1975

Class 2

198419861988199019921994199619982000200220042005 1975

Class 4

1984198519861987198819891990199119921993199419951996199719981999200020012002200320042005

Phase 2. Biodiversity Field Studies

The spatial database of forest disturbance is used to stratify and interpret our field studies investigating the response of 4 major indicator taxa (birds, dung beetles, trees and ants) as a function of disturbance history and time since last disturbance.

Me

an

(±S

E)

corr

ela

tion

co

ee

ffic

ien

t

Butterfl

ies

Large

Mam

mal

s

Lizard

s

Birds

Trees

Arach

nids

Dung bee

tles

Moth

s

Carrio

n flie

s

Fruit

flies

Grass

hoppers

Smal

l mam

mal

s

Orchid

bee

s

Amphib

ians

Bats

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Barlow et al. 2007 PNAS

Both birds and dung beetles are good indicators of community change in most other faunal groups

Barlow et al. 2007 PNAS

Both birds and dung beetles hold large numbers of species that are restricted to primary forest

% Species unique to primary forest

0 10 20 30 40 50 60 70

TreesBirds

AmphibiansLizards

Small mammalsDung beetles

BatsMoths

ButterfliesGrasshoppers

ArachnidsFruit flies

Scavenger fliesLarge mammals

Orchid bees Many wide ranging species

Mostly territorial or habitat specialists

Standardised survey cost ($)

0 2000 4000 6000 8000

% In

dica

tor

spec

ies

0

5

10

15

20

25

30

35

40

Dung beetles

Birds

Moths Small mammals

Gardner, Barlow et al. 2008 Ecology Letters

Both birds and dung beetles are highly cost effective to sample – you get good information on habitat integrity for a low cost

Selecting effective biodiversity indicators

Wide variability in cost of surveying different taxa Some taxa (e.g. birds and dung beetles) are of high

performance for evaluating forest management systems in the Brazilian Amazon because they: Are sensitive indicators of changes in forest integrity Can be surveyed cost effectively

Ants share similar ecological attributes as birds and dung-beetles They are cheap to sample Species rich, with many different functional groups Most do not move large distances from their colonies (if

you find them in a habitat, they come from that habitat).

Methods – Dung Beetles- Baited pitfall traps (human faeces)

- 5 traps per transect, run for 4 days.

- Follows methods discussed at global Scarabnet meetings, and used effectively in many Amazonian studies

- Complemented by un-baited flight intercept traps

Fire-mediated dieback and compositional cascade

Barlow and Peres 2008

Observation and recording

10 point counts per day per site 06:30 h - 09:00 h

10 min. bird observation and recording,

spaced at least 150m each other, intending to avoid double bird-counting (Parker, 1991).

Along each mist-net transects and other trails.

POINT COUNTS

Less individuals sampled compared to point counts

BUT It is independent of observer accuracy

Capture mainly understory birdsSamples species that are not singing

Possibility to mark (banding) and measure

MIST NETS

MEASURING

We will measure:

Wings

Tarsus

Bill

Weight

Expected Results

A large-scale comparison of effects of fire on Amazonian bird communities.

A long-term comparison of effects of fire on Amazonian bird communities.

One of the best overviews comparing Amazonian bird communities in different

Amazonian States (~500 sp).

Phase 3: Characterizing Fires (Starting June 2008)

Integration of Landsat based analyses of forest disturbance with MODIS-derived fire products will enable us to accurately separate fires into their three main types;

(1) deforestation fires, where slash is burned, creating relatively hot fires that burn for several hours;

2) maintenance fires, which rapidly burn as narrow fire lines through grass and early second growth;

3) forest fires, escaped fires in standing forests which vary from extremely low intensity in previously undisturbed forests to high intensity in previously burned or logged forests

Phase 4: Spatio-socioeconomic modeling (starting June 2008)

We will use spatial regressions of economic (farmgate prices for soybean and beef), physical-geographic (precipitation, soil types, vegetation types, distance to previous deforestation, and land protection status (e.g. indigenous lands, conservation units)) and land cover (disturbance history) factors to model probability surfaces of fire ignition and fire spread.

MODIS fire detections will be used to validate the ignition event model for 2000-2009 and the composite burned area product (Phase 3) will be used to validate the fire spread model over the same time period.

Once validated, the models will be run using likely economic and rainfall scenarios to create spatio-temporal predictions of disturbance frequency and expected biodiversity impacts for the 2010-2019 time period.

Reprise of Project Objectives1) Develop a basin-wide spatial database of all forest disturbance (selective

logging, fragmentation, fire, deforestation) from 2000-2009, based on NDFI analyses of annual Landsat imagery.

2) Derive regional estimation functions of expected biodiversity similarity based on disturbance history (disturbance metric) and time-since-last-disturbance (resilience metric) derived from stratified field data collected for four separate taxa (woody plants, birds, dung beetles and ants).

3) Develop a basin-wide spatial and temporal datasets of all fires by type (1) deforestation fires; 2) maintenance fires; 3) forest fires, using MODIS and Landsat data.

4) Model economic, physical-geographic and land cover factors affecting fire ignition and spread from 2000-2009 to create probability surfaces of fire ignition and fire spread.

5) Create a basin-wide map of probable biodiversity alterations in current standing forests across the Brazilian Amazon and predictions of future changes in these conditions over the next 10 years (2010-2019) based on likely economic and climate scenarios. (Starting late 2009)