1. Biodiversity Implications of Forest Disturbance and Related Landscape Dynamics in the Brazilian Amazon Mark A. Cochrane 1,2, David P. Roy 1, Carlos Souza Jr. 2, Jos Barlow 3, Eugenio Arima 4, Izaya Numata 1, Christopher P. Barber 1, Luiz Mestre 1, Rafael Andrade 1, and Sanath Kumar 1 1 Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD USA 2 IMAZON, Instituto do Homem e Meio Ambiente da Amazônia, Belém, PA Brazil 3 Lancaster University, Lancaster United Kingdom 4 Hobart and William Smith Colleges, Geneva NY USA

2. 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

3. The Brazilian Amazon Amazon humid tropical forest biomes: ~ 6.4 km 2 Portion in Brazil: ~ 4 km 2 Source: WWF

5. Human 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 km 2 / year

13. Forest wildfires: Interact with ongoing threats to the Amazon

14. 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.

15. Phase 1. Imagery Acquisition and Processing a) Paragominas,Pará State - 223/62 Soil GV NDFI NPV

16. Image Processing Steps Shade Soil NPV GV Image Registration Radiance Conversion Correct Haze? Atmospheric Correction (ACORN) Yes No Estimate Visibility and water vapor Apply Carlotto’s Technique (1) PRE-PROCESSING Landsat Reflectance Space Pixel Purity Index - (PPI) Visualization Scatter matrix Spectral curves 40 million pixels (2) Build Spectral Library Generic Image Endmembers SVDC (3) SMA Landsat NDFI (4) Enhance and Detect Canopy Damage Extract Patios CCA Canopy Damage Soil ≥ 10% 1 pixel ≤ Area ≤ 4 pixels NDFI ≤ 0.75 GV + NPV + Soil + Shade = 1 Souza Jr. et al. (2005), RSE

17. Haze Correction Contaminated ImageCorrected Image Ji-Parana, 231/67 – R3, G2, B1

18. Normalized Difference Fraction Index -1 ≤ NDFI ≤1 NDFI low to moderate NDFI near 1 High GV Low NPV and Soil Low to moderate GV Moderate to high NPV and Soil Souza Jr. et al. (2005), RSE

19. NDFI 226/68 - 2001 (Sinop - MT) Roads Logged Forest

20. NDFI 226/68 - 2000 (Sinop - MT)

21. NDFI 226/68 - 2001 (Sinop - MT)

22. NDFI 226/68 - 2003 (Sinop - MT)

23. Mapping Forest Damage History

24. Characterizing Forest Fragmentation

25. Age map Derived from Landsat time series Used for calculation of fragmentation features Forest Pasture S.G.Forest Time series Land cover mapAge 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

26. Persistence of Forest Edge (Ariquemes) Remaining edge %

27. Edge length classification using ISO data 1975198419861988199019921994199619982000200220042005 Class 1 1975 Class 2 198419861988199019921994 19961998200020022004 20051975 Class 4 1984198519861987198819891990199119921993199419951996199719981999200020012002200320042005

28. 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.

29. Barlow et al. 2007 PNAS Both birds and dung beetles are good indicators of community change in most other faunal groups

30. Barlow et al. 2007 PNAS Both birds and dung beetles hold large numbers of species that are restricted to primary forest Many wide ranging species Mostly territorial or habitat specialists

31. Dung beetles Birds MothsSmall 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

32. 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).

33. 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

34. Fire-mediated dieback and compositional cascade Barlow and Peres 2008

35. 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

36. Less individuals sampled compared to point counts BUT It is independent of observer accuracy Capture mainly understory birds Samples species that are not singing Possibility to mark (banding) and measure MIST NETS

37. MEASURING We will measure: Wings Tarsus Bill Weight

38. 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).

39. 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

40. 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.

41. Reprise of Project Objectives 1) 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)