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How deregulation, drought and increasing fire impact Amazonian biodiversity

Abstract

Biodiversity contributes to the ecological and climatic stability of the Amazon Basin1,2, but is increasingly threatened by deforestation and fire3,4. Here we quantify these impacts over the past two decades using remote-sensing estimates of fire and deforestation and comprehensive range estimates of 11,514 plant species and 3,079 vertebrate species in the Amazon. Deforestation has led to large amounts of habitat loss, and fires further exacerbate this already substantial impact on Amazonian biodiversity. Since 2001, 103,079–189,755 km2 of Amazon rainforest has been impacted by fires, potentially impacting the ranges of 77.3–85.2% of species that are listed as threatened in this region5. The impacts of fire on the ranges of species in Amazonia could be as high as 64%, and greater impacts are typically associated with species that have restricted ranges. We find close associations between forest policy, fire-impacted forest area and their potential impacts on biodiversity. In Brazil, forest policies that were initiated in the mid-2000s corresponded to reduced rates of burning. However, relaxed enforcement of these policies in 2019 has seemingly begun to reverse this trend: approximately 4,253–10,343 km2 of forest has been impacted by fire, leading to some of the most severe potential impacts on biodiversity since 2009. These results highlight the critical role of policy enforcement in the preservation of biodiversity in the Amazon.

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Fig. 1: Overview of plant and vertebrate species richness and fire-impacted forest in the Amazon Basin.
Fig. 2: Cumulative effects of fire on biodiversity in the Amazon rainforest.
Fig. 3: Newly fire-impacted forest in Brazil (based on MODIS burned area).
Fig. 4: Newly fire-impacted forest area and the impacts on plant and vertebrate species in Brazil.

Data availability

The plant occurrences from the BIEN database are accessible using the RBIEN package (https://github.com/bmaitner/RBIEN). The climatic data are accessible from http://worldclim.org and the soil data are available from http://soilgrids.org. MODIS active fire and burned area products are available at http://modis-fire.umd.edu. The MODIS Vegetation Continuous Fields data are publicly available from https://lpdaac.usgs.gov/products/mod44bv006/. The annual forest loss layers are available from http://earthenginepartners.appspot.com/science-2013-global-forest. The plant range maps are accessible at https://github.com/shandongfx/paper_Amazon_biodiversity_2021. The vertebrate range maps are available from https://www.iucnredlist.org/resources/spatial-data-download. The SPEI data are available from SPEI Global Drought Monitor (https://spei.csic.es/map).

Code availability

The code to process the remote-sensing data is available at https://github.com/shandongfx/paper_Amazon_biodiversity_2021.

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Acknowledgements

We acknowledge the herbaria that contributed data to this work: HA, FCO, MFU, UNEX, VDB, ASDM, BPI, BRI, CLF, L, LPB, AD, TAES, FEN, FHO, A, ANSM, BCMEX, RB, TRH, AAH, ACOR, AJOU, UI, AK, ALCB, AKPM, EA, AAU, ALU, AMES, AMNH, AMO, ANA, GH, ARAN, ARM, AS, CICY, ASU, BAI, AUT, B, BA, BAA, BAB, BACP, BAF, BAL, COCA, BARC, BBS, BC, BCN, BCRU, BEREA, BG, BH, BIO, BISH, SEV, BLA, BM, MJG, BOL, CVRD, BOLV, BONN, BOUM, BR, BREM, BRLU, BSB, BUT, C, CAMU, CAN, CANB, CAS, CAY, CBG, CBM, CEN, CEPEC, CESJ, CHR, ENCB, CHRB, CIIDIR, CIMI, CLEMS, COA, COAH, COFC, CP, COL, COLO, CONC, CORD, CPAP, CPUN, CR, CRAI, FURB, CU, CRP, CS, CSU, CTES, CTESN, CUZ, DAO, HB, DAV, DLF, DNA, DS, DUKE, DUSS, E, HUA, EAC, ECU, EIF, EIU, GI, GLM, GMNHJ, K, GOET, GUA, EKY, EMMA, HUAZ, ERA, ESA, F, FAA, FAU, UVIC, FI, GZU, H, FLAS, FLOR, HCIB, FR, FTG, FUEL, G, GB, GDA, HPL, GENT, GEO, HUAA, HUJ, CGE, HAL, HAM, IAC, HAMAB, HAS, HAST, IB, HASU, HBG, IBUG, HBR, IEB, HGI, HIP, IBGE, ICEL, ICN, ILL, SF, NWOSU, HO, HRCB, HRP, HSS, HU, HUAL, HUEFS, HUEM, HUSA, HUT, IAA, HYO, IAN, ILLS, IPRN, FCQ, ABH, BAFC, BBB, INPA, IPA, BO, NAS, INB, INEGI, INM, MW, EAN, IZTA, ISKW, ISC, GAT, IBSC, UCSB, ISU, IZAC, JBAG, JE, SD, JUA, JYV, KIEL, ECON, TOYA, MPN, USF, TALL, RELC, CATA, AQP, KMN, KMNH, KOR, KPM, KSTC, LAGU, UESC, GRA, IBK, KTU, KU, PSU, KYO, LA, LOMA, SUU, UNITEC, NAC, IEA, LAE, LAF, GMDRC, LCR, LD, LE, LEB, LI, LIL, LINN, AV, HUCP, MBML, FAUC, CNH, MACF, CATIE, LTB, LISI, LISU, MEXU, LL, LOJA, LP, LPAG, MGC, LPD, LPS, IRVC, MICH, JOTR, LSU, LBG, WOLL, LTR, MNHN, CDBI, LYJB, LISC, MOL, DBG, AWH, NH, HSC, LMS, MELU, NZFRI, M, MA, UU, UBT, CSUSB, MAF, MAK, MB, KUN, MARY, MASS, MBK, MBM, UCSC, UCS, JBGP, OBI, BESA, LSUM, FULD, MCNS, ICESI, MEL, MEN, TUB, MERL, CGMS, FSU, MG, HIB, TRT, BABY, ETH, YAMA, SCFS, SACT, ER, JCT, JROH, SBBG, SAV, PDD, MIN, SJSU, MISS, PAMP, MNHM, SDSU, BOTU, MPU, MSB, MSC, CANU, SFV, RSA, CNS, JEPS, BKF, MSUN, CIB, VIT, MU, MUB, MVFA, SLPM, MVFQ, PGM, MVJB, MVM, MY, PASA, N, HGM, TAM, BOON, MHA, MARS, COI, CMM, NA, NCSC, ND, NU, NE, NHM, NHMC, NHT, UFMA, NLH, UFRJ, UFRN, UFS, ULS, UNL, US, NMNL, USP, NMR, NMSU, XAL, NSW, ZMT, BRIT, MO, NCU, NY, TEX, U, UNCC, NUM, O, OCLA, CHSC, LINC, CHAS, ODU, OKL, OKLA, CDA, OS, OSA, OSC, OSH, OULU, OXF, P, PACA, PAR, UPS, PE, PEL, SGO, PEUFR, PH, PKDC, SI, PMA, POM, PORT, PR, PRC, TRA, PRE, PY, QMEX, QCA, TROM, QCNE, QRS, UH, R, REG, RFA, RIOC, RM, RNG, RYU, S, SALA, SANT, SAPS, SASK, SBT, SEL, SING, SIU, SJRP, SMDB, SNM, SOM, SP, SRFA, SPF, STL, STU, SUVA, SVG, SZU, TAI, TAIF, TAMU, TAN, TEF, TENN, TEPB, TI, TKPM, TNS, TO, TU, TULS, UADY, UAM, UAS, UB, UC, UCR, UEC, UFG, UFMT, UFP, UGDA, UJAT, ULM, UME, UMO, UNA, UNM, UNR, UNSL, UPCB, UPNA, USAS, USJ, USM, USNC, USZ, UT, UTC, UTEP, UV, VAL, VEN, VMSL, VT, W, WAG, WII, WELT, WIS, WMNH, WS, WTU, WU, Z, ZSS, ZT, CUVC, AAS, AFS, BHCB, CHAM, FM, PERTH and SAN. X.F., D.S.P., E.A.N., A.L. and J.R.B. were supported by the University of Arizona Bridging Biodiversity and Conservation Science program. Z.L. was supported by NSFC (41922006) and K. C. Wong Education Foundation. The BIEN working group was supported by the National Center for Ecological Analysis and Synthesis, a centre funded by NSF EF-0553768 at the University of California, Santa Barbara, and the State of California. Additional support for the BIEN working group was provided by iPlant/Cyverse via NSF DBI-0735191. B.J.E., B.M. and C.M. were supported by NSF ABI-1565118. B.J.E. and C.M. were supported by NSF ABI-1565118 and NSF HDR-1934790. B.J.E., L.H. and P.R.R. were supported by the Global Environment Facility SPARC project grant (GEF-5810). D.D.B. was supported in part by NSF DEB-1824796 and NSF DEB-1550686. S.R.S. was supported by NSF DEB-1754803. X.F. and A.L. were partly supported by NSF DEB-1824796. B.J.E. and D.M.N. were supported by NSF DEB-1556651. M.M.P. is supported by the São Paulo Research Foundation (FAPESP), grant 2019/25478-7. D.M.N. was supported by Instituto Serrapilheira/Brazil (Serra-1912-32082). E.I.N. was supported by NSF HDR-1934712. We thank L. López-Hoffman and L. Baldwin for constructive comments.

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Contributions

X.F. conceived the idea, which was refined by discussion with D.S.P., C.M., B.M., P.R.R., E.A.N., B.L.B., A.L., J.R.B., D.D.B., J.R.S., K.C.E. and B.J.E.; X.F. and Z.L. processed the remote-sensing data; C.M., X.F., B.M., B.L.B., D.S.P. and B.J.E. conducted the analyses of plant data; P.R.R., C.M., B.M., X.F. and D.S.P. conducted the analyses of vertebrate data; X.F., C.M., S.R.S. and E.A.N. processed the drought data; D.S.P., X.F., C.M., P.R.R. and B.M. designed the illustrations with help from B.J.E., D.D.B., K.C.E. and E.A.N.; E.A.N., X.F., and D.S.P. conducted the statistical analyses with help from B.J.E.; X.F., B.J.E., B.M., A.L., J.R.B., D.S.P., C.M., E.A.N., Z.L. and P.R.R. wrote the original draft; all authors contributed to interpreting the results and the editing of manuscript drafts. B.J.E., C.M., K.C.E. and D.D.B. led to the acquisition of the financial support for the project. X.F., C.M., B.M., D.S.P., P.R.R., Z.L., E.A.N. and B.J.E. contributed equally to data, analyses and writing.

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Correspondence to Xiao Feng.

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Extended data figures and tables

Extended Data Fig. 1 Fire-impacted forest and forest loss in the Amazon Basin.

ah, Visualization of fire-impacted forest (a, b), forest loss without fire (c, d), fire-impacted forest with forest loss (e, f), and fire-impacted forest without forest loss (g, h) in the Amazon Basin based on MODIS burned area (left panels) and active fire (right panels). Data in ad are resampled from the 500m (MODIS burned area) or 1 km (MODIS active fire) to 10 km resolution using mean function and thresholded at 0.01 to illustrate the temporal dynamics. Black represents non-forested areas masked out from this study. The cumulative fire-impacted forest is classified into two categories: fire-impacted forest with forest loss (e, f) and fire-impacted forest without forest loss (g, h). Data in eh are resampled to 10 km using mean function to illustrate the cumulative percentages of impacts.

Extended Data Fig. 2 Scatter plot of species’ range impacted by fire.

Scatter plot of species’ range size in Amazon forest (x-axis) and percentage of total range impacted by fire (red) and forest loss without fire (black) up to 2019 for plants (left panel) and vertebrates (right panel).

Extended Data Fig. 3 Density plot of species’ cumulative range impacted by fire.

Density plot of species’ cumulative range impacted by fire. The different colours represent years 2001-2019. The x-axis is log10 transformed.

Extended Data Fig. 4 Summary of forest impacts in the Amazon Basin.

Areas of forest impact in the Amazon Basin estimated from MODIS burned area (top) and MODIS active fire (bottom).

Extended Data Fig. 5 Cumulative impacts on biodiversity in the Amazon Basin.

Cumulative effects of forest loss without fire on biodiversity in the Amazon rainforest. In the left panels, the black and grey shading represent the cumulative forest loss without fire based on MODIS burned area and MODIS active fire, respectively. Coloured areas represent the lower and upper bounds of cumulative numbers of a, plant and c, vertebrate species’ ranges impacted. Right panels depict the relationships between the cumulative forest loss without fire (based on MODIS burned area) and cumulative number of b, plant and d, vertebrate species. Coloured lines represent predicted values of an ordinary least squares linear regression and grey bands define the two-sided 95% confidence interval (two-sided, p values = 0.00). The silhouette of the tree is from http://phylopic.org/; silhouette of the monkey is courtesy of Mathias M. Pires.

Extended Data Fig. 6 Fire-impacted forest in Brazil.

Newly fire-impacted forest in Brazil (based on MODIS active fire). a shows the area of fire-impacted forest not explained by drought conditions. Different colours represent years from different policy regimes: pre-regulations in light red (mean value in dark red), regulation in grey (mean value in black dashed line), and 2019 in blue. The y-axis represents the difference between actual area and area predicted by drought conditions calibrated by data from regulation years (Methods). A positive value on the y-axis represents more area than expected, using the regulation years as a baseline. b shows a scatter plot of newly fire-impacted forest in Brazil and drought conditions (SPEI); The lines represent the ordinary least squares linear regression between fire-impacted forest and drought conditions for pre-regulation (red) and regulation (black) respectively.

Extended Data Fig. 7 Fire-impacted forest in different countries.

The contribution (0–1) of different countries to the newly fire-impacted forest each year based on MODIS active fire (top) and MODIS burned area (bottom).

Extended Data Figure 8 Impacts of fire on forest and biodiversity in Brazil.

a, Newly fire-impacted forest, b, new range impact on plants and c, new range impacts on vertebrate species in Brazil each year (based on MODIS active fire) that are not predicted by drought conditions. The colours represent three policy regimes: pre-regulation in red, regulation in grey and 2019 in blue. The y-axis represents the difference between actual value (area or range impacted by fire) and the values predicted by drought conditions calibrated by data from regulation years (Methods). A positive value on the y-axis represents more area or range impacted by fire than the expectation using the regulation years as a baseline. The dotted lines represent a smooth curve fitted to the values based on the loess method.

Extended Data Table 1 Summary of fire-impacted forest
Extended Data Table 2 Summary of regression analyses

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Feng, X., Merow, C., Liu, Z. et al. How deregulation, drought and increasing fire impact Amazonian biodiversity. Nature 597, 516–521 (2021). https://doi.org/10.1038/s41586-021-03876-7

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