Concerted political attention has focused on reducing deforestation1,2,3, and this remains the cornerstone of most biodiversity conservation strategies4,5,6. However, maintaining forest cover may not reduce anthropogenic forest disturbances, which are rarely considered in conservation programmes6. These disturbances occur both within forests, including selective logging and wildfires7,8, and at the landscape level, through edge, area and isolation effects9. Until now, the combined effect of anthropogenic disturbance on the conservation value of remnant primary forests has remained unknown, making it impossible to assess the relative importance of forest disturbance and forest loss. Here we address these knowledge gaps using a large data set of plants, birds and dung beetles (1,538, 460 and 156 species, respectively) sampled in 36 catchments in the Brazilian state of Pará. Catchments retaining more than 69–80% forest cover lost more conservation value from disturbance than from forest loss. For example, a 20% loss of primary forest, the maximum level of deforestation allowed on Amazonian properties under Brazil’s Forest Code5, resulted in a 39–54% loss of conservation value: 96–171% more than expected without considering disturbance effects. We extrapolated the disturbance-mediated loss of conservation value throughout Pará, which covers 25% of the Brazilian Amazon. Although disturbed forests retained considerable conservation value compared with deforested areas, the toll of disturbance outside Pará’s strictly protected areas is equivalent to the loss of 92,000–139,000 km2 of primary forest. Even this lowest estimate is greater than the area deforested across the entire Brazilian Amazon between 2006 and 2015 (ref. 10). Species distribution models showed that both landscape and within-forest disturbances contributed to biodiversity loss, with the greatest negative effects on species of high conservation and functional value. These results demonstrate an urgent need for policy interventions that go beyond the maintenance of forest cover to safeguard the hyper-diversity of tropical forest ecosystems.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Boucher, D., Elias, P., Faires, J. & Smith, S. Deforestation Success Stories: Tropical Nations Where Forest Protection and Reforestation Policies Have Worked. Union of Concerned Scientists June 2014 Report (2014)
Nepstad, D. et al. The end of deforestation in the Brazilian Amazon. Science 326, 1350–1351 (2009)
Soares-Filho, B. S. et al. Modelling conservation in the Amazon basin. Nature 440, 520–523 (2006)
Convention on Biological Diversity. Strategic Plan for Biodiversity 2011–2020, Aichi Biodiversity Targetshttps://www.cbd.int/sp/default.shtml (2015)
Legislative Database of the Food and Agricultural Organization of the United Nations (FAOLEX). Brazilian Environmental Law number 12.651 (25 March 2012)
Panfil, S. N. & Harvey, C. A. REDD+ and Biodiversity Conservation: A review of the biodiversity goals, monitoring methods and impacts of 80 REDD+ projects. Conserv. Lett. 9, 143–150 (2015)
Aragão, L. E. O. C. & Shimabukuro, Y. E. The incidence of fire in Amazonian forests with implications for REDD. Science 328, 1275–1278 (2010)
Burivalova, Z., S¸ekerciog˘lu, C. H. & Koh, L. P. Thresholds of logging intensity to maintain tropical forest biodiversity. Curr. Biol. 24, 1893–1898 (2014)
Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. Camb. Philos. Soc. 81, 117–142 (2006)
Instituto Nacional de Pesquisas Espaciais (INPE). Projeto Prodes: Amazon deforestation database. Available at www.obt.inpe.br/prodes (2015)
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013)
Sloan, S. & Sayer, J. Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries. For. Ecol. Manage. 352, 134–145 (2015)
Barlow, J. & Peres, C. A. Avifaunal responses to single and recurrent wildfires in Amazonian forests. Ecol. Appl. 14, 1358–1373 (2004)
Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015)
Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011)
Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R. & Zelazowski, P. Tropical Forests in the Anthropocene. Annu. Rev. Environ. Resour. 39, 125–159 (2014)
Morton, D. C., Le Page, Y., DeFries, R., Collatz, G. J. & Hurtt, G. C. Understorey fire frequency and the fate of burned forests in southern Amazonia. Phil. Trans. R. Soc. B 368, 1–8 (2013)
Gardner, T. A. et al. A social and ecological assessment of tropical land uses at multiple scales: the Sustainable Amazon Network. Phil. Tran. R. Soc. B 368, 20120166 (2013)
Berenguer, E. et al. A large-scale field assessment of carbon stocks in human-modified tropical forests. Glob. Chang. Biol. 20, 3713–3726 (2014)
da Silva, J. M. C., Rylands, A. B. & Da Fonseca, G. A. B. The fate of the Amazonian areas of endemism. Conserv. Biol. 19, 689–694 (2005)
International Union of Forest Research Organizations (IUFRO). Understanding Relationships between Biodiversity, Carbon, Forests and People: The Key to Achieving REDD+ Objectives (eds Parrotta, J. A., Wildburger, C. & Mansourian, S. ) (2012)
Manne, L. L., Brooks, T. M. & Pimm, S. L. Relative risk of extinction of passerine birds on continents and islands. Nature 399, 258–261 (1999)
Purvis, A., Gittleman, J. L., Cowlishaw, G. & Mace, G. M. Predicting extinction risk in declining species. Proc. Biol. Sci. 267, 1947–1952 (2000)
Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009)
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009)
Baker, T. R. et al. Variation in wood density determines spatial patterns in Amazonian forest biomass. Glob. Chang. Biol. 10, 545–562 (2004)
Banks-Leite, C. et al. Assessing the utility of statistical adjustments for imperfect detection in tropical conservation science. J. Appl. Ecol. 51, 849–859 (2014)
Gestão de Florestas Públicas – Relatório 2015. Brasília: MMA/SFB Available at http://www.florestal.gov.br/publicacoes/instrumento-de-gestao (2015)
Chen, Y. et al. Forecasting fire season severity in South America using sea surface temperature anomalies. Science 334, 787–791 (2011)
Ferreira, J. et al. Environment and Development. Brazil’s environmental leadership at risk. Science 346, 706–707 (2014)
Lees, A. C. et al. One hundred and thirty-five years of avifaunal surveys around Santarem, central Brazilian Amazon. Rev. Bras. Ornitol. 21, 16–57 (2013)
Lees, A. C. et al. Paragominas: a quantitative baseline inventory of an eastern Amazonian avifauna. Rev. Bras. Ornitol. 20, 93–118 (2012)
Barrio, J. Hunting pressure on cracids (Cracidae: Aves) in forest concessions in Peru. Rev. Peru. Biol. 18, 225–230 (2011)
Zanne A. E. et al. Data from: Towards a worldwide wood economics spectrum. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.234 (2009)
Instituto Nacional de Pesquisas Espaciais (INPE). Terraclass data 2010; available at http://www.Inpe.Br/cra/projetos_pesquisas/terraclass2010 (2013)
Souza, C. M. Jr. et al. Ten-year landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sens. 5, 5493–5513 (2013)
Ferraz, S. F. D., Vettorazzi, C. A. & Theobald, D. M. Using indicators of deforestation and land-use dynamics to support conservation strategies: A case study of central Rondonia, Brazil. For. Ecol. Manage. 257, 1586–1595 (2009)
Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 879–888 (2012)
Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 133, 225–245 (2000)
This work was supported by grants from Brazil (CNPq 574008/2008-0, 458022/2013-6, and 400640/2012-0; Embrapa SEG:02.08.06.005.00; The Nature Conservancy – Brasil; CAPES scholarships) the UK (Darwin Initiative 17-023; NE/F01614X/1; NE/G000816/1; NE/F015356/2; NE/l018123/1; NE/K016431/1), Formas 2013-1571, and Australian Research Council grant DP120100797. Institutional support was provided by the Herbário IAN in Belém, LBA in Santarém and FAPEMAT. R.M. and J.R.T. were supported by Australian Research Council grant DP120100797. This is paper no. 49 in the Sustainable Amazon Network series.
The authors declare no competing financial interests.
Extended data figures and tables
a, The location of Paragominas and Santarém within Pará. b, c, The distribution of study catchments (n = 36) within Paragominas and Santarém, respectively. d, The distribution of study plots (n = 175) in example catchments spanning the gradient of primary forest. Selected catchments are shown in red in a and b. e, Sampling design within each plot.
a–c, The richness of forest species in secondary forests (SF), pastures (PA), and mechanised agricultural lands (AG) relative to the average richness of forest species in all undisturbed and disturbed primary forests (dashed line) in Paragominas (green) and Santarém (orange). Panels show the convex hull (a), automatic (b) and high basal area filters (c) used to classify forest species (see Methods).
a–d, Estimates of conservation value in the Paragominas (circles) and Santarém (triangles) study regions from large-stemmed plants (a) small-stemmed plants (b) birds (c) and dung beetles (d). Dashed lines show expectations without disturbance. Grey lines show all regressions, with the black solid line showing the median response (see Methods). Values were standardized across study regions and taxa. There was no significant difference between taxa in the median estimate (F3;117 = 1.36, P = 0.26, ANCOVA).
Extended Data Figure 4 Range of conservation value estimates using three alternative sets of reference plots.
Mean species density (de) is measured by: all disturbed and undisturbed plots in the least disturbed reference catchments (grey shaded region), all undisturbed plots throughout a region (green shaded region), and undisturbed plots in the reference catchments (purple shaded region). See Methods for details.
a–h, Species AUCcv values for each variable in Paragominas (a, c, e, g) and Santarém (b, d, f, h) for large-stemmed plants (a, b), small-stemmed plants (c, d), birds (e, f) and beetles (g, h). Variable importance was measured by the mean AUCcv over all well-modelled species (see Methods). Variable colours denote group membership: green, orange and blue represent landscape disturbance, within-forest disturbance and natural variables, respectively (see Methods for variable descriptions). Letters show the results for multiple pair-wise comparisons of group means using Tukey’s range test. Variables which do not share a letter have significantly different mean importance (P < 0.05).
a–h, Species’ AUCcv values for each variable in Paragominas (a, c, e, g) and Santarém (b, d, f, h) for large-stemmed plants (a, b), small-stemmed plants (c, d), birds (e, f) and beetles (g, h). Variable importance was measured by the mean AUCcv over all well-modelled species (see Methods). Variable colours denote group membership: green, orange and blue represent landscape disturbance, within-forest disturbance and natural variables, respectively (see Methods for variable descriptions). Letters show the results for multiple pair-wise comparisons of group means using Tukey’s range test. Variables which do not share a letter have significantly different mean importance (P < 0.05).
a–h, Species’ AUCcv values for each variable in Paragominas (a, c, e, g) and Santarém (b, d, f, h) for large-stemmed plants (a, b), small-stemmed plants (c, d), birds (e, f) and beetles (g, h). Variable importance is measured by the mean AUCcv over all well-modelled species (see Methods). Variable colours denote group membership: green, orange and blue represent landscape disturbance, within-forest disturbance and natural variables, respectively (see Methods for variable descriptions). Letters show the results for multiple pair-wise comparisons of group means using Tukey’s range test. Variables which do not share a letter have significantly different mean importance (P < 0.05).
a–h, The odds of detecting small-stemmed plants (a–d) and dung beetles (e–h) species groups along gradients of landscape disturbance (a, b, e, f) and within-forest disturbance (c, d, g, h) in Paragominas (a, c, e, g) and Santarém (b, d, f, h) (see Methods). Species groups, shown by different coloured lines, are composed of species with similar disturbance responses (see Methods). Line thickness represents the relative size of the groups.
About this article
Cite this article
Barlow, J., Lennox, G., Ferreira, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016). https://doi.org/10.1038/nature18326
Journal of Sustainable Tourism (2021)
Land Use Policy (2021)
Ecosystem Services (2020)
Royal Society Open Science (2020)
Forest Policy and Economics (2020)