Update of the Zero Draft of the Post-2020 Global Biodiversity Framework (Convention on Biological Diversity, 2020); https://www.cbd.int/doc/c/3064/749a/0f65ac7f9def86707f4eaefa/post2020-prep-02-01-en.pdf
Corlett, R. T. et al. Impacts of the coronavirus pandemic on biodiversity conservation. Biol. Conserv. 246, 108571 (2020).
Google Scholar
Singh, R. et al. Impact of the COVID-19 pandemic on rangers and the role of rangers as a planetary health service. Parks 27, 119–134 (2021).
Google Scholar
Hockings, M. et al. COVID‐19 and protected and conserved areas. Parks 26, 7–24 (2020).
Google Scholar
Waithaka, J. The Impact of COVID-19 Pandemic on Africa’s Protected Areas Operations and Programmes (IUCN, 2020); https://www.iucn.org/sites/dev/files/content/documents/2020/report_on_the_impact_of_covid_19_doc_july_10.pdf
Lindsey, P. et al. Conserving Africa’s wildlife and wildlands through the COVID-19 crisis and beyond. Nat. Ecol. Evol. 4, 1300–1310 (2020).
Google Scholar
Amador-Jiménez, M., Millner, N., Palmer, C., Pennington, R. T. & Sileci, L. The unintended impact of Colombia’s COVID-19 lockdown on forest fires. Environ. Resour. Econ. 76, 1081–1105 (2020).
Google Scholar
Poulter, B., Freeborn, P. H., Matt Jolly, W. & Morgan Varner, J. COVID-19 lockdowns drive decline in active fires in southeastern United States. Proc. Natl Acad. Sci. USA 118, e2015666118 (2021).
Google Scholar
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).
Google Scholar
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).
Google Scholar
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23215 (2019).
Google Scholar
Tabor, K. et al. Evaluating the effectiveness of conservation and development investments in reducing deforestation and fires in Ankeniheny–Zahemena Corridor, Madagascar. PLoS ONE 12, e0190119 (2017).
Google Scholar
Cochrane, M. A. Fire science for rainforests. Nature 421, 913–919 (2003).
Google Scholar
Driscoll, D. A. et al. How fire interacts with habitat loss and fragmentation. Biol. Rev. 96, 976–998 (2021).
Google Scholar
Nelson, A. & Chomitz, K. M. Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: a global analysis using matching methods. PLoS ONE 6, e22722 (2011).
Google Scholar
Carlson, K. M. et al. Effect of oil palm sustainability certification on deforestation and fire in Indonesia. Proc. Natl Acad. Sci. USA 115, 121–126 (2018).
Google Scholar
Turco, M. et al. Skilful forecasting of global fire activity using seasonal climate predictions. Nat. Commun. 9, 2718 (2018).
Google Scholar
Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).
Google Scholar
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).
Google Scholar
Brooks, T. M. et al. Global biodiversity conservation priorities. Science 313, 58–61 (2006).
Google Scholar
Jones, J. P. G. et al. Last chance for Madagascar’s biodiversity. Nat. Sustain. 2, 350–352 (2019).
Google Scholar
Gardner, C. J. et al. The rapid expansion of Madagascar’s protected area system. Biol. Conserv. 220, 29–36 (2018).
Google Scholar
Hockley, N., Mandimbiniaina, R. & Rakotonarivo, O. S. Fair and equitable conservation: do we really want it, and if so, do we know how to achieve it? Madag. Conserv. Dev. 13, 3–5 (2018).
Google Scholar
Corson, C. in Conservation and Environmental Management in Madagascar (ed. Scales, I. R.) 193–215 (Routledge, 2014).
Davies, B. et al. Community factors and excess mortality in first wave of the COVID-19 pandemic in England. Nat. Commun. 12, 3755 (2021).
Google Scholar
Kull, C. A. & Lehmann, C. E. R. in The New Natural History of Madagascar (ed. Goodman, S. M.) 197–203 (Princeton Univ. Press, in the press).
Razafindrakoto, M., Roubaud, F. & Wachsberger, J.-M. Puzzle and Paradox: A Political Economy of Madagascar (Cambridge Univ. Press, 2020).
Ruggiero, P. G. C., Pfaff, A., Nichols, E., Rosa, M. & Metzger, J. P. Election cycles affect deforestation within Brazil’s Atlantic Forest. Conserv. Lett. 14, e12818 (2021).
Google Scholar
Morpurgo, J., Kissling, W. D., Tyrrell, P., Negret, P. J. & Allan, J. R. The role of elections as drivers of tropical deforestation. Preprint at bioRxiv https://doi.org/10.1101/2021.05.04.442551 (2021).
Tourism in Madagascar (WorldData, 2021); https://www.worlddata.info/africa/madagascar/tourism.php
Rapport annuel d’activites 2018 (Madagascar National Parks, 2018).
Vyawahare, M. As minister and activists trade barbs, Madagascar’s forests burn. Mongabay (17 December 2020).
Cochrane, M. A. in Tropical Fire Ecology: Climate Change, Land Use, and Ecosystem Dynamics (ed. Cochrane, M. A.) 389–426 (Springer-Verlag, 2009); https://doi.org/10.1007/978-3-540-77381-8_14
Cochrane, M. A. in Tropical Rainforest Responses to Climatic Change (eds Bush, M. et al.) 213–240 (Springer, 2011); https://doi.org/10.1007/978-3-642-05383-2_7
Mondal, N. & Sukumar, R. Fires in seasonally dry tropical forest: testing the varying constraints hypothesis across a regional rainfall gradient. PLoS ONE 11, e0159691 (2016).
Google Scholar
Madagascar Economic Update: COVID-19 Increases Poverty, a New Reform Momentum is Needed to Build Back Stronger (World Bank, 2020); https://www.worldbank.org/en/country/madagascar/publication/madagascar-economic-update-covid-19-increases-poverty-a-new-reform-momentum-is-needed-to-build-back-stronger
Baker, A. Climate, not conflict. Madagascar’s famine is the first in modern history to be solely caused by global warming. Time (20 July 2021).
Graham, V. et al. Management resourcing and government transparency are key drivers of biodiversity outcomes in Southeast Asian protected areas. Biol. Conserv. 253, 108875 (2021).
Google Scholar
Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).
Google Scholar
Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).
Google Scholar
Eklund, J., Coad, L., Geldmann, J. & Cabeza, M. What constitutes a useful measure of protected area effectiveness? A case study of management inputs and protected area impacts in Madagascar. Conserv. Sci. Pract. 1, e107 (2019).
Nolte, C. & Agrawal, A. Linking management effectiveness indicators to observed effects of protected areas on fire occurrence in the Amazon rainforest. Conserv. Biol. 27, 155–165 (2013).
Google Scholar
Schleicher, J., Peres, C. A. & Leader-Williams, N. Conservation performance of tropical protected areas: how important is management? Conserv. Lett. 12, e12650 (2019).
Google Scholar
Schroeder, W., Oliva, P., Giglio, L. & Csiszar, I. A. The new VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sens. Environ. 143, 85–96 (2014).
Google Scholar
Forest Monitoring Designed for Action (Global Forest Watch, 2021); https://www.globalforestwatch.org/
Musinsky, J. et al. Conservation impacts of a near real-time forest monitoring and alert system for the tropics. Remote Sens. Ecol. Conserv 4, 189–196 (2018).
Google Scholar
Ramo, R. et al. African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data. Proc. Natl Acad. Sci. USA 118, e2011160118 (2021).
Google Scholar
Global Economic Prospects, June 2021 (World Bank, 2021).
Razanatsoa, E. et al. Fostering local involvement for biodiversity conservation in tropical regions: lessons from Madagascar during the COVID‐19 pandemic. Biotropica 53, 994–1003 (2021).
Google Scholar
Nolte, C., Agrawal, A., Silvius, K. M. & Soares-Filho, B. S. Governance regime and location influence avoided deforestation success of protected areas in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 110, 4956–4961 (2013).
Google Scholar
ArcGIS 10.8 for Desktop (ESRI, 2021).
Python Language Reference v.3.8.5 (Python Software Foundation, 2021); http://www.python.org
R Core Team R: A Language and Environment for Statistical Computing. R version 4.0.2 (R Foundation for Statistical Computing, 2020); https://www.R-project.org/
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).
The World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, 2020); www.protectedplanet.net
Goodman, S. M., Raherilalao, J. M. & Wohlhauser, S. The Terrestrial Protected Areas of Madagascar: Their History, Description, and Biota (Association Vahatra, 2018).
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).
Google Scholar
NRT VIIRS 375 m Active Fire Product VNP14IMGT (NASA, 2020); https://doi.org/10.5067/FIRMS/VIIRS/VNP14IMGT_NRT.002
Chen, D., Shevade, V., Baer, A. E. & Loboda, T. V. Missing burns in the high northern latitudes: the case for regionally focused burned area products. Remote Sens. 13, 4145 (2021).
Google Scholar
Schroeder, W. & Giglio, L. NASA VIIRS Land Science Investigator Processing System (SIPS) Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m & 750 m Active Fire Products: Product User’s Guide Version 1.4 (NASA, 2018).
Global Precipitation Measurement: Precipitation Data Directory (NASA, 2020); https://gpm.nasa.gov/data/directory
Global Precipitation Measurement: The Tropical Rainfall Measuring Mission (TRMM) (NASA, 2020) https://gpm.nasa.gov/missions/trmm
Hantson, S. et al. Rare, intense, big fires dominate the global tropics under drier conditions. Sci. Rep. 7, 14374 (2017).
Google Scholar
Zeileis, A., Kleiber, C. & Jackman, S. Regression models for count data in R. J. Stat. Softw. https://doi.org/10.18637/jss.v027.i08 (2008).
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R 261–293 (Springer, 2009).
Joseph, M. B. et al. Spatiotemporal prediction of wildfire extremes with Bayesian finite sample maxima. Ecol. Appl. 29, e01898 (2019).
Google Scholar
Guo, F. et al. Comparison of six generalized linear models for occurrence of lightning-induced fires in northern Daxing’an Mountains, China. J. For. Res. 27, 379–388 (2016).
Google Scholar
Garay, A. M., Hashimoto, E. M., Ortega, E. M. M. & Lachos, V. H. On estimation and influence diagnostics for zero-inflated negative binomial regression models. Comput. Stat. Data Anal. 55, 1304–1318 (2011).
Google Scholar
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).
Shcherbakov, M. V. et al. A survey of forecast error measures. World Appl. Sci. J. 24, 171–176 (2013).
Efron, B. Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26 (1979).
Google Scholar
Canty, A. & Ripley, B. boot: Bootstrap R (S-Plus) Functions. R package version 1.3-28 (2021).
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