Antonelli, A., Smith, R. J. & Simmonds, M. S. J. Unlocking the properties of plants and fungi for sustainable development. Nat. Plants 5, 1100–1102 (2019).
Google Scholar
Chen, I.-C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).
Google Scholar
IPBES. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://doi.org/10.5281/zenodo.3553579 (2019).
Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science. 355, eaai9214 (2017).
Google Scholar
Warren, R. et al. Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat. Clim. Chang. 3, 678–682 (2013).
Google Scholar
Destro, G. F. G., Fernandes, V., Andrade, A. F. A., De Marco, P. & Terribile, L. C. Back home? Uncertainties for returning seized animals to the source-areas under climate change. Glob. Change Biol. 25, 3242–3253 (2019).
Google Scholar
Travis, J. M. J. et al. Dispersal and species’ responses to climate change. Oikos 122, 1532–1540 (2013).
IPCC. Summary for policymakers. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (2014).
IPCC. Summary for policymakers. In Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (eds Shukla, P.R. & J. Skea, E. C.) (2019).
IPCC. Special Report on 1.5 degrees: Summary for Policymakers. In Global Warming of 1.5°C. An IPCC Special Report on the Impacts of global Warming of 1.5°C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Cha (2018).
Ulloa Ulloa, C. et al. An integrated assessment of the vascular plant species of the Americas. Science 358, 1614–1617 (2017).
Google Scholar
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
Google Scholar
Coradin, L., Siminski, A. & Reis, A. Espécies Nativas da Flora Brasileira de Valor Econômico Atual e Potencial – Plantas para o futuro – Região Sul. (Ministério do Meio Ambiente, 2011).
Nair, P. K. R. An introduction to agroforestry (Springer, 1993).
Sinclair, F. L. A general classification of agroforestry practice. Agrofor. Syst. 46, 161–180 (1999).
Somarriba, E. Revisiting the past: An essay on agroforestry definition. Agrofor. Syst. 19, 233–240 (1992).
Cerda, R. et al. Contribution of cocoa agroforestry systems to family income and domestic consumption: Looking toward intensification. Agrofor. Syst. 88, 957–981 (2014).
Montagnini, F. Integrating Landscapes: Agroforestry for Biodiversity Conservation and Food Sovereignty Vol. 12 (Springer, New York, 2017).
Siddique, I., Dionísio, A. C. & Simões-Ramos, G. A. Construindo Conhecimentos Sobre Agroflorestas em Rede. (UFSC, 2017).
Jose, S. Agroforestry for conserving and enhancing biodiversity. Agrofor. Syst. 85, 1–8 (2012).
Sistla, S. A. et al. Agroforestry Practices Promote Biodiversity and Natural Resource Diversity in Atlantic Nicaragua. PLoS One 11, e0162529 (2016).
Google Scholar
Santos, P. Z. F., Crouzeilles, R. & Sansevero, J. B. B. Can agroforestry systems enhance biodiversity and ecosystem service provision in agricultural landscapes? A meta-analysis for the Brazilian Atlantic Forest. For. Ecol. Manage. 433, 140–145 (2019).
Reppin, S., Kuyah, S., de Neergaard, A., Oelofse, M. & Rosenstock, T. S. Contribution of agroforestry to climate change mitigation and livelihoods in Western Kenya. Agrofor. Syst. 94, 203–220 (2020).
Marconi, L. & Armengot, L. Complex agroforestry systems against biotic homogenization: The case of plants in the herbaceous stratum of cocoa production systems. Agric. Ecosyst. Environ. 287, 106664 (2020).
Google Scholar
Somarriba, E. et al. Carbon stocks and cocoa yields in agroforestry systems of Central America. Agric. Ecosyst. Environ. 173, 46–57 (2013).
De Stefano, A. & Jacobson, M. G. Soil carbon sequestration in agroforestry systems: A meta-analysis. Agrofor. Syst. 92, 285–299 (2017).
Gomes, L. C. et al. Agroforestry systems can mitigate the impacts of climate change on coffee production: A spatially explicit assessment in Brazil. Agric. Ecosyst. Environ. 294, 106858 (2020).
Kofsky, J., Zhang, H. & Song, B.-H. The Untapped Genetic Reservoir: The Past, Current, and Future Applications of the Wild Soybean (Glycine soja). Front. Plant Sci. 9, 285–299 (2018).
Lorenzi, H. Arvores Brasileiras. (Plantarum, 2016).
Zwiener, V. P. et al. Planning for conservation and restoration under climate and land use change in the Brazilian Atlantic Forest. Divers. Distrib. 23, 955–966 (2017).
Zechini, A. A. et al. Genetic conservation of Brazilian Pine (Araucaria angustifolia) through traditional land use. Econ. Bot. 72, 166–179 (2018).
Donazzolo, J., Stefenon, V. M., Guerra, M. P. & Nodari, R. O. On farm management of Acca sellowiana (Myrtaceae) as a strategy for conservation of species genetic diversity. Sci. Hortic. (Amsterdam) 259, 108826 (2020).
Google Scholar
Favreto, R., Mello, R. S. P. & de Moura Baptista, L. R. Growth of Euterpe edulis Mart (Arecaceae) under forest and agroforestry in southern Brazil. Agrofor. Syst. https://doi.org/10.1007/s10457-010-9321-z (2010).
Google Scholar
Siminski, A., dos Santos, K. L. & Wendt, J. G. N. Rescuing agroforestry as strategy for agriculture in Southern Brazil. J. For. Res. 27, 739–746 (2016).
da Silva, L. C. R., Machado, S. A., Galvão, F. & Filho, A. F. Floristic evolution in an agroforestry system cultivation in Southern Brazil. An. Acad. Bras. Cienc. https://doi.org/10.1590/0001-3765201620150026 (2016).
Google Scholar
Gomes, V. H. F. et al. Species distribution modelling: Contrasting presence-only models with plot abundance data. Sci. Rep. 8, 1003 (2018).
Google Scholar
Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).
Google Scholar
Raes, N. & Aguirre-Gutiérrez, J. A Modeling Framework to Estimate and Project Species Distributions in Space and Time Pontocaspian biodiversity RIse and DEmise View project Current and Future Biodiversity Patterns in Mainland Southeast Asia View project. (2018).
Brooks, T. M. et al. Measuring terrestrial area of habitat (AOH) and its utility for the IUCN red list. Trends Ecol. Evol. 34, 977–986 (2019).
Google Scholar
Soberon, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform. 2, 1–10 (2005).
Levis, C. et al. Persistent effects of pre-Columbian plant domestication on Amazonian forest composition. Science 355, 925–931 (2017).
Google Scholar
Reis, M. S. et al. Domesticated landscapes in Araucaria Forests, Southern Brazil: A multispecies local conservation-by-use system. Front. Ecol. Evol. 6, 1–14 (2018).
IUCN Standards and Petitions Committee. Guidelines for Using the IUCN Red List Categories and Criteria. Version 14. Prep. by Stand. Petitions Comm. (2019).
Gomes, V. H. F., Vieira, I. C. G., Salomão, R. P. & ter Steege, H. Amazonian tree species threatened by deforestation and climate change. Nat. Clim. Chang. 9, 547–553 (2019).
Google Scholar
Guo, Y. et al. Prediction of the potential geographic distribution of the ectomycorrhizal mushroom Tricholoma matsutake under multiple climate change scenarios. Sci. Rep. 7, 46221 (2017).
Google Scholar
Rodrigues, P., Silva, J., Eisenlohr, P. & Schaefer, C. Climate change effects on the geographic distribution of specialist tree species of the Brazilian tropical dry forests. Braz. J. Biol. 75, 679–684 (2015).
Google Scholar
Wilson, O. J., Walters, R. J., Mayle, F. E., Lingner, D. V. & Vibrans, A. C. Cold spot microrefugia hold the key to survival for Brazil’s critically endangered araucaria tree. Glob. Chang. Biol. 25, 4339–4351 (2019).
Google Scholar
Cámara-Leret, R. et al. Climate change threatens New Guinea’s biocultural heritage. Sci. Adv. 5, eaaz1455 (2019).
Google Scholar
Esser, L. F., Saraiva, D. D. & Jarenkow, J. A. Future uncertainties for the distribution and conservation of Paubrasilia echinata under climate change. Acta Bot. Brasilica 33, 770–776 (2019).
Lima, V. P., Marchioro, C. A., Joner, F., ter Steege, H. & Siddique, I. Extinction threat to neglected Plinia edulis exacerbated by climate change, yet likely mitigated by conservation through sustainable use. Austral Ecol. 45, 376–383 (2020).
Santini, L., Benítez-López, A., Maiorano, L., Čengić, M. & Huijbregts, M. A. J. Assessing the reliability of species distribution projections in climate change research. Divers. Distrib. 27, 1–16 (2021).
Raes, N. et al. Historical distribution of Sundaland’s Dipterocarp rainforests at Quaternary glacial maxima. Proc. Natl. Acad. Sci. 111, 16790–16795 (2014).
Google Scholar
Vaz, Ú. L. & Nabout, J. C. Using ecological niche models to predict the impact of global climate change on the geographical distribution and productivity of Euterpe oleracea Mart. (Arecaceae) in the Amazon. Acta Bot. Brasilica 30, 290–295 (2016).
Sánchez-Fernández, D. et al. Thermal niche estimators and the capability of poor dispersal species to cope with climate change. Sci. Rep. 6, 23381 (2016).
Google Scholar
de Lima, R. A. F. et al. How much do we know about the endangered Atlantic Forest? Reviewing nearly 70 years of information on tree community surveys. Biodivers. Conserv. 24, 2135–2148 (2015).
Ribeiro, M. C. et al. The Brazilian Atlantic Forest: A Shrinking Biodiversity Hotspot (Springer, New York, 2011).
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272 (2018).
Google Scholar
Siddique, I. et al. Woody species richness drives synergistic recovery of socio-ecological multifunctionality along early tropical dry forest regeneration. For. Ecol. Manag. 482, 118848 (2021).
Harvey, C. A. et al. Climate-smart landscapes: Opportunities and challenges for integrating adaptation and mitigation in tropical agriculture. Conserv. Lett. 7, 77–90 (2014).
Schneidewind, U. et al. Carbon stocks, litterfall and pruning residues in monoculture and agroforestry cacao production systems. Exp. Agric. 55, 452–470 (2019).
Dinesh, D., Campbell, B. M., Bonilla-findji, O. & Richards, M. 10 Best Bet Innovations for Adaptation in Agriculture: A supplement to the UNFCCC NAP Technical Guidelines. Working paper 215 (2017).
Lin, B. B., Perfecto, I. & Vandermeer, J. Synergies between agricultural intensification and climate change could create surprising vulnerabilities for crops. Bioscience 58, 847–854 (2008).
Perfecto, I., John Vandermeer & Angus Wright. 2019 Nature’s Matrix: Linking Agriculture, Biodiversity Conservation and Food Sovereignty. (Routledge, 2019).
Hannah, L. et al. 30% land conservation and climate action reduces tropical extinction risk by more than 50%. Ecography 43, 943–953 (2020).
Zizka, A. et al. Biogeography and conservation status of the pineapple family (Bromeliaceae). Divers. Distrib. 26, 183–195 (2020).
Elias, G. A., Lima, J. M. T. & dos Santos, R. Threatened flora from the State of Santa Catarina, Brazil: Arecaceae. Hoehnea 46, e322018 (2019).
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752–1246752 (2014).
Google Scholar
Brancalion, P. H. S. et al. What makes ecosystem restoration expensive? A systematic cost assessment of projects in Brazil. Biol. Conserv. 240, 108274 (2019).
Crouzeilles, R. et al. There is hope for achieving ambitious Atlantic Forest restoration commitments. Perspect. Ecol. Conserv. 17, 80–83 (2019).
Magnago, L. F. S. et al. Would protecting tropical forest fragments provide carbon and biodiversity cobenefits under REDD+?. Glob. Chang. Biol. 21, 3455–3468 (2015).
Google Scholar
Rodrigues, A. C., Villa, P. M. & Neri, A. V. Fine-scale topography shape richness, community composition, stem and biomass hyperdominant species in Brazilian Atlantic forest. Ecol. Indic. 102, 208–217 (2019).
de Lima, R. A. F. et al. The erosion of biodiversity and biomass in the Atlantic Forest biodiversity hotspot. Nat. Commun. 11, 6347 (2020).
Google Scholar
Loreau, M. Reconciling utilitarian and non-utilitarian approaches to biodiversity conservation. Ethics Sci. Environ. Polit. 14, 27–32 (2014).
Berkes, F. & Folke, C. Linking social and ecological resilience and sustainability. In Linking Social and Ecological Systems. Management Practices and Social Mechanisms for Building Resilience (Cambridge University Press, Cambridge, 2000).
Fernandes, R. C. & Piovezana, L. The Kaingang perspectives on land and environmental rights in the south of Brazil. Ambient. Soc. 18, 111–128 (2015).
Machado Mello, A. J. & Peroni, N. Cultural landscapes of the Araucaria Forests in the northern plateau of Santa Catarina, Brazil. J. Ethnobiol. Ethnomed. 11, 51 (2015).
Google Scholar
Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N. & Zimmermann, N. E. Uncertainty in ensembles of global biodiversity scenarios. Nat. Commun. 10, 1446 (2019).
Google Scholar
Zurell, D. et al. A standard protocol for reporting species distribution models. Ecography 43, 1261–1277 (2020).
Warren, D. L., Matzke, N. J. & Iglesias, T. L. Evaluating presence-only species distribution models with discrimination accuracy is uninformative for many applications. J. Biogeogr. 47, 167–180 (2020).
Leroy, B. et al. Without quality presence-absence data, discrimination metrics such as TSS can be misleading measures of model performance. J. Biogeogr. 45, 1994–2002 (2018).
Araujo, M. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).
Google Scholar
Raes, N. & ter Steege, H. A null-model for significance testing of presence-only species distribution models. Ecography 30, 727–736 (2007).
Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B Biol. Sci. 285, 20180792 (2018).
Loiselle, B. A. et al. Avoiding pitfalls of using species distribution models in conservation planning. Conserv. Biol. 17, 1591–1600 (2003).
Bean, W. T., Stafford, R. & Brashares, J. S. The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models. Ecography 35, 250–258 (2012).
Meyer, A. L. S., Pie, M. R. & Passos, F. C. Assessing the exposure of lion tamarins (Leontopithecus spp.) to future climate change. Am. J. Primatol. 76, 551–562 (2014).
Google Scholar
Araújo, M. B. & Pearson, R. G. Equilibrium of species’ distributions with climate. Ecography 28, 693–695 (2005).
Guillera-Arroita, G. et al. Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292 (2015).
Bascompte, J., García, M. B., Ortega, R., Rezende, E. L. & Pironon, S. Mutualistic interactions reshuffle the effects of climate change on plants across the tree of life. Sci. Adv. 5, eaav2539 (2019).
Google Scholar
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).
Google Scholar
Thuiller, W. et al. Predicting global change impacts on plant species’ distributions: Future challenges. Perspect. Plant Ecol. Evol. Syst. 9, 137–152 (2008).
Mayle, F. E. Millennial-scale dynamics of southern Amazonian rain forests. Science 290, 2291–2294 (2000).
Google Scholar
Bullock, J. M. et al. Human-mediated dispersal and the rewiring of spatial networks. Trends Ecol. Evol. 33, 958–970 (2018).
Google Scholar
Ordonez, J. C. Constraints and opportunities for tree diversity management along the forest transition curve to achieve multifunctional agriculture. Curr. Opin. Environ. Sustain. 6, 54–60 (2014).
Levis, C. et al. How People Domesticated Amazonian Forests. Front. Ecol. Evol. 5, 171 (2018).
Mendes, P., Velazco, S. J. E., de Andrade, A. F. A. & De Marco, P. Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy. Ecol. Modell. 431, 109180 (2020).
GBIF. GBIF Occurrence. https://www.gbif.org, https://doi.org/10.15468/dl.vjezvb (2019)
Carvalho, G. flora: Tools for Interacting with the Brazilian Flora 2020. R package version 0.3.0. (2017).
Raes, N. Partial versus full species distribution models. Nat. Conserv. 10, 127–138 (2012).
Zizka, A. et al. CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases. Methods Ecol. Evol. 10, 744–751 (2019).
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2020).
Oliveira, U. et al. The strong influence of collection bias on biodiversity knowledge shortfalls of Brazilian terrestrial biodiversity. Divers. Distrib. 22, 1232–1244 (2016).
Daru, B. H. et al. Widespread sampling biases in herbaria revealed from large-scale digitization. New Phytol. 217, 939–955 (2018).
Google Scholar
Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
Proosdij, A. S. J., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).
Beaumont, L. J. et al. Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges?. Ecol. Modell. 342, 135–146 (2016).
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Fourcade, Y., Besnard, A. G. & Secondi, J. Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Glob. Ecol. Biogeogr. 27, 245–256 (2018).
Austin, M. P. & Van Niel, K. P. Improving species distribution models for climate change studies: Variable selection and scale. J. Biogeogr. 38, 1–8 (2011).
Woodward, F. I. Climate and Plant Distribution. (Cambridge Univ. Press., 1987).
IUCN. Plant Growth Forms Classification Scheme. Version: 1.0. https://www.iucnredlist.org/resources/classification-schemes (2020).
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
Fremout, T. et al. Mapping tree species vulnerability to multiple threats as a guide to restoration and conservation of tropical dry forests. Glob. Chang. Biol. 26, 3552–3568 (2020).
Google Scholar
Naimi, B. Package ‘ usdm ’. R Topics Document (2015).
Syfert, M. M. et al. Using species distribution models to inform IUCN Red List assessments. Biol. Conserv. 177, 174–184 (2014).
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).
Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).
Muñoz-Pajares, A. J. et al. Niche differences may explain the geographic distribution of cytotypes in Erysimum mediohispanicum. Plant Biol. 20, 139–147 (2018).
Google Scholar
Peng, L.-P. et al. Modelling environmentally suitable areas for the potential introduction and cultivation of the emerging oil crop Paeonia ostii in China. Sci. Rep. 9, 3213 (2019).
Google Scholar
Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
Boucher-Lalonde, V., Morin, A. & Currie, D. J. How are tree species distributed in climatic space? A simple and general pattern. Glob. Ecol. Biogeogr. 21, 1157–1166 (2012).
Elith, J., Ferrier, S., Huettmann, F. & Leathwick, J. The evaluation strip: A new and robust method for plotting predicted responses from species distribution models. Ecol. Modell. 186, 280–289 (2005).
Jiménez-Valverde, A. & Lobo, J. M. Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecologica 31, 361–369 (2007).
Google Scholar
Betts, J. et al. A framework for evaluating the impact of the IUCN Red List of threatened species. Conserv. Biol. 34, 632–643 (2020).
Google Scholar
ter Steege, H. et al. Estimating the global conservation status of more than 15,000 Amazonian tree species. Sci. Adv. 1, e1500936 (2015).
Google Scholar
Dauby, G. et al. ConR : An R package to assist large-scale multispecies preliminary conservation assessments using distribution data. Ecol. Evol. 7, 11292–11303 (2017).
Google Scholar
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