Lewis, S. L. & Maslin, M. A. Defining the anthropocene. Nature 519, 171–180 (2015).
Google Scholar
Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).
Google Scholar
Pinto-Ledezma, J. N. & Rivero Mamani, M. L. Temporal patterns of deforestation and fragmentation in lowland Bolivia: Implications for climate change. Clim. Change 127, 43–54 (2014).
Google Scholar
Allen, J. M., Folk, R. A., Soltis, P. S., Soltis, D. E. & Guralnick, R. P. Biodiversity synthesis across the green branches of the tree of life. Nat. Plants 5, 11–13 (2019).
Google Scholar
Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, IPBES. Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Accessed 15 Feb 2021. https://zenodo.org/record/3553579. https://doi.org/10.5281/ZENODO.3553579 (2019).
Cavender-Bares, J., Balvanera, P., King, E. & Polasky, S. Ecosystem service trade-offs across global contexts and scales. Ecol. Soc. 20, art22 (2015).
Google Scholar
Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).
Google Scholar
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
Google Scholar
Watson, J. E. M. et al. Set a global target for ecosystems. Nature 578, 360–362 (2020).
Google Scholar
Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 3, 539–551 (2019).
Google Scholar
Mateo, R. G., Mokany, K. & Guisan, A. Biodiversity models: What if unsaturation is the rule?. Trends Ecol. Evol. 32, 556–566 (2017).
Google Scholar
Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).
Google Scholar
Ferrier, S. & Guisan, A. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43, 393–404 (2006).
Google Scholar
Guisan, A. & Rahbek, C. SESAM—A new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages: Predicting spatio-temporal patterns of species assemblages. J. Biogeogr. 38, 1433–1444 (2011).
Google Scholar
Cavender-Bares, J., Schweiger, A. K., Pinto-Ledezma, J. N. & Meireles, J. E. Applying remote sensing to biodiversity science. in Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J. et al.) 13–42 (Springer, 2020). https://doi.org/10.1007/978-3-030-33157-3_2.
Google Scholar
Fawcett, D. et al. Advancing retrievals of surface reflectance and vegetation indices over forest ecosystems by combining imaging spectroscopy, digital object models, and 3D canopy modelling. Remote Sens. Environ. 204, 583–595 (2018).
Google Scholar
Randin, C. F. et al. Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sens. Environ. 239, 111626 (2020).
Google Scholar
Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).
Google Scholar
D’Amen, M., Pradervand, J.-N. & Guisan, A. Predicting richness and composition in mountain insect communities at high resolution: A new test of the SESAM framework: Community-level models of insects. Glob. Ecol. Biogeogr. 24, 1443–1453 (2015).
Google Scholar
Pottier, J. et al. The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients: Climate and species assembly predictions. Glob. Ecol. Biogeogr. 22, 52–63 (2013).
Google Scholar
Zurell, D. et al. Testing species assemblage predictions from stacked and joint species distribution models. J. Biogeogr. 47, 101–113 (2020).
Google Scholar
D’Amen, M. et al. Improving spatial predictions of taxonomic, functional and phylogenetic diversity. J. Ecol. 106, 76–86 (2018).
Google Scholar
Dobrowski, S. Z. et al. Modeling plant ranges over 75 years of climate change in California, USA: Temporal transferability and species traits. Ecol. Monogr. 81, 241–257 (2011).
Google Scholar
Soria-Auza, R. W. et al. Impact of the quality of climate models for modelling species occurrences in countries with poor climatic documentation: A case study from Bolivia. Ecol. Model. 221, 1221–1229 (2010).
Google Scholar
Rocchini, D. et al. Satellite remote sensing to monitor species diversity: Potential and pitfalls. Remote Sens. Ecol. Conserv. 2, 25–36 (2016).
Google Scholar
Schulte to Bühne, H. & Pettorelli, N. Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science. Methods Ecol. Evol. 9, 849–865 (2018).
Google Scholar
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
Google Scholar
Hobi, M. L. et al. A comparison of Dynamic Habitat Indices derived from different MODIS products as predictors of avian species richness. Remote Sens. Environ. 195, 142–152 (2017).
Google Scholar
Radeloff, V. C. et al. The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity. Remote Sens. Environ. 222, 204–214 (2019).
Google Scholar
Pinto-Ledezma, J. N. & Cavender-Bares, J. Using remote sensing for modeling and monitoring species distributions. In Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J., Gamon, J. A. & Townsend, P. A.) 199–223 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-33157-3_9.
Fernández, N., Ferrier, S., Navarro, L. M. & Pereira, H. M. Essential biodiversity variables: Integrating in-situ observations and remote sensing through modeling. In Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J., Gamon, J. A. & Townsend, P. A.) 485–501 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-33157-3_18.
Skidmore, A. K. et al. Priority list of biodiversity metrics to observe from space. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01451-x (2021).
Google Scholar
Saatchi, S., Buermann, W., ter Steege, H., Mori, S. & Smith, T. B. Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sens. Environ. 112, 2000–2017 (2008).
Google Scholar
He, K. S. et al. Will remote sensing shape the next generation of species distribution models?. Remote Sens. Ecol. Conserv. 1, 4–18 (2015).
Google Scholar
Cord, A. F., Meentemeyer, R. K., Leitão, P. J. & Václavík, T. Modelling species distributions with remote sensing data: Bridging disciplinary perspectives. J. Biogeogr. 40, 2226–2227 (2013).
Google Scholar
Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 16024 (2016).
Google Scholar
Scherrer, D., D’Amen, M., Fernandes, R. F., Mateo, R. G. & Guisan, A. How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer. Methods Ecol. Evol. 9, 2155–2166 (2018).
Google Scholar
Cavender-Bares, J. Diversification, adaptation, and community assembly of the American oaks (Quercus), a model clade for integrating ecology and evolution. New Phytol. 221, 669–692 (2019).
Google Scholar
Cavender-Bares, J., Ackerly, D. D., Baum, D. A. & Bazzaz, F. A. Phylogenetic overdispersion in Floridian oak communities. Am. Nat. 163, 823–843 (2004).
Google Scholar
Cavender-Bares, J. et al. The role of diversification in community assembly of the oaks (Quercus L.) across the continental U.S. Am. J. Bot. 105, 565–586 (2018).
Google Scholar
Colwell, R. K. & Rangel, T. F. Hutchinson’s duality: The once and future niche. Proc. Natl. Acad. Sci. 106, 19651–19658 (2009).
Google Scholar
Townsend Peterson, A. et al. Ecological Niches and Geographic Distributions. (Princeton University Press, 2011).
Google Scholar
Cavender-Bares, J., Fontes, G. C. & Pinto-Ledezma, J. Open questions in understanding the adaptive significance of plant functional trait variation within a single lineage. New Phytol. https://doi.org/10.1111/nph.16652 (2020).
Google Scholar
Cavender-Bares, J., Kitajima, K. & Bazzaz, F. A. Multiple trait associations in relation to habitat differentiation among 17 Floridian oak species. Ecol. Monogr. 74, 635–662 (2004).
Google Scholar
Menges, E. S. & Hawkes, C. V. Interactive effects of fire and microhabitat on plants of Florida scrub. Ecol. Appl. 8, 935–946 (1998).
Google Scholar
Calabrese, J. M., Certain, G., Kraan, C. & Dormann, C. F. Stacking species distribution models and adjusting bias by linking them to macroecological models: Stacking species distribution models. Glob. Ecol. Biogeogr. 23, 99–112 (2014).
Google Scholar
Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl. Acad. Sci. 104, 13384–13389 (2007).
Google Scholar
Pinto-Ledezma, J. N., Jahn, A. E., Cueto, V. R., Diniz-Filho, J. A. F. & Villalobos, F. Drivers of phylogenetic assemblage structure of the Furnariides, a widespread clade of lowland neotropical birds. Am. Nat. 193, E41–E56 (2019).
Google Scholar
Gamon, J. A. et al. Consideration of scale in remote sensing of biodiversity. In Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J., Gamon, J. A. & Townsend, P. A.) 425–447 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-33157-3_16.
Pollock, L. J. et al. Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods Ecol. Evol. 5, 397–406 (2014).
Google Scholar
Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).
Google Scholar
Ovaskainen, O. Joint Species Distribution Modelling: with Applications in R (Cambridge University Press, 2020).
Google Scholar
Poggiato, G. et al. On the interpretations of joint modeling in community ecology. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.01.002 (2021).
Google Scholar
Wilkinson, D. P., Golding, N., Guillera-Arroita, G., Tingley, R. & McCarthy, M. A. Defining and evaluating predictions of joint species distribution models. Methods Ecol. Evol. 12, 394–404 (2021).
Google Scholar
Bystrova, D. et al. Clustering species with residual covariance matrix in joint species distribution models. Front. Ecol. Evol. 9, 601384 (2021).
Google Scholar
Mateo, R. G. et al. Hierarchical species distribution models in support of vegetation conservation at the landscape scale. J. Veg. Sci. 30, 386–396 (2019).
Google Scholar
Petitpierre, B. et al. Will climate change increase the risk of plant invasions into mountains?. Ecol. Appl. 26, 530–544 (2016).
Google Scholar
Cavender-Bares, J. et al. Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales. Am. J. Bot. 104, 966–969 (2017).
Google Scholar
Schweiger, A. K. et al. Spectral Niches Reveal Taxonomic Identity and Complementarity in Plant Communities. (2020) https://doi.org/10.1101/2020.04.24.060483.
Cavender-Bares, J. et al. Associations of leaf spectra with genetic and phylogenetic variation in oaks: Prospects for remote detection of biodiversity. Remote Sens. 8, 221 (2016).
Google Scholar
Meireles, J. E. et al. Leaf reflectance spectra capture the evolutionary history of seed plants. New Phytol. 228, 485–493 (2020).
Google Scholar
Williams, L. J. et al. Remote spectral detection of biodiversity effects on forest biomass. Nat. Ecol. Evol. 5, 46–54 (2021).
Google Scholar
Alonso, K. et al. Data products, quality and validation of the DLR earth sensing imaging spectrometer (DESIS). Sensors 19, 4471 (2019).
Google Scholar
Stavros, E. N. et al. ISS observations offer insights into plant function. Nat. Ecol. Evol. 1, 0194 (2017).
Google Scholar
Féret, J.-B. & Asner, G. P. Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol. Appl. 24, 1289–1296 (2014).
Google Scholar
Cavender-Bares, J. et al. BII-Implementation: The causes and consequences of plant biodiversity across scales in a rapidly changing world. Res. Ideas Outcomes 7, e63850 (2021).
Google Scholar
Hipp, A. L. et al. Sympatric parallel diversification of major oak clades in the Americas and the origins of Mexican species diversity. New Phytol. 217, 439–452 (2018).
Google Scholar
Cavender-Bares, J. et al. Phylogeny and biogeography of the American live oaks (Quercus subsection Virentes): A genomic and population genetics approach. Mol. Ecol. 24, 3668–3687 (2015).
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).
Google Scholar
Lobo, J. M., Jiménez-Valverde, A. & Hortal, J. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33, 103–114 (2010).
Google Scholar
Barnett, D. T. et al. The plant diversity sampling design for The National Ecological Observatory Network. Ecosphere 10, e02603 (2019).
Deblauwe, V. et al. Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics: Remotely sensed climate data for tropical species distribution models. Glob. Ecol. Biogeogr. 25, 443–454 (2016).
Google Scholar
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. R package version 1.3. https://CRAN.R-project.org/package=dismo (2020).
Myneni, R. B. et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).
Google Scholar
Gower, S. T., Kucharik, C. J. & Norman, J. M. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sens. Environ. 70, 29–51 (1999).
Google Scholar
Reich, P. B. Key canopy traits drive forest productivity. Proc. R. Soc. B Biol. Sci. 279, 2128–2134 (2012).
Google Scholar
Xiao, Z. et al. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans. Geosci. Remote Sens. 52, 209–223 (2014).
Google Scholar
Soberón, J. Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10, 1115–1123 (2007).
Google Scholar
Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).
Google Scholar
Dubuis, A. et al. Predicting spatial patterns of plant species richness: A comparison of direct macroecological and species stacking modelling approaches: Predicting plant species richness. Divers. Distrib. 17, 1122–1131 (2011).
Google Scholar
Schoener, T. W. Anolis lizards of Bimini: Resource partition in a complex fauna. Ecology 49, 704–726 (1968).
Google Scholar
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).
Google Scholar
Cooper, J. C. & Soberón, J. Creating individual accessible area hypotheses improves stacked species distribution model performance. Glob. Ecol. Biogeogr. 27, 156–165 (2018).
Google Scholar
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many? How to use pseudo-absences in niche modelling?. Methods Ecol. Evol. 3, 327–338 (2012).
Google Scholar
Carlson, C. J. et al. The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock and wildlife. Nat. Microbiol. 4, 1337–1343 (2019).
Google Scholar
Chipman, H. A., George, E. I. & McCulloch, R. E. BART: Bayesian additive regression trees. Ann. Appl. Stat. 4, 266–298 (2010).
Google Scholar
Yen, J. D. L., Thomson, J. R., Vesk, P. A. & Mac Nally, R. To what are woodland birds responding? Inference on relative importance of in-site habitat variables using several ensemble habitat modelling techniques. Ecography 34, 946–954 (2011).
Google Scholar
Carlson, C. J. embarcadero: Species distribution modelling with Bayesian additive regression trees in r. Methods Ecol. Evol. 11, 850–858 (2020).
Google Scholar
Dorie, V. dbarts: Discrete Bayesian Additive Regression Trees Sampler. (2020).
Hastie, T. & Tibshirani, R. Bayesian backfitting. Stat. Sci. 15(3), 196–223 (2000).
Google Scholar
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS): Assessing the accuracy of distribution models. J. Appl. Ecol. 43, 1223–1232 (2006).
Google Scholar
Di Cola, V. et al. ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).
Google Scholar
Hipp, A. L. et al. Genomic landscape of the global oak phylogeny. New Phytol. 226, 1198–1212 (2020).
Google Scholar
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).
Google Scholar
Kruschke, J. Doing Bayesian Data Analysis, 2nd Ed. (2014).
Mills, J. A. & Parent, O. Bayesian MCMC estimation. In Handbook of Regional Science (eds Fischer, M. M. & Nijkamp, P.) 1571–1595 (Springer, Berlin, 2014). https://doi.org/10.1007/978-3-642-23430-9_89.
Google Scholar
Carpenter, B. et al. Stan : A probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).
Google Scholar
Goodrich, B., Gabry, J., Ali, I. & Brilleman, S. rstanarm: Bayesian applied regression modeling via Stan. (2020).
Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Google Scholar
Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).
Google Scholar
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