Abarenkov, K., Somervuo, P., Nilsson, R. H., Kirk, P. M., Huotari, T., Abrego, N. & Ovaskainen, O. (2018). Protax-fungi: A web-based tool for probabilistic taxonomic placement of fungal internal transcribed spacer sequences. New Phytol., 220, 517–525.CrossRefGoogle ScholarPubMed

Abrego, N., Christensen, M., Bässler, C., Ainsworth, A. M. & Heilmann-Clausen, J. (2017b). Understanding the distribution of wood-inhabiting fungi in European beech reserves from species-specific habitat models. Fungal Ecol., 27, 168–174.Google Scholar

Abrego, N., Norberg, A. & Ovaskainen, O. (2017a). Measuring and predicting the influence of traits on the assembly processes of wood-inhabiting fungi. J. Ecol., 105, 1070–1081.Google Scholar

Adams, R. P., Murray, I. & MacKay, D. J. C. (2009). Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. Proceedings of the 26th International Conference on Machine Learning, 9–16.CrossRefGoogle Scholar

Akaike, H. (1974). A new look at the statistical model identification. IEEE Trans. Autom. Contr., 19, 716–723.Google Scholar

Algar, A. C., Kharouba, H. M., Young, E. R. & Kerr, J. T. (2009). Predicting the future of species diversity: Macroecological theory, climate change, and direct tests of alternative forecasting methods. Ecography, 32, 22–33.Google Scholar

Amarasekare, P. & Nisbet, R. (2001). Spatial heterogeneity, source‐sink dynamics, and the local coexistence of competing species. Am. Nat., 158, 572–584.Google Scholar

Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecol., 26, 32–46.Google Scholar

Anderson, M. J., Crist, T. O., Chase, J. M., Vellend, M., Inouye, B. D., Freestone, A. L., Sanders, N. J., Cornell, H. V., Comita, L. S., Davies, K. F., Harrison, S. P., Kraft, N. J. B., Stegen, J. C. & Swenson, N. G. (2011). Navigating the multiple meanings of β diversity: A roadmap for the practicing ecologist. Ecol. Lett., 14, 19–28.Google Scholar

Anderson, T. W. (1951). Estimating linear restrictions on regression coefficients for multivariate normal distributions. Ann. Math. Statist., 22, 327–351.Google Scholar

Araújo, M. B. & Guisan, A. (2006). Five (or so) challenges for species distribution modelling. J. Biogeogr., 33, 1677–1688.Google Scholar

Araújo, M. B. & Luoto, M. (2007). The importance of biotic interactions for modelling species distributions under climate change. Global Ecol. Biogeogr., 16, 743–753.Google Scholar

Araújo, M. B. & Peterson, A. T. (2012). Uses and misuses of bioclimatic envelope modeling. Ecology, 93, 1527–1539.Google Scholar

Araújo, M. B., Anderson, R. P., Márcia Barbosa, A., Beale, C. M., Dormann, C. F., Early, R., Garcia, R. A., Guisan, A., Maiorano, L., Naimi, B., O’Hara, R. B., Zimmermann, N. E. & Rahbek, C. (2019). Standards for distribution models in biodiversity assessments. Sci. Adv., 5(1), eaat4858.Google Scholar

Arrhenius, O. (1921). Species and area. J. Ecol., 9, 95–99.Google Scholar

Asher, J., Warren, M., Fox, R., Harding, P., Jeffcoate, G. & Jeffcoate, S. (2001). Millennium Atlas of Butterflies in Britain and Ireland. Oxford University Press.Google Scholar

Barberán, A., Ladau, J., Leff, J. W., Pollard, K. S., Menninger, H. L., Dunn, R. R. & Fierer, N. (2015). Continental-scale distributions of dust-associated bacteria and fungi. Proc. Natl. Acad. Sci. USA, 112, 5756–5761.Google Scholar

Barry, S. C. & Welsh, A. H. (2002). Generalized additive modelling and zero inflated count data. Ecol. Model., 157, 179–188.Google Scholar

Bates, D., Mächler, M., Bolker, B. & Walker, S. (2015). Fitting linear mixed-effects models using lme4. J. Stat. Soft., 67(1), 1–48.Google Scholar

Bauman, D., Drouet, T., Fortin, M. & Dray, S. (2018). Optimizing the choice of a spatial weighting matrix in eigenvector-based methods. Ecology, 99, 2159–2166.Google Scholar

Beale, C. M., Lennon, J. J., Elston, D. A., Brewer, M. J. & Yearsley, J. M. (2007). Red herrings remain in geographical ecology: A reply to Hawkins et al. (2007). Ecography, 30, 845–847.Google Scholar

Beaulieu, J. M., Jhwueng, D., Boettiger, C. & O’Meara, B. C. (2012). Modeling stabilizing selection: Expanding the Ornstein–Uhlenbeck model of adaptive evolution. Evolution, 66, 2369–2383.Google Scholar

Beck, J., Böller, M., Erhardt, A. & Schwanghart, W. (2014). Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecol. Inform., 19, 10–15.Google Scholar

Beissinger, S. R., Iknayan, K. J., Guillera-Arroita, G., Zipkin, E. F., Dorazio, R. M., Royle, J. A. & Kéry, M. (2016). Incorporating imperfect detection into joint models of communities: A response to Warton et al. Methods Ecol. Evol., 31, 736–737.Google Scholar

Bhattacharya, A. & Dunson, D. B. (2011). Sparse Bayesian infinite factor models. Biometrika, 98, 291–306.Google Scholar

Blonder, B. (2018). Hypervolume concepts in niche- and trait-based ecology. Ecography, 41, 1441–1455.Google Scholar

Boakes, E. H., McGowan, P. J. K., Fuller, R. A., Chang-qing, D., Clark, N. E., O’Connor, K. & Mace, G. M. (2010). Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS. Biol., 8, 1–11.Google Scholar

Bohmann, K., Evans, A., Gilbert, M. T. P., Carvalho, G. R., Creer, S., Knapp, M., Yu, D. W. & de Bruyn, M. (2014). Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol., 29, 358–367.Google Scholar

Bokma, F., Bokma, J. & Mönkkönen, M. (2001). Random processes and geographic species richness patterns: Why so few species in the North?. Ecography, 24, 43–49.CrossRefGoogle Scholar

Bolker, B. M. (2008). Ecological Models and Data in R. Princeton University Press.Google Scholar

Booth, T. H., Nix, H. A., Busby, J. R. & Hutchinson, M. F. (2014). Bioclim: The first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Diversity Distrib., 20, 1–9.Google Scholar

Borcard, D. & Legendre, P. (2002). All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model., 153, 51–68.Google Scholar

Borcard, D., Gillet, F. & Legendre, P. (2011). Numerical Ecology with R. Springer.Google Scholar

Boulangeat, I., Gravel, D. & Thuiller, W. (2012). Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances. Ecol. Lett., 15, 584–593.Google Scholar

Box, G. E. P. (1976). Science and statistics. J. Am. Stat. Assoc., 71, 791–799.Google Scholar

Box, G. E. P. (1979). Robustness in the strategy of scientific model building. Eds. Launer, Robert L. & Wilkinson, Graham N.. In: Robustness in Statistics, Academic Press, 201–236.CrossRefGoogle Scholar

Bray, J. R. & Curtis, J. T. (1957). An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr., 27, 325–349.Google Scholar

Breiner, F. T., Guisan, A., Bergamini, A. & Nobis, M. P. (2015). Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol., 6, 1210–1218.CrossRefGoogle Scholar

Brooker, R. W., Maestre, F. T., Callaway, R. M., Lortie, C. L., Cavieres, L. A., Kunstler, G., Liancourt, P., Tielbörger, K., Travis, J. M. J., Anthelme, F., Armas, C., Coll, L., Corcket, E., Delzon, S., Forey, E., Kikvidze, Z., Olofsson, J., Pugnaire, F., Quiroz, C. L., Saccone, P., Schiffers, K., Seifan, M., Touzard, B. & Michalet, R. (2008). Facilitation in plant communities: The past, the present, and the future. J. Ecol., 96, 18–34.Google Scholar

Brooks, S. P. & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat., 7, 434–455.Google Scholar

Brotons, L., Herrando, S. & Pla, M. (2007). Updating bird species distribution at large spatial scales: Applications of habitat modelling to data from long-term monitoring programs. Divers. Distrib., 13, 276–288.Google Scholar

Brown, A. M., Warton, D. I., Andrew, N. R., Binns, M., Cassis, G. & Gibb, H. (2014). The fourth-corner solution: Using predictive models to understand how species traits interact with the environment. Methods Ecol. Evol., 5, 344–352.Google Scholar

Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. (2003). Inclusion of facilitation into ecological theory. Trends Ecol. Evol., 18, 119–125.Google Scholar

Brus, D. J., Hengeveld, G. M., Walvoort, D. J. J., Goedhart, P. W., Heidema, A. H., Nabuurs, G. J. & Gunia, K. (2012). Statistical mapping of tree species over Europe. Eur. J. Forest. Res., 131, 145–157.Google Scholar

Burnham, K. P. & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer.Google Scholar

Burns, A. R., Stephens, W. Z., Stagaman, K., Wong, S., Rawls, J. F., Guillemin, K. & Bohannan, B. J. M. (2015). Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J., 10, 655.Google Scholar

Bush, A., Sollmann, R., Wilting, A., Bohmann, K., Cole, B., Balzter, H., Martius, C., Zlinszky, A., Calvignac-Spencer, S., Cobbold, C. A., Dawson, T. P., Emerson, B. C., Ferrier, S., Gilbert, M. T., Herold, M., Jones, L., Leendertz, F. H., Matthews, L., Millington, J. D. A., Olson, J. R., Ovaskainen, O., Raffaelli, D., Reeve, R., Rödel, M., Rodgers, T. W., Snape, S., Visseren-Hamakers, I., Vogler, A. P., White, P. C. L., Wooster, M. J. & Yu, D. W. (2017). Connecting Earth observation to high-throughput biodiversity data. Nature Ecol. Evol., 1, 0176.Google Scholar

Cadotte, M. W., Arnillas, C. A., Livingstone, S. W. & Yasui, S. E. (2015). Predicting communities from functional traits. Trends Ecol. Evol., 30, 510–511.Google Scholar

Calabrese, J. M., Certain, G., Kraan, C. & Dormann, C. F. (2014). Stacking species distribution models and adjusting bias by linking them to macroecological models. Global Ecol. Biogr., 23, 99–112.Google Scholar

Cale, W. G., Henebry, G. M. & Yeakley, J. A. (1989). Inferring process from pattern in natural communities. Bioscience, 39, 600–605.Google Scholar

Carpenter, S. R. (1996). Microcosm experiments have limited relevance for community and ecosystem ecology. Ecology, 77, 677–680.Google Scholar

Ceballos, G., Ehrlich, P. R., Barnosky, A. D., García, A., Pringle, R. M. & Palmer, T. M. (2015). Accelerated modern human-induced species losses: Entering the sixth mass extinction. Sci. Adv. 1(5), e1400253.Google Scholar

Charrad, M., Ghazzali, N., Boiteau, V. & Niknafs, A. (2014). NbClust: An R package for determining the relevant number of clusters in a data set. J. Stat. Softw., 61(6), 1–36.Google Scholar

Chase, J. M. & Leibold, M. A. (2003). Ecological Niches: Linking Classical and Contemporary Approaches. University of Chicago Press.Google Scholar

Chase, J. M., McGill, B. J., Thompson, P. L., Antão, L. H., Bates, A. E., Blowes, S. A., Dornelas, M., Gonzalez, A., Magurran, A. E., Supp, S. R., Winter, M., Bjorkman, A. D., Bruelheide, H., Byrnes, J. E. K., Cabral, J. S., Elahi, R., Gomez, C., Guzman, H. M., Isbell, F., Myers-Smith, I., Jones, H. P., Hines, J., Vellend, M., Waldock, C. & O’Connor, M. (2019). Species richness change across spatial scales. Oikos, 128, 1079–1091.Google Scholar

Chave, J., Muller‐Landau, H. & Levin, S. (2002). Comparing classical community models: Theoretical consequences for patterns of diversity. Am. Nat., 159, 1–23.Google Scholar

Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y. & Li, Y. (2018). Xgboost: Extreme gradient boosting. R package version 0.71.2.Google Scholar

Clark, J. S., Gelfand, A. E., Woodall, C. W. & Zhu, K. (2014). More than the sum of the parts: Forest climate response from joint species distribution models. Ecol. Appl., 24, 990–999.Google Scholar

Clark, J. S., Nemergut, D., Seyednasrollah, B., Turner, P. J. & Zhang, S. (2017). Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data. Ecol. Monogr., 87, 34–56.Google Scholar

Clements, F. E. (1916). Plant Succession: Analysis of the Development of Vegetation. Publication no. 242 . Carnegie Institute of Washington.Google Scholar

Clements, F. E. (1936). Nature and structure of the climax. J. Ecol., 24, 252–284.Google Scholar

Cornelissen, J. H. C., Lavorel, S., Garnier, E., Díaz, S., Buchmann, N., Gurvich, D. E., Reich, P. B., Steege, H., Morgan, H. D., Heijden, M. G. A., Pausas, J. G. & Poorter, H. (2003). A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot., 51, 335–380.Google Scholar

Cramp, S., Simmons, K. E. L. & Perrins, C. M. (1977–1994). Handbook of the Birds of Europe, the Middle East, and North Africa: The Birds of the Western Palearctic. Oxford University Press.Google Scholar

Crookston, N. & Finley, A. (2008). yaImpute: An R package for kNN imputation. J. Stat. Softw., 23(10), 1–16.Google Scholar

D’Amen, M., Rahbek, C., Zimmermann, N. E. & Guisan, A. (2017). Spatial predictions at the community level: From current approaches to future frameworks. Biol. Rev., 92, 169–187.Google Scholar

Damgaard, C. (2015). Modelling pin-point cover data of complementary vegetation classes. Ecol. Inform., 30, 179–184.Google Scholar

Damgaard, C. (2008). Modelling pin-point plant cover data along an environmental gradient. Ecol. Model., 214, 404–410.CrossRefGoogle Scholar

Damschen, E. I., Harrison, S. & Grace, J. B. (2010). Climate change effects on an endemic-rich edaphic flora: Resurveying Robert H. Whittaker’s Siskiyou sites (Oregon, USA). Ecology, 91, 3609–3619.Google Scholar

Damuth, J. (1981). Population density and body size in mammals. Nature, 290, 699–700.Google Scholar

De’ath, G., Therneau, T. M., Atkinson, B., Ripley, B. & Oksanen, J. (2014). Mvpart: Multivariate partitioning. R package version 1.6-2.Google Scholar

Dengler, J. (2009). Which function describes the species–area relationship best? A review and empirical evaluation. J. Biogeogr., 36, 728–744.Google Scholar

Diamond, J. M. (1975). Assembly of Species Communities. Eds. Cody, M. L., & Diamond, J. M.. In: Ecology and Evolution of Communities. Harvard University Press, 342–444.Google Scholar

Dolédec, S., Chessel, D., ter Braak, C. J. F. & Champely, S. (1996). Matching species traits to environmental variables: A new three-table ordination method. Environ. Ecol. Stat., 3, 143–166.Google Scholar

Dormann, C. F. (2007a). Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecol. Biogeogr., 16, 129–138.Google Scholar

Dormann, C. F. (2007b). Promising the future? global change projections of species distributions. Basic Appl. Ecol., 8, 387–397.Google Scholar

Dormann, C. F., Bobrowski, M., Dehling, D. M., Harris, D. J., Hartig, F., Lischke, H., Moretti, M. D., Pagel, J., Pinkert, S., Schleuning, M., Schmidt, S. I., Sheppard, C. S., Steinbauer, M. J., Zeuss, D. & Kraan, C. (2018). Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Global Ecol. Biogeogr., 27, 1004–1016.Google Scholar

Dormann, C. F., McPherson, J. M., Araújo, M. B., Bivand, R., Bolliger, J., Carl, G., Davies, R. G., Hirzel, A., Jetz, W., Daniel Kissling, W., Kühn, I., Ohlemüller, R., Peres-Neto, P. R., Reineking, B., Schröder, B., Schurr, F. M. & Wilson, R. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography, 30, 609–628.CrossRefGoogle Scholar

Dormann, C. F., Schymanski, S. J., Cabral, J., Chuine, I., Graham, C., Hartig, F., Kearney, M., Morin, X., Römermann, C., Schröder, B. & Singer, A. (2012). Correlation and process in species distribution models: Bridging a dichotomy. J. Biogeogr., 39, 2119–2131.Google Scholar

Dray, S., Dufour, A. & Thioulouse, J. (2018). Ade4: Analysis of ecological data: Exploratory and Euclidean methods in environmental sciences.Google Scholar

Dray, S., Bauman, D., Blanchet, G., Borcard, D., Clappe, S., Guenard, G., Jombart, T., Larocque, G., Legendre, P., Madi, N. & Wagner, H. H. (2019). Adespatial: Multivariate multiscale spatial analysis. R package version 0.3-7.Google Scholar

Dray, S. & Legendre, P. (2008). Testing the species traits–environment relationships: The fourth-corner problem revisited. Ecology, 89, 3400–3412.Google Scholar

Dray, S., Legendre, P. & Peres-Neto, P. R. (2006). Spatial modelling: A comprehensive framework for Principal Coordinate Analysis of Neighbour Matrices (PCNM). Ecol. Model., 196, 483–493.Google Scholar

Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. (2012). Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol., 29, 1969–1973.Google Scholar

Duan, L. L., Johndrow, J. E. & Dunson, D. B. (2018). Scaling up data augmentation MCMC via calibration. J. Mach. Learn. Res., 19, 2575–2608.Google Scholar

Dunson, D. B. (2018). Statistics in the big data era: Failures of the machine. Stat. Probabil. Lett., 136, 4–9.Google Scholar

Dunstan, P. K., Foster, S. D. & Darnell, R. (2011). Model based grouping of species across environmental gradients. Ecol. Model., 222, 955–963.Google Scholar

Durante, D., Scarpa, B. & Dunson, D. B. (2014). Locally adaptive factor processes for multivariate time series. J. Mach. Learn. Res., 15, 1493–1522.Google Scholar

Elith, J. & Leathwick, J. R. (2009). Species distribution models: Ecological explanation and prediction across space and time. Annu .Rev. Ecol. Evol. Syst., 40, 677–697.Google Scholar

Elton, C. (1966). The Patterns of Animal Communities. 1st edn. John Wiley & Sons, London; Methuen; New York.Google Scholar

Elton, C. (1927). Animal Ecology. University of Chicago Press.Google Scholar

European Environment Agency. (2016). Corine land cover (CLC) 2012, version 18.5.1. available at http://Land.copernicus.eu/pan-european/corine-land-cover/clc-2012/viewGoogle Scholar

Fauth, J. E., Bernardo, J., Camara, M., Resetarits, W. J., Van Buskirk, J. & McCollum, S. A. (1996). Simplifying the jargon of community ecology: A conceptual approach. Am. Nat., 147, 282–286.Google Scholar

Ferrier, S. & Guisan, A. (2006). Spatial modelling of biodiversity at the community level. J. Appl. Ecol., 43, 393–404.Google Scholar

Fick, S. E. & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol., 37, 4302–4315.Google Scholar

Fisher, R. A., Corbet, A. S. & Williams, C. B. (1943). The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol., 12, 42–58.Google Scholar

Fithian, W., Elith, J., Hastie, T. & Keith, D. A. (2015). Bias correction in species distribution models: Pooling survey and collection data for multiple species. Methods Ecol. Evol., 6, 424–438.CrossRefGoogle ScholarPubMed

Foster, S. D., Givens, G. H., Dornan, G. J., Dunstan, P. K. & Darnell, R. (2013). Modelling biological regions from multi-species and environmental data. Environmetrics, 24, 489–499.Google Scholar

Fox, E. B. & Dunson, D. B. (2015). Bayesian nonparametric covariance regression. J. Mach. Learn. Res., 16, 2501–2542.Google Scholar

Fox, G. A., Negrete-Yankelevich, S. & Sosa, V. J. (2015). Ecological Statistics: Contemporary Theory and Application. Oxford University Press.Google Scholar

Franklin, J. (2009). Mapping Species Distributions, Spatial Inference and Prediction. Cambridge University Press.Google Scholar

Garbelotto, M. & Gonthier, P. (2013). Biology, epidemiology, and control of Heterobasidion species worldwide. Annu. Rev. Phytopathol., 51, 39–59.Google Scholar

Gause, G. F. (1934). The Struggle for Existence. Williams and Wilkins.Google Scholar

GBIF (2018) The Global Biodiversity Information Facility. Available at: www.gbif.org/what-is-gbifGoogle Scholar

Gelfand, A. E., Fuentes, M., Hoeting, J. A. & Smith, R. L. (2019). Handbook of Environmental and Ecological Statistics. Chapman & Hall/CRC Press.Google Scholar

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis. 3rd edn. Chapman & Hall.Google Scholar

Gelman, A. & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statist. Sci., 7, 457–472.Google Scholar

Geweke, J. (1996). Bayesian reduced rank regression in econometrics. J. Econom., 75, 121–146.Google Scholar

Geweke, J. & Zhou, G. (1996). Measuring the price of the arbitrage pricing theory. Rev. Financ. Stud., 9, 557–587.Google Scholar

Gleason, H. A. (1926). The individualistic concept of the plant association. J. Torrey Bot. Soc., 53, 7–26.Google Scholar

Gneiting, T. & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Am. Stat., 102, 359–378.Google Scholar

Goldberg, D. E. (1990). Components of Resource Competition in Plant Communities. Eds. Grace, J. B. & Tilman, D.). In: Perspectives on Plant Competition ,Academic Press, 27–49.Google Scholar

Golding, N. & Harris, D. J. (2015). BayesComm: Bayesian community ecology analysis. R package version 0.1-1.Google Scholar

Gonçalves, F. B. & Gamerman, D. (2018). Exact Bayesian inference in spatiotemporal cox processes driven by multivariate Gaussian processes. J. R. Stat. Soc. B, 80, 157–175.Google Scholar

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ., 202, 18–27.Google Scholar

Gotelli, N., Hart, E. & Ellison, A. (2015). EcoSimR: Null model analysis for ecological data. R package version 0.1.0.Google Scholar

Gotelli, N. J. (2000). Null model analysis of species co-occurrence patterns. Ecology, 81, 2606–2621.Google Scholar

Gotelli, N. J., Anderson, M. J., Arita, H. T., Chao, A., Colwell, R. K., Connolly, S. R., Currie, D. J., Dunn, R. R., Graves, G. R., Green, J. L., Grytnes, J., Jiang, Y., Jetz, W., Kathleen Lyons, S., McCain, C. M., Magurran, A. E., Rahbek, C., Rangel, T. F. L. V. B., Soberón, J., Webb, C. O. & Willig, M. R. (2009). Patterns and causes of species richness: A general simulation model for macroecology. Ecol. Lett., 12, 873–886.Google Scholar

Götzenberger, L., de Bello, F., Bråthen, K. A., Davison, J., Dubuis, A., Guisan, A., Lepš, J., Lindborg, R., Moora, M., Pärtel, M., Pellissier, L., Pottier, J., Vittoz, P., Zobel, K. & Zobel, M. (2012). Ecological assembly rules in plant communities-approaches, patterns and prospects. Biol. Rev., 87, 111–127.Google Scholar

Gower, J. C. (1966). Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.Google Scholar

Grafen, A. & Hails, R. (2002). Modern Statistics for the Life Sciences. Oxford University Press.Google Scholar

Grenouillet, G., Buisson, L., Casajus, N. & Lek, S. (2011). Ensemble modelling of species distribution: The effects of geographical and environmental ranges. Ecography, 34, 9–17.Google Scholar

Grime, J. P. (1973). Competitive exclusion in herbaceous vegetation. Nature, 242, 344–347.Google Scholar

Grinnell, J. (1917). The niche-relationships of the California thrasher. The Auk, 34, 427–433.Google Scholar

Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. (2011). Multimodel inference in ecology and evolution: Challenges and solutions. J. Evol. Biol., 24, 699–711.Google Scholar

Guillera-Arroita, G. (2017). Modelling of species distributions, range dynamics and communities under imperfect detection: Advances, challenges and opportunities. Ecography, 40: 281–295.Google Scholar

Guisan, A., Edwards, T. C. & Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecol. Model., 157, 89–100.Google Scholar

Guisan, A. & Rahbek, C. (2011). SESAM – A new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. J. Biogeogr., 38, 1433–1444.Google Scholar

Guisan, A. & Thuiller, W. (2005). Predicting species distribution: Offering more than simple habitat models. Ecol. Lett., 8, 993–1009.Google Scholar

Guisan, A., Thuiller, W. & Zimmermann, N. (2017). Habitat Suitability and Distribution Models: With Applications in R. Cambridge University Press.Google Scholar

Guisan, A. & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecol. Model., 135, 147–186.Google Scholar

Hanski, I. (2016). Messages from Islands: A Global Biodiversity Tour. 1st edn. University of Chicago Press.Google Scholar

Harris, D. J. (2015). Generating realistic assemblages with a joint species distribution model. Methods Ecol. Evol., 6, 465–473.Google Scholar

Harvey, P. H. & Pagel, M. D. (1991). The Comparative Method in Evolutionary Biology. 1st edn. Oxford University Press.Google Scholar

Hassell, M. P. (1975). Density-dependence in single-species populations. J. Anim. Ecol., 44, 283–295.Google Scholar

Hawkins, B. A., Field, R., Cornell, H. V., Currie, D. J., Guégan, J., Kaufman, D. M., Kerr, J. T., Mittelbach, G. G., Oberdorff, T., O’Brien, E. M., Porter, E. E. & Turner, J. R. G. (2003). Energy, water, and broad-scale geographic patterns of species richness. Ecology, 84, 3105–3117.Google Scholar

Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D. & New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res., 113.Google Scholar

He, Q., Bertness, M. D. & Altieri, A. H. (2013). Global shifts towards positive species interactions with increasing environmental stress. Ecol. Lett., 16, 695–706.Google Scholar

Hebert, P. D., Alina, C., Ball, S. L. & deWaard, J. R. (2003). Biological identifications through DNA barcodes. Proc. R. Soc. Lond. Ser. B Biol. Sci., 270, 313–321.Google Scholar

Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol., 25, 1965–1978.Google Scholar

Hijmans, R. J. & Graham, C. H. (2006). The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biol., 12, 2272–2281.Google Scholar

Hijmans, R. J., Phillips, S. J., Leathwick, J. R. & Elith, J. (2017). Dismo: Species distribution modeling. R package version 1.Google Scholar

Hill, N. A., Foster, S. D., Duhamel, G., Welsford, D., Koubbi, P. & Johnson, C. R. (2017). Model-based mapping of assemblages for ecology and conservation management: A case study of demersal fish on the Kerguelen plateau. Divers. Distrib., 23, 1216–1230.Google Scholar

Holmes, I., Harris, K. & Quince, C. (2012). Dirichlet multinomial mixtures: Generative models for microbial metagenomics. PLoS ONE, 7, 1–15.Google Scholar

Holyoak, M., Leibold, M. A. & Holt, R. D. (2005). Metacommunities: Spatial Dynamics and Ecological Communities. 1st edn. The University of Chicago Press.Google Scholar

Hubbell, S. P. (2001). The Unified Neutral Theory of Biodiversity and Biogeography. Princeton Univ. Press.Google Scholar

Hui, F. K. C. (2017). Boral: Bayesian ordination and regression AnaLysis. R package version 1.4.Google Scholar

Hui, F. K. C., Taskinen, S., Pledger, S., Foster, S. D. & Warton, D. I. (2015). Model-based approaches to unconstrained ordination. Methods Ecol. Evol., 6, 399–411.Google Scholar

Hui, F. K. C., Warton, D. I., Foster, S. D. & Dunstan, P. K. (2013). To mix or not to mix: Comparing the predictive performance of mixture models vs. separate species distribution models. Ecology, 94, 1913–1919.Google Scholar

Hutchinson, G. E. (1959). Homage to Santa Rosalia or why are there so many kinds of animals. Am. Nat., 93, 145–159.Google Scholar

iNaturalist. (2019) iNaturalist research-grade observations [WWW document]. www.inaturalist.org.Google Scholar

Ives, A. R. & Helmus, M. R. (2011). Generalized linear mixed models for phylogenetic analyses of community structure. Ecol. Monogr., 81, 511–525.Google Scholar

Izenman, A. J. (1975). Reduced-rank regression for the multivariate linear model. J. Multivariate Anal., 5, 248–264.Google Scholar

Jarzyna, M. A. & Jetz, W. (2018). Taxonomic and functional diversity change is scale dependent. Nat. Commun., 9, 2565.Google Scholar

Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. (2012). The global diversity of birds in space and time. Nature, 491, 444.Google Scholar

Johnson, J. B. & Omland, K. S. (2004). Model selection in ecology and evolution. Trends Ecol. Evol., 19, 101–108.Google Scholar

Johnston, A., Ausden, M., Dodd, A. M., Bradbury, R. B., Chamberlain, D. E., Jiguet, F., Thomas, C. D., Cook, A. S. C. P., Newson, S. E., Ockendon, N., Rehfisch, M. M., Roos, S., Thaxter, C. B., Brown, A., Crick, H. Q. P., Douse, A., McCall, R. A., Pontier, H., Stroud, D. A., Cadiou, B., Crowe, O., Deceuninck, B., Hornman, M. & Pearce-Higgins, J. (2013). Observed and predicted effects of climate change on species abundance in protected areas. Nat. Clim. Change, 3, 1055.Google Scholar

Jordano, P. (1987). Patterns of mutualistic interactions in pollination and seed dispersal: Connectance, dependence asymmetries, and coevolution. Am. Nat., 129, 657–677.Google Scholar

Jung, V., Violle, C., Mondy, C., Hoffmann, L. & Muller, S. (2010). Intraspecific variability and trait-based community assembly. J. Ecol., 98, 1134–1140.Google Scholar

Kassambara, A. & Mundt, F. (2019). Factoextra: Extract and visualize the results of multivariate data analyses. R package version 1.0.6.Google Scholar

Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H. C., Violle, C., Harrison, S. P., van Bodegom, P. M., Reichstein, M., Enquist, B. J., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., Atkin, O., Bahn, M., Baker, T. R., Baldocchi, D., Bekker, R., Blanco, C. C., Blonder, B., Bond, W. J., Bradstock, R., Bunker, D. E., Casanoves, F., Cavender-Bares, J., Chambers, J. Q., Chapin Iii, F.S., Chave, J., Coomes, D., Cornwell, W. K., Craine, J. M., Dobrin, B. H., Duarte, L., Durka, W., Elser, J., Esser, G., Estiarte, M., Fagan, W. F., Fang, J., Fernández-Méndez, F., Fidelis, A., Finegan, B., Flores, O., Ford, H., Frank, D., Freschet, G. T., Fyllas, N. M., Gallagher, R. V., Green, W. A., Gutierrez, A. G., Hickler, T., Higgins, S. I., Hodgson, J. G., Jalili, A., Jansen, S., Joly, C. A., Kerkhoff, A. J., Kirkup, D., Kitajima, K., Kleyer, M., Klotz, S., Knops, J. M. H., Kramer, K., Kühn, I., Kurokawa, H., Laughlin, D., Lee, T. D., Leishman, M., Lens, F., Lenz, T., Lewis, S. L., Lloyd, J., Llusià, J., Louault, F., Ma, S., Mahecha, M. D., Manning, P., Massad, T., Medlyn, B. E., Messier, J., Moles, A. T., Müller, S. C., Nadrowski, K., Naeem, S., Niinemets, Ü, Nöllert, S., Nüske, A., Ogaya, R., Oleksyn, J., Onipchenko, V. G., Onoda, Y., Ordoñez, J., Overbeck, G., Ozinga, W. A., Patiño, S., Paula, S., Pausas, J. G., Peñuelas, J., Phillips, O. L., Pillar, V., Poorter, H., Poorter, L., Poschlod, P., Prinzing, A., Proulx, R., Rammig, A., Reinsch, S., Reu, B., Sack, L., Salgado-Negret, B., Sardans, J., Shiodera, S., Shipley, B., Siefert, A., Sosinski, E., Soussana, J. -., Swaine, E., Swenson, N., Thompson, K., Thornton, P., Waldram, M., Weiher, E., White, M., White, S., Wright, S. J., Yguel, B., Zaehle, S., Zanne, A. E. & Wirth, C. (2011). TRY – a global database of plant traits. Global Change Biol., 17, 2905–2935.Google Scholar

Kearney, M. (2006). Habitat, environment and niche: What are we modelling?. Oikos, 115, 186–191.Google Scholar

Keddy, P. A. (1992). Assembly and response rules: Two goals for predictive community ecology. J. Veg. Sci., 3, 157–164.Google Scholar

Kessler, M. (2009). The impact of population processes on patterns of species richness: Lessons from elevational gradients. Basic Appl. Ecol., 10, 295–299.Google Scholar

Kingsland, S. E. (1986). Modeling nature: Episodes in the history of population ecology. J. Hist. Biol., 19(2), 313–314.Google Scholar

Kissling, W. D., Dormann, C. F., Groeneveld, J., Hickler, T., Kühn, I., McInerny, G. J., Montoya, J. M., Römermann, C., Schiffers, K., Schurr, F. M., Singer, A., Svenning, J., Zimmermann, N. E. & O’Hara, R. B. (2012). Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents. J. Biogeogr., 39, 2163–2178.Google Scholar

Köppen, W. (1884). Die wärmezonen der erde, nach der dauer der heissen, gemässigten und kalten zeit und nach der wirkung der wärme auf die organische welt betrachtet [the thermal zones of the Earth according to the duration of hot, moderate and cold periods and to the impact of heat on the organic world]. Meteorologische Zeitschrift (published 2011), 20(3), 351–360.Google Scholar

Krebs, C. J. (1972). Ecology, the Experimental Analysis of Distribution and Abundance. Harper & Row.Google Scholar

Kukkala, A. S. & Moilanen, A. (2013). Core concepts of spatial prioritisation in systematic conservation planning. Biol. Rev., 88, 443–464.Google Scholar

Ladle, R. J. & Whittaker, R. J. (2011). Conservation Biogeography. Wiley-Blackwell.Google Scholar

Laughlin, D. C., Joshi, C., van Bodegom, P. M., Bastow, Z. A. & Fulé, P. Z. (2012). A predictive model of community assembly that incorporates intraspecific trait variation. Ecol. Lett., 15, 1291–1299.Google Scholar

Lavorel, S. & Garnier, E. (2002). Predicting changes in community composition and ecosystem functioning from plant traits: Revisiting the holy grail. Funct. Ecol., 16, 545–556.Google Scholar

Lawton, J. H. (1999). Are there general laws in ecology?. Oikos, 84, 177–192.Google Scholar

le Roux, P. C., Pellissier, L., Wisz, M. S. & Luoto, M. (2014). Incorporating dominant species as proxies for biotic interactions strengthens plant community models. J. Ecol., 102, 767–775.Google Scholar

Legendre, P. (1993). Spatial autocorrelation: Trouble or new paradigm? Ecology, 74, 1659–1673.Google Scholar

Legendre, P. & Anderson, M. J. (1999). Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr., 69, 1–24.Google Scholar

Legendre, P., Borcard, D. & Peres-Neto, P. (2005). Analyzing beta diversity: Partitioning the spatial variation of community composition data. Ecol. Monogr., 75, 435–450.Google Scholar

Legendre, P., Dale, M. R. T., Fortin, M., Gurevitch, J., Hohn, M. & Myers, D. (2002). The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography, 25, 601–615.Google Scholar

Legendre, P., Galzin, R. & Harmelin-Vivien, M. (1997). Relating behavior to habitat: Solutions to the fourth-corner problem. Ecology, 78, 547–562.Google Scholar

Legendre, P. & Legendre, L. (2012). Numerical Ecology. 3rd edn. Elsevier.Google Scholar

Lehtomäki, J. & Moilanen, A. (2013). Methods and workflow for spatial conservation prioritization using Zonation. Environ. Modell. Softw., 47, 128–37.Google Scholar

Leibold, M. A. (1995). The niche concept revisited: Mechanistic models and community context. Ecology, 76, 1371–1382.Google Scholar

Leibold, M. A. & Chase, J. M. (2018). Metacommunity Ecology. 1st edn. Princeton University Press.Google Scholar

Leibold, M. A., Holyoak, M., Mouquet, N., Amarasekare, P., Chase, J. M., Hoopes, M. F., Holt, R. D., Shurin, J. B., Law, R., Tilman, D., Loreau, M. & Gonzalez, A. (2004). The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett., 7, 601–613.Google Scholar

Levine, S. H. (1976). Competitive interactions in ecosystems. Am. Nat., 110, 903–910.Google Scholar

Liaw, A. & Wiener, M. (2002). Classification and regression by randomForest. R News, 2, 18–22.Google Scholar

Lindström, Å, Green, M., Husby, M., Kålås, J. A. & Lehikoinen, A. (2015). Large-scale monitoring of waders on their boreal and arctic breeding grounds in northern Europe. Ardea, 103(1), 3–15.Google Scholar

Lomolino, M. V., Riddle, B. R., Whittaker, R. J. & Brown, J. H. (2010). Biogeography. 4th edn. Sinauer Associates.Google Scholar

Lou, M. & Golding, G. B. (2012). The effect of sampling from subdivided populations on species identification with DNA barcodes using a Bayesian statistical approach. Mol. Phylogenet. Evol., 65(2), 765–773.Google Scholar

MacArthur, R. H. (1972). Geographical Ecology: Patterns in the Distribution of Species. Princeton University Press.Google Scholar

MacArthur, R. H. & Levins, R. (1967). The limiting similarity, convergence, and divergence of coexisting species. Am. Nat., 101, 377–385.Google Scholar

MacArthur, R. H. & Wilson, E. O. (1967). The Theory of Island Biogeography. Princeton University Press.Google Scholar

Madon, B., Warton, D. I. & Araújo, M. B. (2013). Community-level vs species-specific approaches to model selection. Ecography, 36, 1291–1298.Google Scholar

Magurran, A. E. (2004). Measuring Biological Diversity. 1st edn. Wiley-Blackwell.Google Scholar

Magurran, A. E. & Henderson, P. A. (2003). Explaining the excess of rare species in natural species abundance distributions. Nature, 422, 714.Google Scholar

Margules, C. & Sarkar, S. (2007). Systematic Conservation Planning. Cambridge University Press.Google Scholar

Margules, C. R. & Pressey, R. L. (2000). Systematic conservation planning. Nature, 405, 243–253.Google Scholar

Marincowitz, S., Crous, P. W., Groenewald, J. Z. & Wingfield, M. J. (2008). Microfungi occurring on proteaceae in the fynbos. CBS Fungal Biodiversity Centre.Google Scholar

May, R. M. (1971). Stability in multispecies community models. Math. Biosci., 12, 59–79.Google Scholar

McArdle, B. H. & Anderson, M. J. (2001). Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology, 82, 290–297.Google Scholar

McCune, J. L. (2016). Species distribution models predict rare species occurrences despite significant effects of landscape context. J. Appl. Ecol., 53, 1871–1879.Google Scholar

McGill, B. J. (2003). A test of the unified neutral theory of biodiversity. Nature, 422, 881.Google Scholar

McGill, B. J. (2010). Towards a unification of unified theories of biodiversity. Ecol. Lett., 13, 627–642.Google Scholar

McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. (2006b). Rebuilding community ecology from functional traits. Trends Ecol. Evol., 21, 178–185.Google Scholar

McGill, B. J., Etienne, R. S., Gray, J. S., Alonso, D., Anderson, M. J., Benecha, H. K., Dornelas, M., Enquist, B. J., Green, J. L., He, F., Hurlbert, A. H., Magurran, A. E., Marquet, P. A., Maurer, B. A., Ostling, A., Soykan, C. U., Ugland, K. I. & White, E. P. (2007). Species abundance distributions: Moving beyond single prediction theories to integration within an ecological framework. Ecol. Lett., 10, 995–1015.Google Scholar

McGill, B. J., Maurer, B. A. & Weiser, M. D. (2006a). Empirical evaluation of neutral theory. Ecology, 87, 1411–1423.Google Scholar

Meier, E. S., Edwards, T. C. Jr, Kienast, F., Dobbertin, M. & Zimmermann, N. E. (2011). Co-occurrence patterns of trees along macro-climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L. J. Biogeogr., 38, 371–382.Google Scholar

Merow, C., Smith, M. J., Edwards, T. C. Jr, Guisan, A., McMahon, S. M., Normand, S., Thuiller, W., Wüest, R. O., Zimmermann, N. E. & Elith, J. (2014). What do we gain from simplicity versus complexity in species distribution models?. Ecography, 37, 1267–1281.Google Scholar

Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. & Leisch, F. (2017). e1071: Misc functions of the department of statistics, probability theory group (formerly: E1071). R package version 1.6-8.Google Scholar

Midgley, G. F., Hannah, L., Millar, D., Rutherford, M. C. & Powrie, L. W. (2002). Assessing the vulnerability of species richness to anthropogenic climate change in a biodiversity hotspot. Global Ecol. Biogeogr., 11, 445–451.Google Scholar

Milborrow, S. (2017). Earth: Multivariate adaptive regression splines. R package version 4.5.1.Google Scholar

Miller, J., Damschen, E. & Ives, A. (2018). Data from: Functional traits and community composition: A comparison among community-weighted means, weighted correlations, and multilevel models. Dryad Digital Repository, doi:10.5061/dryad.7gj0s3b.Google Scholar

Miller, J. E. D., Damschen, E. I. & Ives, A. R. (2019). Functional traits and community composition: A comparison among community-weighted means, weighted correlations, and multilevel models. Methods Ecol. Evol., 10, 415–425.Google Scholar

Mitchell, T. J. & Beauchamp, J. J. (1988). Bayesian variable selection in linear regression. J. Am. Stat. Assoc., 83, 1023–1032.Google Scholar

Mod, H. K., le Roux, P. C. & Luoto, M. (2014). Outcomes of biotic interactions are dependent on multiple environmental variables. J. Veg. Sci., 25, 1024–1032.Google Scholar

Mod, H. K., le Roux, P. C., Guisan, A. & Luoto, M. (2015). Biotic interactions boost spatial models of species richness. Ecography, 38, 913–921.Google Scholar

Moilanen, A. (2007). Landscape Zonation, benefit functions and target-based planning: Unifying reserve selection strategies. Biol. Conserv., 134, 571–579.Google Scholar

Møller, J., Syversveen, A. R. & Waagepetersen, R. P. (1998). Log Gaussian Cox processes. Scand. J. Stat., 25, 451–482.Google Scholar

Mönkkönen, M. & Forsman, J. T. (2002). Heterospecific attraction among forest birds: A review. Ornithol. Sci., 1, 41–51.Google Scholar

Morin, P. J. (2011). Community Ecology, 2nd Edition. John Wiley & Sons,Ltd.Google Scholar

Morin, X., Viner, D. & Chuine, I. (2008). Tree species range shifts at a continental scale: New predictive insights from a process-based model. J. Ecol., 96, 784–794.Google Scholar

Nascimento, F. F., Reis, M. D. & Yang, Z. (2017). A biologist’s guide to Bayesian phylogenetic analysis. Nat. Ecol. Evol., 1, 1446–1454.Google Scholar

Nix, H. A. (1986). A Biogeographic Analysis of Australian Elapid Snakes. Ed. Longmore, R. In: Australian Flora and Fauna Series 7 . Bureau of Flora and Fauna, 4–15.Google Scholar

Norberg, A., Abrego, N., Blanchet, F. G., Adler, F. R., Anderson, B. J., Anttila, J., Araújo, M. B., Dallas, T., Dunson, D., Elith, J., Foster, S. D., Fox, R., Franklin, J., Godsoe, W., Guisan, A., O’Hara, B., Hill, N. A., Holt, R. D., Hui, F. K. C., Husby, M., Kålås, J. A., Lehikoinen, A., Luoto, M., Mod, H. K., Newell, G., Renner, I., Roslin, T., Soininen, J., Thuiller, W., Vanhatalo, J., Warton, D., White, M., Zimmermann, N. E., Gravel, D. & Ovaskainen, O. (2019). A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecol. Monogr., 89(3):e01370.Google Scholar

O’Hara, R. B. & Sillanpӓӓ, M. J. (2009). A review of Bayesian variable selection methods: What, how and which. Bayesian Anal., 4, 85–117.Google Scholar

Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O’Hara, B., Simpson, G. L., Solymos, P., Henry, M., Stevens, H. & Wagner, H. (2019). Vegan: Community ecology package. R package version 2.5-5.Google Scholar

Ovaskainen, O., Abrego, N., Halme, P. & Dunson, D. (2016a). Using latent variable models to identify large networks of species-to-species associations at different spatial scales. Methods Ecol. Evol., 7, 549–555.Google Scholar

Ovaskainen, O., Finkelshtein, D., Kutoviy, O., Cornell, S., Bolker, B. & Kondratiev, Y. (2014). A general mathematical framework for the analysis of spatiotemporal point processes. Theor. Ecol., 7, 101–113.Google Scholar

Ovaskainen, O., Hottola, J. & Siitonen, J. (2010). Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions. Ecology, 9, 2514–2521.Google Scholar

Ovaskainen, O., Roy, D. B., Fox, R. & Anderson, B. J. (2016b). Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods Ecol. Evol., 7, 428–436.Google Scholar

Ovaskainen, O., Rybicki, J. & Abrego, N. (2019). What can observational data reveal about metacommunity processes? Ecography, 42, 1877–1886.Google Scholar

Ovaskainen, O., Schigel, D., Ali-Kovero, H., Auvinen, P., Paulin, L., Norden, B. & Norden, J. (2013). Combining high-throughput sequencing with fruit body surveys reveals contrasting life-history strategies in fungi. ISME J., 7, 1696–1709.Google Scholar

Ovaskainen, O. & Soininen, J. (2011). Making more out of sparse data: Hierarchical modeling of species communities. Ecology, 92, 289–295.Google Scholar

Ovaskainen, O., Tikhonov, G., Dunson, D., Grøtan, V., Engen, S., Sæther, B. & Abrego, N. (2017a). How are species interactions structured in species-rich communities? A new method for analysing time-series data. Proc. R. Soc. Lond. Ser. B Biol. Sci., 284.Google Scholar

Ovaskainen, O., Tikhonov, G., Norberg, A., Blanchet, F. G., Duan, L., Dunson, D., Roslin, T. & Abrego, N. (2017b). How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett., 20, 561–576.Google Scholar

Pacifici, K., Reich, B. J., Miller, D. A. W., Gardner, B., Stauffer, G., Singh, S., McKerrow, A. & Collazo, J. A. (2017). Integrating multiple data sources in species distribution modeling: A framework for data fusion*. Ecology, 98, 840–850.Google Scholar

Pearce, J. & Ferrier, S. (2000). Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model., 133, 225–245.Google Scholar

Pearman, P. B., Randin, C. F., Broennimann, O., Vittoz, P., Knaap, W. O. v. d., Engler, R., Lay, G. L., Zimmermann, N. E. & Guisan, A. (2008). Prediction of plant species distributions across six millennia. Ecol. Lett., 11, 357–369.Google Scholar

Pearson, R. G. & Dawson, T. P. (2003). Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful? Global Ecol. Biogeogr., 12, 361–371.Google Scholar

Pearson, R. G., Thuiller, W., Araújo, M. B., Martinez-Meyer, E., Brotons, L., McClean, C., Miles, L., Segurado, P., Dawson, T. P. & Lees, D. C. (2006). Model-based uncertainty in species range prediction. J. Biogeogr., 33, 1704–1711.Google Scholar

Pellissier, L., Albouy, C., Bascompte, J., Farwig, N., Graham, C., Loreau, M., Maglianesi, M. A., Melián, C. J., Pitteloud, C., Roslin, T., Rohr, R., Saavedra, S., Thuiller, W., Woodward, G., Zimmermann, N. E. & Gravel, D. (2018). Comparing species interaction networks along environmental gradients. Biol. Rev., 93, 785–800.Google Scholar

Pellissier, L., Anne Bråthen, K., Pottier, J., Randin, C. F., Vittoz, P., Dubuis, A., Yoccoz, N. G., Alm, T., Zimmermann, N. E. & Guisan, A. (2010). Species distribution models reveal apparent competitive and facilitative effects of a dominant species on the distribution of tundra plants. Ecography, 33, 1004–1014.CrossRefGoogle Scholar

Peres-Neto, P. R., Olden, J. D. & Jackson, D. A. (2001). Environmentally constrained null models: Site suitability as occupancy criterion. Oikos, 93, 110–120.Google Scholar

Peterson, A. T., Soberón, J., Pearson, R. G., Anderson, R. P., Martínez-Meyer, E., Nakamura, M. & Araújo, M. B. (2011). Ecological Niches and Geographic Distributions. 1st edn. Princeton University Press.Google Scholar

Phillips, S. J., Anderson, R. P. & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecol. Model., 190, 231–259.Google Scholar

Plummer, M., Best, N., Cowles, K., Vines, K., Sarkar, D., Bates, D., Almond, R. & Magnusson, A. (2018). Coda: Output Analysis and Diagnostics for MCMC, R Package Version 0.19-2.Google Scholar

Pocheville, A. (2015). Chapter 26. The Ecological Niche: History and Recent Controversies. Eds. Heams, T., Huneman, P. Lecointre, G. & Silberstein, M. In: Handbook of Evolutionary Thinking in the Sciences. Springer, 547–586.Google Scholar

Pollock, L. J., Morris, W. K. & Vesk, P. A. (2012). The role of functional traits in species distributions revealed through a hierarchical model. Ecography, 35, 716–725.Google Scholar

Pollock, L. J., Tingley, R., Morris, W. K., Golding, N., O’Hara, R. B., Parris, K. M., Vesk, P. A. & McCarthy, M. A. (2014). Understanding co-occurrence by modelling species simultaneously with a joint species distribution model (JSDM). Methods Ecol. Evol., 5, 397–406.Google Scholar

Poorter, L. & Bongers, F. (2006). Leaf traits are good predictors of plant performance across 53 rain forest species. Ecology, 87, 1733–1743.Google Scholar

Preston, F. W. (1948). The commonness, and rarity, of species. Ecology, 29, 254–283.Google Scholar

Preston, F. W. (1960). Time and space and the variation of species. Ecology, 41, 612–627.Google Scholar

R Development Core Team. (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.Google Scholar

Randin, C. F., Dirnböck, T., Dullinger, S., Zimmermann, N. E., Zappa, M. & Guisan, A. (2006). Are niche-based species distribution models transferable in space?. J. Biogeogr., 33, 1689–1703.Google Scholar

Rangel, T.F., Diniz-Filho, J. & Colwell, R. (2007). Species richness and evolutionary niche dynamics: A spatial pattern oriented simulation experiment. Am. Nat., 170, 602–616.Google Scholar

Renner, I. W., Elith, J., Baddeley, A., Fithian, W., Hastie, T., Phillips, S. J., Popovic, G. & Warton, D. I. (2015). Point process models for presence-only analysis. Methods Ecol. Evol., 6, 366–379.Google Scholar

Renner, I. W. & Warton, D. I. (2013). Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics, 69, 274–281.Google Scholar

Ricklefs, R. E. (1987). Community diversity: Relative roles of local and regional processes. Science, 235, 167–171.Google Scholar

Ricklefs, R. E. (1990). Ecology. 3rd edn. W. H. Freeman and Co.Google Scholar

Ricklefs, R. E. (2008). Disintegration of the ecological community. Am. Nat., 172, 741–750.Google Scholar

Ridgeway, G. (2017). Gbm: Generalized boosted regression models. R package version 2.1.3.Google Scholar

Root, R. B. (1967). The niche exploitation pattern of the blue-gray gnatcatcher. Ecol. Monogr., 37, 317–350.Google Scholar

Saint-Germain, M., Buddle, C. M., Larrivée, M., Mercado, A., Motchula, T., Reichert, E., Sackett, T. E., Sylvain, Z. & Webb, A. (2007). Should biomass be considered more frequently as a currency in terrestrial arthropod community analyses? J. Appl. Ecol., 44, 330–339.Google Scholar

Saurola, P., Valkama, J. & Velmala, J. (2013). The Finnish bird ringing atlas. Vol 1.Google Scholar

Schindler, D. W. (1998). Whole-ecosystem experiments: Replication versus realism: The need for ecosystem-scale experiments. Ecosystems, 1, 323–334.Google Scholar

Schoch, C. L., Seifert, K. A., Huhndorf, S., Robert, V., Spouge, J. L., Levesque, C. A., Chen, W. & Fungal Barcoding Consortium. (2012). Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for fungi. Proc. Natl. Acad. Sci. USA, 109, 6241–6246.Google Scholar

Schwarz, G. (1978). Estimating the dimension of a model. Ann. Stat, 6, 461–464.Google Scholar

Sebastián-González, E., Sánchez-Zapata, J. A., Botella, F. & Ovaskainen, O. (2010). Testing the heterospecific attraction hypothesis with time-series data on species co-occurrence. Proc. R. Soc. Lond. B Biol. Sci., 277, 2983–2990.Google Scholar

Segurado, P., Araújo, M. B. & Kunin, W. E. (2006). Consequences of spatial autocorrelation for niche-based models. J. Appl. Ecol., 43, 433–444.Google Scholar

Simberloff, D. S. & Wilson, E. O. (1969). Experimental zoogeography of islands: The colonization of empty islands. Ecology, 50, 278–296.Google Scholar

Smith, T. W. & Lundholm, J. T. (2010). Variation partitioning as a tool to distinguish between niche and neutral processes. Ecography, 33, 648–655.Google Scholar

Somervuo, P., Yu, D. W., Xu, C. C. Y., Ji, Y., Hultman, J., Wirta, H. & Ovaskainen, O. (2017). Quantifying uncertainty of taxonomic placement in DNA barcoding and metabarcoding. Methods Ecol. Evol., 8, 398–407.Google Scholar

Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Van, D. L. (2002). Bayesian measures of model complexity and fit. J. R. Statist. Soc. B, 64, 583–639.Google Scholar

Stroud, J. T., Bush, M. R., Ladd, M. C., Nowicki, R. J., Shantz, A. A. & Sweatman, J. (2015). Is a community still a community? Reviewing definitions of key terms in community ecology. Ecol. Evol., 5, 4757–4765.Google Scholar

Talluto, M. V., Boulangeat, I., Ameztegui, A., Aubin, I., Berteaux, D., Butler, A., Doyon, F., Drever, C. R., Fortin, M., Franceschini, T., Liénard, J., McKenney, D., Solarik, K. A., Strigul, N., Thuiller, W. & Gravel, D. (2016). Cross-scale integration of knowledge for predicting species ranges: A metamodeling framework. Glob. Ecol. Biogeogr., 25, 238–249.Google Scholar

ter Braak, C. J. F. (1986). Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology, 67, 1167–1179.Google Scholar

Thorson, J. T., Ianelli, J. N., Larsen, E. A., Ries, L., Scheuerell, M. D., Szuwalski, C. & Zipkin, E. F. (2016). Joint dynamic species distribution models: A tool for community ordination and spatio-temporal monitoring. Global Ecol.Biogeogr., 25, 1144–1158.Google Scholar

Thorson, J. T., Scheuerell, M. D., Shelton, A. O., See, K. E., Skaug, H. J. & Kristensen, K. (2015). Spatial factor analysis: A new tool for estimating joint species distributions and correlations in species range. Methods Ecol. Evol., 6, 627–637.Google Scholar

Thuiller, W., Brotons, L., Araújo, M. B. & Lavorel, S. (2004). Effects of restricting environmental range of data to project current and future species distributions. Ecography, 27, 165–172.Google Scholar

Thuiller, W., Münkemüller, T., Lavergne, S., Mouillot, D., Mouquet, N., Schiffers, K. & Gravel, D. (2013). A road map for integrating eco-evolutionary processes into biodiversity models. Ecol. Lett., 16, 94–105.Google Scholar

Tikhonov, G., Abrego, N., Dunson, D. & Ovaskainen, O. (2017). Using joint species distribution models for evaluating how species-to-species associations depend on the environmental context. Methods Ecol. Evol., 8, 443–452.Google Scholar

Tikhonov, G., Duan, L., Abrego, N., Newell, G., White, M., Dunson, D. & Ovaskainen, O. (2020a). Computationally efficient joint species distribution modeling of big spatial data. Ecology, e02929.Google Scholar

Tikhonov, G., Opedal, Ø, Abrego, N., Lehikoinen, A., de Jonge, M. M., Oksanen, J. & Ovaskainen, O. (2020b). Joint species distribution modelling with the R-package Hmsc. Methods Ecol. Evol., 11, 442–447.Google Scholar

Tilman, D. (1994). Competition and biodiversity in spatially structured habitats. Ecology, 75, 2–16.Google Scholar

Tjur, T. (2009). Coefficients of determination in logistic regression models – A new proposal: The coefficient of discrimination. Am. Stat., 63, 366–372.Google Scholar

Tobler, M. W., Kéry, M., Hui, F. K. C., Guillera-Arroita, G., Knaus, P. & Sattler, T. (2019). Joint species distribution models with species correlations and imperfect detection. Ecology, 100, e02754.Google Scholar

Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. (2017). Taxonomic bias in biodiversity data and societal preferences. Sci. Rep., 7, 9132.Google Scholar

Tuomisto, H. (2010). A diversity of beta diversities: Straightening up a concept gone awry. part 1. defining beta diversity as a function of alpha and gamma diversity. Ecography, 33, 2–22.Google Scholar

Valkama, J., Saurola, P., Lehikoinen, A., Lehikoinen, E., Piha, M., Sola, P. & Velmala, W. (2014). The Finnish bird ringing atlas. Vol 2.Google Scholar

Vellend, M. (2016). The Theory of Ecological Communities. 1st edn. Princeton University Press.Google Scholar

Vellend, M. (2010). Conceptual synthesis in community ecology. Q. Rev. Biol., 85, 183–206.Google Scholar

Wallace, A. R. (1876). The Geographical Distribution of Animals, with a Study of the Relations of Living and Extinct Faunas as Elucidating the Past Changes of the Earth’s Surface. Macmillan and Co.Google Scholar

Warton, D. I., Blanchet, F. G., O’Hara, R. B., Ovaskainen, O., Taskinen, S., Walker, S. C. & Hui, F. K. C. (2015). So many variables: Joint modeling in community ecology. Trends Ecol. Evol., 30, 766–779.Google Scholar

Watanabe, S. (2013). A widely applicable Bayesian information criterion. J. Mach. Learn. Res., 14, 867–897.Google Scholar

Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res., 11, 3571–3594.Google Scholar

Wei, T., Simko, V., Levy, L., Xie, Y., Jin, Y. & Zemla, J. (2017). Package ‘corrplot’: Visualization of a Correlation Matrix. R package version 0.84.Google Scholar

Whitfeld, T. J. S., Kress, W. J., Erickson, D. L. & Weiblen, G. D. (2012). Change in community phylogenetic structure during tropical forest succession: Evidence from New Guinea. Ecography, 35, 821–830.Google Scholar

Whittaker, R. H. (1960). Vegetation of the Siskiyou mountains, Oregon and California. Ecol. Monogr., 30, 279–338.Google Scholar

Whittaker, R. H. (1962). Classification of natural communities. Bot.Rev., 28, 1–239.Google Scholar

Whittaker, R. H. (1972). Evolution and measurement of species diversity. Taxon, 21, 213–251.Google Scholar

Whittaker, R. H. (1975). Communities and Ecosystems. Macmillan, New York.Google Scholar

Wiens, J. J., Ackerly, D. D., Allen, A. P., Anacker, B. L., Buckley, L. B., Cornell, H. V., Damschen, E. I., Jonathan Davies, T., Grytnes, J., Harrison, S. P., Hawkins, B. A., Holt, R. D., McCain, C. M. & Stephens, P. R. (2010). Niche conservatism as an emerging principle in ecology and conservation biology. Ecol. Lett., 13, 1310–1324.Google Scholar

Wilman, H., Belmaker, J., Simpson, J., d. l. Rosa, C., Rivadeneira, M. M. & Jetz, W. (2014). EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology, 95, 2027–2027.Google Scholar

Wilson, K. A., Underwood, E. C., Morrison, S. A., Klausmeyer, K. R., Murdoch, W. W., Reyers, B., Wardell-Johnson, G., Marquet, P. A., Rundel, P. W., McBride, M. F., Pressey, R. L., Bode, M., Hoekstra, J. M., Andelman, S., Looker, M., Rondinini, C., Kareiva, P., Shaw, M. R. & Possingham, H. P. (2007). Conserving biodiversity efficiently: What to do, where, and when. PLoS Biol., 5, 1–12.Google Scholar

Wisz, M. S., Pottier, J., Kissling, W. D., Pellissier, L., Lenoir, J., Damgaard, C. F., Dormann, C. F., Forchhammer, M. C., Grytnes, J., Guisan, A., Heikkinen, R. K., Høye, T. T., Kühn, I., Luoto, M., Maiorano, L., Nilsson, M., Normand, S., Öckinger, E., Schmidt, N. M., Termansen, M., Timmermann, A., Wardle, D. A., Aastrup, P. & Svenning, J. (2013). The role of biotic interactions in shaping distributions and realised assemblages of species: Implications for species distribution modelling. Biol. Rev., 88, 15–30.Google Scholar

Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Statist. Soc. B, 73, 3–36.Google Scholar

Wootton, J. T. (2005). Field parameterization and experimental test of the Neutral Theory of Biodiversity. Nature, 433, 309.Google Scholar

Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F., Parnell, S., Fielding, A. H., Bamford, A. J., Ban, S., Barbosa, A. M., Dormann, C. F., Elith, J., Embling, C. B., Ervin, G. N., Fisher, R., Gould, S., Graf, R. F., Gregr, E. J., Halpin, P. N., Heikkinen, R. K., Heinänen, S., Jones, A. R., Krishnakumar, P. K., Lauria, V., Lozano-Montes, H., Mannocci, L., Mellin, C., Mesgaran, M. B., Moreno-Amat, E., Mormede, S., Novaczek, E., Oppel, S., Ortuño Crespo, G., Peterson, A. T., Rapacciuolo, G., Roberts, J. J., Ross, R. E., Scales, K. L., Schoeman, D., Snelgrove, P., Sundblad, G., Thuiller, W., Torres, L. G., Verbruggen, H., Wang, L., Wenger, S., Whittingham, M. J., Zharikov, Y., Zurell, D. & Sequeira, A. M. M. (2018). Outstanding challenges in the transferability of ecological models. Trends Ecol. Evol., 33, 790–802.Google Scholar