Aarssen, L. W. and Schamp, B. S. 2002. Predicting distributions of species richness and species size in regional floras: applying the species pool hypothesis to the habitat template model. Perspectives in Plant Ecology Evolution and Systematics, 5, 3–12.
Ackerly, D. D. 2003. Community assembly, niche conservatism, and adaptive evolution in changing environments. International Journal of Plant Sciences, 164, S165–S184.
Agresti, A. 1990. Categorical Data Analysis. New York: Wiley.
Agresti, A. 1999. Modelling ordered categorical data: recent advances and future challenges. Statistics in Medicine, 18, 2191–2207.
Aguiar-Pulido, V., Huang, W., Suarez-Ulloa, V., et al. 2016. Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis. Evolutionary Bioinformatics Online, 12, 5.
Akaike, H. 1974. A new look at statistical model identification. IEEE Transactions on Automatic Control, AU-19, 716–722.
Alagador, D., Martins, M. J., Cerdeira, J. O., Cabeza, M. and Araújo, M. B. 2011. A probability-based approach to match species with reserves when data are at different resolutions. Biological Conservation, 144, 811–820.
Albert, C. H., Yoccoz, N. G., Edwards, T. C., et al. 2010. Sampling in ecology and evolution: bridging the gap between theory and practice. Ecography, 33, 1028–1037.
Albouy, C., Velez, L., Coll, M., et al. 2014. From projected species distribution to food-web structure under climate change. Global Change Biology, 20, 730–741.
Algar, A. C., Kharouba, H. M., Young, E. R. and 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.
Allen, T. F. H. and Hoekstra, T. W. 1992. Towards a Unified Ecology. New York: Columbia University Press.
Allouche, O., Tsoar, A. and Kadmon, R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43, 1223–1232.
Amante, C. and Eakins, B. W. 2009. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum.
Anderson, B. J., Akcakaya, H. R., Araujo, M. B., et al. 2009. Dynamics of range margins for metapopulations under climate change. Proceedings of the Royal Society B. Biological Sciences, 276, 1415–1420.
Anderson, R. P. and Raza, A. 2010. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. Journal of Biogeography, 37, 1378–1393.
Anderson, R. P., Peterson, A. T. and Gomez-Laverde, M. 2002. Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos, 98, 3–16.
Anderson, R. P., Lew, D. and Peterson, A. T. 2003. Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecological Modelling, 162, 211–232.
Araújo, M. B. and Guisan, A. 2006. Five (or so) challenges for species distribution modelling. Journal of Biogeography, 33, 1677–1688.
Araújo, M. B. and Luoto, M. 2007. The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography, 16, 743–753.
Araújo, M. B. and New, M. 2007. Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22, 42–47.
Araújo, M. B. and Pearson, R. G. 2005. Equilibrium of species’ distributions with climate. Ecography, 28, 693–695.
Araújo, M. B. and Peterson, A. T. 2012. Uses and misuses of bioclimatic envelope modeling. Ecology, 93, 1527–1539.
Araújo, M. B. and Rahbek, C. 2006. How does climate change affect biodiversity? Science, 313, 1396–1397.
Araújo, M. B., Cabeza, M., Thuiller, W., Hannah, L. and Williams, P. H. 2004. Would climate change drive species out of reserves? An assessment of existing reserve-selection methods. Global Change Biology, 10, 1618–1626.
Araújo, M. B., Pearson, R. G., Thuiller, W. and Erhard, M. 2005a. Validation of species-climate impact models under climate change. Global Change Biology, 11, 1504–1513.
Araújo, M. B., Thuiller, W., Williams, P. H. and Reginster, I. 2005b. Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Global Ecology and Biogeography, 14, 17–30.
Araújo, M. B., Thuiller, W. and Pearson, R. G. 2006. Climate warming and the decline of amphibians and reptiles in Europe. Journal of Biogeography, 33, 1712–1728.
Araújo, M. B., Nogues-Bravo, D., Diniz-Filho, J. A. F., et al. 2008. Quaternary climate changes explain diversity among reptiles and amphibians. Ecography, 31, 8–15.
Araújo, M. B., Alagador, D., Cabeza, M., Nogues-Bravo, D. and Thuiller, W. 2011. Climate change threatens European conservation areas. Ecology Letters, 14, 484–492.
Araújo, M. B., Ferri-Yanez, F., Bozinovic, F., et al. 2013. Heat freezes niche evolution. Ecology Letters, 16, 1206–1219.
Arlot, S. and Celisse, A. 2010. A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79.
Austin, M. 1987. Models for the analysis of species’ response to environmental gradients. Vegetatio, 69, 35–45.
Austin, M. 1990. Community theory and competition in vegetation. Perspectives on Plant Competition, 215, 238.
Austin, M. and Gaywood, M. 1994. Current problems of environmental gradients and species response curves in relation to continuum theory. Journal of Vegetation Science, 5, 473–482.
Austin, M., Nicholls, A., Doherty, M. and Meyers, J. 1994. Determining species response functions to an environmental gradient by means of a β-function. Journal of Vegetation Science, 5, 215–228.
Austin, M. and Smith, T. 1989. A new model for the continuum concept. Plant Ecology, 83, 35–47.
Austin, M., Belbin, L., Meyers, J., Doherty, M. and Luoto, M. 2006. Evaluation of statistical models used for predicting plant species distributions: role of artificial data and theory. Ecological Modelling, 199, 197–216.
Austin, M. P. 1971. Role of regression analysis in plant ecology. Proceedings of the Ecological Society of Australia, 6, 63–75.
Austin, M. P. 1985. Continuum concept, ordination methods, and niche theory. Annual Review of Ecology and Systematics, 16, 39–61.
Austin, M. P. 1992. Modeling the environmental niche of plants – implications for plant community response to elevated CO2 levels. Australian Journal of Botany, 40, 615–630.
Austin, M. P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 101–118.
Austin, M. P. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling, 200, 1–19.
Austin, M. P. and Van Niel, K. P. 2011. Improving species distribution models for climate change studies: variable selection and scale. Journal of Biogeography, 38, 1–8.
Austin, M. P., Nicholls, A. O. and Margules, C. R. 1990. Measurement of the realized qualitative niche: environmental niches of 5 Eucalyptus species. Ecological Monographs, 60, 161–177.
Ba, J., Hou, Z., Platvoet, D., Zhu, L. and Li, S. 2010. Is Gammarus tigrinus (Crustacea, Amphipoda) becoming cosmopolitan through shipping? Predicting its potential invasive range using ecological niche modeling. Hydrobiologia, 649, 183–194.
Bahn, V. and McGill, B. J. 2007. Can niche-based distribution models outperform spatial interpolation? Global Ecology and Biogeography, 16, 733–742.
Bahn, V., J O’Connor, R. and B Krohn, W. 2006. Importance of spatial autocorrelation in modeling bird distributions at a continental scale. Ecography, 29, 835–844.
Barbet-Massin, M., Thuiller, W. and Jiguet, F. 2010. How much do we overestimate future local extinction rates when restricting the range of occurrence data in climate suitability models? Ecography, 33, 878–886.
Barbet-Massin, M., Jiguet, F., Albert, C. H. and Thuiller, W. 2012. Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution, 3, 327–338.
Barry, S. and Elith, J. 2006. Error and uncertainty in habitat models. Journal of Applied Ecology, 43, 413–423.
Bartholome, E. and Belward, A. S. 2005. GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26, 1959–1977.
Barve, N., Barve, V., Jiménez-Valverde, A., et al. 2011. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling, 222, 1810–1819.
Bascompte, J., Jordano, P., Melian, C. J. and Olesen, J. M. 2003. The nested assembly of plant–animal mutualistic networks. Proceedings of the National Academy of Sciences of the United States of America, 100, 9383–9387.
Baselga, A. and Araújo, M. B. 2009. Individualistic vs community modelling of species distributions under climate change. Ecography, 32, 55–65.
Beale, C. M., Lennon, J. J., Yearsley, J. M., Brewer, M. J. and Elston, D. A. 2010. Regression analysis of spatial data. Ecology Letters, 13, 246–264.
Beale, C. M., Baker, N. E., Brewer, M. J. and Lennon, J. J. 2013. Protected area networks and savannah bird biodiversity in the face of climate change and land degradation. Ecology Letters, 16, 1061–1068.
Beaumont, L. J. and Hughes, L. 2002. Potential changes in the distributions of latitudinally restricted Australian butterfly species in response to climate change. Global Change Biology, 8, 954–971.
Beaumont, L. J., Gallagher, R. V., Thuiller, W., et al. 2009. Different climatic envelopes among invasive populations may lead to underestimations of current and future biological invasions. Diversity and Distributions, 15, 409–420.
Beerling, D. J., Huntley, B. and Bailey, J. P. 1995. Climate and the distribution of Fallopia japonica: use of an introduced species to test the predictive capacity of response surfaces. Journal of Vegetation Science, 6, 269–282.
Bell, D. M., Bradford, J. B. and Lauenroth, W. K. 2014. Early indicators of change: divergent climate envelopes between tree life stages imply range shifts in the western United States. Global Ecology and Biogeography, 23, 168–180.
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. and Courchamp, F. 2012. Impacts of climate change on the future of biodiversity. Ecology Letters, 15, 365–377.
Belward, A. S., Estes, J. E. and Kline, K. D. 1999. The IGBP-DIS global 1-km land-cover data set DISCover: a project overview. Photogrammetric Engineering and Remote Sensing, 65, 1013–1020.
Bendall, M. L., Stevens, S. L., Chan, L.-K., et al. 2016. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. The ISME Journal, 10, 1589–1601.
Berrocal, V. J., Craigmile, P. F. and Guttorp, P. 2012. Regional climate model assessment using statistical upscaling and downscaling techniques. Environmetrics, 23, 482–492.
Berteaux, D., Humphries, M. M., Krebs, C. J., et al. 2006. Constraints to projecting the effects of climate change on mammals. Climate Research, 32, 151–158.
Bertrand, R., Lenoir, J., Piedallu, C., et al. 2011. Changes in plant community composition lag behind climate warming in lowland forests. Nature, 479, 517–520.
Bertrand, R., Perez, V. and Gégout, J. C. 2012. Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: the case of Quercus pubescens in France. Global Change Biology, 18, 2648–2660.
Binzenhofer, B., Schroder, B., Strauss, B., Biedermann, R. and Settele, J. 2005. Habitat models and habitat connectivity analysis for butterflies and burnet moths: the example of Zygaena carniolica and Coenonympha arcania. Biological Conservation, 126, 247–259.
Blondel, J. and Aronson, J. 1995. Biodiversity and ecosystem function in the Mediterranean basin: human and non-human determinants. In Davis, G. W. and Richardson, D. M. (eds), Ecological Studies. Berlin: Springer-Verlag, pp. 43–119.
Bocedi, G., Zurell, D., Reineking, B. and Travis, J. M. J. 2014. Mechanistic modelling of animal dispersal offers new insights into range expansion dynamics across fragmented landscapes. Ecography, 37, 1240–1253.
Bombi, P. and D’Amen, M. 2012. Scaling down distribution maps from atlas data: a test of different approaches with virtual species. Journal of Biogeography, 39, 640–651.
Bombi, P., Salvi, D., Vignoli, L. and Bologna, M. A. 2009. Modelling Bedriaga’s rock lizard distribution in Sardinia: an ensemble approach. Amphibia–Reptilia, 30, 413–424.
Booth, T. H., Nix, H. A., Busby, J. R. and Hutchinson, M. F. 2014. BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Diversity and Distributions, 20, 1–9.
Botkin, D. B., Saxe, H., Araujo, M. B., et al. 2007. Forecasting the effects of global warming on biodiversity. Bioscience, 57, 227–236.
Boucher, D. H., James, S. and Keeler, K. H. 1982. The ecology of mutualism. Annual Review of Ecology and Systematics, 13, 315–347.
Boucher, F. C., Thuiller, W., Roquet, C., et al. 2012. Reconstructing the origins of high-alpine niches and cushion life form in the genus Androsace sl (Primulaceae). Evolution, 66, 1255–1268.
Boulangeat, I., Gravel, D. and Thuiller, W. 2012a. Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances. Ecology Letters, 15, 584–593.
Boulangeat, I., Philippe, P., Abdulhak, S., et al. 2012b. Improving plant functional groups for dynamic models of biodiversity: at the crossroads between functional and community ecology. Global Change Biology, 18, 3464–3475.
Boulangeat, I., Georges, D. and Thuiller, W. 2014. FATE-HD: a spatially and temporally explicit integrated model for predicting vegetation structure and diversity at regional scale. Global Change Biology, 20, 2368–2378.
Box, E. O. 1981. Macroclimate and Plant Forms: An Introduction to Predictive Modeling in Phytogeography, The Hague: Junk.
Boyce, M. S., Vernier, P. R., Nielsen, S. E. and Schmiegelow, F. K. A. 2002. Evaluating resource selection functions. Ecological Modelling, 157, 281–300.
Braconnot, P., Otto-Bliesner, B., Harrison, S., et al. 2007. Results of PMIP2 coupled simulations of the mid-holocene and last glacial maximum. Part I: experiments and large-scale features. Climate of the Past, 3, 261–277.
Breiman, L. 1996. Bagging predictors. Machine Learning, 24, 123–140.
Breiman, L. 2001. Random forests. Machine Learning, 45, 5–32.
Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. 1984. Classification and Regression Trees, New York: Chapman and Hall.
Breiner, F. T., Guisan, A., Bergamini, A. and Nobis, M. P. 2015. Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution, 6(10), 1210–1218.
Broennimann, O. and Guisan, A. 2008. Predicting current and future biological invasions: both native and invaded ranges matter. Biology Letters, 4, 585–589.
Broennimann, O., Thuiller, W., Hughes, G., et al. 2006. Do geographic distribution, niche property and life form explain plants’ vulnerability to global change? Global Change Biology, 12, 1079–1093.
Broennimann, O., Treier, U. A., Müller-Schärer, H., et al. 2007. Evidence of climatic niche shift during biological invasion. Ecology Letters, 10, 701–709.
Broennimann, O., Fitzpatrick, M. C., Pearman, P. B., et al. 2012. Measuring ecological niche overlap from occurrence and spatial environmental data. Global Ecology and Biogeography, 21, 481–497.
Broennimann, O., Mráz, P., Petitpierre, B., Guisan, A. and Müller-Schärer, H. 2014a. Contrasting spatio-temporal climatic niche dynamics during the eastern and western invasions of spotted knapweed in North America. Journal of Biogeography, 41, 1126–1136.
Broennimann, O., Ursenbacher, S., Meyer, A., et al. 2014c. Influence of climate on the presence of colour polymorphism in two montane reptile species. Biology Letters, 10, 20140638.
Broitman, B., Szathmary, P., Mislan, K., Blanchette, C. and Helmuth, B. 2009. Predator–prey interactions under climate change: the importance of habitat vs body temperature. Oikos, 118, 219–224.
Brook, B. W., Akçakaya, H. R., Keith, D. A., et al. 2009. Integrating bioclimate with population models to improve forecasts of species extinctions under climate change. Biology Letters, 5, 723–725.
Brotons, L., Thuiller, W., Araújo, M. B. and Hirzel, A. H. 2004. Presence–absence vs presence-only modelling methods for predicting bird habitat suitability. Ecography, 27, 165–172.
Brown, A. M., Warton, D. I., Andrew, N. R., et al. 2014. The fourth-corner solution: using predictive models to understand how species traits interact with the environment. Methods in Ecology and Evolution, 5, 344–352.
Brown, J. H. 1971. Mechanisms of competitive exclusion between two species of chipmunks. Ecology, 52, 305–311.
Brown, J. H., Stevens, G. C. and Kaufman, D. M. 1996. The geographic range: size, shape, boundaries, and internal structure. Annual Review of Ecology and Systematics, 597–623.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. and West, G. B. 2004. Toward a metabolic theory of ecology. Ecology, 85, 1771–1789.
Bruno, J. F., Stachowicz, J. J. and Bertness, M. D. 2003. Inclusion of facilitation into ecological theory. Trends in Ecology and Evolution, 18, 119–125.
Brzeziecki, B., Kienast, F. and Wildi, O. 1995. Modelling potential impacts of climate change on the spatial distribution of zonal forest communities in Switzerland. Journal of Vegetation Science, 6, 257–268.
Buckley, L. B. 2008. Linking traits to energetics and population dynamics to predict lizard ranges in changing environments. The American Naturalist, 171, E1–E19.
Buckley, L. B., Davies, T. J., Ackerly, D. D., et al. 2010. Phylogeny, niche conservatism and the latitudinal diversity gradient in mammals. Proceedings of the Royal Society of London B: Biological Sciences, 277, 2131–2138.
Buisson, L., Thuiller, W., Lek, S., Lim, P. and Grenouillet, G. 2008. Climate change hastens the turnover of stream fish assemblages. Global Change Biology, 14, 2232–2248.
Buisson, L., Thuiller, W., Casajus, N., Lek, S. and Grenouillet, G. 2010. Uncertainty in ensemble forecasting of species distribution. Global Change Biology, 16, 1145–1157.
Buisson, L., Grenouillet, G., Villéger, S., Canal, J. and Laffaille, P. 2013. Toward a loss of functional diversity in stream fish assemblages under climate change. Global Change Biology, 19, 387–400.
Burgman, M. A. and Fox, J. C. 2003. Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. Animal Conservation, 6, 19–28.
Burnham, K. P. and Anderson, D. R. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Berlin: Springer.
Busby, J. R. 1991. BIOCLIM: a bioclimate analysis and prediction system. In Margules, C. R. and Austin, M. P. (eds.), Nature Conservation: Cost Effective Biological Surveys and Data Analysis. Canberra, Australia: CSIRO, pp. 64–68.
Calenge, C. and Basille, M. 2008. A general framework for the statistical exploration of the ecological niche. Journal of Theoretical Biology, 252, 674–685.
Caley, M. J. and Schluter, D. 1997. The relationship between local and regional diversity. Ecology, 78, 70–80.
Callaway, R. M. 1995. Positive interactions among plants (interpreting botanical progress). The Botanical Review, 61, 306–349.
Calvete, C., Estrada, R., Miranda, M. A., et al. 2008. Modelling the distributions and spatial coincidence of bluetongue vectors Culicoides imicola and the Culicoides obsoletus group throughout the Iberian peninsula. Medical and Veterinary Entomology, 22, 124–134.
Carl, G. and Kühn, I. 2007. Analyzing spatial autocorrelation in species distributions using Gaussian and logit models. Ecological Modelling, 207, 159–170.
Carlson, B. Z., Georges, D., Rabatel, A., et al. 2014. Accounting for tree line shift, glacier retreat and primary succession in mountain plant distribution models. Diversity and Distributions, 20, 1379–1391.
Carnaval, A. C., Hickerson, M. J., Haddad, C. F., Rodrigues, M. T. and Moritz, C. 2009. Stability predicts genetic diversity in the Brazilian Atlantic forest hotspot. Science, 323, 785–789.
Carpenter, G., Gillison, A. N. and Winter, J. 1993. DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation, 2, 667–680.
Carroll, C., Johnson, D. S., Dunk, J. R. and Zielinski, W. J. 2010. Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring. Conservation Biology, 24, 1538–1548.
Carvalho, S. B., Brito, J. C., Crespo, E. G., Watts, M. E. and Possingham, H. P. 2011. Conservation planning under climate change: toward accounting for uncertainty in predicted species distributions to increase confidence in conservation investments in space and time. Biological Conservation, 144, 2020–2030.
Chase, J. M. and Leibold, M. A. 2003. Ecological Niches. Chicago, IL: The University of Chicago Press.
Chave, J. 2013. The problem of pattern and scale in ecology: what have we learned in 20 years? Ecology Letters, 16, 4–16.
Chefaoui, R. M. and Lobo, J. M. 2008. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecological Modelling, 210, 478–486.
Chen, I. C., Hill, J. K., Ohlemuller, R., Roy, D. B. and Thomas, C. D. 2011. Rapid range shifts of species associated with high levels of climate warming. Science, 333, 1024–1026.
Christie, K. S., Gilbert, S. L., Brown, C. L., Hatfield, M. and Hanson, L. 2016. Unmanned aircraft systems in wildlife research: current and future applications of a transformative technology. Frontiers in Ecology and the Environment, 14, 241–251.
Civantos, E., Thuiller, W., Maiorano, L., Guisan, A. and Araújo, M. B. 2012. Potential impacts of climate change on ecosystem services in Europe: the case of pest control by vertebrates. Bioscience, 62, 658–666.
Claeskens, G. and Hjort, N. L. 2008. Model Selection and Model Averaging. Cambridge, UK: University Press Cambridge.
Clark, J. S., Dietze, M., Chakraborty, S., et al. 2007. Resolving the biodiversity paradox. Ecology Letters, 10, 647–659.
Clark, J. S., Gelfand, A. E., Woodall, C. W. and Zhu, K. 2014. More than the sum of the parts: forest climate response from joint species distribution models. Ecological Applications, 24, 990–999.
Clausen, J., Keck, D. and Hiesey, W. M. 1948. Experimental studies on the nature of species. III. Environmental responses of climatic races of Achillea. Carnegie Inst. Wash., 581.
Coetzee, B. W. T., Robertson, M. P., Erasmus, B. F. N., van Rensburg, B. J. and Thuiller, W. 2009. Ensemble models predict important bird areas in southern Africa will become less effective for conserving endemic birds under climate change. Global Ecology and Biogeography, 18, 701–710.
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.
Cohen, J. 1968. Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70, 213.
Colwell, R. K. and Futuyma, D. J. 1971. Measurement of niche breadth and overlap. Ecology, 52, 567–576.
Colwell, R. K. and Rangel, T. F. 2009. Hutchinson’s duality: the once and future niche. Proceedings of the National Academy of Sciences, 106, 19651–19658.
Connell, J. H. 1961. The influence of interspecific competition and other factors on the distribution of the barnacle Chthamalus stellatus. Ecology, 42, 710–723.
Corsi, F., de Leeuw, J. and Skidmore, A. 2000. Modeling Species Distribution with GIS. In Boitani, L. and Fuller, T. K. (eds), Research Techniques in Animal Ecology: Controversies and Consequences. New York: Columbia University Press, pp. 389–434.
Côté, I. M. and Reynolds, J. D. 2002. Predictive ecology to the rescue? Science, 298, 1181–1182.
Cox, B. 2001. The biogeographic regions reconsidered. Journal of Biogeography, 28, 511–523.
Crase, B., Liedloff, A. C. and Wintle, B. A. 2012. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography, 35, 879–888.
Crase, B., Liedloff, A., Vesk, P. A., Fukuda, Y. and Wintle, B. A. 2014. Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts. Global Change Biology, 20, 2566–2579.
Cressie, N. 1993. Geostatistics: a tool for environmental models. In Goodchild, M. F., Parks, B. O. and Steyaert, L. T. (eds.), Environmental Modeling with GIS. Oxford, UK: Oxford University Press, pp. 414–421.
Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T. and Mynsberge, A. R. 2011. Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science, 331, 324–327.
Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A. and Hess, K. T. 2007. Random forests for classification in ecology. Ecology, 88, 2783–2792.
D’Amen, M., Zimmermann, N. E. and Pearman, P. B. 2013. Conservation of phylogeographic lineages under climate change. Global Ecology and Biogeography, 22, 93–104.
D’Amen, M., Dubuis, A., Fernandes, R. F., et al. 2015a. Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models. Journal of Biogeography, 42, 1255–1266.
D’Amen, M., Zimmermann, N. E., Rahbek, C. and Guisan, A. 2015b. Spatial prediction at the community level: state of the art and future perspectives. Biological Reviews, 92, 169–187.
Daly, C., Neilson, R. P. and Phillips, D. L. 1994. A statistical topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology, 33, 140–158.
Davis, E. B., McGuire, J. L. and Orcutt, J. D. 2014. Ecological niche models of mammalian glacial refugia show consistent bias. Ecography, 37, 1133–1138.
Davis, M. B. 1989. Lags in vegetation response to greenhouse warming. Climatic Change, 15, 75–82.
Dawson, T. P., Curran, P. J. and Plummer, S. E. 1998. The biochemical decomposition of slash pine needles from reflectance spectra using neural networks. International Journal of Remote Sensing, 19, 1433–1438.
De’Ath, G. 2007. Boosted trees for ecological modeling and prediction. Ecology, 88, 243–251.
De’Ath, G. and Fabricius, K. E. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81, 3178–3192.
de Oliveira, S. V., Escobar, L. E., Peterson, A. T. and Gurgel-Gonçalves, R. 2013. Potential geographic distribution of hantavirus reservoirs in Brazil. Plos One, 8, e85137.
de Witte, L. C. and Stöcklin, J. 2010. Longevity of clonal plants: why it matters and how to measure it. Annals of botany, 1–12.
Defossez, E., Courbaud, B., Marcais, B., et al. 2011. Do interactions between plant and soil biota change with elevation? A study on Fagus sylvatica. Biology Letters, 7, 699–701.
DeFries, R., Hansen, M., Steininger, M., et al. 1997. Subpixel forest cover in central Africa from multisensor, multitemporal data. Remote Sensing of Environment, 60, 228–246.
DeFries, R. S., Townshend, J. R. G. and Hansen, M. C. 1999. Continuous fields of vegetation characteristics at the global scale at 1-km resolution. Journal of Geophysical Research: Atmospheres, 104, 16911–16923.
Delisle, F., Lavoie, C., Jean, M. and Lachance, D. 2003. Reconstructing the spread of invasive plants: taking into account biases associated with herbarium specimens. Journal of Biogeography, 30, 1033–1042.
Del Monte-Luna, P., Brook, B. W., Zetina-Rejón, M. J. and Cruz-Escalona, V. H. 2004. The carrying capacity of ecosystems. Global Ecology and Biogeography, 13, 485–495.
Delzon, S., Urli, M., Samalens, J.-C., et al. 2013. Field evidence of colonisation by Holm oak, at the northern margin of its distribution range, during the Anthropocene period. Plos One, 8 e80443.
Dennis, R. L., Sparks, T. H. and Hardy, P. B. 1999. Bias in butterfly distribution maps: the effects of sampling effort. Journal of Insect Conservation, 3, 33–42.
DeVaney, S. C., McNyset, K. M., Williams, J. B., Peterson, A. T. and Wiley, E. O. 2009. A tale of four “carp’’: invasion potential and ecological niche modeling. Plos One, 4.
Di Cola, V., Broennimann, O., Petitpierre, B., et al. 2017. ecospat: an R package for the support of spatial analyses and modelling of species niches and distributions. Ecography, 40, 774–787.
Dietz, H. and Edwards, P. J. 2006. Recognition that causal processes change during plant invasion helps explain conflicts in evidence. Ecology, 87, 1359–1367.
Diniz, J. A. F., Bini, L. M., Rangel, T. F., et al. 2009. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography, 32, 897–906.
Diniz-Filho, J. A. F., Bini, L. M. and Hawkins, B. A. 2003. Spatial autocorrelation and red herrings in geographical ecology. Global Ecology and Biogeography, 12, 53–64.
Dirnbock, T., Greimler, J., Lopez, P. and Stuessy, T. F. 2003. Predicting future threats to the native vegetation of Robinson Crusoe Island, Juan Fernandez Archipelago, Chile. Conservation Biology, 17, 1650–1659.
Dobrowski, S. Z., Thorne, J. H., Greenberg, J. A., et al. 2011. Modeling plant ranges over 75 years of climate change in California, USA: temporal transferability and species traits. Ecological Monographs, 81, 241–257.
Dobrowski, S. Z., Abatzoglou, J., Swanson, A. K., et al. 2013. The climate velocity of the contiguous United States during the 20th century. Global Change Biology, 19, 241–251.
Doebeli, M. and Dieckmann, U. 2003. Speciation along environmental gradients. Nature, 421, 259–264.
Dolédec, S., Chessel, D. and Gimaret-Carpentier, C. 2000. Niche separation in community analysis: a new method. Ecology, 81, 2914–2927.
Dorazio, R. M. 2014. Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Global Ecology and Biogeography, 23, 1472–1484.
Dormann, C. F. 2007a. Assessing the validity of autologistic regression. Ecological Modelling, 207, 234–242.
Dormann, C. F. 2007b. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecology and Biogeography, 30, 609–628.
Dormann, C. F., McPherson, J. M., Araujo, M. B., et al. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 30, 609–628.
Dormann, C. F., Purschke, O., Marquez, J. R. G., Lautenbach, S. and Schroder, B. 2008. Components of uncertainty in species distribution analysis: a case study of the great grey shrike. Ecology, 89, 3371–3386.
Dormann, C. F., Schymanski, S. J., Cabral, J., et al. 2012. Correlation and process in species distribution models: bridging a dichotomy. Journal of Biogeography, 39, 2119–2131.
Dormann, C. F., Elith, J., Bacher, S., et al. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36, 27–46.
Drake, J. M., Randin, C. and Guisan, A. 2006. Modelling ecological niches with support vector machines. Journal of Applied Ecology, 43, 424–432.
Dray, S., Chessel, D. and Thioulouse, J. 2003. Co-inertia analysis and the linking of ecological data tables. Ecology, 84, 3078–3089.
Dubuis, A., Pottier, J., Rion, V., et al. 2011. Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches. Diversity and Distributions, 17, 1122–1131.
Dubuis, A., Giovanettina, S., Pellissier, L., et al. 2013. Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables. Journal of Vegetation Science, 24, 593–606.
Duckworth, J., Bunce, R. and Malloch, A. 2000. Vegetation–environment relationships in Atlantic European calcareous grasslands. Journal of Vegetation Science, 11, 15–22.
Dullinger, S., Gattringer, A., Thuiller, W., et al. 2012. Extinction debt of high-mountain plants under twenty-first-century climate change. Nature Climate Change, 2, 619–622.
Edwards, T. C., Cutler, D. R., Zimmermann, N. E., Geiser, L. and Alegria, J. 2005. Model-based stratifications for enhancing the detection of rare ecological events. Ecology, 86, 1081–1090.
Edwards, T. C., Cutler, D. R., Zimmermann, N. E., Geiser, L. and Moisen, G. G. 2006. Effects of sample survey design on the accuracy of classification tree models in species distribution models. Ecological Modelling, 199, 132–141.
Efron, B. and Gong, G. 1983. A leisurely look at the bootstrap, the jackknife, and cross- validation. American Statistician, 37, 36–48.
Efron, B. and Tibshirani, R. 1993. An Introduction to the Bootstrap, New York: Chapman and Hall.
Efron, B. and Tibshirani, R. 1997. Improvements on cross-validation: The .632+ bootstrap method. Journal of the American Statistical Association, 92, 548–560.
Elith, J. 2002. Predicting the distribution of plants. PhD Thesis. Melbourne, Australia: The University of Melbourne.
Elith, J. and Leathwick, J. R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology Evolution and Systematics, 40, 677–697.
Elith, J., Burgman, M. A. and Regan, H. M. 2002. Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecological Modelling, 157, 313–329.
Elith, J., Ferrier, S., Huettmann, F. and Leathwick, J. 2005. The evaluation strip: a new and robust method for plotting predicted responses from species distribution models. Ecological Modelling, 186, 280–289.
Elith, J., Graham, C. H., Anderson, R. P., et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151.
Elith, J., Leathwick, J. R. and Hastie, T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–813.
Elith, J., Kearney, M. and Phillips, S. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution, 1, 330–342.
Elith, J., Phillips, S. J., Hastie, T., et al. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43–57.
Ellenberg, H. 1953. Physiologisches und ökologisches Verhalten derselben Pflanzenarten. Berichte der Deutschen Botanischen Gesellschaft, 65, 351–362.
Ellenberg, H. 1954. Ueber einige fortschritte der kausalen Vegetationskunde. Vegetatio, 5–6, 199–211.
Ellenberg, H. 1988. Vegetation Ecology of Central Europe, 4th edn. Cambridge, UK: Cambridge University Press.
Elton, C. 1927. Animal Ecology, London: Sidgwick and Jackson.
Emery, N. C., Ewanchuck, P. J. and Bertness, M. D. 2001. Competition and salt marsh plant zonation: stress tolerators my be dominant competitors. Ecology, 82, 2471–2485.
Engler, R. and Guisan, A. 2009. MigClim: predicting plant distribution and dispersal in a changing climate. Diversity and Distributions, 15, 590–601.
Engler, R., Guisan, A. and Rechsteiner, L. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology, 41, 263–274.
Engler, R., Randin, C. F., Vittoz, P., et al. 2009. Predicting future distributions of mountain plants under climate change: does dispersal capacity matter? Ecography, 32, 34–45.
Engler, R., Randin, C. F., Thuiller, W., et al. 2011a. 21st century climate change threatens mountain flora unequally across Europe. Global Change Biology, 17, 2330–2341.
Engler, R., Randin, C. R., Thuiller, W., et al. 2011b. Climate change impacts on European mountain plant diversity. Global Change Biology, 17, 2330–2341.
Engler, R., Hordijk, W. and Guisan, A. 2012. The MIGCLIM R package–seamless integration of dispersal constraints into projections of species distribution models. Ecography, 35, 872–878.
Espíndola, A., Pellissier, L., Maiorano, L., et al. 2012. Predicting present and future intra-specific genetic structure through niche hindcasting across 24 millennia. Ecology Letters, 15, 649–657.
Estrada-Peña, A. and Thuiller, W. 2008. An assessment of the effect of data partitioning on the performance of modelling algorithms for habitat suitability for ticks. Medical and Veterinary Entomology, 22, 248–257.
Estrada-Peña, A. and Venzal, J. M. 2007. Climate niches of tick species in the Mediterranean region: modeling of occurrence data, distributional constraints, and impact of climate change. Journal of Medical Entomology, 44, 1130–1138.
Evans, M. E., Smith, S. A., Flynn, R. S. and Donoghue, M. J. 2009. Climate, niche evolution, and diversification of the “bird-cage” evening primroses (Oenothera, Sections Anogra and Kleinia). The American Naturalist, 173, 225–240.
Farber, O. and Kadmon, R. 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecological Modelling, 160, 115–130.
Fernandes, R. F., Vicente, J. R., Georges, D., et al. 2014. A novel downscaling approach to predict plant invasions and improve local conservation actions. Biological Invasions, 16, 2577–2590.
Ferreira, M. P., Zortea, M., Zanotta, D. C., Shimabukuro, Y. E. and de Souza Filho, C. R. 2016. Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data. Remote Sensing of Environment, 179, 66–78.
Ferrier, S. 1984. The status of the Rufous Scrub-Bird Atrichornis rufescens: habitat, geographical variation and abundance. PhD Thesis. Armidale, Australia: University of New England.
Ferrier, S. and Guisan, A. 2006. Spatial modelling of biodiversity at the community level. Journal of Applied Ecology, 43, 393–404.
Ferrier, S. and Watson, G. 1997. An Evaluation of the Effectiveness of Environmental Surrogates and Modelling Techniques in Predicting the Distribution of Biological Diversity. Canberra, Australia: NSW National Parks and Wildlife Service.
Ferrier, S., Drielsma, M., Manion, G. and Watson, G. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity in north-east New South Wales. II. Community-level modelling. Biodiversity and Conservation, 11, 2309–2338.
Ferrier, S., Manion, G., Elith, J. and Richardson, K. 2007. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and Distributions, 13, 252–264.
Fielding, A. H. 2002. What are the appropriate characteristics of an accuracy measure? In Scott, J. M., Heglund, P. J., Morrison, M. L., et al. (eds), Predicting Species Occurrences: Issues of Accuracy and Scale. Covelo, California: Island Press.
Fielding, A. H. and Bell, J. F. 1997. A review of methods for the assessment of prediction errors in conservation presence–absence models. Environmental Conservation, 24, 38–49.
Filchak, K. E., Roethele, J. B. and Feder, J. L. 2000. Natural selection and sympatric divergence in the apple maggot Rhagoletis pomonella. Nature, 407, 739–742.
Fithian, W., Elith, J., Hastie, T. and Keith, D. A. 2015. Bias correction in species distribution models: pooling survey and collection data for multiple species. Methods in Ecology and Evolution, 6, 424–438.
Fitzpatrick, M. C. and Hargrove, W. W. 2009. The projection of species distribution models and the problem of non-analog climate. Biodiversity and Conservation, 18, 2255–2261.
Fitzpatrick, M. C., Weltzin, J. F., Sanders, N. J. and Dunn, R. R. 2007. The biogeography of prediction error: why does the introduced range of the fire ant over-predict its native range? Global Ecology and Biogeography, 16, 24–33.
Foden, W., Midgley, G. F., Hughes, G., et al. 2007. A changing climate is eroding the geographical range of the Namib Desert tree aloe through population declines and dispersal lags. Diversity and Distributions, 13, 645–653.
Fondi, M., Karkman, A., Tamminen, M. V., et al. 2016. “Every gene is everywhere but the environment selects”: global geolocalization of gene sharing in environmental samples through network analysis. Genome Biology and Evolution, 8, 1388–1400.
Forbes, A. D. 1995. Classification-algorithm evaluation: five performance measures based on confusion matrices. Journal of Clinical Monitoring, 11, 189–206.
Fordham, D. A., Resit Akçakaya, H., Araújo, M. B., et al. 2012. Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? Global Change Biology, 18, 1357–1371.
Fraker, M. E. and Peacor, S. D. 2008. Statistical tests for biological interactions: a comparison of permutation tests and analysis of variance. Acta Oecologica-International Journal of Ecology, 33, 66–72.
Franklin, J. 1995. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography, 19, 474–499.
Franklin, J. 2010a. Mapping Species Distribution: Spatial Inference and Prediction. Cambridge, UK: Cambridge University Press.
Franklin, J. 2010b. Moving beyond static species distribution models in support of conservation biogeography. Diversity and Distributions, 16, 321–330.
Franklin, J., Davis, F. W., Ikegami, M., et al. 2013. Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Global Change Biology, 19, 473–483.
Fraser, L. H. and Keddy, P. A. 2005. Can competitive ability predict structure in experimental plant communities? Journal of Vegetation Science, 16, 571–578.
Freeman, E. A. and Moisen, G. G. 2008. A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecological Modelling, 217, 48–58.
Friedl, M. A., Sulla-Menashe, D., Tan, B., et al. 2010. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114, 168–182.
Friedman, J. H. 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29, 1189–1232.
Friedman, J. H. 2002. Stochastic gradient boosting. Computational Statistics and Data Analysis, 38, 367–378.
Friedman, J. H. and Meulman, J. J. 2003. Multiple additive regression trees with application in epidemiology. Statistics in Medicine, 22, 1365–1381.
Friedman, J. H., Hastie, T. J. and Tibshirani, R. 2000. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28, 337–374.
Gallagher, R. V., Beaumont, L. J., Hughes, L. and Leishman, M. R. 2010. Evidence for climatic niche and biome shifts between native and novel ranges in plant species introduced to Australia. Journal of Ecology, 98, 790–799.
Gallant, D., Slough, B. G., Reid, D. G. and Berteaux, D. 2012. Arctic fox versus red fox in the warming Arctic: four decades of den surveys in north Yukon. Polar Biology, 35, 1421–1431.
Gallien, L., Münkemüller, T., Albert, C. H., Boulangeat, I. and Thuiller, W. 2010. Predicting potential distributions of invasive species: where to go from here? Diversity and Distributions, 16, 331–342.
Gallien, L., Douzet, R., Pratte, S., Zimmermann, N. E. and Thuiller, W. 2012. Invasive species distribution models: how violating the equilibrium assumption can create new insights? Global Ecology and Biogeography, 21, 1126–1136.
Gallien, L., Mazel, F., Lavergne, S., et al. 2015. Contrasting the effects of environment, dispersal and biotic interactions to explain the distribution of invasive plants in alpine communities. Biological Invasions, 17, 1407–1423.
Gao, B. C. 1996. NDWI: A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.
Gause, G. F. 1936. The Struggle for Existence. Baltimore, MD: Williams and Wilkins.
Gehrig-Fasel, J., Guisan, A. and Zimmermann, N. E. 2007. Tree line shifts in the Swiss Alps: climate change or land abandonment? Journal of Vegetation Science, 18, 571–582.
Gelfand, A. E., Silander, J. A., Wu, S., et al. 2006. Explaining species distribution patterns through hierarchical modeling. Bayesian Analysis, 1, 41–92.
Gellrich, M. and Zimmermann, N. E. 2007. Investigating the regional-scale pattern of agricultural land abandonment in the Swiss mountains: a spatial statistical modelling approach. Landscape and Urban Planning, 79, 65–76.
Goldewijk, K. K. 2001. Estimating global land use change over the past 300 years: the HYDE Database. Global Biogeochemical Cycles, 15, 417–433.
Golding, N. and Purse, B. V. 2016. Fast and flexible Bayesian species distribution modelling using Gaussian processes. Methods in Ecology and Evolution.
Gotelli, N. J., Graves, G. R. and Rahbek, C. 2010. Macroecological signals of species interactions in the Danish avifauna. Proceedings of the National Academy of Sciences, 107, 5030–5035.
Grace, J. B. and Wetzel, R. G. 1981. Habitat partitioning and competitive displacement in Cattails (Typha): experimental field studies. American Naturalist, 118, 463–474.
Graham, C. H., Ferrier, S., Huettman, F., Moritz, C. and Peterson, A. T. 2004a. New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology and Evolution, 19, 497–503.
Graham, C. H., Ron, S. R., Santos, J. C., Schneider, C. J. and Moritz, C. 2004b. Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution, 58, 1781–1793.
Grant, P. R. and Grant, B. R. 2009. The secondary contact phase of allopatric speciation in Darwin’s finches. Proceedings of the National Academy of Sciences of the United States of America, 106, 20141–20148.
Gravel, D., Massol, F., Canard, E., Mouillot, D. and Mouquet, N. 2011. Trophic theory of island biogeography. Ecology Letters, 14, 1010–1016.
Graves, G. R. and Rahbek, C. 2005. Source pool geometry and the assembly of continental avifaunas. Proceedings of the National Academy of Sciences of the United States of America, 102, 7871–7876.
Green, R. H. 1971. Multivariate statistical approach to Hutchinsonian niche: bivalve molluscs of central Canada. Ecology, 52, 543–556.
Green, R. H. 1979. Sampling Design and Statistical Methods for Environmental Biologists. New York: John Wiley.
Grenouillet, G., Buisson, L., Casajus, N. and Lek, S. 2011. Ensemble modelling of species distribution: the effects of geographical and environmental ranges. Ecography, 34, 9–17.
Grinnell, J. 1917. The niche-relationships of the California Trasher. Auk, 34, 131–135.
Guillera-Arroita, G., Lahoz-Monfort, J. J. and Elith, J. 2014. Maxent is not a presence–absence method: a comment on Thibaud et al. Methods in Ecology and Evolution, 5, 1192–1197.
Guisan, A. and Harrell, F. E. 2000. Ordinal response regression models in ecology. Journal of Vegetation Science, 11, 617–626.
Guisan, A. and Hofer, U. 2003. Predicting reptile distributions at the mesoscale: relation to climate and topography. Journal of Biogeography, 30, 1233–1243.
Guisan, A. and Rahbek, C. 2011. SESAM: a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. Journal of Biogeography, 38, 1433–1444.
Guisan, A. and Theurillat, J. P. 2000. Equilibrium modeling of alpine plant distribution: how far can we go? Phytocoenologia, 30, 353–384.
Guisan, A. and Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993–1009.
Guisan, A. and Zimmermann, N. E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147–186.
Guisan, A., Weiss, S. B. and Weiss, A. D. 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecology, 143, 107–122.
Guisan, A., Edwards, J., Thomas, C. and Hastie, T. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157, 89–100.
Guisan, A., Broennimann, O., Engler, R., et al. 2006a. Using niche-based models to improve the sampling of rare species. Conservation Biology, 20, 501–511.
Guisan, A., Lehmann, A., Ferrier, S., et al. 2006b. Making better biogeographical predictions of species’ distributions. Journal of Applied Ecology, 43, 386–392.
Guisan, A., Graham, C. H., Elith, J., Huettmann, F. and NCEAS Species Distribution Modelling Group 2007a. Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions, 13, 332–340.
Guisan, A., Zimmermann, N. E., Elith, J., et al. 2007b. What matters for predicting the occurrences of trees: techniques, data, or species’ characteristics? Ecological Monographs, 77, 615–630.
Guisan, A., Petitpierre, B., Broennimann, O., et al. 2012. Response to comment on “Climatic niche shifts are rare among terrestrial plant invaders”. Science, 338, 193.
Guisan, A., Tingley, R., Baumgartner, J. B., et al. 2013. Predicting species distributions for conservation decisions. Ecology Letters, 16, 1424–1435.
Guisan, A., Petitpierre, B., Broennimann, O., Daehler, C. and Kueffer, C. 2014. Unifying niche shift studies: insights from biological invasions. Trends in Ecology and Evolution, 29, 260–269.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. and Tatham, R. L. 2006. Multivariate Data Analysis. Upper Saddle River, NJ: Pearson Prentice Hall.
Hakkarainen, H., Mykra, S., Kurki, S., Tornberg, R. and Jungell, S. 2004. Competitive interactions among raptors in boreal forests. Oecologia, 141, 420–424.
Halvorsen, R., Mazzoni, S., Bryn, A. and Bakkestuen, V. 2015. Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt. Ecography, 38, 172–183.
Hanberry, B. B., He, H. S. and Palik, B. J. 2012. Pseudoabsence generation strategies for species distribution models. Plos One, 7.
Hansen, M. C., Defries, R. S., Townshend, J. R. G. and Sohlberg, R. 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21, 1331–1364.
Hansen, M. C., DeFries, R. S., Townshend, J. R. G., et al. 2002. Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sensing of Environment, 83, 303–319.
Hanski, I. and Gilpin, M. E. 1997. Metapopulation Biology, San Diego, CA: Academic Press.
Hanspach, J., Kühn, I., Pompe, S. and Klotz, S. 2010. Predictive performance of plant species distribution models depends on species traits. Perspectives in Plant Ecology, Evolution and Systematics, 12, 219–225.
Hanssen, A. J. and Kuipers, W. J. 1965. On the relationship between the frequency of rain and various meteorological parameters. Meded Verhand, 81, 2–15.
Harrell, F. E. 2001. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Berlin: Springer.
Harrell, F. E., Lee, K. L. and Mark, D. B. 1996. Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine, 15, 361–387.
Harris, D. J. 2015. Generating realistic assemblages with a joint species distribution model. Methods in Ecology and Evolution, 6, 465–473.
Harte, J. 2011. Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics. London: Oxford University Press.
Hastie, T. and Tibishirani, R. 1986. Generalized additive models. Statistical Science, 1, 297–318.
Hastie, T. J. and Tibshirani, R. 1990. Generalized Additive Models, London: Chapman and Hall.
Hastie, T., Tibshirani, R. and Buja, A. 1994. Flexible discriminant analysis by optimal scoring. Journal of the American Statistical Association, 89(428), 1255–1270.
Hastie, T., Tibshirani, R. and Friedman, J. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Berlin: Springer.
Hastings, D. A., Dunbar, P. K., Elphingstone, G. M., et al. 1999. The Global Land One-kilometer Base Elevation (GLOBE) Digital Elevation Model, Version 1.0. Boulder, CO: National Oceanic and Atmospheric Administration, National Geophysical Data Center.
Hausser, J. (ed.) 1995. Säugetiere der Schweitz. Atlas des Mammifères de Suisse. Mammiferi della Svizzera. Basel: Birkhäuser Verlag.
Hautier, Y., Randin, C. F., Stocklin, J. and Guisan, A. 2009. Changes in reproductive investment with altitude in an alpine plant. Journal of Plant Ecology, 2, 125–134.
Hawkins, B. A. 2012a. Are multiple regression models of spatially structured data to be trusted? Journal of Biogeography, 39, 998.
Hawkins, B. A. 2012b. Eight (and a half) deadly sins of spatial analysis. Journal of Biogeography, 39, 1–9.
Hector, A., von Felten, S., Hautier, Y., Weilenmann, M. and Bruelheide, H. 2012. Effects of dominance and diversity on productivity along Ellenberg’s experimental water table gradients. Plos One, 7, e43358.
Heikkinen, R. K., Luoto, M., Araujo, M. B., et al. 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography, 30, 751–777.
Heikkinen, R. K., Luoto, M., Virkkala, R., Pearson, R. G. and Korber, J. H. 2007. Biotic interactions improve prediction of boreal bird distributions at macro-scales. Global Ecology and Biogeography, 16, 754–763.
Heinanen, S. and von Numers, M. 2009. Modelling species distribution in complex environments: an evaluation of predictive ability and reliability in five shorebird species. Diversity and Distributions, 15, 266–279.
Heinrichs, J. A., Bender, D. J., Gummer, D. L. and Schumaker, N. H. 2010. Assessing critical habitat: evaluating the relative contribution of habitats to population persistence. Biological Conservation, 143, 2229–2237.
Heller, H. C. and Gates, D. 1971. Altitudinal zonation of chipmunks (Eutamias): energy budgets. Ecology, 52, 424–443.
Hernandez, P. A., Graham, C. H., Master, L. L. and Albert, D. L. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29, 773–785.
Hijmans, R. J. 2012. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology, 93, 679–688.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. and Jarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965–1978.
Hintikka, V. 1963. Über das Großklima einiger Pflanzenareale in zwei Klimakoordinatensystemen dargestellt. Annales Botanici Societatis Zoologicae Botanicae Fennicae ‘Vanamo’. 34, 1–64.
Hirzel, A. and Guisan, A. 2002. Which is the optimal sampling strategy for habitat suitability modelling. Ecological Modelling, 157, 331–341.
Hirzel, A., Helfer, V. and Metral, F. 2001. Assessing habitat-suitability models with a virtual species. Ecological Modelling, 145, 111–121.
Hirzel, A. H. and Arlettaz, R. 2003. Modeling habitat suitability for complex species distributions by environmental-distance geometric mean. Environmental Management, 32, 614–623.
Hirzel, A. H., Hausser, J., Chessel, D. and Perrin, N. 2002a. Ecological niche factor analysis: how to compute habitat suitability maps without absence data? Ecology, 83, 2027–2036.
Hirzel, A. H., Hausser, J. and N., P. 2002b. Biomapper 2.0, Bern: Division of Conservation Biology.
Hirzel, A. H., Posse, B., Oggier, P. A., et al. 2004. Ecological requirements of reintroduced species and the implications for release policy: the case of the bearded vulture. Journal of Applied Ecology, 41, 1103–1116.
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. and Guisan, A. 2006. Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199, 142–152.
Hoerl, A. E. and Kennard, R. W. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 12, 55–67.
Holdridge, L. R. 1967. Life Zone Ecology, San Jose, Costa Rica: Tropical Science Center.
Hooten, M. B., Larsen, D. R. and Wikle, C. K. 2003. Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model. Landscape Ecology, 18, 487–502.
Hortal, J., De MarcoJr, P., Santos, A. and Diniz-Filho, J. A. F. 2012. Integrating biogeographical processes and local community assembly. Journal of Biogeography, 39, 627–628.
Hoskin, C. J., Higgie, M., McDonald, K. R. and Moritz, C. 2005. Reinforcement drives rapid allopatric speciation. Nature, 437, 1353–1356.
Howard, C., Stephens, P. A., Pearce-Higgins, J. W., Gregory, R. D. and Willis, S. G. 2014. Improving species distribution models: the value of data on abundance. Methods in Ecology and Evolution, 5, 506–513.
Howe, A. and Chain, P. S. 2015. Challenges and opportunities in understanding microbial communities with metagenome assembly (accompanied by IPython Notebook tutorial). Frontiers in Microbiology, 6, 678.
Hubbell, S. P. 2001. The Unified Neutral Theory of Biodiversity and Biogeography. Princeton, NJ: Princeton University Press.
Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309.
Hugall, A., Moritz, C., Moussalli, A. and Stanisic, J. 2002. Reconciling paleodistribution models and comparative phylogeography in the Wet Tropics rainforest land snail Gnarosophia bellendenkerensis (Brazier 1875). Proceedings of the National Academy of Sciences, 99, 6112–6117.
Hui, F. K., Warton, D. I. and Foster, S. D. 2015. Multi-species distribution modeling using penalized mixture of regressions. The Annals of Applied Statistics, 9, 866–882.
Huntley, B., Green, R. E., Collingham, Y. C., et al. 2004. The performance of models relating species geographical distributions to climate is independent of trophic level. Ecology Letters, 7, 417–426.
Hurtt, G. C., Chini, L. P., Frolking, S., et al. 2011. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change, 109, 117–161.
Huston, M. A. 2002. Introductory essay: critical issues for improving predictions. In Scott, J. M., Heglund, P. J., Morrison, M. L., et al. (eds.) Predicting Species Occurrences: Issues of Accuracy and Scale. Covelo, California: Island Press, pp. 7–21.
Hutchinson, G. E. 1957. Population studies. Animal ecology and demography: concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22, 415–427.
Hutchinson, M. F. 1995. Interpolating mean rainfall using thin-plate smoothing splines. International Journal of Geographical Information Systems, 9, 385–403.
Ings, T. C., Montoya, J. M., Bascompte, J., et al. 2009. Ecological networks: beyond food webs. Journal of Animal Ecology, 78, 253–269.
IPCC 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, NY: Cambridge University Press.
IPCC 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, NY: Cambridge University Press.
IUCN 2001. IUCN Red List Categories and Criteria: Version 3.1. IUCN Species Survival Commissio.n Gland, Switzerland and Cambridge, UK: IUCN.
Iverson, L. R. and Prasad, A. M. 1998. Predicting abundance of 80 tree species following climate change in the eastern United States. Ecological Monographs, 68, 465–485.
Iverson, L. R. and Prasad, A. M. 2001. Potential changes in tree species richness and forest community types following climate change. Ecosystems, 4, 186–199.
Iverson, L. R., Prasad, A. M., Matthews, S. N. and Peters, M. 2008. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management, 254, 390–406.
Ives, A. R. and Helmus, M. R. 2011. Generalized linear mixed models for phylogenetic analyses of community structure. Ecological Monographs, 81, 511–525.
Jaberg, C. and Guisan, A. 2001. Modelling the distribution of bats in relation to landscape structure in a temperate mountain environment. Journal of Applied Ecology, 38, 1169–1181.
Jackson, S. T. and Overpeck, J. T. 2000. Responses of plant populations and communities to environmental changes of the late Quaternary. Paleobiology, 26, 194–220.
Jackson, S. T. and Williams, J. W. 2004. Modern analogs in Quaternary paleoecology: here today, gone yesterday, gone tomorrow? Annual Review of Earth and Planetary Sciences, 32, 495–537.
Jalas, J. and Suominen, J. 1972–1996. The Atlas Florae Europaeae. Helsinki, Committee for Mapping the Flora of Europe and Societas Biologica Fennica Vanamo.
James, G., Witten, D., Hastie, T. and Tibshirani, R. 2013. An Introduction to Statistical Learning. Berlin: Springer.
Jamil, T., Ozinga, W. A., Kleyer, M. and ter Braak, C. J. 2013. Selecting traits that explain species–environment relationships: a generalized linear mixed model approach. Journal of Vegetation Science, 24, 988–1000.
Jardine, N. 1972. Computational methods in the study of plant distributions. In Valentine D. H. (ed.), Taxonomy, Phytogeography and Evolution. London: Academic Press, pp. 381–393.
Jeltsch, F., Moloney, K. A., Schurr, F. M., Kochy, M. and Schwager, M. 2008. The state of plant population modelling in light of environmental change. Perspectives in Plant Ecology Evolution and Systematics, 9, 171–189.
Jensen, O. P., Seppelt, R., Miller, T. J. and Bauer, L. J. 2005. Winter distribution of blue crab Callinectes sapidus in Chesapeake Bay: application and cross-validation of a two-stage generalized additive model. Marine Ecology-Progress Series, 299, 239–255.
Jimenez-Valverde, A. and Lobo, J. M. 2007. Threshold criteria for conversion of probability of species presence to either-or presence–absence. Acta Oecologica – International Journal of Ecology, 31, 361–369.
Jimenez-Valverde, A., Lobo, J. M. and Hortal, J. 2009. The effect of prevalence and its interaction with sample size on the reliability of species distribution models. Community Ecology, 10, 196–205.
Jimenez-Alfaro, B., Draper, D. and Nogues-Bravo, D. 2012. Modeling the potential area of occupancy at fine resolution may reduce uncertainty in species range estimates. Biological Conservation, 147, 190–196.
Johnson, C. J. and Gillingham, M. P. 2005. An evaluation of mapped species distribution models used for conservation planning. Environmental Conservation, 32, 117–128.
Johnson, C. J. and Gillingham, M. P. 2008. Sensitivity of species-distribution models to error, bias, and model design: an application to resource selection functions for woodland caribou. Ecological Modelling, 213, 143–155.
Johnson, J. B. and Omland, K. S. 2004. Model selection in ecology and evolution. Trends in Ecology and Evolution, 19, 101–108.
Johnston, T. H. 1924. The relation of climate to the spread of prickly pear. Transactions of the Royal Society of South Australia, 48, 269–295.
Jones, H. G. 1992. Plants and Microclimate. A Quantitative Approach to Environmental Plant Physiology, Cambridge, UK: Cambridge University Press.
Ju, J. C., Kolaczyk, E. D. and Gopal, S. 2003. Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing. Remote Sensing of Environment, 84, 550–560.
Kadmon, R., Farber, O. and Danin, A. 2004. Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecological Applications, 14, 401–413.
Kaplan, J. O., Krumhardt, K. M. and Zimmermann, N. E. 2009. The prehistoric and preindustrial deforestation of Europe. Quaternary Science Reviews, 28, 3016–3034.
Kauth, R. J. and Thomas, G. S. 1976. The tasselled cap: a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. LARS Symposia, 159.
Kearney, M. 2006. Habitat, environment and niche: what are we modelling? Oikos, 115, 186–191.
Kearney, M. and Porter, W. P. 2004. Mapping the fundamental niche: physiology, climate, and the distribution of a nocturnal lizard. Ecology, 85, 3119–3131.
Kearney, M. and Porter, W. 2009. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecology Letters, 12, 334–350.
Keil, P., Belmaker, J., Wilson, A. M., Unitt, P. and Jetz, W. 2013. Downscaling of species distribution models: a hierarchical approach. Methods in Ecology and Evolution, 4, 82–94.
Keil, P., Wilson, A. M. and Jetz, W. 2014. Uncertainty, priors, autocorrelation and disparate data in downscaling of species distributions. Diversity and Distributions, 20, 797–812.
Keith, D. A., Akcakaya, H. R., Thuiller, W., et al. 2008. Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models. Biology Letters, 4, 560–563.
Kerr, J. T. and Ostrovsky, M. 2003. From space to species: ecological applications for remote sensing. Trends in Ecology and Evolution, 18, 299–305.
Kharouba, H. M., McCune, J. L., Thuiller, W. and Huntley, B. 2013. Do ecological differences between taxonomic groups influence the relationship between species’ distributions and climate? A global meta-analysis using species distribution models. Ecography, 36, 657–664.
King, A. 1991. Translating models across scales in a landscape. In Turner, M. G. and Gardner, R. H. (eds.), Quantitative Methods in Landscape Ecology. New York, NY: Springer, pp. 479–517.
Kissling, W. D., Dormann, C. F., Groeneveld, J., et al. 2012. Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents. Journal of Biogeography, 39, 2163–2178.
Knutti, R. 2010. The end of model democracy? Climatic Change, 102, 395–404.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J. and Meehl, G. A. 2010. Challenges in combining projections from multiple climate models. Journal of Climate, 23, 2739–2758.
Körner, C. 2003. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems; With 47 Tables. Berlin: Springer.
Körner, C. 2007. The use of “altitude” in ecological research. Trends in Ecology and Evolution, 22, 569–574.
Körner, C. and Paulsen, J. 2004. A world-wide study of high altitude treeline temperatures. Journal of Biogeography, 31, 713–732.
Kremen, C., Cameron, A., Moilanen, A., et al. 2008. Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science, 320, 222–226.
Kühn, I. 2007. Incorporating spatial autocorrelation may invert observed patterns. Diversity and Distributions, 13, 66–69.
Kühn, I. and Dormann, C. F. 2012. Less than eight (and a half) misconceptions of spatial analysis. Journal of Biogeography, 39, 995–998.
Kühn, I., Nobis, M. P. and Durka, W. 2009. Combining spatial and phylogenetic eigenvector filtering in trait analysis. Global Ecology and Biogeography, 18, 745–758.
Lahoz-Monfort, J. J., Guillera-Arroita, G. and Wintle, B. A. 2014. Imperfect detection impacts the performance of species distribution models. Global Ecology and Biogeography, 23, 504–515.
Landis, J. R. and Koch, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics, 159–174.
Larsen, P. E., Scott, N., Post, A. F., et al. 2015. Satellite remote sensing data can be used to model marine microbial metabolite turnover. The ISME Journal, 9, 166–179.
Larson, M. A., Thompson, F. R., Millspaugh, J. J., Dijak, W. D. and Shifley, S. R. 2004. Linking population viability, habitat suitability, and landscape simulation models for conservation planning. Ecological Modelling, 180, 103–118.
Lassueur, T., Joost, S. and Randin, C. F. 2006. Very high resolution digital elevation models: Do they improve models of plant species distribution? Ecological Modelling, 198, 139–153.
Lavergne, S. and Molofsky, J. 2007. Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proceedings of the National Academy of Sciences of the United States of America, 104, 3883–3888.
Lawler, J. J., White, D., Neilson, R. P. and Blaustein, A. R. 2006. Predicting climate-induced range shifts: model differences and model reliability. Global Change Biology, 12, 1568–1584.
Lawler, J. J., Shafer, S. L., White, D., et al. 2009. Projected climate-induced faunal change in the Western Hemisphere. Ecology, 90, 588–597.
Le Cessie, S. and van Houwelingen, J. C. 1992. Ridge estimators in logistic regression. Applied Statistics, 41, 191–201.
Leathwick, J., Elith, J. and Hastie, T. 2006. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecological Modelling, 199, 188–196.
Leathwick, J., Moilanen, A., Francis, M., et al. 2008. Novel methods for the design and evaluation of marine protected areas in offshore waters. Conservation Letters, 1, 91–102.
Leathwick, J. R. 1998. Are New Zealand’s Nothofagus species in equilibrium with their environment? Journal of Vegetation Science, 9, 719–732.
Leathwick, J. R. 2002. Intra-generic competition among Nothofagus in New Zealand’s primary indigenous forests. Biodiversity and Conservation, 11, 2177–2187.
Leathwick, J. R. and Austin, M. P. 2001. Competitive interactions between tree species in New Zealand’s old-growth indigenous forests. Ecology, 82, 2560–2573.
Leathwick, J. R., Rowe, D., Richardson, J., Elith, J. and Hastie, T. 2005. Using multivariate adaptive regression splines to predict the distributions of New Zealand’s freshwater diadromous fish. Freshwater Biology, 50, 2034–2052.
Legendre, P. 1993. Spatial autocorrelation: trouble or new paradigm? Ecology, 74, 1659–1673.
Legendre, P., Dale, M. R., Fortin, M. J., et al. 2002. The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography, 25, 601–615.
Legendre, P., Borcard, D. and Peres-Neto, P. R. 2005. Analyzing beta diversity: partitioning the spatial variation of community composition data. Ecological Monographs, 75, 435–450.
Lehmann, A., Overton, J. M. and Leathwick, J. R. 2002. GRASP: generalized regression analysis and spatial prediction. Ecological Modelling, 157, 189–207.
Leibold, M. A. 1995. The niche concept revisited: mechanistic models and community context. Ecology, 76, 1371–1382.
Leibold, M. A. and McPeek, M. A. 2006. Coexistence of the niche and neutral perspectives in community ecology. Ecology, 87, 1399–1410.
Lek, S., Delacoste, M., Baran, P., et al. 1996. Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling, 90, 39–52.
Lenoir, J. and Svenning, J. C. 2015. Climate-related range shifts–a global multidimensional synthesis and new research directions. Ecography, 38, 15–28.
Lenoir, J., Gégout, J.-C., Marquet, P. A., de Ruffray, P. and Brisse, H. 2008. A significant upward shift in plant species optimum elevation during the 20th century. Science, 320, 1768–1771.
Lenoir, J., Gégout, J.-C., Guisan, A., et al. 2010. Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate. Ecography, 33, 295–303.
Lima-Ribeiro, M. S., Nogues-Bravo, D., Marske, K. A., et al. 2012. Human arrival scenarios have a strong influence on interpretations of the late Quaternary extinctions. Proceedings of the National Academy of Sciences of the United States of America, 109, E2409–E2410.
Linacre, J. 2008. The expected value of a point-biserial (or similar) correlation. Rasch Measurement Transactions, 22, 1154–1157.
Linder, H. P., Bykova, O., Dyke, J.,et al. 2012. Biotic modifiers, environmental modulation and species distribution models. Journal of Biogeography, 39, 2179–2190.
Link, W. A. and Barker, R. J. 2006. Model weights and the foundations of multimodel inference. Ecology, 87, 2626–2635.
Lira-Noriega, A. and Peterson, A. T. 2014. Range-wide ecological niche comparisons of parasite, hosts and dispersers in a vector-borne plant parasite system. Journal of Biogeography, 41, 1664–1673.
Lischke, H., Zimmermann, N. E., Bolliger, J., Rickebusch, S. and Loffler, T. J. 2006. TreeMig: A forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecological Modelling, 199, 409–420.
Lischke, H., Löffler, T. J., Thornton, P. E. and Zimmermann, N. E. 2007. Model up-scaling in landscape research. In Kienast, F., Wildi, O. and Ghosh, S. (eds), A Changing World: Challenges for Landscape Research. Dordrecht, The Netherlands: Springer.
Liu, C., White, M. and Newell, G. 2013. Selecting thresholds for the prediction of species occurrence with presence-only data. Journal of Biogeography, 40, 778–789.
Liu, C. R., Berry, P. M., Dawson, T. P. and Pearson, R. G. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography, 28, 385–393.
Liu, C. R., White, M. and Newell, G. 2011. Measuring and comparing the accuracy of species distribution models with presence–absence data. Ecography, 34, 232–243.
Loarie, S. R., Duffy, P. B., Hamilton, H., et al. 2009. The velocity of climate change. Nature, 462, 1052-U111.
Lobo, J. M., Jimenez-Valverde, A. and Real, R. 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17, 145–151.
Lobo, J. M. and Tognelli, M. F. 2011. Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data. Journal for Nature Conservation, 19, 1–7.
Loiselle, B. A., Howell, C. A., Graham, C. H., et al. 2003. Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology, 17, 1591–1600.
Loiselle, B. A., Jorgensen, P. M., Consiglio, T., et al. 2008. Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? Journal of Biogeography, 35, 105–116.
Lomba, A., Pellissier, L., Randin, C., et al. 2010. Overcoming the rare species modelling paradox: a novel hierarchical framework applied to an Iberian endemic plant. Biological Conservation, 143, 2647–2657.
Lomolino, M. V., Riddle, B. R., Whittaker, R. J. and Brown, J. H. 2010. Biogeography. Sunderland, MA: Sinauer Associates.
Lorenzen, E. D., Nogues-Bravo, D., Orlando, L., et al. 2011. Species-specific responses of Late Quaternary megafauna to climate and humans. Nature, 479, 359-U195.
Lortie, C. J., Brooker, R. W., Choler, P., et al. 2004. Rethinking plant community theory. Oikos, 107, 433–438.
Lowry, J., Ramsey, R. D., Thomas, K., et al. 2007. Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework: a case study of the Southwest Regional Gap Analysis Project (SWReGAP). Remote Sensing of Environment, 108, 59–73.
Luoto, M., Heikkinen, R. K., Poyry, J. and Saarinen, K. 2006. Determinants of the biogeographical distribution of butterflies in boreal regions. Journal of Biogeography, 33, 1764–1778.
Luoto, M., Virkkala, R. and Heikkinen, R. K. 2007. The role of land cover in bioclimatic models depends on spatial resolution. Global Ecology and Biogeography, 16, 34–42.
Lyet, A., Thuiller, W., Cheylan, M. and Besnard, A. 2013. Fine-scale regional distribution modelling of rare and threatened species: bridging GIS Tools and conservation in practice. Diversity and Distributions, 19, 651–663.
Lyons, K. G. and Schwartz, M. W. 2001. Rare species loss alters ecosystem function–invasion resistance. Ecology Letters, 4, 358–365.
Lyons, K. G., Brigham, C., Traut, B. and Schwartz, M. W. 2005. Rare species and ecosystem functioning. Conservation Biology, 19, 1019–1024.
Mac Nally, R. 2000. Regression and model-building in conservation biology, biogeography and ecology: the distinction between – and reconciliation of – “predictive” and “explanatory” models. Biodiversity and Conservation, 9, 655–671.
MacArthur, R. H. 1968. The theory of the niche. In Lewontin, R. C. (ed.), Population Biology and Evolution. Syracuse, NY: Syracuse University Press.
MacArthur, R. H. 1972. Geographical Ecology. New-York: Harper & Row.
Maggini, R., Lehmann, A., Zimmermann, N. E. and Guisan, A. 2006. Improving generalized regression analysis for the spatial prediction of forest communities. Journal of Biogeography, 33, 1729–1749.
Maher, S. P., Randin, C. F., Guisan, A. and Drake, J. M. 2014. Pattern-recognition ecological niche models fit to presence-only and presence–absence data. Methods in Ecology and Evolution, 5, 761–770.
Maiorano, L., Cheddadi, R., Zimmerman, N. E., et al. 2013. Building the niche through time: using 13,000 years of data to predict the effects of climate change on tree species in Europe. Global Ecology and Biogeography, 22, 302–317.
Malanson, G. P., Westman, W. E. and Yan, Y.-L. 1992. Realized versus fundamental niche functions in a model of chaparral response to climatic change. Ecological Modelling, 64, 261–277.
Manel, D., Dias, J. M., Buckton, S. T. and Ormerod, S. J. 1999a. Alternative methods for predicting species distribution: an illustration with Himalayan river birds. Journal of Applied Ecology, 36, 734–747.
Manel, S., Dias, J.-M. and Ormerod, S. J. 1999b. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological Modelling, 120, 337–347.
Manel, S., Williams, H. C. and Ormerod, S. J. 2001. Evaluating presence–absence models in ecology: the need to account for prevalence. Journal of Applied Ecology, 38, 921–931.
Manly, B. F. J. (ed.) 2006. Randomization, Bootstrap and Monte Carlo Methods in Biology. 3rd edition. London: Chapman & Hall/CRC.
Marcelli, M., Polednik, L., Polednikova, K. and Fusillo, R. 2012. Land use drivers of species re-expansion: inferring colonization dynamics in Eurasian otters. Diversity and Distributions.
Margules, C. R. and Austin, M. P. 1991. Nature Conservation: Cost Effective Biological Survey and Data Analysis. Canberra, Australia: CSIRO.
Marion, G., McInerny, G. J., Pagel, J., et al. 2012. Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche. Journal of Biogeography, 39, 2225–2239.
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K. and Thuiller, W. 2009. Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions, 15, 59–69.
Martínez-Meyer, E., Peterson, A. T. and Hargrove, W. W. 2004. Ecological niches as stable distributional constraints on mammal species, with implications for Pleistocene extinctions and climate change projections for biodiversity. Global Ecology and Biogeography, 13, 305–314.
Mateo, R. G., Felicísimo, Á. M., Pottier, J., Guisan, A. and Muñoz, J. 2012. Do stacked species distribution models reflect altitudinal diversity patterns? Plos One, 7, e32586.
Mateo, R. G., Broennimann, O., Petitpierre, B., et al. 2015. What is the potential of spread in invasive bryophytes? Ecography, 38(5), 480–487.
Mathys, L., Guisan, A., Kellenberger, T. W. and Zimmermann, N. E. 2009. Evaluating effects of spectral training data distribution on continuous field mapping performance. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 665–673.
Mauri, A., Davis, B. A. S. and Kaplan, J. O. 2014. The influence of atmospheric circulation on the mid-holocene climate of Europe: a data–model comparison. Climate of the Past, 10, 1925–1938.
McCullagh, P. and Nelder, J. A. 1989a. Generalized Linear Models. London: Chapman & Hall.
McCullagh, P. and Nelder, J. A. 1989b. Generalized Linear Models. 2nd edition. London: Chapman & Hall.
McGill, B. J. 2010. Matters of scale. Science, 328, 575–576.
McInerny, G. J. and Purves, D. W. 2011. Fine-scale environmental variation in species distribution modelling: regression dilution, latent variables and neighbourly advice. Methods in Ecology and Evolution, 2, 248–257.
McPherson, J. M., Jetz, W. and Rogers, D. J. 2004. The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? Journal of Applied Ecology, 41, 811–823.
McPherson, J. M., Jetz, W. and Rogers, D. J. 2006. Using coarse-grained occurrence data to predict species distributions at finer spatial resolutions-possibilities and limitations. Ecological Modelling, 192, 499–522.
Meehl, G. A., Goddard, L., Murphy, J., et al. 2009. Decadal prediction: can it be skilful? Bulletin of the American Meteorological Society, 90, 1467–1485.
Meentemeyer, R. K., Anacker, B. L., Mark, W. and Rizzo, D. M. 2008. Early detection of emerging forest disease using dispersal estimation and ecological niche modeling. Ecological Applications, 18, 377–390.
Meier, E. S., Kienast, F., Pearman, P. B., et al. 2010. Biotic and abiotic variables show little redundancy in explaining tree species distributions. Ecography, 33, 1038–1048.
Meier, E. S., Edwards, T. C., Kienast, F., Dobbertin, M. and 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. Journal of Biogeography, 38, 371–382.
Meier, E. S., Lischke, H., Schmatz, D. R. and Zimmermann, N. E. 2012. Climate, competition and connectivity affect future migration and ranges of European trees. Global Ecology and Biogeography, 21, 164–178.
Meller, L., Cabeza, M., Pironon, S., et al. 2014. Ensemble distribution models in conservation prioritization: from consensus predictions to consensus reserve networks. Diversity and Distributions, 20, 309–321.
Menard, S. W. 2002. Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage.
Merow, C., Smith, M. J., Edwards, T. C., et al. 2014. What do we gain from simplicity versus complexity in species distribution models? Ecography, 37, 1267–1281.
Mesgaran, M. B., Cousens, R. D. and Webber, B. L. 2014. Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity and Distributions, 20, 1147–1159.
Meyer, C., Kreft, H., Guralnick, R. and Jetz, W. 2015. Global priorities for an effective information basis of biodiversity distributions. Nature Communications, 6.
Meynard, C. N. and Kaplan, D. M. 2013. Using virtual species to study species distributions and model performance. Journal of Biogeography, 40, 1–8.
Meynard, C. N. and Quinn, J. F. 2007. Predicting species distributions: a critical comparison of the most common statistical models using artificial species. Journal of Biogeography, 34, 1455–1469.
Meyneeke, J. O. 2004. Effects of global climate change on geographic distributions of vertebrates in North Queensland. Ecological Modelling, 174, 347–357.
Mitchell, M. S., Lancia, R. A. and Gerwin, J. A. 2001. Using landscape-level data to predict the distribution of birds on a managed forest: effects of scale. Ecological Applications, 11, 1692–1708.
Mod, H. K., Scherrer, D., Luoto, M. and Guisan, A. 2016. What we use is not what we know: environmental predictors in plant distribution models. Journal of Vegetation Science, 27(6), 1308–1322.
Moisen, G. G. and Frescino, T. S. 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling, 157, 209–225.
Mokany, K., Harwood, T. D., Williams, K. J. and Ferrier, S. 2012. Dynamic macroecology and the future for biodiversity. Global Change Biology, 18, 3149–3159.
Moloney, K. A. and Jeltsch, F. 2008. Space matters: novel developments in plant ecology through spatial modelling. Perspectives in Plant Ecology Evolution and Systematics, 9, 119–120.
Monserud, R. A. and Leemans, R. 1992. Comparing global vegetation maps with the Kappa statistic. Ecological Modelling, 62, 275–293.
Montgomery, D. C. and Peck, E. A. 1982. Introduction to Linear Regression Analysis. New York, Wiley.
Morales, J. M., Moorcroft, P. R., Matthiopoulos, J., et al. 2010. Building the bridge between animal movement and population dynamics. Philosophical Transactions of the Royal Society B: Biological Sciences, 365, 2289–2301.
Moretti, M., Conedera, M., Moresi, R. and Guisan, A. 2006. Modelling the influence of change in fire regime on the local distribution of a Mediterranean pyrophytic plant species (Cistus salviifolius) at its northern range limit. Journal of Biogeography, 33, 1492–1502.
Morgan, J. N. and Sonquist, J. A. 1963. Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association, 58, 415–434.
Moritz, C., Patton, J. L., Conroy, C. J.,et al. 2008. Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science, 322, 261–264.
Mouillot, D., Bellwood, D. R., Baraloto, C., et al.2013. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biology, 11, e1001569.
Münkemüller, T., De Bello, F., Meynard, C., et al. 2012. From diversity indices to community assembly processes: a test with simulated data. Ecography, 35, 468–480.
Münkemüller, T., Boucher, F. C., Thuiller, W. and Lavergne, S. 2015. Phylogenetic niche conservatism: common pitfalls and ways forward. Functional Ecology, 29(5), 627–639.
Murphy, A. H. and Winkler, R. L. 1987. A general framework for forecast verification. Monthly Weather Review, 115, 1330–1338.
Naujokaitis-Lewis, I. R., Curtis, J. M., Tischendorf, L., et al. 2013. Uncertainties in coupled species distribution–metapopulation dynamics models for risk assessments under climate change. Diversity and Distributions, 19, 541–554.
Nenzén, H. K. and Araújo, M. 2011. Choice of threshold alters projections of species range shifts under climate change. Ecological Modelling, 222, 3346–3354.
Nenzén, H. K., Swab, R. M., Keith, D. A. and Araújo, M. B. 2012. demoniche: an R-package for simulating spatially-explicit population dynamics. Ecography, 35, 577–580.
Nesme, J., Achouak, W., Agathos, S. N., et al. 2016. Back to the future of soil metagenomics. Frontiers in Microbiology, 7, 73.
Newbold, T., Reader, T., El-Gabbas, A., et al. 2010. Testing the accuracy of species distribution models using species records from a new field survey. Oikos, 119, 1326–1334.
Nieto-Lugilde, D., Lenoir, J., Abdulhak, S., et al. 2015. Tree cover at fine and coarse spatial grains interacts with shade tolerance to shape plant species distributions across the Alps. Ecography, 38, 578–589.
Nix, H., McMahon, J. and Mackenzie, D. 1977. Potential areas of production and the future of pigeon pea and other grain legumes in Australia. In Wallis, E. Amp, S., and Whiteman, P. C. (eds), The Potential for Pigeon Pea in Australia. Proceedings of Pigeon Pea (Cajanus cajan (L.) Millsp.) Field Day. Queensland, Australia: University of Queensland, pp. 5/1–5/12.
Nogues-Bravo, D. 2009. Predicting the past distribution of species climatic niches. Global Ecology and Biogeography, 18, 521–531.
Nogues-Bravo, D., Rodiguez, J., Hortal, J., Batra, P. and Araujo, M. B. 2008. Climate change, humans, and the extinction of the woolly mammoth. Plos Biology, 6, 685–692.
Normand, S., Treier, U. A., Randin, C., Vittoz, P., Guisan, A. and Svenning, J. C. 2009. Importance of abiotic stress as a range-limit determinant for European plants: insights from species responses to climatic gradients. Global Ecology and Biogeography, 18, 437–449.
Normand, S., Ricklefs, R. E., Skov, F., Bladt, J., Tackenberg, O. and Svenning, J.-C. 2011. Postglacial migration supplements climate in determining plant species ranges in Europe. Proceedings of the Royal Society of London B: Biological Sciences, 278, 3644–3653.
Normand, S., Randin, C., Ohlemueller, R., et al. 2013. A greener Greenland? Climatic potential and long-term constraints on future expansions of trees and shrubs. Philosophical Transactions of the Royal Society B-Biological Sciences, 368.
O’Neill, R. V., DeAngelis, D. L., Waide, J. B. and Allen, T. F. H. 1986. A hierarchical concept of ecosystems, Princeton, NJ, USA, Princeton University Press.
Olden, J. D. 2003. A species-specific approach to modeling biological communities and its potential for conservation. Conservation Biology, 17, 854–863.
Olwoch, J. M., Rautenbach, C. J. D., Erasmus, B. F. N., Engelbrecht, F. A. and van Jaarsveld, A. S. 2003. Simulating tick distributions over sub-Saharan Africa: the use of observed and simulated climate surfaces. Journal of Biogeography, 30, 1221–1232.
Osborne, P. E. and Suarez-Seoane, S. 2002. Should data be partitioned spatially before building large-scale distribution models? Ecological Modelling, 157, 249–259.
Osborne, P. E., Foody, G. M. and Suárez-Seoane, S. 2007. Non-stationarity and local approaches to modelling the distributions of wildlife. Diversity and Distributions, 13, 313–323.
Ottaviani, D., Lasinio, G. J. and Boitani, L. 2004. Two statistical methods to validate habitat suitability models using presence-only data. Ecological Modelling, 179, 417–443.
Oulas, A., Pavloudi, C., Polymenakou, P., et al. 2015. Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies. Bioinformatics and Biology Insights, 9, 75.
Ovaskainen, O. and Meerson, B. 2010. Stochastic models of population extinction. Trends in Ecology and Evolution, 25, 643–652.
Ovaskainen, O. and Soininen, J. 2011. Making more out of sparse data: hierarchical modeling of species communities. Ecology, 92, 289–295.
Pagel, J. and Schurr, F. M. 2012. Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics. Global Ecology and Biogeography, 21, 293–304.
Parviainen, M., Marmion, M., Luoto, M., Thuiller, W. and Heikkinen, R. K. 2009. Using summed individual species models and state-of-the-art modelling techniques to identify threatened plant species hotspots. Biological Conservation, 142, 2501–2509.
Patsiou, T. S., Conti, E., Zimmermann, N. E., Theodoridis, S. and Randin, C. F. 2014. Topo-climatic microrefugia explain the persistence of a rare endemic plant in the Alps during the last 21 millennia. Global Change Biology, 20, 2286–2300.
Pearce, J. and Ferrier, S. 2000. Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling, 133, 225–245.
Pearce, J. and Ferrier, S. 2001. The practical value of modelling relative abundance of species for regional conservation planning: a case study. Biological Conservation, 98, 33–43.
Pearce, J. and Lindenmayer, D. 1998. Bioclimatic analysis to enhance reintroduction biology of the endangered helmeted honeyeater (Lichenostomus melanops cassidix) in southeastern Australia. Restoration Ecology, 6, 238–243.
Pearce, J. L. and Boyce, M. S. 2006. Modelling distribution and abundance with presence-only data. Journal of Applied Ecology, 43, 405–412.
Pearlstine, L. G., Smith, S. E., Brandt, L. A., et al. 2002. Assessing state-wide biodiversity in the Florida Gap analysis project. Journal of Environmental Management, 66, 127–144.
Pearman, P. B., Guisan, A., Broennimann, O. and Randin, C. F. 2008a. Niche dynamics in space and time. Trends in Ecology and Evolution, 23, 149–158.
Pearman, P. B., Randin, C. F., Broennimann, O., et al. 2008b. Prediction of plant species distributions across six millennia. Ecology Letters, 11, 357–369.
Pearman, P. B., D’Amen, M., Graham, C. H., Thuiller, W. and Zimmermann, N. E. 2010. Within-taxon niche structure: niche conservatism, divergence and predicted effects of climate change. Ecography, 33, 990–1003.
Pearson, R. G. and Dawson, T. P. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography, 12, 361–372.
Pearson, R. G., Dawson, T. P., Berry, P. M. and Harrison, P. A. 2002. SPECIES: a spatial evaluation of climate impact on the envelope of species. Ecological Modelling, 154, 289–300.
Pearson, R. G., Dawson, T. P. and Liu, C. 2004. Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography, 27, 285–298.
Pearson, R. G., Thuiller, W., Araujo, M. B., et al. 2006. Model-based uncertainty in species range prediction. Journal of Biogeography, 33, 1704–1711.
Pearson, R. G., Raxworthy, C. J., Nakamura, M. and Peterson, A. T. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34, 102–117.
Pellet, J., Guisan, A. and Perrin, N. 2004. A concentric analysis of the impact of urbanization on the threatened European tree frog in an agricultural landscape. Conservation Biology, 18, 1599–1606.
Pellissier, L., Brathen, K. A., Pottier, J., et al. 2010. Species distribution models reveal apparent competitive and facilitative effects of a dominant species on the distribution of tundra plants. Ecography, 33, 1004–1014.
Pellissier, L., Alvarez, N. and Guisan, A. 2012a. Pollinators as drivers of plant distribution and assemblage into communities. In Patiny, S. (ed.), Evolution of Plant–Pollinator Interactions. Cambridge: Cambridge University Press.
Pellissier, L., Fiedler, K., Ndribe, C., et al. 2012b. Shifts in species richness, herbivore specialization, and plant resistance along elevation gradients. Ecology and Evolution, 2, 1818–1825.
Pellissier, L., Bråthen, K. A., Vittoz, P. A., et al. 2013a. Thermal niches are more conserved at cold than warm limits in arctic-alpine plant species. Global Ecology and Biogeography, 22, 933–941.
Pellissier, L., Meltofte, H., Hansen, J., et al. 2013b. Suitability, success and sinks: how do predictions of nesting distributions relate to fitness parameters in high arctic waders? Diversity and Distributions, 19, 1496–1505.
Pellissier, L., Pinto-Figueroa, E., Niculita-Hirzel, H., et al. 2013c. Plant species distributions along environmental gradients: do belowground interactions with fungi matter? Frontiers in plant science, 4, 1–9.
Pellissier, L., Rohr, R. P., Ndiribe, C., et al. 2013d. Combining food web and species distribution models for improved community projections. Ecology and Evolution, 3, 4572–4583.
Peltier, W. R. 2004. Global glacial isostasy and the surface of the ice-age earth: The ice-5G (VM2) model and grace. Annual Review of Earth and Planetary Sciences, 32, 111–149.
Peppler-Lisbach, C. and Schroder, B. 2004. Predicting the species composition of Nardus stricta communities by logistic regression modelling. Journal of Vegetation Science, 15, 623–634.
Peters, J., De Baets, B., Verhoest, N. E. C., et al. 2007. Random forests as a tool for ecohydrological distribution modelling. Ecological Modelling, 207, 304–318.
Peters, R. H. 1991. A Critique for Ecology, Cambridge, UK: Cambridge University Press.
Peterson, A. T. 2003. Predicting the geography of species’ invasions via ecological niche modeling. Quarterly Review of Biology, 78, 419–433.
Peterson, A. T. 2006. Ecologic niche modeling and spatial patterns of disease transmission. Emerging Infectious Diseases, 12, 1822–1826.
Peterson, A. T. 2011. Ecological niche conservatism: a time-structured review of evidence. Journal of Biogeography, 38, 817–827.
Peterson, A. T., Ortega-Huerta, M. A., Bartley, J., et al. 2002a. Future projections for Mexican faunas under global climatic change scenarios. Nature, 416, 626–629.
Peterson, A. T., Sanchez-Cordero, V., Ben Beard, C. and Ramsey, J. M. 2002b. Ecologic niche modeling and potential reservoirs for Chagas disease, Mexico. Emerging Infectious Diseases, 8, 662–667.
Peterson, A. T., Papes, M. and Soberon, J. 2008a. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling, 213, 63–72.
Peterson, A. T., Stewart, A., Mohamed, K. I. and Araújo, M. B. 2008b. Shifting global invasive potential of European plants with climate change. PLoS One, 3, e2441.
Peterson, A. T., Soberon, J., Pearson, R. G., et al. 2011. Ecological Niches and Geographic Distributions, Princeton, NJ: Princeton University Press.
Petitpierre, B., Kueffer, C., Broennimann, O., et al. 2012. Climatic niche shifts are rare among terrestrial plant invaders. Science, 335, 1344–1348.
Petitpierre, B., McDougall, K., Seipel, T., et al. 2016. Will climate change increase the risk of plant invasions into mountains? Ecological Applications, 26, 530–544.
Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C. and Guisan, A. 2017. Selecting predictors to maximize the transferability of species distribution models: lessons from cross-continental plants invasions. Global Ecology and Biogeography, 26(3), 275–287.
Phillips, S. J. and Elith, J. 2010. POC plots: calibrating species distribution models with presence-only data. Ecology, 91, 2476–2484.
Phillips, S. J. and Dudík, M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161–175.
Phillips, S. J., Dudík, M. and Schapire, R. E. 2004. A maximum entropy approach to species distribution modeling. In Proceedings of the 21st International Conference on Machine Learning. Association for Computing Machinery: Banff, Canada, p. 83.
Phillips, S. J., Anderson, R. P. and Schapire, R. E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259.
Phillips, S. J., Dudik, M., Elith, J., et al. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications, 19, 181–197.
Pickett, S. T. A. and Bazzaz, F. A. 1978. Organization of an assemblage of early successional species on a soil-moisture gradient. Ecology, 59, 1248–1255.
Pio, D. V., Engler, R., Linder, H. P., et al. 2014. Climate change effects on animal and plant phylogenetic diversity in southern Africa. Global Change Biology, 20, 1538–1549.
Polis, G. A., Anderson, W. B. and Holt, R. D. 1997. Toward an integration of landscape and food web ecology: the dynamics of spatially subsidized food webs. Annual Review of Ecology and Systematics, 28, 289–316.
Pollock, L. J., Morris, W. K. and Vesk, P. A. 2012. The role of functional traits in species distributions revealed through a hierarchical model. Ecography, 35, 716–725.
Pollock, L. J., Tingley, R., Morris, W. K., et al. 2014. Understanding co-occurrence by modelling species simultaneously with a joint species distribution model (JSDM). Methods in Ecology and Evolution, 5, 397–406.
Pongratz, J., Reick, C., Raddatz, T. and Claussen, M. 2008. A reconstruction of global agricultural areas and land cover for the last millennium. Global Biogeochemical Cycles, 22, GB3018, 1–16.
Pottier, J., Dubuis, A., Pellissier, L., et al. 2013. The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients. Global Ecology and Biogeography, 22, 52–63.
Pottier, J., Malenovský, Z., Psomas, A., et al. 2014. Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy. Biology Letters, 10, 20140347.
Pradervand, J.-N., Dubuis, A., Pellissier, L., Guisan, A. and Randin, C. 2014. Very high resolution environmental predictors in species distribution models: moving beyond topography? Progress in Physical Geography, 38, 79–96.
Prasad, A. M., Iverson, L. R. and Liaw, A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9, 181–199.
Pulliam, H. R. 2000. On the relationship between niche and distribution. Ecology Letters, 3, 349–361.
Quetier, F., Rivoal, F., Marty, P., et al. 2010. Social representations of an alpine grassland landscape and socio-political discourses on rural development. Regional Environmental Change, 10, 119–130.
Quinlan, J. R. 1986. Induction of decision trees. Machine Learning, 1, 81–106.
Ramankutty, N. and Foley, J. A. 1999. Estimating historical changes in global land cover: croplands from 1700 to 1992. Global Biogeochemical Cycles, 13, 997–1027.
Randin, C. F., Dirnbock, T., Dullinger, S., et al. 2006. Are niche-based species distribution models transferable in space? Journal of Biogeography, 33, 1689–1703.
Randin, C. F., Engler, R., Normand, S., et al. 2009. Climate change and plant distribution: local models predict high-elevation persistence. Global Change Biology, 15, 1557–1569.
Randin, C. F., Paulsen, J., Vitasse, Y., et al. 2013. Do the elevational limits of deciduous tree species match their thermal latitudinal limits? Global Ecology and Biogeography, 22, 913–923.
Raxworthy, C. J., Martinez-Meyer, E., Horning, N., et al. 2003. Predicting distributions of known and unknown reptile species in Madagascar. Nature, 426, 837–841.
Regan, H. M., Hierl, L. A., Franklin, J., et al. 2008. Species prioritization for monitoring and management in regional multiple species conservation plans. Diversity and Distributions, 14, 462–471.
Reid, P. C., Lancelot, C., Gieskes, W. W. C., Hagmeier, E. and Weichart, G. 1990. Phytoplankton of the North-Sea and its dynamics: a review. Netherlands Journal of Sea Research, 26, 295–331.
Reineking, B. and Schroder, B. 2006. Constrain to perform: regularization of habitat models. Ecological Modelling, 193, 675–690.
Renner, I. W. and Warton, D. I. 2013. Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics, 69, 274–281.
Renner, I. W., Elith, J., Baddeley, A., et al. 2015. Point process models for presence-only analysis. Methods in Ecology and Evolution, 6, 366–379.
Richardson, D. M., Pysek, P., Rejmanek, M., et al. 2000. Naturalization and invasion of alien plants: concepts and definitions. Diversity and Distributions, 6, 93–107.
Ricklefs, R. E. 1987. Community diversity: relative roles of local and regional processes. Science, 235, 167–171.
Ricklefs, R. E. 2008. Disintegration of the ecological community. The American Naturalist, 172, 741–750.
Ridgeway, G. 1999. The state of boosting. Computing Science and Statistics, 31, 172–181.
Ripley, B. D. 1996. Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.
Robertson, M. P., Caithness, N. and Villet, M. H. 2001. A PCA-based modelling technique for predicting environmental suitability for organisms from presence records. Diversity and Distributions, 7, 15–27.
Robertson, M. P., Villet, M. H. and Palmer, A. R. 2004. A fuzzy classification technique for predicting species’ distributions: applications using invasive alien plants and indigenous insects. Diversity and Distributions, 10.
Robinson, A. P., Lane, S. E. and Thérien, G. 2011. Fitting forestry models using generalized additive models: a taper model example. Canadian Journal of Forest Research, 41, 1909–1916.
Rödder, D., Nekum, S., Cord, A. F. and Engler, J. O. 2016. Coupling satellite data with species distribution and connectivity models as a tool for environmental management and planning in matrix-sensitive species. Environmental Management, 1–14.
Rodríguez, J. P., Brotons, L., Bustamante, J. and Seoane, J. 2007. The application of predictive modelling of species distribution to biodiversity conservation. Diversity and Distributions, 13, 243–251.
Root, T. L., Price, J. T., Hall, K. R., et al. 2003. Fingerprints of global warming on wild animals and plants. Nature, 421, 57–60.
Roura-Pascual, N., Suarez, A. V., Gomez, C., et al. 2004. Geographical potential of Argentine ants (Linepithema humile Mayr) in the face of global climate change. Proceedings of the Royal Society B: Biological Sciences, 271, 2527–2534.
Roura-Pascual, N., Suarez, A. V., McNyset, K. M., et al. 2006. Niche differentiation and fine-scale projections for Argentine ants based on remotely sensed data. Ecological Applications, 16, 1832–1841.
Rouse, J. W., Haas R. H., Schell J. A. and Deering D. W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. In Fraden S. C., Marcanti E. P. and Becker M. A. (eds), Third ERTS-1 Symposium, 10–14 Dec. 1973, NASA SP-351, Washington DC: NASA, pp. 309–317.
Rykiel, E. J. J. 1996. Testing ecological models: the meaning of validation. Ecological Modelling, 90, 229–244.
Sala, O. E., Chapin, F. S., Armesto, J. J., et al. 2000. Global biodiversity scenarios for the year 2100. Science, 287, 1770–1774.
Salisbury, E. J. 1929. The biological equipment of species in relation to competition. Journal of Ecology, 17, 197–222.
Saltre, F., Saint-Amant, R., Gritti, E. S., et al. 2013. Climate or migration: what limited European beech post-glacial colonization? Global Ecology and Biogeography, 22, 1217–1227.
Sandel, B., Arge, L., Dalsgaard, B., et al. 2011. The Influence of Late Quaternary climate-change velocity on species endemism. Science, 334, 660–664.
Sargent, R. D. and Ackerly, D. D. 2008. Plant–pollinator interactions and the assembly of plant communities. Trends in Ecology and Evolution, 23, 123–130.
Scherrer, D., Massy, S., Meier, S., Vittoz, P. and Guisan, A. 2017. Assessing and predicting shifts in mountain forest composition across 25 years of climate change. Diversity and Distributions, 23, 517–528.
Schlossberg, S. and King, D. I. 2009. Modeling animal habitats based on cover types: a critical review. Environmental Management, 43, 609–618.
Schneider, D. C. 2001. The rise of the concept of scale in ecology. Bioscience, 51, 545–553.
Schoener, T. W. 1989. Food webs from the small to the large. Ecology, 70, 1559–1589.
Schorr, G., Holstein, N., Pearman, P. B., Guisan, A. and Kadereit, J. W. 2012. Integrating species distribution models (SDMs) and phylogeography for two species of Alpine primula. Ecology and Evolution, 2, 1260–1277.
Schorr, G., Pearman, P. B., Guisan, A. and Kadereit, J. W. 2013. Combining palaeodistribution modelling and phylogeographical approaches for identifying glacial refugia in Alpine primula. Journal of Biogeography, 40, 1947–1960.
Schurr, F. M., Pagel, J., Cabral, J. S., et al. 2012. How to understand species’ niches and range dynamics: a demographic research agenda for biogeography. Journal of Biogeography, 39, 2146–2162.
Schwartz, M. W. 2012. Using niche models with climate projections to inform conservation management decisions. Biological Conservation, 155, 149–156.
Schwarz, M. and Zimmermann, N. E. 2005. A new GLM-based method for mapping tree cover continuous fields using regional MODIS reflectance data. Remote Sensing of Environment, 95, 428–443.
Scott, J. M., Davis, F., Csuti, B., et al. 1993. Gap analysis: a geographic approach to protection of biological diversity. Wildlife Monographs, 123, 1–41.
Scott, J. M., Davis, F. W., McGhie, R. G., et al. 2001. Nature reserves: do they capture the full range of America’s biological diversity? Ecological Applications, 11, 999–1007.
Scott, J. M., Heglund, P. J., Haufler, J. B., et al. (eds) 2002. Predicting Species Occurrences: Issues of Accuracy and Scale, Covelo, CA: Island Press.
Segurado, P. and Araujo, M. B. 2004. An evaluation of methods for modelling species distributions. Journal of Biogeography, 31, 1555–1568.
Segurado, P., Araujo, M. B. and Kunin, W. E. 2006. Consequences of spatial autocorrelation for niche-based models. Journal of Applied Ecology, 43, 433–444.
Seo, C., Thorne, J. H., Hannah, L. and Thuiller, W. 2009. Scale effects in species distribution models: implications for conservation planning under climate change. Biology Letters, 5, 39–43.
Serra-Diaz, J. M., Franklin, J., Ninyerola, M., et al. 2014. Bioclimatic velocity: the pace of species exposure to climate change. Diversity and Distributions, 20, 169–180.
Serra-Varela, M., Grivet, D., Vincenot, L., Broennimann, O.,et al. 2015. Does phylogeographical structure relate to climatic niche divergence? A test using maritime pine (Pinus pinaster Ait.). Global Ecology and Biogeography, 24, 1302–1313.
Sexton, J. O., Song, X.-P., Feng, M., et al. 2013. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. International Journal of Digital Earth, 6, 427–448.
Shipley, B., Vile, D. and Garnier, E. 2006. From plant traits to plant communities: a statistical mechanistic approach to biodiversity. Science, 314, 812–814.
Shipley, B., Laughlin, D. C., Sonnier, G. and Otfinowski, R. 2011. A strong test of a maximum entropy model of trait-based community assembly. Ecology, 92, 507–517.
Silvertown, J. 2004. Plant coexistence and the niche. Trends in Ecology and Evolution, 19, 605–611.
Silvertown, J., Dodd, M., Gowing, D., Lawson, C. and McConway, K. 2006. Phylogeny and the hierarchical organization of plant diversity. Ecology, 87, S39–S49.
Simmons, R. E., Barnard, P., Dean, W. R. J., et al. 2004. Climate change and birds: perspectives and prospects from southern Africa. Ostrich, 75, 295–308.
Smith, T. M. and Smith, R. L. 2015. Elements of Ecology, 9th edn. San Francisco, CA: Pearson Education Ltd.
Snell, R. S., Huth, A., Nabel, J. E. M. S., et al. 2014. Using dynamic vegetation models to simulate plant range shifts. Ecography, 37, 1184–1197.
Soberón, J. 2007. Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters, 10, 1115–1123.
Soberón, J. and Nakamura, M. 2009. Niches and distributional areas: concepts, methods, and assumptions. Proceedings of the National Academy of Sciences, 106, 19644–19650.
Sokol, E. R., Benfield, E., Belden, L. K. and Valett, H. M. 2011. The assembly of ecological communities inferred from taxonomic and functional composition. The American Naturalist, 177, 630–644.
Stachowicz, J. J. 2001. Mutualism, facilitation, and the structure of ecological communities. Bioscience, 51, 235–246.
Stockwell, D. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13, 143–158.
Stockwell, D. R. B. and Peterson, A. T. 2002a. Controlling bias in biodiversity data. In Scott, J. M., Heglund, P. J., Morrison, M. L., Haufler, J. B., Raphael, M. G., Wall, W. A. and Samson, F. B. (eds), Predicting Species Occurrences: Issues of Accuracy and Scale. Covelo, CA: Island Press, pp. 537–546.
Stockwell, D. R. B. and Peterson, A. T. 2002b. Effects of sample size on accuracy of species distribution models. Ecological Modelling, 148, 1–13.
Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T. and Zeileis, A. 2008. Conditional variable importance for random forests. BMC Bioinformatics, 9(1), 307.
Strobl, C., Malley, J. and Tutz, G. 2009. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14, 323.
Suarez-Seoane, S., Virgos, E., Terroba, O., Pardavila, X. and Barea-Azcon, J. M. 2014. Scaling of species distribution models across spatial resolutions and extents along a biogeographic gradient. The case of the Iberian mole Talpa occidentalis. Ecography, 37, 279–292.
Sutherst, R. W., Maywald, G. F. and Bourne, A. S. 2007. Including species interactions in risk assessments for global change. Global Change Biology, 13, 1843–1859.
Svenning, J. C. and Skov, F. 2004. Limited filling of the potential range in European tree species. Ecology Letters, 7, 565–573.
Svenning, J. C. and Skov, F. 2007. Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation? Ecology Letters, 10, 453–460.
Svenning, J. C., Normand, S. and Skov, F. 2006. Range filling in European trees. Journal of Biogeography, 33, 2018–2021.
Svenning, J. C., Normand, S. and Kageyama, M. 2008. Glacial refugia of temperate trees in Europe: insights from species distribution modelling. Journal of Ecology, 96, 1117–1127.
Svenning, J. C., Flojgaard, C., Marske, K. A., Nogues-Bravo, D. and Normand, S. 2011. Applications of species distribution modeling to paleobiology. Quaternary Science Reviews, 30, 2930–2947.
Swets, J. A. 1988. Measuring the accuracy of diagnostic systems. Science, 240, 1285–1293.
Syfert, M. M., Smith, M. J. and Coomes, D. A. 2013. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. Plos One, 8, e55158.
Talluto, M., Boulangeat, I., Ameztegui, A., et al. 2016. Cross-scale integration of knowledge for predicting species ranges: a metamodeling framework. Global Ecology and Biogeography, 25, 238–249.
Tang, L. and Shao, G. 2015. Drone remote sensing for forestry research and practices. Journal of Forestry Research, 26, 791–797.
Tautz, D. 2003. Evolutionary biology: splitting in space. Nature, 421, 225–226.
Team, R. C. 2014. R: A language and environment for statistical computing. In R Foundation For Statistical Computing, V. www.R-project.org/
Tebaldi, C. and Knutti, R. 2007. The use of the multi-model ensemble in probabilistic climate projections. Philosophical Transactions of the Royal Society A: Mathematical Physical and Engineering Sciences, 365, 2053–2075.
ter Braak, C. J. F. 1986. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology, 67, 1167–1179.
Thibaud, E., Petitpierre, B., Broennimann, O., Davison, A. C. and Guisan, A. 2014. Measuring the relative effect of factors affecting species distribution model predictions. Methods in Ecology and Evolution, 5(9), 947–955.
Thomas, C. D., Cameron, A., Green, R. E., et al. 2004. Extinction risk from climate change. Nature, 427, 145–148.
Thornton, P. E. and Running, S. W. 1999. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology, 93, 211–228.
Thornton, P. E., Running, S. W. and White, M. A. 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology, 190, 214–251.
Thornton, P. E., Thornton, M. M., Mayer, B. W., et al. 2017. Daymet: daily surface weather data on a 1-km grid for North America. Version 3. Oak Ridge, TN: ORNL DAAC. Available at: https://doi.org/10.3334/ORNLDAAC/1328.
Thuiller, W. 2003. BIOMOD: optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology, 9, 1353–1362.
Thuiller, W. 2004. Patterns and uncertainties of species’ range shifts under climate change. Global Change Biology, 10, 2020–2027.
Thuiller, W. 2007. Biodiversity: climate change and the ecologist. Nature, 448, 550–552.
Thuiller, W., Araujo, M. B. and Lavorel, S. 2003a. Generalized models vs. classification tree analysis: predicting spatial distributions of plant species at different scales. Journal of Vegetation Science, 14, 669–680.
Thuiller, W., Vayreda, J., Pino, J., et al. 2003b. Large-scale environmental correlates of forest tree distributions in Catalonia (NE Spain). Global Ecology and Biogeography, 12, 313–325.
Thuiller, W., Araujo, M. B., Pearson, R. G., et al. 2004a. Uncertainty in predictions of extinction risk. Nature, 430, 34.
Thuiller, W., Brotons, L., Araújo, M. B. and Lavorel, S. 2004b. Effects of restricting environmental range of data to project current and future species distributions. Ecography, 27, 165–172.
Thuiller, W., Lavorel, S., Midgley, G., Lavergne, S. and Rebelo, T. 2004c. Relating plant traits and species distributions along bioclimatic gradients for 88 Leucadendron taxa. Ecology, 85, 1688–1699.
Thuiller, W., Lavorel, S., Araujo, M. B., Sykes, M. T. and Prentice, I. C. 2005a. Climate change threats to plant diversity in Europe. Proceedings of the National Academy of Sciences of the United States of America, 102, 8245–8250.
Thuiller, W., Richardson, D. M., Pysek, P., et al. 2005b. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Global Change Biology, 11, 2234–2250.
Thuiller, W., Midgley, G. F., Rouget, M. and Cowling, R. M. 2006. Predicting patterns of plant species richness in megadiverse South Africa. Ecography, 29, 733–744.
Thuiller, W., Albert, C., Araujo, M. B., et al. 2008. Predicting global change impacts on plant species’ distributions: future challenges. Perspectives in Plant Ecology Evolution and Systematics, 9, 137–152.
Thuiller, W., Lafourcade, B., Engler, R. and Araujo, M. B. 2009. BIOMOD: a platform for ensemble forecasting of species distributions. Ecography, 32, 369–373.
Thuiller, W., Albert, C. H., Dubuis, A., Randin, C. and Guisan, A. 2010. Variation in habitat suitability does not always relate to variation in species’ plant functional traits. Biology Letters, rsbl20090669.
Thuiller, W., Lavergne, S., Roquet, C., et al. 2011. Consequences of climate change on the tree of life in Europe. Nature, 470, 531–534.
Thuiller, W., Münkemüller, T., Lavergne, S.,et al. 2013. A road map for integrating eco-evolutionary processes into biodiversity models. Ecology Letters, 16, 94–105.
Thuiller, W., Guéguen, M., Georges, D., et al. 2014a. Are different facets of plant diversity well protected against climate and land cover changes? A test study in the French Alps. Ecography, 37, 1254–1266.
Thuiller, W., Münkemüller, T., Schiffers, K. H., et al. 2014b. Does probability of occurrence relate to population dynamics? Ecography, 37, 1155–1166.
Thuiller, W., Pironon, S., Psomas, A., et al. 2014c. The European functional tree of bird life in the face of global change. Nature Communications, 5.
Thuiller, W., Pollock, L. J., Gueguen, M. and Münkemüller, T. 2015. From species distributions to meta-communities. Ecology Letters 18(12), 1321–1328.
Tibshirani, R. 1996. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society Series B: Methodological, 58, 267–288.
Tibshirani, R. 1997. The LASSO method for variable selection in the Cox model. Statistics in Medicine, 16, 385–395.
Tingley, M. W., Monahan, W. B., Beissinger, S. R. and Moritz, C. 2009. Birds track their Grinnellian niche through a century of climate change. Proceedings of the National Academy of Sciences of the United States of America, 106, 19637–19643.
Tsoar, A., Allouche, O., Steinitz, O., Rotem, D. and Kadmon, R. 2007. A comparative evaluation of presence-only methods for modelling species distribution. Diversity and Distributions, 13, 397–405.
Tuanmu, M. N., Vina, A., Roloff, G. J., et al. 2011. Temporal transferability of wildlife habitat models: implications for habitat monitoring. Journal of Biogeography, 38, 1510–1523.
Tulloch, A. I., Sutcliffe, P., Naujokaitis-Lewis, I.,et al. 2016. Conservation planners tend to ignore improved accuracy of modelled species distributions to focus on multiple threats and ecological processes. Biological Conservation, 199, 157–171.
Vale, C. G., Tarroso, P. and Brito, J. C. 2014. Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara-Sahel transition zone. Diversity and Distributions, 20, 20–33.
Van Horne, B. 2002. Approaches to Habitat Modeling: The Tensions between Pattern and Process and between Specificity and Generality. In Scott, J. M., Heglund, P. J., Haufler, J. B., et al. (eds), Predicting Species Occurrences: Issues of Accuracy and Scale. Covelo, CA: Island Press.
Van Niel, K. P. and Austin, M. P. 2007. Predictive vegetation modeling for conservation: Impact of error propagation from digital elevation data. Ecological Applications, 17, 266–280.
Vandermeer, J. H. 1972. Niche theory. Annual Review of Ecology and Systematics, 3, 107–132.
VanDerWal, J., Shoo, L. P., Graham, C. and William, S. E. 2009. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecological Modelling, 220, 589–594.
Vaughan, I. P. and Ormerod, S. J. 2005. The continuing challenges of testing species distribution models. Journal of Applied Ecology, 42, 720–730.
Veloz, S. D. 2009. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. Journal of Biogeography, 36, 2290–2299.
Venables, W. N. and Ripley, B. D. 2002. Modern Applied Statistisc with S. Dordrecht, The Netherlands: Springer.
Verbyla, D. L. and Litvaitis, J. A. 1989. Resampling methods for evaluating classification accuracy of wildlife habitat models. Environmental Management, 13, 783–787.
Verner, J., Morrison, M. L. and Ralph, C. J. 1986. Wildlife 2000: Modelling Habitat Relationships of Terrestrial Vertebrates, Madison, WI: University of Wisconsin Press.
Vesk, P. A. 2013. How traits determine species responses to environmental gradients. Journal of Vegetation Science, 24, 977–978.
Vetaas, O. R. 2002. Realized and potential climate niches: a comparison of four Rhododendron tree species. Journal of Biogeography, 29, 545–554.
Vicente, J., Fernandes, R., Randin, C., et al. 2013. Will climate change drive alien invasive plants into areas of high protection value? An improved model-based regional assessment to prioritise the management of invasions. Journal of Environmental Management, 131, 185–195.
Vincent, P. J. and Haworth, J. M. 1983. Poisson regression models of species abundance. Journal of Biogeography, 10, 153–160.
Vittoz, P. and Engler, R. 2007. Seed dispersal distances: a typology based on dispersal modes and plant traits. Botanica Helvetica, 117, 109–124.
Walther, G. R., Berger, S. and Sykes, M. T. 2005. An ecological ‘footprint’ of climate change. Proceedings of the Royal Society B: Biological Sciences, 272, 1427–1432.
Ward, G., Hastie, T., Barry, S., Elith, J. and Leathwick, J. R. 2009. Presence-only data and the EM algorithm. Biometrics, 65, 554–563.
Warren, D. L., Glor, R. E. and Turelli, M. 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution, 62, 2868–2883.
Warton, D. I., Blanchet, F. G., O’Hara, , et al. 2015. So many variables: joint modeling in community ecology. Trends in Ecology and Evolution, 30, 766–779.
Weiher, E. and Keddy, P. 2001. Ecological Assembly Rules: Perspectives, Advances, Retreats, Cambridge, UK: Cambridge University Press.
Weisberg, S. 1980. Applied Linear Regression, New York, NY: Wiley.
Wenger, S. J. and Olden, J. D. 2012. Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods in Ecology and Evolution, 3, 260–267.
Wenger, S. J., Som, N. A., Dauwalter, D. C., et al. 2013. Probabilistic accounting of uncertainty in forecasts of species distributions under climate change. Global Change Biology, 19, 3343–3354.
Westman, W. E. 1991. Measuring realized niche spaces: climatic response of chaparral and coastal sage scrub. Ecology, 72, 1678–1684.
Wharton, T. N. and Kriticos, D. J. 2004. The fundamental and realized niche of the Monterey pine aphid, Essigella californica (Essig) (Hemiptera: Aphididae): implications for managing softwood plantations in Australia. Diversity and Distributions, 10, 253–262.
Whittaker, R. H. 1967. Gradient analysis of vegetation. Biological Reviews, 42, 207–264.
Whittaker, R. H., Levin, S. A. and Root, R. B. 1973. Niche, habitat, and ecotope. American Naturalist, 107, 321–338.
Whittingham, M. J., Stephens, P. A., Bradbury, R. B. and Freckleton, R. P. 2006. Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology, 75, 1182–1189.
Wiens, J. A. 1989. Spatial scaling in ecology. Functional Ecology, 3, 385–397.
Wiens, J. J. and Graham, C. H. 2005. Niche conservatism: Integrating evolution, ecology, and conservation biology. Annual Review of Ecology Evolution and Systematics, 36, 519–539.
Wiens, T. S., Dale, B. C., Boyce, M. S. and Kershaw, G. P. 2008. Three way k-fold cross-validation of resource selection functions. Ecological Modelling, 212, 244–255.
Williams, J. W. and Jackson, S. T. 2007. Novel climates, no-analog communities, and ecological surprises. Frontiers in Ecology and the Environment, 5, 475–482.
Williams, J. W., Jackson, S. T. and Kutzbacht, J. E. 2007. Projected distributions of novel and disappearing climates by 2100 AD. Proceedings of the National Academy of Sciences of the United States of America, 104, 5738–5742.
Williams, R. A. J., Fasina, F. O. and Peterson, A. T. 2008. Predictable ecology and geography of avian influenza (H5N1) transmission in Nigeria and West Africa. Transactions of the Royal Society of Tropical Medicine and Hygiene, 102, 471–479.
Willis, K. J. and McElwain, J. C. 2002. The Evolution of Plants. Oxford, UK: Oxford University Press.
Willis, K. J. and Whittaker, R. J. 2002. Ecology. Species diversity, Scale matters. Science, 295, 1245–1248.
Wintle, B. A., McCarthy, M. A., Volinsky, C. T. and Kavanagh, R. P. 2003. The use of Bayesian model averaging to better represent uncertainty in ecological models. Conservation Biology, 17, 1579–1590.
Wintle, B. A., Elith, J. and Potts, J. M. 2005. Fauna habitat modelling and mapping: a review and case study in the Lower Hunter Central Coast region of NSW. Austral Ecology, 30, 719–738.
Wisz, M. S. and Guisan, A. 2009. Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data. BMC Ecology, 9, 8.
Wisz, M. S., Hijmans, R. J., Li, J., et al. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14, 763–773.
Wisz, M. S., Pottier, J., Kissling, W. D., et al. 2013. The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biological Reviews, 88, 15–30.
Wisz, M. S., Broennimann, O., Grønkjær, P., et al. 2015. Arctic warming will promote Atlantic–Pacific fish interchange. Nature Climate Change, 5, 261–265.
Wolmarans, R., Robertson, M. P. and van Rensburg, B. J. 2010. Predicting invasive alien plant distributions: how geographical bias in occurrence records influences model performance. Journal of Biogeography, 37, 1797–1810.
Wood, S. 2006. Generalized Additive Models: An Introduction with R. London: CRC Press.
Wood, S. N., Goude, Y. and Shaw, S. 2015. Generalized additive models for large data sets. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64, 139–155.
Woodward, F. I. 1987. Climate and Plant Distribution, Cambridge, UK: Cambridge University Press.
Woodward, F. I. and Kelly, C. K. 2003. Why are species not more widely distributed? Physiological and environmental limits. In Blackburn, T. M. and Gaston, K. J. (eds), Macroecology. Oxford, UK: Blackwell.
Wu, J. 1999. Hierarchy and scaling: extrapolating information along a scaling ladder. Canadian Journal of Remote Sensing, 25, 367–380.
Wu, J. G. 2004. Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecology, 19, 125–138.
Yañez-Arenas, C., Peterson, A. T., Mokondoko, P., Rojas-Soto, O. and Martínez-Meyer, E. 2014. The use of ecological niche modeling to infer potential risk areas of snakebite in the Mexican state of Veracruz. PLoS One, 9, e100957.
Yates, C. J., McNeill, A., Elith, J. and Midgley, G. F. 2010. Assessing the impacts of climate change and land transformation on Banksia in the South West Australian Floristic Region. Diversity and Distributions, 16, 187–201.
Zanini, F., Pellet, J. and Schmidt, B. R. 2009. The transferability of distribution models across regions: an amphibian case study. Diversity and Distributions, 15, 469–480.
Zhang, J., Hu, J., Lian, J., et al. 2016. Seeing the forest from drones: testing the potential of lightweight drones as a tool for long-term forest monitoring. Biological Conservation, 198, 60–69.
Zhu, G. and Peterson, A. T. 2014. Potential geographic distribution of the novel avian-origin influenza A (H7N9) Virus. PLoS One, 9, e93390.
Zimmermann, N. E., Yoccoz, N. G., Edwards, T. C., et al. 2009. Climatic extremes improve predictions of spatial patterns of tree species. Proceedings of the National Academy of Sciences of the United States of America, 106, 19723–19728.
Zimmermann, N. E., Edwards, T. C., Graham, C. H., Pearman, P. B. and Svenning, J. C. 2010. New trends in species distribution modelling. Ecography, 33, 985–989.
Zobel, M. 1997. The relative role of species pools in determining plant species richness: an alternative explanation of species coexistence. Trends in Ecology and Evolution, 12, 266–269.
Zurell, D., Berger, U., Cabral, J. S., et al. 2010. The virtual ecologist approach: simulating data and observers. Oikos, 119, 622–635.
Zurell, D., Elith, J. and Schröder, B. 2012. Predicting to new environments: tools for visualizing model behaviour and impacts on mapped distributions. Diversity and Distributions, 18, 628–634.
