Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-27T07:42:44.180Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  14 September 2017

Antoine Guisan ,
Wilfried Thuiller  and
Niklaus E. Zimmermann
Antoine Guisan
Affiliation:
Université de Lausanne, Switzerland
Wilfried Thuiller
Affiliation:
CNRS, Université Grenoble Alpes
Niklaus E. Zimmermann
Affiliation:
Swiss Federal Research Institute WSL
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Habitat Suitability and Distribution Models
With Applications in R
 , pp. 417 - 457
Publisher: Cambridge University Press
Print publication year: 2017

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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.CrossRefGoogle Scholar
Ackerly, D. D. 2003. Community assembly, niche conservatism, and adaptive evolution in changing environments. International Journal of Plant Sciences, 164, S165–S184.CrossRefGoogle Scholar
Agresti, A. 1990. Categorical Data Analysis. New York: Wiley.Google Scholar
Agresti, A. 1999. Modelling ordered categorical data: recent advances and future challenges. Statistics in Medicine, 18, 2191–2207.3.0.CO;2-M>CrossRefGoogle ScholarPubMed
Aguiar-Pulido, V., Huang, W., Suarez-Ulloa, V., et al. 2016. Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis. Evolutionary Bioinformatics Online, 12, 5.Google ScholarPubMed
Akaike, H. 1974. A new look at statistical model identification. IEEE Transactions on Automatic Control, AU-19, 716–722.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Allen, T. F. H. and Hoekstra, T. W. 1992. Towards a Unified Ecology. New York: Columbia University Press.Google Scholar
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.CrossRefGoogle Scholar
Amante, C. and Eakins, B. W. 2009. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Araújo, M. B. and Guisan, A. 2006. Five (or so) challenges for species distribution modelling. Journal of Biogeography, 33, 1677–1688.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Araújo, M. B. and New, M. 2007. Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22, 42–47.CrossRefGoogle ScholarPubMed
Araújo, M. B. and Pearson, R. G. 2005. Equilibrium of species’ distributions with climate. Ecography, 28, 693–695.CrossRefGoogle Scholar
Araújo, M. B. and Peterson, A. T. 2012. Uses and misuses of bioclimatic envelope modeling. Ecology, 93, 1527–1539.CrossRefGoogle ScholarPubMed
Araújo, M. B. and Rahbek, C. 2006. How does climate change affect biodiversity? Science, 313, 1396–1397.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Araújo, M. B., Ferri-Yanez, F., Bozinovic, F., et al. 2013. Heat freezes niche evolution. Ecology Letters, 16, 1206–1219.CrossRefGoogle ScholarPubMed
Arlot, S. and Celisse, A. 2010. A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40–79.CrossRefGoogle Scholar
Austin, M. 1987. Models for the analysis of species’ response to environmental gradients. Vegetatio, 69, 35–45.CrossRefGoogle Scholar
Austin, M. 1990. Community theory and competition in vegetation. Perspectives on Plant Competition, 215, 238.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Austin, M. and Smith, T. 1989. A new model for the continuum concept. Plant Ecology, 83, 35–47.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Austin, M. P. 1971. Role of regression analysis in plant ecology. Proceedings of the Ecological Society of Australia, 6, 63–75.Google Scholar
Austin, M. P. 1985. Continuum concept, ordination methods, and niche theory. Annual Review of Ecology and Systematics, 16, 39–61.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Austin, M. P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 101–118.CrossRefGoogle Scholar
Austin, M. P. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling, 200, 1–19.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Bahn, V. and McGill, B. J. 2007. Can niche-based distribution models outperform spatial interpolation? Global Ecology and Biogeography, 16, 733–742.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Barry, S. and Elith, J. 2006. Error and uncertainty in habitat models. Journal of Applied Ecology, 43, 413–423.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Baselga, A. and Araújo, M. B. 2009. Individualistic vs community modelling of species distributions under climate change. Ecography, 32, 55–65.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
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.Google ScholarPubMed
Berrocal, V. J., Craigmile, P. F. and Guttorp, P. 2012. Regional climate model assessment using statistical upscaling and downscaling techniques. Environmetrics, 23, 482–492.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
Botkin, D. B., Saxe, H., Araujo, M. B., et al. 2007. Forecasting the effects of global warming on biodiversity. Bioscience, 57, 227–236.CrossRefGoogle Scholar
Boucher, D. H., James, S. and Keeler, K. H. 1982. The ecology of mutualism. Annual Review of Ecology and Systematics, 13, 315–347.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.Google Scholar
Box, E. O. 1981. Macroclimate and Plant Forms: An Introduction to Predictive Modeling in Phytogeography, The Hague: Junk.CrossRefGoogle Scholar
Boyce, M. S., Vernier, P. R., Nielsen, S. E. and Schmiegelow, F. K. A. 2002. Evaluating resource selection functions. Ecological Modelling, 157, 281–300.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Breiman, L. 1996. Bagging predictors. Machine Learning, 24, 123–140.CrossRefGoogle Scholar
Breiman, L. 2001. Random forests. Machine Learning, 45, 5–32.CrossRefGoogle Scholar
Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. 1984. Classification and Regression Trees, New York: Chapman and Hall.Google Scholar
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.CrossRefGoogle Scholar
Broennimann, O. and Guisan, A. 2008. Predicting current and future biological invasions: both native and invaded ranges matter. Biology Letters, 4, 585–589.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Broennimann, O., Treier, U. A., Müller-Schärer, H., et al. 2007. Evidence of climatic niche shift during biological invasion. Ecology Letters, 10, 701–709.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Broennimann, O., Di Cola, V. and Guisan, A. (2016). ecospat: Spatial Ecology Miscellaneous Methods. R package version 2.1.1. http://CRAN.R-project.org/package=ecospat.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Brown, J. H. 1971. Mechanisms of competitive exclusion between two species of chipmunks. Ecology, 52, 305–311.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Buckley, L. B. 2008. Linking traits to energetics and population dynamics to predict lizard ranges in changing environments. The American Naturalist, 171, E1–E19.CrossRefGoogle ScholarPubMed
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.Google ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Burnham, K. P. and Anderson, D. R. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Berlin: Springer.Google Scholar
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.Google Scholar
Calenge, C. and Basille, M. 2008. A general framework for the statistical exploration of the ecological niche. Journal of Theoretical Biology, 252, 674–685.CrossRefGoogle ScholarPubMed
Caley, M. J. and Schluter, D. 1997. The relationship between local and regional diversity. Ecology, 78, 70–80.CrossRefGoogle Scholar
Callaway, R. M. 1995. Positive interactions among plants (interpreting botanical progress). The Botanical Review, 61, 306–349.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Carl, G. and Kühn, I. 2007. Analyzing spatial autocorrelation in species distributions using Gaussian and logit models. Ecological Modelling, 207, 159–170.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Chase, J. M. and Leibold, M. A. 2003. Ecological Niches. Chicago, IL: The University of Chicago Press.CrossRefGoogle Scholar
Chave, J. 2013. The problem of pattern and scale in ecology: what have we learned in 20 years? Ecology Letters, 16, 4–16.CrossRefGoogle Scholar
Chefaoui, R. M. and Lobo, J. M. 2008. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecological Modelling, 210, 478–486.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Claeskens, G. and Hjort, N. L. 2008. Model Selection and Model Averaging. Cambridge, UK: University Press Cambridge.Google Scholar
Clark, J. S., Dietze, M., Chakraborty, S., et al. 2007. Resolving the biodiversity paradox. Ecology Letters, 10, 647–659.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.CrossRefGoogle Scholar
Cohen, J. 1968. Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70, 213.CrossRefGoogle ScholarPubMed
Colwell, R. K. and Futuyma, D. J. 1971. Measurement of niche breadth and overlap. Ecology, 52, 567–576.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Connell, J. H. 1961. The influence of interspecific competition and other factors on the distribution of the barnacle Chthamalus stellatus. Ecology, 42, 710–723.CrossRefGoogle Scholar
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.Google Scholar
Côté, I. M. and Reynolds, J. D. 2002. Predictive ecology to the rescue? Science, 298, 1181–1182.CrossRefGoogle ScholarPubMed
Cox, B. 2001. The biogeographic regions reconsidered. Journal of Biogeography, 28, 511–523.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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, 140–158.2.0.CO;2>CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Davis, M. B. 1989. Lags in vegetation response to greenhouse warming. Climatic Change, 15, 75–82.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
De’Ath, G. 2007. Boosted trees for ecological modeling and prediction. Ecology, 88, 243–251.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Dietz, H. and Edwards, P. J. 2006. Recognition that causal processes change during plant invasion helps explain conflicts in evidence. Ecology, 87, 1359–1367.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Doebeli, M. and Dieckmann, U. 2003. Speciation along environmental gradients. Nature, 421, 259–264.CrossRefGoogle ScholarPubMed
Dolédec, S., Chessel, D. and Gimaret-Carpentier, C. 2000. Niche separation in community analysis: a new method. Ecology, 81, 2914–2927.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Dormann, C. F. 2007a. Assessing the validity of autologistic regression. Ecological Modelling, 207, 234–242.CrossRefGoogle Scholar
Dormann, C. F. 2007b. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecology and Biogeography, 30, 609–628.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Drake, J. M., Randin, C. and Guisan, A. 2006. Modelling ecological niches with support vector machines. Journal of Applied Ecology, 43, 424–432.CrossRefGoogle Scholar
Dray, S., Chessel, D. and Thioulouse, J. 2003. Co-inertia analysis and the linking of ecological data tables. Ecology, 84, 3078–3089.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Duckworth, J., Bunce, R. and Malloch, A. 2000. Vegetation–environment relationships in Atlantic European calcareous grasslands. Journal of Vegetation Science, 11, 15–22.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Efron, B. and Gong, G. 1983. A leisurely look at the bootstrap, the jackknife, and cross- validation. American Statistician, 37, 36–48.Google Scholar
Efron, B. and Tibshirani, R. 1993. An Introduction to the Bootstrap, New York: Chapman and Hall.CrossRefGoogle Scholar
Efron, B. and Tibshirani, R. 1997. Improvements on cross-validation: The .632+ bootstrap method. Journal of the American Statistical Association, 92, 548–560.Google Scholar
Elith, J. 2002. Predicting the distribution of plants. PhD Thesis. Melbourne, Australia: The University of Melbourne.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Elith, J., Leathwick, J. R. and Hastie, T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–813.CrossRefGoogle ScholarPubMed
Elith, J., Kearney, M. and Phillips, S. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution, 1, 330–342.CrossRefGoogle Scholar
Elith, J., Phillips, S. J., Hastie, T., et al. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43–57.CrossRefGoogle Scholar
Ellenberg, H. 1953. Physiologisches und ökologisches Verhalten derselben Pflanzenarten. Berichte der Deutschen Botanischen Gesellschaft, 65, 351–362.Google Scholar
Ellenberg, H. 1954. Ueber einige fortschritte der kausalen Vegetationskunde. Vegetatio, 5–6, 199–211.CrossRefGoogle Scholar
Ellenberg, H. 1988. Vegetation Ecology of Central Europe, 4th edn. Cambridge, UK: Cambridge University Press.Google Scholar
Elton, C. 1927. Animal Ecology, London: Sidgwick and Jackson.Google Scholar
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.CrossRefGoogle Scholar
Engler, R. and Guisan, A. 2009. MigClim: predicting plant distribution and dispersal in a changing climate. Diversity and Distributions, 15, 590–601.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Engler, R., Randin, C. R., Thuiller, W., et al. 2011b. Climate change impacts on European mountain plant diversity. Global Change Biology, 17, 2330–2341.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Ferrier, S. and Guisan, A. 2006. Spatial modelling of biodiversity at the community level. Journal of Applied Ecology, 43, 393–404.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Forbes, A. D. 1995. Classification-algorithm evaluation: five performance measures based on confusion matrices. Journal of Clinical Monitoring, 11, 189–206.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Franklin, J. 1995. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography, 19, 474–499.CrossRefGoogle Scholar
Franklin, J. 2010a. Mapping Species Distribution: Spatial Inference and Prediction. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Franklin, J. 2010b. Moving beyond static species distribution models in support of conservation biogeography. Diversity and Distributions, 16, 321–330.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Fraser, L. H. and Keddy, P. A. 2005. Can competitive ability predict structure in experimental plant communities? Journal of Vegetation Science, 16, 571–578.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Friedman, J. H. 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29, 1189–1232.CrossRefGoogle Scholar
Friedman, J. H. 2002. Stochastic gradient boosting. Computational Statistics and Data Analysis, 38, 367–378.CrossRefGoogle Scholar
Friedman, J. H. and Meulman, J. J. 2003. Multiple additive regression trees with application in epidemiology. Statistics in Medicine, 22, 1365–1381.CrossRefGoogle ScholarPubMed
Friedman, J. H., Hastie, T. J. and Tibshirani, R. 2000. Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28, 337–374.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Gause, G. F. 1936. The Struggle for Existence. Baltimore, MD: Williams and Wilkins.Google Scholar
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.CrossRefGoogle Scholar
Gelfand, A. E., Silander, J. A., Wu, S., et al. 2006. Explaining species distribution patterns through hierarchical modeling. Bayesian Analysis, 1, 41–92.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Goldewijk, K. K. 2001. Estimating global land use change over the past 300 years: the HYDE Database. Global Biogeochemical Cycles, 15, 417–433.CrossRefGoogle Scholar
Golding, N. and Purse, B. V. 2016. Fast and flexible Bayesian species distribution modelling using Gaussian processes. Methods in Ecology and Evolution.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Grace, J. B. and Wetzel, R. G. 1981. Habitat partitioning and competitive displacement in Cattails (Typha): experimental field studies. American Naturalist, 118, 463–474.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Gravel, D., Massol, F., Canard, E., Mouillot, D. and Mouquet, N. 2011. Trophic theory of island biogeography. Ecology Letters, 14, 1010–1016.CrossRefGoogle ScholarPubMed
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.Google Scholar
Green, R. H. 1971. Multivariate statistical approach to Hutchinsonian niche: bivalve molluscs of central Canada. Ecology, 52, 543–556.CrossRefGoogle ScholarPubMed
Green, R. H. 1979. Sampling Design and Statistical Methods for Environmental Biologists. New York: John Wiley.Google Scholar
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.CrossRefGoogle Scholar
Grinnell, J. 1917. The niche-relationships of the California Trasher. Auk, 34, 131–135.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Guisan, A. and Harrell, F. E. 2000. Ordinal response regression models in ecology. Journal of Vegetation Science, 11, 617–626.CrossRefGoogle Scholar
Guisan, A. and Hofer, U. 2003. Predicting reptile distributions at the mesoscale: relation to climate and topography. Journal of Biogeography, 30, 1233–1243.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Guisan, A. and Theurillat, J. P. 2000. Equilibrium modeling of alpine plant distribution: how far can we go? Phytocoenologia, 30, 353–384.CrossRefGoogle Scholar
Guisan, A. and Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993–1009.CrossRefGoogle ScholarPubMed
Guisan, A. and Zimmermann, N. E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147–186.CrossRefGoogle Scholar
Guisan, A., Weiss, S. B. and Weiss, A. D. 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecology, 143, 107–122.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Guisan, A., Lehmann, A., Ferrier, S., et al. 2006b. Making better biogeographical predictions of species’ distributions. Journal of Applied Ecology, 43, 386–392.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Guisan, A., Tingley, R., Baumgartner, J. B., et al. 2013. Predicting species distributions for conservation decisions. Ecology Letters, 16, 1424–1435.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Hakkarainen, H., Mykra, S., Kurki, S., Tornberg, R. and Jungell, S. 2004. Competitive interactions among raptors in boreal forests. Oecologia, 141, 420–424.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Hanberry, B. B., He, H. S. and Palik, B. J. 2012. Pseudoabsence generation strategies for species distribution models. Plos One, 7.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Hanski, I. and Gilpin, M. E. 1997. Metapopulation Biology, San Diego, CA: Academic Press.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Harrell, F. E. 2001. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Berlin: Springer.CrossRefGoogle Scholar
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, 361–387.3.0.CO;2-4>CrossRefGoogle ScholarPubMed
Harris, D. J. 2015. Generating realistic assemblages with a joint species distribution model. Methods in Ecology and Evolution, 6, 465–473.CrossRefGoogle Scholar
Harte, J. 2011. Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics. London: Oxford University Press.CrossRefGoogle Scholar
Hastie, T. and Tibishirani, R. 1986. Generalized additive models. Statistical Science, 1, 297–318.Google Scholar
Hastie, T. J. and Tibshirani, R. 1990. Generalized Additive Models, London: Chapman and Hall.Google Scholar
Hastie, T., Tibshirani, R. and Buja, A. 1994. Flexible discriminant analysis by optimal scoring. Journal of the American Statistical Association, 89(428), 1255–1270.Google Scholar
Hastie, T., Tibshirani, R. and Friedman, J. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Berlin: Springer.CrossRefGoogle Scholar
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.Google Scholar
Hausser, J. (ed.) 1995. Säugetiere der Schweitz. Atlas des Mammifères de Suisse. Mammiferi della Svizzera. Basel: Birkhäuser Verlag.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Hawkins, B. A. 2012a. Are multiple regression models of spatially structured data to be trusted? Journal of Biogeography, 39, 998.CrossRefGoogle Scholar
Hawkins, B. A. 2012b. Eight (and a half) deadly sins of spatial analysis. Journal of Biogeography, 39, 1–9.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Heller, H. C. and Gates, D. 1971. Altitudinal zonation of chipmunks (Eutamias): energy budgets. Ecology, 52, 424–443.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Hijmans, R., Phillips, S., Leathwick, J. and Elith, J. 2013. dismo: Species distribution modeling. R package version 0.9–.. http://CRAN.R-project.org/package=dismo.Google Scholar
Hijmans, R. J. 2012. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology, 93, 679–688.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Hintikka, V. 1963. Über das Großklima einiger Pflanzenareale in zwei Klimakoordinatensystemen dargestellt. Annales Botanici Societatis Zoologicae Botanicae Fennicae ‘Vanamo’. 34, 1–64.Google Scholar
Hirzel, A. and Guisan, A. 2002. Which is the optimal sampling strategy for habitat suitability modelling. Ecological Modelling, 157, 331–341.CrossRefGoogle Scholar
Hirzel, A., Helfer, V. and Metral, F. 2001. Assessing habitat-suitability models with a virtual species. Ecological Modelling, 145, 111–121.CrossRefGoogle Scholar
Hirzel, A. H. and Arlettaz, R. 2003. Modeling habitat suitability for complex species distributions by environmental-distance geometric mean. Environmental Management, 32, 614–623.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Hirzel, A. H., Hausser, J. and N., P. 2002b. Biomapper 2.0, Bern: Division of Conservation Biology.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Hoerl, A. E. and Kennard, R. W. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 12, 55–67.CrossRefGoogle Scholar
Holdridge, L. R. 1967. Life Zone Ecology, San Jose, Costa Rica: Tropical Science Center.Google Scholar
Homer, C. H., Fry, J. A. and Barnes, C. A. 2012. The national land cover database. US Geological Survey Fact Sheet 2012–3020. Reston, VA: USGS. Available at: http://pubs.usgs.gov/fs/2012/3020/fs2012-3020.pdf.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Hoskin, C. J., Higgie, M., McDonald, K. R. and Moritz, C. 2005. Reinforcement drives rapid allopatric speciation. Nature, 437, 1353–1356.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Hubbell, S. P. 2001. The Unified Neutral Theory of Biodiversity and Biogeography. Princeton, NJ: Princeton University Press.Google Scholar
Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Hutchinson, G. E. 1957. Population studies. Animal ecology and demography: concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22, 415–427.CrossRefGoogle Scholar
Hutchinson, M. F. 1995. Interpolating mean rainfall using thin-plate smoothing splines. International Journal of Geographical Information Systems, 9, 385–403.CrossRefGoogle Scholar
Ings, T. C., Montoya, J. M., Bascompte, J., et al. 2009. Ecological networks: beyond food webs. Journal of Animal Ecology, 78, 253–269.CrossRefGoogle ScholarPubMed
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.Google Scholar
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.Google Scholar
IUCN 2001. IUCN Red List Categories and Criteria: Version 3.1. IUCN Species Survival Commissio.n Gland, Switzerland and Cambridge, UK: IUCN.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Ives, A. R. and Helmus, M. R. 2011. Generalized linear mixed models for phylogenetic analyses of community structure. Ecological Monographs, 81, 511–525.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Jalas, J. and Suominen, J. 1972–1996. The Atlas Florae Europaeae. Helsinki, Committee for Mapping the Flora of Europe and Societas Biologica Fennica Vanamo.Google Scholar
James, G., Witten, D., Hastie, T. and Tibshirani, R. 2013. An Introduction to Statistical Learning. Berlin: Springer.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Johnson, C. J. and Gillingham, M. P. 2005. An evaluation of mapped species distribution models used for conservation planning. Environmental Conservation, 32, 117–128.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Johnson, J. B. and Omland, K. S. 2004. Model selection in ecology and evolution. Trends in Ecology and Evolution, 19, 101–108.CrossRefGoogle ScholarPubMed
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.Google Scholar
Jones, H. G. 1992. Plants and Microclimate. A Quantitative Approach to Environmental Plant Physiology, Cambridge, UK: Cambridge University Press.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Kaplan, J. O., Krumhardt, K. M. and Zimmermann, N. E. 2009. The prehistoric and preindustrial deforestation of Europe. Quaternary Science Reviews, 28, 3016–3034.CrossRefGoogle Scholar
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.Google Scholar
Kearney, M. 2006. Habitat, environment and niche: what are we modelling? Oikos, 115, 186–191.CrossRefGoogle Scholar
Kearney, M. and Porter, W. P. 2004. Mapping the fundamental niche: physiology, climate, and the distribution of a nocturnal lizard. Ecology, 85, 3119–3131.CrossRefGoogle Scholar
Kearney, M. and Porter, W. 2009. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecology Letters, 12, 334–350.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Kerr, J. T. and Ostrovsky, M. 2003. From space to species: ecological applications for remote sensing. Trends in Ecology and Evolution, 18, 299–305.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
Knutti, R. 2010. The end of model democracy? Climatic Change, 102, 395–404.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Körner, C. 2003. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems; With 47 Tables. Berlin: Springer.CrossRefGoogle Scholar
Körner, C. 2007. The use of “altitude” in ecological research. Trends in Ecology and Evolution, 22, 569–574.CrossRefGoogle Scholar
Körner, C. and Paulsen, J. 2004. A world-wide study of high altitude treeline temperatures. Journal of Biogeography, 31, 713–732.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Kühn, I. 2007. Incorporating spatial autocorrelation may invert observed patterns. Diversity and Distributions, 13, 66–69.CrossRefGoogle Scholar
Kühn, I. and Dormann, C. F. 2012. Less than eight (and a half) misconceptions of spatial analysis. Journal of Biogeography, 39, 995–998.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Landis, J. R. and Koch, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics, 159–174.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Lawler, J. J., Shafer, S. L., White, D., et al. 2009. Projected climate-induced faunal change in the Western Hemisphere. Ecology, 90, 588–597.CrossRefGoogle ScholarPubMed
Le Cessie, S. and van Houwelingen, J. C. 1992. Ridge estimators in logistic regression. Applied Statistics, 41, 191–201.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Leathwick, J. R. 1998. Are New Zealand’s Nothofagus species in equilibrium with their environment? Journal of Vegetation Science, 9, 719–732.CrossRefGoogle Scholar
Leathwick, J. R. 2002. Intra-generic competition among Nothofagus in New Zealand’s primary indigenous forests. Biodiversity and Conservation, 11, 2177–2187.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Legendre, P. 1993. Spatial autocorrelation: trouble or new paradigm? Ecology, 74, 1659–1673.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Lehmann, A., Overton, J. M. and Leathwick, J. R. 2002. GRASP: generalized regression analysis and spatial prediction. Ecological Modelling, 157, 189–207.CrossRefGoogle Scholar
Leibold, M. A. 1995. The niche concept revisited: mechanistic models and community context. Ecology, 76, 1371–1382.CrossRefGoogle Scholar
Leibold, M. A. and McPeek, M. A. 2006. Coexistence of the niche and neutral perspectives in community ecology. Ecology, 87, 1399–1410.CrossRefGoogle ScholarPubMed
Lek, S., Delacoste, M., Baran, P., et al. 1996. Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling, 90, 39–52.CrossRefGoogle Scholar
Lenoir, J. and Svenning, J. C. 2015. Climate-related range shifts–a global multidimensional synthesis and new research directions. Ecography, 38, 15–28.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Linacre, J. 2008. The expected value of a point-biserial (or similar) correlation. Rasch Measurement Transactions, 22, 1154–1157.Google Scholar
Linder, H. P., Bykova, O., Dyke, J.,et al. 2012. Biotic modifiers, environmental modulation and species distribution models. Journal of Biogeography, 39, 2179–2190.CrossRefGoogle Scholar
Link, W. A. and Barker, R. J. 2006. Model weights and the foundations of multimodel inference. Ecology, 87, 2626–2635.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Loarie, S. R., Duffy, P. B., Hamilton, H., et al. 2009. The velocity of climate change. Nature, 462, 1052-U111.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Lomolino, M. V., Riddle, B. R., Whittaker, R. J. and Brown, J. H. 2010. Biogeography. Sunderland, MA: Sinauer Associates.Google Scholar
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.CrossRefGoogle ScholarPubMed
Lortie, C. J., Brooker, R. W., Choler, P., et al. 2004. Rethinking plant community theory. Oikos, 107, 433–438.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Lyons, K. G. and Schwartz, M. W. 2001. Rare species loss alters ecosystem function–invasion resistance. Ecology Letters, 4, 358–365.CrossRefGoogle Scholar
Lyons, K. G., Brigham, C., Traut, B. and Schwartz, M. W. 2005. Rare species and ecosystem functioning. Conservation Biology, 19, 1019–1024.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
MacArthur, R. H. 1968. The theory of the niche. In Lewontin, R. C. (ed.), Population Biology and Evolution. Syracuse, NY: Syracuse University Press.Google Scholar
MacArthur, R. H. 1972. Geographical Ecology. New-York: Harper & Row.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Manly, B. F. J. (ed.) 2006. Randomization, Bootstrap and Monte Carlo Methods in Biology. 3rd edition. London: Chapman & Hall/CRC.Google Scholar
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.CrossRefGoogle Scholar
Margules, C. R. and Austin, M. P. 1991. Nature Conservation: Cost Effective Biological Survey and Data Analysis. Canberra, Australia: CSIRO.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Mateo, R. G., Broennimann, O., Petitpierre, B., et al. 2015. What is the potential of spread in invasive bryophytes? Ecography, 38(5), 480–487.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
McCullagh, P. and Nelder, J. A. 1989a. Generalized Linear Models. London: Chapman & Hall.CrossRefGoogle Scholar
McCullagh, P. and Nelder, J. A. 1989b. Generalized Linear Models. 2nd edition. London: Chapman & Hall.CrossRefGoogle Scholar
McGill, B. J. 2010. Matters of scale. Science, 328, 575–576.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Menard, S. W. 2002. Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Meyer, C., Kreft, H., Guralnick, R. and Jetz, W. 2015. Global priorities for an effective information basis of biodiversity distributions. Nature Communications, 6.CrossRefGoogle ScholarPubMed
Meynard, C. N. and Kaplan, D. M. 2013. Using virtual species to study species distributions and model performance. Journal of Biogeography, 40, 1–8.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Meyneeke, J. O. 2004. Effects of global climate change on geographic distributions of vertebrates in North Queensland. Ecological Modelling, 174, 347–357.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Moisen, G. G. and Frescino, T. S. 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling, 157, 209–225.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Monserud, R. A. and Leemans, R. 1992. Comparing global vegetation maps with the Kappa statistic. Ecological Modelling, 62, 275–293.CrossRefGoogle Scholar
Montgomery, D. C. and Peck, E. A. 1982. Introduction to Linear Regression Analysis. New York, Wiley.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Mouillot, D., Bellwood, D. R., Baraloto, C., et al.2013. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biology, 11, e1001569.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.Google Scholar
Murphy, A. H. and Winkler, R. L. 1987. A general framework for forecast verification. Monthly Weather Review, 115, 1330–1338.2.0.CO;2>CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Nenzén, H. K. and Araújo, M. 2011. Choice of threshold alters projections of species range shifts under climate change. Ecological Modelling, 222, 3346–3354.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Nesme, J., Achouak, W., Agathos, S. N., et al. 2016. Back to the future of soil metagenomics. Frontiers in Microbiology, 7, 73.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Nogues-Bravo, D. 2009. Predicting the past distribution of species climatic niches. Global Ecology and Biogeography, 18, 521–531.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.Google ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Olden, J. D. 2003. A species-specific approach to modeling biological communities and its potential for conservation. Conservation Biology, 17, 854–863.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Osborne, P. E. and Suarez-Seoane, S. 2002. Should data be partitioned spatially before building large-scale distribution models? Ecological Modelling, 157, 249–259.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Ovaskainen, O. and Meerson, B. 2010. Stochastic models of population extinction. Trends in Ecology and Evolution, 25, 643–652.CrossRefGoogle ScholarPubMed
Ovaskainen, O. and Soininen, J. 2011. Making more out of sparse data: hierarchical modeling of species communities. Ecology, 92, 289–295.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Pearce, J. and Ferrier, S. 2000. Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling, 133, 225–245.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Pearce, J. L. and Boyce, M. S. 2006. Modelling distribution and abundance with presence-only data. Journal of Applied Ecology, 43, 405–412.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Pearman, P. B., Randin, C. F., Broennimann, O., et al. 2008b. Prediction of plant species distributions across six millennia. Ecology Letters, 11, 357–369.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Pearson, R. G., Thuiller, W., Araujo, M. B., et al. 2006. Model-based uncertainty in species range prediction. Journal of Biogeography, 33, 1704–1711.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Peters, R. H. 1991. A Critique for Ecology, Cambridge, UK: Cambridge University Press.Google Scholar
Peterson, A. T. 2003. Predicting the geography of species’ invasions via ecological niche modeling. Quarterly Review of Biology, 78, 419–433.CrossRefGoogle ScholarPubMed
Peterson, A. T. 2006. Ecologic niche modeling and spatial patterns of disease transmission. Emerging Infectious Diseases, 12, 1822–1826.CrossRefGoogle ScholarPubMed
Peterson, A. T. 2011. Ecological niche conservatism: a time-structured review of evidence. Journal of Biogeography, 38, 817–827.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Peterson, A. T., Papes, M. and Soberon, J. 2008a. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling, 213, 63–72.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Peterson, A. T., Soberon, J., Pearson, R. G., et al. 2011. Ecological Niches and Geographic Distributions, Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Petitpierre, B., Kueffer, C., Broennimann, O., et al. 2012. Climatic niche shifts are rare among terrestrial plant invaders. Science, 335, 1344–1348.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Phillips, S. J. and Elith, J. 2010. POC plots: calibrating species distribution models with presence-only data. Ecology, 91, 2476–2484.CrossRefGoogle ScholarPubMed
Phillips, S. J. and Dudík, M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161–175.Google Scholar
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.Google Scholar
Phillips, S. J., Anderson, R. P. and Schapire, R. E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Pottier, J., Malenovský, Z., Psomas, A., et al. 2014. Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy. Biology Letters, 10, 20140347.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Pulliam, H. R. 2000. On the relationship between niche and distribution. Ecology Letters, 3, 349–361.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Quinlan, J. R. 1986. Induction of decision trees. Machine Learning, 1, 81–106.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Reineking, B. and Schroder, B. 2006. Constrain to perform: regularization of habitat models. Ecological Modelling, 193, 675–690.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Ricklefs, R. E. 1987. Community diversity: relative roles of local and regional processes. Science, 235, 167–171.CrossRefGoogle ScholarPubMed
Ricklefs, R. E. 2008. Disintegration of the ecological community. The American Naturalist, 172, 741–750.CrossRefGoogle ScholarPubMed
Ridgeway, G. 1999. The state of boosting. Computing Science and Statistics, 31, 172–181.Google Scholar
Ripley, B. D. 1996. Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Rykiel, E. J. J. 1996. Testing ecological models: the meaning of validation. Ecological Modelling, 90, 229–244.CrossRefGoogle Scholar
Sala, O. E., Chapin, F. S., Armesto, J. J., et al. 2000. Global biodiversity scenarios for the year 2100. Science, 287, 1770–1774.CrossRefGoogle ScholarPubMed
Salisbury, E. J. 1929. The biological equipment of species in relation to competition. Journal of Ecology, 17, 197–222.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Sandel, B., Arge, L., Dalsgaard, B., et al. 2011. The Influence of Late Quaternary climate-change velocity on species endemism. Science, 334, 660–664.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Schlossberg, S. and King, D. I. 2009. Modeling animal habitats based on cover types: a critical review. Environmental Management, 43, 609–618.CrossRefGoogle ScholarPubMed
Schneider, D. C. 2001. The rise of the concept of scale in ecology. Bioscience, 51, 545–553.CrossRefGoogle Scholar
Schoener, T. W. 1989. Food webs from the small to the large. Ecology, 70, 1559–1589.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Schwartz, M. W. 2012. Using niche models with climate projections to inform conservation management decisions. Biological Conservation, 155, 149–156.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Segurado, P. and Araujo, M. B. 2004. An evaluation of methods for modelling species distributions. Journal of Biogeography, 31, 1555–1568.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Shipley, B., Vile, D. and Garnier, E. 2006. From plant traits to plant communities: a statistical mechanistic approach to biodiversity. Science, 314, 812–814.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Silvertown, J. 2004. Plant coexistence and the niche. Trends in Ecology and Evolution, 19, 605–611.CrossRefGoogle Scholar
Silvertown, J., Dodd, M., Gowing, D., Lawson, C. and McConway, K. 2006. Phylogeny and the hierarchical organization of plant diversity. Ecology, 87, S39–S49.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Smith, T. M. and Smith, R. L. 2015. Elements of Ecology, 9th edn. San Francisco, CA: Pearson Education Ltd.Google Scholar
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.CrossRefGoogle Scholar
Soberón, J. 2007. Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters, 10, 1115–1123.CrossRefGoogle ScholarPubMed
Soberón, J. and Nakamura, M. 2009. Niches and distributional areas: concepts, methods, and assumptions. Proceedings of the National Academy of Sciences, 106, 19644–19650.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Stachowicz, J. J. 2001. Mutualism, facilitation, and the structure of ecological communities. Bioscience, 51, 235–246.CrossRefGoogle Scholar
Stockwell, D. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13, 143–158.CrossRefGoogle Scholar
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.Google Scholar
Stockwell, D. R. B. and Peterson, A. T. 2002b. Effects of sample size on accuracy of species distribution models. Ecological Modelling, 148, 1–13.CrossRefGoogle Scholar
Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T. and Zeileis, A. 2008. Conditional variable importance for random forests. BMC Bioinformatics, 9(1), 307.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Svenning, J. C. and Skov, F. 2004. Limited filling of the potential range in European tree species. Ecology Letters, 7, 565–573.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Svenning, J. C., Normand, S. and Skov, F. 2006. Range filling in European trees. Journal of Biogeography, 33, 2018–2021.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Swets, J. A. 1988. Measuring the accuracy of diagnostic systems. Science, 240, 1285–1293.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Tang, L. and Shao, G. 2015. Drone remote sensing for forestry research and practices. Journal of Forestry Research, 26, 791–797.CrossRefGoogle Scholar
Tautz, D. 2003. Evolutionary biology: splitting in space. Nature, 421, 225–226.CrossRefGoogle ScholarPubMed
Team, R. C. 2014. R: A language and environment for statistical computing. In R Foundation For Statistical Computing, V. www.R-project.org/Google Scholar
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.CrossRefGoogle ScholarPubMed
ter Braak, C. J. F. 1986. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology, 67, 1167–1179.CrossRefGoogle Scholar
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.Google Scholar
Thomas, C. D., Cameron, A., Green, R. E., et al. 2004. Extinction risk from climate change. Nature, 427, 145–148.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Thuiller, W. 2003. BIOMOD: optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology, 9, 1353–1362.CrossRefGoogle Scholar
Thuiller, W. 2004. Patterns and uncertainties of species’ range shifts under climate change. Global Change Biology, 10, 2020–2027.CrossRefGoogle Scholar
Thuiller, W. 2007. Biodiversity: climate change and the ecologist. Nature, 448, 550–552.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Thuiller, W., Araujo, M. B., Pearson, R. G., et al. 2004a. Uncertainty in predictions of extinction risk. Nature, 430, 34.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Thuiller, W., Lafourcade, B., Engler, R. and Araujo, M. B. 2009. BIOMOD: a platform for ensemble forecasting of species distributions. Ecography, 32, 369–373.CrossRefGoogle Scholar
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.Google Scholar
Thuiller, W., Lavergne, S., Roquet, C., et al. 2011. Consequences of climate change on the tree of life in Europe. Nature, 470, 531–534.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Thuiller, W., Münkemüller, T., Schiffers, K. H., et al. 2014b. Does probability of occurrence relate to population dynamics? Ecography, 37, 1155–1166.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Thuiller, W., Pollock, L. J., Gueguen, M. and Münkemüller, T. 2015. From species distributions to meta-communities. Ecology Letters 18(12), 1321–1328.Google Scholar
Tibshirani, R. 1996. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society Series B: Methodological, 58, 267–288.Google Scholar
Tibshirani, R. 1997. The LASSO method for variable selection in the Cox model. Statistics in Medicine, 16, 385–395.3.0.CO;2-3>CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle ScholarPubMed
Vandermeer, J. H. 1972. Niche theory. Annual Review of Ecology and Systematics, 3, 107–132.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Vaughan, I. P. and Ormerod, S. J. 2005. The continuing challenges of testing species distribution models. Journal of Applied Ecology, 42, 720–730.CrossRefGoogle Scholar
Veloz, S. D. 2009. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. Journal of Biogeography, 36, 2290–2299.CrossRefGoogle Scholar
Venables, W. N. and Ripley, B. D. 2002. Modern Applied Statistisc with S. Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
Verbyla, D. L. and Litvaitis, J. A. 1989. Resampling methods for evaluating classification accuracy of wildlife habitat models. Environmental Management, 13, 783–787.CrossRefGoogle Scholar
Verner, J., Morrison, M. L. and Ralph, C. J. 1986. Wildlife 2000: Modelling Habitat Relationships of Terrestrial Vertebrates, Madison, WI: University of Wisconsin Press.Google Scholar
Vesk, P. A. 2013. How traits determine species responses to environmental gradients. Journal of Vegetation Science, 24, 977–978.CrossRefGoogle Scholar
Vetaas, O. R. 2002. Realized and potential climate niches: a comparison of four Rhododendron tree species. Journal of Biogeography, 29, 545–554.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Vincent, P. J. and Haworth, J. M. 1983. Poisson regression models of species abundance. Journal of Biogeography, 10, 153–160.CrossRefGoogle Scholar
Vittoz, P. and Engler, R. 2007. Seed dispersal distances: a typology based on dispersal modes and plant traits. Botanica Helvetica, 117, 109–124.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Ward, G., Hastie, T., Barry, S., Elith, J. and Leathwick, J. R. 2009. Presence-only data and the EM algorithm. Biometrics, 65, 554–563.CrossRefGoogle ScholarPubMed
Warren, D. L., Glor, R. E. and Turelli, M. 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution, 62, 2868–2883.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Weiher, E. and Keddy, P. 2001. Ecological Assembly Rules: Perspectives, Advances, Retreats, Cambridge, UK: Cambridge University Press.Google Scholar
Weisberg, S. 1980. Applied Linear Regression, New York, NY: Wiley.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Westman, W. E. 1991. Measuring realized niche spaces: climatic response of chaparral and coastal sage scrub. Ecology, 72, 1678–1684.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Whittaker, R. H. 1967. Gradient analysis of vegetation. Biological Reviews, 42, 207–264.CrossRefGoogle ScholarPubMed
Whittaker, R. H., Levin, S. A. and Root, R. B. 1973. Niche, habitat, and ecotope. American Naturalist, 107, 321–338.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Wiens, J. A. 1989. Spatial scaling in ecology. Functional Ecology, 3, 385–397.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Willis, K. J. and McElwain, J. C. 2002. The Evolution of Plants. Oxford, UK: Oxford University Press.Google Scholar
Willis, K. J. and Whittaker, R. J. 2002. Ecology. Species diversity, Scale matters. Science, 295, 1245–1248.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Wisz, M. S., Broennimann, O., Grønkjær, P., et al. 2015. Arctic warming will promote Atlantic–Pacific fish interchange. Nature Climate Change, 5, 261–265.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Wood, S. 2006. Generalized Additive Models: An Introduction with R. London: CRC Press.CrossRefGoogle Scholar
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.Google Scholar
Woodward, F. I. 1987. Climate and Plant Distribution, Cambridge, UK: Cambridge University Press.Google Scholar
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.Google Scholar
Wu, J. 1999. Hierarchy and scaling: extrapolating information along a scaling ladder. Canadian Journal of Remote Sensing, 25, 367–380.CrossRefGoogle Scholar
Wu, J. G. 2004. Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecology, 19, 125–138.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Zhu, G. and Peterson, A. T. 2014. Potential geographic distribution of the novel avian-origin influenza A (H7N9) Virus. PLoS One, 9, e93390.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Zurell, D., Berger, U., Cabral, J. S., et al. 2010. The virtual ecologist approach: simulating data and observers. Oikos, 119, 622–635.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×