[Akaike, H. 1973. Information theory as an extension of the maximum likelihood principle. - In: Petrov, B.N. & Csaki, F. (eds), Second international symposium on information theory, Akademiai Kiado, Budapest, pp. 267-281.]Search in Google Scholar
[*Anderson, R.P. & Gonzalez, I.J. 2011. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. - Ecol. Modelling 222: 2796-2811.10.1016/j.ecolmodel.2011.04.011]Search in Google Scholar
[*Anderson, R.P. & 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. - J. Biogeogr. 37: 1378-1393.10.1111/j.1365-2699.2010.02290.x]Search in Google Scholar
[Anonymous 2010. R version 2.11 for Windows. - The R foundation for statistical computing (http://cran.r-project.org)]Search in Google Scholar
[*Aranda, S.C. & Lobo, J.M. 2011. How well does presence-only-based species distribution modelling predict assemblage diversity? A case study of the Tenerife flora. - Ecography 34: 31-38.10.1111/j.1600-0587.2010.06134.x]Search in Google Scholar
[Araújo, M.B. & Guisan, A. 2006. Five (or so) challenges for species distribution modelling. - J. Biogeogr. 33: 1677-1688.10.1111/j.1365-2699.2006.01584.x]Search in Google Scholar
[Araújo, M.B., Pearson, R.G., Thuiller, W. & Erhard, M. 2005. Validation of species-climate impact models under climate change. - Global Change Biol. 11: 1504-1513.10.1111/j.1365-2486.2005.01000.x]Search in Google Scholar
[*Auestad, I., Halvorsen, R., Bakkestuen, V. & Erikstad, L. 2011. Utbredelsesmodellering av fremmede invaderende karplanter langs veg. - Dir. Naturforv. Utredn. 2011: 2: 1-30.]Search in Google Scholar
[Austin, M.P. 1999. A silent clash of paradigms: some inconsistencies in community ecology. - Oikos 86: 170-178.10.2307/3546582]Search in Google Scholar
[Austin, M.P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. - Ecol. Modelling 157: 101-118.10.1016/S0304-3800(02)00205-3]Search in Google Scholar
[Austin, M. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. - Ecol. Modelling 200: 1-19.10.1016/j.ecolmodel.2006.07.005]Search in Google Scholar
[Bakkestuen, V., Aarrestad, P.A., Stabbetorp, O.E., Erikstad, L. & Eilertsen, O. 2010. Vegetation composition, gradients and environment relationships of birch forest in six reference areas in Norway. - Sommerfeltia 33: 1-226.]Search in Google Scholar
[Barbosa, A.M. 2009. Transferability of environmental favourability models in geographic space: the case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain. - Ecol. Modelling 220: 747-754.10.1016/j.ecolmodel.2008.12.004]Search in Google Scholar
[Barry, S. & Elith, J. 2006. Error and uncertainty in habitat models. - J. appl. Ecol. 43: 413-423.10.1111/j.1365-2664.2006.01136.x]Search in Google Scholar
[*Bartel, R.A. & Sexton, J.O. 2009. Monitoring habitat dynamics for rare and endangered species using satellite images and niche-based models. - Ecography 32: 888-896.10.1111/j.1600-0587.2009.05797.x]Search in Google Scholar
[*Bedia, J., Busqué, J. & Gutiérrez, J.M. 2011. Predicting plant species distribution across an alpine rangeland in northern Spain: a comparison of probabilistic methods. - Appl. Veg. Sci. 14: 415-432.10.1111/j.1654-109X.2011.01128.x]Search in Google Scholar
[Berger, A.L., Della Pietra, S.A. & Della Pietra, V.J. 1996. A maximum entropy approach to natural language processing. - Comput. Linguist. 22: 39-71.]Search in Google Scholar
[Bini, L.M., Diniz-Filho, A.F., Rangel, T.F.L.V.B., Akre, T.S.B., Albaladejo, R.G., Albuquerque, F.S., Aparicio, A., Araújo, M.B., Baselga, A., Beck, J., Bellocq, M.I., Böhning-Gaese, K., Borges, P.A.V., Castro-Parga, I., Chey, V.K., Chown, S.L., de Marco, P.J., Dobkin, D.S., Ferrer-Castán, D., Field, R., Filloy, J., Fleishman, E., Gómez, J.F., Hortal, J., Iverson, J.B., Kerr, J.T., Kissling, W.D., Kitching, I.J., León-Cortés, J.L., Lobo, J.M., Montoya, D., Morales-Castilla, I., Moreno, J.C., Oberdorff, T., Olalla-Tárraga, M.Á., Pausas, J.G., Qian, H., Rahbek, C., Rodríguez, M.Á., Rueda, M., Ruggiero, A., Sackmann, P., Sanders, N.J., Terribile, L.C., Vetaas, O.R. & Hawkins, B.A. 2009. Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression. - Ecography 32: 193-204.10.1111/j.1600-0587.2009.05717.x]Search in Google Scholar
[*Bradley, B.A., Wilcove, D.S. & Oppenheimer, M. 2010. Climate change increases risk of plant invasion in the Eastern United States. - Biol. Invasions 12: 1855-1872.10.1007/s10530-009-9597-y]Search in Google Scholar
[*Braunisch, V. & Suchant, R. 2010. Predicting species distributions based on incomplete survey data: the trade-off between precision and scale. - Ecography 33: 826-840.10.1111/j.1600-0587.2009.05891.x]Search in Google Scholar
[Brown, K.A., Spector, S. & Wu, W. 2008. Multi-scale analysis of species introductions: combining landscape and demographic models to improve management decisions about non-native species. - J. appl. Ecol. 45: 1639-1648.10.1111/j.1365-2664.2008.01550.x]Search in Google Scholar
[*Buermann, W., Saatchi, S., Smith, T.B., Zutta, B.R., Chaves, J.A., Milá, B. & Graham, C.H. 2008. Predicting species distributions across the Amazonian and Andean regions using remote sensing data. - J. Biogeogr. 35: 1160-1176.10.1111/j.1365-2699.2007.01858.x]Search in Google Scholar
[Burnham, K.P. & Anderson, D.R. 2002. Model selection and multimodel inferences: a practical information-theoretic approach, ed. 2. - Springer, New York.]Search in Google Scholar
[*Carnaval, A.C. & Moritz, C. 2008. Historical climate modelling predicts patterns of current biodiversity in the Brazilian Atlantic forest. - J. Biogeogr. 35: 1187-1201.10.1111/j.1365-2699.2007.01870.x]Search in Google Scholar
[*Carroll, C., Johnson, D.S., Dunk, J.R. & Zielinski, W.J. 2010. Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring. - Conserv. Biol. 24: 1538-1548.10.1111/j.1523-1739.2010.01528.x20497204]Search in Google Scholar
[*Cordellier, M. & Pfenninger, M. 2009. Inferring the past to predict the future: climate modelling predictions and phylogeography for the freshwater gastropod Radix balthica (Pulmonata Basommatophora). - Molec. Ecol. 18: 534-544.10.1111/j.1365-294X.2008.04042.x19161472]Search in Google Scholar
[*Costa, G.C., Nogueira, C., Machado, R.B. & Colli, G.R. 2010. Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot. - Biodiv. Conserv. 19: 883-899.10.1007/s10531-009-9746-8]Search in Google Scholar
[Crawley, M.J. 2007. The R book. - Wiley, Chichester.10.1002/9780470515075]Search in Google Scholar
[*Cunningham, H.R., Rissler, L.J. & Apodaca, J.J. 2009. Competition at the boundary in the slimy salamander: using reciprocal transplants for studies on the role of biotic interactions in spatial distributions. - J. Anim. Ecol. 78: 52-62.10.1111/j.1365-2656.2008.01468.x]Search in Google Scholar
[Danz, N.P., Reich, P.B., Frelich, L.E. & Niemi, G.J. 2011. Vegetation controls vary across space and spatial scale in a historic grassland-forest biome boundary. - Ecography 34: 402-414.10.1111/j.1600-0587.2010.06561.x]Search in Google Scholar
[De'ath, G. 2007. Boosted trees for ecological modeling and prediction. - Ecology 88: 243-251.10.1890/0012-9658(2007)88[243:BTFEMA]2.0.CO;2]Search in Google Scholar
[Della Pietra, S., Della Pietra, V. & Lafferty, J. 1997. Inducing features of random fields. - IEEE Trans. Pattern Anal. Mach. Intell. 19: 1-13.10.1109/34.588021]Search in Google Scholar
[DeLong, E.R., DeLong, D.M. & Clarke-Pearson, D.L. 1988. Comparing the areas under two or more correlated receiver operating characteristic curves: a non-parametric approach - Biometrics 44: 837-845.10.2307/2531595]Search in Google Scholar
[*DeMatteo, K.E. & Loiselle, B.A. 2008. New data on the status and distribution of the bush dog (Speothos venaticus): evaluating its quality of protection and directing research efforts. - Biol. Conserv. 141: 2494-2505.10.1016/j.biocon.2008.07.010]Search in Google Scholar
[*Diniz-Filho, J.A.F., Bini, L.M., Rangel, T.F., Loyola, R.D., Hof, C., Nogués-Bravo, D. & Araújo, M.B. 2009. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. - Ecography 32: 897-906.10.1111/j.1600-0587.2009.06196.x]Search in Google Scholar
[Dobrowski, S.Z., Safford, H., D., Cheng, Y.B. & Ustin, S.L. 2008. Mapping mountain vegetation using species distribution modeling, image-based texture analysis, and object-based classification. - Appl. Veg. Sci. 11: 499-508.]Search in Google Scholar
[Dormann, C. 2011. Modelling species’ distributions. - In: Jopp, F., Reuter, H. & Breckling, B. (eds), Modelling complex ecological dynamics: an introduction into ecological modelling, Springer, Berlin, pp. 179-196.]Search in Google Scholar
[Dormann, C.F., McPherson, J.M., Araújo, M.B., Bivand, R., Bolliger, J., Carl, G., Davies, R.G., Hirzel, A., Jetz, W., Kissling, W.D., Kühn, I., Ohlemüller, R., Peres-Neto, P.R., Reineking, B., Schröder, B., Schurr, F.M. & Wilson, R. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. - Ecography 30: 609-628.10.1111/j.2007.0906-7590.05171.x]Search in Google Scholar
[Dubuis, A., Pottier, J., Rion, V., Pellissier, L., Theurillat, J.-P. & Guisan, A. 2011. Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches. - Divers. Distrib. 17: 1122-1131.10.1111/j.1472-4642.2011.00792.x]Search in Google Scholar
[Dudík, M. & Phillips, S.J. 2009. Generative and discriminative learning with unknown labeling bias. - Adv. neural Inf. Process. Syst. 21: 401-408.]Search in Google Scholar
[Dudík, M., Phillips, S.J. & Schapire, R.E. 2007. Maximum entropy density estimation with generalized regularization and an application to species distribution modeling. - J. Machine Learning Res. 8: 1217-1260.]Search in Google Scholar
[*Echarri, F., Tambussi, C. & Hospitaleche, C.A. 2009. Predicting the distribution of the crested tinamous, Eudromia spp. (Aves, Tinamiformes). - J. Ornithol. 150: 75-84.10.1007/s10336-008-0319-5]Search in Google Scholar
[*Edrén, S.M.C., Wisz, M.S., Teilmann, J., Dietz, R. & Söderkvist, J. 2010. Modelling spatial patterns in harbour porpoise satellite telemetry data using maximum entropy. - Ecography 33: 698-708.10.1111/j.1600-0587.2009.05901.x]Search in Google Scholar
[*Edvardsen, A., Bakkestuen, V. & Halvorsen, R. 2011. A fine-grained spatial prediction model for the red-listed vascular plant Scorzonera humilis. - Nord. J. Bot. 29: 495-504.10.1111/j.1756-1051.2010.00984.x]Search in Google Scholar
[Edwards, T.C.J., Cutler, D.R., Zimmermann, N.E., Geiser, L. & Alegria, J. 2005. Model-based stratifications for enhancing the detection of rare ecological events. - Ecology 86: 1081-1090.10.1890/04-0608]Search in Google Scholar
[Edwards, T.C.J., Cutler, D.R., Zimmermann, N.E., Geiser, L. & Moisen, G.G. 2006. Effects of sample survey design on the accuracy of classification tree models in species distribution models. - Ecol. Modelling 199: 132-141.10.1016/j.ecolmodel.2006.05.016]Search in Google Scholar
[Elith, J. & Graham, C.H. 2009. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. - Ecography 32: 66-77.10.1111/j.1600-0587.2008.05505.x]Search in Google Scholar
[Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M.M., Peterson, A.T., Phillips, S.J., Richardson, K., Scachetti-Pereira, R., Schapire, R.E., Soberón, J., Williams, S., Wisz, M.S. & Zimmermann, N.E. 2006. Novel methods improve prediction of species’ distributions from occurrence data. - Ecography 29: 129-151. *Elith, J., Kearney, M. & Phillips, S. 2010. The art of modelling range-shifting species. - Methods Ecol. Evol. 1: 330-342.]Search in Google Scholar
[Elith, J. & Leathwick, J.R. 2009. Species distribution models: ecological explanation and prediction across space and time. - A. Rev. Ecol. Evol. Syst. 40: 677-697.10.1146/annurev.ecolsys.110308.120159]Search in Google Scholar
[Elith, J., Leathwich, J.R. & Hastie, T. 2008. A working guide to boosted regression trees. - J. Anim. Ecol. 77: 802-813.10.1111/j.1365-2656.2008.01390.x18397250]Search in Google Scholar
[Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E. & Yates, C.J. 2011. A statistical explanation of MaxEnt for ecologists. - Divers. Distrib. 17: 43-57.10.1111/j.1472-4642.2010.00725.x]Search in Google Scholar
[*Feeley, K.J. & Silman, M.R. 2011. Keep collecting: accurate species distribution modelling requires more collections than previously thought. - Divers. Distrib. 17: 1132-1140.10.1111/j.1472-4642.2011.00813.x]Search in Google Scholar
[*Ficetola, G.F., Thuiller, W. & Miaud, C. 2007. Prediction and validation of the potential global distribution of a problematic invasive alien species. - Divers. Distrib. 13: 476-485.10.1111/j.1472-4642.2007.00377.x]Search in Google Scholar
[Fielding, A.H. & Bell, J.E. 1997. A review of methods for the assessment of prediction errors in conservation presence-absence models. - Environm. Conserv. 24: 38-49. *Fitzpatrick, M.C., Gove, A.D., Sanders, N.J. & Dunn, R.R. 2008. Climate change, plant migration, and range collapse in a global biodiversity hotspot: the Banksia (Proteaceae) of Western Australia. - Global Change Biol. 14: 1337-1352.]Search in Google Scholar
[Franklin, J. 2009. Mapping species distributions: spatial inference and prediction. - Cambridge University Press, Cambridge.10.1017/CBO9780511810602]Search in Google Scholar
[Franklin, J. 2010. Moving beyond static species distribution models in support of conservation biogeography. - Divers. Distrib. 16: 321-330.10.1111/j.1472-4642.2010.00641.x]Search in Google Scholar
[Franklin, J., Wejnert, K.E., Hathaway, S.A., Rochester, C.J. & Fisher, R.N. 2009. Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California. - Divers. Distrib. 15: 167-177.10.1111/j.1472-4642.2008.00536.x]Search in Google Scholar
[Friedman, J.H. 1991. Multivariate adaptive regression splines. - Ann. Statist. 19: 1-67.10.1214/aos/1176347963]Search in Google Scholar
[*Gaikwad, J., Wilson, P.D. & Ranganathan, S. 2011. Ecological niche modeling of customary medicinal plant species used by Australian aborigines to identify species-rich and culturally valuable areas for conservation. - Ecol. Modelling 222: 3437-3443.10.1016/j.ecolmodel.2011.07.005]Search in Google Scholar
[*Gastón, A. & García-Viñas, J.I. 2011. Modelling species distributions with penalised logistic regressions: a comparison with maximum entropy models. - Ecol. Modelling 222: 2037-2041.10.1016/j.ecolmodel.2011.04.015]Search in Google Scholar
[Gellrich, M. & Zimmermann, N.E. 2007. Investigating the regional-scale pattern of agricultural land abandonment in the Swiss mountains: a spatial statistical modelling approach. - Landsc. Urban Planning 79: 65-76.10.1016/j.landurbplan.2006.03.004]Search in Google Scholar
[*Gibson, L., Barrett, B. & Burbridge, A. 2007. Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot. - Divers. Distrib. 13: 704-713.10.1111/j.1472-4642.2007.00365.x]Search in Google Scholar
[Gini, C. 1912. Variabilità e mutabilità - Bologna, Cuppini.]Search in Google Scholar
[*Giovanelli, J.G.R., Haddad, C.F.B. & Alexandrino, J. 2008. Predicting the potential distribution of the alien invasive American bullfrog (Lithobates catesbeianus) in Brazil. - Biol. Invasions 10: 585-590.10.1007/s10530-007-9154-5]Search in Google Scholar
[*Gormley, A.M., Forsyth, D.M., Griffioen, P., Lindeman, M., Ramsey, D.S.L., Scroggie, M.P. & Woodford, L. 2011. Using presence-only and presence-absence data to estimate the current and potential distributions of established invasive species. - J. appl. Ecol. 48: 25-34.10.1111/j.1365-2664.2010.01911.x]Search in Google Scholar
[*Graham, C.H. & Hijmans, R.J. 2006. A comparison of methods for mapping species ranges and species richness. - Global Ecol. Biogeogr. 15: 578-587.10.1111/j.1466-8238.2006.00257.x]Search in Google Scholar
[*Graham, C.H., VanDerWal, J., Phillips, S.J., Moritz, C. & Williams, S.E. 2010. Dynamic refugia and species persistence: tracking spatial shifts in habitat through time. - Ecography 33: 1062-1069.10.1111/j.1600-0587.2010.06430.x]Search in Google Scholar
[Guisan, A., Broennimann, O., Engler, R., Vust, M., Yoccoz, N.G., Lehmann, A. & Zimmermann, N.E. 2006. Using niche-based models to improve the sampling of rare species. - Conserv. Biol. 20: 501-511.10.1111/j.1523-1739.2006.00354.x]Search in Google Scholar
[Guisan, A., Graham, C.H., Elith, J., Huettmann, F. & Group, N.S.D.M. 2007. Sensitivity of predictive species distribution models to change in grain size. - Divers. Distrib. 13: 332-340.10.1111/j.1472-4642.2007.00342.x]Search in Google Scholar
[Guisan, A. & Zimmermann, N.E. 2000. Predictive habitat distribution models in ecology. - Ecol. Modelling 135: 147-186.10.1016/S0304-3800(00)00354-9]Search in Google Scholar
[Halvorsen, R. 2012. A gradient analytic perspective on distribution modelling. - Sommerfeltia, submitted manuscript.10.2478/v10208-011-0015-3]Search in Google Scholar
[Hanley, J.A. & McNeil, B.J. 1982. The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. - Radiology 143: 29-36.10.1148/radiology.143.1.70637477063747]Search in Google Scholar
[Hastie, T., Tibshirani, R. & Friedman, J. 2009. The elements of statistical learning, ed. 2. - Springer, New York.10.1007/978-0-387-84858-7]Search in Google Scholar
[Hengl, T., Sierdsema, H., Radović, A. & Dilo, A. 2009. Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, ENFA and regressionkriging. - Ecol. Modelling 220: 3499-3511.]Search in Google Scholar
[*Hernandez, P.A., Graham, C.H., Master, L.L. & Albert, D.L. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. - Ecography 29: 773-785.10.1111/j.0906-7590.2006.04700.x]Search in Google Scholar
[Hijmans, R.J. & Elith, J. 2011. Species distribution modelling with R. - http://cran.r-project.org/ web/packages/dismo/vignettes/sdm.pdf, The R foundation for statistical computing.]Search in Google Scholar
[*Hijmans, R.J. & Graham, C.H. 2006. The ability of climate envelope models to predict the effect of climate change on species distributions. - Global Change Biol. 12: 2272-2281.10.1111/j.1365-2486.2006.01256.x]Search in Google Scholar
[Hirzel, A.H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. 2006. Evaluating the ability of habitat suitability models to predict species presences. - Ecol. Modelling 199: 142-152.10.1016/j.ecolmodel.2006.05.017]Search in Google Scholar
[Hjort, J. & Marmion, M. 2009. Periglacial distribution modelling with a boosting method. - Permafr. periglac. Proc 20: 15-25.10.1002/ppp.629]Search in Google Scholar
[*Hoffman, J.D., Aguilar-Amuchastegui, N. & Tyre, A.J. 2010. Use of simulated data from a processbased habitat model to evaluate methods for predicting species occurrence. - Ecography 33: 656-666.10.1111/j.1600-0587.2009.05495.x]Search in Google Scholar
[Hortal, J., Jiménez-Valverde, A., Gómez, J.F., Lobo, J.M. & Baselga, A. 2008. Historical bias in biodiversity inventories affects the observed environmental niche of the species. - Oikos 117: 847-858.10.1111/j.0030-1299.2008.16434.x]Search in Google Scholar
[Huisman, J., Olff, H. & Fresco, L.F.M. 1993. A hierarchical set of models for species response analysis. - J. Veg. Sci. 4: 37-46.10.2307/3235732]Search in Google Scholar
[Jaynes, E.T. 1957. Information theory and statistical mechanics. - Phys. Rev. 106: 620-630.10.1103/PhysRev.106.620]Search in Google Scholar
[Jaynes, E.T. 1957. Information theory and statistical mechanics. II. - Phys. Rev. 108: 171-190.10.1103/PhysRev.108.171]Search in Google Scholar
[Jaynes, E.T. 2003. Probability theory: the logic of science. - Cambridge University Press, Cambridge.10.1017/CBO9780511790423]Search in Google Scholar
[Jiménez-Valverde, A., Lobo, J. & Hortal, J. 2008. Not as good as they seem: the importance of concepts in species distribution modelling. - Divers. Distrib. 14: 885-890.10.1111/j.1472-4642.2008.00496.x]Search in Google Scholar
[Kadmon, R., Farber, O. & Danin, A. 2004. Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. - Ecol. Appl. 14: 401-413.10.1890/02-5364]Search in Google Scholar
[*Kharouba, H.M., Algar, A.C. & Kerr, J.T. 2009. Historically calibrated predictions of butterfly species’ range shift using global change as a pseudo-experiment. - Ecology 90: 2213-2222.10.1890/08-1304.119739383]Search in Google Scholar
[*Ko, C.Y., Root, T.L. & Lee, P.F. 2011. Movement distances enhance validity of predictive models. - Ecol. Modelling 222: 947-954.10.1016/j.ecolmodel.2010.12.001]Search in Google Scholar
[Kodric-Brown, A. & Brown, J.H. 1993. Incomplete data sets in community ecology and biogeography: a cautionary tale. - Ecol. Appl. 3: 736-742.10.2307/194210427759285]Search in Google Scholar
[Kullback, S. 1959. Information theory and statistics. - New York, Wiley.]Search in Google Scholar
[*Lahoz-Monfort, J.J., Guillera-Arroita, G., Milner-Gulland, E.J., Young, R.P. & Nicholson, E. 2010. Satellite imagery as a single source of predictor variables for habitat suitability modelling: how Landsat can inform the conservation of a critically endangered lemur. - J. appl. Ecol. 47: 1094-1102.10.1111/j.1365-2664.2010.01854.x]Search in Google Scholar
[*Lamb, J.M., Ralph, T.M.C., Goodman, S.M., Bogdanowicz, W.,, Fahr, J., Gajewska, M., Bates, P.J.J., Eger, J., Benda, P. & & Taylor, P.J. 2008. Phylogeography and predicted distribution of African-Arabian and Malagasy populations of giant mastiff bats, Otomops spp. (Chiroptera: Molossidae). - Acta chiropterol. 10: 21-40.]Search in Google Scholar
[Leathwick, J.R., Elith, J. & Hastie, T. 2006. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. - Ecol. Modelling 199: 188-196.10.1016/j.ecolmodel.2006.05.022]Search in Google Scholar
[Legendre, P. & Legendre, L. 1998. Numerical ecology, ed. 2. - Elsevier, Amsterdam.]Search in Google Scholar
[Lobo, J.M. 2008. More complex distribution models or more representative data? - Biodiv. Inform. 5: 14-19.10.17161/bi.v5i0.40]Search in Google Scholar
[Lobo, J.M., Jiménez-Valverde, A. & Hortal, J. 2010. The uncertain nature of absences and their importance in species distribution modelling. - Ecography 33: 103-114.10.1111/j.1600-0587.2009.06039.x]Search in Google Scholar
[Lobo, J.M., Jiménez-Valverde, A. & Real, R. 2008. AUC: a misleading measure of the performance of predictive distribution models. - Global Ecol. Biogeogr. 17: 145-151.10.1111/j.1466-8238.2007.00358.x]Search in Google Scholar
[*Loiselle, B.A., Jørgensen, P.M., Consiglio, T., Jiménez, I., Blake, J.G., Lohmann, L.G. & Montiel, O.M. 2008. Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? - J. Biogeogr. 35: 105-116.]Search in Google Scholar
[*Lozier, J.D., Aniello, P. & Hickerson, M.J. 2009. Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling. - J. Biogeogr. 36: 1623-1627.10.1111/j.1365-2699.2009.02152.x]Search in Google Scholar
[Luoto, M., Pöyry, J., Heikkinen, R.K. & Saarinen, K. 2005. Uncertainty of bioclimatic envelope models based on the geographical distribution of species. - Global Ecol. Biogeogr. 14: 575-584.10.1111/j.1466-822X.2005.00186.x]Search in Google Scholar
[McCarthy, K.P., Fletcher, R.J.J., Rota, C.T. & Hutto, R.L. 2011. Predicting species distributions from samples collected along roadsides. - Conserv. Biol. 26: 68-77.10.1111/j.1523-1739.2011.01754.x]Search in Google Scholar
[Maggini, R., Lehmann, A., Zimmermann, N.E. & Guisan, A. 2006. Improving generalized regression analysis for the spatial prediction of forest communities. - J. Biogeogr. 33: 1729-1749.10.1111/j.1365-2699.2006.01465.x]Search in Google Scholar
[*Marini, M.Á., Barbet-Massin, M., Lopes, L. & Jiguet, F. 2010. Predicting the occurrence of rare Brazilian birds with species distribution models. - J. Ornithol. 151: 857-866.10.1007/s10336-010-0523-y]Search in Google Scholar
[*Marino, J., Bennett, M., Cossios, D., Iriarte, A., Lucherini, M., Pliscoff, P., Sillero-Zubiri, C., Villalba, L. & Walker, S. 2011. Bioclimatic constraints to Andean cat distribution: a modelling application for rare species. - Divers. Distrib. 17: 311-322.10.1111/j.1472-4642.2011.00744.x]Search in Google Scholar
[Marmion, M., Luoto, M., Heikkinen, R.K. & Thuiller, W. 2009a. The performance of state-of-the-art modelling techniques depends on geographical distribution of species. - Ecol. Modelling 220: 3512-3520. *Mateo, R.G., Croat, T.B., Felicísimo, Á.M. & Muñoz, J. 2010. Profile or group discriminative techniques? Generating reliable species distribution models using pseudo-absences and target-group absences from natural history collections. - Divers. Distrib. 16: 84-94.]Search in Google Scholar
[*Merckx, B., Steyaert, M., Vanreusel, A., Vincx, M. & Vanaverbeke, J. 2011. Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling. - Ecol. Modelling 222: 588-597.]Search in Google Scholar
[Metz, C.E. 1978. Basic principles of ROC curve analysis. - Semin. nucl. Med. 8: 283-298.10.1016/S0001-2998(78)80014-2]Search in Google Scholar
[Michel, P., Overton, J.M., Mason, N.W.H., Hurst, J.M. & Lee, W.G. 2011. Species-environment relationships of mosses in New Zealand indigenous forest and shrubland ecosystems. - Pl. Ecol. 212: 353-367.10.1007/s11258-010-9827-5]Search in Google Scholar
[Minchin, P.R. 1989. Montane vegetation of the Mt. Field massif, Tasmania: a test of some hypotheses about properties of community patterns. - Vegetatio 83: 97-110.]Search in Google Scholar
[*Monterroso, P., Brito, J.C., Ferreras, P. & Alves, P.C. 2009. Spatial ecology of the European wildcat in a Mediterranean ecosystem: dealing with small radio-tracking datasets in species conservation. - J. Zool. 279: 27-35.10.1111/j.1469-7998.2009.00585.x]Search in Google Scholar
[Mouton, A.M., de Baets, B. & Goethals, P.L.M. 2010. Ecological relevance of performance criteria for species distribution models. - Ecol. Modelling 221: 1995-2002.10.1016/j.ecolmodel.2010.04.017]Search in Google Scholar
[Murphy, A.H. & Winkler, R.L. 1987. A general framework for forecast verification. - Mon. Weather Rev. 115: 1330-1338.10.1175/1520-0493(1987)115<1330:AGFFFV>2.0.CO;2]Search in Google Scholar
[*Murray-Smith, C., Brummitt, N.A., Oliveira-Filho, A.T., Bachman, S., Moat, J., Lughadha, E.M.N. & Lucas, E.J. 2009. Plant diversity hotspots in the Atlantic coastal forests of Brazil. - Conserv. Biol. 23: 151-163.10.1111/j.1523-1739.2008.01075.x]Search in Google Scholar
[Myers, R.H., Montgomery, D.C. & Vining, G.G. 2002. Generalized linear models with applications in engineering and the sciences. - Wiley, New York.]Search in Google Scholar
[*Niamir, A., Skidmore, A.K., Toxopeus, A.G., Muñoz, A.R. & Real, R. 2011. Finessing atlas data for species distribution models. - Divers. Distrib. 17: 1173-1185.10.1111/j.1472-4642.2011.00793.x]Search in Google Scholar
[Nóbrega, C.C. & de Marco, P.J. 2011. Unprotecting the rare species: a niche-based gap analysis for odonates in a core Cerrado area. - Divers. Distrib. 17: 491-505.10.1111/j.1472-4642.2011.00749.x]Search in Google Scholar
[Økland, R.H. 1990a. Vegetation ecology: theory, methods and applications with reference to Fennoscandia. - Sommerfeltia Suppl. 1: 1-233.]Search in Google Scholar
[Økland, R.H. 1992. Studies in SE Fennoscandian mires: relevance to ecological theory. - J. Veg. Sci. 3: 279-284.10.2307/3235693]Search in Google Scholar
[Økland, R.H. 2007. Wise use of statistical tools in ecological field studies. - Folia geobot. 42: 123-140.10.1007/BF02893879]Search in Google Scholar
[Økland, R.H., Rydgren, K. & Økland, T. 2003. Plant species composition of boreal spruce swamp forests: closed doors and windows of opportunity. - Ecology 84: 1909-1919.10.1890/0012-9658(2003)084[1909:PSCOBS]2.0.CO;2]Search in Google Scholar
[Økland, R.H., Økland, T. & Rydgren, K. 2001. Vegetation-environment relationships of boreal spruce swamp forests in Østmarka Nature Reserve, SE Norway. - Sommerfeltia 29: 1-190.10.2478/som-2001-0001]Search in Google Scholar
[Oksanen, J. & Minchin, P.R. 2002. Continuum theory revisited: what shape are species responses along ecological gradients? - Ecol. Modelling 157: 119-129.10.1016/S0304-3800(02)00190-4]Search in Google Scholar
[*Parisien, M.A. & Moritz, M.A. 2009. Environmental controls on the distribution of wildfire at multiple spatial scales. - Ecol. Monogr. 79: 127-154.10.1890/07-1289.1]Search in Google Scholar
[*Parolo, G., Rossi, G. & Ferrarini, A. 2008. Toward improved species niche modelling: Arnica montana in the Alps as a case study. - J. appl. Ecol. 45: 1410-1418.10.1111/j.1365-2664.2008.01516.x]Search in Google Scholar
[Pearce, J.L. & Boyce, M.S. 2006. Modelling distribution and abundance with presence-only data. - J. appl. Ecol. 43: 405-412.10.1111/j.1365-2664.2005.01112.x]Search in Google Scholar
[Pearce, J. & Ferrier, S. 2000a. An evaluation of alternative algorithms for fitting species distribution models using logistic regression. - Ecol. Modelling 128: 127-147.10.1016/S0304-3800(99)00227-6]Search in Google Scholar
[Pearce, J.L. & Ferrier, S. 2000b. Evaluating the predictive performance of habitat models developed using logistic regression. - Ecol. Modelling 133: 225-245.10.1016/S0304-3800(00)00322-7]Search in Google Scholar
[*Pearson, R.G., Raxworthy, C.J., Nakamura, M. & Peterson, A.T. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. - J. Biogeogr. 34: 102-117.10.1111/j.1365-2699.2006.01594.x]Search in Google Scholar
[Peterson, A.T., Papes, M. & Eaton, M. 2007. Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. - Ecography 30: 550-560.10.1111/j.0906-7590.2007.05102.x]Search in Google Scholar
[Peterson, A.T., Soberón, J., Pearson, R.G., Anderson, R.P., Martínez-Meyer, E., Nakamura, M. & Araújo, M.B. 2011. Ecological niches and geographic distributions. - Monogr. Pop. Biol. 49: 1-314.10.23943/princeton/9780691136868.003.0003]Search in Google Scholar
[Phillips, S.J. 2011. A brief tutorial on Maxent. - AT&T Research, Princeton, NJ.]Search in Google Scholar
[Phillips, S.J., Anderson, R.P. & Schapire, R.E. 2006. Maximum entropy modeling of species geographic distributions. - Ecol. Modelling 190: 231-259.10.1016/j.ecolmodel.2005.03.026]Search in Google Scholar
[Phillips, S.J. & Dudík, M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. - Ecography 31: 161-175.10.1111/j.0906-7590.2008.5203.x]Search in Google Scholar
[Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, A., Leathwich, J.R. & Ferrier, S. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. - Ecol. Appl. 19: 181-197.10.1890/07-2153.119323182]Search in Google Scholar
[Phillips, S.J., Dudík, M. & Schapire, R. 2004. A maximum entropy approach to species distribution modeling. - In: Anonymous (ed.), Proceedings of the 21st international conference on machine learning, ACM Press, New York, pp. 655-662.]Search in Google Scholar
[Phillips, S. & Elith, J. 2010. POC-plots: calibrating species distribution models using presenceonly data. - Ecology 91: 2476-2484.10.1890/09-0760.120836469]Search in Google Scholar
[*Pineda, E. & Lobo, J.M. 2009. Assessing the accuracy of species distribution models to predict amphibian species richness patterns. - J. anim. Ecol. 78: 182-190.10.1111/j.1365-2656.2008.01471.x18771504]Search in Google Scholar
[Platts, P.J., Ahrends, A., Gereau, R.E., McClean, C.J., Lovett, J.C., Marshall, A.R., Pellikka, P.K.E., Mulligan, M., Fanning, E. & Marchant, R. 2010. Can distribution models help refine inventory- based estimates of conservation priority? A case study in the Eastern Arc forests of Tanzania and Kenya. - Divers. Distrib. 16: 628-642.10.1111/j.1472-4642.2010.00668.x]Search in Google Scholar
[*Raes, N. & ter Steege, H. 2007. A null-model for significance testing of presence-only species distribution models. - Ecography 30: 727-736.10.1111/j.2007.0906-7590.05041.x]Search in Google Scholar
[Randin, C.F., Dirnböck, T., Dullinger, S., Zimmermann, N.E., Zappa, M. & Guisan, A. 2006. Are nichebased species distribution models transferable in space? - J. Biogeogr. 33: 1689-1703.10.1111/j.1365-2699.2006.01466.x]Search in Google Scholar
[*Rebelo, H. & Jones, G. 2010. Ground validation of presence-only modelling with rare species: a case study on barbastelles Barbastella barbastellus (Chiroptera: Vespertilionidae). - J. appl. Ecol. 47: 410-420.10.1111/j.1365-2664.2009.01765.x]Search in Google Scholar
[Reineking, B. & Schröder, B. 2006. Constrain to perform: regularization of habitat models. - Ecol. Modelling 193: 675-690.10.1016/j.ecolmodel.2005.10.003]Search in Google Scholar
[*Reside, A.E., Watson, I., VanDerWal, J. & Kutt, A.S. 2011. Incorporating low-resolution historic species location data decreases performance of distribution models. - Ecol. Modelling 222: 3444-3448.10.1016/j.ecolmodel.2011.06.015]Search in Google Scholar
[*Riordan, E.C. & Rundel, P.W. 2009. Modelling the distribution of a threatened habitat: the California sage scrub. - J. Biogeogr. 36: 2176-2188.10.1111/j.1365-2699.2009.02151.x]Search in Google Scholar
[Robertson, M.P., Cumming, G.S. & Erasmus, B.F.N. 2010. Getting the most out of atlas data. - Divers. Distrib. 16: 363-375.10.1111/j.1472-4642.2010.00639.x]Search in Google Scholar
[*Rota, C.T., Fletcher, R., Jr., Evans, J.M. & Hutto, R.L. 2011. Does accounting for imperfect detection improve species distribution models? - Ecography 34: 659-670.10.1111/j.1600-0587.2010.06433.x]Search in Google Scholar
[*Roura-Pascual, N., Brotons, L., Peterson, A.T. & Thuiller, W. 2009. Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula. - Biol. Invasions 11: 1017-1031.10.1007/s10530-008-9313-3]Search in Google Scholar
[Roxburgh, S.H. & Mokany, K. 2010. On testing predictions of species relative abundance from maximum entropy optimisation. - Oikos 119: 583-590.10.1111/j.1600-0706.2009.17772.x]Search in Google Scholar
[*Rupprecht, F., Oldeland, J. & Finckh, M. 2011. Modelling potential distribution of the threatened tree species Juniperus oxycedrus: how to evaluate the predictions of different modelling approaches? - J. Veg. Sci. 22: 647-659.10.1111/j.1654-1103.2011.01269.x]Search in Google Scholar
[Rydgren, K., Halvorsen, R., Auestad, I. & Hamre, L.N. in press. Ecological design is more important than compensatory mitigation for successful restoration of alpine spoil heaps. - Rest. Ecol. in press.]Search in Google Scholar
[Rydgren, K., Økland, R.H. & Økland, T. 2003. Species response curves along environmental gradients: a case study from SE Norwegian swamp forests. - J. Veg. Sci. 14: 869-880.10.1111/j.1654-1103.2003.tb02220.x]Search in Google Scholar
[Santika, T. 2011. Assessing the effect of prevalence on the predictive performance of species distribution models using simulated data. - Global Ecol. Biogeogr. 20: 181-192.10.1111/j.1466-8238.2010.00581.x]Search in Google Scholar
[Santika, T. & Hutchinson, M.F. 2009. The effect of species response form on species distribution model prediction and inference. - Ecol. Modelling 220: 2365-2379.10.1016/j.ecolmodel.2009.06.004]Search in Google Scholar
[Schwarz, G. 1978. Estimating the dimension of a model. - Ann. Statist. 6: 461-464.10.1214/aos/1176344136]Search in Google Scholar
[Segurado, P., Araújo, M.B. & Kunin, W.E. 2006. Consequences of spatial autocorrelation for nichebased models. - J. appl. Ecol. 43: 433-444.10.1111/j.1365-2664.2006.01162.x]Search in Google Scholar
[*Sérgio, C., Figueira, R., Draper, D., Menezes, R. & Sousa, A.J. 2007. Modelling bryophyte distribution based on ecological information for extent of occurrence assessment. - Biol. Conserv. 135: 341-351.10.1016/j.biocon.2006.10.018]Search in Google Scholar
[Shipley, B. 2010. Community assembly, natural selection and maximum entropy models. - Oikos 119: 604-609.]Search in Google Scholar
[Shipley, B., Vile, D. & Garnier, É. 2006. From plant traits to plant communities: a statistical mechanistic approach to biodiversity. - Science 314: 812-814.10.1126/science.1131344]Search in Google Scholar
[Sokal, R.R. & Rohlf, F.J. 1995. Biometry, ed. 3. - Freeman, New York.]Search in Google Scholar
[*Stachura-Skierczyńska, K., Tumiel, T. & Skierczyński, M. 2009. Habitat prediction model for three-toed woodpecker and its implications for the conservation of biologically valuable forests. - For. Ecol. Mgmt 258: 697-703.10.1016/j.foreco.2009.05.007]Search in Google Scholar
[Steyerberg, E.W., Eijkemans, M.J., Harrell Jr., F.E. & Habbema, J.D. 2000. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. - Statist. Med. 19: 1059-1079.10.1002/(SICI)1097-0258(20000430)19:8<1059::AID-SIM412>3.0.CO;2-0]Search in Google Scholar
[*Suárez-Seoane, S., García de la Morena, E.L., Prieto, M.B.M., Osborne, P.E. & de Juana, E. 2008. Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetraxtetrax) distribution. - Ecol. Modelling 219: 17-29.10.1016/j.ecolmodel.2008.07.035]Search in Google Scholar
[Suárez-Seoane, S., Osborne, P.E. & Rosema, A. 2004. Can climate data from METEOSAT improve wildlife distribution models? - Ecography 27: 629-636.10.1111/j.0906-7590.2004.03939.x]Search in Google Scholar
[Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. - Science 240: 1285-1293.10.1126/science.3287615]Search in Google Scholar
[*Svenning, J.-C., Normand, S. & Kageyama, M. 2008. Glacial refugia of temperate trees in Europe: insights from species distribution modelling. - J. Ecol. 96: 1117-1127.10.1111/j.1365-2745.2008.01422.x]Search in Google Scholar
[*Synes, N.W. & Osborne, P.E. 2011. Choice of predictor variables as a source of uncertainty in continental-scale species distribution modelling under climate change. - Global Ecol. Biogeogr. 20: 904-914.10.1111/j.1466-8238.2010.00635.x]Search in Google Scholar
[ter Braak, C.J.F. & Prentice, I.C. 1988. A theory of gradient analysis. - Adv. ecol. Res. 18: 271-317.10.1016/S0065-2504(08)60183-X]Search in Google Scholar
[*Thompson, G.D., Robertson, M.-P., Webber, B.L., Richardson, D.M., Le Roux, J.J. & Wilson, J.R.U. 2011. Predicting the subspecific identity of invasive species using distribution models: Acacia saligna as an example. - Divers. Distrib. 17: 1001-1014.10.1111/j.1472-4642.2011.00820.x]Search in Google Scholar
[*Thorn, J.S., Nijman, V., Smith, D. & Nekaris, K.A.I. 2009. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus). - Divers. Distrib. 15: 289-298.10.1111/j.1472-4642.2008.00535.x]Search in Google Scholar
[Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. - J. R. Statist. Soc. Ser. B 58: 267-288.10.1111/j.2517-6161.1996.tb02080.x]Search in Google Scholar
[Tingley, R. & Herman, T.B. 2009. Land-cover data improve bioclimatic models for anurans and turtles at a regional scale. - J. Biogeogr. 36: 1656-1672.10.1111/j.1365-2699.2009.02117.x]Search in Google Scholar
[*Tinoco, B.A., Astudillo, P.X., Latta, S.C. & Graham, C.H. 2009. Distribution, ecology and conservation of an endangered Andean hummingbird: the violet-throated metaltail (Metallurabaroni). - Bird Conserv. Int. 19: 63-76. *Tittensor, D.P., Baco, A.R., Brewin, P.E., Clark, M.R., Consalvey, M., Hall-Spencer, J., Rowden, A.A., Schlacher, T., Stocks, K.I. & Rogers, A.D. 2009. Predicting global habitat suitability for stony corals on seamounts. - J. Biogeogr. 36: 1111-1128. *Tognelli, M.F., Roig-Juñent, S.A., Marvaldi, A.E., Flores, G.E. & Lobo, J.M. 2009. An evaluation of methods for modelling distribution of Patagonian insects. - Revta chil. Hist. nat. 82: 347-360.]Search in Google Scholar
[Trivedi, M.R., Berry, P.M., Morecroft, M.D. & Dawson, T.P. 2008. Spatial scale affects bioclimate model projections of climate change impacts on mountain plants. - Global Change Biol. 14: 1089-1103.10.1111/j.1365-2486.2008.01553.x]Search in Google Scholar
[*Urbina-Cardona, J.N. & Flores-Villela, O. 2010. Ecological-niche modeling and prioritization of conservation-area networks for Mexican herpetofauna. - Conserv. Biol. 24: 1031-1041.10.1111/j.1523-1739.2009.01432.x20345399]Search in Google Scholar
[*Václávík, T. & Meentemeyer, R.K. 2009. Invasive species distribution modeling (iSDM): are absence data and dispersal constraints needed to predict actual distributions? - Ecol. Modelling 220: 3248-3258.10.1016/j.ecolmodel.2009.08.013]Search in Google Scholar
[*VanDerWal, J., Shoo, L.P., Graham, C.H. & Williams, S.E. 2009a. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? - Ecol. Modelling 220: 589-594.10.1016/j.ecolmodel.2008.11.010]Search in Google Scholar
[*VanDerWal, J., Shoo, L.P. & Williams, S.P. 2009b. New approaches to understanding late Quaternary climate fluctuations and refugial dynamics in Australian wet tropical rain forests. - J. Biogeogr. 36: 291-301. van Neil, K.P. & Austin, M.P. 2007. Predictive vegetation modeling for conservation: impact of error propagation from digital elevation data. - Ecol. Appl. 17: 266-280.]Search in Google Scholar
[Varela, S., Rodríguez, J. & Lobo, J.M. 2009. Is current climatic equilibrium a guarantee for the transferability of distribution model predictions? A case study of the spotted hyena. - J. Biogeogr. 36: 1645-1655.10.1111/j.1365-2699.2009.02125.x]Search in Google Scholar
[Vaughan, I.P. & Ormerod, S.J. 2003. Improving the quality of distribution models for conservation by addressing shortcomings in the field collection of training data. - Conserv. Biol. 17: 1601-1611.10.1111/j.1523-1739.2003.00359.x]Search in Google Scholar
[*Veloz, S.D. 2009. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. - J. Biogeogr. 36: 2290-2299.10.1111/j.1365-2699.2009.02174.x]Search in Google Scholar
[Venables, W.N. & Ripley, B.D. 2002. Modern applied statistics with S. - Springer, New York.10.1007/978-0-387-21706-2]Search in Google Scholar
[*Verbruggen, H., Tyberghein, L., Pauly, K., Vlaeminck, C., van Nieuwenhuyze, K., Kooistra, W., Leliaert, F. & de Clerck, O. 2009. Macroecology meets macroevolution: evolutionary niche dynamics in the seaweed Halimeda. - Global Ecol. Biogeogr. 18: 393-405.10.1111/j.1466-8238.2009.00463.x]Search in Google Scholar
[*Wang, Y., Xie, B., Wan, F., Xiao, Q. & Dai, L. 2007. The potential geographic distribution of Radopholus similis in China. - Agric. Sci. China 6: 1444-1449.10.1016/S1671-2927(08)60006-1]Search in Google Scholar
[*Ward, D.F. 2007. Modelling the potential geographic distribution of invasive ant species in New Zealand. - Biol. Invasions 9: 723-735.10.1007/s10530-006-9072-y]Search in Google Scholar
[Ward, G., Hastie, T., Barry, S., Elith, J. & Leathwick, J.R. 2009. Presence-only data and the EM algorithm. - Biometrics 65: 554-563.10.1111/j.1541-0420.2008.01116.x482188618759851]Search in Google Scholar
[*Warren, D.L., Glor, R.E. & Turelli, M. 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. - Evolution 62: 2868-2883.10.1111/j.1558-5646.2008.00482.x18752605]Search in Google Scholar
[Warren, D.L., Glor, R.E. & Turelli, M. 2010. ENMTools: a toolbox for comparative studies of environmental niche models. - Ecography 33: 607-611.10.1111/j.1600-0587.2009.06142.x]Search in Google Scholar
[Warren, D.L. & Seifert, S.N. 2011. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. - Ecol. Appl. 21: 335-342.10.1890/10-1171.121563566]Search in Google Scholar
[*Webber, B.L., Yates, C.J., La Maitre, D.C., Scott, J.K., Kriticos, D.J., Ota, N., McNeill, A., Le Roux, J.J. & Midgley, G.F. 2011. Modelling horses for novel climate courses: insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models. - Divers. Distrib. 17: 978-1000.10.1111/j.1472-4642.2011.00811.x]Search in Google Scholar
[*Weber, T.C. 2011. Maximum entropy modeling of mature hardwood forest distribution in four U.S. states. - For. Ecol. Mgmt 261: 779-788.10.1016/j.foreco.2010.12.009]Search in Google Scholar
[Whittaker, R.J., Araújo, M.B., Jepson, P., Ladle, R.J., J.E.M., W. & Willis, K.J. 2005. Conservation biogeography: assessment and prospect. - Divers. Distrib. 11: 3-23.10.1111/j.1366-9516.2005.00143.x]Search in Google Scholar
[*Willems, E.P. & Hill, R.A. 2009. A critical assessment of two species distribution models: a case study of the vervet monkey (Cercopithecus aethiops). - J. Biogeogr. 36: 2300-2312.10.1111/j.1365-2699.2009.02166.x]Search in Google Scholar
[*Williams, J.N., Seo, C.W., Thorne, J., Nelson, J.K., Erwin, S., O’Brien, J.M. & Schwartz, M.W. 2009. Using species distribution models to predict new occurrences for rare plants. - Divers.10.1111/j.1472-4642.2009.00567.x]Search in Google Scholar
[Distrib. 15: 565-576.]Search in Google Scholar
[*Wisz, M.S., Hijmans, R.J., Li, J., Peterson, A.T., Graham, C.H., Guisan, A. & NCEAS Predicting Species Distributions Working Group 2008. Effects of sample size on the performance of species distribution models. - Divers. Distrib. 14: 763-773.10.1111/j.1472-4642.2008.00482.x]Search in Google Scholar
[Wohlgemuth, T., Nobis, M.P., Kienast, F. & Plattner, M. 2008. Modelling vascular plant diversity at the landscape scale using systematic samples. - J. Biogeogr. 35: 1226-1240.10.1111/j.1365-2699.2008.01884.x]Search in Google Scholar
[Wollan, A.K., Bakkestuen, V. & Halvorsen, R. 2011. Romlig prediksjonsmodellering av åpen grunnlendt kalkmark i Oslofjord-området. - Univ. Oslo NatHist. Mus. Rapp. 11: 176-196.]Search in Google Scholar
[*Wollan, A.K., Bakkestuen, V., Kauserud, H., Gulden, G. & Halvorsen, R. 2008. Modelling and predicting fungal distribution patterns using herbarium data. - J. Biogeogr. 35: 2298-2310.10.1111/j.1365-2699.2008.01965.x]Search in Google Scholar
[*Wolmarans, R., Robertson, M.P. & van Rensburg, B.J. 2010. Predicting invasive alien plant distributions: how geographical bias in occurrence records influences model performance. - J. Biogeogr. 37: 1797-1810.10.1111/j.1365-2699.2010.02325.x]Search in Google Scholar
[Wood, S.N. 2006. Generalized additive models. - Chapman & Hall, London.10.1201/9781420010404]Search in Google Scholar
[*Yates, C., McNeill, A., Elith, J. & Midgley, G. 2010. Assessing the impacts of climate change and land transformation on Banksia in the South West Australian floristic region. - Divers. Distrib. 16: 187-201.10.1111/j.1472-4642.2009.00623.x]Search in Google Scholar
[*Yesson, C. & Culham, A. 2006. A phyloclimatic study of Cyclamen. - BMC evol. Biol. 6: 72: 1-23.10.1186/1471-2148-6-72159975516987413]Search in Google Scholar
[*Yost, A.C., Petersen, S.L., Gregg, M. & Miller, R. 2008. Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using maximum entropy and a longterm dataset from southern Oregon. - Ecol. Informatics 3: 375-386.10.1016/j.ecoinf.2008.08.004]Search in Google Scholar
[*Young, B.F., Franke, I., Hernandez, P.A., Herzog, S.K., Paniagua, L., Tovar, C. & Valqui, T. 2009. Using spatial models to predict areas of endemism and gaps in the protection of Andean slope birds. - Auk 126: 554-565.10.1525/auk.2009.08155]Search in Google Scholar
[Zielinski, W.J., Dunk, J.R., Yaeger, J.S. & LaPlante, D.W. 2010. Developing and testing a landscapescale habitat suitability model for fisher (Martes pennanti) in forests of interior northern California. - For. Ecol. Mgmt 260: 1579-1591.10.1016/j.foreco.2010.08.006]Search in Google Scholar
[Zuur, A.F., Ieno, E.N. & Smith, G.M. 2007. Analysing ecological data. - Springer, New York.10.1007/978-0-387-45972-1]Search in Google Scholar