Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models

Allouche, 2006, Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS), J. Appl. Ecol., 43, 1223, 10.1111/j.1365-2664.2006.01214.x Alvarado-Serrano, 2014, Ecological niche models in phylogeographic studies: applications, advances and precautions, Mol. Ecol. Resour., 14, 233, 10.1111/1755-0998.12184 Araújo, 2012, Uses and misuses of bioclimatic envelope modeling, Ecology, 93, 1527, 10.1890/11-1930.1 Barve, 2011, The crucial role of the accessible area in ecological niche modeling and species distribution modeling, Ecol. Model., 222, 1810, 10.1016/j.ecolmodel.2011.02.011 Benscoter, 2013, Threatened and endangered subspecies with vulnerable ecological traits also have high susceptibility to sea level rise and habitat fragmentation, PLoS ONE, 8, e70647, 10.1371/journal.pone.0070647 Blaustein, 2008, Biodiversity hotspot: the Florida panhandle, Bioscience, 58, 784, 10.1641/B580904 Booth, 2014, BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies, Divers. Distrib., 20, 1, 10.1111/ddi.12144 Braunisch, 2013, Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change, Ecography, 36, 971, 10.1111/j.1600-0587.2013.00138.x Breiman, 2001, Random forests, Mach. Learn., 45, 15 Bucklin, 2015, Comparing species distribution models constructed with different subsets of environmental predictors, Divers. Distrib., 21, 23, 10.1111/ddi.12247 Bucklin, 2013, Climate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States, Reg. Environ. Change, 13, 57, 10.1007/s10113-012-0389-z Buisson, 2010, Uncertainty in ensemble forecasting of species distribution, Glob. Change Biol., 16, 1145, 10.1111/j.1365-2486.2009.02000.x Burnham, 2002 Carroll, 2010, Optimizing resiliency of reserve networks to climate change: multispecies conservation planning in the Pacific Northwest, USA, Glob. Change Biol., 16, 891, 10.1111/j.1365-2486.2009.01965.x Catano, 2014, Using scenario planning to evaluate the impacts of climate change on wildlife populations and communities in the Florida Everglades, Environ. Manage. Cruz-Cárdenas, 2014, Potential species distribution modeling and the use of principal component analysis as predictor variables, Rev. Mexicana Biodiver., 85, 189, 10.7550/rmb.36723 Cutler, 2007, Random forests for classification in ecology, Ecology, 88, 2783, 10.1890/07-0539.1 Diniz-Filho, 2009, Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change, Ecography, 32, 897, 10.1111/j.1600-0587.2009.06196.x Dormann, 2013, Collinearity: a review of methods to deal with it and a simulation study evaluating their performance, Ecography, 36, 27, 10.1111/j.1600-0587.2012.07348.x Dormann, 2008, Components of uncertainty in species distribution analysis: a case study of the Great Grey Shrike, Ecology, 89, 3371, 10.1890/07-1772.1 Elith, 2002, Mapping epistemic uncertainties and vague concepts in predictions of species distribution, Ecol. Model., 157, 313, 10.1016/S0304-3800(02)00202-8 Elith, 2006, Novel methods improve prediction of species’ distributions from occurrence data, Ecography, 29, 129, 10.1111/j.2006.0906-7590.04596.x Elith, 2008, A working guide to boosted regression trees, J. Anim. Ecol., 77, 802, 10.1111/j.1365-2656.2008.01390.x Elith, 2011, A statistical explanation of MaxEnt for ecologists, Divers. Distrib., 17, 43, 10.1111/j.1472-4642.2010.00725.x Fielding, 1997, A review of methods for the assessment of prediction errors in conservation presence/absence models, Environ. Conserv., 24, 38, 10.1017/S0376892997000088 Fitzpatrick, 2013, MaxEnt versus MaxLike: empirical comparisons with ant species distributions, Ecosphere, 4, 55, 10.1890/ES13-00066.1 Fourcade, 2014, Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias, PLoS ONE, 9, e97122, 10.1371/journal.pone.0097122 Franklin, 2009 Gritti, 2013, Estimating consensus and associated uncertainty between inherently different species distribution models, Meth. Ecol. Evol., 4, 442, 10.1111/2041-210X.12032 Guisan, 2007, What matters most for predicting the occurrences of trees: techniques, data, or species characteristics?, Ecol. Monogr., 77, 615, 10.1890/06-1060.1 Hanspach, 2011, Geographical patterns in prediction errors of species distribution models, Glob. Ecol. Biogeogr., 20, 779, 10.1111/j.1466-8238.2011.00649.x Heikkinen, 2006, Methods and uncertainties in bioclimate envelope modeling under climate change, Prog. Phys. Geogr., 30, 751, 10.1177/0309133306071957 Hernandez, 2006, The effect of sample size and species characteristics on performance of different species distribution modeling methods, Ecography, 29, 773, 10.1111/j.0906-7590.2006.04700.x Hijmans, 2005, Very high resolution interpolated climate surfaces for global land areas, Int. J. Clim., 25, 1965, 10.1002/joc.1276 Hirzel, 2002, Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data?, Ecology, 83, 2027, 10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2 Jaeschke, 2012, Biotic interactions in the face of climate change: a comparison of three modelling approaches, PLoS ONE, e51472, 10.1371/journal.pone.0051472 Kampichler, 2010, Classification in conservation biology: a comparison of five machine-learning methods, Ecol. Inform., 5, 441, 10.1016/j.ecoinf.2010.06.003 Knight, 2011 Lobo, 2007, AUC: a misleading measure of the performance of predictive distribution models, Glob. Ecol. Biogeogr., 17, 145, 10.1111/j.1466-8238.2007.00358.x Lobo, 2010, The uncertain nature of absences and their importance in species distribution modelling, Ecography, 33, 103, 10.1111/j.1600-0587.2009.06039.x Luoto, 2007, The role of land cover in bioclimatic models depends on spatial resolution, Glob. Ecol. Biogeogr., 16, 34, 10.1111/j.1466-8238.2006.00262.x Marmion, 2009, Evaluation of consensus methods in predictive species distribution modelling, Divers. Distrib., 15, 59, 10.1111/j.1472-4642.2008.00491.x McCullugh, 1989 Muñoz, 2004, Comparison of statistical methods commonly used in predictive modelling, J. Veg. Sci., 15, 285, 10.1111/j.1654-1103.2004.tb02263.x Nakicenović, 2000 New, 2002, A high-resolution data set of surface climate over global land areas, Clim. Res., 21, 1, 10.3354/cr021001 Nicholls, 2008 Nix, 1986, A biogeographic analysis of Australian elapid snakes, 4 Oliver, 2012, Population density but not stability can be predicted from species distribution models, J. Appl. Ecol., 49, 581 Oppel, 2012, Comparison of five modelling techniques to predict spatial distribution and abundance of seabirds, Biol. Conserv., 156, 94, 10.1016/j.biocon.2011.11.013 Pagel, 2012, Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics, Glob. Ecol. Biogeogr., 21, 293, 10.1111/j.1466-8238.2011.00663.x Peters, 2013, The challenge to keep global warming below 2°C, Nat. Clim. Change, 3, 4, 10.1038/nclimate1783 Phillips, 2006, Maximum entropy modeling of species geographic distributions, Ecol. Model., 190, 231, 10.1016/j.ecolmodel.2005.03.026 Phillips, 2009, Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data, Ecol. Appl., 19, 181, 10.1890/07-2153.1 R Development Core Team, 2013 Real, 2010, Species distribution models in climate change scenarios are still not useful for informing public policy: an uncertainty assessment using fuzzy logic, Ecography, 33, 304 Reece, 2013, A vulnerability assessment of 300 species in Florida: threats from sea level rise, land use and climate change, PLoS ONE, 8, e80658, 10.1371/journal.pone.0080658 Royle, 2012, Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions, Methods Ecol. Evol., 3, 545, 10.1111/j.2041-210X.2011.00182.x SAS Institute, 2013 Shirley, 2013, Species distribution modeling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions, Divers. Distrib., 2013, 1 Stefanova, 2012, A proxy for high-resolution regional reanalysis for the southeast United States: assessment of precipitation variability in dynamically downscaled reanalyses, Clim. Dynam., 38, 2449, 10.1007/s00382-011-1230-y Stein, 2002 Synes, 2011, Choice of predictor variables as a source of uncertainty in continental-scale species distribution modelling under climate change, Glob. Ecol. Biogeogr., 20, 904, 10.1111/j.1466-8238.2010.00635.x Tabor, 2010, Globally downscaled climate projections for assessing the conservation impacts of climate change, Ecol. Appl., 20, 554, 10.1890/09-0173.1 Thuiller, 2014 Tsoar, 2007, A comparative evaluation of presence-only methods for modelling species distribution, Divers. Distrib., 13, 397, 10.1111/j.1472-4642.2007.00346.x University of Florida, 2014 Valle, 2013, Comparing the performance of species distribution models of Zostera marina: implications for conservation, J. Sea Res., 83, 56, 10.1016/j.seares.2013.03.002 VanDerWal, 2009, Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know?, Ecol. Model., 220, 589, 10.1016/j.ecolmodel.2008.11.010 Watling, 2012, Do bioclimate variables improve performance of climate envelope models?, Ecol. Model., 246, 79, 10.1016/j.ecolmodel.2012.07.018 Wenger, 2013, Probabilistic accounting of uncertainty in forecasts of species distributions under climate change, Glob. Change Biol., 19, 3343 Wilson, 2005, Sensitivity of conservation planning to different approaches to using predicted species distribution data, Biol. Conserv., 122, 99, 10.1016/j.biocon.2004.07.004 Wisz, 2009, Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data, BMC Ecol., 9, 8, 10.1186/1472-6785-9-8