A framework for using niche models to estimate impacts of climate change on species distributions

Predicting species geographic distributions in the future is an important yet exceptionally challenging endeavor. Overall, it requires a two‐step process: (1) a niche model characterizing suitability, applied to projections of future conditions and linked to (2) a dispersal/demographic simulation estimating the species’ future occupied distribution. Despite limitations, for the vast majority of species, correlative approaches are the most feasible avenue for building niche models. In addition to myriad technical issues regarding model building, researchers should follow critical principles for selecting predictor variables and occurrence data, demonstrating effective performance in prediction across space, and extrapolating into nonanalog conditions. Many of these principles relate directly to the niche space, dispersal/demographic noise, biotic noise, and human noise assumptions defined here. Issues requiring progress include modeling interactions between abiotic variables, integrating biotic variables, considering genetic heterogeneity, and quantifying uncertainty. Once built, the niche model identifying currently suitable conditions must be processed to approximate the areas that the species occupies. That estimate serves as a seed for the simulation of persistence, dispersal, and establishment in future suitable areas. The dispersal/demographic simulation also requires data regarding the species’ dispersal ability and demography, scenarios for future land use, and the capability of considering multiple interacting species simultaneously.

[1]  Robert P. Anderson,et al.  Making better Maxent models of species distributions: complexity, overfitting and evaluation , 2014 .

[2]  Jacquelyn L Gill,et al.  Model systems for a no‐analog future: species associations and climates during the last deglaciation , 2013, Annals of the New York Academy of Sciences.

[3]  Greta Bocedi,et al.  Effects of local adaptation and interspecific competition on species’ responses to climate change , 2013, Annals of the New York Academy of Sciences.

[4]  P. Zarnetske,et al.  Moving forward: dispersal and species interactions determine biotic responses to climate change , 2013, Annals of the New York Academy of Sciences.

[5]  Lauren B. Buckley Get real: putting models of climate change and species interactions in practice , 2013, Annals of the New York Academy of Sciences.

[6]  Elinore J. Theobald,et al.  How will biotic interactions influence climate change–induced range shifts? , 2013, Annals of the New York Academy of Sciences.

[7]  S. Higgins,et al.  Impacts of past habitat loss and future climate change on the range dynamics of South African Proteaceae , 2013 .

[8]  B. McGill,et al.  Testing the predictive performance of distribution models , 2013 .

[9]  T. Dawson,et al.  The incidence and implications of clouds for cloud forest plant water relations. , 2013, Ecology letters.

[10]  W. D. Kissling,et al.  The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling , 2012, Biological reviews of the Cambridge Philosophical Society.

[11]  M. Uriarte,et al.  Moving forward in global-change ecology: capitalizing on natural variability , 2013, Ecology and evolution.

[12]  Michael D. Cramer,et al.  A physiological analogy of the niche for projecting the potential distribution of plants , 2012 .

[13]  S. Higgins,et al.  Temperature dependence of the reproduction niche and its relevance for plant species distributions , 2012 .

[14]  S. Higgins,et al.  Connecting dynamic vegetation models to data – an inverse perspective , 2012 .

[15]  R. Etienne,et al.  Stitch the niche – a practical philosophy and visual schematic for the niche concept , 2012 .

[16]  Boris Schröder,et al.  How to understand species’ niches and range dynamics: a demographic research agenda for biogeography , 2012 .

[17]  Juliano Sarmento Cabral,et al.  Linking ecological niche, community ecology and biogeography: insights from a mechanistic niche model , 2012 .

[18]  Carsten F. Dormann,et al.  Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents , 2012 .

[19]  M. Kearney,et al.  Correlation and process in species distribution models: bridging a dichotomy , 2012 .

[20]  J. Muñoz,et al.  Use of ring recoveries to predict habitat suitability in small passerines , 2012 .

[21]  Antoine Guisan,et al.  The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models , 2012 .

[22]  T. Booth,et al.  Developing biodiverse plantings suitable for changing climatic conditions 1: Underpinning scientific methods , 2012 .

[23]  W. Godsoe,et al.  How do species interactions affect species distribution models , 2012 .

[24]  Forecasting cloud forest in eastern and southern Mexico: conservation insights under future climate change scenarios , 2012, Biodiversity and Conservation.

[25]  K. Beard,et al.  Predicting the distribution potential of an invasive frog using remotely sensed data in Hawaii , 2012 .

[26]  V. Grimm,et al.  Uncertainty in predictions of range dynamics: black grouse climbing the Swiss Alps , 2012 .

[27]  Robert P. Anderson,et al.  Harnessing the world's biodiversity data: promise and peril in ecological niche modeling of species distributions , 2012, Annals of the New York Academy of Sciences.

[28]  Kimberly S. Sheldon,et al.  On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change , 2012, Proceedings of the Royal Society B: Biological Sciences.

[29]  B. Otto‐Bliesner,et al.  No‐analog climates and shifting realized niches during the late quaternary: implications for 21st‐century predictions by species distribution models , 2012 .

[30]  Brendan A. Wintle,et al.  Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? , 2012 .

[31]  G. Powell,et al.  Vegetation dynamics and avian seasonal migration: clues from remotely sensed vegetation indices and ecological niche modelling , 2012 .

[32]  D J Lewis,et al.  Economic-based projections of future land use in the conterminous United States under alternative policy scenarios. , 2012, Ecological applications : a publication of the Ecological Society of America.

[33]  M. Kirkpatrick,et al.  How do genetic correlations affect species range shifts in a changing environment? , 2012, Ecology letters.

[34]  Dirk R. Schmatz,et al.  Climate, competition and connectivity affect future migration and ranges of European trees , 2012 .

[35]  Bruce L. Webber,et al.  CliMond: global high‐resolution historical and future scenario climate surfaces for bioclimatic modelling , 2012 .

[36]  F. Schurr,et al.  Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics , 2012 .

[37]  Ilkka Hanski,et al.  Eco‐evolutionary dynamics in a changing world , 2012, Annals of the New York Academy of Sciences.

[38]  Nico Cellinese,et al.  Evolutionary informatics: unifying knowledge about the diversity of life. , 2012, Trends in ecology & evolution.

[39]  J. Hellmann,et al.  The influence of species interactions on geographic range change under climate change , 2012, Annals of the New York Academy of Sciences.

[40]  Luc De Meester,et al.  A crucial step toward realism: responses to climate change from an evolving metacommunity perspective , 2011, Evolutionary applications.

[41]  Q. Guo,et al.  Latitudinal shifts of introduced species: possible causes and implications , 2012, Biological Invasions.

[42]  R. A. Garcia,et al.  Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates , 2011, Global Change Biology.

[43]  Robert P. Anderson,et al.  Ecological Niches and Geographic Distributions , 2011 .

[44]  J. Wiens The niche, biogeography and species interactions , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[45]  Robert P. Anderson,et al.  Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent , 2011 .

[46]  A. Peterson,et al.  The crucial role of the accessible area in ecological niche modeling and species distribution modeling , 2011 .

[47]  A. Peterson Ecological niche conservatism: a time‐structured review of evidence , 2011 .

[48]  I. Hanski Habitat Loss, the Dynamics of Biodiversity, and a Perspective on Conservation , 2011, AMBIO.

[49]  A. Peterson,et al.  Use of niche models in invasive species risk assessments , 2011, Biological Invasions.

[50]  Niklaus E. Zimmermann,et al.  Co‐occurrence patterns of trees along macro‐climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L. , 2011 .

[51]  Otso Ovaskainen,et al.  Making more out of sparse data: hierarchical modeling of species communities. , 2011, Ecology.

[52]  M. Austin,et al.  Improving species distribution models for climate change studies: variable selection and scale , 2011 .

[53]  Trevor Hastie,et al.  A statistical explanation of MaxEnt for ecologists , 2011 .

[54]  C. Graham,et al.  New trends in species distribution modelling , 2010 .

[55]  Steven J. Phillips,et al.  The art of modelling range‐shifting species , 2010 .

[56]  J. Hellmann,et al.  Adaptation to host plants may prevent rapid insect responses to climate change , 2010 .

[57]  J M J Travis,et al.  Local adaptation and the evolution of species' ranges under climate change. , 2010, Journal of theoretical biology.

[58]  A. Gelfand,et al.  Modeling large scale species abundance with latent spatial processes , 2010, 1011.3327.

[59]  M. Angilletta,et al.  Can mechanism inform species' distribution models? , 2010, Ecology letters.

[60]  Robert P. Anderson,et al.  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 , 2010 .

[61]  Wilfried Thuiller,et al.  BioMove – an integrated platform simulating the dynamic response of species to environmental change , 2010 .

[62]  Robert D Holt,et al.  A framework for community interactions under climate change. , 2010, Trends in ecology & evolution.

[63]  S. Jackson,et al.  Balancing biodiversity in a changing environment: extinction debt, immigration credit and species turnover. , 2010, Trends in ecology & evolution.

[64]  Tim Newbold,et al.  Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models , 2010 .

[65]  Jorge Soberón Niche and area of distribution modeling: a population ecology perspective , 2010 .

[66]  Robert P. Anderson,et al.  Chapter 2. Taxonomy, Distribution, and Natural History of the Genus Heteromys (Rodentia: Heteromyidae) in Central and Eastern Venezuela, with the Description of a New Species from the Cordillera de la Costa , 2009 .

[67]  S. Beissinger,et al.  Detecting range shifts from historical species occurrences: new perspectives on old data. , 2009, Trends in ecology & evolution.

[68]  J. Lobo,et al.  Is current climatic equilibrium a guarantee for the transferability of distribution model predictions? A case study of the spotted hyena , 2009 .

[69]  D. Nogues‐Bravo,et al.  Predicting the past distribution of species climatic niches. , 2009 .

[70]  Jason D. K. Dzurisin,et al.  Translocation experiments with butterflies reveal limits to enhancement of poleward populations under climate change , 2009, Proceedings of the National Academy of Sciences.

[71]  Jeremy VanDerWal,et al.  Abundance and the Environmental Niche: Environmental Suitability Estimated from Niche Models Predicts the Upper Limit of Local Abundance , 2009, The American Naturalist.

[72]  W. Hargrove,et al.  The projection of species distribution models and the problem of non-analog climate , 2009, Biodiversity and Conservation.

[73]  M. Kearney,et al.  Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges. , 2009, Ecology letters.

[74]  J. Elith,et al.  Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .

[75]  J. Elith,et al.  Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models , 2009 .

[76]  Brian Huntley,et al.  Dynamic distribution modelling : predicting the present from the past , 2009 .

[77]  Alberto Jiménez-Valverde,et al.  Not as good as they seem: the importance of concepts in species distribution modelling , 2008 .

[78]  Wilfried Thuiller,et al.  Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models , 2008, Biology Letters.

[79]  Jaime A. Chaves,et al.  Predicting species distributions across the Amazonian and Andean regions using remote sensing data , 2008 .

[80]  Steven J. Phillips Transferability, sample selection bias and background data in presence‐only modelling: a response to Peterson et al. (2007) , 2008 .

[81]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

[82]  Marcel E Visser,et al.  Keeping up with a warming world; assessing the rate of adaptation to climate change , 2008, Proceedings of the Royal Society B: Biological Sciences.

[83]  C O M M E N T A,et al.  Can remote sensing of land cover improve species distribution modelling ? , 2008 .

[84]  M. Araújo,et al.  The importance of biotic interactions for modelling species distributions under climate change , 2007 .

[85]  Alan Hastings,et al.  Ecological and evolutionary insights from species invasions. , 2007, Trends in ecology & evolution.

[86]  J. Hellmann,et al.  Constraints and reinforcement on adaptation under climate change: Selection of genetically correlated traits , 2007 .

[87]  S. Suárez‐Seoane,et al.  Non‐stationarity and local approaches to modelling the distributions of wildlife , 2007 .

[88]  John E. Kutzbach,et al.  Projected distributions of novel and disappearing climates by 2100 AD , 2006, Proceedings of the National Academy of Sciences.

[89]  Helen T. Murphy,et al.  Accounting for regional niche variation in habitat suitability models , 2007 .

[90]  M. Araújo,et al.  Five (or so) challenges for species distribution modelling , 2006 .

[91]  T. Dawson,et al.  Model‐based uncertainty in species range prediction , 2006 .

[92]  M. Araújo,et al.  How Does Climate Change Affect Biodiversity? , 2006, Science.

[93]  A. Agrawal,et al.  Biotic interactions and plant invasions. , 2006, Ecology letters.

[94]  Michael J. Papaik,et al.  Neighborhood analyses of canopy tree competition along environmental gradients in New England forests. , 2006, Ecological applications : a publication of the Ecological Society of America.

[95]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[96]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[97]  Wilfried Thuiller,et al.  Using niche‐based modelling to assess the impact of climate change on tree functional diversity in Europe , 2006 .

[98]  L. Rockwood Introduction to population ecology , 2006 .

[99]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[100]  C. Graham,et al.  Niche Conservatism: Integrating Evolution, Ecology, and Conservation Biology , 2005 .

[101]  I. Côté,et al.  Mutualism or parasitism? The variable outcome of cleaning symbioses , 2005, Biology Letters.

[102]  M. Araújo,et al.  Reducing uncertainty in projections of extinction risk from climate change , 2005 .

[103]  M. Cadotte Ecological Niches: Linking Classical and Contemporary Approaches , 2004, Biodiversity & Conservation.

[104]  A. Peterson,et al.  Biodiversity informatics: managing and applying primary biodiversity data. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[105]  S. Lavorel,et al.  Effects of restricting environmental range of data to project current and future species distributions , 2004 .

[106]  Robert P. Anderson,et al.  Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador , 2004 .

[107]  T. Dawson,et al.  Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? , 2003 .

[108]  Robert P. Anderson,et al.  Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela , 2003 .

[109]  Robert P. Anderson,et al.  Taxonomy, Distribution, and Natural History of the Genus Heteromys (Rodentia: Heteromyidae) in Western Venezuela, with the Description of a Dwarf Species from the Península de Paraguaná , 2003 .

[110]  Robert P. Anderson Taxonomy, distribution, and natural history of the genus Heteromys (Rodentia, Heteromyidae) in western Venezuela : with the description of a dwarf species from the Península de Paraguaná. American Museum novitates ; no. 3396 , 2003 .

[111]  Kevin J. Gaston,et al.  The structure and dynamics of geographic ranges , 2003 .

[112]  Patrick E. Osborne,et al.  Should data be partitioned spatially before building large-scale distribution models? , 2002 .

[113]  M. Austin Spatial prediction of species distribution: an interface between ecological theory and statistical modelling , 2002 .

[114]  Robert P. Anderson,et al.  A New Species of Spiny Pocket Mouse (Heteromyidae: Heteromys) Endemic to Western Ecuador , 2002 .

[115]  Robert P. Anderson,et al.  Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice , 2002 .

[116]  Robert P. Anderson,et al.  Geographical distributions of spiny pocket mice in South America: insights from predictive models , 2002 .

[117]  J. Overpeck,et al.  Responses of plant populations and communities to environmental changes of the late Quaternary , 2000, Paleobiology.

[118]  M. Austin,et al.  Current approaches to modelling the environmental niche of eucalypts: implication for management of forest biodiversity , 1996 .

[119]  L. Holdridge,et al.  Forest environments in tropical life zones: a pilot study. , 1971 .