Combining static and dynamic variables in species distribution models under climate change

1.Methods used to predict shifts in species' ranges because of climate change commonly involve species distribution (niche) modelling using climatic variables, future values of which are predicted for the next several decades by general circulation models. However, species' distributions also depend on factors other than climate, such as land cover, land use and soil type. Changes in some of these factors, such as soil type, occur over geologic time and are thus imperceptible over the timescale of these types of projections. Other factors, such as land use and land cover, are expected to change over shorter timescales, but reliable projections are not available. Some important predictor variables, therefore, must be treated as unchanging, or static, whether because of the properties of the variable or out of necessity. The question of how best to combine dynamic variables predicted by climate models with static variables is not trivial and has been dealt with differently in studies to date. Alternative methods include using the static variables as masks, including them as independent explanatory variables in the model, or excluding them altogether. 2.Using a set of simulated species, we tested various methods for combining static variables with future climate scenarios. Our results showed that including static variables in the model with the dynamic variables performed better or no worse than either masking or excluding the static variables. 3.The difference in predictive ability was most pronounced when there is an interaction between the static and dynamic variables. 4.For variables such as land use, our results indicate that if such variables affect species distributions, including them in the model is better than excluding them, even though this may mean making the unrealistic assumption that the variable will not change in the future. 5.These results demonstrate the importance of including static and dynamic non-climate variables in addition to climate variables in species distribution models designed to predict future change in a species' habitat or distribution as a result of climate change. © 2011 The Authors. Methods in Ecology and Evolution © 2011 British Ecological Society.

[1]  D. Chessel,et al.  ECOLOGICAL-NICHE FACTOR ANALYSIS: HOW TO COMPUTE HABITAT-SUITABILITY MAPS WITHOUT ABSENCE DATA? , 2002 .

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

[3]  Steven J. Phillips,et al.  Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. , 2009, Ecological applications : a publication of the Ecological Society of America.

[4]  O. Edenhofer,et al.  Intergovernmental Panel on Climate Change (IPCC) , 2013 .

[5]  A. Townsend Peterson,et al.  The influence of spatial errors in species occurrence data used in distribution models , 2007 .

[6]  S. Lavorel,et al.  Do we need land‐cover data to model species distributions in Europe? , 2004 .

[7]  Corey J A Bradshaw,et al.  Synergies among extinction drivers under global change. , 2008, Trends in ecology & evolution.

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

[9]  Kimberly P. Van Niel,et al.  Impact of landscape predictors on climate change modelling of species distributions: a case study with Eucalyptus fastigata in southern New South Wales, Australia , 2011 .

[10]  Kees Klein Goldewijk,et al.  Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1 , 2010 .

[11]  Vincent R. Gray.  Climate Change 2007: The Physical Science Basis Summary for Policymakers , 2007 .

[12]  M. Sykes,et al.  Climate change threats to plant diversity in Europe. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

[14]  J. Travis.  Climate change and habitat destruction: a deadly anthropogenic cocktail , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

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

[16]  Simon Ferrier,et al.  Evaluating the predictive performance of habitat models developed using logistic regression , 2000 .

[17]  O. Phillips,et al.  Extinction risk from climate change , 2004, Nature.

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

[19]  M. Araújo,et al.  Climate warming and the decline of amphibians and reptiles in Europe , 2006 .

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

[21]  H. Possingham,et al.  From climate change predictions to actions – conserving vulnerable animal groups in hotspots at a regional scale , 2010 .

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

[23]  S. Williams,et al.  Climate change in Australian tropical rainforests: an impending environmental catastrophe , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[24]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[25]  T. Dawson,et al.  Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data , 2004 .

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

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

[28]  J. Blackard,et al.  Journal of Applied , 2006 .

[29]  Damien A. Fordham,et al.  Dynamics of range margins for metapopulations under climate change , 2009, Proceedings of the Royal Society B: Biological Sciences.

[30]  Ned Horning,et al.  Remote Sensing for Ecology and Conservation: A Handbook of Techniques , 2010 .

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

[32]  A. Prasad,et al.  PREDICTING ABUNDANCE OF 80 TREE SPECIES FOLLOWING CLIMATE CHANGE IN THE EASTERN UNITED STATES , 1998 .

[33]  Brian Huntley,et al.  Projected impacts of climate change on a continent-wide protected area network. , 2009, Ecology letters.

[34]  Kees Klein Goldewijk,et al.  The HYDE 3.1 spatially explicit database of human‐induced global land‐use change over the past 12,000 years , 2011 .

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

[36]  David R. B. Stockwell,et al.  Future projections for Mexican faunas under global climate change scenarios , 2002, Nature.

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

[38]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[39]  H. Resit Akçakaya,et al.  Integrating bioclimate with population models to improve forecasts of species extinctions under climate change , 2009, Biology Letters.