Sensitivity of predictive species distribution models to change in grain size

Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence‐only data for 50 species in five different regions, to test whether: (1) a 10‐fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.

[1]  S. Franklin,et al.  Seismic Cutlines, Changing Landscape Metrics and Grizzly Bear Landscape use in Alberta , 2005, Landscape Ecology.

[2]  R. Tibshirani,et al.  Flexible Discriminant Analysis by Optimal Scoring , 1994 .

[3]  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.

[4]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[5]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[6]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[7]  S. Ferrier,et al.  Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling , 2004, Biodiversity & Conservation.

[8]  M. Ehlers,et al.  Review article: Thirty years of analysing and modelling avian habitat relationships using satellite imagery data: a review , 2005 .

[9]  Eric R. Ziegel,et al.  Generalized Linear Models , 2002, Technometrics.

[10]  Catherine H. Graham,et al.  A comparison of methods for mapping species ranges and species richness , 2006 .

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

[12]  A. Peterson,et al.  New developments in museum-based informatics and applications in biodiversity analysis. , 2004, Trends in ecology & evolution.

[13]  David R. B. Stockwell,et al.  Effects of sample size on accuracy of species distribution models , 2002 .

[14]  A. Guisan,et al.  Predicting reptile distributions at the mesoscale: relation to climate and topography , 2003 .

[15]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[16]  J. Busby.  BIOCLIM - a bioclimate analysis and prediction system , 1991 .

[17]  C. S. Holling.  Cross-Scale Morphology, Geometry, and Dynamics of Ecosystems , 1992 .

[18]  G. Carpenter,et al.  DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals , 1993, Biodiversity & Conservation.

[19]  Paul H. Williams,et al.  Downscaling European species atlas distributions to a finer resolution: implications for conservation planning , 2005 .

[20]  F. Huettmann,et al.  Large-scale effects on the spatial distribution of seabirds in the Northwest Atlantic , 2006, Landscape Ecology.

[21]  P. Hernandez,et al.  The effect of sample size and species characteristics on performance of different species distribution modeling methods , 2006 .

[22]  David R. B. Stockwell,et al.  The GARP modelling system: problems and solutions to automated spatial prediction , 1999, Int. J. Geogr. Inf. Sci..

[23]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[24]  Walter Jetz,et al.  Using coarse-grained occurrence data to predict species distributions at finer spatial resolutions—possibilities and limitations , 2006 .

[25]  A. Guisan,et al.  An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data , 2004 .

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

[27]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[28]  P. McCullagh,et al.  Generalized Linear Models, 2nd Edn. , 1990 .

[29]  Monica G. Turner,et al.  Scale and heterogeneity in habitat selection by elk in Yellowstone National Park , 2003 .

[30]  J. Reynolds,et al.  Predictive Ecology to the Rescue? , 2002, Science.

[31]  Michael Drielsma,et al.  Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling , 2002, Biodiversity & Conservation.

[32]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[33]  L. Belbin,et al.  Evaluation of statistical models used for predicting plant species distributions: Role of artificial data and theory , 2006 .