species distribution modeling

Spatial inference and prediction with biogeographical data (2005-2008) with Jennifer Miller

National Science Foundation Grant BSC-0452389


Maps of actual or potential species distributions are required for many aspects of resource management and conservation planning including biodiversity assessment, habitat management and restoration, single- and multiple species and habitat conservation plans, population viability analysis, modeling community and ecosystem dynamics, and predicting the effects of climate change on species and ecosystems. A growing number of quantitative methods are being used both inferentially, to identify the parameters that determine habitat suitability, and predictively, to assign habitat value to locations where biological survey data are lacking (most of the earth’s surface). There are three impediments to the effective use of these modeling tools by both researchers and conservation and resource managers: a) too few of the existing applications explicitly incorporate the spatial dependence inherent in biospatial data into the modeling methods b) the statistical and GIS modeling tools are not always well integrated, and, c) the proliferation of potential methods and conflicting results regarding their efficacy is daunting to users. The investigators 1) synthesized existing information on spatial prediction using biogeographical data, 2) strategically planned and execute a set of modeling experiments, and, based on these, 3) developed a framework to guide the operational use of these methods for biodiversity assessment and landscape management. Comparative modeling experiments were executed using species distribution and abundance data spanning the three major ecological regions in southern California (desert, mountain, coastal), for plants from vegetation surveys and reptiles and amphibians (herptiles) surveyed in a multi-year monitoring program. The methods tested includeed  parametric and non-parametric statistical (generalized) models, machine learning approaches, and those incorporating spatial dependence (regression kriging, spatial autoregressive models).

The research is innovative because it provided a broad comparison of modeling methods for real biological datasets that vary in their sample design, measurement scale, and spatial dependence, but were collected in the same bioregion, and focused on biogeographical modeling of spatial dependence in plant and animal species distribution and abundance. It will result in a framework that can be used by researchers and resource managers to select an approach to modeling that is best suited to their biogeographical data and questions. The project directly benefited society because it was collaborative with the Biological Resources Division of the US Geological Survey, the federal agency with a leadership role in spatial data archiving and analysis and biological information infrastructure. Thus, the framework and recommendations were directly conveyed to resource and data managers. 


  • Franklin, J., 2010. Mapping Species Distributions: Spatial Inference and Prediction, Cambridge University Press, Cambridge, UK. ISBN 978-0-521-87635-3 hb; 978-0-521-7002-3 pb. 338 pp.
  • Syphard, A. D. and Franklin, J., 2010, Species’ functional type affects the accuracy of species distribution models for plants in southern California, Journal of Vegetation Science 21(1):177-189. (Both authors contributed equally to this paper.) Published 2/10. DOI: 10.1111/j.1654
  • Syphard, A. D. and Franklin, J., 2009, Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors, Ecography 32:907-918. published 12/08/09. doi: 10.1111/j.1600-0587.2009.05883.x
  • Franklin, J., Wejnert, K., Hathaway, S., Rochester, C. and Fisher, R., 2009, Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California, Diversity and Distributions 15: 167-177; published 1/09. DOI: 10.1111/j.1472-4642.2008.00536.x
  • Miller, J., J. Franklin and R. Aspinall, 2007, Incorporating spatial dependence in predictive vegetation models, Ecological Modelling 202: 225-242. doi:10.1016/j.ecolmodel.2006.12.012
  • Miller, J., and J. Franklin, 2006, Explicitly incorporating spatial dependence in predictive vegetation models as explanatory variables: a Mojave Desert case study, Journal of Geographical Systems 9(4): 411-435. DOI 10.1007/s10109-006-0035-8.

Earlier Publications on this Research Topic

  • Scull, P., O.A. Chadwick, J. Franklin, and G. Okin, 2005, A comparison of prediction methods to create spatially distributed soil property maps using soil survey data for an alluvial basin in the Mojave Desert, California, Professional Geographer vol. 57, no. 3, pp. 423-437.
  • Scull, P., J. Franklin, and O. Chadwick, 2005, The application of classification tree analysis to soil type prediction in a desert landscape, Ecological Modelling vol 181, no. 1, pp. 1-15.
  • Scull, P., J. Franklin, and D. McArthur, 2003, Predictive soil mapping: a review, Progress in Physical Geography, vol. 27, no. 2, pp. 171-197.
  • Miller, J. and J. Franklin, 2002, Predictive vegetation modeling with spatial dependence -- vegetation Alliances in the Mojave Desert, Ecological Modelling vol. 157, pp. 227-247. t
  • Franklin, J., 2002, Enhancing a regional vegetation map with predictive models of dominant plant species in chaparral, Applied Vegetation Science vol. 5, pp. 135-146.
  • Meentemeyer, R., A. Moody, and J. Franklin. 2001, Landscape-scale patterns of shrub-species abundance in California chaparral: the role of topographically mediated resource gradients, Plant Ecology, vol. 156, no. 1, pp. 19-41.
  • Franklin, J., C. E. Woodcock, and R. Warbington, 2000, Digital vegetation maps of forest lands in California: Integrating satellite imagery, GIS modeling, and field data in support of resource management, Photogrammetric Engineering and Remote Sensing, vol. 66, no. 10, pp. 1209-1217.
  • Franklin, J., 1998, Predicting the distributions of shrub species in California chaparral and coastal sage communities from climate and terrain-derived variables, Journal of Vegetation Science, vol. 9, pp. 733-748.
  • Franklin, J., 1995, Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography vol. 19, no. 4, pp. 494-519.