New paradigms for modelling species distributions

1The management of both desirable and undesirable species requires an understanding of the factors determining their distribution. Quantitative distribution models offer simple methods for formulating the species–habitat link and the means not only for predicting where species should occur, but also for understanding the factors involved. Generalized linear modelling, in particular, links the incidence of species to habitat variables, and has increasingly formed the backbone of the modelling approaches used. New ‘data technologies’, such as remote sensing and geographical information systems, have further broadened these modelling applications to almost any ecological system and any species for which there are distribution data.2Many previous approaches have aimed to identify the most parsimonious model with the best suite of predictors, selected on the basis of null hypothesis testing. However, information-theoretic approaches based on Akaike's information criterion allow the selection of a best approximating model or a subset of models from a set of candidates. Information-theoretic approaches require a deeper understanding of the biology of the system modelled and may well become an improved paradigm for species distribution modelling.3Synthesis and applications. This special profile of six papers demonstrates the development in methodology used in species distribution modelling. The papers show how information-theoretic approaches can be coupled with emerging data technologies to address issues of conservation significance. With conservation biology and applied ecology at the forefront of many of the basic science developments so far, we expect these methods to pervade other areas of ecological research more fully in future.

[1]  Lesley Gibson,et al.  Spatial prediction of rufous bristlebird habitat in a coastal heathland: a GIS-based approach , 2004 .

[2]  Mark S. Boyce,et al.  A quantitative approach to conservation planning: using resource selection functions to map the distribution of mountain caribou at multiple spatial scales , 2004 .

[3]  Annett Bartsch,et al.  Modelling habitat selection and distribution of the critically endangered Jerdon's courser Rhinoptilus bitorquatus in scrub jungle: an application of a new tracking method , 2004 .

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

[5]  Atte Moilanen,et al.  Combining probabilities of occurrence with spatial reserve design , 2004 .

[6]  Gordon B. Stenhouse,et al.  Removing GPS collar bias in habitat selection studies , 2004 .

[7]  Ian Phillip Vaughan,et al.  Improving the Quality of Distribution Models for Conservation by Addressing Shortcomings in the Field Collection of Training Data , 2003 .

[8]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[9]  L. L. Eberhardt,et al.  What should we do about hypothesis testing , 2003 .

[10]  G. Griffiths,et al.  Conservation strategy maps: a tool to facilitate biodiversity action planning illustrated using the heath fritillary butterfly , 2003 .

[11]  Nancy Vaughan,et al.  Habitat associations of European hares Lepus europaeus in England and Wales: implications for farmland management , 2003 .

[12]  S. Suárez‐Seoane,et al.  Large-scale habitat selection by agricultural steppe birds in Spain: identifying species–habitat responses using generalized additive models , 2002 .

[13]  A. Møller,et al.  The distribution and colony size of barn swallows in relation to agricultural land use , 2002 .

[14]  Thorsten Wiegand,et al.  Assessing the suitability of central European landscapes for the reintroduction of Eurasian lynx , 2002 .

[15]  A. Guisan,et al.  Modelling the distribution of bats in relation to landscape structure in a temperate mountain environment , 2001 .

[16]  S. Manel,et al.  Evaluating presence-absence models in ecology: the need to account for prevalence , 2001 .

[17]  Simon Ferrier,et al.  Incorporating expert opinion and fine-scale vegetation mapping into statistical models of faunal distribution , 2001 .

[18]  Dr Robert Bryant,et al.  Modelling landscape-scale habitat use using GIS and remote sensing : a case study with great bustards , 2001 .

[19]  Richard B. Bradbury,et al.  Habitat associations and breeding success of yellowhammers on lowland farmland , 2000 .

[20]  P. Donald,et al.  Local extinction of British farmland birds and the prediction of further loss , 2000 .

[21]  Stéphanie Manel,et al.  Testing large-scale hypotheses using surveys: the effects of land use on the habitats, invertebrates and birds of Himalayan rivers , 2000 .

[22]  S. Langton,et al.  Habitat models of bird species' distribution: an aid to the management of coastal grazing marshes. , 2000 .

[23]  B. Huntley,et al.  Predicting the spatial distribution of non‐indigenous riparian weeds: issues of spatial scale and extent , 2000 .

[24]  David Gutiérrez,et al.  Habitat‐based statistical models for predicting the spatial distribution of butterflies and day‐flying moths in a fragmented landscape , 2000 .

[25]  David R. Anderson,et al.  Model Selection and Multimodel Inference , 2003 .

[26]  Steve Cherry,et al.  STATISTICAL TESTS IN PUBLICATIONS OF THE WILDLIFE SOCIETY , 1998 .

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

[28]  S. T. Buckland,et al.  An autologistic model for the spatial distribution of wildlife , 1996 .

[29]  Michael J. Crawley,et al.  GLIM for Ecologists , 1994 .

[30]  John Hinde,et al.  Statistical Modelling in GLIM. , 1989 .