Identifying core areas in a species’ range using temporal suitability analysis: an example using little bustards Tetrax Tetrax L. in ... View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2006-07-09

AUTHORS

Patrick E. Osborne , Susana Suárez-Seoane

ABSTRACT

Variations in habitat quality impact on breeding success, leading to strong selection pressure for the best sites to be occupied first during a population increase and last during a decline. Coupled with dispersal and metapopulation processes, the result is that snapshot surveys of wildlife distributions may fail to reveal core areas that conservation seeks to protect. At a local scale, territory occupancy is a good indicator of quality but data are not readily available to assess occupancy for rarer species, in remote areas, and over large spatial extents. We introduce temporal suitability analysis as a way to generate an analogue of occupancy from a single survey and illustrate it using data on the little bustard in Spain. We first used Generalised Additive Modelling (GAM) to build a predictive distribution model using Geographic Information System (GIS) coverages and satellite imagery, and then applied the model retrospectively to a time series of satellite images to produce one distribution map for each year. These annual maps differed in the extent of Spain predicted as suitable for little bustards. By overlaying the maps, we identified areas predicted as suitable in one to n years. We show that this temporal suitability map correlates with a conventional habitat suitability map based on a single year but contains extra information on hierarchical use of habitats and the lag between suitability and use. The technique may be applied at a variety of spatial scales to reveal changes in expected occupancy as land use or external factors determining land cover types vary over time. More... »

PAGES

263-276

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4020-6865-2_19

DOI

http://dx.doi.org/10.1007/978-1-4020-6865-2_19

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042025470


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