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Spatially Explicit Population Models: Current Forms and Future Uses
Author(s) -
Dunning John B.,
Stewart David J.,
Danielson Brent J.,
Noon Barry R.,
Root Terry L.,
Lamberson Roland H.,
Stevens Ernest E.
Publication year - 1995
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.2307/1942045
Subject(s) - population , landscape epidemiology , ecology , landscape ecology , spatial ecology , geography , environmental resource management , population model , scale (ratio) , habitat , temporal scales , land use , variety (cybernetics) , simulation modeling , computer science , cartography , environmental science , artificial intelligence , biology , mathematics , demography , mathematical economics , sociology
Spatially explicit population models are becoming increasingly useful tools for population ecologists, conservation biologists, and land managers. Models are spatially explicit when they combine a population simulator with a landscape map that describes the spatial distribution of landscape features. With this map, the locations of habitat patches, individuals, and other items of interest are explicitly incorporated into the model, and the effect of changing landscape features on population dynamics can be studied. In this paper we describe the structure of some spatially explicit models under development and provide examples of current and future research using these models. Spatially explicit models are important tools for investigating scale‐related questions in population ecology, especially the response of organisms to habitat change occurring at a variety of spatial and temporal scales. Simulation models that incorporate real‐world landscapes, as portrayed by landscape maps created with geographic information systems, are also proving to be crucial in the development of management strategies in response to regional land‐use and other global change processes. Spatially explicit population models will increase our ability to accurately model complex landscapes, and therefore should improve both basic ecological knowledge of landscape phenomena and applications of landscape ecology to conservation and management.