Major issues in threat analysis and resolving such problems: an addendum to the GAP analysis
Identification of regions that warrant conservation attention is a top priority among global environmental concerns. Conventionally, this objective was achieved via recognizing natural landscapes based on the number of IUCN Red Listed species, percentage of endemism and species diversity. A recent...
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Format: | Article |
Language: | English |
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Wildlife Information Liaison Development Society
2012-04-01
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Series: | Journal of Threatened Taxa |
Online Access: | http://threatenedtaxa.org/index.php/JoTT/article/view/746 |
Summary: | Identification of regions that warrant conservation attention is a top priority among global environmental concerns. Conventionally, this objective was achieved via recognizing natural landscapes based on the number of IUCN Red Listed species, percentage of endemism and species diversity. A recent innovation in conservation biology is the use of GIS-based threat analysis models to identify key areas of conservation importance. Compared with GAP Analysis, which only identifies biodiversity-rich unprotected lands, threat analysis serves as a rigorous tool in conservation planning which specifically recognizes threats and habitat suitability to different taxa based on a spatially-explicit analysis. Threat analysis is a highly flexible process which involves building up a model with multiple independent (without autocorrelations) variables that both positively and negatively affect distribution and population persistence of a concerned species. Parameters include rate of land-use change, population density, population growth rate, land management regimes, protection status, habitat suitability and land stewardship. Threat analysis models can be used to understand the current status of a particular species (or a community) and can be used to project future trends about the species under consideration. This publication provides an overview of uses of GIS-based threat analyses in conservation biology and provides insights on the limitations of these models and the directions that should be taken in future. |
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ISSN: | 0974-7893 0974-7907 |