New research presents reserve selection using nonlinear species distribution models
New research to be published in the June 2005 issue of The American Naturalist is among the first studies to present a computationally feasible solution for doing large-scale spatial reserve planning (a.k.a. spatial optimization) in a manner that predicted effects of landscape structure to species distributions are accounted for in a near- optimal manner. If effects of landscape structure on species occurrence are accounted for, it becomes apparent that good reserve structures contain relatively large blocks of land that occur in an aggregated manner.
Reserve design concerns the selection of land parcels for conservation, either via protection or habitat management measures. In general, it would be most beneficial for nature if all remaining natural or semi-natural regions could be protected. However, conservation resources are limited and thus the question becomes, which land parcels do we choose to obtain highest benefits for our money. Spatial reserve planning is typically based on currently observed (or predicted) distributions of species, and the essential aim is to identify areas of overlap where valuable occurrences of many species can be obtained efficiently. Spatial reserve planning is computationally heavy, if the question is "which 100,000 hectares do I choose from an area of a million hectares," the number of potential reserve structures is huge.
Computational reserve planning is further complicated by the fact that species occurrence is in reality not independent from the spatial structure of a reserve network. If habitat is lost outside designated protection areas, e.g. agricultural fields, new housing areas and car parks are built next to the protected areas, then some species are bound to be affected negatively by added disturbance and by the disappearance of suitable dispersal habitat from between individual reserve sites. In other words, groups of populations may be hurt by habitat loss or degradation from between individual populations.
Source: Eurekalert & othersLast reviewed: By John M. Grohol, Psy.D. on 21 Feb 2009
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