z-logo
Premium
Influencia de los Objetivos de Representación sobre el Área Total de Redes de Áreas de Conservación
Author(s) -
JUSTUS JAMES,
FULLER TREVON,
SARKAR SAHOTRA
Publication year - 2008
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1111/j.1523-1739.2008.00928.x
Subject(s) - representation (politics) , geography , computer science , political science , law , politics
  Systematic conservation planning typically requires specification of quantitative representation targets for biodiversity surrogates such as species, vegetation types, and environmental parameters. Targets are usually specified either as the minimum total area in a conservation‐area network in which a surrogate must be present or as the proportion of a surrogate's existing spatial distribution required to be in the network. Because the biological basis for setting targets is often unclear, a better understanding of how targets affect selection of conservation areas is needed. We studied how the total area of conservation‐area networks depends on percentage targets ranging from 5% to 95%. We analyzed 12 data sets of different surrogate distributions from 5 regions: Korea, Mexico, Québec, Queensland, and West Virginia. To assess the effect of spatial resolution on the target‐area relationship, we also analyzed each data set at 7 spatial resolutions ranging from 0.01°× 0.01° to 0.10°× 0.10°. Most of the data sets showed a linear relationship between representation targets and total area of conservation‐area networks that was invariant across changes in spatial resolution. The slope of this relationship indicated how total area increased with target level, and our results suggest that greater surrogate representation requires significantly more area. One data set exhibited a highly nonlinear relationship. The results for this data set suggest a new method for setting targets on the basis of the functional form of target‐area relationships. In particular, the method shows how the target‐area relationship can provide a rationale for setting targets solely on the basis of distributional information about surrogates.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here