Multivariate perspectives on patch use by masked bobwhites
Academic Article
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
Models that discriminate habitat patches acceptable to wildlife assist biologists in managing habitat, evaluating the effects of management treatments, and selecting areas for development or preservation. We used neural network modeling to discriminate between used and random patches for the endangered masked bobwhite (Colinus virginianus ridgwayi) in Sonora, Mexico, and Arizona during 1994-96. Input variables, thought to encompass the habitat space of bobwhites, were canopy coverage of woody vegetation (%), exposure of bare ground (%), exposure to ground predators (m2), exposure to aerial predators (m3), and operative temperature (C). A neural model developed with data from Mexico correctly classified 87.4% of patches for training (n = 483) and validation data (n = 118). The model developed for Arizona correctly classified 82.3% of patches for training data (n = 265) and 78.1% for validation data (n = 64). Mathematical transplants of Mexico bobwhites to Arizona habitat and of Arizona bobwhites to Mexico habitat revealed that bobwhites from Mexico (native) were adapted to a broader range of conditions than those in Arizona (reintroduced). For masked bobwhites and probably other species, the contingent nature of habitat features in a multivariate sense may permit the redress of a habitat deficiency without addressing the perceived deficiency per se.
