A significant problem in wildlife management is identifying ““good”” habitat for species within the short time frames demanded by policy makers. Statistical models of the response of species presence/absence to predictor variables are one solution, widely known as habitat modeling. We use a ““virtual ecologist”” to test logistic regression as a means of developing habitat models within a spatially explicit, individual-based simulation that allows habitat quality to influence either fecundity or survival with a continuous scale. The basic question is how good are logistic regression models of habitat quality at identifying habitat where birth rates are high and death rates low (i.e., ““source”” habitat)? We find that, even when all the important variables are perfectly measured, and there is no error in surveying the species of interest, demographic stochasticity and the limiting effect of localized dispersal generally prevent an explanation of much more than half of the variation in territory occupancy as a function of habitat quality. This is true regardless of whether fecundity or survival is influenced by habitat quality. In addition, habitat models only detect a significant effect of habitat on territory occupancy when habitat quality is spatially autocorrelated. We find that habitat models based on logistic regression really measure the ability of the species to reach and colonize areas, not birth or death rates.
Ecological Applications – Ecological Society of America
Published: Dec 1, 2001
Keywords: demographic stochasticity ; dispersal ; habitat quality––occupancy relationships ; habitat vs. individual-based model ; life history parameters ; logistic regression ; observed pattern ; Petauroides volans ; source vs. sink habitat ; territory occupancy ; virtual ecologist
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