Inferring process from pattern: Can territory occupancy provide information about life history parameters?
Authored by HP Possingham, AJ Tyre, DB Lindenmayer
Date Published: 2001
DOI: 10.2307/3061091
Sponsors:
National Science and Engineering Research Council of Canada (NSERC)
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
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.
Tags
Landscape
Habitat use
Population-dynamics
Spatial-distribution
Natal dispersal
Arboreal
marsupials
Eastern australia
Central highlands
Eucalypt forests
Greater glider