Research Task: 8327CND.1.0
Task Manager: Brian Cade
Unexplained heterogeneity in statistical models of animal responses to their physical environment is reasonable to expect, because the measured habitat resources are a constraint on—but not the sole determinant of—abundance, survival, fecundity, or fitness. Typically, it is impossible to know whether the habitat factors measured are actually limiting at the time and location of sampling. Our ecological understanding and reliability of management predictions based on animal habitat models can be improved by shifting focus from estimating expected values (means) of responses to estimating intervals of responses associated with multiple percentiles of a distribution. Regression quantiles are an easily implemented approach for estimating intervals of responses in multiple regression models of animal responses to habitat. This methodology can be applied to a variety of ecological analyses where hidden biases may occur in observational data. This task will compare the statistical performance of the conventional asymptotic rankscore test (used for testing hypotheses and constructing confidence intervals for regression quantile estimates) with a new permutation variant of the rankscore test and a permutation drop in dispersion test. Evaluation conditions will be structured to match the range of sample sizes, variable types, covariance among predictors, and hypotheses typically encountered by investigators building models of animal habitat relationships with multiple linear regression models. In addition, case studies for selected terrestrial and aquatic species will demonstrate the utility of building more reliable habitat models.
For more information contact Brian Cade
U.S. Department of the Interior | U.S. Geological Survey
URL: http://www.fort.usgs.gov/Research/research_tasks.asp
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