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Lawler A 《Science (New York, N.Y.)》1995,269(5231):1665
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Lawler A 《Science (New York, N.Y.)》1995,267(5197):446-447
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The ability to predict species occurrences quickly is often crucial for managers and conservation biologists with limited
time and funds. We used measured associations with landscape patterns to build accurate predictive habitat models that were
quickly and easily applied (i.e., required no additional data collection in the field to make predictions). We used classification
trees (a nonparametric alternative to discriminant function analysis, logistic regression, and other generalized linear models)
to model nesting habitat of red-naped sapsuckers (Sphyrapicus nuchalis), northern flickers (Colaptes auratus),tree swallows (Tachycineta bicolor), and mountain chickadees (Parus gambeli) in the Uinta Mountains of northeastern Utah, USA. We then tested the predictive capability of the models with independent
data collected in the field the following year. The models built for the northern flicker, red-naped sapsucker, and tree swallow
were relatively accurate (84%, 80%, and 75% nests correctly classified,respectively)compared to the models for the mountain
chickadee (50% nests correctly classified). All four models were more selective than a null model that predicted habitat based
solely on a gross association with aspen forests. We conclude that associations with landscape patterns can be used to build
relatively accurate, easy to use, predictive models for some species. Our results stress, however, that both selecting the
proper scale at which to assess landscape associations and empirically testing the models derived from those associations
are crucial for building useful predictive models.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献