| Summary: | Uncertainty in the location of in situ wildlife observations may impair the performance of habitat models based on those observations. In this thesis, I explore the effects of location uncertainty on inference in species-habitat models using a simulation approach to propagate uncertainty in habitat models and quantify its effects. Using a point survey of overwintering migratory birds at Poyang Lake, China, I applied Monte Carlo methods to characterize the uncertainty that results when the observer locations and species abundances are known, but the specific directions and distances of the observations (i.e. the specific location of the observed landscape) are unknown. Habitat models of a tuber-feeding swan (tundra swan; Cygnus columbianus) indicated that the abundance of this species increased with the amount of shallow water, a land-cover class likely to contain submerged aquatic vegetation communities; and that of a grazing goose (white-fronted goose; Anser albifrons albifrons) increased with the amount of sparse live wetland vegetation, a land-cover class found in the transition space between mudflats and perennial wetland vegetation. The incorporation of location uncertainty into the habitat models of tundra swans produced uncertainties in the inferred relationship to shallow water, as indicated by changes from positive to negative parameter coefficient estimates, in 15% of simulated models while the relationship between white-fronted goose abundance and sparse live wetland vegetation was positive for 98% of model runs. The causes of these changes in inference were highly dependent on landscape configurations and therefore difficult to predict or generalize. I found that fixed-radius models (i.e., models constructed assuming that the observed area was a uniform-sized circular area around each sample point) were consistent in terms of direction of effects with the simulation results but should be used with caution when interpreting the strength of the species-habitat relationship from these models due to high ...
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