Spatiotemporal models for aerial survey counts: An application to ice-associated seals in the bering sea

Background/Question/Methods:

Ecologists often estimate animal abundance by fitting models to count data. Such models often require that animal density remains constant across the landscape where sampling is being conducted.  This assumption is problematic for animals inhabiting dynamic landscapes or otherwise exhibiting considerable spatio-temporal variation in density, and may be an impediment to inference about how changes in environmental conditions affect animals' spatial distribution.  A variety of models have been developed for analyzing spatio-temporal variation in count data, but there has been little comparison of the efficacy of alternative modeling approaches for estimating animal abundance.  We compare a suite of novel and existing spatio-temporal hierarchical models for animal count data that permit animal density to vary over space and time.  Models varied by the nature of the temporal structure (i.e., descriptive or dynamical), and whether total expected abundance was assumed constant over time.  We gauge the relative performance of alternative spatio-temporal models when confronted with simulated and real datasets from dynamic animal populations.  For the latter, we analyze spotted seal counts from an aerial survey of the Bering Sea where the quantity and quality of suitable habitat (sea ice) changed dramatically while surveys were being conducted.

Results/Conclusions

Simulation analyses suggested that multiple types of spatio-temporal models provide reasonable inference (low positive bias, high precision) about animal abundance, but have potential for overestimating precision.  Analysis of spotted seal data indicated that several model formulations, including those based on a log-Gaussian Cox process, had a tendency to overestimate abundance.  By contrast, a model that included a population closure assumption and a scale prior on total abundance produced estimates that largely conformed to our a priori expectation. Although care must be taken to tailor models to match the study population and survey data available, we argue that hierarchical spatio-temporal statistical models represent a powerful way forward for estimating abundance and explaining variation in the distribution of dynamical populations.