Spatial models of sparse data to inform cetacean conservation planning: an example from Oman

Abstract

Habitat models are tools for understanding the relationship between cetaceans and their environment, from which patterns of the animals' space use can be inferred and management strate- gies developed. Can working with space use alone be sufficient for management, when habitat can- not be modeled? Here, we analyzed cetacean sightings data collected from small boat surveys off the coast of Oman between 2000 and 2003. The waters off Oman are used by the Endangered Arabian Sea population of humpback whales. Our data were collected primarily for photo-identification, using a haphazard sampling regime, either in areas where humpback whales were thought to be relatively abundant, or in areas that were logistically easy to survey. This leads to spatially autocorrelated data that are not amenable to analysis using standard approaches. We used quasi-Poisson generalized lin- ear models and semi-parametric spatial filtering to assess the distribution of humpback and Bryde's whales in 3 areas off Oman relative to 3 simple physiographic variables in a survey grid. Our analysis focused on the spatial eigenvector filtering of models, coupled with the spatial distribution of model residuals, rather than just on model predictions. Spatial eigenvector filtering accounts for spatial autocorrelation in models, allowing inference to be made regarding the relative importance of partic- ular areas. As an exemplar of this approach, we demonstrate that the Dhofar coast of southern Oman is important habitat for the Arabian Sea population of humpback whales. We also suggest how con- servation planning for mitigating impacts on humpback whales off the Dhofar coast could start.