Fellow Marmamers I'm pleased to announce the publication of a new paper, "Spatial models of sparse data to inform cetacean conservation planning: an example from Oman ", by myself, Gianna Minton, Tim Collins, Ken Findlay, Andy Willson and Robert Baldwin. The paper is out in the latest issue of Endangered Species Research, and is part of the ESR Special on "Beyond Marine Mammal Habitat Modeling: Applications For Ecology And Conservation". It's Open Access and can be downloaded from the Endangered Species Research website at: http://www.int-res.com/abstracts/esr/v15/n1/p39-52/ Note that the modeling technique outlined in the paper can reduce spatial autocorrelation due to haphazard sampling, making it likely useful for other photo-identification studies, or those conducted from platforms of opportunity (e.g. whale-watching vessels). The Abstract is: 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 strategies developed. Can working with space use alone be sufficient for management, when habitat cannot 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 linear 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 particular 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 conservation planning for mitigating impacts on humpback whales off the Dhofar coast could start. Peter Corkeron -- Leader, Large Whale Team Protected Species Branch NOAA Northeast Fisheries Science Center 166 Water St Woods Hole MA 02543 phone: 508-495-2191