Aerial survey estimates of polar bears and their tracks in the Chukchi Sea.

Polar bears are of international conservation concern due to climate change but are difficult to study because of low densities and an expansive, circumpolar distribution. In a collaborative U.S.

Taxonomy-based hierarchical analysis of natural mortality: polar and subpolar phocid seals.

Knowledge of life-history parameters is frequently lacking in many species and populations, often because they are cryptic or logistically challenging to study, but also because life-history parameters can be difficult to estimate with adequate precision. We suggest using hierarchical Bayesian analysis (HBA) to analyze variation in life-history parameters among related species, with prior variance components representing shared taxonomy, phenotypic plasticity, and observation error. We develop such a framework to analyze U-shaped natural mortality patterns typical of mammalian life history from a variety of sparse datasets.

A Bayesian Analysis of Abundance, Trend, and Population Viability for Harbor Seals in Iliamna Lake, Alaska.

Harbor seals in Iliamna Lake, Alaska, are a small, isolated population, and one of only two freshwater populations of harbor seals in the world, yet little is known about their abundance or risk for extinction. Bayesian hierarchical models were used to estimate abundance and trend of this population. Observational models were developed from aerial survey and harvest data, and they included effects for time of year and time of day on survey counts.

Habitat selection and seasonal movements of young bearded seals (Erignathus barbatus) in the Bering Sea.

The first year of life is typically the most critical to a pinniped's survival, especially for Arctic phocids which are weaned at only a few weeks of age and left to locate and capture prey on their own. Their seasonal movements and habitat selection are therefore important factors in their survival. During a cooperative effort between scientists and subsistence hunters in October 2004, 2005, and 2006, 13 female and 13 male young (i.

The commercial harvest of ice-associated seals in the Sea of Okhotsk, 1972-1994.

Sealing log books from 75 out of 79 commercial harvest cruises carried out between 1972 and 1994 in the Sea of Okhotsk, Russia, were analyzed to describe spatial and temporal allocation of ice-associated seal harvest effort, species composition of catches, total harvest rates, and related parameters for species including ringed (Pusa hispida), ribbon (Histriophoca fasciata), bearded (Erignathus barbatus) and spotted (Phoca largha) seal. Variations in catch per unit effort were explored in relation to year, sea ice conditions, day of the year, and geographic location. In most years, the harvest was predominantly represented by ringed seals (mean = 0.

On Extrapolating Past the Range of Observed Data When Making Statistical Predictions in Ecology.

Ecologists are increasingly using statistical models to predict animal abundance and occurrence in unsampled locations. The reliability of such predictions depends on a number of factors, including sample size, how far prediction locations are from the observed data, and similarity of predictive covariates in locations where data are gathered to locations where predictions are desired. In this paper, we propose extending Cook's notion of an independent variable hull (IVH), developed originally for application with linear regression models, to generalized regression models as a way to help assess the potential reliability of predictions in unsampled areas.

Spatially Estimating Disturbance of Harbor Seals (Phoca vitulina).

Tidewater glacial fjords in Alaska provide habitat for some of the largest aggregations of harbor seals (Phoca vitulina), with calved ice serving as platforms for birthing and nursing pups, molting, and resting. These fjords have also been popular destinations for tour ships for more than a century, with dramatic increases in vessel traffic since the 1980s. Seals on ice are known to flush into the water when approached by tour ships, but estimating the exposure to disturbance across populations is difficult.

Accommodating species identification errors in transect surveys.

Ecologists often use transect surveys to estimate the density and abundance of animal populations. Errors in species classification are often evident in such surveys, yet few statistical methods exist to properly account for them. In this paper, we examine biases that result from species misidentification when ignored, and we develop statistical models to provide unbiased estimates of density in the face of such errors.

Haul-out behavior of harbor seals (Phoca vitulina) in Hood Canal, Washington.

The goal of this study was to model haul-out behavior of harbor seals (Phoca vitulina) in the Hood Canal region of Washington State with respect to changes in physiological, environmental, and temporal covariates. Previous research has provided a solid understanding of seal haul-out behavior. Here, we expand on that work using a generalized linear mixed model (GLMM) with temporal autocorrelation and a large dataset.

A Bayesian hierarchical model of Antarctic fur seal foraging and pup growth related to sea ice and prey abundance.

We created a Bayesian hierarchical model (BHM) to investigate ecosystem relationships between the physical ecosystem (sea ice extent), a prey measure (krill density), predator behaviors (diving and foraging effort of female Antarctic fur seals, Arctocephalus gazella, with pups) and predator characteristics (mass of maternal fur seals and pups). We collected data on Antarctic fur seals from 1987/1988 to 1994/1995 at Seal Island, Antarctica. The BHM allowed us to link together predators and prey into a model that uses all the data efficiently and accounts for major sources of uncertainty.

Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data?

Quasi-Poisson and negative binomial regression models have equal numbers of parameters, and either could be used for overdispersed count data. While they often give similar results, there can be striking differences in estimating the effects of covariates. We explain when and why such differences occur.