State-space models explicitly separate uncertainty associated with unobserved, time-varying parameters from that which arises from sampling the population. The statistical aspects of formal state-space models are appealing and these models are becoming more widely used for assessments. However, treating natural mortality as known and constant across ages continues to be common practice. We developed a state-space, age-structured assessment model that allowed different assumptions for natural mortality and the degree of temporal stochasticity in abundance. We fit a suite of models where natural mortality was either age-invariant or an allometric function of mass and interannual transitions of abundance were deterministic or stochastic to observations on Gulf of Maine – Georges Bank Acadian redfish (Sebastes fasciatus). We found that allowing stochasticity in the interannual transition in abundance was important and estimating age-invariant natural mortality was sufficient. A simulation study showed low bias in annual biomass estimation when the estimation and simulation model matched and the Akaike imformation criterion accurately measured relative model performance, but it was important to allow simulated data sets to include the stochasticity in interannual transitions of abundance-at-age.

a b s t r a c tMagnetic deterrents have recently been employed to assess their ability to reduce elasmobranch mortality in beach nets. With previous studies exhibiting promise, the present study examined the ability of a magnetic barrier technology, known as the Sharksafe Barrier, to exclude bull sharks (Carcharhinus leucas) from bait, and how behavioral interactions may change with variations in environmental and biological factors. Generalized linear mixed model analyses based on 114, 30-min trials illustrate that all interacting C. leucas were successfully excluded from baited procedural control and magnetic regions (i.e. zero entrances through either region). Avoidance and pass around frequencies significantly differed from the control region and were based on situational context. To enhance behavioral analysis techniques, an Adaptive Resolution Imaging Sonar (ARIS) was employed which revealed that C. leucas distance from and swim speed associated with the magnetic barrier region were significantly greater than those associated with the procedural control region. This study demonstrates the Sharksafe barrier's effectiveness in excluding C. leucas from baited regions, regardless of variations in biological and/or environmental parameters. While other bather protection systems (e.g. beach nets and drumlines) continue to be used, this study exhibits promise that the Sharksafe barrier can be an eco-friendly alternative to beach nets.

We developed a model for in-season age-specific forecasts of salmon returns using preseason return forecasts, age composition of in-season returns, cumulative in-season returns by fishing district, and age composition and an index of abundance from an in-season test fishery. We apply this method to the sockeye salmon (Oncorhynchus nerka) fishery in the Bristol Bay districts of Alaska. The model generates point estimates and Bayesian probability distributions for return numbers by age and river, and it provides an integrated framework for including all of the major data sources currently used in in-season forecasting. We evaluated model performance using early-season data from 1999–2001 and compared the effects of four information sets on forecast accuracy. The four information sets were as follows: I, district-specific inshore return data; II, inshore return data and test fishery data; III, inshore return data and preseason forecasts; IV, inshore return data, test fishery data, and preseason forecasts. Forecasts from information sets II, III, and IV were less biased than those from information set I. However, in terms of the forecast interval, forecasts from information set II were best because the 95% highest posterior density regions of forecasts from information set II covered the actual returns most frequently.

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