Changes in bottom-up forcing are fundamental drivers of fish population dynamics. Recent literature has highlighted the need to incorporate the role of dynamic environmental conditions in stock assessments as a key step towards adaptive fishery management. Combining a bioclimate envelope model and a population dynamic model, we propose a model-based approach that can incorporate ecosystem products into single-species stock assessments. The framework was applied to a commercially important American lobster (Homarus americanus) stock in the Northwest Atlantic. The bioclimate envelope model was used to hindcast temporal variability in a lobster recruitment habitat suitability index (HSI) using bottom temperature and salinity. The climate-driven HSI was used to inform the lobster recruitment dynamics within the size-structured population dynamics model. The performance of the assessment model with an environment-explicit recruitment function is evaluated by comparing relevant assessment outputs such as recruitment, annual fishing mortality, and magnitude of retrospective biases. The environmentally-informed assessment model estimated (i) higher recruitment and lower fishing mortality and (ii) reduced retrospective patterns. This analysis indicates that climate-driven changes in lobster habitat suitability contributed to increased lobster recruitment and present potential improvement to population assessment. Our approach is extendable to other stocks that are impacted by similar environmental variability.
Fish aggregation devices (FADs) have been used extensively in the tuna purse seine fishery since the 1980s. This long-term modification of natural habitat has generated discussions as to whether FADs impact movement patterns of tuna species. We examined this question using data collected from the skipjack tuna (Katsuwonus pelamis) fishery. We used the longitudinal gravitational center of catch (G) to examine temporal variability in skipjack movement in the Western and Central Pacific Ocean, and related this to El Niño Southern Oscillation (ENSO) events. We found that in most cases G for free-swimming school sets changed with the onset of ENSO events, while G for floating-object-associated school sets remained relatively constant. This suggests that skipjack exhibit distinguishable behavioral strategies in response to ENSO events: they either react by moving long distances or they associate with floating objects. There has been no previous attempt to evaluate the interaction between FADs and the environmentally-determined movement of skipjack; this study shows evidence of an interaction, which should be considered when managing skipjack populations.
Atlantic cod (Gadus morhua) in the Northwest Atlantic off New England and southern Atlantic Canada exhibit a complex population structure. This region has three independently assessed stocks [Georges Bank, Gulf of Maine (GOM), and the 4X stock], all of which are known to mix with each other. Assessments of these stocks, however, assume no interpopulation mixing. Using simulations, we evaluated impacts of ignoring mixing resulting from seasonal migrations on the GOM assessment. The dynamics of the three stocks were simulated according to different scenarios of interstock mixing, and a statistical catch-at-age stock assessment model was fitted to the simulated GOM data with and without mixing. The results suggest that, while mixing causes measurable bias in the assessment, under the conditions tested, this model still performed well. Of the bias that does exist, spawning-stock biomass estimates are relatively sensitive to mixing compared with estimates of recruitment and exploitation rate. The relative timing of seasonal migration of the three stocks plays a critical role in determining the magnitude of bias. The scale and trends among years in the bias were driven by how representative the catch and survey data were for the GOM stock; this representation changed with the mixing rates.
Conventional yield-per-recruit (Y/R) and spawning-stock biomass-per-recruit (SSB/R) models make no allowance for spatial heterogeneity in fishing mortality, natural mortality, or growth across the stock area, although variability in these processes can affect model results. For example, areas with higher growth and/or lower natural mortality rates should be fished at a lower rate to maximize Y/R; however, these areas may be especially attractive to fishers and are often fished harder. Here, Y/R and SSB/R models are developed that simultaneously account for spatial heterogeneity in growth and fishing effort. These models are applied to the US Atlantic sea scallop (Placopecten magellanicus) fishery. The spatial variability in growth uses depth-integrated models from the literature and variability in effort is based on, alternatively, uniform, observed, and relative-optimal spatial harvesting distributions. The observed effort patterns are derived from vessel monitoring system positions, and illustrate one application for these widely collected but underutilized spatial data. In this example, the distribution of observed fishing effort reduces Y/R compared with the relative-optimal, or the uniform effort distribution implicitly assumed by conventional Y/R analysis. SSB/R was in some cases considerably higher under the relative-optimal distribution of effort than when calculated using observed or uniform effort patterns. Such more realistic spatially integrated Y/R and SSB/R models can help to evaluate the impact of effort patterns on fishery yield and stock egg production. These models demonstrate that the spatial distribution of effort can be as important as the overall average fishing mortality when managing fisheries to optimize Y/R, SSB/R, and yield.
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