Fish and Fisheries最新文献

Scientific advice for fisheries management rarely takes into account how fishers react to regulations, which can lead to unexpected results and unrealistic expectations of the effectiveness of the management measures. Short-term decisions about when and where to fish are one of the greatest sources of uncertainty in predicting management outcomes. Several models have been developed to predict how fishers allocate effort in space and time, including mechanistic methods such as gravity and dynamic state variable models, and statistical methods such as random utility and Markov models. These have been individually used to predict effort allocation for various fisheries, but there is no comparative synthesis of their structure and characteristics. We demonstrate strong theoretical links between utility and choice in gravity, random utility, Markov and dynamic state variable models. Using an advanced event-based simulation framework, we find that mechanistic models bias effort allocation to certain areas when applying commonly used strong assumptions about drivers of effort allocation; and conversely, statistical models accurately predict the distribution of fishing effort under business as usual. However, predictive performance degrades with previously unobserved dynamics, such as a spatial closure. Mechanistic models were less suited to general application under business as usual but provide a useful framework for testing hypotheses about a fishery system in response to policy change. Comparison of simple model formulations yielded significant insight into the characteristics of the models and how they could be used to evaluate alternative management approaches for mixed fisheries.

Species with complex life cycles, such as anadromous fish that perform spawning migrations between freshwater and the ocean, may be particularly sensitive to global change because freshwater and marine habitats experience distinct shifts in climate and ecosystem dynamics. Abundances of wild steelhead trout (Oncorhynchus mykiss) have declined across most of their range over the past 40–50 years. We examined whether declines in steelhead survival can be linked to changing climate conditions and species interactions. A novel hierarchical integrated population model that accounts for the species' complex life history was fitted to data from multiple wild steelhead populations on the Washington coast, U.S.A. The model estimates recruitment residuals and kelt survival rates as time-varying processes, which reflect annual variation in survival before and after first maturation. We found that survival rates of immature steelhead (recruits) and adult steelhead (kelts) have declined over time and that survival trends across populations were strongly associated with climate and ecosystem change, specifically summer sea surface temperature and pink salmon abundance in the North Pacific Ocean, the NPGO index and river flows. Including these drivers in the model reduced unexplained annual variation in shared recruitment and kelt survival anomalies and largely accounted for their negative long-term trends. Our findings provide evidence that rising temperatures and increased interspecific competition at sea have contributed to declines in steelhead survival over the last five decades. Considering projected warming and high pink salmon abundances in the ocean, steelhead will likely continue to experience low marine survival rates.

Salmonid fishes are one of the best studied fish taxa, but little is known about their biomass distribution. We created a dataset using published material for over 1000 rivers with estimated salmonid biomass, covering 27 countries, and 11 species. The distribution of salmonid biomass and production across streams was skewed to the right with a mean biomass and production of 5.2 g/m² (range = 0–70.3 g/m²) and 6.3 g/m²/year (range = 0.03–50.2 g/m²/year), respectively. The top 10% and 1% of salmonid streams in the world had a biomass > 11.9 and 36.5 g/m², respectively, and a production > 13.9 and 25.6 g/m²/year, respectively. Salmonid production was positively correlated with biomass (r = 0.82, n = 194), with a mean production to biomass (P/B) ratio of 1.08, which differed among species. Mean biomass declined 38% over time, from 8.6 g/m² before 1980 to 5.4 g/m² in 2000–2020. Biomass was also higher in small streams (< 10 m wide) and in streams where smaller areas were sampled. Brown trout (Salmo trutta) streams represented a higher proportion of those with biomass > 10 g/m² than many other species. In addition to the variables mentioned above, salmonid biomass in streams was affected by species, season, method of sampling, elevation, latitude, and migratory strategy. Expanding the list of variables would be useful for developing models to predict salmonid biomass and the conditions for an outstanding salmonid stream, defined as a stream which has a biomass estimate in the top 1% worldwide.

Mixed fisheries exploit fish stocks that are heterogeneously distributed in space using gears that are not species selective. This poses a challenge for management as catch limits for less productive stocks constrain catches of more productive stocks leading to losses in yield and economic value. Decoupling catches of stocks caught together in mixed fisheries could increase potential yields and may be achieved through changes in spatial fishing patterns. This study identifies fine-scale spatial patterns of retained catches for the UK otter trawlers in UK waters by combining vessel monitoring system positional information and logbook data on retained catches. Spatially contiguous units were identified through a combination of Principal Components Analysis and spatial clustering. Our results show a complex spatial structure in the fish assemblage which differs across UK sea areas, with greater similarity between the Northern North Sea and West of Scotland compared to other sea areas. Through simulation, we highlight how fine-scale spatially resolved fisheries data can identify areas where choke risks from catches of low-quota, low-productivity species associated with target species can be reduced. This underscores the value of fine-scale data for enhancing efficiency and sustainability in mixed fisheries. Wider benefits from the use of fine-scale data include the ability to identify consistent spatial métier definitions for use in modelling technical interactions. Ultimately, our study informs strategies and approaches that decouple catches of low-quota, low-productivity species caught together in mixed fisheries, improving sustainability and the conservation of living resources under complex management challenges.

Although the eco-evolutionary consequences of dispersal and exploitation are increasingly recognised, consideration of these effects and how they interact for management and conservation remains limited. We addressed this gap by examining population exploitation within a metapopulation framework, using Atlantic salmon as a case study. We compared eco-evolutionary consequences of alternative exploitation strategies by incorporating selective exploitation based on life-history traits and spatial dimension of exploitation (i.e., whether populations were net exporters or importers of individuals). We used a demo-genetic agent-based model to examine demographic and evolutionary consequences of these strategies across a gradient of population-specific exploitation rates. At the metapopulation scale, we found both lower abundance and earlier sexual maturation with increasing exploitation, particularly when fishing was selective on larger individuals. The spatial selectivity of exploitation had an overall additional detrimental effect on metapopulation performance and fisheries yield, and induced stronger evolutionary changes than when exploitation was evenly spread over all populations. We discuss the implications of metapopulation functioning for species management and how considering dispersal patterns and intensity might change how we apply harvest. Nevertheless, our findings suggest that the safest approach remains to distribute exploitation efforts evenly across all populations, especially in the absence of variation in intrinsic productivity. However, this strategy might not completely prevent negative consequences at the local scale. Therefore, we advise managers to critically assess the relevance of our results and dispersal assumptions in the specific cases they may have to deal with.