Productive fisheries are strongly linked to the ecological state of the essential habitats. In this study, we developed a methodology to assess the most important reproduction habitats of fish by using larval survey data and Bayesian species distribution models that predict the spatial distribution and abundance of fish larvae. Our case study with four commercially and ecologically important fish species in the coastal zone of the northern Baltic Sea demonstrated that the production of fish stocks can be concentrated to an extremely limited area compared with the entire suitable production area. The area suitable for larval production varied from 3.7% to 99.8% among species, but the smallest area responsible for 80% of the cumulative larval production was two to five times more limited, varying from 1.4% to 52.9% among species. Hence, instead of the traditional approach of modeling only habitat suitability for fish production, marine spatial planning and management should take into account the areal production potential. Moreover, the developed methodology enables linking of the total production potential across the whole distribution area to fisheries stock assessment and management.

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Species distribution models (SDM) are a key tool in ecology, conservation and management of natural resources. Two key components of the state-ofthe-art SDMs are the description for species distribution response along environmental covariates and the spatial random effect that captures deviations from the distribution patterns explained by environmental covariates. Joint species distribution models (JSDMs) additionally include interspecific correlations which have been shown to improve their descriptive and predictive performance compared to single species models. However, current JSDMs are restricted to hierarchical generalized linear modeling framework. Their limitation is that parametric models have trouble in explaining changes in abundance due, for example, highly nonlinear physical tolerance limits which is particularly important when predicting species distribution in new areas or under scenarios of environmental change. On the other hand, semi-parametric response functions have been shown to improve the predictive performance of SDMs in these tasks in single species models.Here, we propose JSDMs where the responses to environmental covariates are modeled with additive multivariate Gaussian processes coded as linear models of coregionalization. These allow inference for wide range of functional forms and interspecific correlations between the responses. We propose also an efficient approach for inference with Laplace approximation and parameterization of the interspecific covariance matrices on the euclidean space. We demonstrate the benefits of our model with two small scale examples and one real world case study. We use cross-validation to compare the proposed model to analogous semi-parametric single species models and parametric single and joint species models in interpolation and extrapolation tasks. The proposed model outperforms the alternative models in all cases. We also show that the proposed model can be seen as an extension of the current state-of-the-art JSDMs to semi-parametric models.MSC 2010 subject classifications: Primary 60G15, 60K35; secondary 62P12.

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The number of cormorants has rapidly increased in the northernmost Baltic Sea. In 2018, 50 km × 50 km ICES catch rectangle 55H1 had 3140 breeding pairs. To estimate the predation effect of cormorants on perch populations, we Passive Integrated Tags tagged 1977 perch and 9.9% of tags were found. The median instantaneous cormorant-induced mortality during the breeding time, with consumption by non-breeding individuals, was estimated at 0.23 and at 0.35 during the whole residing period. We estimated with a yeild-per-recruit model that the long-term maximum loss of perch yield of tagged sub-population would be at 80% probability interval 32–67%, and when extended to the entire 55H1, 10–33%, respectively. The cormorants’ share of the >2-year-old perch biomass and production would be 8%, while that of other natural mortality would be 63% and that of fishing 29% in 55H1. The yield-per-recruit-results should be interpreted as an estimate of maximum cormorant effect because the dependence of predation rate on prey density was not accounted for, and density-dependence of growth, mortality, and reproduction of perch could partly compensate the loss. The results indicate that high density of cormorants can reduce the perch stocks and catches locally.

We explore the mortality rate of disentangled sea trout in whitefish fishery using gillnets with a 35-43 mm bar length. The study was conducted during the main fishing seasons in the Gulf of Bothnia in the northern Baltic Sea. Overall 59.5% of the sea trout were alive at the end of a 2-7 day observation period following release from the gillnets. Altogether, 12.1% of the captured fish were found dead in gillnets and 28.4% died due to injuries during an extended observation period. The average length of the captured sea trout was 435 mm, indicating that the majority were spending their first or second year at sea. The proportion of the survived and not injured (no observed damage) fish was highest in larger fish, >450 mm. The injured and not injured fish died equally frequently. Post capture survival was not connected to the removal time from gillnets or type of observed injuries but to the type of entanglement. Most of the fish were entangled by a mesh around the body, which caused extensive scale loss and open sores on the skin. The smallest fish may have had internal wounds that were not registered in this study. These results can be used in fisheries management to estimate the mortality of multi-species gillnet fishing to sea trout populations in relation to management actions.

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