Responses of coastal fishery resources to rapid environmental changes

Abstract

Coastal systems experience strong impacts of ongoing environmental change, affecting fish communities and subsequently fishery yields. In the Baltic Sea, the combined effects of climate‐induced changes and eutrophication‐related pressures constitute major threats to its living resources. Although much work has been devoted to uncovering environmental impacts on the commercially most valuable fish stocks, only little is known about community‐wide responses of fished species and how environmental change may affect their yield. In this study, the authors use a joint species distribution modelling framework to disentangle environmental impacts on species‐specific fishery yields of 16 fished species along the coast of Finland over four decades. The authors show that environmental covariates substantially contributed to variations in fishery yields and are likely to have strong impacts on fished resources also in the future. Salinity and near‐bottom oxygen concentration emerged as the strongest environmental drivers of yields at the community level, whereas temperature was particularly important for cod (Gadus morhua) and sprat (Sprattus sprattus) yields. The authors found shore density to be an important predictor for fisheries resources especially for freshwater fish. The results of this study suggest that the changes in environmental conditions during the past four decades had a positive effect on the yields of freshwater and warm‐affinity species, whereas yields of marine cold‐affinity species have been mainly negatively affected by contracting favourable habitats, becoming warmer and less saline.

Macrosystem community change in lake phytoplankton and its implications for diversity and function

“…Before the analysis, we ln-transformed total phosphorous concentrations, water colour, lake area and retention days to achieve a more homogeneous distribution of the variables. To account for temporal trends in community trajectories not explained by the included covariates, we also added year as a linear fixed effect (Peltonen & Weigel, 2022). We assumed that species niches might include their optimum at intermediate values of physico-chemical variables, hence we implemented these covariates as quadratic response functions (Antão et al, 2022;Weigel et al, 2021) patterns, we included random effects at three spatial scales: site, nested in watershed, nested in river basin.…”

“…The random effect of site was assumed to be spatially explicit and was implemented through the computationally efficient predictive Gaussian process for big spatial data (Tikhonov, Duan, et al, 2020 ). To account for the temporal stochasticity of the data, we also included year as random effect (Peltonen & Weigel, 2022 ). HMSC involves a hierarchical structure, examining how species responses to environmental covariates depend on species traits and phylogenetic relationships.…”

Stock Assessment of the Commercial Small Pelagic Fishes in the Beibu Gulf, the South China Sea, 2006–2020

“…The specific contents can be summarized as suspending fishing operations during the fishing moratorium, limiting national total fisheries production, and setting targets for negative growth in fishing capacity, respectively. Although these measures have been effective, frequent human activities (e.g., marine fishing or pollution) and climate change will inevitably have a strong impact on coastal fishery ecosystems due to ongoing environmental changes, affecting fish communities and subsequently affecting fishery yields [11]. Therefore, it is necessary to evaluate the effectiveness of past fishery management, which is crucial for the development of management measures that are more suitable to the current status of local fishery resources [12].…”