Fishing and Global Warming Affect Fish Spatial Dynamics and Sustainability

Prof. Chih-Hao Hsieh (謝志豪) and his master’s student Jheng-Yu Wang (王政喻) from NTU’s Institute of Oceanography together with Assistant Prof. Ting-Chun Kuo (郭庭君) of National Taiwan Ocean University (NTOU)’s Institute of Marine Affairs and Resource Management, found that fishing-induced age truncation and warming temperatures drove marine fishes into uneven spatial distribution, weakening their sustainability. This study, published in Nature Communications (May 26), provided empirical evidence for long-lasting hypotheses explaining complex mechanisms underlying spatial distribution of marine fishes. These findings bear important implications for fisheries management.

Populations with a more uniform spatial distribution (i.e., lower spatial variability) have better bet-hedging capacity to withstand environmental variability. The spatial structure of a population is usually not stationary and can constantly change over time. Theories suggest that population spatial structure adjusts in response to changes of several factors. For example, age composition of a population can affect its spatial structure because different age classes will occupy different habitats according to their own living requirements; thus, changes in age structure would alter population spatial structure. In addition, populations may expand or shrink their spatial distribution following increased or decreased abundance to have balanced living conditions. Also, environmental change plays a role in influencing spatial structure via inducing varying living conditions in space and driving movement of a population for adapting to changing environments.

While theories have suggested that population age structure, abundance, and environmental change may affect population spatial structure, clear empirical evidence for the causal effects is lacking. Existing analyses on the determining factors of species spatial structure are generally based on linear approaches, which can yield misleading results as correlation does not necessarily imply causality. This study provided empirical evidence for the aforementioned causal relationships using long-term fish survey data and applying nonlinear dynamical approaches called empirical dynamic modeling (EDM). These methods, in contrast to linear approaches, can distinguish causality from correlation by depicting underlying mechanisms of a dynamical system. The research team applied these methods to quantitatively measure causal relationships, identifying both common and species-specific patterns.

The team used a 25-year spatiotemporal data from the North Sea to examine if population spatial variability responds to changing age diversity, abundance, and environmental conditions for nine exploited fish species. The results showed that population spatial variability changed over time in response to variations in age diversity, abundance, or environmental conditions for all study species. More importantly, the team found that reduced age diversity, warming and spatial-varying temperatures could elevate population spatial variability. However, reduced abundance could either increase or decrease population spatial variability, which was probably regulated by the aggregation tendency of a species. These results suggested that fish populations might be more unevenly distributed in space under heavily fishing pressure, as fishing could change age structure and reduce population abundance. In addition, global warming also enhances population spatial variability, making the populations more vulnerable to environmental variability. This study implied the potential double jeopardy of fishing activities and environmental changes on fish populations, highlighting the importance of considering spatial dynamics in fisheries management.