鱼的栖息地:鱼类栖息地和康复至关重要

钓鱼对鱼类栖息地的影响

Peter j .奥斯特和理查德·w·兰顿

doi:https://doi.org/10.47886/9781888569124.ch13

Abstract. -1996 Magnuson-Stevens渔业保护和管理法案》规定,区域性渔业管理委员会必须指定必要的鱼类栖息地(EFH)为每一个物种,管理评估EFH渔业的影响,并在需要的地方为EFH开发保护措施。钓鱼的合成影响栖息地产生帮助渔业管理委员会在评估捕鱼活动的影响。广泛的研究了,钓鱼的栖息地(即报道的影响。,structural habitat components, community structure, and ecosystem processes) for a diversity of habitats and fishing gear types. Commonalities of all studies included immediate effects on species composition and diversity and a reduction in habitat complexity. Studies of acute effects were found to be a good predictor of chronic effects. Recovery after fishing was more variable depending on habitat type, life history strategy of component species, and the natural disturbance regime. The ultimate goal of gear impact studies should not be to retrospectively analyze environmental impacts but ultimately to develop the ability to predict outcomes of particular management regimes. Synthesizing the results of these studies into predictive numerical models is not currently possible. However, conceptual models can coalesce the patterns found over the range of observations and can be used to predict effects of gear impacts within the framework of current ecological theory. Initially, it is useful to consider fishes’ use of habitats along a gradient of habitat complexity and environmental variability. Such considerations can be facilitated by a model of gear impacts on a range of seafloor types based on changes in structural habitat values. Disturbance theory provides the framework for predicting effects of habitat change based on spatial patterns of disturbance. Alternative community state models and type 1–type 2 disturbance patterns may be used to predict the general outcome of habitat management. Primary data are lacking on the spatial extent of fishing-induced disturbance, the effects of specific gear types along a gradient of fishing effort, and the linkages between habitat characteristics and the population dynamics of fishes. Adaptive and precautionary management practices will therefore be required until empirical data become available for validating model predictions.