A perspective on needed research, modeling, and management approaches that can enhance Great Lakes fisheries management under changing ecosystem conditions

Journal of Great Lakes Research(2016)

引用 14|浏览55
暂无评分
摘要
The Great Lakes Fishery Commission sponsored a 2-day workshop that sought to enhance the ability of Great Lakes agencies to understand, predict, and ideally manage fisheries production in the face of changes in natural and anthropogenic forcings (e.g., climate, invasive species, and nutrients). The workshop brought together 18 marine and freshwater researchers with collective expertise in aquatic ecology, physical oceanography, limnology, climate modeling, and ecosystem modeling, and two individuals with fisheries management expertise. We report on the outcome of a writing exercise undertaken as part of this workshop that challenged each participant to identify three needs, which if addressed, could most improve the ability of Great Lakes agencies to manage their fisheries in the face of ecosystem change. Participant responses fell into two categories. The first identified gaps in ecological understanding, including how physical and biological processes can regulate early life growth and survival, how life-history strategies vary across species and within populations, and how anthropogenic stressors (e.g., nutrient runoff, climate change) can interact to influence fish populations. The second category pointed to the need for improved approaches to research (e.g., meta-analytic, comparative, spatial translation) and management (e.g., mechanistic management models, consideration of multi-stock management), and also identified the need for improved predictive models of the physical environment and associated ecosystem monitoring programs. While some progress has been made toward addressing these needs, we believe that a continued focus will be necessary to enable optimal fisheries management responses to forthcoming ecosystem change.
更多
查看译文
关键词
Physical–biological coupling,Fish recruitment,Trophic state change,Climate change,Non-native species
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要