The impact of ecological dynamics and statistical properties in fisheries data on the sustainability of fish populations and harvest

自20世纪初以来，渔业科学从捕捞量与潜在鱼类丰度和用于捕捞它们的捕捞活动（努力）成正比的假设发展而来。这导致了广泛使用CPUE（每单元捕获次数）作为索引丰度。到20世纪末，人们普遍认识到，由于鱼类聚集，渔获量与丰度之间缺乏相称性。但是，通常不调查捕获量和努力量这两个已知量之间的比例关系。在为更详细的人口评估模型将努力量转换为捕捞死亡率时，也隐含着比例关系。基于证据的管理需要尽可能准确的丰度信息，但捕获量、努力量和丰度之间复杂且不成比例的关系会破坏这种准确性。我的实验室正在进行的任务是确定行为和统计效应对商业捕捞和努力量之间观察到的关系的影响，以及它们对鱼类丰度估计的影响。我实验室之前的工作表明，当地的CPUE可能会受到渔获者的行为的影响，他们对周围地区的丰度做出反应，或者在多物种渔业中选择性地追求一个（或几个）物种。随着船队总努力的增加，船舶之间的竞争性干扰会导致效率的下降。这些影响的影响可能因鱼类和鱼类采集者的空间分布而异。标准的统计方法没有考虑到努力中的观察误差，即使在不存在歧化的情况下也会导致明显的歧化。一般来说，我们预计捕获量和努力量由于统计和行为原因似乎不成比例，从单套（拖网、陷阱、刺网等）的水平到通常报告的年度汇总摘要。为了理清跨尺度捕捞量和努力量之间的关系，我的团队将对商业渔业数据采用各种定量模型和方法（统计、行为、经济、模拟），以便1)确定捕捞量和努力量的潜在比例，2)调整不成比例的捕捞标准化，3)确定捕捞量和努力量数据系列中的因果关系和复杂性，4)预测空间选择性捕捞对鱼类生产力的影响。5)评价这些现象对种群评估和渔业可持续性的潜在影响。这项研究将改变渔业资源的保护和管理方式，提高我们发现商业渔业可持续性威胁的能力。支持这项研究的学生（5名硕士，1名博士）将培养毕业后立即参与渔业研究，收获管理和保护的技能。

Since the early 20th century fisheries science has developed from the assumption that catch is proportional to both the underlying fish abundance and the amount of fishing activity (effort) applied to pursue them. This led to the widespread use of CPUE (catch-per-unit-effort) as an index abundance. By the end of 20th century the lack of proportionality between catch and abundance, due to fish aggregation, was broadly appreciated. However, the proportionality between catch and effort, the two known quantities, was not usually investigated. A proportional relationship was also implicit when converting effort into fishing mortality for more detailed population assessment models. Evidence based management requires the most accurate abundance information possible, but complex and disproportionate relationships between catch, effort, and abundance can undermine this accuracy. My lab's ongoing mission is to establish the impact of behavioural and statistical effects on the observed relationship between commercial catch and effort and their influence on estimates of fish abundance. Previous work in my lab has shown that local CPUE can be biased by the behaviour of fish harvesters who react to abundance in surrounding areas or who selectively pursue one (or a few species) in a multispecies fishery. Competitive interference among vessels can result in a decline in efficiency as total fleet effort increases. The impact of these influences may vary with the spatial distribution of fish and fish harvesters. Standard statistical methods that do not account for observation error in effort can result in apparent disproportionalities even where they do not exist. Generally, we expect catch and effort to appear to be disproportionate for statistical as well as behavioural reasons, from the level of single sets (trawls, traps, gillnets, etc.) to the aggregated annual summaries typically reported. To disentangle the relationship between catch and effort across scales, my group will employ a variety of quantitative models and methods (statistical, behavioural, economic, simulation) to commercial fisheries data in order to 1) determine the underlying proportionality of catch and effort, 2) adjust catch standardizations for disproportionalities, 3) identify causality and complexity in catch and effort data series, 4) predict the impact of spatially selective fishing on fish productivity, and 5) evaluate the potential effect of these phenomena on population assessments and fishery sustainability. This research will transform the use of fishing effort in conservation and management, improving our ability to detect threats to sustainability in commercial fisheries. The students (5 M.Sc., 1 Ph.D.) supporting this research will develop skills to immediately participate in fisheries research, harvest management, and conservation upon graduation.