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

自世纪初以来，渔业科学的发展是基于这样一种假设，即渔获量与潜在的鱼类丰度和捕捞活动（努力）的数量成正比。这导致了CPUE（单位努力量渔获量）作为丰度指数的广泛使用。到了世纪末，人们普遍认识到，由于鱼类聚集，渔获量和丰度之间缺乏比例关系。然而，通常不调查渔获量和努力量这两个已知数量之间的比例。在为更详细的种群评估模型将努力量转换为捕捞死亡率时，也隐含着一种比例关系。循证管理需要尽可能准确的丰度信息，但渔获量、努力量和丰度之间复杂和不成比例的关系可能会破坏这种准确性。我实验室正在进行的使命是确定行为和统计效应对商业捕捞量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。 我实验室以前的工作表明，当地的CPUE可能会受到鱼类捕捞者的行为的影响，这些捕捞者对周围地区的丰富度做出反应，或者在多物种渔业中选择性地追求一个（或几个物种）。船舶之间的竞争性干扰可能导致效率下降，因为船队总努力增加。这些影响的影响可能因鱼和捕鱼者的空间分布而异。不考虑观测误差的标准统计方法可能会导致明显的不确定性，即使它们不存在。一般来说，我们预期，从单套（拖网、捕网、刺网等）的角度来看，渔获量和努力量由于统计和行为的原因似乎不成比例。通常报告的年度汇总摘要。为了理清渔获量和努力量之间的关系，我的团队将采用各种定量模型和方法（统计、行为、经济、模拟）与商业渔业数据相结合，以便1）确定渔获量和努力量的基本比例，2）调整渔获量的合理性，3）确定渔获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）评估这些现象对种群评估和渔业可持续性的潜在影响。这项研究将改变在养护和管理中对捕捞努力量的使用，提高我们发现商业渔业可持续性威胁的能力。学生（5个硕士，1名博士）支持这项研究将发展技能，毕业后立即参与渔业研究，收获管理和保护。

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.