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，渔业科学从这样一种假设发展而来，即渔获量与潜在鱼类丰度和捕捞活动(努力)的数量成正比。这导致了CPUE(每单位努力捕获量)作为丰度指标的广泛使用。到20世纪末，由于鱼类聚集，捕获量和丰度之间缺乏相称性，这一点得到了广泛的认可。然而，渔获量和努力量这两个已知数量之间的比例通常不会被调查。在将努力转化为捕捞死亡率以用于更详细的种群评估模型时，比例关系也是隐含的。基于证据的管理需要尽可能准确的丰富信息，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。我的实验室的持续任务是确定行为和统计效应对观察到的商业渔获量和努力之间的关系的影响，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可能会受到对周围地区丰度做出反应或选择性地在多物种渔业中捕捞一种(或几种)鱼类的渔民的行为的影响。随着船队总努力的增加，船只之间的竞争干扰可能会导致效率下降。这些影响的影响可能会因鱼类和捕鱼机的空间分布而有所不同。没有考虑努力中的观测误差的标准统计方法可能会导致明显的不相称性，即使在不存在的情况下也是如此。一般而言，我们预计，从单套渔获量(拖网、诱捕器、刺网等)的水平来看，由于统计和行为原因，捕获量和努力似乎不成比例。到通常报告的年度汇总摘要。为了弄清渔获量和捕捞努力之间的跨尺度关系，我的小组将对商业渔业数据使用各种量化模型和方法(统计、行为、经济、模拟)，以便1)确定渔获量和捕捞努力的基本比例，2)调整捕捞不相称性的渔获量标准化，3)确定渔获量和捕捞活动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，以及5)评估这些现象对种群评估和渔业可持续性的潜在影响。这项研究将改变捕捞努力在养护和管理中的使用，提高我们发现商业渔业可持续性威胁的能力。学生(理科硕士5人，博士1人)支持这项研究将培养毕业后立即参与渔业研究、捕捞管理和养护的技能。

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.