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首页> 外文期刊>ICES Journal of Marine Science >Modelling community structure and species co-occurrence using fishery observer data
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Modelling community structure and species co-occurrence using fishery observer data

机译：使用渔业观察员数据对社区结构和物种共生建模

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In this study, we modelled fishery observer data to compare methods of identifying community structure using cluster analyses to determine stratifications and probabilistic models for examining species co-occurrence in the Gulf of Mexico deepwater reef fish fishery. Comparing cluster analysis methods, the correlation measure of dissimilarity in combination with average agglomerative linkage was the most efficient method for determining species relationships using simulated random species as a comparison tool. Cluster analysis revealed distinct species stratifications and in combination with multiscale bootstrapping generated probabilities indicating the strength of stratifications in the fishery. A more parsimonious approach with probabilistic models was also developed to quantify pairwise species co-occurrence as random, positive, or negative based on the observed vs. expected fishing sets with co-occurrence. For the most common species captured, the probabilistic models predicted positive or negative co-occurrence between 84.2% of the pairwise combinations examined. These methods provide fishery managers tools for determining multispecies quota allocations and offer insights into other by catch species of interest.
机译：在这项研究中，我们建立了渔业观察者数据的模型，以比较使用聚类分析确定分层和概率模型来检验群落结构的方法，以检验墨西哥湾深水礁鱼渔业中物种共生的情况。与聚类分析方法相比，相异性的相关度量与平均聚集链接的组合是使用模拟随机物种作为比较工具确定物种关系的最有效方法。聚类分析显示了不同的物种分层，并结合了多尺度自举产生的概率，表明了在渔业中分层的强度。还开发了一种使用概率模型的更简化方法，以基于同时发生的观察到的和预期的捕鱼集将成对物种同时出现量化为随机，阳性或阴性。对于捕获的最常见物种，概率模型预测在检查的成对组合中有84.2％的阳性或阴性同时出现。这些方法为渔业管理人员提供了用于确定多物种配额分配的工具，并提供了对感兴趣的捕捞物种的了解。

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