Machine learning highlights the importance of primary and secondary production in determining habitat for marine fish and macroinvertebrates
Machine learning highlights the importance of primary and secondary production in determining habitat for marine fish and macroinvertebrates
复制标题
机器学习强调初级和次级生产在确定海洋鱼类和大型无脊椎动物栖息地方面的重要性
DOI：
10.1002/aqc.3527
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发表时间：
2021
影响因子：
2.4
通讯作者：
Kisei R. Tanaka
中科院分区：
文献类型：
--
作者：
K. Friedland;Michelle Bachman;A. Davies;R. Frelat;M. C. McManus;R. Morse;B. Pickens;S. Smoliński;Kisei R. Tanaka
海洋生物的物种分布模型越来越多地用于空间规划、保护和渔业管理等一系列应用。这些模型是使用各种数学形式构建的，并利用物理和生物独立变量；然而，所谓的第一代模型主要遵循线性模型或平滑样条的形式，由鱼类调查中收集的数据提供信息。在一系列用机器学习方法建立的物种发生模型中，测试了不同类别变量的性能，具体评估了低营养级数据的潜在贡献。基于对美国东北大陆架鱼类和大型无脊椎动物的缺失/存在分类，拟合了随机森林模型。潜在变量包括物理变量、初级生产变量、次级生产变量和地形变量。对于可接受的模型拟合，计算了六个变量重要性度量，它们共同表明物理和次要生产变量对所有模型的贡献最大。相反，地形变量对这些模型的贡献最小。考虑到所有性能指标的多变量分析强化了水深和温度在确定物种存在和不存在方面的作用；叶绿素浓度和一些特定的浮游动物类群，如绿媒和paracalanus parvus，也有重要的贡献，且具有强烈的季节变化。我们的研究结果表明，较低营养水平的变量，如果可用，是有价值的物种分布模型的海洋生物的创建。
Species distribution models for marine organisms are increasingly used for a range of applications, including spatial planning, conservation, and fisheries management. These models have been constructed using a variety of mathematical forms and drawing on both physical and biological independent variables; however, what might be called first‐generation models have mainly followed the form of linear models, or smoothing splines, informed by data collected in the context of fish surveys.The performance of different classes of variables were tested in a series of species occurrence models built with machine learning methods, specifically evaluating the potential contribution of lower trophic level data. Random forest models were fitted based on the classification of the absence/presence for fish and macroinvertebrates surveyed on the US Northeast Continental Shelf.The potential variables included physical, primary production, secondary production, and terrain variables. For accepted model fits, six variable importance measures were computed, which collectively showed that physical and secondary production variables make the greatest contribution across all models. In contrast, terrain variables made the least contribution to these models.Multivariable analyses that account for all performance measures reinforce the role of water depth and temperature in defining species presence and absence; however, chlorophyll concentration and some specific zooplankton taxa, such asMetridia lucensandParacalanus parvus, also make important contributions with strong seasonal variations.Our results suggest that lower trophic level variables, if available, are valuable in the creation of species distribution models for marine organisms.