Canadian Journal of Fisheries and Aquatic Sciences, Ahead of Print.
Scale and hierarchy have received less attention in aquatic compared to terrestrial systems. Walleye (Sander vitreus) spawning habitat offers an opportunity to investigate scale’s importance. We estimated lake-, transect-, and quadrat-scale influences on nearshore walleye egg deposition in 28 Minnesota lakes from 2016–2018. Random forest models (RFM) estimated importance of predictive variables to walleye egg deposition. Predictive accuracies of a multi-scale classification tree (CT) and a quadrat-scale CT were compared. RFM results suggested that five of our variables were unimportant when predicting egg deposition. The multi-scale CT was more accurate than the quadrat-scale CT when predicting egg deposition. Both model results suggest that in-lake egg deposition by walleye is regulated by hierarchical abiotic processes and that silt–clay abundance at the transect-scale (reef-scale) is more important than abundance at the quadrat-scale (within-reef). Our results show machine learning can be used for scale-optimization and potentially to determine cross-scale interactions. Further incorporation of scale and hierarchy into studies of aquatic systems will increase our understanding of species–habitat relationships, especially in lentic systems where multi-scale approaches are rarely used.

中文翻译：

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机器学习通过产生大眼鱼应用于静息栖息地，展示了在水生研究中考虑多个空间尺度的好处

加拿大渔业和水生科学杂志，印刷前。
与陆地系统相比，水生系统的规模和等级受到的关注较少。Walleye (Sander vitreus) 产卵栖息地提供了研究规模重要性的机会。我们估计了 2016 年至 2018 年明尼苏达州 28 个湖泊中湖泊、横断面和样方尺度对近岸角膜白斑卵沉积的影响。随机森林模型 (RFM) 估计了预测变量对角膜白斑卵沉积的重要性。比较了多尺度分类树 (CT) 和样方尺度 CT 的预测精度。RFM 结果表明，我们的五个变量在预测卵子沉积时并不重要。在预测卵子沉积时，多尺度 CT 比样方尺度 CT 更准确。两个模型结果都表明，角膜白斑的湖内卵沉积受分级非生物过程的调节，样带尺度（珊瑚礁尺度）的淤泥-粘土丰度比样方尺度（礁内）的丰度更重要。我们的结果表明机器学习可用于规模优化，并可能用于确定跨规模交互。将尺度和等级进一步纳入水生系统研究将增加我们对物种 - 栖息地关系的理解，特别是在很少使用多尺度方法的静息系统中。