Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, 1125 Colonel by Drive, Ottawa, ON, K1S 5B6, Canada.
Department of Biology, Dalhousie University, 1355 Oxford Street, Halifax, NS, B4H 4R2, Canada.
Oecologia. 2020 Oct;194(1-2):283-298. doi: 10.1007/s00442-020-04753-2. Epub 2020 Oct 1.
Information on ecological systems often comes from diverse sources with varied levels of complexity, bias, and uncertainty. Accordingly, analytical techniques continue to evolve that address these challenges to reveal the characteristics of ecological systems and inform conservation actions. We applied multiple statistical learning algorithms (i.e., machine learning) with a range of information sources including fish tracking data, environmental data, and visual surveys to identify potential spawning aggregation sites for a marine fish species, permit (Trachinotus falcatus), in the Florida Keys. Recognizing the potential complementarity and some level of uncertainty in each information source, we applied supervised (classic and conditional random forests; RF) and unsupervised (fuzzy k-means; FKM) algorithms. The two RF models had similar predictive performance, but generated different predictor variable importance structures and spawning site predictions. Unsupervised clustering using FKM identified unique site groupings that were similar to the likely spawning sites identified with RF. The conservation of aggregate spawning fish species depends heavily on the protection of key spawning sites; many of these potential sites were identified here for permit in the Florida Keys, which consisted of relatively deep-water natural and artificial reefs with high mean permit residency periods. The application of multiple machine learning algorithms enabled the integration of diverse information sources to develop models of an ecological system. Faced with increasingly complex and diverse data sources, ecologists, and conservation practitioners should find increasing value in machine learning algorithms, which we discuss here and provide resources to increase accessibility.
有关生态系统的信息通常来自来源多样、复杂程度不同、存在偏差和不确定性的信息源。因此，分析技术在不断发展，以应对这些挑战，揭示生态系统的特征，并为保护行动提供信息。我们应用了多种统计学习算法（即机器学习），并结合了一系列信息源，包括鱼类跟踪数据、环境数据和视觉调查，以确定佛罗里达群岛中一种海洋鱼类（黄鳍鲷）的潜在产卵聚集区。我们认识到每个信息源都存在潜在的互补性和一定程度的不确定性，因此应用了监督（经典和条件随机场；RF）和无监督（模糊 K 均值；FKM）算法。两种 RF 模型的预测性能相似，但生成了不同的预测变量重要性结构和产卵点预测。使用 FKM 的无监督聚类识别出了独特的站点分组，这些分组与 RF 识别出的可能产卵点相似。聚集产卵鱼类的保护在很大程度上依赖于关键产卵地的保护；在这里，为黄鳍鲷确定了许多潜在的产卵地，这些潜在的产卵地位于佛罗里达群岛的相对深水区，包括具有高平均黄鳍鲷居留期的天然和人工礁石。应用多种机器学习算法可以整合多种信息源来开发生态系统模型。面对日益复杂和多样化的数据源，生态学家和保护从业者应该会发现机器学习算法越来越有价值，我们在此讨论了这些算法，并提供了资源以提高其可访问性。
