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具有不同程度的复杂性，偏见和不确定性的各种来源。因此，分析技术不断发展以应对这些挑战，以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），并提供了一系列信息源，包括鱼类跟踪数据，环境数据和视觉调查，以识别佛罗里达州海洋鱼类的潜在产卵聚集点（Trachinotus falcatus）按键 认识到每个信息源中潜在的互补性和一定程度的不确定性，我们应用了监督（经典和条件随机森林； RF）和无监督（模糊k-手段; FKM）算法。这两个RF模型具有相似的预测性能，但是生成了不同的预测变量重要性结构和产卵站点预测。使用FKM的无监督聚类可确定独特的站点分组，这些站点分组类似于用RF标识的可能的产卵站点。总产卵鱼类物种的保护在很大程度上取决于关键产卵地点的保护；这些潜在地点中有许多是在佛罗里达礁岛上确定的许可证地点，其中包括相对较高的平均许可居住期的深水天然和人工礁石。多种机器学习算法的应用使各种信息源得以集成，从而开发出生态系统的模型。面对日益复杂和多样化的数据源，生态学家