ABSTRACT: Understanding diet flexibility is important for resource management as climate change alters ecological communities. However, food web complexity often limits our ability to predict how changes in prey communities may alter predator diets. Stomach content and stable isotope analyses are traditionally used to evaluate trophic interactions, but costs and logistical constraints can limit their efficacy. Using boosted regression tree (BRT) models, we predicted how juvenile bull shark Carcharhinus leucas diets respond to shifts in potential prey communities using patterns of shark and prey distributions, and size-based differences in shark gape widths. BRT models were based on bull shark diets from published literature and long-term monitoring of sharks and prey in a coastal estuary in the western Gulf of Mexico. In situ diet data were used to test model accuracy, which revealed that BRT models effectively predicted the most abundant prey families in the diets of bull sharks (Sciaenidae: ~37%; Ariidae: ~34%), with Pearson’s correlation rates as high as 0.778 for predictions and in situ diet data. Inaccuracies were evident for rarer prey families (e.g. Mugilidae), which was attributed to monitoring limitations, elucidating how BRT models can be improved before future application. High model accuracy suggests BRTs may serve as an appropriate complement to stomach content and stable isotope analyses when monitoring data of predators and potential prey are available. Such results are promising for reducing stressful or harmful sampling and broadening the applications of current monitoring programs used to assess changes in species densities and distributions, particularly for resource-limited management agencies.

中文翻译：

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增强回归树模型可预测亚热带河口的幼年公鲨的饮食

摘要：随着气候变化改变生态群落，了解饮食的灵活性对于资源管理至关重要。但是，食物网的复杂性通常会限制我们预测猎物群落变化可能如何改变捕食者饮食的能力。胃内容物和稳定的同位素分析通常用于评估营养相互作用，但是成本和后勤约束可能会限制其功效。使用增强回归树（BRT）模型，我们预测了幼年公牛鲨Carcharhinus leucas饮食使用鲨鱼和猎物的分布模式以及鲨鱼无隙宽度的大小差异来响应潜在猎物群落的变化。BRT模型基于公开文献中的公牛鲨鱼饮食以及对墨西哥湾西部沿海河口的鲨鱼和猎物的长期监测。原位饮食数据用于检验模型的准确性，这表明BRT模型有效地预测了公鲨饮食中最丰富的猎物家族（（科：〜37％；;科：〜34％），皮尔森的相关率高达0.778用于预测和就地饮食数据。稀有猎物家族（例如，Mugilidae）的不准确性很明显，这归因于监测的局限性，阐明了在未来应用之前如何改进BRT模型。较高的模型准确性表明，当有捕食者和潜在猎物的监测数据可用时，BRT可以作为胃含量和稳定同位素分析的适当补充。这些结果有望减少压力或有害采样，并扩大目前用于评估物种密度和分布变化的监测计划的应用，特别是对于资源有限的管理机构而言。