Torres Leigh G, Read Andrew J, Halpin Patrick
Duke University Marine Laboratory, Nicholas School of the Environment and Earth Sciences, 135 Duke Marine Lab Road, Beaufort, North Carolina 28516, USA.
Ecol Appl. 2008 Oct;18(7):1702-17. doi: 10.1890/07-1455.1.
Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.
捕食者和猎物会根据彼此、资源和避难所的可利用性以及它们相互作用的时间和空间尺度进行自我分布。捕食者分布的预测模型通常依靠这些关系，通过纳入环境变异性和猎物可利用性的数据来确定捕食者的栖息地选择模式。这种预测建模方法在海洋系统中是适用的，因为在海洋系统中对捕食者进行观测在后勤上存在困难，这凸显了准确模型的必要性。在本文中，我们探讨在美国佛罗里达州佛罗里达湾，将猎物分布数据纳入宽吻海豚（Tursiops truncatus）栖息地选择的精细尺度预测模型中是否能提高预测能力。在顶级海洋捕食者的栖息地模型中，环境特征常被用作预测变量，其假设是这些环境特征可作为猎物分布的替代指标。我们通过比较海豚分布和鱼类捕获率对相同环境变量的反应来检验这一假设的有效性。接下来，测试四个模型（有无猎物分布数据）的预测能力，以确定是否无需描述猎物分布就能预测海豚的栖息地选择。最终分析确定基于显著环境特征对高鱼类捕获区域进行建模所生成的海豚分布预测图的准确性。我们使用空间分析和独立数据集来训练和测试模型。我们的结果表明，由于栖息地高度异质性和猎物斑块的空间变异性，如果将环境变量用作捕食者分布的预测变量，而不是依赖猎物数据作为解释变量，沿海栖息地海豚栖息地选择的精细尺度模型会更成功。然而，基于环境变异性将猎物分布作为响应变量进行预测建模，确实对海豚栖息地选择，尤其是觅食栖息地，产生了较高的预测性能。
