USGS Patuxent Wildlife Research Center, 12100 Beech Forest Rd., Laurel, Maryland 20708, USA.
Ecol Appl. 2012 Oct;22(7):1962-72. doi: 10.1890/11-1936.1.
The ability to accurately predict patterns of species' occurrences is fundamental to the successful management of animal communities. To determine optimal management strategies, it is essential to understand species-habitat relationships and how species habitat use is related to natural or human-induced environmental changes. Using five years of monitoring data in the Chesapeake and Ohio Canal National Historical Park, Maryland, USA, we developed four multispecies hierarchical models for estimating amphibian wetland use that account for imperfect detection during sampling. The models were designed to determine which factors (wetland habitat characteristics, annual trend effects, spring/summer precipitation, and previous wetland occupancy) were most important for predicting future habitat use. We used the models to make predictions about species occurrences in sampled and unsampled wetlands and evaluated model projections using additional data. Using a Bayesian approach, we calculated a posterior distribution of receiver operating characteristic area under the curve (ROC AUC) values, which allowed us to explicitly quantify the uncertainty in the quality of our predictions and to account for false negatives in the evaluation data set. We found that wetland hydroperiod (the length of time that a wetland holds water), as well as the occurrence state in the prior year, were generally the most important factors in determining occupancy. The model with habitat-only covariates predicted species occurrences well; however, knowledge of wetland use in the previous year significantly improved predictive ability at the community level and for two of 12 species/species complexes. Our results demonstrate the utility of multispecies models for understanding which factors affect species habitat use of an entire community (of species) and provide an improved methodology using AUC that is helpful for quantifying the uncertainty in model predictions while explicitly accounting for detection biases.
准确预测物种出现模式对于成功管理动物群落至关重要。为了确定最佳管理策略，了解物种-栖息地关系以及物种栖息地利用与自然或人为环境变化的关系至关重要。我们使用美国马里兰州切萨皮克和俄亥俄运河国家历史公园五年的监测数据，开发了四个用于估计两栖动物湿地利用的多物种层次模型，这些模型考虑了采样过程中的不完全检测。这些模型旨在确定哪些因素（湿地栖息地特征、年度趋势效应、春夏降水和以前的湿地占有）对预测未来栖息地利用最重要。我们使用这些模型来预测在采样和未采样湿地中物种的出现情况，并使用额外的数据评估模型预测。使用贝叶斯方法，我们计算了接收者操作特征曲线下面积（ROC AUC）值的后验分布，这使我们能够明确量化预测质量的不确定性，并在评估数据集时考虑到假阴性。我们发现，湿地的水期（湿地保持水的时间长度）以及前一年的出现状态通常是决定占有状态的最重要因素。仅具有栖息地协变量的模型可以很好地预测物种的出现情况；然而，上一年湿地利用情况的知识显著提高了社区水平和 12 个物种/物种复合体中的两个的预测能力。我们的研究结果表明，多物种模型对于理解哪些因素影响整个群落（物种）的物种栖息地利用具有实用性，并提供了一种使用 AUC 来量化模型预测不确定性的改进方法，同时明确考虑了检测偏差。
