Modelling spatial patterns in harbour porpoise satellite telemetry data using maximum entropy
Modelling spatial patterns in harbour porpoise satellite telemetry data using maximum entropy
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DOI：
10.1111/j.1600-0587.2009.05901.x
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发表时间：
2010-09-01
期刊：
影响因子：
5.9
通讯作者：
Soderkvist, Johan
中科院分区：
文献类型：
--
作者：
Edren, Susi M. C.;Wisz, Mary S.;Soderkvist, Johan
人们对欧盟水域的港鼠海豚分布知之甚少，对它们分布的建模预测可以为未来海洋环境开发的战略空间规划提供信息，以避免潜在的冲突。我们使用基于最大熵的建模工具：Maxent分析了丹麦内陆水域39只港鼠海豚Phocoena Phocoena的卫星遥测数据。Maxent不需要缺席数据，并且已被证明对小样本量、抽样偏差和位置误差特征的数据有效。对于每个季节，我们使用迭代自举程序从39个标记个体中随机选择最精确的记录，并基于假设可作为港湾鼠海豚猎物丰度良好代理的解释性环境变量对汇总记录进行Maxent。在我们的环境变量中，海岸距离和底部盐度最有解释力，它们的响应形状在大多数季节相对一致。模型的预测能力（由ROC-AUC评估）在季节范围内为0.70至0.86。据预测，卡特加特南部、带海、波罗的海大部分西部和海湾在不同季节发生的可能性相对较高。相比之下，卡特加特中部和波罗的海的南部和东部的利姆和达斯岭始终显示出低概率的发生。发生概率最低的地区通常具有较高的预测不确定性。我们的方法对陆地和海洋环境中卫星标记动物的分析具有启示意义。通过将引导程序与Maxent相结合，我们规避了卫星遥测数据带来的一些统计挑战，以生成丹麦内部水域的空间预测。
The distribution of harbour porpoises in EU waters is poorly understood, and modelled predictions of their distributions could inform the strategic spatial planning of future exploitation of the marine environment to avoid potential conflicts. We analysed satellite telemetry data from 39 harbour porpoises Phocoena phocoena in inner Danish waters using a modelling tool rooted in maximum entropy: Maxent. Maxent does not require absence data and has been shown to be effective for data characterised by small sample size, sampling bias and locational errors. For each season we used an iterative bootstrapping procedure to randomly select among the most precise records from each of the 39 tagged individuals, and ran Maxent on pooled records based on explanatory environmental variables hypothesised to serve as good proxies for harbour porpoise prey abundance. Among our environmental variables, distance to coast and bottom salinity had the most explanatory power, and their response shapes were relatively consistent across most seasons. The predictive power of the models (assessed by ROC-AUC) ranged from 0.70 to 0.86 within seasons. The southern Kattegat, the Belt Seas, most western part of the Baltic Sea and the Sound were predicted to have relatively high probabilities of occurrence across seasons. In contrast, the central part of Kattegat and the Baltic Sea south and east of Limhamn and Darss Ridge consistently showed low probabilities of occurrence. Areas with the lowest probabilities of occurrence were generally characterised by high predictive uncertainty. Our methods have implications for the analyses of satellite tagged animals in terrestrial and marine environments. By coupling a bootstrapping procedure with Maxent we circumvented some of the statistical challenges presented by satellite telemetry data to generate spatial predictions within the inner Danish waters.