Biogeography Branch, Center for Coastal Monitoring and Assessment, National Oceanic and Atmospheric Administration, Silver Spring, Maryland, United States of America.
PLoS One. 2011;6(5):e20583. doi: 10.1371/journal.pone.0020583. Epub 2011 May 26.
Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5-300 m radii) surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT) and Maximum Entropy Species Distribution Modelling (MaxEnt). The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided 'outstanding' model predictions (AUC = >0.9) for three of five fish species. MaxEnt provided 'outstanding' model predictions for two of five species, with the remaining three models considered 'excellent' (AUC = 0.8-0.9). In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy) than BRT (68% map accuracy). We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support conservation prioritization in marine protected area design, zoning in marine spatial planning, and ecosystem-based fisheries management.
有效管理珊瑚礁生态系统的两个主要限制因素是缺乏海洋物种空间分布的信息和驱动分布模式的相互作用环境变量的数据。海洋遥感技术的进步，以及景观生态学和先进生态位模型技术的新颖结合，为可靠地对许多公里长的珊瑚礁生态系统中的海洋物种分布进行建模和制图提供了前所未有的机会。我们开发了一种多尺度方法，使用三维海底形态和跨架位置来预测五个常见加勒比鱼类物种的空间分布。从周围鱼类调查点的高分辨率水深测量中量化了景观地形，共使用了五个空间尺度（半径 5-300 米）。使用两种性能较高的机器学习算法：Boosted Regression Trees (BRT) 和最大熵物种分布建模 (MaxEnt) 评估了模型性能和地图准确性。两个最重要的预测因子是跨架的地理位置，其次是地形复杂度的度量。预测因子的贡献在物种之间有所不同，但在空间尺度上很少变化。BRT 为五个鱼类物种中的三个提供了“出色”的模型预测（AUC = >0.9）。MaxEnt 为五个物种中的两个提供了“出色”的模型预测，其余三个模型被认为是“优秀”的（AUC = 0.8-0.9）。相比之下，MaxEnt 的空间预测准确性明显更高（92%的地图准确性），而 BRT 的准确性较低（68%的地图准确性）。我们证明，通过对跨架地理位置和海底结构地形异质性之间的相互作用进行建模，可以对一系列关键鱼类物种进行可靠的空间预测。这种多尺度、分析性的方法是一种新的、具有成本效益的重要工具，可以准确划定重要鱼类栖息地，并支持海洋保护区设计、海洋空间规划中的分区以及基于生态系统的渔业管理中的保护优先级。
