Environmental controls on the global distribution of shallow-water coral reefs
Environmental controls on the global distribution of shallow-water coral reefs
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DOI：
10.1111/j.1365-2699.2012.02706.x
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发表时间：
2012-08-01
影响因子：
3.9
通讯作者：
Hendy, Erica J.
中科院分区：
文献类型：
--
作者：
Couce, Elena;Ridgwell, Andy;Hendy, Erica J.
目的阐明珊瑚礁的环境极限对于预测未来气候变化对这些生态系统及其全球分布的影响至关重要。物种分布模型的最新发展和全面的全球环境数据集的提供，为重新评估控制全球范围内珊瑚礁分布的环境因素以及比较不同的物种分布模型技术的性能提供了机会。地理位置全球浅水区。方法SDM方法采用最大熵(Maxent)和两种有无方法：分类回归树(CART)和增强回归树(BRT)。考虑的预测变量包括海表面温度(SST)、盐度、文石饱和状态(OArag)、营养物质、辐照度、水透明度、沙尘、海流速度和气旋活动强度。对于许多变量，在每周、每月和每年的平均时间尺度上，同时考虑了平均值和标准偏差。所有这些都被转换到一个1度x 1度的全球网格中，以生成珊瑚礁概率图，以便与已知位置进行比较。根据受试者工作特征(ROC)曲线和曲线下面积(AUC)分数来比较模型的性能。通过测试数据上的假阳性和阴性错误的错误分类图来探索潜在的地理偏差。结果增强回归树始终优于其他方法，尽管Maxent的表现也是可以接受的。主要的环境预测因子是温度变量(年平均SST和月、周最低SST)，其次是区域之间的相对重要性、营养物质、光有效性和OArag。在主要珊瑚省之间没有发现SDM表现的系统性偏差，但对于包含边缘非珊瑚礁形成珊瑚群落的单元，例如百慕大，更有可能出现假阴性。主要结论BRT和Maxent模型之间的一致性为在与全球气候模型相关的空间尺度(c.1度x 1度)探索珊瑚礁生态系统的环境界限提供了预测性信心。虽然与SST有关的变量在珊瑚礁分布模型中占主导地位，但营养物质、OArag和光照对开发巴哈马群岛、南太平洋和珊瑚三角等区域的珊瑚礁存在模型至关重要。SST驱动的概率在低温下的陡峭响应表明，珊瑚礁栖息地的纬向扩张对全球变暖非常敏感。
Aim Elucidating the environmental limits of coral reefs is central to projecting future impacts of climate change on these ecosystems and their global distribution. Recent developments in species distribution modelling (SDM) and the availability of comprehensive global environmental datasets have provided an opportunity to reassess the environmental factors that control the distribution of coral reefs at the global scale as well as to compare the performance of different SDM techniques. Location Shallow waters world-wide. Methods The SDM methods used were maximum entropy (Maxent) and two presence/absence methods: classification and regression trees (CART) and boosted regression trees (BRT). The predictive variables considered included sea surface temperature (SST), salinity, aragonite saturation state (OArag), nutrients, irradiance, water transparency, dust, current speed and intensity of cyclone activity. For many variables both mean and SD were considered, and at weekly, monthly and annually averaged time-scales. All were transformed to a global 1 degrees x 1 degrees grid to generate coral reef probability maps for comparison with known locations. Model performance was compared in terms of receiver operating characteristic (ROC) curves and area under the curve (AUC) scores. Potential geographical bias was explored via misclassification maps of false positive and negative errors on test data. Results Boosted regression trees consistently outperformed other methods, although Maxent also performed acceptably. The dominant environmental predictors were the temperature variables (annual mean SST, and monthly and weekly minimum SST), followed by, and with their relative importance differing between regions, nutrients, light availability and OArag. No systematic bias in SDM performance was found between major coral provinces, but false negatives were more likely for cells containing marginal non-reef-forming coral communities, e.g. Bermuda. Main conclusions Agreement between BRT and Maxent models gives predictive confidence for exploring the environmental limits of coral reef ecosystems at a spatial scale relevant to global climate models (c. 1 degrees x 1 degrees). Although SST-related variables dominate the coral reef distribution models, contributions from nutrients, OArag and light availability were critical in developing models of reef presence in regions such as the Bahamas, South Pacific and Coral Triangle. The steep response in SST-driven probabilities at low temperatures indicates that latitudinal expansion of coral reef habitat is very sensitive to global warming.