Explaining island‐wide geographical patterns of Caribbean fish diversity: A multi‐scale seascape ecology approach
Explaining island‐wide geographical patterns of Caribbean fish diversity: A multi‐scale seascape ecology approach
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解释加勒比鱼类多样性的全岛地理格局：多尺度海景生态学方法
DOI：
10.1111/maec.12434
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
S. Pittman
中科院分区：
文献类型：
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作者：
Linn Sekund;S. Pittman
珊瑚礁海域鱼类多样性的地理格局是由许多相互作用的环境变量在多个空间尺度上运行。确定一套解释鱼类多样性空间格局的变量对生态学至关重要，并为海洋保护的优先事项提供信息，特别是在保护生物多样性最高的珊瑚礁是主要目标的情况下。然而，传统的斑块内变量与周围海景的空间格局的相对重要性仍然不清楚的珊瑚礁鱼类生态。应用景观生态学中的多尺度海景方法，对不同空间尺度下的多种变量进行了量化和解释，包括：（i）来自野外数据的斑块内结构属性（5x 1平方米的取样单位面积）; ㈡海底地图上的海景几何图（10-50米半径海景单位）;和波曝光从水动力模型（240米分辨率）的251个珊瑚礁调查地点在美属维尔京群岛。使用单一分类和回归树（CART）和增强回归树（TreeNet）的集合的非参数统计学习技术被用于：（一）建模交互作用;和（二）从多个空间尺度（1- 196 350平方米）的多个数据类型（潜水员调查，地形模型，栖息地地图）中确定最有影响力的环境预测因子。将连续响应变量分类为二进制类别，并预测鱼类物种丰富度热点（前10%丰富度）的存在和不存在，增加了模型的预测性能。最好的CART模型预测鱼类丰富度热点的准确率为80%。在1平方米的样方和周围的海底地形（150米半径的海景单位）的地形复杂性，从高分辨率的地形模型测量的活石珊瑚的丰度之间的统计相互作用最好的解释了鱼类丰富的热点的地理格局。预测整个海景鱼类多样性连续变化的模型的性能相对较差，可能是由于结构退化导致珊瑚礁结构普遍同质化，从而使多样性与环境的关系脱钩。
Geographical patterning of fish diversity across coral reef seascapes is driven by many interacting environmental variables operating at multiple spatial scales. Identifying suites of variables that explain spatial patterns of fish diversity is central to ecology and informs prioritization in marine conservation, particularly where protection of the highest biodiversity coral reefs is a primary goal. However, the relative importance of conventional within-patch variables versus the spatial patterning of the surrounding seascape is still unclear in the ecology of fishes on coral reefs. A multi-scale seascape approach derived from landscape ecology was applied to quantify and examine the explanatory roles of a wide range of variables at different spatial scales including: (i) within-patch structural attributes from field data (5 × 1 m2 sample unit area); (ii) geometry of the seascape from sea-floor maps (10–50 m radius seascape units); and wave exposure from a hydrodynamic model (240 m resolution) for 251 coral reef survey sites in the US Virgin Islands. Non-parametric statistical learning techniques using single classification and regression trees (CART) and ensembles of boosted regression trees (TreeNet) were used to: (i) model interactions; and (ii) identify the most influential environmental predictors from multiple data types (diver surveys, terrain models, habitat maps) across multiple spatial scales (1–196,350 m2). Classifying the continuous response variables into a binary category and instead predicting the presence and absence of fish species richness hotspots (top 10% richness) increased the predictive performance of the models. The best CART model predicted fish richness hotspots with 80% accuracy. The statistical interaction between abundance of living scleractinian corals measured by SCUBA divers within 1 m2 quadrats and the topographical complexity of the surrounding sea-floor terrain (150 m radius seascape unit) measured from a high-resolution terrain model best explained geographical patterns in fish richness hotspots. The comparatively poor performance of models predicting continuous variability in fish diversity across the seascape could be a result of a decoupling of the diversity-environment relationship owing to structural degradation leading to a widespread homogenization of coral reef structure.