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首页> 外文期刊>Ocean & coastal management >Species composition and turnover models provide robust approximations of biodiversity in marine conservation planning
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Species composition and turnover models provide robust approximations of biodiversity in marine conservation planning

机译：物种组成和营业额模型为海洋保护计划中的生物多样性提供了强大的近似

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Robust design of Marine Protected Areas in ocean environments is often challenging due to inadequate knowledge of biodiversity patterns, reflecting difficulties in the prediction of species distributions from sampling data that are often sparse or inadequate. Models that combine species and environmental data, such as Gradient Forests (GF), provide one analytical approach to this problem, efficiently combining available information to produce spatial models of species turnover throughout an area of interest. Spatial estimates of species turnover can then be classified to estimate spatial patterns in species composition; however, the performance of GF-based classifications within a conservation planning context has not previously been evaluated. Here we assess the utility for conservation planning (using the software Zonation) of a GF-based hierarchical classification that summarises spatial patterns in demersal fish composition in the oceans around New Zealand. Progressively more complex Zonation analyses assessed the effects of (i) varying the number of classification groups, (ii) adding information describing species turnover, and (iii) adding information describing spatial variation in demersal fish species richness. The best-performing GF-based conservation ranking used layers describing the distributions of 30 classification groups, demersal fish species turnover between these groups, and species richness. Conservation outcomes from this ranking were only marginally less efficient than those from a more conventional ranking that used 217 individual species distribution layers (7% less efficient). This indicates that GF-based classifications may provide a practical alternative for marine conservation planning. Additional advantages arise from the greater ease with which a single classification layer summarising complex biodiversity patterns can facilitate decision-making in participatory stakeholder processes.
机译：由于生物多样性模式的知识不足，在海洋环境中的海洋保护区的强大设计往往是具有挑战性的，这反映了从通常稀疏或不充分的采样数据预测物种分布的困难。结合物种和环境数据的模型，例如渐变森林（GF），为此问题提供了一种分析方法，有效地结合了可用信息，以在整个感兴趣领域产生物种营业额的空间模型。然后可以将物种周转的空间估计分类以估计物种组成中的空间模式;但是，先前尚未评估保护规划上下文中的基于GF的分类的性能。在这里，我们评估了基于GF的等级分类的保护计划（使用软件区划）的效用，这些分类分类总结了新西兰海洋中的岩体鱼组合物中的空间模式。逐步更复杂的分区分析评估（i）改变分类组的数量，（ii）添加描述物种营业额的信息，以及（iii）添加描述倒数鱼类种类丰富的空间变化的信息。最佳的基于GF的保护排名使用描述了30个分类群的分布，这些群体之间的分裂鱼类成交以及物种丰富性。来自该排名的保护结果仅效率低于使用217个单独的物种分布层（效率低7％）的更常规排名的效率。这表明基于GF的分类可以提供海洋保护计划的实际替代方案。额外的优点因其摘要复杂的生物多样性模式的单个分类层而易于易于促进参与性利益相关者流程的决策。

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