使用随机森林模型预测中生动物的丰度，以定义深海采矿环境中的保护区和影响区,Journal of Applied Ecology

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使用随机森林模型预测中生动物的丰度，以定义深海采矿环境中的保护区和影响区
Journal of Applied Ecology ( IF 5.0 ) Pub Date : 2020-05-03 , DOI: 10.1111/1365-2664.13621
Katja Uhlenkott _{1,

2} , Annemiek Vink ₃ , Thomas Kuhn ₃ , Pedro Martínez Arbizu _{1,

2}

Affiliation

在多金属结核的商业深海开采中，经济利益浓厚，因此需要在克拉里昂·克利珀顿断裂带（CCZ）的深海平原中定义合适的保存区。但是，除了基于船的多波束数据外，在大范围的地理区域中仅可获得稀疏的连续环境信息。
我们使用随机森林方法，在较大的地理尺度上测试了在高生物分类水平上对鱼类群落丰富度和多样性进行建模的潜力。基于船的多波束测深和反向散射信号是11个预测变量以及海底多金属结核丰度的唯一建模来源。连续的动物群落预测已与所有可用的环境变量结合在一起，并使用k均值聚类划分为代表深海生境的类别。
结果表明，基于船舶，多波束的预测因子可用于计算大型动物群落分布的预测模型。不同的动物动物响应变量之间的预测分布有所不同。
为了评估预测，还额外计算了1000次重复的随机森林回归，将每个站点上不同数量的采样位置和并行样本进行积分。较大数量的并行样本对于减轻小型动物群落分布显着变化的影响特别有用。但是，大量的采样位置甚至更重要，它将更多的环境条件自然可变性整合到模型中。
综合与应用。多金属结核勘探承包商必须在其许可区域内界定潜在的采矿和保护区。保护区的生物多样性和环境应代表受采矿影响的地点。我们预测的meiofauna分布和所获得的栖息地图是必不可少的第一步，可以识别具有相似生态条件的区域。这样，不仅可以根据专家意见和环境代理来定义保护区，而且可以整合来自底栖动物群落分布的证据。

"点击查看英文标题和摘要"

Predicting meiofauna abundance to define preservation and impact zones in a deep‐sea mining context using random forest modelling

There is a strong economic interest in commercial deep‐sea mining of polymetallic nodules and therefore a need to define suitable preservation zones in the abyssal plain of the Clarion Clipperton Fracture Zone (CCZ). However, besides ship‐based multibeam data, only sparse continuous environmental information is available over large geographic scales.
We test the potential of modelling meiofauna abundance and diversity on high taxonomic level on large geographic scale using a random forest approach. Ship‐based multibeam bathymetry and backscatter signal are the only sources for 11 predictor variables, as well as the modelled abundance of polymetallic nodules on the seafloor. Continuous meiofauna predictions have been combined with all available environmental variables and classified into classes representing abyssal habitats using k‐means clustering.
Results show that ship‐based, multibeam‐derived predictors can be used to calculate predictive models for meiofauna distribution on a large geographic scale. Predicted distribution varies between the different meiofauna response variables.
To evaluate predictions, random forest regressions were additionally computed with 1,000 replicates, integrating varying numbers of sampling positions and parallel samples per site. Higher numbers of parallel samples are especially useful to smoothen the influence of the remarkable variability of meiofauna distribution on a small scale. However, a high number of sampling positions is even more important, integrating a greater amount of natural variability of environmental conditions into the model.
Synthesis and applications. Polymetallic nodule exploration contractors are required to define potential mining and preservation zones within their licence area. The biodiversity and the environment of preservation zones should be representative of the sites that will be impacted by mining. Our predicted distributions of meiofauna and the derived habitat maps are an essential first step to enable the identification of areas with similar ecological conditions. In this way, it is possible to define preservation zones not only based on expert opinion and environmental proxies but also integrating evidence from the distribution of benthic communities.

更新日期：2020-05-03

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