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首页> 外文期刊>ICES Journal of Marine Science >Spatial prediction of demersal fish diversity in the Baltic Sea: comparison of machine learning and regression-based techniques
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Spatial prediction of demersal fish diversity in the Baltic Sea: comparison of machine learning and regression-based techniques

机译：波罗的海深海鱼类多样性的空间预测：机器学习和基于回归的技术的比较

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摘要

Marine spatial planning (MSP) is considered a valuable tool in the ecosystem-based management of marine areas. Predictive modelling may be applied in the MSP framework to obtain spatially explicit information about biodiversity patterns. The growing number of statistical approaches used for this purpose implies the urgent need for comparisons between different predictive techniques. In this study, we evaluated the performance of selected machine learning and regression-based methods that were applied for modelling fish community indices. We hypothesized that habitat features can influence fish assemblage and investigated the effect of environmental gradients on demersal fish diversity (species richness and Shannon-Weaver Index). We used fish data from the Baltic International Trawl Surveys (2001-2014) and maps of six potential predictors: bottom salinity, depth, seabed slope, growth season bottom temperature, seabed sediments and annual mean bottom current velocity. We compared the performance of six alternative modelling approaches: generalized linear models, generalized additive models, multivariate adaptive regression splines, support vector machines, boosted regression trees and random forests. We applied repeated 10-fold cross-validation, using accuracy as the measure of model quality. Finally, we selected random forest as the best performing algorithm and implemented it for the spatial prediction of fish diversity from the Baltic Proper to the Kattegat. To obtain information on the data reliability and confidence of the developed models, which are essential for MSP, we estimated the uncertainty of predictions with standard deviation of predictions obtained from all the trees in the ensemble random forest method. We showed how state-of-the-art predictive techniques, based on easily available data and simple Geographic Information System tools, can be used to obtain reliable spatial information about fish diversity. Our comparative work highlighted the potential of machine learning method to reduce prediction error in modelling of demersal fish diversity in the framework of MSP.
机译：海洋空间规划（MSP）被认为是基于生态系统的海洋区域管理中的宝贵工具。可以在MSP框架中应用预测建模，以获得有关生物多样性模式的空间明确信息。为此目的越来越多的统计方法意味着迫切需要在不同的预测技术之间进行比较。在这项研究中，我们评估了选定的机器学习和基于回归的方法的性能，这些方法用于建模鱼类群落指数。我们假设栖息地的特征会影响鱼类的聚集，并研究了环境梯度对沉鱼多样性的影响（物种丰富度和香农-韦弗指数）。我们使用了波罗的海国际拖网调查（2001-2014）的鱼类数据以及六个潜在预测因子的地图：底部盐度，深度，海床坡度，生长季节底部温度，海床沉积物和年平均底部流速。我们比较了六种替代建模方法的性能：广义线性模型，广义加性模型，多元自适应回归样条，支持向量机，增强回归树和随机森林。我们使用准确性作为模型质量的度量，应用了重复的10倍交叉验证。最后，我们选择随机森林作为最佳算法，并将其用于从波罗的海到卡特加特海域鱼类多样性的空间预测。为了获得有关已开发模型的数据可靠性和置信度（这对于MSP至关重要）的信息，我们使用整体随机森林法估计了从所有树木获得的预测的标准偏差，并估计了预测的不确定性。我们展示了如何基于易于获得的数据和简单的地理信息系统工具，使用最新的预测技术来获取有关鱼类多样性的可靠空间信息。我们的比较工作突显了机器学习方法在MSP框架下减少深海鱼类多样性建模中的预测误差的潜力。

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