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首页> 外文期刊>Marine ecology progress series >Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks
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Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks

机译：使用人工神经网络预测地中海深海鱼类的分布

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摘要

Predicting the occurrence of economically important demersal fish in a multispecies marine environment can be of considerable value to fisheries management and protection of biodiversity. Here, 2 predictive modelling principles were utilised, artificial neural network (ANN) and discriminant function analysis (DFA), to develop presence/absence models for 3 species (anglerfish Lophius budegassa; hake Merluccius merluccius; red mullet Mullus barbatus) in the Mediterranean Sea. ANN-based models of demersal fish distribution outperformed conventional models and attained better recognition and prediction performance. Results indicated the ability of ANN's to predict presence more accurately than DFA when tested against independent field data. More precisely, sensitivity values obtained using DFA were 62.1% for anglerfish, 5.8% for hake and 59.8% for red mullet whereas using ANN were 75, 71 and 72.9% respectively. The accuracy of test data was 79.6% for anglerfish, 49.5 % for hake and 83.3 % for red mullet using DFA and 83.7, 83.3 and 85.6 % respectively using a back-propagation ANN. After learning from a set of selected patterns, the neural network (NN) models displayed a relatively high demersal fish classification accuracy, which was consistent with present understanding of the aggregating effects of the examined variables on these species' distribution. Predicting presence or absence was found to be easier for red mullet and anglerfish than for hake. The present results also suggested that the main processes modulating the occurrence of anglerfish, hake and red mullet in the NE Mediterranean Sea can be approximated by linear functions only to a limited extent. Due to their ability to mimic non-linear systems, ANNs proved far more effective in modelling the distribution of these species in the marine ecosystem. The main results and the ANN potential to predict suitable habitat profiles and structural characteristics of species assemblages are discussed.
机译：预测在多种海洋环境中具有重要经济价值的沉鱼可能对渔业管理和生物多样性保护具有重要价值。在这里，利用了2种预测建模原理，即人工神经网络（ANN）和判别函数分析（DFA），为地中海的3种鱼类（angle鱼Lophius budegassa；无须鳕Merluccius merluccius；红鱼Mullus barbatus）开发了存在/不存在模型。 。基于人工神经网络的深海鱼类分布模型优于传统模型，并获得了更好的识别和预测性能。结果表明，相对于独立的现场数据进行测试，人工神经网络比DFA能够更准确地预测存在。更准确地说，使用DFA获得的敏感度值对angle鱼为62.1％，对无须鳕为5.8％，对红鱼为59.8％，而使用ANN分别为75、71和72.9％。使用DFA对angle鱼进行测试数据的准确性为79.6％，对无须鳕为49.5％，对鱼为83.3％，使用反向传播ANN分别对83.7％，83.3％和85.6％。从一组选定的模式中学习后，神经网络（NN）模型显示出相对较高的海底鱼类分类精度，这与当前对所检查变量对这些物种分布的聚集效应的了解相一致。发现预测红and鱼和r鱼的存在与否比无须鳕要容易。目前的结果还表明，调节东北地中海沿岸琵琶鱼，鳕鱼和红鱼的发生的主要过程只能在有限的范围内通过线性函数近似。由于具有模拟非线性系统的能力，因此人工神经网络在模拟这些物种在海洋生态系统中的分布方面被证明更为有效。讨论了主要结果和人工神经网络潜力，以预测合适的生境概况和物种集合的结构特征。

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