Energy availability and habitat heterogeneity predict global riverine fish diversity
Energy availability and habitat heterogeneity predict global riverine fish diversity
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DOI：
10.1038/34899
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发表时间：
1998-01-22
期刊：
影响因子：
64.8
通讯作者：
Oberdorff, T
中科院分区：
文献类型：
--
作者：
Guégan, JF;Lek, S;Oberdorff, T
可以使用人工神经网络 (ANN) 程序对全球河流鱼类物种丰富度模式的控制过程进行建模。这些人工神经网络是计算机辅助识别领域的最新发展，与传统技术有很大不同(1,2)。在这里，我们利用人工神经网络的潜力来处理一些持续存在的模糊和非线性问题，这些问题困扰着物种多样性预测的经典统计方法。我们表明，全球范围内的河流鱼类多样性模式可以通过当地河流条件的地理模式成功预测。通过 ANN 方法拟合的非线性关系充分描述了数据，我们的结果解释了高达 93% 的物种丰富度总变化。这些发现强调了能源可用性和栖息地异质性对全球鱼类多样性模式的主导影响。我们的结果强化了物种能量理论 (3)，并与最近对北美哺乳动物物种的研究 (4) 进行了对比，但更有趣的是，它们证明了 ANN 方法在生态学中的适用性。
Processes governing patterns of richness of riverine fish species at the global level can be modelled using artificial neural network (ANN) procedures. These ANNs are the most recent development in computer-aided identification and are very different from conventional techniques(1,2). Here we use the potential of ANNs to deal with some of the persistent fuzzy and nonlinear problems that confound classical statistical methods for species diversity prediction. We show that riverine fish diversity patterns on a global scale can be successfully predicted by geographical patterns in local river conditions. Nonlinear relationships, fitted by ANN methods, adequately describe the data, with up to 93 per cent of the total variation in species richness being explained by our results. These findings highlight the dominant effect of energy availability and habitat heterogeneity on patterns of global fish diversity. Our results reinforce the species-energy theory(3) and contrast with those from a recent study on North American mammal species(4), but, more interestingly, they demonstrate the applicability of ANN methods in ecology.