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首页> 外文期刊>Marine ecology progress series >Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau
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Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau

机译：改善数据贫乏地区物种分布模型的方法：以克格伦高原的南极亚底栖生物为例

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摘要

Species distribution models (SDMs) are essential tools to aid conservation biologists in evaluating the combined effects of environmental change and human activities on natural habitats and for the development of relevant conservation plans. However, modeling species distributions over vast and remote regions is often challenging due to poor and heterogeneous data sets, and this raises questions regarding the relevance of the modeling procedures. In recent years, there have been many methodological developments in SDM procedures using virtual species and broad data sets, but few solutions have been proposed to deal with poor or heterogeneous data. In the present work, we address this methodological challenge by studying the performance of different modeling procedures based on 4 real species, using presence-only data compiled from various oceanographic surveys on the Kerguelen Plateau (Southern Ocean). We followed a practical protocol to test for the reliability and performance of the models and to correct for limited and aggregated data, as well as accounting for spatial and temporal sampling biases. Our results show that producing reliable SDMs is feasible as long as the amount and quality of available data allow testing and correcting for these biases. However, we found that SDMs could be corrected for spatial and temporal heterogeneities in only 1 of the 4 species we examined, highlighting the need to consider all potential biases when modeling species distributions. Finally, we show that model reliability and performance also depend on the interaction between the incompleteness of the data and species niches, with the distribution of narrow-niche species being less sensitive to data gaps than species occupying wider niches.
机译：物种分布模型（SDM）是帮助保护生物学家评估环境变化和人类活动对自然栖息地的综合影响以及制定相关保护计划的重要工具。然而，由于数据集的多样性和异构性，在广阔和偏远地区对物种分布进行建模通常具有挑战性，这引发了有关建模程序相关性的问题。近年来，在使用虚拟物种和广泛数据集的SDM程序中，已经有了许多方法学方面的发展，但是很少提出解决不良或异构数据的解决方案。在当前的工作中，我们通过使用基于Kerguelen高原（南洋）的各种海洋学调查汇编的仅存在数据，研究了基于4种真实物种的不同建模程序的性能，从而解决了该方法学难题。我们遵循实用的协议来测试模型的可靠性和性能，并校正有限和汇总的数据，并考虑空间和时间采样偏差。我们的结果表明，只要可用数据的数量和质量允许测试和纠正这些偏差，就可以生产可靠的SDM。但是，我们发现在我们检查的4个物种中，只有1个可以纠正SDM的空间和时间异质性，这突出说明了在对物种分布进行建模时需要考虑所有潜在的偏差。最后，我们表明模型的可靠性和性能还取决于数据不完全性与物种生态位之间的相互作用，与生态位较窄的物种相比，生态位较窄的物种对数据缺口的敏感性较低。

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