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Using Species Distribution Models For Fungi

机译：利用物种分布模型进行真菌

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Species distribution models (SDMs) are an emerging tool in the study of fungi, and their use is expanding across species and research topics. To summarise progress to date and to highlight important considerations for future users, we review 283 studies that apply SDMs to fungi. We found that macrofungi, lichens, and pathogenic microfungi are most often studied. While many studies only aim to model species response to environmental covariates, the use of SDMs for explicitly predicting fungal occurrence in space and time is growing. Many studies collect fungal occurrence data, but the use of pre-collected records from reference collections and citizen science programs is increasing. Challenges of applying SDMs to fungi include detection and sampling biases, and uncertainties in identification and taxonomy. Further, finding environmental covariates at appropriate spatial and temporal scales is important, as fungi can respond to fine-scale environmental patterns. Fine-scale covariate data can be difficult to gather across space, but we show remote-sensing measurements are viable for fungi SDMs. For those fungi interacting with host species, host information is also important, and can be used as covariates in SDMs. We also highlight that competition among fungi, and dispersal, can affect observed distributions, with the latter particularly prominent for invasive fungi. We show how one can account for these processes in models, when suitable data are available. Finally, we note that environmental DNA records create new opportunities and challenges for future modelling efforts, and discuss the difficulties in predicting invasions and climate change impacts. The application of SDMs to fungi has already provided interesting lessons on how to adapt modelling tools for specific questions, and fungi will continue to be relevant test subjects for further technical development of SDMs. (C) 2020 British Mycological Society. Published by Elsevier Ltd. All rights reserved.
机译：物种分布模型（SDMS）是对真菌研究的新兴工具，它们的使用正在扩展物种和研究主题。为了迄今为止的进展，并突出对未来用户的重要考虑，我们审查了283项对真菌施加SDM的研究。我们发现Macrofungi，地衣和病原微生物最常被研究。虽然许多研究仅旨在模拟物种对环境协变量的反应，但使用SDMS用于明确预测空间和时间的真菌发生。许多研究会收集真菌发生数据，但使用从参考资料和公民科学计划的预收集的记录正在增加。将SDMS应用于真菌的挑战包括检测和取样偏见，以及识别和分类中的不确定性。此外，在适当的空间和时间尺度上发现环境协变量是重要的，因为真菌可以响应微尺度的环境模式。精细的协变量数据可能难以聚集在空间上，但我们显示遥感测量对于真菌SDMS是可行的。对于与宿主物种进行交互的那些真菌，主机信息也很重要，并且可以用作SDMS中的协变量。我们还强调了真菌之间的竞争和分散，可以影响观察到的分布，后者对侵袭性真菌特别突出。我们展示如何在适当的数据时展示在模型中的这些过程。最后，我们注意到环境DNA记录为未来的建模努力创造了新的机遇和挑战，并讨论了预测入侵和气候变化影响的困难。 SDMS对真菌的应用已经提供了有关如何对特定问题进行建模工具的有趣课程，并且真菌将继续具有相关的测试科目，以获得SDM的进一步技术开发。 （c）2020英国Mycological社会。 elsevier有限公司出版。保留所有权利。

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