Using Species Distribution Models For Fungi. Issue 2 (June 2020)

• Record Type: Journal Article
• Authors: Hao, Tianxiao; Guillera-Arroita, Gurutzeta; May, Tom W.; Lahoz-Monfort, José J.; Elith, Jane
• Is Part Of: Fungal biology reviews. Volume 34:Issue 2(2020)
• Page Start: 74
• Page End: 88
• Publication Date: 2020-06
• Subjects: Biogeography -- Biotic interaction -- Citizen science -- Conservation mycology -- Ecological niche modelling -- Fungal biogeography -- Fungal pathogen; Mycology -- Periodicals; Fungi -- Periodicals; Mycologie -- Périodiques; Champignons -- Périodiques; Electronic journals; Periodicals; Périodiques; 579.505
• DOI: 10.1016/j.fbr.2020.01.002
• Languages: English
• ISSNs: 1749-4613

Abstract

Abstract: 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. Highlights: Species distribution models (SDMs) are emerging in mycology. Finding scale-appropriate covariates is important to SDMs for fungi. Pre-collected fungi data likely contain sampling bias and issues in identification. Imperfect detection should be accounted for in sporing body survey data. Methods are available for including biotic interactions and dispersal in models.