Biogeography of plant‐associated fungal symbionts in mountain ecosystems: A meta‐analysis

Aim Predicting the potential for climate change to disrupt host–microbe symbioses requires basic knowledge of the biogeography of these consortia. In plants, fungal symbionts can ameliorate the abiotic stressors that accompany climate warming and thus could influence plants under a changing climate. Forecasting future plant–microbe interactions first requires knowledge of current fungal symbiont distributions, which are poorly resolved relative to the distributions of plants. Location We used meta-analysis to summarize the biogeographic distributions of plant-fungal symbionts in mountain ecosystems worldwide, because these ecosystems are likely to be among the first to experience climate change-induced range shifts. Methods We analysed 374 records from 53 publications to identify general trends, pinpoint areas in need of greater study and develop reporting guidelines to facilitate future syntheses. Results Elevational patterns varied strongly among fungal and plant functional groups. Fungal diversity and abundance increased with altitude for the ectomycorrhizal fungi. However, arbuscular mycorrhizal fungi and localized foliar endophytes declined in either abundance or diversity with altitude. In shrubs, fungal abundance increased with elevation, but in C3 grasses, fungal abundance declined with elevation. Altitudinal patterns in fungal composition were stronger than gradients in fungal abundance or diversity, suggesting that species turnover contributes more to elevational gradients in fungal symbionts than does variation in abundance or richness. Plant functional groups were overrepresented by C3 grasses and trees, with surprisingly few data on sedges or shrubs, despite their ecological dominance in mountain ecosystems. Similarly, epichloae, ericoid mycorrhizal fungi and root endophytes were understudied relative to other fungal groups. Main Conclusions Meta-analysis revealed broad biogeographic patterns in plant-fungal symbiont abundance, diversity and composition that inform predictions of future distributions.