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Restinga ectomycorrhizae: a work in progress.

Ariadne Nobrega Marinho FurtadoMarco LeonardiOrnella ComandiniMaria Alice NevesAndrea C Rinaldi
Published in: F1000Research (2023)
Background: The Brazilian Atlantic Forest is one of the most biodiverse terrestrial ecoregions of the world. Among its constituents, restinga vegetation makes a particular case, acting as a buffer zone between the oceans and the forest. Covering some 80% of Brazilian coastline (over 7,300 km in length), restinga is a harsh environment where plants and fungi interact in complex ways that just now are beginning to be unveiled. Ectomycorrhizal symbiosis, in particular, plays a so far ungauged and likely underestimated role. We recently described the morpho-anatomical and molecular features of the ectomycorrhizae formed by several basidiomycetous mycobionts on the host plant Guapira opposita , but the mycorrhizal biology of restinga is still largely unexplored. Here, we report new data on the ectomycorrhizal fungal symbionts of G. opposita , based on the collection of sporomata and ectomycorrhizal root tips in restinga stands occurring in southern Brazil. Methods: To obtain a broader view of restinga mycorrhizal and ecological potential, we compiled a comprehensive and up-to-date checklist of fungal species reported or supposed to establish ectomycorrhizae on restinga-inhabiting host plants, mainly on the basis of field observations. Results: Our list comprises some 726 records, 74 of which correspond to putative ectomycorrhizal taxa specifically associated with restinga. These include several members of Boletaceae , Amanita , Tomentella / Thelephora , Russula / Lactifluus , and Clavulina , as well as hypogeous fungi, like the recently described Longistriata flava . Conclusions: Our survey reveals a significant diversity of the restinga ectomycorrhizal mycobiota, indicating the importance of this symbiosis for the ecological functioning of a unique yet poorly known and threatened ecosystem.
Keyphrases
  • climate change
  • electronic health record
  • machine learning