Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach

NEPOTE VALENTIN D; VOYRON S.; SOTERAS F.; IRIARTE H. J.; GIOVANNINI A.; LUMINI, E.; LUGO M. A.

PeerJ

Peer Journal Publishing

Año: 2023 p. 1 - 30

2167-8359

The biogeographic region of Argentinean Puna mainly extends at elevations higherthan 3,000 m within the Andean Plateau and hosts diverse ecological communitieshighly adapted to extreme aridity and low temperatures. Soils of Puna are typicallypoorly evolved and geomorphology is shaped by drainage networks, resulting inhighly vegetated endorheic basins and hypersaline basins known as salar or salt flats.Local communities rely on soil fertility for agricultural practices and on pastures forlivestock rearing. From this perspective, investigating the scarcely exploredmicrobiological diversity of these soils as indicators of ecosystems functioning mighthelp to predict the fragility of these harsh environments. In this study we collectedsoil samples from 28 points, following a nested design within three different macrohabitats,i.e., Puna grassland, hypersaline salar and family-run crop fields. Total fungiand arbuscular mycorrhizal fungi (AMF) occurrence were analyzed using eDNAsequencing. In addition, the significance of soil salinity and organic matter content assignificant predictors of AMF occurrence, was assessed through Generalized LinearMixed Modeling. We also investigated whether intensive grazing by cattle and lamain Puna grasslands may reduce the presence of AMF in these highly disturbed soils,driving or not major ecological changes, but no consistent results were found,suggesting that more specific experiments and further investigations may address thequestion more specifically. Finally, to predict the suitability for AMF in the differentmacro-habitats, Species Distribution Modeling (SDM) was performed within anenvironmental coherent area comprising both the phytogeographic regions of Punaand Altoandino. We modeled AMF distribution with a maximum entropy approach,including bioclimatic and edaphic predictors and obtaining maps of environmentalsuitability for AMF within the predicted areas. To assess the impact of farming onAMF occurrence, we set a new series of models excluding the cultivated Chaupi Rodeo samples. Overall, SDM predicted a lower suitability for AMF in hypersalinesalar areas, while grassland habitats and a wider temperature seasonality rangeappear to be factors significantly related to AMF enrichment, suggesting a main roleof seasonal dynamics in shaping AMF communities. The highest abundance of AMFwas observed in Vicia faba crop fields, while potato fields yielded a very low AMFoccurrence. The models excluding the cultivated Chaupi Rodeo samples highlightedthat if these cultivated areas had theoretically remained unmanaged habitats of Punaand Altoandino, then large-scale soil features and local bioclimatic constraints wouldlikely support a lower suitability for AMF. Using SDM we evidenced the influence ofbioclimatic, edaphic and anthropic predictors in shaping AMF occurrence andhighlighted the relevance of considering human activities to accurately predict AMFdistribution.