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

Autores
Nepote Valentin, Davide; Voyron, Samuele; Soteras, María Florencia; Iriarte, Hebe Jorgelina; Giovannini, Andrea; Lumini, Erica; Lugo, Mónica Alejandra
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The biogeographic region of Argentinean Puna mainly extends at elevations higher than 3,000 m within the Andean Plateau and hosts diverse ecological communities highly adapted to extreme aridity and low temperatures. Soils of Puna are typically poorly evolved and geomorphology is shaped by drainage networks, resulting in highly vegetated endorheic basins and hypersaline basins known as salar or salt flats. Local communities rely on soil fertility for agricultural practices and on pastures for livestock rearing. From this perspective, investigating the scarcely explored microbiological diversity of these soils as indicators of ecosystems functioning might help to predict the fragility of these harsh environments. In this study we collected soil samples from 28 points, following a nested design within three different macro-habitats, i.e., Puna grassland, hypersaline salar and family-run crop fields. Total fungi and arbuscular mycorrhizal fungi (AMF) occurrence were analyzed using eDNA sequencing. In addition, the significance of soil salinity and organic matter content as significant predictors of AMF occurrence, was assessed through Generalized Linear Mixed Modeling. We also investigated whether intensive grazing by cattle and lama in 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 the question more specifically. Finally, to predict the suitability for AMF in the different macro-habitats, Species Distribution Modeling (SDM) was performed within an environmental coherent area comprising both the phytogeographic regions of Puna and Altoandino. We modeled AMF distribution with a maximum entropy approach, including bioclimatic and edaphic predictors and obtaining maps of environmental suitability for AMF within the predicted areas. To assess the impact of farming on AMF occurrence, we set a new series of models excluding the cultivated Chaupi Rodeo samples. Overall, SDM predicted a lower suitability for AMF in hypersaline salar areas, while grassland habitats and a wider temperature seasonality range appear to be factors significantly related to AMF enrichment, suggesting a main role of seasonal dynamics in shaping AMF communities. The highest abundance of AMF was observed in Vicia faba crop fields, while potato fields yielded a very low AMF occurrence. The models excluding the cultivated Chaupi Rodeo samples highlighted that if these cultivated areas had theoretically remained unmanaged habitats of Puna and Altoandino, then large-scale soil features and local bioclimatic constraints would likely support a lower suitability for AMF. Using SDM we evidenced the influence of bioclimatic, edaphic and anthropic predictors in shaping AMF occurrence and highlighted the relevance of considering human activities to accurately predict AMF distribution.
Fil: Nepote Valentin, Davide. University Of Turin. Life Sciences And Systems Biology; Italia
Fil: Voyron, Samuele. University Of Turin. Life Sciences And Systems Biology; Italia. National Research Council; Italia
Fil: Soteras, María Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina
Fil: Iriarte, Hebe Jorgelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis; Argentina. Universidad Nacional de San Luis; Argentina
Fil: Giovannini, Andrea. University of Turin. Life Sciences And Systems Biology; Italia
Fil: Lumini, Erica. Institute For Sustainable Plant Protection ; Italia. National Research Council; Italia
Fil: Lugo, Mónica Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis; Argentina. Universidad Nacional de San Luis; Argentina
Materia
ARGENTINEAN PUNA
SPECIES DISTRIBUTION MODELING (SDM)
FUNGAL METABARCODING
ARBUSCULAR MYCORRHIZAL FUNGI (ANF)
MAXENT
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/221762

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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approachNepote Valentin, DavideVoyron, SamueleSoteras, María FlorenciaIriarte, Hebe JorgelinaGiovannini, AndreaLumini, EricaLugo, Mónica AlejandraARGENTINEAN PUNASPECIES DISTRIBUTION MODELING (SDM)FUNGAL METABARCODINGARBUSCULAR MYCORRHIZAL FUNGI (ANF)MAXENThttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1The biogeographic region of Argentinean Puna mainly extends at elevations higher than 3,000 m within the Andean Plateau and hosts diverse ecological communities highly adapted to extreme aridity and low temperatures. Soils of Puna are typically poorly evolved and geomorphology is shaped by drainage networks, resulting in highly vegetated endorheic basins and hypersaline basins known as salar or salt flats. Local communities rely on soil fertility for agricultural practices and on pastures for livestock rearing. From this perspective, investigating the scarcely explored microbiological diversity of these soils as indicators of ecosystems functioning might help to predict the fragility of these harsh environments. In this study we collected soil samples from 28 points, following a nested design within three different macro-habitats, i.e., Puna grassland, hypersaline salar and family-run crop fields. Total fungi and arbuscular mycorrhizal fungi (AMF) occurrence were analyzed using eDNA sequencing. In addition, the significance of soil salinity and organic matter content as significant predictors of AMF occurrence, was assessed through Generalized Linear Mixed Modeling. We also investigated whether intensive grazing by cattle and lama in 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 the question more specifically. Finally, to predict the suitability for AMF in the different macro-habitats, Species Distribution Modeling (SDM) was performed within an environmental coherent area comprising both the phytogeographic regions of Puna and Altoandino. We modeled AMF distribution with a maximum entropy approach, including bioclimatic and edaphic predictors and obtaining maps of environmental suitability for AMF within the predicted areas. To assess the impact of farming on AMF occurrence, we set a new series of models excluding the cultivated Chaupi Rodeo samples. Overall, SDM predicted a lower suitability for AMF in hypersaline salar areas, while grassland habitats and a wider temperature seasonality range appear to be factors significantly related to AMF enrichment, suggesting a main role of seasonal dynamics in shaping AMF communities. The highest abundance of AMF was observed in Vicia faba crop fields, while potato fields yielded a very low AMF occurrence. The models excluding the cultivated Chaupi Rodeo samples highlighted that if these cultivated areas had theoretically remained unmanaged habitats of Puna and Altoandino, then large-scale soil features and local bioclimatic constraints would likely support a lower suitability for AMF. Using SDM we evidenced the influence of bioclimatic, edaphic and anthropic predictors in shaping AMF occurrence and highlighted the relevance of considering human activities to accurately predict AMF distribution.Fil: Nepote Valentin, Davide. University Of Turin. Life Sciences And Systems Biology; ItaliaFil: Voyron, Samuele. University Of Turin. Life Sciences And Systems Biology; Italia. National Research Council; ItaliaFil: Soteras, María Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Iriarte, Hebe Jorgelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis; Argentina. Universidad Nacional de San Luis; ArgentinaFil: Giovannini, Andrea. University of Turin. Life Sciences And Systems Biology; ItaliaFil: Lumini, Erica. Institute For Sustainable Plant Protection ; Italia. National Research Council; ItaliaFil: Lugo, Mónica Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis; Argentina. Universidad Nacional de San Luis; ArgentinaPeerJ, Inc.2023-01-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/221762Nepote Valentin, Davide; Voyron, Samuele; Soteras, María Florencia; Iriarte, Hebe Jorgelina; Giovannini, Andrea; et al.; Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach; PeerJ, Inc.; PeerJ; 11; 12-1-2023; 1-302167-8359CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://peerj.com/articles/14651info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:00:20Zoai:ri.conicet.gov.ar:11336/221762instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:00:20.42CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach
title Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach
spellingShingle Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach
Nepote Valentin, Davide
ARGENTINEAN PUNA
SPECIES DISTRIBUTION MODELING (SDM)
FUNGAL METABARCODING
ARBUSCULAR MYCORRHIZAL FUNGI (ANF)
MAXENT
title_short Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach
title_full Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach
title_fullStr Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach
title_full_unstemmed Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach
title_sort Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach
dc.creator.none.fl_str_mv Nepote Valentin, Davide
Voyron, Samuele
Soteras, María Florencia
Iriarte, Hebe Jorgelina
Giovannini, Andrea
Lumini, Erica
Lugo, Mónica Alejandra
author Nepote Valentin, Davide
author_facet Nepote Valentin, Davide
Voyron, Samuele
Soteras, María Florencia
Iriarte, Hebe Jorgelina
Giovannini, Andrea
Lumini, Erica
Lugo, Mónica Alejandra
author_role author
author2 Voyron, Samuele
Soteras, María Florencia
Iriarte, Hebe Jorgelina
Giovannini, Andrea
Lumini, Erica
Lugo, Mónica Alejandra
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv ARGENTINEAN PUNA
SPECIES DISTRIBUTION MODELING (SDM)
FUNGAL METABARCODING
ARBUSCULAR MYCORRHIZAL FUNGI (ANF)
MAXENT
topic ARGENTINEAN PUNA
SPECIES DISTRIBUTION MODELING (SDM)
FUNGAL METABARCODING
ARBUSCULAR MYCORRHIZAL FUNGI (ANF)
MAXENT
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The biogeographic region of Argentinean Puna mainly extends at elevations higher than 3,000 m within the Andean Plateau and hosts diverse ecological communities highly adapted to extreme aridity and low temperatures. Soils of Puna are typically poorly evolved and geomorphology is shaped by drainage networks, resulting in highly vegetated endorheic basins and hypersaline basins known as salar or salt flats. Local communities rely on soil fertility for agricultural practices and on pastures for livestock rearing. From this perspective, investigating the scarcely explored microbiological diversity of these soils as indicators of ecosystems functioning might help to predict the fragility of these harsh environments. In this study we collected soil samples from 28 points, following a nested design within three different macro-habitats, i.e., Puna grassland, hypersaline salar and family-run crop fields. Total fungi and arbuscular mycorrhizal fungi (AMF) occurrence were analyzed using eDNA sequencing. In addition, the significance of soil salinity and organic matter content as significant predictors of AMF occurrence, was assessed through Generalized Linear Mixed Modeling. We also investigated whether intensive grazing by cattle and lama in 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 the question more specifically. Finally, to predict the suitability for AMF in the different macro-habitats, Species Distribution Modeling (SDM) was performed within an environmental coherent area comprising both the phytogeographic regions of Puna and Altoandino. We modeled AMF distribution with a maximum entropy approach, including bioclimatic and edaphic predictors and obtaining maps of environmental suitability for AMF within the predicted areas. To assess the impact of farming on AMF occurrence, we set a new series of models excluding the cultivated Chaupi Rodeo samples. Overall, SDM predicted a lower suitability for AMF in hypersaline salar areas, while grassland habitats and a wider temperature seasonality range appear to be factors significantly related to AMF enrichment, suggesting a main role of seasonal dynamics in shaping AMF communities. The highest abundance of AMF was observed in Vicia faba crop fields, while potato fields yielded a very low AMF occurrence. The models excluding the cultivated Chaupi Rodeo samples highlighted that if these cultivated areas had theoretically remained unmanaged habitats of Puna and Altoandino, then large-scale soil features and local bioclimatic constraints would likely support a lower suitability for AMF. Using SDM we evidenced the influence of bioclimatic, edaphic and anthropic predictors in shaping AMF occurrence and highlighted the relevance of considering human activities to accurately predict AMF distribution.
Fil: Nepote Valentin, Davide. University Of Turin. Life Sciences And Systems Biology; Italia
Fil: Voyron, Samuele. University Of Turin. Life Sciences And Systems Biology; Italia. National Research Council; Italia
Fil: Soteras, María Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina
Fil: Iriarte, Hebe Jorgelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis; Argentina. Universidad Nacional de San Luis; Argentina
Fil: Giovannini, Andrea. University of Turin. Life Sciences And Systems Biology; Italia
Fil: Lumini, Erica. Institute For Sustainable Plant Protection ; Italia. National Research Council; Italia
Fil: Lugo, Mónica Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis; Argentina. Universidad Nacional de San Luis; Argentina
description The biogeographic region of Argentinean Puna mainly extends at elevations higher than 3,000 m within the Andean Plateau and hosts diverse ecological communities highly adapted to extreme aridity and low temperatures. Soils of Puna are typically poorly evolved and geomorphology is shaped by drainage networks, resulting in highly vegetated endorheic basins and hypersaline basins known as salar or salt flats. Local communities rely on soil fertility for agricultural practices and on pastures for livestock rearing. From this perspective, investigating the scarcely explored microbiological diversity of these soils as indicators of ecosystems functioning might help to predict the fragility of these harsh environments. In this study we collected soil samples from 28 points, following a nested design within three different macro-habitats, i.e., Puna grassland, hypersaline salar and family-run crop fields. Total fungi and arbuscular mycorrhizal fungi (AMF) occurrence were analyzed using eDNA sequencing. In addition, the significance of soil salinity and organic matter content as significant predictors of AMF occurrence, was assessed through Generalized Linear Mixed Modeling. We also investigated whether intensive grazing by cattle and lama in 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 the question more specifically. Finally, to predict the suitability for AMF in the different macro-habitats, Species Distribution Modeling (SDM) was performed within an environmental coherent area comprising both the phytogeographic regions of Puna and Altoandino. We modeled AMF distribution with a maximum entropy approach, including bioclimatic and edaphic predictors and obtaining maps of environmental suitability for AMF within the predicted areas. To assess the impact of farming on AMF occurrence, we set a new series of models excluding the cultivated Chaupi Rodeo samples. Overall, SDM predicted a lower suitability for AMF in hypersaline salar areas, while grassland habitats and a wider temperature seasonality range appear to be factors significantly related to AMF enrichment, suggesting a main role of seasonal dynamics in shaping AMF communities. The highest abundance of AMF was observed in Vicia faba crop fields, while potato fields yielded a very low AMF occurrence. The models excluding the cultivated Chaupi Rodeo samples highlighted that if these cultivated areas had theoretically remained unmanaged habitats of Puna and Altoandino, then large-scale soil features and local bioclimatic constraints would likely support a lower suitability for AMF. Using SDM we evidenced the influence of bioclimatic, edaphic and anthropic predictors in shaping AMF occurrence and highlighted the relevance of considering human activities to accurately predict AMF distribution.
publishDate 2023
dc.date.none.fl_str_mv 2023-01-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/221762
Nepote Valentin, Davide; Voyron, Samuele; Soteras, María Florencia; Iriarte, Hebe Jorgelina; Giovannini, Andrea; et al.; Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach; PeerJ, Inc.; PeerJ; 11; 12-1-2023; 1-30
2167-8359
CONICET Digital
CONICET
url http://hdl.handle.net/11336/221762
identifier_str_mv Nepote Valentin, Davide; Voyron, Samuele; Soteras, María Florencia; Iriarte, Hebe Jorgelina; Giovannini, Andrea; et al.; Modeling geographic distribution of arbuscular mycorrhizal fungi from molecular evidence in soils of Argentinean Puna using a maximum entropy approach; PeerJ, Inc.; PeerJ; 11; 12-1-2023; 1-30
2167-8359
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
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