Predicting dissolved organic matter lability and carbon accumulation in temperate freshwater ecosystems

Autores
Bastidas Navarro, Marcela; Schenone, Luca; Martyniuk, Nicolás; Vega, Evelyn; Balseiro, Esteban; Modenutti, Beatriz
Año de publicación
2020
Idioma
inglés
Tipo de recurso
conjunto de datos
Estado
versión aceptada
Descripción
Dissolved organic matter (DOM) dynamics influence aquatic ecosystem metabolism with ecological and biogeochemical effects. During microbial degradation, certain DOM molecules accumulate in the environments constituting the residual refractory pool that has a key role in the global carbon cycle by sequestering carbon in lakes and oceans. The present study aims to model the factors driving bacterial C-consumption, and thus predicting the potential residual carbon accumulation. We developed mechanistic models to represent bacterial C-consumption, considering the contribution of DOM quality and P and N concentrations in the total carbon pool. Based on 82 different environments we establish DOM components and nutrient concentration for deep lakes, shallow lakes, high altitude lakes, low-order streams, and wetlands from North-Andean Patagonian glacial lake district (around 41°S). We applied Bayesian methods to estimate model parameters from laboratory C-lability experiments performed in 29 environments. We tested the predictive accuracy of our models with an external dataset consisting of C-lability experiments with natural lake water enriched with organic matter from different sources. We found a model that performed excellently in both, fit to training data and prediction to external experiments. Based on the selected model, an increase in P concentration stimulates C-consumption, and an increase in the proportion of DOM protein-like compounds reduces the amount of residual C. Based on the predictive accuracy, we demonstrated that our model is very useful to anticipate C accumulation due to changes in the inputs to water bodies.
Fil: Bastidas Navarro, Marcela. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Bastidas Navarro, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Schenone, Luca. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Schenone, Luca. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Martyniuk, Nicolás. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Martyniuk, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Vega, Evelyn. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Vega, Evelyn. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Balseiro, Esteban. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Balseiro, Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Modenutti, Beatriz. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Modenutti, Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Materia
Bayesian
PARAFAC
Microbial respiration
Dissolved organic matter
Modeling
Forcasting
Ciencias de la tierra y Medio ambiente
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
Repositorio Digital Institucional (UNCo)
Institución
Universidad Nacional del Comahue
OAI Identificador
oai:rdi.uncoma.edu.ar:uncomaid/15901
Acceder
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oai_identifier_str oai:rdi.uncoma.edu.ar:uncomaid/15901
network_acronym_str RDIUNCO
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network_name_str Repositorio Digital Institucional (UNCo)
spelling Predicting dissolved organic matter lability and carbon accumulation in temperate freshwater ecosystemsBastidas Navarro, MarcelaSchenone, LucaMartyniuk, NicolásVega, EvelynBalseiro, EstebanModenutti, BeatrizBayesianPARAFACMicrobial respirationDissolved organic matterModelingForcastinghttps://purl.org/becyt/ford/1.5Ciencias de la tierra y Medio ambienteDissolved organic matter (DOM) dynamics influence aquatic ecosystem metabolism with ecological and biogeochemical effects. During microbial degradation, certain DOM molecules accumulate in the environments constituting the residual refractory pool that has a key role in the global carbon cycle by sequestering carbon in lakes and oceans. The present study aims to model the factors driving bacterial C-consumption, and thus predicting the potential residual carbon accumulation. We developed mechanistic models to represent bacterial C-consumption, considering the contribution of DOM quality and P and N concentrations in the total carbon pool. Based on 82 different environments we establish DOM components and nutrient concentration for deep lakes, shallow lakes, high altitude lakes, low-order streams, and wetlands from North-Andean Patagonian glacial lake district (around 41°S). We applied Bayesian methods to estimate model parameters from laboratory C-lability experiments performed in 29 environments. We tested the predictive accuracy of our models with an external dataset consisting of C-lability experiments with natural lake water enriched with organic matter from different sources. We found a model that performed excellently in both, fit to training data and prediction to external experiments. Based on the selected model, an increase in P concentration stimulates C-consumption, and an increase in the proportion of DOM protein-like compounds reduces the amount of residual C. Based on the predictive accuracy, we demonstrated that our model is very useful to anticipate C accumulation due to changes in the inputs to water bodies.Fil: Bastidas Navarro, Marcela. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.Fil: Bastidas Navarro, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Schenone, Luca. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.Fil: Schenone, Luca. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Martyniuk, Nicolás. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.Fil: Martyniuk, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Vega, Evelyn. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.Fil: Vega, Evelyn. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Balseiro, Esteban. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.Fil: Balseiro, Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Modenutti, Beatriz. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.Fil: Modenutti, Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Universidad Nacional del ComahueInstituto de Investigaciones en Biodiversidad y Medioambiente2020-08-26info:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_ddb1info:ar-repo/semantics/conjuntoDeDatosinfo:eu-repo/semantics/dataSetapplication/ms-excelapplication/octet-streamapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheethttp://rdi.uncoma.edu.ar/handle/uncomaid/15901engARGinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:Repositorio Digital Institucional (UNCo)instname:Universidad Nacional del Comahue2025-10-16T10:05:42Zoai:rdi.uncoma.edu.ar:uncomaid/15901instacron:UNCoInstitucionalhttp://rdi.uncoma.edu.ar/Universidad públicaNo correspondehttp://rdi.uncoma.edu.ar/oaimirtha.mateo@biblioteca.uncoma.edu.ar; adriana.acuna@biblioteca.uncoma.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:71082025-10-16 10:05:42.798Repositorio Digital Institucional (UNCo) - Universidad Nacional del Comahuefalse
dc.title.none.fl_str_mv Predicting dissolved organic matter lability and carbon accumulation in temperate freshwater ecosystems
title Predicting dissolved organic matter lability and carbon accumulation in temperate freshwater ecosystems
spellingShingle Predicting dissolved organic matter lability and carbon accumulation in temperate freshwater ecosystems
Bastidas Navarro, Marcela
Bayesian
PARAFAC
Microbial respiration
Dissolved organic matter
Modeling
Forcasting
Ciencias de la tierra y Medio ambiente
title_short Predicting dissolved organic matter lability and carbon accumulation in temperate freshwater ecosystems
title_full Predicting dissolved organic matter lability and carbon accumulation in temperate freshwater ecosystems
title_fullStr Predicting dissolved organic matter lability and carbon accumulation in temperate freshwater ecosystems
title_full_unstemmed Predicting dissolved organic matter lability and carbon accumulation in temperate freshwater ecosystems
title_sort Predicting dissolved organic matter lability and carbon accumulation in temperate freshwater ecosystems
dc.creator.none.fl_str_mv Bastidas Navarro, Marcela
Schenone, Luca
Martyniuk, Nicolás
Vega, Evelyn
Balseiro, Esteban
Modenutti, Beatriz
author Bastidas Navarro, Marcela
author_facet Bastidas Navarro, Marcela
Schenone, Luca
Martyniuk, Nicolás
Vega, Evelyn
Balseiro, Esteban
Modenutti, Beatriz
author_role author
author2 Schenone, Luca
Martyniuk, Nicolás
Vega, Evelyn
Balseiro, Esteban
Modenutti, Beatriz
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Bayesian
PARAFAC
Microbial respiration
Dissolved organic matter
Modeling
Forcasting
Ciencias de la tierra y Medio ambiente
topic Bayesian
PARAFAC
Microbial respiration
Dissolved organic matter
Modeling
Forcasting
Ciencias de la tierra y Medio ambiente
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
dc.description.none.fl_txt_mv Dissolved organic matter (DOM) dynamics influence aquatic ecosystem metabolism with ecological and biogeochemical effects. During microbial degradation, certain DOM molecules accumulate in the environments constituting the residual refractory pool that has a key role in the global carbon cycle by sequestering carbon in lakes and oceans. The present study aims to model the factors driving bacterial C-consumption, and thus predicting the potential residual carbon accumulation. We developed mechanistic models to represent bacterial C-consumption, considering the contribution of DOM quality and P and N concentrations in the total carbon pool. Based on 82 different environments we establish DOM components and nutrient concentration for deep lakes, shallow lakes, high altitude lakes, low-order streams, and wetlands from North-Andean Patagonian glacial lake district (around 41°S). We applied Bayesian methods to estimate model parameters from laboratory C-lability experiments performed in 29 environments. We tested the predictive accuracy of our models with an external dataset consisting of C-lability experiments with natural lake water enriched with organic matter from different sources. We found a model that performed excellently in both, fit to training data and prediction to external experiments. Based on the selected model, an increase in P concentration stimulates C-consumption, and an increase in the proportion of DOM protein-like compounds reduces the amount of residual C. Based on the predictive accuracy, we demonstrated that our model is very useful to anticipate C accumulation due to changes in the inputs to water bodies.
Fil: Bastidas Navarro, Marcela. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Bastidas Navarro, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Schenone, Luca. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Schenone, Luca. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Martyniuk, Nicolás. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Martyniuk, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Vega, Evelyn. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Vega, Evelyn. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Balseiro, Esteban. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Balseiro, Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Modenutti, Beatriz. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina.
Fil: Modenutti, Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
description Dissolved organic matter (DOM) dynamics influence aquatic ecosystem metabolism with ecological and biogeochemical effects. During microbial degradation, certain DOM molecules accumulate in the environments constituting the residual refractory pool that has a key role in the global carbon cycle by sequestering carbon in lakes and oceans. The present study aims to model the factors driving bacterial C-consumption, and thus predicting the potential residual carbon accumulation. We developed mechanistic models to represent bacterial C-consumption, considering the contribution of DOM quality and P and N concentrations in the total carbon pool. Based on 82 different environments we establish DOM components and nutrient concentration for deep lakes, shallow lakes, high altitude lakes, low-order streams, and wetlands from North-Andean Patagonian glacial lake district (around 41°S). We applied Bayesian methods to estimate model parameters from laboratory C-lability experiments performed in 29 environments. We tested the predictive accuracy of our models with an external dataset consisting of C-lability experiments with natural lake water enriched with organic matter from different sources. We found a model that performed excellently in both, fit to training data and prediction to external experiments. Based on the selected model, an increase in P concentration stimulates C-consumption, and an increase in the proportion of DOM protein-like compounds reduces the amount of residual C. Based on the predictive accuracy, we demonstrated that our model is very useful to anticipate C accumulation due to changes in the inputs to water bodies.
publishDate 2020
dc.date.none.fl_str_mv 2020-08-26
dc.type.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_ddb1
info:ar-repo/semantics/conjuntoDeDatos
info:eu-repo/semantics/dataSet
status_str acceptedVersion
format dataSet
dc.identifier.none.fl_str_mv http://rdi.uncoma.edu.ar/handle/uncomaid/15901
url http://rdi.uncoma.edu.ar/handle/uncomaid/15901
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
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application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
dc.coverage.none.fl_str_mv ARG
dc.publisher.none.fl_str_mv Universidad Nacional del Comahue
Instituto de Investigaciones en Biodiversidad y Medioambiente
publisher.none.fl_str_mv Universidad Nacional del Comahue
Instituto de Investigaciones en Biodiversidad y Medioambiente
dc.source.none.fl_str_mv reponame:Repositorio Digital Institucional (UNCo)
instname:Universidad Nacional del Comahue
reponame_str Repositorio Digital Institucional (UNCo)
collection Repositorio Digital Institucional (UNCo)
instname_str Universidad Nacional del Comahue
repository.name.fl_str_mv Repositorio Digital Institucional (UNCo) - Universidad Nacional del Comahue
repository.mail.fl_str_mv mirtha.mateo@biblioteca.uncoma.edu.ar; adriana.acuna@biblioteca.uncoma.edu.ar
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score 12.8982525

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