A common framework to model recovery in disturbed tropical forests
Despite their exceptional biodiversity and carbon stocks, more than 80% of tropical forests are disturbed. However, a lot of interrogations remain around the ability of vegetation attributes in tropical forests to recover from the various anthropogenic disturbances coexisting in many tropical landscapes. While these different disturbances are usually studied separately, this work provides, for the first time, a common modelling framework of vegetation attribute recovery in differently disturbed forests. We develop an original Bayesian hierarchical model of recovery trajectories, considering disturbed forests in a common framework, through a disturbance intensity gradient. As a case study, we test our modelling approach on data from two long-term experiments, Tirimbina (Costa Rica) and Paracou (French Guiana), where forest permanent sample plots have been set up following selective logging (63.25 ha), agriculture (4 ha), and clearcutting+fire (6.25 ha). We build a modelling framework that stands out by: (i) its interpretability, with model parameters having a clear ecological meaning; (ii) its robustness, allowing to compare parameter values amongst ecological systems to ensure that predictions are ecologically sound; (iii) its versatility to consider disturbance intensity through postdisturbance changes in a structural variable, either as input data, or as a transformed parameter without requiring pre-disturbance monitoring; (iv) its flexibility to explicitly consider, and test, the effects on forest recovery of various disturbance types, along an intensity gradient, in a single integrative model. First conclusions drawn from our common framework underline the strongest above-ground biomass and diversity recovery rate offered by selective logging, compared to agriculture and clearcutting+fire, as well as the strong effect of disturbance intensity on taxonomic composition recovery. Considering disturbed forests in a common framework might help managers to know which disturbance types need to be firmly avoided, which intensity range makes human activities sustainable in forested environments, and where inexpensive natural regeneration should be favoured over active restoration, such as tree planting. Testing this framework with various monitoring tools to estimate disturbance intensity and model vegetation attribute recovery, i.e., with forest monitoring or remote sensing data, will be the next step to make it widely applicable.
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Elsevier
2023
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| Subjects: | SUCESION VEGETAL, PLANT SUCCESSION, CONSERVACION DE LA NATURALEZA, NATURE CONSERVATION, MODELIZACION DEL MEDIO AMBIENTE, ENVIRONMENTAL MODELLING, BOSQUES TROPICALES, TROPICAL FORESTS, |
| Online Access: | https://repositorio.catie.ac.cr/handle/11554/4672 |
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SUCESION VEGETAL PLANT SUCCESSION CONSERVACION DE LA NATURALEZA NATURE CONSERVATION MODELIZACION DEL MEDIO AMBIENTE ENVIRONMENTAL MODELLING BOSQUES TROPICALES TROPICAL FORESTS SUCESION VEGETAL PLANT SUCCESSION CONSERVACION DE LA NATURALEZA NATURE CONSERVATION MODELIZACION DEL MEDIO AMBIENTE ENVIRONMENTAL MODELLING BOSQUES TROPICALES TROPICAL FORESTS |
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SUCESION VEGETAL PLANT SUCCESSION CONSERVACION DE LA NATURALEZA NATURE CONSERVATION MODELIZACION DEL MEDIO AMBIENTE ENVIRONMENTAL MODELLING BOSQUES TROPICALES TROPICAL FORESTS SUCESION VEGETAL PLANT SUCCESSION CONSERVACION DE LA NATURALEZA NATURE CONSERVATION MODELIZACION DEL MEDIO AMBIENTE ENVIRONMENTAL MODELLING BOSQUES TROPICALES TROPICAL FORESTS Maurent, Eliott autor 349416 Delgado, Diego autor 9 Finegan, Bryan autor 1 Ngo-Bieng, Marie Ange autor 348498 y otros 5 autores más A common framework to model recovery in disturbed tropical forests |
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Despite their exceptional biodiversity and carbon stocks, more than 80% of tropical forests are disturbed. However, a lot of interrogations remain around the ability of vegetation attributes in tropical forests to recover from the various anthropogenic disturbances coexisting in many tropical landscapes. While these different disturbances are usually studied separately, this work provides, for the first time, a common modelling framework of vegetation attribute recovery in differently disturbed forests. We develop an original Bayesian hierarchical model of recovery trajectories, considering disturbed forests in a common framework, through a disturbance intensity gradient. As a case study, we test our modelling approach on data from two long-term experiments, Tirimbina (Costa Rica) and Paracou (French Guiana), where forest permanent sample plots have been set up following selective logging (63.25 ha), agriculture (4 ha), and clearcutting+fire (6.25 ha). We build a modelling framework that stands out by: (i) its interpretability, with model parameters having a clear ecological meaning; (ii) its robustness, allowing to compare parameter values amongst ecological systems to ensure that predictions are ecologically sound; (iii) its versatility to consider disturbance intensity through postdisturbance changes in a structural variable, either as input data, or as a transformed parameter without requiring pre-disturbance monitoring; (iv) its flexibility to explicitly consider, and test, the effects on forest recovery of various disturbance types, along an intensity gradient, in a single integrative model. First conclusions drawn from our common framework underline the strongest above-ground biomass and diversity recovery rate offered by selective logging, compared to agriculture and clearcutting+fire, as well as the strong effect of disturbance intensity on taxonomic composition recovery. Considering disturbed forests in a common framework might help managers to know which disturbance types need to be firmly avoided, which intensity range makes human activities sustainable in forested environments, and where inexpensive natural regeneration should be favoured over active restoration, such as tree planting. Testing this framework with various monitoring tools to estimate disturbance intensity and model vegetation attribute recovery, i.e., with forest monitoring or remote sensing data, will be the next step to make it widely applicable. |
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SUCESION VEGETAL PLANT SUCCESSION CONSERVACION DE LA NATURALEZA NATURE CONSERVATION MODELIZACION DEL MEDIO AMBIENTE ENVIRONMENTAL MODELLING BOSQUES TROPICALES TROPICAL FORESTS |
| author |
Maurent, Eliott autor 349416 Delgado, Diego autor 9 Finegan, Bryan autor 1 Ngo-Bieng, Marie Ange autor 348498 y otros 5 autores más |
| author_facet |
Maurent, Eliott autor 349416 Delgado, Diego autor 9 Finegan, Bryan autor 1 Ngo-Bieng, Marie Ange autor 348498 y otros 5 autores más |
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Maurent, Eliott autor 349416 |
| title |
A common framework to model recovery in disturbed tropical forests |
| title_short |
A common framework to model recovery in disturbed tropical forests |
| title_full |
A common framework to model recovery in disturbed tropical forests |
| title_fullStr |
A common framework to model recovery in disturbed tropical forests |
| title_full_unstemmed |
A common framework to model recovery in disturbed tropical forests |
| title_sort |
common framework to model recovery in disturbed tropical forests |
| publisher |
Elsevier |
| publishDate |
2023 |
| url |
https://repositorio.catie.ac.cr/handle/11554/4672 |
| work_keys_str_mv |
AT maurenteliottautor349416 acommonframeworktomodelrecoveryindisturbedtropicalforests AT delgadodiegoautor9 acommonframeworktomodelrecoveryindisturbedtropicalforests AT fineganbryanautor1 acommonframeworktomodelrecoveryindisturbedtropicalforests AT ngobiengmarieangeautor348498 acommonframeworktomodelrecoveryindisturbedtropicalforests AT yotros5autoresmas acommonframeworktomodelrecoveryindisturbedtropicalforests AT maurenteliottautor349416 commonframeworktomodelrecoveryindisturbedtropicalforests AT delgadodiegoautor9 commonframeworktomodelrecoveryindisturbedtropicalforests AT fineganbryanautor1 commonframeworktomodelrecoveryindisturbedtropicalforests AT ngobiengmarieangeautor348498 commonframeworktomodelrecoveryindisturbedtropicalforests AT yotros5autoresmas commonframeworktomodelrecoveryindisturbedtropicalforests |
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1768989129076375552 |
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KOHA-OAI-BVE:1509492023-06-14T13:59:38ZA common framework to model recovery in disturbed tropical forests Maurent, Eliott autor 349416 Delgado, Diego autor 9 Finegan, Bryan autor 1 Ngo-Bieng, Marie Ange autor 348498 y otros 5 autores más textElsevier2023engpdfDespite their exceptional biodiversity and carbon stocks, more than 80% of tropical forests are disturbed. However, a lot of interrogations remain around the ability of vegetation attributes in tropical forests to recover from the various anthropogenic disturbances coexisting in many tropical landscapes. While these different disturbances are usually studied separately, this work provides, for the first time, a common modelling framework of vegetation attribute recovery in differently disturbed forests. We develop an original Bayesian hierarchical model of recovery trajectories, considering disturbed forests in a common framework, through a disturbance intensity gradient. As a case study, we test our modelling approach on data from two long-term experiments, Tirimbina (Costa Rica) and Paracou (French Guiana), where forest permanent sample plots have been set up following selective logging (63.25 ha), agriculture (4 ha), and clearcutting+fire (6.25 ha). We build a modelling framework that stands out by: (i) its interpretability, with model parameters having a clear ecological meaning; (ii) its robustness, allowing to compare parameter values amongst ecological systems to ensure that predictions are ecologically sound; (iii) its versatility to consider disturbance intensity through postdisturbance changes in a structural variable, either as input data, or as a transformed parameter without requiring pre-disturbance monitoring; (iv) its flexibility to explicitly consider, and test, the effects on forest recovery of various disturbance types, along an intensity gradient, in a single integrative model. First conclusions drawn from our common framework underline the strongest above-ground biomass and diversity recovery rate offered by selective logging, compared to agriculture and clearcutting+fire, as well as the strong effect of disturbance intensity on taxonomic composition recovery. Considering disturbed forests in a common framework might help managers to know which disturbance types need to be firmly avoided, which intensity range makes human activities sustainable in forested environments, and where inexpensive natural regeneration should be favoured over active restoration, such as tree planting. Testing this framework with various monitoring tools to estimate disturbance intensity and model vegetation attribute recovery, i.e., with forest monitoring or remote sensing data, will be the next step to make it widely applicable. Despite their exceptional biodiversity and carbon stocks, more than 80% of tropical forests are disturbed. However, a lot of interrogations remain around the ability of vegetation attributes in tropical forests to recover from the various anthropogenic disturbances coexisting in many tropical landscapes. While these different disturbances are usually studied separately, this work provides, for the first time, a common modelling framework of vegetation attribute recovery in differently disturbed forests. We develop an original Bayesian hierarchical model of recovery trajectories, considering disturbed forests in a common framework, through a disturbance intensity gradient. As a case study, we test our modelling approach on data from two long-term experiments, Tirimbina (Costa Rica) and Paracou (French Guiana), where forest permanent sample plots have been set up following selective logging (63.25 ha), agriculture (4 ha), and clearcutting+fire (6.25 ha). We build a modelling framework that stands out by: (i) its interpretability, with model parameters having a clear ecological meaning; (ii) its robustness, allowing to compare parameter values amongst ecological systems to ensure that predictions are ecologically sound; (iii) its versatility to consider disturbance intensity through postdisturbance changes in a structural variable, either as input data, or as a transformed parameter without requiring pre-disturbance monitoring; (iv) its flexibility to explicitly consider, and test, the effects on forest recovery of various disturbance types, along an intensity gradient, in a single integrative model. First conclusions drawn from our common framework underline the strongest above-ground biomass and diversity recovery rate offered by selective logging, compared to agriculture and clearcutting+fire, as well as the strong effect of disturbance intensity on taxonomic composition recovery. Considering disturbed forests in a common framework might help managers to know which disturbance types need to be firmly avoided, which intensity range makes human activities sustainable in forested environments, and where inexpensive natural regeneration should be favoured over active restoration, such as tree planting. Testing this framework with various monitoring tools to estimate disturbance intensity and model vegetation attribute recovery, i.e., with forest monitoring or remote sensing data, will be the next step to make it widely applicable. SUCESION VEGETALPLANT SUCCESSIONCONSERVACION DE LA NATURALEZANATURE CONSERVATIONMODELIZACION DEL MEDIO AMBIENTEENVIRONMENTAL MODELLINGBOSQUES TROPICALESTROPICAL FORESTShttps://repositorio.catie.ac.cr/handle/11554/4672 |