How to Kill a Tree: Empirical Mortality Models for 18 Species and Their Performance in a Dynamic Forest Model. Hülsmann, L., Bugmann, H., Cailleret, M., & Brang, P. 28(2):522–540. ![How to Kill a Tree: Empirical Mortality Models for 18 Species and Their Performance in a Dynamic Forest Model [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Dynamic Vegetation Models (DVMs) are designed to be suitable for simulating forest succession and species range dynamics under current and future conditions based on mathematical representations of the three key processes regeneration, growth, and mortality. However, mortality formulations in DVMs are typically coarse and often lack an empirical basis, which increases the uncertainty of projections of future forest dynamics and hinders their use for developing adaptation strategies to climate change. Thus, sound tree mortality models are highly needed. We developed parsimonious, species-specific mortality models for 18 European tree species using $>$90,000 records from inventories in Swiss and German strict forest reserves along a considerable environmental gradient. We comprehensively evaluated model performance and incorporated the new mortality functions in the dynamic forest model ForClim. Tree mortality was successfully predicted by tree size and growth. Only a few species required additional covariates in their final model to consider aspects of stand structure or climate. The relationships between mortality and its predictors reflect the indirect influences of resource availability and tree vitality, which are further shaped by species-specific attributes such as maximum longevity and shade tolerance. Considering that the behavior of the models was biologically meaningful, and that their performance was reasonably high and not impacted by changes in the sampling design, we suggest that the mortality algorithms developed here are suitable for implementation and evaluation in DVMs. In the DVM ForClim, the new mortality functions resulted in simulations of stand basal area and species composition that were generally close to historical observations. However, ForClim performance was poorer than when using the original, coarse mortality formulation. The difficulties of simulating stand structure and species composition, which were most evident for Fagus sylvatica L. and in long-term simulations, resulted from feedbacks between simulated growth and mortality as well as from extrapolation to very small and very large trees. Growth and mortality processes and their species-specific differences should thus be revisited jointly, with a particular focus on small and very large trees in relation to their shade tolerance.
@article{hulsmannHowKillTree2018,
title = {How to Kill a Tree: Empirical Mortality Models for 18 Species and Their Performance in a Dynamic Forest Model},
author = {Hülsmann, Lisa and Bugmann, Harald and Cailleret, Maxime and Brang, Peter},
date = {2018-03},
journaltitle = {Ecological Applications},
volume = {28},
pages = {522--540},
issn = {1051-0761},
doi = {10.1002/eap.1668},
url = {https://doi.org/10.1002/eap.1668},
abstract = {Dynamic Vegetation Models (DVMs) are designed to be suitable for simulating forest succession and species range dynamics under current and future conditions based on mathematical representations of the three key processes regeneration, growth, and mortality. However, mortality formulations in DVMs are typically coarse and often lack an empirical basis, which increases the uncertainty of projections of future forest dynamics and hinders their use for developing adaptation strategies to climate change. Thus, sound tree mortality models are highly needed. We developed parsimonious, species-specific mortality models for 18 European tree species using {$>$}90,000 records from inventories in Swiss and German strict forest reserves along a considerable environmental gradient. We comprehensively evaluated model performance and incorporated the new mortality functions in the dynamic forest model ForClim. Tree mortality was successfully predicted by tree size and growth. Only a few species required additional covariates in their final model to consider aspects of stand structure or climate. The relationships between mortality and its predictors reflect the indirect influences of resource availability and tree vitality, which are further shaped by species-specific attributes such as maximum longevity and shade tolerance. Considering that the behavior of the models was biologically meaningful, and that their performance was reasonably high and not impacted by changes in the sampling design, we suggest that the mortality algorithms developed here are suitable for implementation and evaluation in DVMs. In the DVM ForClim, the new mortality functions resulted in simulations of stand basal area and species composition that were generally close to historical observations. However, ForClim performance was poorer than when using the original, coarse mortality formulation. The difficulties of simulating stand structure and species composition, which were most evident for Fagus sylvatica L. and in long-term simulations, resulted from feedbacks between simulated growth and mortality as well as from extrapolation to very small and very large trees. Growth and mortality processes and their species-specific differences should thus be revisited jointly, with a particular focus on small and very large trees in relation to their shade tolerance.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14686303,dbh,empirical-equation,forest-dynamics,forest-resources,modelling-uncertainty,precipitation,temperature,tree-mortality,uncertainty},
number = {2}
}
Downloads: 0
{"_id":"duQHdB36cENro2cg6","bibbaseid":"hlsmann-bugmann-cailleret-brang-howtokillatreeempiricalmortalitymodelsfor18speciesandtheirperformanceinadynamicforestmodel","authorIDs":[],"author_short":["Hülsmann, L.","Bugmann, H.","Cailleret, M.","Brang, P."],"bibdata":{"bibtype":"article","type":"article","title":"How to Kill a Tree: Empirical Mortality Models for 18 Species and Their Performance in a Dynamic Forest Model","author":[{"propositions":[],"lastnames":["Hülsmann"],"firstnames":["Lisa"],"suffixes":[]},{"propositions":[],"lastnames":["Bugmann"],"firstnames":["Harald"],"suffixes":[]},{"propositions":[],"lastnames":["Cailleret"],"firstnames":["Maxime"],"suffixes":[]},{"propositions":[],"lastnames":["Brang"],"firstnames":["Peter"],"suffixes":[]}],"date":"2018-03","journaltitle":"Ecological Applications","volume":"28","pages":"522–540","issn":"1051-0761","doi":"10.1002/eap.1668","url":"https://doi.org/10.1002/eap.1668","abstract":"Dynamic Vegetation Models (DVMs) are designed to be suitable for simulating forest succession and species range dynamics under current and future conditions based on mathematical representations of the three key processes regeneration, growth, and mortality. However, mortality formulations in DVMs are typically coarse and often lack an empirical basis, which increases the uncertainty of projections of future forest dynamics and hinders their use for developing adaptation strategies to climate change. Thus, sound tree mortality models are highly needed. We developed parsimonious, species-specific mortality models for 18 European tree species using $>$90,000 records from inventories in Swiss and German strict forest reserves along a considerable environmental gradient. We comprehensively evaluated model performance and incorporated the new mortality functions in the dynamic forest model ForClim. Tree mortality was successfully predicted by tree size and growth. Only a few species required additional covariates in their final model to consider aspects of stand structure or climate. The relationships between mortality and its predictors reflect the indirect influences of resource availability and tree vitality, which are further shaped by species-specific attributes such as maximum longevity and shade tolerance. Considering that the behavior of the models was biologically meaningful, and that their performance was reasonably high and not impacted by changes in the sampling design, we suggest that the mortality algorithms developed here are suitable for implementation and evaluation in DVMs. In the DVM ForClim, the new mortality functions resulted in simulations of stand basal area and species composition that were generally close to historical observations. However, ForClim performance was poorer than when using the original, coarse mortality formulation. The difficulties of simulating stand structure and species composition, which were most evident for Fagus sylvatica L. and in long-term simulations, resulted from feedbacks between simulated growth and mortality as well as from extrapolation to very small and very large trees. Growth and mortality processes and their species-specific differences should thus be revisited jointly, with a particular focus on small and very large trees in relation to their shade tolerance.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-14686303,dbh,empirical-equation,forest-dynamics,forest-resources,modelling-uncertainty,precipitation,temperature,tree-mortality,uncertainty","number":"2","bibtex":"@article{hulsmannHowKillTree2018,\n title = {How to Kill a Tree: Empirical Mortality Models for 18 Species and Their Performance in a Dynamic Forest Model},\n author = {Hülsmann, Lisa and Bugmann, Harald and Cailleret, Maxime and Brang, Peter},\n date = {2018-03},\n journaltitle = {Ecological Applications},\n volume = {28},\n pages = {522--540},\n issn = {1051-0761},\n doi = {10.1002/eap.1668},\n url = {https://doi.org/10.1002/eap.1668},\n abstract = {Dynamic Vegetation Models (DVMs) are designed to be suitable for simulating forest succession and species range dynamics under current and future conditions based on mathematical representations of the three key processes regeneration, growth, and mortality. However, mortality formulations in DVMs are typically coarse and often lack an empirical basis, which increases the uncertainty of projections of future forest dynamics and hinders their use for developing adaptation strategies to climate change. Thus, sound tree mortality models are highly needed. We developed parsimonious, species-specific mortality models for 18 European tree species using {$>$}90,000 records from inventories in Swiss and German strict forest reserves along a considerable environmental gradient. We comprehensively evaluated model performance and incorporated the new mortality functions in the dynamic forest model ForClim. Tree mortality was successfully predicted by tree size and growth. Only a few species required additional covariates in their final model to consider aspects of stand structure or climate. The relationships between mortality and its predictors reflect the indirect influences of resource availability and tree vitality, which are further shaped by species-specific attributes such as maximum longevity and shade tolerance. Considering that the behavior of the models was biologically meaningful, and that their performance was reasonably high and not impacted by changes in the sampling design, we suggest that the mortality algorithms developed here are suitable for implementation and evaluation in DVMs. In the DVM ForClim, the new mortality functions resulted in simulations of stand basal area and species composition that were generally close to historical observations. However, ForClim performance was poorer than when using the original, coarse mortality formulation. The difficulties of simulating stand structure and species composition, which were most evident for Fagus sylvatica L. and in long-term simulations, resulted from feedbacks between simulated growth and mortality as well as from extrapolation to very small and very large trees. Growth and mortality processes and their species-specific differences should thus be revisited jointly, with a particular focus on small and very large trees in relation to their shade tolerance.},\n keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14686303,dbh,empirical-equation,forest-dynamics,forest-resources,modelling-uncertainty,precipitation,temperature,tree-mortality,uncertainty},\n number = {2}\n}\n\n","author_short":["Hülsmann, L.","Bugmann, H.","Cailleret, M.","Brang, P."],"key":"hulsmannHowKillTree2018","id":"hulsmannHowKillTree2018","bibbaseid":"hlsmann-bugmann-cailleret-brang-howtokillatreeempiricalmortalitymodelsfor18speciesandtheirperformanceinadynamicforestmodel","role":"author","urls":{"Paper":"https://doi.org/10.1002/eap.1668"},"keyword":["*imported-from-citeulike-INRMM","~INRMM-MiD:c-14686303","dbh","empirical-equation","forest-dynamics","forest-resources","modelling-uncertainty","precipitation","temperature","tree-mortality","uncertainty"],"downloads":0},"bibtype":"article","biburl":"https://tmpfiles.org/dl/58794/INRMM.bib","creationDate":"2020-07-02T22:41:09.807Z","downloads":0,"keywords":["*imported-from-citeulike-inrmm","~inrmm-mid:c-14686303","dbh","empirical-equation","forest-dynamics","forest-resources","modelling-uncertainty","precipitation","temperature","tree-mortality","uncertainty"],"search_terms":["kill","tree","empirical","mortality","models","species","performance","dynamic","forest","model","hülsmann","bugmann","cailleret","brang"],"title":"How to Kill a Tree: Empirical Mortality Models for 18 Species and Their Performance in a Dynamic Forest Model","year":null,"dataSources":["DXuKbcZTirdigFKPF"]}