Periodic Reporting for period 1 - FORECAST (Predicting mixed forests responses to climate change and drought stress with a plant hydraulics model)
Periodo di rendicontazione: 2021-03-08 al 2023-03-07
The frequency and intensity of droughts are expected to increase in many regions of the world due to global climate change. Droughts can lead to widespread tree mortality, leading to a loss of carbon stored in forests that will feedback to exacerbate climate change. Thus, modelling forest responses to drought is critical for predicting climate change socio-economic and ecological impacts and informing conservation strategies. However, current models are not capable of adequately representing forest responses to drought, especially for mixed forests due to their diversity in species and functional traits.
The main objective of FORECAST was to develop a model for mixed forests based on known biophysical constraints, plant hydraulic functional traits and optimization theory. The developed model can be used to predict how diverse species growing in mixed forests will respond to different scenarios of climate change. The model´s outputs allow calculating carbon assimilation due to photosynthesis and mortality risk of plants due to hydraulic damage. This data can help managers guide different forestry practices in order to maximize a forest´s resilience so that it can keep providing products and services.
The main objective of FORECAST was to develop a model for mixed forests based on known biophysical constraints, plant hydraulic functional traits and optimization theory. The developed model can be used to predict how diverse species growing in mixed forests will respond to different scenarios of climate change. The model´s outputs allow calculating carbon assimilation due to photosynthesis and mortality risk of plants due to hydraulic damage. This data can help managers guide different forestry practices in order to maximize a forest´s resilience so that it can keep providing products and services.
The fellow completed all training during the period reported. This includes on hands training in plant hydraulics, ecophysiology and forest modelling, completing courses on tree climbing and university teaching, gaining mentoring and teaching experience, and acquiring project management skills.
Main FORECAST results are the development of a model written in R that can be used for predicting mixed forests responses to environmental variables. Key output elements of this model are assimilation and drought-stress indicators. Detailed evaluation with trait data and environmental measurements from Hayedo de Montejo (Madrid, Spain) is still in progress. However, the model’s performance was tested against gas exchange data from an eddy-covariance tower and the results from this comparison have been published in Journal of Advances in Modeling Earth Systems.
The model has the potential to be adapted as a tool that managers from a forest can use to predict the effects of climate change or silvicultural practices such as thinning. It can also be used to determine which species will be more vulnerable under new environmental conditions. This can allow managers to decide if they want to perform silvicultural practices that favour those vulnerable species or prefer to accelerate change in species composition of forests for those that will be more resilient to future environmental conditions. The final version of the model will be archived in a public repository so that it is freely accessible for its use.
FORECAST hypothesis and results have been disseminated targeting different audiences. The project in its results were presented to scientists at European Geophysical Union General Assembly in Vienna (May 2022), in seminars at Universidad Politécnica de Madrid (UPM), and in a scientific paper. FORECAST was presented to stakeholders and managers during a secondment at Dirección General de Biodiversidad y Recursos Naturales de la Comunidad de Madrid. The project has also been presented to students of UPM during lectures, field trips, and the elaboration of final degree project and a master thesis. The fellows also presented the project to primary and secondary school students during the “Science is Wonderful” (November 2021) event organized by the European Commission. Finally, social media accounts and the researcher’s website were also used for disseminating results.
Main FORECAST results are the development of a model written in R that can be used for predicting mixed forests responses to environmental variables. Key output elements of this model are assimilation and drought-stress indicators. Detailed evaluation with trait data and environmental measurements from Hayedo de Montejo (Madrid, Spain) is still in progress. However, the model’s performance was tested against gas exchange data from an eddy-covariance tower and the results from this comparison have been published in Journal of Advances in Modeling Earth Systems.
The model has the potential to be adapted as a tool that managers from a forest can use to predict the effects of climate change or silvicultural practices such as thinning. It can also be used to determine which species will be more vulnerable under new environmental conditions. This can allow managers to decide if they want to perform silvicultural practices that favour those vulnerable species or prefer to accelerate change in species composition of forests for those that will be more resilient to future environmental conditions. The final version of the model will be archived in a public repository so that it is freely accessible for its use.
FORECAST hypothesis and results have been disseminated targeting different audiences. The project in its results were presented to scientists at European Geophysical Union General Assembly in Vienna (May 2022), in seminars at Universidad Politécnica de Madrid (UPM), and in a scientific paper. FORECAST was presented to stakeholders and managers during a secondment at Dirección General de Biodiversidad y Recursos Naturales de la Comunidad de Madrid. The project has also been presented to students of UPM during lectures, field trips, and the elaboration of final degree project and a master thesis. The fellows also presented the project to primary and secondary school students during the “Science is Wonderful” (November 2021) event organized by the European Commission. Finally, social media accounts and the researcher’s website were also used for disseminating results.
The progress beyond the state of the art achieved is the development of a model that can more accurately predict the responses of mixed forests to environmental change. This is a process-based model that relies on plant functional traits and forest stand characteristics for its parameterization. The model outputs allow inferring the amount of carbon dioxide that the stand can assimilate through photosynthesis and the risk of mortality due to drought-stress. This model has the potential to become a tool that helps forest managers decide what silvicultural practices they will apply in a stand to ensure the forest can still provide products and services that society requires in a sustainable way.