Posted in | News | Climate Change | Ecosystems

Researchers Develop New Model to Forecast Forest Tree Growth Based on Climate Factors

As a result of climate change, forest mortality has increased in a variety of tree species and habitats. Transplantation options tailored to evolutionary mechanisms, such as the idea of transferring trees to more appropriate climates, are being investigated in response to this worrying scenario.

Researchers Develop New Model to Forecast Forest Tree Growth Based on Climate Factors.

Image Credit: © Unité conservatoire génétique de Lacanau

A team of INRAE and CNRS scientists has created models based on height growth in maritime pine to anticipate how trees will respond in a particular environment.

Their findings reveal that models that include genomic and meteorological data forecast tree height growth clearer than models based solely on climatic factors. These findings could quickly translate into practical uses in forest conservation and management, particularly through transplantation tactics. The study was published in The American Naturalist on April 29th, 2022.

For forest ecosystems to operate and survive, trees are required. However, if the global change increases, some tree populations that are slow to adjust may face population reduction or even extinction.

To avert such eventualities, conservation and forest management measures can be applied, such as relocating trees to more appropriate climates (assisted gene flow) or vulnerable populations with low genetic diversity (evolutionary rescue).

Since such techniques bind forest management officials for several years, it is crucial to predict how transplanted trees will react in their new surroundings.

Until now, prediction models have been based mainly on the climate of origin of transplanted tree populations. However, genomic data provide valuable information on adaptive processes in trees, such as growth. With climatic and genomic information more and more accessible, thanks to the continually decreasing cost of sequencing technology, the research team developed models combining these two types of data to improve the robustness and accuracy of predictions.

A Model-Based On a Large-Scale Experimental Scheme of Maritime Pine in France, Spain, and Portugal

The models were created using maritime pine, a representative species of the Mediterranean area. With trees from 34 maritime pine groups obtained throughout the species’ natural range, an experimental monitoring system was built at five sites in France (Cestas Pierroton), Spain (Asturias, Cáceres, and Madrid) and Portugal (Fundão).

Researchers concentrated on forecasting tree height growth, which is important in both economic and ecological aspects because the fastest growing trees have a better chance of survival and reproduction.

The results reveal that the diverse gene pools from which they come, as well as the different conditions in which they have developed, account for observed height disparities in maritime pine. When compared to models based solely on climatic data, models incorporating climatic and genomic data, enhanced projections of population height growth by an average of 14–25% based on the experimental area.

The observations have the potential to help explain how transplanted tree populations adjust to a changing habitat from the perspective of forest conservation and management.

Journal Reference:

Archambeau, J., et al. (2022) Combining climatic and genomic data improves range-wide tree height growth prediction in a forest tree. The American Naturalist.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.