About this Research Topic
In recent decades, climate change has modified the growth of forests, mainly due to increasing temperature and altered precipitation patterns. Several biogeographical models demonstrate a poleward shift of boreal and hardwood tree species distribution at a higher rate than in past glacial times. Due to climate change, some models project a 50% loss in the geographic range of some tree species by 2100, resulting in diversity loss and impacting forest productivity. In addition, rising air temperatures decrease precipitation and increase solar radiation, which may enhance several critical constraints to plant growth. This environmental variability, owing to climate change, could potentially disrupt temporal changes in species richness, demographic rates, and biomass. Despite these predictions, more detailed knowledge of which climate variables might have the most significant impact on tree growth is needed. By thoroughly investigating climate variables, we gain a deeper understanding of the diverse ecosystem services they provide, encompassing not just environmental benefits but also socio-economic and cultural values.
Mitigating climate change's impact on forests requires a multi-pronged approach, focusing on how tree species adapt to climate variations. We can develop resilient forest management strategies by understanding the interplay between climate factors, soil composition, and species performance. Leveraging predictive models, especially machine learning techniques like random forests, is crucial. These models provide insights into key parameters, such as basal area and stem density, informing adaptive strategies that ensure forest health amidst evolving climate conditions. This integrated approach, combining empirical data and advanced modeling, enhances our ability to protect forests against the challenges of a changing climate.
We aim to underscore the pivotal role of predictive modeling in unraveling the intricate dynamics of growth within forest ecosystems grappling with the uncertainties of climate change. Our primary focus is on how these models serve as invaluable tools to inform adaptive forest management strategies, thereby ensuring the sustained vitality of species in the face of escalating temperatures and unpredictable climate patterns. The overarching scope of this initiative includes the critical integration of empirical data and cutting-edge modeling techniques. Through this synergy, we aim to facilitate the development of agile strategies that can effectively fortify forests against the formidable challenges posed by an evolving climate landscape.
Call for Submissions: We cordially invite contributions from authors across the scientific community, including original research articles, case studies, opinion papers, short-communication pieces, methods, reviews, and perspective papers, all aimed at exploring various facets of this vital theme. While we encourage diverse perspectives and approaches, submissions may encompass, but are not limited to, the following subtopics:
1) Enhanced Niche Models and Mechanistic Approaches: -Investigating advancements in niche models or mechanistic (physiological) approaches for predicting tree species distribution and understanding growth patterns across spatial and temporal dimensions.
2) Climate Change and Tree Species Distribution: -Delving into the potential impacts of climate change on the distribution and growth dynamics of key tree species and their ecological significance.
3) Modeling Regional Species Diversity Change: -Utilizing species distribution models to simulate changes in regional species diversity under climate change, offering insights into ecological shifts.
4) Adapting Forest Management: Modeling the Impact of Non-Native Species and Changing Distributions: -We're exploring the suitability of non-native tree species in new environments, assessing their growth dynamics, and understanding the implications of changing tree species distributions on sustainable forest management.
Join us in contributing to advancing our collective understanding of forest ecosystems in the context of a changing climate. Together, we can forge innovative strategies to ensure the resilience and survival of these invaluable ecosystems. Your contributions will be instrumental in shaping a sustainable future for our forests.
Mitigating climate change's impact on forests requires a multi-pronged approach, focusing on how tree species adapt to climate variations. We can develop resilient forest management strategies by understanding the interplay between climate factors, soil composition, and species performance. Leveraging predictive models, especially machine learning techniques like random forests, is crucial. These models provide insights into key parameters, such as basal area and stem density, informing adaptive strategies that ensure forest health amidst evolving climate conditions. This integrated approach, combining empirical data and advanced modeling, enhances our ability to protect forests against the challenges of a changing climate.
We aim to underscore the pivotal role of predictive modeling in unraveling the intricate dynamics of growth within forest ecosystems grappling with the uncertainties of climate change. Our primary focus is on how these models serve as invaluable tools to inform adaptive forest management strategies, thereby ensuring the sustained vitality of species in the face of escalating temperatures and unpredictable climate patterns. The overarching scope of this initiative includes the critical integration of empirical data and cutting-edge modeling techniques. Through this synergy, we aim to facilitate the development of agile strategies that can effectively fortify forests against the formidable challenges posed by an evolving climate landscape.
Call for Submissions: We cordially invite contributions from authors across the scientific community, including original research articles, case studies, opinion papers, short-communication pieces, methods, reviews, and perspective papers, all aimed at exploring various facets of this vital theme. While we encourage diverse perspectives and approaches, submissions may encompass, but are not limited to, the following subtopics:
1) Enhanced Niche Models and Mechanistic Approaches: -Investigating advancements in niche models or mechanistic (physiological) approaches for predicting tree species distribution and understanding growth patterns across spatial and temporal dimensions.
2) Climate Change and Tree Species Distribution: -Delving into the potential impacts of climate change on the distribution and growth dynamics of key tree species and their ecological significance.
3) Modeling Regional Species Diversity Change: -Utilizing species distribution models to simulate changes in regional species diversity under climate change, offering insights into ecological shifts.
4) Adapting Forest Management: Modeling the Impact of Non-Native Species and Changing Distributions: -We're exploring the suitability of non-native tree species in new environments, assessing their growth dynamics, and understanding the implications of changing tree species distributions on sustainable forest management.
Join us in contributing to advancing our collective understanding of forest ecosystems in the context of a changing climate. Together, we can forge innovative strategies to ensure the resilience and survival of these invaluable ecosystems. Your contributions will be instrumental in shaping a sustainable future for our forests.
Keywords: Forest, climate change, random forest (bagging) and machine learning, forest growth, tree migration
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
