Biology subjects

de Coligny, F.

Publications and source records attributed to de Coligny, F..

4 recordsLinked to original sources

bioRxiv2025

PhorEau: a new process-based model to predict forest functioning, from tree ecophysiology to forest dynamics and biogeography

Climate change impacts forest functioning and dynamics, yet significant uncertainties persist regarding the interactions between species composition, demographic processes, and environmental drivers. While the effects of climate change on individual plant ecophysiology are better understood, few robust tools integrate these processes dynamically, hindering accurate projections and recommendations for long-term sustainable forest management. Forest gap models strike a balance between complexity and generality and are widely used in predictive forest ecology. However, their lack of explicit representation of critical processes--such as plant phenology and water use--limits their ability to fully capture tree sensitivity to climate change, calling into question the robustness of their future predictions. Therefore, incorporating trait- and process-based representations of climate-sensitive processes within gap models is a crucial step toward generating realistic predictions of forest evolution under climate change. In this study, we coupled the ForCEEPS gap model, validated across a broad range of forest types and environmental conditions in Europe, with two process-based models: a plant phenology model (PHENOFIT) and a plant hydraulics model (SurEAU), each parameterized for the main European tree species. We then evaluated the performance of the resulting PHOREAU model across multiple processes, metrics, and time- and spatial-scales, thereby minimizing the risk of equifinality. PHOREAU demonstrated robust capabilities in predicting fine hydraulic processes at both the forest and stand scales for various species and forest types. This, combined with its enhanced ability to predict stand leaf areas from inventories, led to modest improvements in annual growth predictions compared to the original ForCEEPS model and a strong capacity to predict potential community compositions. By integrating recent advancements in plant hydraulics, phenology, and competition for light and water into a dynamic, individual-based framework, the PHOREAU model bridges the gap between trait diversity and long-term forest productivity and resilience. It offers insights into complex emergent properties and trade-offs linked to diversity effects under extreme climatic events, with significant implications for sustainable forest management strategies.

ecology↗

bioRxiv2020

Managing mixed stands can mitigate severe climate change impacts on ecosystem functioning

Climate change affects forest ecosystem processes and related services due to increasing temperature and increasing extreme drought event frequency. This effect can be direct through the alteration of the physiological responses of trees, but also indirect, by modifying interactions between trees and thus changing communities composition. Such changes might affect species richness with high impacts on ecosystem functioning, especially productivity. Regarding management issues, mixed stands are usually considered a good option to maintain forest cover and ecosystem services under climate change. However, the possibility to maintain these mixed stands with management actions with positive effects on forest functioning under climate change remains uncertain and deserves further investigations. Relying on a simulation-based study with a forest gap model, we thus addressed the following questions: (1) Are monospecific stands vulnerable to climate change? (2) Would mixed stands significantly mitigate climate change effects on forest productivity and wood production under climate change? (3) Would conversion to mixed stand management affect significantly forest productivity and wood production under climate change compare to monospecific management? With a 150 years simulation approach, we quantified potential climate change effect (using RCP 8.5) compared to present climate and managements effect in the French Alps, focusing on five tree species. The gap-model we used included a management module, which allowed testing six silvicultural scenarios on different stands, with various composition, structure or environmental conditions, under climate change. These simulations showed that monospecific stands currently growing in stressful conditions would be too vulnerable to climate change to be maintained. Managing mixed stands or conversion from pure to mixed stands would make it possible to maintain higher productivity in the long-term than monospecific stands, even under severe climate change. This pattern depends to species and sites considered. Our results will feed into discussion on forest management in the context of climate change.

ecology↗

bioRxiv2020

Beyond forest succession: a gap model to study ecosystem functioning and tree community composition under climate change

Climate change impacts forest functioning and dynamics, and large uncertainties remain regarding the interactions between species composition, demographic processes, and environmental drivers. There are few robust tools available to link these processes, which precludes accurate projections and recommendations for long-term forest management. Forest gap-models present a balance between complexity and generality and are widely used in predictive forest ecology. However, their relevance to tackle questions about the links between species composition, climate and forest functioning is unclear. In this regard, demonstrating the ability of gap-models to predict the growth of forest stands at the annual time scale - representing a sensitive and integrated signal of tree functioning and mortality risk - appears as a fundamental step. In this study, we aimed at assessing the ability of a gap-model to accurately predict forest growth in the short-term and potential community composition in the long-term, across a wide range of species and environmental conditions. To do so, we present the gap-model ForCEEPS, calibrated using an original parameterization procedure for the main tree species in France. ForCEEPS was shown to satisfactorily predict forest annual growth (averaged over a few years) at the plot level from mountain to Mediterranean climates, regardless the species. Such an accuracy was not gained at the cost of losing precision for long-term predictions, as the model showed a strong ability to predict potential community composition along a gradient of sites with contrasted conditions. The mechanistic relevance of ForCEEPS parameterization was explored by showing the congruence between the values of key model parameter and species functional traits. We further showed that accounting for the spatial configuration of crowns within forest stands, the effects of climatic constraints and the variability of shade tolerances in the species community are all crucial to better predict short-term productivity with gap-models. The dual ability of predicting short-term functioning and long-term community composition, as well as the balance between generality and realism (i.e., predicting accuracy) of the new generation of gap-models may open great perspectives for the exploration of the biodiversity-ecosystem functioning relationships, species coexistence mechanisms, and the impacts of climate change on forest ecosystems.

ecology↗

bioRxiv2020

A demogenetic agent based model for the evolution of traits and genome architecture under sexual selection

Sexual selection has long been known to favor the evolution of mating behaviors such as mate preference and competitiveness, and to affect their genetic architecture, for instance by favoring genetic correlation between some traits. Reciprocally, genetic architecture can affect the expression and the evolvability of traits and mating behaviors. But sexual selection is highly context-dependent, making interactions between individuals a central process in evolution, governing the transmission of genotypes to the next generation. This loop between the genetic structure conditioning the expression and evolution of traits and behaviour, and the feedback of this phenotypic evolution on the architecture of the genome in the dynamic context of sexual selection, has yet to be thoroughly investigated. We argue that demogenetic agent-based models (DG-ABM) are especially suited to tackle such a challenge because they allow explicit modelling of both the genetic architecture of traits and the behavioural interactions in a dynamic population context. We here present a DG-ABM able to simultaneously track individual variation in traits (such as gametic investment, preference, competitiveness), fitness and genetic architecture throughout evolution. Using two simulation experiments, we compare various mating systems and show that behavioral interactions during mating triggered some complex feedback in our model, between fitness, population demography, and genetic architecture, placing interactions between individuals at the core of evolution through sexual selection. DG-ABMs can, therefore, relate to theoretical patterns expected at the population level from simpler analytical models in evolutionary biology, and at the same time provide a more comprehensive framework regarding individual trait and behaviour variation, that is usually envisioned separately from genome architecture in behavioural ecology.

evolutionary biology↗