Biology subjects

Boussange, V.

Publications and source records attributed to Boussange, V..

5 recordsLinked to original sources

bioRxiv2026

Dynamic sedimentary ancient DNA models of Fennoscandic Holocene plant communities reveal the role of temperature and competition

Ecosystems are constantly responding to shifting environmental conditions, and the observed structure of ecological communities at any given discrete observation timepoint is part of a larger dynamic response. However, we lack long-term time series of ecosystem responses allowing understanding of the processes driving community dynamics. Sedimentary ancient DNA (sed aDNA) offers novel opportunities to link empirical data with ecological processes, providing near continuous records of plant and animal community changes over millennia. Here, we analyzed metabarcoding data from 10 lakes from Northern Fennoscandia characterizing plant and mammal communities at temporal resolution of 100 years over several millenia. We used sed aDNA data to test how biotic processes, including temperature-dependent plant growth, competition, shading, and herbivory, may have driven the dynamics of Fennoscandic Holocene plant communities since the Last Glacial Maximum. We compared the explanatory power of ordinary differential equation-based models simulating community changes and including these processes. The fit of the models improved with the consideration of temperature dependent densification and biotic interactions, most notably shading. When considering individual taxa, the importance of shading and herbivory was greatest for the dwarf shrubs, while tree and shrub taxa were more often best predicted by models without competition. Our study demonstrates how using sed aDNA time series together with process-based inverse modelling can uncover the mechanisms of species response to climate change, offering potential for more realistic predictions.

ecology↗

bioRxiv2026

A unified framework for prioritizing habitat and connectivity conservation through analytical sensitivity

ContextConnected habitats underpin key ecological processes such as movement, gene flow, and recolonization. As land-use change accelerates habitat loss and fragmentation, there is an urgent need to identify priority areas for conserving habitat and maintaining connectivity. ObjectivesWe develop a unified spatial prioritization framework for functional connectivity that is grounded in metapopulation theory, accommodates species-specific movement behavior, and captures the dual role of landscape units as both habitat providers and connectivity facilitators. MethodsWe formalize the landscape as a network in which nodes represent spatial units characterized by habitat quality and edges encode permeability for movement. Pairwise ecological proximities are defined from species-specific movement capacity and an ecological distance that integrates costs across paths, expressed as least-cost distance, effective resistance, expected cost, or survival probability depending on the movement ecology of the focal species. From these proximities and node qualities, we construct the landscape matrix. Based on the sensitivity of the landscape matrix to local perturbations in habitat quality or permeability, we develop a prioritization approach that identifies where local changes most strongly influence landscape-scale habitat connectivity. ResultsOur framework unifies widely used landscape connectivity metrics across the full spectrum of movement models, including least-cost paths, circuit theory, spatial absorbing Markov chains, and randomized shortest paths. We show that summation-based metrics serve as computationally efficient approximations of metapopulation capacity, enabling evaluation of functional connectivity in large, high-resolution landscapes. We derive sensitivities for these metrics with respect to habitat quality and permeability and show that they correspond to two families of centrality measures: a closeness-like centrality reflecting the influence of habitat quality, and a betweenness-like centrality reflecting the influence of habitat permeability. Applying our prioritization approach based on these sensitivities to a case study involving wild reindeer in Norway, we identify high-value habitat and key movement corridors that closely align with a reference node-removal approach while reducing computation time by several orders of magnitude. ConclusionsBy linking metapopulation theory with sensitivity analysis, our work provides a unified and scalable framework for spatial prioritization of functional connectivity. It supports evidence-based management of fragmented landscapes and advances both theoretical understanding and practical application in landscape ecology.

ecology↗

bioRxiv2025

Spatial Biodiversity Indicators and a Composite Index for Conservation Prioritization in Switzerland

Spatially explicit indicators that quantify to which extent landscapes support biodiversity are essential for guiding evidence-based conservation planning. Here, we present a 25-meter resolution dataset for Switzerland, encompassing three key biodiversity indicators-- Complementarity, Extinction Risk, and Ecological Connectivity--developed across 17 major taxonomic groups, using habitat suitability maps for approximately 7,500 individual species. These three indicators capture (1) the contribution of landscapes to taxonomic, functional, and phylogenetic diversity, (2) species vulnerability to extinction and (3) ecological connectivity. Outputs are provided for both terrestrial and aquatic realms, including versions adjusted for species richness, and integrated into composite indices. In total, 272 spatial layers were produced and made openly accessible. Relationships among the three indicators were analyzed to assess their distinct contributions, and expert-based validations were performed to evaluate their ecological plausibility and relevance for conservation applications. This dataset provides a robust foundation to support spatial planning and conservation decision-making in Switzerland and can be used as a blueprint for analogue integration in other countries.

ecology↗

bioRxiv2022

Mini-batching ecological data to improve ecosystem models with machine learning

Process-based, dynamic models are essential for extrapolating beyond current trends and anticipating biodiversity responses to global change. However, their practical adoption for forecasting purposes remains limited due to difficulties in calibrating them against data and structural inaccuracies in their mathematical formulations. While ecological time series could, in principle, be used to directly estimate model parameters and refine model structures, the large noise levels in ecological datasets and the strong nonlinearity of ecological dynamics challenge conventional calibration methods. Here, we present a robust and scalable calibration framework that addresses these challenges by integrating techniques from scientific computing and deep learning. Our approach combines a segmentation strategy where state variables are estimated independently, differentiable programming for efficient gradient computation, parameter transformations to ensure the feasibility and stability of the model simulations, and mini-batching to accomodate large datasets. Through comprehensive benchmarks using simulated food web dynamics of increasing complexity, we demonstrate that the framework substantially improves the convergence of gradient descent algorithms and Monte Carlo sampling methods, accommodating for realistic scenarios with noisy and partial observations. This yields improved parameter estimation and forecasts within both mode estimation and full posterior distribution contexts. Crucially, we show that the calibration framework scales effectively with both the number of parameters and state variables. The improved convergence and scalability of the calibration framework enables hybrid modeling approaches, where neural networks parameterize complex processes within the dynamic model. In particular, we demonstrate that neural networks can effectively capture environmental dependencies in demographic rates and recover functional responses governing trophic interactions. Neural network-based parameterizations have the capability to improve the structural inaccuracies of models while maintaining ecological interpretability through post-hoc analysis of learned representations. We provide an implementation of the calibration framework and other key utilities as the open-source Julia package HybridDynamicModels.jl, with the hope that the package will facilitate the development of hybrid modeling approaches in quantitative ecology and related fields.

ecology↗

bioRxiv2021

Topology and habitat assortativity drive neutral and adaptive diversification in spatial graphs

Biodiversity results from differentiation mechanisms developing within biological populations. Such mechanisms are influenced by the properties of the landscape over which individuals interact, disperse and evolve. Notably, landscape connectivity and habitat heterogeneity constrain the movement and survival of individuals, thereby promoting differentiation through drift and local adaptation. Nevertheless, the complexity of landscape features can blur our understanding of how they drive differentiation. Here, we formulate a stochastic, eco-evolutionary model where individuals are structured over a graph that captures complex connectivity patterns and accounts for habitat heterogeneity. Individuals possess neutral and adaptive traits, whose divergence results in differentiation at the population level. The modelling framework enables an analytical underpinning of emerging macroscopic properties, which we complement with numerical simulations to investigate how the graph topology and the spatial habitat distribution affect differentiation. We show that in the absence of selection, graphs with high characteristic length and high heterogeneity in degree promote neutral differentiation. Habitat assortativity, a metric that captures habitat spatial autocorrelation in graphs, additionally drives differentiation patterns under habitat-dependent selection. While assortativity systematically amplifies adaptive differentiation, it can foster or depress neutral differentiation depending on the migration regime. By formalising the eco-evolutionary and spatial dynamics of biological populations in complex landscapes, our study establishes the link between landscape features and the emergence of diversification, contributing to a fundamental understanding of the origin of biodiversity gradients. Significance statementIt is not clear how landscape connectivity and habitat heterogeneity influence differentiation in biological populations. To obtain a mechanistic understanding of underlying processes, we construct an individualbased model that accounts for eco-evolutionary and spatial dynamics over graphs. Individuals possess both neutral and adaptive traits, whose co-evolution results in differentiation at the population level. In agreement with empirical studies, we show that characteristic length, heterogeneity in degree and habitat assortativity drive differentiation. By using analytical tools that permit a macroscopic description of the dynamics, we further link differentiation patterns to the mechanisms that generate them. Our study provides support for a mechanistic understanding of how landscape features affect diversification.

evolutionary biology↗