Si-moussi, S. | Search bioRxiv

bioRxiv2025

Synergies and trade-offs between maintaining climate niche variability and preserving climate stability

The impact of climate change on biodiversity accelerates, calling for climate resilient conservation strategies such as protecting areas of high climatic stability (i.e. climate refugia) or protecting the variability of species climatic niches (to preserve adaptive potential). Developing a spatial framework that integrates both strategies, we identify priorities to protect climatic niche components of 1,207 European vertebrates. Priority areas for protecting climatic niches under low climate velocity or low magnitude were respectively found in mountainous/southern regions and in northern/eastern Europe. These synergy areas overlapped by 48-73% with single-objective prioritizations focused on either niche components or climatic stability. Trade-offs occur where climatic niches diversity is high but climate stability is low, such as eastern Europe (velocity) or the Mediterranean and North Fennoscandia (magnitude). Our results reveal spatial mismatches between climate refugia and spatial priorities to preserve adaptive potential, emphasizing the need to combine both strategies in conservation planning.

ecology↗

bioRxiv2024

Causal discovery from ecological time-series with one timestamp and multiple observations

Ecologists often aim to uncover causal relations within ecological systems using observational data. However, many studies still rely mainly on correlation-based approaches, which do not support causal interpretation. Causal discovery methods have recently gained interest, particularly for ecological time-series data. Yet such data are often scarce, as collecting repeated and consistent measurements over long periods is costly, require strict continuation of protocols and is time-consuming. Consequently, ecologists have often access only to single-time-point observational data generated by dynamical systems. In this setting, unobserved past states can induce substantial unmeasured confounding, limiting the ability of standard algorithms such as PC and FCI to recover micro-level causal relations between observed variables. We show that PC and FCI can nevertheless recover meaningful causal information. In particular, they can identify specific cluster-level structures, which we call clustered super-unshielded colliders, which provide information about the partial causal ordering of macro-level variables. We further show that both algorithms can be reduced to a simple procedure, which we call RestPC, that yields the same identifiable information. We illustrate our results using simulated data and two real-world datasets: one on bird abundance, climate, and land cover, and another on soil microbial communities, environmental, terrain, and geochemical variables.

ecology↗