Biology subjects

Si-Moussi, S.

Publications and source records attributed to Si-Moussi, S..

5 recordsLinked to original sources

bioRxiv2026

Ecosystem service gradients at protected area borders reveal multiple patterns and prevalent management conflicts

Protected areas cannot halt biodiversity loss in isolation; integrating them with surrounding human-dominated landscapes is critical. However, this integration is challenged by substantial landscape heterogeneity at their borders, hindering our understanding of cross-border changes in ecosystem service provision. We introduce a novel framework for characterizing these dynamics by analyzing ecosystem service gradients along protected area borders. For 16 protected areas in the French Alps, we assessed 12 ecosystem services using a mix of established biophysical models and novel connectivity-based models for mobile species. These were aggregated into three stakeholder-driven domains reflecting respectively rural, cultural, and urban management priorities. Automated polynomial regression analysis classified borders into five gradient types. The most common were Decreasing Gradients, representing a decline in ecosystem services outside the protected area, and Increasing Gradients, with the opposite pattern. Our analysis reveals these patterns are driven by specific landscape configurations, uncovering frequent trade-offs between the three management priorities, where, for instance, landscapes supporting rural priorities often degrade cultural and urban ones. We also identify key opportunities for synergies, by identifying areas where ecosystem services for all three priority domains increase simultaneously outside the protected area. This spatially explicit typology provides a powerful diagnostic tool for designing targeted interventions, such as prioritizing habitat restoration where ecosystem services decline or managing agricultural landscapes to mitigate conflicts across management priorities, supporting a more effective integration of protected areas into the wider landscape. Article impact statementDetermining ecosystem service gradients at protected area borders reveals management trade-offs, guiding targeted spatial planning.

ecology↗

bioRxiv2026

Correcting overprediction reduces the propagation of uncertainty from species distribution models into spatial conservation prioritization

Effective conservation planning increasingly relies on species distribution models (SDMs) to guide where actions deliver the greatest biodiversity benefits through spatial conservation prioritization. However, SDMs are inherently uncertain, and this uncertainty propagates through prioritization processes, affecting the identification of priority areas and influencing conservation decisions. Here, we evaluate whether correcting SDM overprediction reduces uncertainty propagation into spatial conservation prioritization. Using two large European datasets of vertebrates and invertebrates, we compared unconstrained SDMs with models corrected for overprediction through a Bayesian integration of occurrences, expert range maps, and habitat suitability. We found that overprediction correction reduced spatial and performance uncertainty, with uncertainty strongly structured by model and algorithm choice and amplified when overprediction was not corrected. Although no single modelling adjustment fully eliminates uncertainty propagation from SDMs into prioritization, we demonstrate that overprediction correction consistently reduces it across datasets, taxa, and modelling approaches, highlighting its importance for robust conservation planning.

ecology↗

bioRxiv2025

Beyond species-level planning: The role of bioclimatic variation within species distributions

Conserving biodiversity under a changing climate is a complex challenge that requires comprehensive conservation planning approaches accounting for both current biodiversity patterns and the diverse ecological and environmental changes that species and ecosystems are likely to encounter over time. Systematic conservation planning (SCP) offers a strategic framework to meet this challenge by prioritizing areas that promote species persistence and ecological resilience. Traditionally, SCP focuses on conserving adequate amounts of species distributions to ensure their long-term persistence. More recently, partitioning species distributions into bioclimatic components has emerged to explicitly represent niche variability, enhancing adaptive capacity by preserving local adaptations and genetic diversity across environmental gradients. Despite this conceptual progress, empirical comparisons of species-level and bioclimatic component prioritization remain scarce. This study aimed to compare species-level and bioclimatic component prioritization by assessing their trade-offs and effectiveness in supporting species persistence and ecological resilience. Specifically, we aimed to (i) assess the surrogacy between species-level and bioclimatic component prioritizations, (ii) examine their spatial overlap and divergence, and (iii) quantify and compare environmental heterogeneity within priority areas identified by each approach. We found that species-level and bioclimatic component prioritizations act as reasonable surrogates for one another overall, but species-level prioritization tended to underrepresent the least-covered bioclimatic components, with failures to capture certain components in the top-ranked areas. Spatial overlap between the two approaches was generally high, though it declined with more restrictive thresholds and under future conditions. Additionally, bioclimatic component prioritizations consistently captured higher within-group multivariate dispersion in environmental heterogeneity in selected areas. Our findings highlight that bioclimatic component prioritization captures greater environmental heterogeneity and complements species-based approaches by better representing niche diversity. Integrating both strategies may offer a more robust path toward climate-resilient conservation planning that accounts for ecological requirements and environmental variation.

ecology↗

bioRxiv2025

Threats to Nature's Contributions to People provided by terrestrial vertebrates across Europe

Aim.Species and ecosystem processes offer essential benefits to people, known as Natures Contributions to People (NCP). However, we still lack a comprehensive understanding of NCP provided by terrestrial vertebrates on a large scale, and of the threats they face. To bridge this gap, we built a comprehensive dataset that documents the NCP provided by terrestrial vertebrate species in Europe, and analysed the conservation status and threats to NCP provider species. Location.Europe Methods.We synthesised existing literature on NCP associated with European terrestrial vertebrates, and leveraged ecological traits and trophic interactions from previously established datasets. We identified 15 NCP (10 regulating NCP and 5 non-material NCP), with 860 species providing at least one NCP (out of 1,168 vertebrate species considered in total). Then, we harnessed species distribution data and a novel European land system map to create species-mediated NCP maps across Europe at a 1km{superscript 2} resolution, including societal demand for each NCP. Results.We found that i) for each NCP, at least 25% of NCP provider species are assessed as threatened with extinction; ii) NCP multifunctionality is lowest in high-intensity land systems; and iii) direct exploitation and agricultural intensification are major threats to species-mediated NCP, impacting both non-material and regulating NCP provider species. Main conclusions.Protecting threatened NCP provider species, and reducing direct exploitation are key to maintain regulating and non-material NCP. Our results suggest that de-intensifying agricultural practices, through maintaining heterogeneous mosaic landscapes and promoting diversified practices, could increase NCP multifunctionality. Our work enables a comprehensive understanding of NCP provided by terrestrial vertebrates in Europe, their biogeography, and the threats they face, which can in turn inform spatial conservation planning to improve the conservation of both biodiversity and NCP.

ecology↗

bioRxiv2025

Limits and Promises of Earth Observation Foundation Models in Predicting Multi-Trophic Soil Biodiversity

Soil biodiversity is essential for terrestrial ecosystems, influencing nutrient cycling, carbon sequestration, agricultural productivity, and resilience to environmental changes. Yet, it faces significant threats from land-use changes, pollution, agricultural intensification, and climate change. Effective predictive modeling tools are urgently needed to inform conservation and management strategies. Although species distribution models (SDMs) have been successful for aboveground biodiversity, their application to soil biodiversity is limited by scarce large-scale datasets with spatial mismatches between environmental data and soil habitats. Recent advances, including environmental DNA (eDNA) metabarcoding, now allow extensive multi-taxa assessments of soil biodiversity. Simultaneously, remote sensing technologies provide high-resolution spatial data, potentially overcoming traditional coarse-gridded environmental limitations. This study evaluates Earth Observation Foundation (EOF) models, deep learning models pretrained on massive remote sensing datasets to summarize earth observation images into embeddings, to predict multi-trophic soil biodiversity in the French Alps. We compare models using EOF-derived embeddings from orthophotos with coarse-gridded and high-quality in-situ variables. We modeled relative abundance for 51 trophic groups across seven taxa using Random Forest, Light Gradient Boosting Machine, and Artificial Neural Networks, evaluating four data configurations: coarse-gridded environmental data, high-quality in-situ data, EOF embeddings, and a hybrid embedding-tabular approach. High-quality in-situ climate and soil data consistently delivered the highest predictive accuracy, especially for microbial and fungal groups. EOF embeddings provided valuable spatial context but did not surpass in-situ data performance, showing partial redundancy. Integrating remote sensing data can enhance biodiversity modeling in areas lacking detailed in-situ measurements, underscoring their complementary role in ecological assessments.

ecology↗