Biology subjects

Sabatini, F. M.

Publications and source records attributed to Sabatini, F. M..

6 recordsLinked to original sources

bioRxiv2025

Leveraging roadless areas to close strict protection gaps in the EU

Strictly protected areas are critical to biodiversity conservation, yet only 4% of the EUs land currently meets this designation, falling short of the 10% target outlined in the EU Biodiversity Strategy for 2030. Roadless areas, i.e., portions of land 1 km farther from linear infrastructure, are increasingly recognized for their ecological value and potential role in closing this gap. Using OpenStreetMap data and environmental gradients derived from climate, soil, and topographic layers, we identified the extent of EU roadless areas and assessed their conservation value based on their capacity to complement the existing strict protection network in terms of environmental representativeness. We found that 11.5% of EU land is roadless, with only 18.9% of this already under strict protection. Integrating all roadless areas would raise strict protection coverage to 12.7% and improve representation of abiotic environmental space from 78.8% to 94.4%. However, most roadless areas are extremely fragmented and redundant with respect to their abiotic features. While roadless areas offer a critical opportunity to expand the strict protection network, they cannot solely meet conservation goals. A balanced strategy that includes restoration, coordination across governance levels, and prioritization based on other biodiversity features is essential to effectively meet EU conservation commitments.

ecology↗

bioRxiv2025

From smartphones to satellites: Uniting crowdsourced biodiversity monitoring and Earth observation to fill the gaps in global plant trait mapping

Plant functional traits are fundamental to ecosystem dynamics and Earth system processes, but their global characterization is limited by the availability of field surveys and trait measurements. Recent expansions in biodiversity data aggregation, including large collections of vegetation surveys, citizen science observations, and trait measurements, offer new opportunities to overcome these constraints. Here we demonstrate that combining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km resolution. Our approach achieves high predictive power, reaching correlations up to 0.63 (15 of 31 traits exceeding 0.50) and improved spatial transferability, effectively bridging gaps in under-sampled regions. By capturing a broad range of traits with high spatial coverage, these maps can enhance our understanding of plant community properties and ecosystem functioning globally, and can serve as useful tools in modeling global biogeochemical processes and informing worldwide conservation efforts. Ultimately, our framework highlights the power and necessity of crowdsourced biodiversity data in high-resolution plant trait modeling. We anticipate that advancements in biodiversity data collection and remote sensing capabilities will further refine global trait mapping, fostering a dynamic trait-based understanding of the biosphere.

ecology↗

bioRxiv2024

Land-use impacts on plant functional diversity throughout Europe

AimGlobal biodiversity loss resulting from anthropogenic land-use activities is a pressing concern, requiring precise assessments of impacts at large spatial extents. Existing models mainly focus on species richness and abundance, lacking insights into ecological mechanisms and species roles in ecosystem functioning. To bridge this gap, we conducted an extensive analysis of the impact of human land use on vascular plant functional diversity, across diverse land-use classes and bioregions in Europe, comparing it to traditional metrics. Location: Europe Time period: 1992-2019 Major taxa studied: Vascular plants MethodsIntegrating extensive databases of vegetation plots with spatial data on land use and land cover, we paired plots from areas actively used and modified by humans with plots from natural habitats under similar environmental conditions. Using species occurrences and traits, in each plot we computed three complementary functional diversity metrics (functional richness, evenness, and divergence), species richness and abundance. We assessed the impact of land use by comparing the metrics in the paired plots. ResultsOur findings revealed that, compared to natural habitats, anthropogenic land use exhibits lower functional richness and divergence but higher functional evenness across most land-use classes and bioregions. The response of functional richness was more marked than the other two metrics and especially pronounced in croplands and urban areas and in northern bioregions. Functional richness exhibited a pattern that did not fully overlap with the trend in species richness, providing useful complementary information. Main conclusionsWe provide a large-scale precise assessment of anthropogenic land-use impacts on functional diversity across Europe. Our findings indicate that: (i) human disturbance significantly alters plant functional diversity compared to natural habitats; (ii) this alteration goes in the direction of functional homogenization; (i) functional diversity metrics complement traditional metrics by offering deeper insights into the ecological mechanisms in response to anthropogenic land use.

ecology↗

bioRxiv2023

You shall know a species by the company it keeps: leveraging co-occurrence data to improve ecological prediction

O_LIMaking predictions about species, including how they respond to environmental change, is a central challenge for ecologists. Due to the huge number of species, ecologists seek generalizations based on species traits and phylogenetic relationships, but the predictive power of trait-based and phylogenetic models is often low. Species co-occurrence patterns may contain additional information about species ecological attributes not captured by traits or phylogenies. C_LIO_LIWe propose using ordination to encode the information contained in species co-occurrence data in low-dimensional vectors that can be used to represent species in ecological prediction. We present an efficient method to derive species vectors from co-occurrence data using GloVe, an unsupervised learning algorithm originally designed for language modeling. To demonstrate the method, we used GloVe to generate vectors for nearly 40,000 plant species using co-occurrence statistics derived from a global vegetation dataset and tested their ability to predict elevational range shifts in European montane plant species. C_LIO_LICo-occurrence-based species vectors were weakly correlated with traits or phylogeny, indicating that they encode unique information about species. Models including co-occurrence-based vectors explained twice as much variation in species range shifts as models including only traits or phylogenetic information. C_LIO_LIGiven the widespread availability of species occurrence data, species vectors learned from co-occurrence patterns are a widely applicable and powerful tool for encoding ecological information about species, with many potential applications for describing and predicting the ecology of species, communities, and ecosystems. C_LI

ecology↗

bioRxiv2020

European Primary Forest Database (EPFD) v2.0

Primary forests are scarce in Europe and continue to disappear at an alarming rate. Despite these losses, we know little about where such forests still occur. Here, we present an updated geodatabase and map of Europes known primary forests. Our geodatabase harmonizes 48 different datasets of primary forests, and contains 18,411 individual patches (41.1 Mha) spread across 33 countries. When available, we provide information on each patch (name, location, naturalness, extent and dominant tree species) and the surrounding landscape (biogeographical regions, protection status, potential natural vegetation, current forest extent). To assess the robustness of our geodatabase, we checked each patch for forest disturbance events using Landsat satellite-image time series (1985-2018). We estimate that 94% of the patches in our database did not experience significant disturbances that would alter their primary forest status in the last 30 year. Our database is the most comprehensive dataset on primary forests in Europe, and will be useful for biogeographic and ecological studies, and conservation planning to safeguard these unique forests.

ecology↗

bioRxiv2020

Revealing the functional traits that are linked to hidden environmental factors in community assembly

AimTo identify functional traits that best predict community assembly without knowing the driving environmental factors. MethodsWe propose a new method that is based on the correlation r(XY) between two matrices of potential community composition: matrix X is fuzzy-weighted by trait similarities of species, and matrix Y is derived by Beals smoothing using the probabilities of species co-occurrences. Since matrix X is based on one or more traits, r(XY) measures how well the traits used for fuzzy-weighting reflect the observed co-occurrence patterns. We developed an optimization algorithm that identifies those traits that maximize this correlation, together with an appropriate permutational test for significance. Using metacommunity data generated by a stochastic, individual-based, spatially explicit model, we assessed the type I error and the power of our method across different simulation scenarios, varying environmental filtering parameters, number of traits and trait correlation structures. We then applied the method to real-world community and trait data of dry calcareous grassland communities across Germany to identify, out of 49 traits, the combination of traits that maximizes r(XY). ResultsThe method correctly identified the relevant traits involved in the community assembly mechanisms specified in simulations. It had high power and accurate type I error and was robust against confounding aspects related to interactions between environmental factors, strength of limiting factors, and correlation among traits. In the grassland dataset, the method identified five traits that best explained community assembly. These traits reflected the size and the leaf economics spectrum, which are related to succession and resource supply, factors that may not be always measured in real-world situations. ConclusionsOur method successfully identified the relevant traits mediating community assembly driven by environmental factors which may be hidden for not being measured or accessible at the spatial or temporal scale of the study.

ecology↗