Biology subjects

Mateo, R. G.

Publications and source records attributed to Mateo, R. G..

3 recordsLinked to original sources

bioRxiv2026

Integrating nested species distribution models and multicriteria prioritization into an open-access geoportal for tropical forest restoration

Tropical forests face increasing pressures from climate change and deforestation, which calls for the selection of climate-resilient species for ecological restoration. Species distribution models (SDMs) calibrated solely with regional data in the tropics often suffer from niche truncation due to data scarcity. We applied spatially nested models (N-SDMs), in one of their first applications in the tropics, to assess the current and future climatic suitability of 199 native forest species of continental Ecuador, integrating global and regional data through a nested approach that consistently outperformed regional-only SDMs (AUC = 0.95 vs. 0.83). Given a widespread projected loss of climatic suitability and an insufficient representation within the National System of Protected Areas, passive conservation alone is insufficient, which calls for an active prioritization tool. We developed a restoration potential index (IPR) that provides a continuous score for each species in every cell by integrating current potential presence, future climatic confidence and documented ecosystem services. This species- and cell-level resolution yields a restoration priority ranking that is robust across climate scenarios and is available for any site in the territory, distinguishing species of consolidated priority from those requiring urgent active propagation owing to their reduced current potential extent. All results are made available through the interactive geoportal geoSABINA, providing the first spatially explicit and climate-informed infrastructure to support the implementation of the Ecuadorian National Forest Restoration Plan (PNRF) and equivalent frameworks in the American tropics

plant biology↗

bioRxiv2026

sabinaMBM: An R package for Multiscale Bayesian species distribution Modelling using INLA

1. Regional species distribution models (SDM) calibrated over spatially restricted extents tend to truncate species' ecological niches. Existing nested SDM workflows integrate multi-scale information through sequential combination, which limits formal uncertainty propagation across scales and prevents regional predictions from being explicitly constrained within globally-informed niche boundaries. 2. We introduce sabinaMBM, an R package implementing joint multiscale Bayesian SDM within a unified probabilistic framework built on inlabru/R-INLA. It propagates uncertainty across scales without the computational bottlenecks of MCMC-based approaches. The framework offers multiple coupling architectures ranging from complete independence to hierarchical constraint that can be configured independently for intercepts and covariates. 3. In a range-margin population, hierarchical constraint most improves out-of-sample discrimination where regional data were scarcest, while leaving predictions unchanged where they already suffice, delivering gains precisely where sequential approaches are expected to struggle most. Applied to Quercus petraea across its Iberian trailing-edge, including a spatial field produced the largest single performance gain, consistent across every coupling configuration, and covariate responses diverged by scale for at least one climatic predictor. Under future climate, the constrained model yielded lower suitable habitat estimates and redistributed uncertainty in proportion to cross-scale agreement rather than uniformly. 4. sabinaMBM makes multiscale Bayesian inference accessible without specialist programming. This framework provides robust value for trailing-edge populations and spatial (invasive species) or temporal (climate change) projections where niche truncation risks ecologically implausible outcomes, while simultaneously delivering fine-resolution predictions with properly propagated uncertainty whenever global and regional covariates offer complementary information.

ecology↗

bioRxiv2025

sabinaHSBM: An R package for link prediction network reconstruction using Hierarchical Stochastic Block Models

O_LINetwork analysis is a powerful framework for investigating complex systems across disciplines, including ecology, where it helps uncover patterns in predator-prey, host- parasite, or plant-pollinator interactions. However, ecological network data are often incomplete or error-prone due to sampling limitations, detection failures, and taxonomic uncertainty--leading to missing (false negative) and spurious (false positive) links that obscure structure and hinder inference. The hierarchical stochastic block model (HSBM), particularly in its degree-corrected form, is among the most effective tools for reconstructing networks under such uncertainty. Despite its robustness, the primary implementation of HSBM in the Python-based graph-tool library has remained largely inaccessible to ecologists. C_LIO_LIHere, we introduce sabinaHSBM, the first R package that makes degree-corrected HSBM broadly available through a user-friendly, flexible workflow. By bridging a gap between advanced network modeling and widely used ecological analysis platforms, sabinaHSBM facilitates network reconstruction and link prediction from binary bipartite data. The workflow involves three main steps: (1) preparing input data, (2) estimating posterior link probabilities, and (3) reconstructing the network. The package supports detection of undocumented and spurious links, exploration of hierarchical structure, and propagation of uncertainty throughout. Key features include cross-validation, flexible thresholding, probabilistic evaluation metrics, and two link prediction modes: estimating all link probabilities or identifying undocumented ones. C_LIO_LIWe illustrate the packages functionality through a case study using a published global dataset of carnivore-parasite associations, showing that inferred groupings are phylogenetically clustered. To assess predictive accuracy, we examined the top 10 highest-probability links identified by the model and found published evidence for 8, despite their absence from the original dataset. This highlights the models ability to recover biologically meaningful but underreported interactions. C_LIO_LIBy integrating all components of HSBM-based reconstruction into an accessible R package, sabinaHSBM empowers researchers to improve relational data quality and uncover overlooked patterns in complex ecological networks and beyond. C_LI

ecology↗