Miguel Ángel Rodríguez

Network analysis is a powerful framework for investigating complex systems across disciplines, including ecology. 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. Here, 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 modelling and widely used ecological analysis tools, sabinaHSBM facilitates network reconstruction and link prediction from binary 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 methods: estimating all link probabilities or identifying undocumented ones. We illustrate the package's 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 eight, despite their absence from the original dataset. This highlights the model's ability to recover biologically meaningful but unrecorded interactions. By providing the first accessible R workflow for HSBM‐based network reconstruction, sabinaHSBM enables researchers to quantify uncertainty and recover overlooked interactions in ecological networks.

We present a new database providing spatial data to support plant ecological research and conservation throughout mainland Spain. It integrates high-resolution spatial data of four main categories: (I) plant occurrence data, (II) environmental variables, (III) species distribution models, and (IV) thematic maps for conservation and management. The occurrence dataset includes georeferenced records for 81 tree and 101 shrub native species, and atlas data for 6,456 vascular plants and 1,252 bryophytes. Environmental variables include climatic, edaphic, hydrological, and solar, factors influencing plant distribution. Species distribution models are available for all the trees and shrubs (182 species). Thematic maps include species richness for woody and protected plants, distribution of vegetation types, and forest connectivity. All climatic variables, models, and thematic maps are projected under current and four future climate scenarios (2070–2100). The database is openly available on Zenodo.

Network 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. Here, 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. We illustrate the package’s 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 model’s ability to recover biologically meaningful but underreported interactions. By 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.

Los espacios protegidos de montaña son áreas en las que el turismo es un importante recurso económico, su gestión integra prácticas ganaderas extensivas que coexisten con la fauna salvaje. Esta relación entre ganado, fauna salvaje y personas puede provocar problemas sanitarios relacionados con zoonosis pudiendo afectar a la salud pública, la biodiversidad y la economía. Múltiples zoonosis son transmitidas por ectoparásitos como garrapatas que actúan como reservorio y vector de enfermedades infecciosas siendo los principales hospedadores pequeños mamíferos, ungulados silvestres y ganado. El propósito de este proyecto es plantear la creación de modelos de abundancia de vectores y patógenos teniendo en cuenta la importancia del agua como determinante del uso del espacio. Se están realizando muestreos en el Parque Nacional de Ordesa y Monte Perdido, el Parque Nacional de la Sierra de Guadarrama y el Parque Natural de las Sierras de Cazorla Segura y las Villas; midiendo: la densidad de garrapatas/m2 en diferentes hábitats y distancias al agua; la densidad de ungulados salvajes y domésticos utilizando cámaras trampa, censos visuales en el crepúsculo y entrevistas a ganaderos y personal del parque; la densidad de pequeños mamíferos mediante métodos de captura y recaptura. Las muestras recogidas se analizarán en laboratorio preparando bibliotecas genómicas y análisis matriciales, que permitirán construir redes probabilísticas a partir de las cuales realizar mapas de riesgos que podrían servir de base para la gestión, mitigando y previniendo la transmisión de enfermedades, protegiendo la economía y la salud de la población.

Motivation: We accessed published and unpublished floristic sources to compile a comprehensive species list of the Iberian–Balearic terrestrial vascular flora and generate AFLIBER, an accurate floristic database of georeferenced plant occurrence records.Main type of variable contained: Species distribution data totalling 1,824,549 plant occurrence records corresponding to 6,456 species and subspecies.Spatial location and grain: The western Mediterranean, including inland territories of Spain, Portugal and Andorra and the adjacent archipelagos of Berlengas, Columbretes and the Balearic Islands, covered by 6,316 UTM quadrangular grid cells of 10 km resolution.Time period: All distributional trustable records were considered to create the AFLIBER database, most of them dating from the 1960s onwards.Major taxa and level of measurement: Terrestrial vascular plant species and subspecies.Software format: Data are supplied as comma‐separated text (csv) files.

Earthworm distribution in global soils: Earthworms are key components of soil ecological communities, performing vital functions in decomposition and nutrient cycling through ecosystems. Using data from more than 7000 sites, Phillips et al. developed global maps of the distribution of earthworm diversity, abundance, and biomass (see the Perspective by Fierer). The patterns differ from those typically found in aboveground taxa; there are peaks of diversity and abundance in the mid-latitude regions and peaks of biomass in the tropics. Climate variables strongly influence these patterns, and changes are likely to have cascading effects on other soil organisms and wider ecosystem functions. Science , this issue p. 480 ; see also p. 425

Aim: We test the ability of the biotic exchange across the Bering land bridge coupled to niche conservatism to explain current day mammalian diversity gradients.Location: The Holarctic.Taxon: Mammals.Methods: We compared the diversity within clades that participated in the exchange (colonizers), whose ancestors withstood the Beringian cold temperatures, with that within clades that did not participate (sedentaries). We contrasted biogeographical patterns, tested the ability of environmental models to predict species richness of colonizers and sedentaries across continents and, compared richness‐climate relationships between colonizers and sedentaries controlling for phylogenetic effects.Results: We find that assemblages of colonizers are more diverse towards higher latitudes, opposing the traditional latitudinal diversity gradient which is followed by sedentaries. Despite the long passage of time since this major dispersal event, we find that the geographic distribution of colonizers is more strongly correlated with the distributions of other colonizers inhabiting a different continent than to the distribution of sedentary species.Main conclusions: Our results highlight the importance of historical migrations and dispersal in configuring present‐day diversity gradients. We also suggest that colonizers may be particularly vulnerable to future climate change because of the predicted disproportionate decrease in climate space in the extra‐tropical realm where they are currently most diverse.

Evolutionary history can explain species resemblance to a large extent. Thus, if closely related species share combinations of traits that modulate their response to environmental changes, then phylogeny could predict species sensitivity to novel stressors such as increased levels of deforestation. To test this hypothesis, we used 66,949 plots (25-m-radius) of the Spanish National Forest Inventory and modelled the relationships between local (plot-level) stem density of 61 Holarctic tree species and forest canopy cover measured at local and landscape scales (concentric circles centred on the plots with radiuses of 1.6, 3.2 and 6.4 km, respectively). Then, we used the output model equations to estimate the probability of occurrence of the species as a function of forest canopy cover (i.e. response to forest loss), and quantified the phylogenetic signal in their responses using a molecular phylogeny. Most species showed a lower probability of occurrence when forest canopy cover in the plots (local scale) was low. However, the probability of occurrence of many species increased when forest canopy cover decreased across landscape scales. We detected a strong phylogenetic signal in species response to forest loss at local and small landscape (1.6 km) scales. However, phylogenetic signal was weak and non-significant at intermediate (3.2 km) and large (6.4 km) landscape scales. Our results suggest that phylogenetic information could be used to prioritize forested areas for conservation, since evolutionary history may largely determine species response to forest loss. As such, phylogenetically diverse forests might ensure contrasted responses to deforestation, and thus less abrupt reductions in the abundances of the constituent species.

Phylogenetic imputation has recently emerged as a potentially powerful tool for predicting missing data in functional traits datasets. As such, understanding the limitations of phylogenetic modelling in predicting trait values is critical if we are to use them in subsequent analyses. Previous studies have focused on the relationship between phylogenetic signal and clade‐level prediction accuracy, yet variability in prediction accuracy among individual tips of phylogenies remains largely unexplored. Here, we used simulations of trait evolution along the branches of phylogenetic trees to show how the accuracy of phylogenetic imputations is influenced by the combined effects of 1) the amount of phylogenetic signal in the traits and 2) the branch length of the tips to be imputed. Specifically, we conducted cross‐validation trials to estimate the variability in prediction accuracy among individual tips on the phylogenies (hereafter ‘tip‐level accuracy’). We found that under a Brownian motion model of evolution (BM, Pagel't λ = 1), tip‐level accuracy rapidly decreased with increasing tip branch‐lengths, and only tips of approximately 10% or less of the total height of the trees showed consistently accurate predictions (i.e. cross‐validation R‐squared >0.75). When phylogenetic signal was weak, the effect of tip branch‐length was reduced, becoming negligible for traits simulated with λ < 0.7, where accuracy was in any case low. Our study shows that variability in prediction accuracy among individual tips of the phylogeny should be considered when evaluating the reliability of phylogenetically imputed trait values. To address this challenge, we describe a Monte Carlo‐based method that allows one to estimate the expected tip‐level accuracy of phylogenetic predictions for continuous traits. Our approach identifies gaps in functional trait datasets for which phylogenetic imputation performs poorly, and will help ecologists to design more efficient trait collection campaigns by focusing resources on lineages whose trait values are more uncertain.