Teresa Goicolea

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. 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. 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. 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.

Species distribution models (SDMs) are central tools for predicting and forecasting how species respond to environmental changes, yet their reliability depends on accurately capturing ecological processes across spatial scales while accounting for uneven data availability and resolution. Models calibrated over spatially restricted extents are often prone to truncating species' niches, whereas broad‐scale models typically rely on coarse datasets that overlook fine‐scale habitat constraints. The spatially nested SDM (N‐SDM) approach addresses this challenge by combining global‐ and regional‐level models to couple large‐scale drivers with detailed, high‐resolution environmental data. Here, we present N‐SDM 2.0, a reengineered and extended successor to the original N‐SDM software – the first end‐to‐end high‐performance computing platform for spatially nested species distribution modelling. The new version offers improved usability and modularity, along with an extended range of modelling features, while maintaining continuity for users of the previous release. New modelling features include additional nesting methods, background‐point generation strategies, and cross‐validation options. These developments are complemented by an improved workflow architecture, a refined settings system, streamlined data‐preparation steps, and greater control over data usage across spatial levels as well as error handling. N‐SDM 2.0 is designed for flexible integration into heterogeneous high‐performance computing environments, supporting large‐extent and high‐resolution biodiversity forecasting across an extensive number of species and projection scenarios. Beyond pure technical advances, N‐SDM 2.0 strengthens the conceptual foundation of spatially nested species distribution modelling, introducing new features that form an open and scalable framework for mapping species distributions and robustly projecting their responses to global environmental changes.

Introduction: Habitat fragmentation in agricultural landscapes challenges biodiversity conservation and ecosystem resilience. In Chile's Mediterranean region, restoring habitat connectivity through spatial planning provides efficient opportunities to enhance biodiversity in intensively managed landscapes. Objectives: This study identifies priority ecological corridors based on functional connectivity and evaluates how alternative restoration strategies influence connectivity outcomes in a fragmented agricultural landscape in central Chile. Methods: Functional connectivity was assessed using resistance surfaces for forest mammals and graph‐theory metrics. Least‐cost paths were used to delineate corridor networks, and alternative corridor‐ and riparian‐based restoration scenarios were simulated. Restoration efficiency was evaluated by comparing gains in Equivalent Connected Area (ECA) relative to restored area. Results: Following the identification of 60 functional connectivity corridors, restoring 10% of priority least‐cost corridors was the most spatially efficient strategy (148.6 ECA/ha). A targeted riparian restoration approach near least‐cost corridors also performed well (121.7 ECA/ha), offering a feasible option under land‐use constraints. In contrast, large‐scale restoration along full corridor networks and legal riparian buffers achieved higher total connectivity gains but required substantially larger areas. Conclusions: Our findings highlight the value of spatially explicit corridor planning using least‐cost paths to improve connectivity gains per restored area under intensively managed land‐use constraints.

Aim: Assess the potential effects of climate change on different forest habitats; and outline a climate‐wise conservation network to sustain forest connectivity under current and future climates. Location: Mainland Spain. Time Period: Current and future (2071–2100). Major Taxa Studied: Forest species associated with different vegetation types and dispersal abilities. Methods: We fitted a Random Forest model to predict the distribution of six vegetation types under current and four future climate scenarios. We then assessed forest availability and connectivity for each climate scenario and vegetation type. To define the conservation network, we identified the key habitat patches and corridors for dynamic connectivity using multi‐temporal habitat availability indices. Finally, we analysed how much of the conservation network is expected to change due to climate shifts and how it aligns with existing protected areas. Results: Forests across all vegetation types exhibited substantial northward and upward shifts. Forests adapted to cold or wet conditions (e.g., deciduous, mountain conifers and high‐mountain vegetation) declined in area and connectivity. Warm‐ and dry‐adapted forests (e.g., sclerophyllous, subsclerophyllous and hyperxerophilous vegetation) increased their ranges and/or connectivity. Rates of change in forest area and connectivity were similar within each vegetation type, except for subsclerophyllous vegetation, which lost forest area but gained connectivity. 48% of the proposed conservation network experienced shifts in the vegetation types. Current protected areas covered 41% of the conservation patches across Spain, but less than 7% within the deciduous vegetation type. Main Conclusions: Projected shifts highlight the need for dynamic connectivity analyses to guide effective conservation under changing climate. Forest types exhibited distinct trends, underscoring the need for tailored strategies for each type. The proposed conservation network provides guidance for a proactive enhancement of Spanish forests' resilience. It also serves as a reference for other countries with similar conservation targets, including the European Union Nature Restoration Regulation or Biodiversity Strategy for 2030.

Species distribution models (SDMs) relate species observations to mapped environmental variables to estimate the realized niche of species and predict their distribution. SDMs are key tools for projecting the impact of climate change on species and have been used in many biodiversity assessments. However, when fitted within spatial extents that do not encompass the whole species range (i.e. subrange), the estimated realized environmental niche can be truncated, which can lead to wrong or inaccurate predictions. A simple solution to this niche truncation consists in fitting SDMs at a spatial extent that encompasses the whole species range, but this often implies using a spatial resolution too coarse for local conservation assessments. To keep a fine resolution, a solution is to fit spatially nested SDMs (N‐SDMs), where a whole range, coarse‐grain SDM is combined with a subrange, fine‐grain SDM. N‐SDMs have demonstrated superior performance to subrange (truncated) SDMs in projecting species distributions under climate change and have accordingly regained considerable interest. Here, we review developments, applications and effectiveness of N‐SDMs. We present and discuss existing methods and tools to fit N‐SDMs, and assess when N‐SDMs are not needed. We highlight strengths and weaknesses of N‐SDMs, underline their importance in reducing niche truncation, and identify remaining challenges and future perspectives. Our review highlights that subrange SDMs most often lead to niche truncation and thus to incorrect spatial projections, a problem that can be overcome by using N‐SDMs. We show that the various N‐SDM methods come with their strengths and weaknesses and should be selected depending on the intended goal of the study. Synthesis. N‐SDMs are key tools to develop untruncated regional climate change forecasts of species distributions at fine resolution over restricted extent. While several N‐SDM approaches were proposed, there is currently no universal solution suggesting that further developments and testing are crucial if we are to derive robust future projections of species distributions, at least until SDMs can be applied for most species at high resolution over large geographic extents.

Habitat loss, fragmentation, and climate change threaten biodiversity, emphasizing the need to maintain landscape connectivity for species movement and access to resources. Limited conservation funds and land-use conflicts make prioritizing efficient connectivity measures essential. To address this, we developed the Makurhini R package, an open-source tool for systematically calculating key fragmentation and connectivity metrics. Makurhini provides powerful, integrated analyses and efficiently processes large datasets with parallel processing, accommodating diverse species and landscape contexts. Key features include computing fragmentation metrics, characterizing links using Euclidean, least-cost, and circuit theory approaches, evaluating connectivity with indices such as Probability of Connectivity (PC) and Equivalent Connected Area, and ranking patches and links by their contribution to connectivity. Case studies in Mexico, Colombia, and Spain demonstrate its applicability. Makurhini offers a robust, data-driven framework to support the conservation and restoration of connectivity in fragmented landscapes.

Species distribution models have evolved to combine species‐environment interactions across multiple scales. Spatially nested hierarchical models (NSDMs) offer a promising avenue by integrating datasets and predictive models from broad to fine scales. But a user‐friendly tool to execute these models remains an important ongoing challenge. To address this gap, we introduce the sabinaNSDM R package that provides a straightforward approach to develop NSDMs. This package merges global scale models, capturing extensive ecological niches, with regional scale models featuring high‐resolution covariates, to form a unified hierarchical modelling framework. This toolkit is designed to facilitate the implementation of NSDMs for ecologists, conservationists and researchers aiming to produce more reliable species distribution predictions. sabinaNSDM streamlines the data preparation, calibration, integration and projection of models across two scales. It automates (if necessary) the generation of background points, spatial thinning of species occurrence and absence (if available) data, covariate selection and the generation of NSDMs. This paper outlines the workflow and functions integrated into the sabinaNSDM package, complemented by an applied case study involving a pool of 76 tree species. Consistent with previous publications, the generated NSDMs facilitated precise predictions (mean AUC value through independent evaluation higher than 0.88) of species distributions under current and future environmental scenarios.

Spatial truncation in species distribution models (SDMs) might cause niche truncation and model transferability issues, particularly when extrapolating models to non‐analog environmental conditions. While broad calibration extents reduce truncation issues, they usually overlook local ecological factors driving species distributions at finer resolution. Spatially‐nested hierarchical SDMs (HSDMs) address truncation by merging (a) a global model calibrated with broadly extended, yet typically low‐resolution, basic, and imprecise data; and (b) a regional model calibrated with spatially restricted but more precise and reliable data. This study aimed to examine HSDMs' efficacy to overcome spatial truncation in national‐scale studies. We compared two hierarchical strategies (‘covariate', which uses the global model output as a covariate for the regional model, and ‘multiply', which calculates the geometric mean of the global and regional models) and a non‐hierarchical strategy. The three strategies were compared in terms of niche truncation, environmental extrapolation, model performance, species' predicted distributions and shifts, and trends in species richness. We examined the consistency of the results over two study areas (Spain and Switzerland), 108 tree species, and four future climate scenarios. Only the non‐hierarchical strategy was susceptible to niche truncation, and environmental extrapolation issues. Hierarchical strategies, particularly the ‘covariate' one, presented greater model accuracy than non‐hierarchical strategies. The non‐hierarchical strategy predicted the highest overall values and the lowest decreases over time in species distribution ranges and richness. Differences between strategies were more evident in Switzerland, which was more affected by niche truncation issues. Spain was more negatively affected by climate change and environmental extrapolation. The ‘covariate' strategy exhibited higher model performance than the ‘multiply' one. However, uncertainties regarding model temporal transferability advocate for adopting and further examining multiple hierarchical approaches. This research underscores the importance of adopting spatially‐nested hierarchical SDMs given the compromised reliability of non‐hierarchical approaches due to niche truncation and extrapolation issues.

Forecasting habitat suitability and connectivity can be central to both controlling range expansion of invasive species and promoting native species conservation, especially under changing climate conditions. This study aimed to identify and prioritize areas in Spain to control the expansion of one of the most harmful invasive species in Europe, the American mink, while conserving its counterpart, the endangered European mink, under current and future conditions. We used ensemble habitat suitability and dynamic connectivity models to predict species ranges and movement routes considering likely climate change under three emission scenarios. Then, using habitat availability metrics, we prioritized areas for invasive mink control and native mink conservation and classified them into different management zones that reflected the overlap between species and threat from American to European minks. Results suggest that both species are likely to experience declines in habitat and connectivity under climate change scenarios with significantly larger declines by the end of the century for European minks (72 and 80% respectively) than for American minks (41 and 32%). Priority areas for management of both species varied over time and across emission scenarios, with a general shift in priority habitat towards the North-East of the study area. Our findings demonstrate how habitat suitability and dynamic connectivity approaches can guide long-term management strategies to control invasive species and conserve native species while accounting for likely landscape changes. The simultaneous study of both invasive and native species can support prioritized management action and inform management planning of the intensity, extent, and techniques of intervention depending on the overlap between species.

Forecasting habitat suitability and connectivity can be central to both controlling invasive species expansion and promoting native species conservation, especially under changing climate conditions. This study aimed to identify and prioritize areas in Spain to control the expansion of one of the most harmful invasive species in Europe, while conserving its counterpart, the endangered European mink, under current and future conditions. We used dynamic ensemble habitat suitability and connectivity models to predict species ranges and movement routes considering likely climate change under three emission scenarios. Then, using habitat availability metrics, we prioritized areas for invasive mink control and native mink conservation and classified them into different management zones that reflected the overlap between species and threat from American to European minks. Results suggest that both species are likely to experience declines in habitat and connectivity under climate change scenarios with significantly larger declines by the end of the century for European minks (72 and 80% respectively) than for American minks (41 and 32%). Priority areas for management of both species varied over time and across emission scenarios, with a general shift in priority habitat towards the North-East of the study area. Our findings demonstrate how dynamic habitat suitability and connectivity approaches can guide long-term management strategies to control invasive species and conserve native species while accounting for likely landscape changes. The simultaneous study of both invasive and native species can support prioritized management action and inform management planning of the intensity, extent, and techniques of intervention depending on the overlap between species.