Landscape Ecology

ContextSpecies distribution models are used to predict habitat suitability for a species, by quantifying the environmental characteristics that allow a species to occupy a geographical area. The abundance and range of pine marten (Martes martes) has declined substantially in Great Britain, with remaining populations restricted to Scotland. ObjectivesHere, we perform species distribution modelling using BIOMOD2 platform to determine habitat suitability, and inform the identification of potential reintroduction sites for pine marten in Great Britain. MethodsUsing a global range dataset of 4,189 occurrences and seven environmental variables, ensemble species distribution models were used to predict habitat suitability across Europe at 1 km resolution and Great Britain at 100 m resolution. ResultsAcross the extent of both Europe and Britain, results indicate high suitability in areas with woody vegetation cover in low topographic positions, and notably low in urban areas and extensive areas of arable land. In Britain, high habitat suitability is identified across substantial areas in the South East of England, parts of South West of England, East Yorkshire and Gloucestershire, with pockets of suitable habitat along the West Coast of Britain. The results indicate that elevation and land cover are important drivers of suitability. ConclusionHabitat suitability modelling at a high resolution of 100m proves effective for informing potential reintroduction sites for pine marten in Britain. We also demonstrate the importance of using occurrence data from pine martens global range to predict optimal habitat suitability.

Swidden agriculture is a widespread anthropogenic disturbance regime in tropical forests. Swidden research often posits that aggregate levels of forest disturbance correlate with increased land degradation, however a landscape configuration approach may distinguish when swidden degrades landscapes and when it diversifies them. Here we analyze how the configuration of swidden mosaics relates to vegetation diversity. Using satellite imagery from 18 swidden societies across the African, Southeast Asian, and American tropics, we quantify patch geometry using landscape metrics and estimate vegetation diversity from spectral variation and develop a nonlinear hierarchical Bayesian model that links the structure of swidden mosaics with vegetation diversity. Our analyses reveal three dominant gradients of swidden mosaic patterns: (1) aggregation versus interspersion of land-cover types; (2) spatial dispersion versus synchronization of disturbed patches; and (3) alternative modes of landscape connectivity. Across sites, vegetation diversity exhibits a consistent nonlinear response, peaking at intermediate levels of disturbance intensity. These results demonstrate that swidden agriculture does not produce a singular, degradative outcome. Instead, its effects on vegetation diversity depend on how disturbance is spatially configured. By shifting attention from area-based measures of deforestation to landscape configuration, this study reframes swidden as a spatial process with the potential for diversity-enhancing outcomes.

Resource scarcity prompts animals to adjust their space use in ways that enhance their survival. In wild herbivores, seasonal habitat shifts are well studied; however, little is known about how large herbivores navigate human-dominated landscapes under fluctuating resource conditions. In Hong Kong, feral water buffalo (Bubalus bubalis; henceforth buffalo) experience declines in body condition score during the dry season. While buffalo expand spatial ranges in the dry season, whether this expansion reflects access to improved ecological conditions, and how key anthropogenic factors (like distance to roads and human density) further influence space use, remains understudied. We observed six buffalo herds (n=92 known individuals) across one wet (July-September 2023) and one dry (January-March 2024) season. We recorded herd locations and extracted remotely sensed habitat and environmental data. We used Normalized Difference Vegetation Index (NDVI) as a proxy of vegetation productivity and included elevation, distance to roads, and human density as additional predictors. We hypothesized that dry-season space use would shift toward higher vegetation productivity, and that anthropogenic factors would have weak influence on space use due to the adaption of buffalo in human-dominated landscapes. We found that buffalo used areas with higher vegetation productivity and higher elevation in the dry season than in the wet. Distance to roads and human density had no detectable effect. Our findings reveal that space use by a large herbivore like buffalo in human-dominated landscapes is strategic and resource driven, and that these seasonal shifts may have important implications for local biodiversity and human-animal interactions.

Key Biodiversity Areas (KBAs) are a cornerstone of global biodiversity conservation, influencing international strategic plans and helping protect thousands of species. KBAs are identified through quantitative criteria, among which the most recent is Criterion E. KBA Criterion E uses Spatial Conservation Prioritization techniques to identify highly irreplaceable sites, representing a promising tool for effective expansion of the KBA network. However, it has rarely been tested or applied at large scales. Here, we carried out a continental application of KBA Criterion E in Europe, using Species Distribution Models (SDMs) for 5,529 species of insects and 972 tetrapods. We stress-tested the application of Criterion E by changing the following settings: irreplaceability threshold, metrics of irreplaceability, representation targets, spatial resolution, and cost of planning units. Under the standard Criterion E settings, we identified 23 potential KBAs for insects, mostly along northern European coasts, and 88 for tetrapods, mostly concentrated in Mediterranean islands and southern Europe. These sites slightly overlapped with existing KBAs, showing that Criterion E can capture biodiversity patterns overlooked by other criteria. Our results also showed that the identification of highly irreplaceable areas is very sensitive to analytical choices. The strict irreplaceability threshold currently required, associated with the definition of representation targets, limited the selection of important sites almost exclusively to those containing very narrow-range species, and when such species were absent, important sites were preferentially selected on coasts, where the cost of planning units (represented by land extent) was minimized. Our analysis showed both opportunities and challenges of Criterion E and its applications with SDMs. We propose potential adjustments to the definition and guidelines of Criterion E, to improve its applicability at large spatial scales and on different taxa. Improvements of KBA Criterion E will ensure that KBAs continue to substantially contribute to the global conservation of biodiversity.

AimWhile many studies on ectotherm thermal tolerance consider temperature exposure, the frequency of temperature exposures is emerging as an important and generally overlooked driver of survival and fitness which may influence species ranges. We use a physiologically-informed species distribution model to evaluate the influence of temperature fluctuations on the historical distribution and intensity of defoliation of a Lepidopteran forest pest, and on predicted future defoliation. LocationEastern Canada. Time period2006-2016, projections to 2041-2070. Major taxa studiedChoristoneura fumiferana (Lepidoptera: Tortricidae, spruce budworm). MethodsWe combined publicly-available maps of spruce budworm-induced defoliation between 2006-2016 in the Canadian province of Quebec with climate, forest composition, and de novo temperature fluctuation predictors to train a species distribution model. Our model evaluated how predictor categories influence spruce budworm defoliation and compared these results to a model trained without temperature fluctuations. Additionally, we predicted future spruce budworm defoliation under 2041-2070 climate change conditions using the models trained with and without temperature fluctuation predictors to determine the impact of temperature fluctuations on future defoliation predictions. ResultsWe found that the inclusion of temperature fluctuation predictors improved model performance, and these predictors ranked highly in importance relative to predictors in other categories. The model trained with temperature fluctuation predictors also predicted vastly different defoliation distribution and severity across Quebec, Ontario, and Labrador than the model trained without them under future climate. Main conclusionsOur study reveals the previously overlooked importance of temperature fluctuations on landscape-scale spruce budworm defoliation and demonstrates the importance of including physiologically-informed predictors in species distribution models. It also provides a novel framework for including thermal variation in correlative species distribution models of ectotherms.

The main conservation risks for wild non-human primates (NHP) in Costa Rica, Mesoamerica, is deforestation and the allocation of lands for agriculture. This is because they result in a mosaic of forest patches that differ in size and ecological properties. NHP, being the vertebrates with the highest risk and rate of extinction, slowly adapt to this rapid environmental change, optimizing their metabolic costs to survive and reproduce. One way to balance these costs is to use forest patches depending on the benefits they provide, such as food, shelter, or social contact. To understand the possible environmental factors that predict the usage of a series of 8 connected forest patches by Ateles geoffroyi, Alouata paliatta, and Sapajus imitator we collected demographic, behavioral, climatological and other environmental data from 2018 until 2021. We used information-theoretic metrics to identify the factors that best explained the presence and behavior of the species of interest in the forest patches studied, and fit the data to a set of models built informed a priori. Using the best explanatory factors, we k-fold cross-validated 9 classifier algorithms to identify the best predictive models for the presence of the monkeys studied and their behavioral patterns given the data. Presence was highest in warmer, more humid days, especially when other groups were present in the same patch. Behavioral patterns were different in each patch; monkeys rested more often when other groups of the same species were present, and foraged more during warmer, more humid days, and smaller groups. Predictive models for the presence of the species studied, trained with the 3 best explanatory factors, reached an accuracy between 70-96%, with Gradient Boost Classifier performing the best. In contrast, behavioral patterns were more unpredictable, with the the algorithms tested only reaching between 43-51% accuracy, the AdaBoost Classifier being the best. Our findings suggest that the usage of the 8 forest patches monitored by the monkeys studied depends on patch characteristics, not related to size nor the presence of a reserve, by the presence of other NHP in the patch and the meteorological conditions. Further work on the ecological characteristics of these patches can clarify the mechanisms modulating behavioral patterns.

Bovine tuberculosis (bTB), a zoonotic disease caused by Mycobacterium bovis, continues to challenge eradication efforts in Ireland and the UK, partly due to the role of the European badger (Meles meles) as a wildlife reservoir. Traditional management strategies often rely on sett (burrow) locations to infer badger distribution, which implicitly assumes a correlation with abundance. This study uses data from Irelands national badger culling and vaccination programme (2019-2025) to decouple badger and sett distributions using spatial point process modelling via log-Gaussian Cox processes. By separately modelling the environmental drivers of main sett and badger distributions, and validating outputs for ecological realism with independent badger body weight data, we demonstrate that sett and badger densities are governed by distinct ecological processes. Sett densities are driven by landscape features such as elevation, slope, and proximity to forest edges, while badger densities are more influenced by recent culling history and pasture availability. Our results reveal a spatial mismatch between high-density sett areas and high-density badger areas, highlighting the need for refined metrics in wildlife-based bTB management. These findings underscore the importance of integrating independently derived wildlife distribution models into disease control policies for more sustainable and effective bTB management.

BackgroundClimate change coupled with other anthropogenic pressures may affect species distributions, often causing extinctions at different scales. This is particularly true for species occupying marginal habitats such as gelada, Theropithecus gelada. Our study aimed to model the impact of climate change on the distribution of suitable habitats for geladas and draw conservation implications. Our modelling was based on 285 presence locations of geladas, covering their complete current distribution. We used different techniques to generate pseudoabsence datasets, MaxEnt model complexities, and cut-off thresholds to map the potential distribution of gelada under current and future climates (2050 and 2070). We assembled maps from these techniques to produce a final composite map. We also evaluated the change in the topographic features of gelada over the past 200 years by comparing the topography in current and historical settings. ResultsAll model runs had high performances, AUC = 0.87 - 0.96. Under the current climate, the suitable habitat predicted with high certainty was 90,891 km2, but it decreased remarkably under future climates, -36% by 2050 and -52% by 2070. Whereas no remarkable range shift was predicted under future climates, currently geladas are confined to higher altitudes and complex landscapes compared to historical sightings, probably qualifying geladas as refugee species. ConclusionsOur findings indicated that climate change most likely results in a loss of suitable habitat for geladas, particularly south of the Rift Valley. The difference in topography between current and historical sightings is potentially associated with anthropogenic pressures that drove niche truncation to higher altitudes, undermining the climatic and topographic niche our models predicted. We recommend protecting the current habitats of geladas even when they are forecasted to become climatically unsuitable in the future, in particular for the population south of the Rift Valley.

Population monitoring can help us determine population status and trajectory, but it is important to assess what factors may influence the number of individuals counted. In this study we conducted a complete count of the Critically Endangered Colobus vellerosus in the forests attached to the Boabeng and Fiema communities in central Ghana. We used 157 repeated counts of the same groups, including both good and unreliable counts to assess what factors predict the number of counted individuals in each group. The number of counted individuals increased with proxies for observation condition, observer experience, and habituation. We therefore recommend observer training and careful planning to increase the chances of having good observation. Then, we used the good counts to calculate the population size and group compositions. The obtained maximum number was 393 individuals in 25 groups. There were no significant differences in group sizes or immature to adult female ratios between groups occupying the older growth forest and groups in other forest types. Although there was still a relatively high immature to adult female ratio indicating that the population size may still increase, it does not appear to grow as rapidly as it used to, based on comparisons with previous population counts. Based on these findings, we recommend priority areas to promote conservation success.

O_LILand use change leads to shifts in species ranges and declines in biodiversity across the world. By mapping likely future land use under projections of socio-economic change, these ecological changes can be predicted to inform conservation decision-making. C_LIO_LIWe present a land use modelling approach that enables ecologists to map changes in land use under various socio-economic scenarios at fine spatial resolutions. Its predictions can be used as a direct input to virtually all existing spatially-explicit ecological models. C_LIO_LIThe most commonly used land use modelling approaches provide binary predictions of land use. However, continuous representations of land use have been shown to improve ecological models. Our approach maps the fractional cover of land use within each grid cell, providing higher information content than discrete classes at the same spatial resolution. C_LIO_LIWhen parametrized using data from 1990, the method accurately reproduced land use patterns observed in the Amazon from 1990 until 2018. Predictions were accurate in terms of the fractional amounts allocated across the landscape and the correct identification of areas with declines and increases in different land uses. A small case study showcases the successful application of our model to reproduce patterns of agricultural expansion and habitat decline. C_LIO_LIThe model source code is provided as an open-source R package, making this new, open method available to ecologists to bridge the gap between socio-economic, land use and biodiversity modelling. C_LI

AO_SCPLOWBSTRACTC_SCPLOWThe Theory of Island Biogeography (TIB) promoted the idea that species richness within sites depends on site connectivity, i.e. its connection with surrounding potential sources of immigrants. TIB has been extended to a wide array of fragmented ecosystems, beyond archipelagoes, surfing on the analogy between habitat patches and islands and on the patch-matrix framework. However, patch connectivity often little contributes to explaining species richness in empirical studies. Before interpreting this trend as questioning the broad applicability of TIB principles, one first needs a clear identification of methods and contexts where strong effects of patch structural connectivity are likely to occur. Here, we use spatially explicit simulations of neutral metacommunities to show that patch connectivity effect on local species richness is maximized under a set of specific conditions: (i) patch delineation should be fine enough to ensure that no dispersal limitation occurs within patches, (ii) patch connectivity indices should be scaled according to target organisms dispersal distance and (iii) the habitat amount around sampled sites (within a distance adapted to organisms dispersal) should be highly variable. When those three criteria are met, the absence of an effect of connectivity on species richness should be interpreted as contradicting TIB hypotheses

The negative effect of fragmentation is one of the main concerns in the study of biodiversity loss in landscape ecology. The use of the matrix has been considered an important factor because it can change the relationship of a population with the configuration of the landscape. A systematic way to assess the effect of matrix quality in fragmented landscapes could lead to a better understanding of how matrices can be used to suppress the negative effect of fragmentation. We built a computational individual-based model capable of simulating bi-dimensional landscapes with three types of land cover (habitat, suitable matrix and hostile matrix) and individuals that inhabit those landscapes. We explored in which situations changes in the proportion of the suitable matrix in the landscape and the degree of usability of this suitable matrix can mitigate the negative effect of fragmentation per se. We observed that (i) an increase in the matrix quality (increases in the suitable matrix proportion and/or usability) can suppress the fragmentation effect in 47% of the simulated scenarios; (ii) the less usable the matrix is the more of it is needed to suppress the fragmentation effect; (iii) there is a level of usability below which increasing the suitable matrix proportion does cause the fragmentation effect to cease. These results point toward a landscape management that considers the similarity of the matrix to the native habitat under management. We suggest that an index to measure the usability of elements of the matrix could be an important tool to further the use of computational models in landscape management.

The landscape connectivity of cities is increasingly recognized as crucial for biodiversity conservation and ecosystem services. Yet, modelling ecological connectivity in cities remains challenging because landscape resistance is often based on expert judgment rather than empirical evidence, leading to varying modelling results and limited use for planning. We developed and tested a data-driven framework for empirically parametrizing resistance and movement-distance parameters in functional connectivity models from movement-proxy data - information on the presence/absence of animal movement from direct observation or camera traps. At each step, we ensured that the connectivity model reflected the behavioural and spatial properties of the observations. We applied the framework for the common blackbird (Turdus merula) in Munich, Germany. We used observations of flying blackbirds as movement-proxy data in a logistic regression framework, testing alternative combinations of resistance and movement distances. Model selection identified the parameter sets best supported by the data. The resulting parameters were validated using repeated out-of-sample validation and compared against an expert-based connectivity model. Connectivity derived from empirically estimated parameters increased the probability of observing flying blackbirds. Across repeated validations, the empirical model achieved a mean AUC of 0.76 and R2 of 0.17. It performed moderately better than the expert-based model. Depending on their height, buildings exhibited varying resistance to flying blackbirds. Results indicate that expert assessments may oversimplify urban barriers. The approach provides a transparent, reproducible framework for using movement-proxy data to derive maps of landscape resistance. It offers a step toward more data-driven urban connectivity modelling.

Among vector-borne diseases, tick-borne diseases (TBD) are a major concern for human health. Mapping the distribution of important tick species is thus a major challenge for efficient prevention. Due to its specific ecological requirements, Ixodes ricinus, the main tick species in Europe responsible for TBD transmission, lives mostly in woodlands but also at the interface between woodlands and pastures or crops and along hedgerows. At the landscape scale, extensive variations in tick densities are observed but remain poorly understood. In that aim, we built a statistical model to identify the landscape variables influencing the abundance of questing I. ricinus nymphs, using GLMM approaches and MCMC estimates. This model was fitted on a data set based on a field sampling of ticks conducted during 3 years in 2 different agricultural landscapes in northwest and southwest France, for a total of 5390 sampling units. Among 12 variables investigated, 4 were finally kept in the model: woodland perimeter, woodland distance, road distance and building perimeter. Then, we developed a R package that simulates the abundance of questing nymphs within a given agricultural landscape, taking into account the influence of the different habitats as determined by the above statistical model. The maps obtained as an output from this simulator will be a useful tool for visualizing TBD risk, notably for stake-holders involved in landscape management and public health decisions. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/663759v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@f950aborg.highwire.dtl.DTLVardef@1f0f27org.highwire.dtl.DTLVardef@11bde05org.highwire.dtl.DTLVardef@8d2202_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsIxodes ricinus abondance is influenced by landscape characteristics Tick sampling was carried out in heterogeneous agricultural landscapes Informative variables related to habitats were identified by statistical analysis Woodlands, roads and buildings influence tick densities The resulting model was used to build a simulator of tick at-risk zones

ContextAs global urbanization intensifies, Urban Green Spaces (UGS) are pivotal for biodiversity conservation and climate change mitigation. However, comparative assessments of UGS spatial configuration and connectivity across diverse urban landscapes remain limited. ObjectivesThis study aims to assess the spatial arrangement and connectivity of UGS across 28 European capital cities. Additionally, we evaluate how Network Science metrics derived from Graph Theory can complement traditional landscape ecology metrics to provide a more comprehensive understanding of UGS at a large scale. MethodsWe developed a European Urban Vegetation Map using Earth observation data to classify UGS at 10m resolution across the selected capitals. We then analyzed UGS connectivity for each city utilizing 40 traditional landscape metrics and a Graph-Theory-based approach. ResultsWhile traditional landscape metrics effectively quantified fragmentation, they often remain strongly correlated with total vegetation abundance. In contrast, Network Science metrics provided specific insights into UGS functional connectivity, distinguishing the quality of ecological links beyond spatial proximity. This integration allowed us to cluster European capitals into three distinct typologies: unconnected compact cities, large metropolises with complex peri-urban dynamics, and high-connectivity cities with robust networks. These findings demonstrate that graph-based indices effectively complement traditional metrics, highlighting that relying solely on green space percentage is insufficient for assessing the ecological resilience of urban environments. ConclusionsThese results underscore the relevance of Earth observation-based UGS assessment and demonstrate that graph-based landscape connectivity analysis outperforms simple abundance metrics. Therefore, effective assessment requires integrating structural metrics with graph-based connectivity to support resilient urban biodiversity.

Countering the impacts of habitat loss and fragmentation on ecosystems requires complementing conservation areas with Other Effective area-based Conservation Measures within landscapes to promote biodiversity and multiple ecosystem services (ES). However, critical knowledge gaps persist in where and how natural elements should be restored to improve landscape connectivity to simultaneously support, and reduce trade-offs between biodiversity and ES. In virtual landscape experiments that allow exploring the effects of spatial pattern systematically, we generated alternative landscape restoration scenarios aimed at fostering ecological connectivity. Scenarios varied in the location and size of restored areas complementing existing natural areas. We analysed the impact of these scenarios on four bundles representing distinct priorities of target ES and biodiversity-related values. As expected, all bundles were favoured by increasing restored area in the landscape, but they were promoted by different spatial configurations. Restoration scenarios that fostered high aggregation of natural habitats promoted biodiversity and cultural value-related bundles, while smaller natural elements dispersed throughout the landscape were more beneficial for the sustainable production and climate adaptation bundles. These contrasts were most pronounced at low restoration efforts, where landscape configuration had greatest impacts on biodiversity and ecosystem processes. Effective spatial planning of restoration initiatives within landscapes should consider these trade-offs, along with context-specific constraints, when prioritizing areas for restoration or conservation. Our findings contribute to a more comprehensive understanding of how protected and restored areas can be integrated within landscapes to jointly support connectivity for both biodiversity and people. HighlightsO_LIVirtual landscape restoration options effects on four ecosystem services and biodiversity bundles C_LIO_LIHigh aggregation of restored elements promoted biodiversity and cultural value C_LIO_LILow aggregation promoted sustainable production and climate adaptation C_LIO_LIThese contrasts were most important at low restoration targets C_LIO_LIThese results highlight the importance of configuration trade-offs in restoration planning C_LI

Habitat fragmentation and landscape change are common causes of concern for species persistence, especially for habitat specialists. The composition of the matrix surrounding habitat fragments influences connectivity between them, which affects gene flow across the landscape. This can further impact populations in various ways. The Shola Sky Island landscapes naturally comprise a biphasic, forest-grassland mosaic ecosystem unique to the high-altitude regions of the Western Ghats of India. Earlier, this mosaic consisted of patches of native cloud forests embedded in a large grassland matrix. Over the last few decades, however, extensive invasion (up to 60%) of timber species into native grasslands has inverted this mosaic, i.e., small patches of grasslands are nested in a woodland matrix. We attempt to study the effects of these modifications on functional habitat connectivity in this region by modelling species movement using a circuit theory-based algorithm. We do this for seven Shola endemic, range-restricted bird species; six forest-specialist and one grassland-specialist species, based on a decade of field data. We consider a range of species-environment relationships and dispersal capacities for a past, relatively uninvaded landscape and a present, highly modified landscape. We used bird occupancy data (presence/absence from a total of 720 grid cells from targeted occupancy surveys for forest species and 744 presence locations for grassland species from occupancy surveys combined with opportunistic records) along with remotely sensed landscape, vegetation, climatic and topographic variables. We find that connectivity has increased overall for forest specialists, but has reduced for the grassland species. This pattern is concordant with regions where woodland cover from invasive timber species has expanded over approximately two decades. We also identify species-specific areas of low and high connectivity, which may have implications for gene flow within the landscape. This would help focus conservation efforts and predict how future landscape change might affect species persistence.

ContextConservation planning and land management are inherently spatial processes that are most effective when implemented over large areas.\n\nObjectivesOur objectives were to (i) use existing plot data to aggregate species inventories to growth forms and derive indicators of vegetation structure and composition and ii) generate spatially-explicit, continuous, landscape scaled models of these discrete vegetation indicators, accompanied by maps of model uncertainty.\n\nMethodUsing a case study from New South Wales, Australia, we aggregated floristic observations from 7234 sites into growth forms. We trained ensembles of artificial neural networks (ANN) to predict the distribution of these indicators over a broad region covering 11.5 million hectares. Importantly, we show spatially explicit models of uncertainty so that end-users have a tangible and transparent means of assessing models.\n\nResultsOur key findings were firstly, widely available site-based floristic records can be used to derive aggregated indicators of the structure and composition of plant growth forms. Secondly, ANNs are a powerful method to predict continuous patterns in complex, non-linear data (Pearsons correlation coefficient 0.83 (total native vegetation cover) to 0.42 (forb cover)). Thirdly, maps of the standardised residual error give insight into model performance and provide an assessment of model uncertainty in specific locations.\n\nConclusionsSpatially explicit, continuous representations of vegetation composition and structural complexity can add considerable value to conventional maps of vegetation extent or community type. This application has the potential to enhance the capacity for conservation planners, landscape managers and policy-makers to make informed decisions across landscape and regional scales.

Landscape heterogeneity has an impact on wildlife behavior, their interactions, and their persistence. Urban landscapes are among the worlds most heterogeneous landscapes, yet current global landcover maps classify developed land in a single landcover type. This limits the spatial scale at which urban ecologists can approach research questions. OpenStreetMap (OSM), an open-source mapping platform, can be leveraged to enhance the representation of landscape heterogeneity in developed areas. For this, we extracted OSM features with attributes representing infrastructure, land use and green cover, integrating these into a continental landcover map through a globally applicable computational framework. We validated our OSM-enhanced landcover layer against existing remote sensing, aerial photography, and local governmental maps for 33 cities in North America. Our frameworks output provides an 89% accurate representation of landscape heterogeneity. We discuss caveats, potential improvements, and ecological applications. Our OSM-based landcover enhancement framework will facilitate the use of open-source landscape information for improved ecological modeling and urban planning.

Wildfire regimes in Mediterranean landscapes are undergoing significant changes due to the combined effects of land-use transitions and climate change. In particular, land abandonment increased fuel availability, the expansion of the wildland-urban interface increased ignition frequency, while climate change increases the chances of fire-weather conditions and reduces vegetation recovery capacity. This study presents a modelling framework to investigate the coupled dynamics of fire and vegetation under different fire regimes scenarios, using a case study in central Italy (Monte Pisano). The approach integrates two cellular automata models for vegetation dynamics (Batllori et al., 2017) and for fire-spread (PROPAGATOR; Trucchia et al., 2020). The vegetation model represents succession among six functional classes, including grasslands, shrubs, and trees with different fire-response strategies (seeders and resprouters), while explicitly accounting for post-fire recovery processes. The model was calibrated for the area using historical fire perimeters and vegetation maps over 40 years. Fire spread is simulated probabilistically using PROPAGATOR, driven by fuel types, topography, and weather conditions. A stochastic coupling was implemented by sampling fuel classes from vegetation composition, and by feeding simulated burned areas back into the vegetation model, thus enabling dynamic fire-vegetation feedback. Future wildfire scenarios are constructed by linking ignition probability to fire-weather conditions derived from historical reanalysis data (1981-2023). Extreme fire events are defined based on thresholds of wind speed and fuel moisture, and their probability of occurrence is varied across scenarios to represent increasing climate-driven risk. Simulations are performed over a 100-year horizon starting from current vegetation conditions. Results show that, in the absence of fire, vegetation dynamics lead to dominance of late-successional, fire-resilient species (resprouters). This is particularly evident for low probabilities of extreme fire events, with fire impacts diminishing over time as landscapes become less flammable. However, increasing the frequency of extreme fire conditions resulted in persistent disturbance, maintaining higher proportions of shrubs and early successional vegetation, and sustaining elevated burned areas over time. Overall, the study shows that coupling fire spread and vegetation dynamics provides a useful framework for exploring long-term ecosystem trajectories under climate change. The results highlight the critical role of extreme fire events in shaping landscape resilience and suggest that future management strategies should account for fire-vegetation feedbacks to support more stable and less fire-prone ecosystems.