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1. Habitat fragmentation and isolation as a result of human activities have been recognized as great threats to population viability. Evaluating landscape connectivity in order to identify and protect linkages has therefore become a key challenge in applied ecology and conservation. 2. One useful approach to evaluate connectivity is least-cost path (LCP) analysis. However, several studies have highlighted importance of parameterization with empirical, biologically relevant proxies of factors affecting movements as well as the need to validate the LCP model with an independent dataset. 3. We used LCP analysis incorporating quantitative, empirical data about behaviour of the greater horseshoe bat Rhinolophus ferrumequinum to build up a model of functional connectivity in relation to landscape connecting features. We then validated the accumulated costs surface from the LCP model with two independent datasets; one at an individual level with radiotracking data and one at a population level with acoustic data. 4. When defining resistance, we found that the probability of bat presence in a hedgerow is higher when the distance between hedgerows is below 38 m, and decrease rapidly when gaps are larger than 50 m. The LCP model was validated by both datasets: the independent acoustic data showed that the probability of bat presence was significantly higher in areas with lower accumulated costs, and the radiotracking data showed that foraging was more likely in areas where accumulated costs were significantly lower. 5. Synthesis and applications. Through our modelling approach, we recommend a maximum of 38 m (and no more than 50 m) between connecting features around colonies of greater horseshoe bats. Our quantitative study highlights the value of this framework for conservation: results are directly applicable in the field and the framework can be applied to other species sensitive to habitat loss, including other bats. Provided that it is parameterized with empirical, biologically relevant data, this modelling approach can be used for restoring and evaluating green networks in agri-environmental schemes and management plans.

Least-cost models for focal species are widely used to design wildlife corridors. To evaluate the least-cost modeling approach used to develop 15 linkage designs in southern California, USA, we assessed robustness of the largest and least constrained linkage. Species experts parameterized models for eight species with weights for four habitat factors (land cover, topographic position, elevation, road density) and resistance values for each class within a factor (e.g., each class of land cover). Each model produced a proposed corridor for that species. We examined the extent to which uncertainty in factor weights and class resistance values affected two key conservation-relevant outputs, namely, the location and modeled resistance to movement of each proposed corridor. To do so, we compared the proposed corridor to 13 alternative corridors created with parameter sets that spanned the plausible ranges of biological uncertainty in these parameters. Models for five species were highly robust (mean overlap 88%, little or no increase in resistance). Although the proposed corridors for the other three focal species overlapped as little as 0% (mean 58%) of the alternative corridors, resistance in the proposed corridors for these three species was rarely higher than resistance in the alternative corridors (mean difference was 0.025 on a scale of 1–10; worst difference was 0.39). As long as the model had the correct rank order of resistance values and factor weights, our results suggest that the predicted corridor is robust to uncertainty. The three carnivore focal species, alone or in combination, were not effective umbrellas for the other focal species. The carnivore corridors failed to overlap the predicted corridors of most other focal species and provided relatively high resistance for the other focal species (mean increase of 2.7 resistance units). Least-cost modelers should conduct uncertainty analysis so that decision-makers can appreciate the potential impact of model uncertainty on conservation decisions. Our approach to uncertainty analysis (which can be called a worst-case scenario approach) is appropriate for complex models in which distribution of the input parameters cannot be specified.

Landscape epidemiological studies often produce maps that visualise spatial variation in disease incidence or prevalence. Within England and Wales a component of the spatial variation in the incidence of bovine tuberculosis (bTB) is endemic infection in the European badger (Meles meles) population. This reservoir of infection presents a major obstacle to control and eradication of the disease in cattle, which is currently one of the major issues in agricultural policy in England and Wales. Previous bTB maps have considered the distribution and abundance of badgers, but there is recognition in epidemiology that other landscape characteristics that may relate to disease risk need to be considered. Landscape isolation is particularly relevant for management of disease in wildlife hosts, and especially for bTB in badgers, as the potential for badgers to disperse into and out of areas under management affects the success of culling, and possibly also the success of vaccination. To map the relative levels of landscape isolation for badgers, we used expert opinion of landscape obstacles to dispersal to apply a continuous surface catchment area approach, which is based on the concept of landscape connectivity and the technique of least-cost modelling. Our results indicate that some parts of the landscape are relatively more or less isolated than others. This finding is directly relevant in the context of potential bTB management strategies, and we hope this will encourage collation of data designed to quantify the degree of landscape isolation for wildlife disease hosts, and encourage its consideration when formulating and assessing disease management strategies.

Context Connectivity assessments typically rely on resistance surfaces derived from habitat models, assuming that higher-quality habitat facilitates movement. This assumption remains largely untested though, and it is unlikely that the same environmental factors determine both animal movements and habitat selection, potentially biasing connectivity assessments. Objectives We evaluated how much connectivity assessments differ when based on resistance surfaces from habitat versus movement models. In addition, we tested how sensitive connectivity assessments are with respect to the parameterization of the movement models. Methods We parameterized maximum entropy models to predict habitat suitability, and step selection functions to derive movement models for brown bear ( Ursus arctos ) in the northeastern Carpathians. We compared spatial patterns and distributions of resistance values derived from those models, and locations and characteristics of potential movement corridors. Results Brown bears preferred areas with high forest cover, close to forest edges, high topographic complexity, and with low human pressure in both habitat and movement models. However, resistance surfaces derived from the habitat models based on predictors measured at broad and medium scales tended to underestimate connectivity, as they predicted substantially higher resistance values for most of the study area, including corridors. Conclusions Our findings highlighted that connectivity assessments should be based on movement information if available, rather than generic habitat models. However, the parameterization of movement models is important, because the type of movement events considered, and the sampling method of environmental covariates can greatly affect connectivity assessments, and hence the predicted corridors.

Landscape fragmentation and the elimination of urban green spaces are the results of human activities which put significant pressure on urban sustainability. The planning and developing of urban ecological networks and corridors as an effective approach is a response to rapid urbanization and fragmentation of natural areas. The Tabriz metropolitan as the fourth-largest city in Iran was selected as a case study that has grown rapidly over the past few decades. This study presents a practical approach and framework for assessing and enhancing ecological connectivity of landscape. The framework was developed based on the landscape metrics, graph theory, least-cost modeling, and geographic information system tools during three different periods (1984-2000-2020). The results using the values of landscape metrics such as CA, Edge Distance, Mean Patch Size, Mean Shape Index, large patch index, NP, Landscape shape index, and COHESION indicate that the urban landscape of Tabriz has been more fragmented over the past 3 decades and has lost its connectivity. To reduce the effects of fragmentation and enhance landscape connectivity in study area, we proposed a network of ecological corridors that passes through the core ecological patches. Our results also indicate that the core patches and the least-cost created corridors are mainly located in the suburbs of Tabriz and the central part of the city cannot be suitable for ecological development. Therefore, using the above-mentioned methods could be an effective approach to develop ecological networks and improve landscape connectivity that can encourage urban planners and managers to protect and develop green networks.

Human modifications of the landscapes have led to species with wide home ranges being trapped within protected areas, disrupting their relationship with species in other protected areas. In this regard, identifying potential corridors that species use to migrate and access other resources can help preserve the species’ survival. Iran, with 246 protected areas, is home to a variety of vertebrates, including large mammals. This study aims to identify the least-cost corridors between core habitat areas of two large mammals of brown bear ( Ursus arctos ) and Persian leopard ( Panthera pardus saxicolor ) in Iran, as well as the barriers along these corridors. We also identify areas known as pinch-points or bottlenecks to determine mammalian conservation priorities. For this purpose, after creating a resistance map using species distribution models (SDMs), the Linkage Mapper toolkit was used to model the landscape connectivity. For brown bears, our results showed that the average length of the least-cost corridors in 84 core habitat areas was 74 km. Brown bears have to cross 179 barriers on average to migrate to another region, mostly due to linear infrastructure such as roads. For the Persian leopard, we identified 5 core areas with an average corridor length of 397 km and 36 barriers between them. This study suggests that in addition to determining the least-cost corridors between core habitats, it is necessary to identify the barriers and pinch-points along these corridors. The results of this study offer new insights for protected area managers to better manage these areas.

To understand the long-term fate of fish assemblages in the context of global change and to design efficient restoration measures in river management, it is essential to consider the historical component of these ecosystems. The human-impacted Seine River Basin is a relevant case that has experienced the extinction of diadromous fishes over the last two centuries and has recently witnessed the recolonization of some species. One key issue is to understand the historical evolution of habitat accessibility for these migratory species. Thanks to the unique availability of historical, mainly hand-written sources of multiple types (river engineering projects, navigation maps, paper-based databases on oxygen, etc.), we documented and integrated, in a geographic information system-based database, the changes to physical and chemical barriers in the Seine River from the sea to Paris for three time periods (1900s, 1970s, and 2010s). The potential impact of these changes on the runs of three migratory species that have different migratory behaviors—Atlantic salmon, allis shad, and sea lamprey—was evaluated by ecological connectivity modeling, using a least-cost approach that integrates distance, costs, and risks related to barriers. We found that accessibility was contrasted between species, emphasizing the crucial role of the migration type, period, and level of tolerance to low dissolved oxygen values. The highest disruption of ecological connectivity was visible in the 1970s, when the effects of large hypoxic areas were compounded by those of impassable navigation weirs (i.e., without fish passes). As the approach was able to reveal the relative contribution of physical and chemical barriers on overall functional connectivity, it may constitute a model work in assessing the functioning of large river ecosystems.

Accurate prediction of walking travel rates is central to wide-ranging applications, including modeling historical travel networks, simulating evacuation from hazards, evaluating military ground troop movements, and assessing risk to wildland firefighters. Most of the existing functions for estimating travel rates have focused on slope as the sole landscape impediment, while some have gone a step further in applying a limited set of multiplicative factors to account for broadly defined surface types (e.g., “on-path” vs. “off-path”). In this study, we introduce the Simulating Travel Rates In Diverse Environments (STRIDE) model, which accurately predicts travel rates using a suite of airborne lidar-derived metrics (slope, vegetation density, and surface roughness) that encompass a continuous spectrum of landscape structure. STRIDE enables the accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings. The model explained more than 80% of the variance in the mean travel rates from three separate field experiments, with an average predictive error less than 16%. We demonstrate the use of STRIDE to map least-cost paths, highlighting its propensity for selecting logically consistent routes and producing more accurate yet considerably greater total travel time estimates than a slope-only model.

Context Invasive species pose a significant threat to biodiversity, creating a need for accurate methods to assess their spread. Although multiple introductions are common, estimates of expansion rates often assume a single introduction site due to limited knowledge of population structure. Objectives This multidisciplinary study aimed to develop a novel spatio-temporal approach to delineate potential populations without prior knowledge of population structure. We applied this approach to the Sculptured Resin Bee, Europe’s first non-native bee species, providing regional expansion rate estimates for its spread across Europe. Methods Observation data from 2008 to 2024 were analysed. Based on an environmental suitability map, sequential least-cost modelling was applied in annual time steps, linking each new observation to the nearest known observation via a least-cost path. Populations were delineated by excluding high-cost paths and analysing the connectivity of the remaining paths, and expansion rates were calculated using the distance regression method. Results We identified two populations, which align with known genetic groups in the area of France, Switzerland and Austria. Our modelling results also indicate two additional populations introduced to Italy and Serbia. Expansion rates ranged from 13.3 km/year to 58.6 km/year and peaked at 89.7 km/year during expansion phases, exhibiting a consistent sigmoidal expansion pattern. Conclusions Our spatio-temporal approach delineates introduced populations without prior genetic knowledge, improving expansion rate estimation and informing targeted genetic sampling, monitoring, and management efforts of invasive species.

Planning for a green–blue infrastructure system around big cities, having the shape of a belt, to connect natural areas—such as green spaces, water, and agricultural land—is a solution for mitigating the challenges of climate change and urban sprawl. In this context, this study presents an innovative information technology solution for assessing the connectivity of the green and blue areas in the metropolitan area of Bucharest, Romania. The solution is to try to stop the sprawl of Bucharest into the adjacent rural areas and answer the need for a green infrastructure providing ecosystem services. The methodology uses datasets compatible with the European databases on environmental issues, CORINE Land Cover 2018 and Urban Atlas, and two tools in the ArcGIS PRO 2.9 software package, namely Cost Raster and Cost Connectivity. Based on the results, we developed a framework for implementing a strategy for the green–blue infrastructure for the Bucharest metropolitan area. Our methodology is a starter for planning a green–blue belt for the metropolitan area of Bucharest and a model of good practice in terms of making green–blue infrastructure part of urban and territorial planning.