Ecological modeling requires sufficient spatial resolution and a careful selection of environmental variables to achieve good predictive performance. Although national and international administrations offer fine-scale environmental data, they usually have limited spatial coverage (country or continent). Alternatively, optical and radar satellite imagery is available with high resolutions, global coverage and frequent revisit intervals. Here, we compared the performance of ecological models trained with free satellite data with models fitted using regionally restricted spatial datasets. We developed brown bear habitat suitability and connectivity models from three datasets with different spatial coverage and accessibility. These datasets comprised (1) a Sentinel-1 and 2 land cover map (global coverage); (2) pan-European vegetation and land cover layers (continental coverage); and (3) LiDAR data and the Forest Map of Spain (national coverage). Results show that Sentinel imagery and pan-European datasets are powerful sources to estimate vegetation variables for habitat and connectivity modeling. However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. Therefore, more effort should be taken to improving the thematic resolution of satellite-derived vegetation variables. Our findings support the application of ecological modeling worldwide and can help select spatial datasets according to their coverage and resolution for habitat suitability and connectivity modeling.

Land conversion is causing habitat loss and fragmentation worldwide, particularly in Africa, where the proliferation of agricultural development corridors may threaten vital areas for ecological connectivity and wildlife survival. To conserve connectivity, careful landscape planning is necessary, which strongly relies on remotely sensed land cover maps. Here, we present a remote sensing‐based framework that efficiently identifies priority locations for connectivity conservation. We applied the framework in the Kilombero catchment and development cluster of the Southern Agricultural Growth Corridor (SAGCOT) in Tanzania, where new agricultural development projects could act as barriers for connectivity. Using satellite imagery from Sentinel‐1 and 2, we mapped the mixture of mountain and lowland land covers and habitats with an overall accuracy of 75%. Then, we assessed ecological connectivity to predict African elephant corridors and prioritize them in two ways. First, we identified elephant corridors that contribute the most to current landscape connectivity, and second, we identified those whose restoration would significantly enhance landscape functionality and improve the current connectivity level. We mapped 214 potential elephant corridors, identified 43 of them as priority for conservation, and 43 as target for restoration. Our model predicted four already known corridor areas in and around the Kilombero valley floodplain, and other important corridors not yet identified by previous studies in the south of the basin. Priority elephant corridors inside the floodplain showed narrow widths and low permeability, indicating low functionality in the connectivity network. Nevertheless, the abundance of priority corridors for restoration suggests that connectivity could be recovered if the recommended measures are applied during SAGCOT planning and implementation process. Our findings demonstrate the possibilities of combining multispectral and radar data for guiding biodiversity management in development corridors and for assessing ecological connectivity worldwide.

Wildlife conservation requires a comprehensive understanding of habitat and landscape conditions supporting viable and well‐connected populations, especially for reintroduction efforts. Habitat selection depends on different life processes and behavioural states driving animal movements. However, habitat modelling often considers all species records together or distinguishes only between locations within and outside home ranges. We considered a more detailed characterization of movements, focusing on five distinct movement phases: home ranges, transient residences, excursions, post‐release dispersals and dispersals. Utilizing GPS telemetry data from 124 Iberian lynxes (Lynx pardinus), mainly tracked during a reintroduction programme, we examined their habitat selection and adaptation to heterogeneous, human‐modified and fragmented landscapes. We developed context‐specific mixed‐effects habitat selection models for each phase, incorporating two hierarchical selection scales: resident range and path selection functions at the landscape scale, and step selection functions at the local scale. All lynxes consistently avoided intensive non‐tree cropland and selected mosaics of natural vegetation, including tree, shrubland and grassland cover. Resident lynxes selected areas with low road and human infrastructure densities locally, but no such pattern emerged at the landscape scale, likely due to constraints on establishing large home ranges where infrastructures are widespread and intertwined with natural cover. We observed significant differences in habitat selection among non‐residential phases. During excursions, lynxes avoided areas with high human infrastructure, whereas during dispersals, this avoidance was less pronounced, indicating high behavioural plasticity. The post‐release dispersal phase mirrored dispersals but showed infrastructure avoidance and a higher selection for sheltering features like rugged terrain and shrub cover. Our findings emphasize the importance of distinguishing specific movement phases, and particularly different movement phases outside home ranges, when assessing habitat selection, especially for translocated animals settling in the landscape. This differentiation is essential to (i) identify suitable reintroduction areas with habitat features supporting post‐release movements and settlement; (ii) evaluate habitat conditions of temporary stopovers facilitating long‐distance dispersals, and (iii) achieve accurate connectivity estimates among populations. True dispersal events, as the primary movements facilitating gene flow and range expansion, should be treated separately in landscape permeability studies to effectively guide related conservation efforts.

Context: Identifying the components of movement paths is essential for studying key processes such as space use, habitat selection and connectivity. Although animal movement operates across scales, most segmentation approaches do not differentiate how fine-scale behaviors depend on broader movement phases. Objectives: We introduce a hidden Markov model (HMM) formulation that jointly analyses fine-scale activity modes (inactive, moving) and broader movement phases (resident, non-resident). Our goal is to provide a flexible and accessible method for segmenting movement trajectories into behaviorally meaningful states within longer-term movement patterns. Methods: Our framework uses variables directly derived from GPS tracks—step lengths and turning angles—to identify activity modes, and residence time to characterize broader phases. An asymmetric coupled HMM (ACHMM) structure allows activity modes to depend on movement phases, but not vice versa. We demonstrate the model with two illustrative examples and apply it to telemetry data from Cantabrian brown bears, a species with diverse movement strategies, by modeling state transition probabilities as functions of bear identity and time of day (via splines) to examine interindividual variation in diel activity. Results: The model effectively segmented trajectories into interpretable states—residence areas, stepping-stones, dispersals, excursions—and provided a biologically informed approach to identifying home ranges and core areas. It also showed that the characteristics of activity modes vary with movement phase and revealed interindividual differences and phase-dependent shifts in bear diel activity patterns. Conclusions: The ACHMM framework facilitates integrated ecological inference across movement scales, offering a powerful tool for studying how environmental factors jointly shape animal behaviors and movement strategies.

Context: Understanding large herbivore space use is central to landscape ecology, particularly in human-modified landscapes where movement is increasingly constrained. Neotropical cervids are underrepresented in movement studies, limiting our ability to integrate their spatial behavior into landscape planning and conservation. Objectives: We provide the first evaluation of how marsh deer ( Blastocerus dichotomus ) organize space use and movement across a fragmented wetland landscape. Specifically, we examined whether distinct movement phases differ in their spatial and temporal patterns and how sex and post-release conditions influence these patterns. Methods: We analyzed GPS tracking data from 18 adult marsh deer monitored between 2015 and 2024 in the Paraná River Delta, Argentina. Individual trajectories were segmented into distinct movement phases, and space use and activity patterns were evaluated across spatial and temporal scales. Results: Marsh deer consistently structured their movements into four phases: home ranges, excursions, stepping stones, and dispersals. Home ranges accounted for most space use, indicating strong site fidelity within the landscape. Excursions were common and generally short, contrasting with dispersals that were infrequent and covered longer distances. All rehabilitated individuals performed non-resident post-release movements but eventually established home ranges similarly to free-ranging deer. Stepping stones, although transiently occupied, enabled longer-distance excursions and dispersals across fragmented areas in the landscape and connected otherwise isolated residence zones. Activity patterns were predominantly crepuscular, varied seasonally, and differed between sexes. Conclusions: Our findings reveal that marsh deer navigate fragmented wetland landscapes through a diversity of movement tactics shaped by sex and individual history, and highlight that characterizing the full spectrum of movement is key for identifying spatiotemporal ecological patterns and requirements, and for informing wildlife conservation in human-modified landscapes.