Background When assessing connectivity, it is crucial to rely on accurate modeling frameworks that consider species movement preferences and patterns. One important aspect is the level of randomness or unpredictability in the route selection. In this respect, traditional approaches (based on least-cost path or circuit theory) consider species movements unrealistically as totally deterministic or as totally random. A recent approach (randomized shortest path) advocates for choosing intermediate levels of randomness through a single parameter. This parameter may be optimized by validating connectivity surfaces developed from different levels of randomness against observed movement data. However, connectivity models are seldom validated, and it is still unclear how to approach this task. To address this knowledge gap, this paper aims at comparing different validation methods to infer the optimal randomness level in connectivity studies. Additionally, we aimed to disentangle the practical consequences of applying traditional connectivity approaches versus using an optimized level of movement randomness when delineating corridors. Methods These objectives were accomplished through the study case of the Iberian lynx, an endangered species whose maintenance and recovery depend on the current connectivity among its population nuclei. We firstly determined a conductance surface based on point selection functions accounting for the behavioral state (territorial or exploratory) of individuals. Secondly, we identified the level of randomness that better fits lynxes’ movements with independent GPS locations and different validation techniques. Lastly, we delineated corridors between lynx population nuclei through a) the randomized shortest path approach and the extreme and optimal levels of randomness of each validation method, and b) the traditional connectivity approaches. Results According to all used validation methodologies, models with intermediate levels of randomness outperformed those with extreme randomness levels representing totally deterministic or random movements. We found differences in the optimal randomness level among validation methods but similar results in the delineation of corridors. Our results also revealed that models with extreme randomness levels (deterministic and random walk) of the randomized path approach provided equivalent corridor networks to those from traditional approaches. Moreover, these corridor networks calculated with traditional approaches showed notable differences in patterns from the corridor network calculated with an optimized randomness level. Conclusions Here we presented a connectivity model with a solid biological basis that calibrates the level of movement randomness and is supported by comprehensive validation methods. It is thus a step forward in the search and evaluation of connectivity approaches that lead to improved, efficient, and successful management actions.
Context Climate and land-use changes affect species ranges and movements. However, these changes are usually overlooked in connectivity studies, and this could have adverse consequences in the definition of effective management measures. Objectives We evaluated two ways to incorporate landscape dynamics: (i) by analyzing connectivity as a fluctuating phenomenon (i.e., time-varying connectivity); and (ii) by analyzing species movements from past to current ranges (i.e., spatio-temporal connectivity). We also compared these dynamic approaches with traditional static connectivity methods. Methods We compared the overall connectivity values and the prioritization of critical habitat patches according to dynamic and static approaches using habitat availability metrics (Probability of Connectivity and Equivalent Connected Area). This comparative research was conducted for species associated with broadleaf forests of the different ecoregions of the Iberian Peninsula. We considered species habitat preferences during movement and a wide range of dispersal abilities to assess functional connectivity. Results Static approaches generated varying overall connectivity values and priority patches depending on the time snapshot considered and different from those generated by dynamic approaches. The two dynamic connectivity approaches resulted in very similar priority conservation patches, indicating their potential to guide enduring conservation measures that enhance connectivity between contemporary habitat patches at multiple time snapshots but also species range shifts in time. Conclusions Connectivity is affected by landscape changes, and only dynamic approaches can overcome the issues associated with these changes and provide valuable information to guide improved and enduring measures in changing landscapes.
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.
Landscape connectivity has traditionally been studied for animal species rather than for plants, especially under a multispecies approach. However, connectivity can be equally critical for both fauna and flora and, thus, an essential point in the selection of key management areas and measures. This paper explores a spatially explicit framework to assess the contribution of habitat patches in the conservation and enhancement of plant functional connectivity and habitat availability in a multispecies context. It relies on graph theory and a habitat availability index and differentiates between two management scenarios: (i) conservation; and (ii) restoration, by considering current and potential species distribution based on species distribution models together with a vegetation survey. The results mapped at high spatial resolution priority target areas to apply management measures. We found that intervening in a small proportion of the study area may lead to double the average overall landscape connectivity of the studied species. This study aimed at proposing an innovative methodology that allows studying connectivity for multiple plant species at landscape scale while integrating their individual characteristics. The proposed framework is a step toward incorporating connectivity concerns into plant biodiversity management, based on a better understanding of landscape structure and functionality. Here, we illustrated its significant potential for local conservation and restoration planning and resource optimization.
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.
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