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Abstract Due to the central role of landscape connectivity in many ecological processes, evaluating and accounting for it has gained attention in both theoretical and applied ecological sciences. To address this challenge, researchers often use generic species to simplify multi‐species connectivity assessments. Yet, this approach tends to oversimplify movement behaviour, likely reducing realism and precision of connectivity model outputs. Also, the most widely used methods and theories for assessing landscape connectivity, namely circuit and network theories, have strong limitations. Finally, uncertainty or robustness estimates are rarely integrated in connectivity assessments. Here, we propose a versatile framework, which, instead of using arbitrary defined generic species, first identifies species groups based on species' environmental niches and morphological, biological, and ecological traits. Second, it combines circuit and network theories to take the best of the two methods to assess landscape connectivity for those groups, while integrating uncertainties in modelling choices. Specifically, ecological continuities (i.e. landscape elements contributing to connectivity) are calculated for these groups and used together with group dispersal capacities to derive network‐based connectivity metrics for conservation areas. We detailed our framework through a case study where we assess the connectivity of 1619 protected areas in metropolitan France for 193 vertebrate species. Our study revealed that both the protection of ecological continuities and the connectivity of protected areas for 11 mammal and 19 bird groups, respectively, were quite low, with variations among groups. Different protection types (i.e. national parks, reserves or prefectural orders) contributed unequally to the overall connectivity of group‐specific suitable habitats. Considering uncertainty propagation was crucial, as many connectivity metrics varied among repetitions. The proposed framework combines different connectivity tools to provide a more relevant and comprehensive assessment of landscape connectivity. It can be used to inform the decision‐making process for spatial planning, particularly in the context of connectivity conservation and management, or support theoretical studies to better understand the ecological role of landscape connectivity. Its flexibility allows easy application under various environmental conditions, including future scenarios.

Abstract Background Network theory is largely applied in real-world systems to assess landscape connectivity using empirical or theoretical networks. Empirical networks are usually built from discontinuous individual movement trajectories without knowing the effect of relocation frequency on the assessment of landscape connectivity while theoretical networks generally rely on simple movement rules. We investigated the combined effects of relocation sampling frequency and landscape fragmentation on the assessment of landscape connectivity using simulated trajectories and empirical high-resolution (1 Hz) trajectories of Alpine ibex (Capra ibex). We also quantified the capacity of commonly used theoretical networks to accurately predict landscape connectivity from multiple movement processes. Methods We simulated forager trajectories from continuous correlated biased random walks in simulated landscapes with three levels of landscape fragmentation. High-resolution ibex trajectories were reconstructed using GPS-enabled multi-sensor biologging data and the dead-reckoning technique. For both simulated and empirical trajectories, we generated spatial networks from regularly resampled trajectories and assessed changes in their topology and information loss depending on the resampling frequency and landscape fragmentation. We finally built commonly used theoretical networks in the same landscapes and compared their predictions to actual connectivity. Results We demonstrated that an accurate assessment of landscape connectivity can be severely hampered (e.g., up to 66% of undetected visited patches and 29% of spurious links) when the relocation frequency is too coarse compared to the temporal dynamics of animal movement. However, the level of landscape fragmentation and underlying movement processes can both mitigate the effect of relocation sampling frequency. We also showed that network topologies emerging from different movement behaviours and a wide range of landscape fragmentation were complex, and that commonly used theoretical networks accurately predicted only 30–50% of landscape connectivity in such environments. Conclusions Very high-resolution trajectories were generally necessary to accurately identify complex network topologies and avoid the generation of spurious information on landscape connectivity. New technologies providing such high-resolution datasets over long periods should thus grow in the movement ecology sphere. In addition, commonly used theoretical models should be applied with caution to the study of landscape connectivity in real-world systems as they did not perform well as predictive tools.

Cartographier le niveau d’intensité des pressions sur la biodiversité est un outil précieux pour le pilotage des politiques de conservation. Nous présentons ici le premier catalogue de cartes de ce type en France, développé dans le cadre de la stratégie nationale pour les aires protégées. Il repose sur l’identification des pressions dont l’impact est le plus significatif sur le territoire puis sur la recherche de données appropriées. Sur 41 pressions identifiées, 27 ont pu être cartographiées en métropole et des cartes ont également été produites en Outre-mer. Elles s’appuient sur des données de source et nature très variées. Des cartes de synthèse cumulant le niveau de différentes pressions générées par un même secteur d’activité sont également proposées. Ce travail a vocation à être croisé avec des cartographies d’espèces et habitats patrimoniaux pour prioriser la création d’aires protégées, mais peut aussi être mis à profit par d’autres acteurs de la conservation

Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed population-level, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer (Odocoileus hemionus), plains bison (Bison bison bison) and plains zebra (Equus quagga), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity.

Abstract Landscape complexity can determine the population dynamics of interacting predators and prey. Yet, management plans are commonly developed from aspatial predictive models. This oversight may result in unexpected outcomes or the loss of opportunities to make spatial interventions that would increase a plan's effectiveness. The management of the threatened woodland caribou (Rangifer tarandus caribou), boreal population, provides an example of such shortcomings when using an aspatial approach. Currently, the most influential management recommendation is to maintain at least 65% of undisturbed forests in areas occupied by caribou populations, regardless of the spatial configuration of the forest cover. Using a spatially explicit individual‐based model (IBM), we evaluated the effects of the spatial configuration of cuts and roads on the mortality of boreal caribou living in sympatry with wolves (Canis lupus) and moose (Alces alces), an apparent competitor. Starting with a real forest landscape, we created forest management scenarios of the specific spatial distribution of cuts (mosaic, small, or large agglomeration) with increasing disturbance levels. We then ran the IBM with simulated agents, representing individuals of the three species, moving according to movement rules determined from radio‐collared individuals. We found that movement responses to land cover types and roads differed among species. For example, caribou and moose generally avoided areas close to roads, contrary to wolves. Those differences influenced the mortality of caribou agents, which not only depended on the levels of disturbance but also depended on the spatial distribution of cuts and roads. After controlling for disturbance level, wolves were more successful when forest management required an extensive road network resulting in relatively high habitat fragmentation. Caribou agents experienced lower mortality in landscapes with low densities of road and disturbance‐related edges. The effect remained much stronger, however, for the level than the spatial configuration of human disturbances. Still, our IBM demonstrated how landscape management could be used to manipulate species interactions, with the intent of either increasing or decreasing predation rates on specific populations, depending on management goals.

Abstract Primary production can determine the outcome of management actions on ecosystem properties, thereby defining sustainable management. Yet human agencies commonly overlook spatio-temporal variations in productivity by recommending fixed resource extraction thresholds. We studied the influence of forest productivity on habitat disturbance levels that boreal caribou – a threatened, late-seral ungulate under top-down control – should be able to withstand. Based on 10 years of boreal caribou monitoring, we found that adult survival and recruitment to populations decreased with landscape disturbance, but increased with forest productivity. This benefit of productivity reflected the net outcome of an increase in resources for apparent competitors and predators of caribou, and a more rapid return to the safety of mature conifer forests. We estimated 3-fold differences in forest harvesting levels that caribou populations could withstand due to variations in forest productivity. The adjustment of ecosystem provisioning services to local forest productivity should provide strong conservation and socio-economic advantages.

Conditional logistic regression (CLR) is widely used to analyze habitat selection and movement of animals when resource availability changes over space and time. Observations used for these analyses are typically autocorrelated, which biases model-based variance estimation of CLR parameters. This bias can be corrected using generalized estimating equations (GEE), an approach that requires partitioning the data into independent clusters. Here we establish the link between clustering rules in GEE and their effectiveness to remove statistical biases in variance estimation of CLR parameters. The current lack of guidelines is such that broad variation in clustering rules can be found among studies (e.g., 14-450 clusters) with unknown consequences on the robustness of statistical inference. We simulated datasets reflecting conditions typical of field studies. Longitudinal data were generated based on several parameters of habitat selection with varying strength of autocorrelation and some individuals having more observations than others. We then evaluated how changing the number of clusters impacted the effectiveness of variance estimators. Simulations revealed that 30 clusters were sufficient to get unbiased and relatively precise estimates of variance of parameter estimates. The use of destructive sampling to increase the number of independent clusters was successful at removing statistical bias, but only when observations were temporally autocorrelated and the strength of inter-individual heterogeneity was weak. GEE also provided robust estimates of variance for different magnitudes of unbalanced datasets. Our simulations demonstrate that GEE should be estimated by assigning each individual to a cluster when at least 30 animals are followed, or by using destructive sampling for studies with fewer individuals having intermediate level of behavioural plasticity in selection and temporally autocorrelated observations. The simulations provide valuable information to build reliable habitat selection and movement models that allow for robustness of statistical inference without removing excessive amounts of ecological information.