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Niche modeling is typically used to assess the effects of anthropogenic land use and climate change on species distributions and to inform spatial conservation planning. These models focus on the suitability of local biotic and abiotic conditions for a species in environmental space (E-space). Although movements also affect species occurrence, efforts to formally integrate geographic space (G-space) into niche modeling have been hindered by the lack of comprehensive theoretical frameworks. We propose the "functional habitat" framework to define areas that are simultaneously of high quality in E-space, and functionally connected to other suitable habitats in G-space. Originating in metapopulation ecology, approaches have been developed to assess the amount of suitable connected habitats, based on the proximity between pairs of locations. Using network theory, which operates in topological space (T-space, defined by a network), we extended these metapopulation approaches to integrate movement constraints in G-space with niche modeling in E-space. We demonstrate the functional habitat framework using empirical data (GPS tracking and population monitoring) throughout the European wild mountain reindeer (Rangifer t. tarandus) distribution range. We show that functional habitat outperforms traditional suitability in explaining the species' distribution. This approach integrates effects from habitat loss and fragmentation for spatial conservation planning, and avoids overemphasizing small, inaccessible areas with locally suitable habitats. The functional habitat framework formally integrates biotic, abiotic, and movement constraints in niche modeling using network theory, thus opening a wide range of applications in spatial conservation planning., (© 2023 The Authors. Ecology published by Wiley Periodicals LLC on behalf of The Ecological Society of America.)

Animal space use has been studied by focusing either on geographic (e.g. home ranges, species' distribution) or on environmental (e.g. habitat use and selection) space. However, all patterns of space use emerge from individual movements, which are the primary means by which animals change their environment. Individuals increase their use of a given area by adjusting two key movement components: the duration of their visit and/or the frequency of revisits. Thus, in spatially heterogeneous environments, animals exploit known, high-quality resource areas by increasing their residence time (RT) in and/or decreasing their time to return (TtoR) to these areas. We expected that spatial variation in these two movement properties should lead to observed patterns of space use in both geographic and environmental spaces. We derived a set of nine predictions linking spatial distribution of movement properties to emerging space-use patterns. We predicted that, at a given scale, high variation in RT and TtoR among habitats leads to strong habitat selection and that long RT and short TtoR result in a small home range size. We tested these predictions using moose (Alces alces) GPS tracking data. We first modelled the relationship between landscape characteristics and movement properties. Then, we investigated how the spatial distribution of predicted movement properties (i.e. spatial autocorrelation, mean, and variance of RT and TtoR) influences home range size and hierarchical habitat selection. In landscapes with high spatial autocorrelation of RT and TtoR, a high variation in both RT and TtoR occurred in home ranges. As expected, home range location was highly selective in such landscapes (i.e. second-order habitat selection); RT was higher and TtoR lower within the selected home range than outside, and moose home ranges were small. Within home ranges, a higher variation in both RT and TtoR was associated with higher selectivity among habitat types (i.e. third-order habitat selection). Our findings show how patterns of geographic and environmental space use correspond to the two sides of a coin, linked by movement responses of individuals to environmental heterogeneity. By demonstrating the potential to assess the consequences of altering RT or TtoR (e.g. through human disturbance or climatic changes) on home range size and habitat selection, our work sets the basis for new theoretical and methodological advances in movement ecology., (© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.)

Habitats have substantial influence on the distribution and abundance of animals. Animals' selective movement yields their habitat use. Animals generally are more abundant in habitats that are selected most strongly. Models of habitat selection can be used to distribute animals on the landscape or their distribution can be modelled based on data of habitat use, occupancy, intensity of use or counts of animals. When the population is at carrying capacity or in an ideal-free distribution, habitat selection and related metrics of habitat use can be used to estimate abundance. If the population is not at equilibrium, models have the flexibility to incorporate density into models of habitat selection; but abundance might be influenced by factors influencing fitness that are not directly related to habitat thereby compromising the use of habitat-based models for predicting population size. Scale and domain of the sampling frame, both in time and space, are crucial considerations limiting application of these models. Ultimately, identifying reliable models for predicting abundance from habitat data requires an understanding of the mechanisms underlying population regulation and limitation., (© 2015 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.)

Impediments to animal movement are ubiquitous and vary widely in both scale and permeability. It is essential to understand how impediments alter ecological dynamics via their influence on animal behavioural strategies governing space use and, for anthropogenic features such as roads and fences, how to mitigate these effects to effectively manage species and landscapes. Here, we focused primarily on barriers to movement, which we define as features that cannot be circumnavigated but may be crossed. Responses to barriers will be influenced by the movement capabilities of the animal, its proximity to the barriers, and habitat preference. We developed a mechanistic modelling framework for simultaneously quantifying the permeability and proximity effects of barriers on habitat preference and movement. We used simulations based on our model to demonstrate how parameters on movement, habitat preference and barrier permeability can be estimated statistically. We then applied the model to a case study of road effects on wild mountain reindeer summer movements. This framework provided unbiased and precise parameter estimates across a range of strengths of preferences and barrier permeabilities. The quality of permeability estimates, however, was correlated with the number of times the barrier is crossed and the number of locations in proximity to barriers. In the case study we found that reindeer avoided areas near roads and that roads are semi-permeable barriers to movement. There was strong avoidance of roads extending up to c. 1 km for four of five animals, and having to cross roads reduced the probability of movement by 68·6% (range 3·5-99·5%). Human infrastructure has embedded within it the idea of networks: nodes connected by linear features such as roads, rail tracks, pipelines, fences and cables, many of which divide the landscape and limit animal movement. The unintended but potentially profound consequences of infrastructure on animals remain poorly understood. The rigorous framework for simultaneously quantifying movement, habitat preference and barrier permeability developed here begins to address this knowledge gap., (© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society.)

The loss, fragmentation and degradation of habitat everywhere on Earth prompts increasing attention to identifying landscape features that support animal movement (corridors) or impedes it (barriers). Most algorithms used to predict corridors assume that animals move through preferred habitat either optimally (e.g. least cost path) or as random walkers (e.g. current models), but neither extreme is realistic. We propose that corridors and barriers are two sides of the same coin and that animals experience landscapes as spatiotemporally dynamic corridor-barrier continua connecting (separating) functional areas where individuals fulfil specific ecological processes. Based on this conceptual framework, we propose a novel methodological approach that uses high-resolution individual-based movement data to predict corridor-barrier continua with increased realism. Our approach consists of two innovations. First, we use step selection functions (SSF) to predict friction maps quantifying corridor-barrier continua for tactical steps between consecutive locations. Secondly, we introduce to movement ecology the randomized shortest path algorithm (RSP) which operates on friction maps to predict the corridor-barrier continuum for strategic movements between functional areas. By modulating the parameter Ѳ, which controls the trade-off between exploration and optimal exploitation of the environment, RSP bridges the gap between algorithms assuming optimal movements (when Ѳ approaches infinity, RSP is equivalent to LCP) or random walk (when Ѳ → 0, RSP → current models). Using this approach, we identify migration corridors for GPS-monitored wild reindeer (Rangifer t. tarandus) in Norway. We demonstrate that reindeer movement is best predicted by an intermediate value of Ѳ, indicative of a movement trade-off between optimization and exploration. Model calibration allows identification of a corridor-barrier continuum that closely fits empirical data and demonstrates that RSP outperforms models that assume either optimality or random walk. The proposed approach models the multiscale cognitive maps by which animals likely navigate real landscapes and generalizes the most common algorithms for identifying corridors. Because suboptimal, but non-random, movement strategies are likely widespread, our approach has the potential to predict more realistic corridor-barrier continua for a wide range of species., (© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.)

Habitat selection studies generally assume that animals select habitat and food resources at multiple scales to maximise their fitness. However, animals sometimes prefer habitats of apparently low quality, especially when considering the costs associated with spatially heterogeneous human disturbance. We used spatial variation in human disturbance, and its consequences on lynx survival, a direct fitness component, to test the Hierarchical Habitat Selection hypothesis from a population of Eurasian lynx Lynx lynx in southern Norway. Data from 46 lynx monitored with telemetry indicated that a high proportion of forest strongly reduced the risk of mortality from legal hunting at the home range scale, while increasing road density strongly increased such risk at the finer scale within the home range. We found hierarchical effects of the impact of human disturbance, with a higher road density at a large scale reinforcing its negative impact at a fine scale. Conversely, we demonstrated that lynx shifted their habitat selection to avoid areas with the highest road densities within their home ranges, thus supporting a compensatory mechanism at fine scale enabling lynx to mitigate the impact of large-scale disturbance. Human impact, positively associated with high road accessibility, was thus a stronger driver of lynx space use at a finer scale, with home range characteristics nevertheless constraining habitat selection. Our study demonstrates the truly hierarchical nature of habitat selection, which aims at maximising fitness by selecting against limiting factors at multiple spatial scales, and indicates that scale-specific heterogeneity of the environment is driving individual spatial behaviour, by means of trade-offs across spatial scales.

Animal movements are the primary behavioural adaptation to spatiotemporal heterogeneity in resource availability. Depending on their spatiotemporal scale, movements have been categorized into distinct functional groups (e.g. foraging movements, dispersal, migration), and have been studied using different methodologies. We suggest striving towards the development of a coherent framework based on the ultimate function of all movement types, which is to increase individual fitness through an optimal exploitation of resources varying in space and time. We developed a novel approach to simultaneously study movements at different spatiotemporal scales based on the following proposed theory: the length and frequency of animal movements are determined by the interaction between temporal autocorrelation in resource availability and spatial autocorrelation in changes in resource availability. We hypothesized that for each time interval the spatiotemporal scales of moose Alces alces movements correspond to the spatiotemporal scales of variation in the gains derived from resource exploitation when taking into account the costs of movements (represented by their proxies, forage availability NDVI and snow depth respectively). The scales of change in NDVI and snow were quantified using wave theory, and were related to the scale of moose movement using linear mixed models. In support of the proposed theory we found that frequent, smaller scale movements were triggered by fast, small-scale ripples of changes, whereas infrequent, larger scale movements matched slow, large-scale waves of change in resource availability. Similarly, moose inhabiting ranges characterized by larger scale waves of change in the onset of spring migrated longer distances. We showed that the scales of movements are driven by the scales of changes in the net profitability of trophic resources. Our approach can be extended to include drivers of movements other than trophic resources (e.g. population density, density of related individuals, predation risk) and may facilitate the assessment of the impact of environmental changes on community dynamics and conservation., (© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.)

Identifying factors shaping variation in resource selection is central for our understanding of the behaviour and distribution of animals. We examined summer habitat selection and space use by 108 Global Positioning System (GPS)-collared moose in Norway in relation to sex, reproductive status, habitat quality, and availability. Moose selected habitat types based on a combination of forage quality and availability of suitable habitat types. Selection of protective cover was strongest for reproducing females, likely reflecting the need to protect young. Males showed strong selection for habitat types with high quality forage, possibly due to higher energy requirements. Selection for preferred habitat types providing food and cover was a positive function of their availability within home ranges (i.e. not proportional use) indicating functional response in habitat selection. This relationship was not found for unproductive habitat types. Moreover, home ranges with high cover of unproductive habitat types were larger, and smaller home ranges contained higher proportions of the most preferred habitat type. The distribution of moose within the study area was partly related to the distribution of different habitat types. Our study shows how distribution and availability of habitat types providing cover and high-quality food shape ungulate habitat selection and space use.

The field of habitat ecology has been muddled by imprecise terminology regarding what constitutes habitat, and how importance is measured through use, selection, avoidance and other bio-statistical terminology. Added to the confusion is the idea that habitat is scale-specific. Despite these conceptual difficulties, ecologists have made advances in understanding 'how habitats are important to animals', and data from animal-borne global positioning system (GPS) units have the potential to help this clarification. Here, we propose a new conceptual framework to connect habitats with measures of animal performance itself--towards assessing habitat-performance relationship (HPR). Long-term studies will be needed to estimate consequences of habitat selection for animal performance. GPS data from wildlife can provide new approaches for studying useful correlates of performance that we review. Recent examples include merging traditional resource selection studies with information about resources used at different critical life-history events (e.g. nesting, calving, migration), uncovering habitats that facilitate movement or foraging and, ultimately, comparing resources used through different life-history strategies with those resulting in death. By integrating data from GPS receivers with other animal-borne technologies and combining those data with additional life-history information, we believe understanding the drivers of HPRs will inform animal ecology and improve conservation.

The relationship between individual performance and nonrandom use of habitat is fundamental to ecology; however, empirical tests of this relationship remain limited, especially for higher orders of selection like that of the home range. We quantified the association between lifetime reproductive success (LRS) and variables describing lifetime home ranges during the period of maternal care (spring to autumn) for 77 female roe deer (Capreolus capreolus) at Trois-Fontaines, Champagne-Ardenne, France (1976-2000). We maintained population growth rate (adjusted to account for removals of non-focal animals) near rmax, which enabled us to define the fitness-habitat relationship in the absence of density effects. Using a negative binomial model, we showed that a roe deer's incorporation into its home range of habitat components important to food, cover, and edge (meadows, thickets, and increased density of road allowances) was significantly related to LRS. Further, LRS decreased with increasing age of naturally reclaimed meadows at the time of a deer's birth, which may have reflected a cohort effect related to, but not entirely explained by, a decline in quality of meadows through time. Predictive capacity of the selected model, estimated as the median correlation (rs) between predicted and observed LRS among deer of cross-validation samples, was 0.55. The strength of this relationship suggests that processes like selection of the site of a home range during dispersal may play a more important role in determining fitness of individuals than previously thought. Individual fitness of highly sedentary income breeders with high reproductive output such as roe deer should be more dependent on home range quality during the period of maternal care compared to capital breeders with low reproductive output. Identification of the most important habitat attributes to survival and reproduction at low density (low levels of intraspecific competition) may prove useful for defining habitat value ("intrinsic habitat value").