Restrictions on roaming Until the past century or so, the movement of wild animals was relatively unrestricted, and their travels contributed substantially to ecological processes. As humans have increasingly altered natural habitats, natural animal movements have been restricted. Tucker et al. examined GPS locations for more than 50 species. In general, animal movements were shorter in areas with high human impact, likely owing to changed behaviors and physical limitations. Besides affecting the species themselves, such changes could have wider effects by limiting the movement of nutrients and altering ecological interactions. Science , this issue p. 466

Abstract. Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. abstract: Understanding animal movement is a key challenge in ecology and conservation biology. Relocation data often represent a complex mixture of different movement behaviors, and reliably decomposing this mix into its component parts is an unresolved problem in movement ecology. Traditional approaches, such as composite random walk models, require that the timescales characterizing the movement are all similar to the usually arbitrary data-sampling rate. Movement behaviors such as long-distance searching and fine-scale foraging, however, are often intermixed but operate on vastly different spatial and temporal scales. An approach that integrates the full sweep of movement behaviors across scales is currently lacking.Here we show how the semivariance function (SVF) of a stochastic movement process can both identify multiple movement modes and solve the sampling rate problem. We express a broad range of continuous-space, continuous-time stochastic movement models in terms of their SVFs, connect them to relocation data via variogram regression, and compare them using standard model selection techniques. We illustrate our approach using Mongolian gazelle relocation data and show that gazelle movement is characterized by ballistic foraging movements on a 6-h timescale, fast diffusive searching with a 10-week timescale, and asymptotic diffusion over longer timescales.

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Aim To demonstrate how the interrelations of individual movements form largescale population-level movement patterns and how these patterns are associated with the underlying landscape dynamics by comparing ungulate movements across species.Locations Arctic tundra in Alaska and Canada, temperate forests in Massachusetts, Patagonian Steppes in Argentina, Eastern Steppes in Mongolia. MethodsWe used relocation data from four ungulate species (barren-ground caribou, Mongolian gazelle, guanaco and moose) to examine individual movements and the interrelation of movements among individuals. We applied and developed a suite of spatial metrics that measure variation in movement among individuals as population dispersion, movement coordination and realized mobility. Taken together, these metrics allowed us to quantify and distinguish among different large-scale population-level movement patterns such as migration, range residency and nomadism. We then related the population-level movement patterns to the underlying landscape vegetation dynamics via long-term remote sensing measurements of the temporal variability, spatial variability and unpredictability of vegetation productivity. ResultsMoose, which remained in sedentary home ranges, and guanacos, which were partially migratory, exhibited relatively short annual movements associated with landscapes having very little broad-scale variability in vegetation. Caribou and gazelle performed extreme long-distance movements that were associated with broad-scale variability in vegetation productivity during the peak of the growing season. Caribou exhibited regular seasonal migration in which individuals were clustered for most of the year and exhibited coordinated movements. In contrast, gazelle were nomadic, as individuals were independently distributed and moved in an uncoordinated manner that relates to the comparatively unpredictable (yet broad-scale) vegetation dynamics of their landscape. Main conclusionsWe show how broad-scale landscape unpredictability may lead to nomadism, an understudied type of long-distance movement. In contrast to classical migration where landscapes may vary at broad scales but in a predictable manner, long-distance movements of nomadic individuals are uncoordinated and independent from other such individuals. Landscapes with little broad-scale variability in vegetation productivity feature smaller-scale movements and allow for range residency. Nomadism requires distinct integrative conservation strategies that facilitate long-distance movements across the entire landscape and are not limited to certain migration corridors.

Movement of organisms is one of the key mechanisms shaping biodiversity, e.g. the distribution of genes, individuals and species in space and time. Recent technological and conceptual advances have improved our ability to assess the causes and consequences of individual movement, and led to the emergence of the new field of ‘movement ecology’. Here, we outline how movement ecology can contribute to the broad field of biodiversity research, i.e. the study of processes and patterns of life among and across different scales, from genes to ecosystems, and we propose a conceptual framework linking these hitherto largely separated fields of research. Our framework builds on the concept of movement ecology for individuals, and demonstrates its importance for linking individual organismal movement with biodiversity. First, organismal movements can provide ‘mobile links’ between habitats or ecosystems, thereby connecting resources, genes, and processes among otherwise separate locations. Understanding these mobile links and their impact on biodiversity will be facilitated by movement ecology, because mobile links can be created by different modes of movement (i.e., foraging, dispersal, migration) that relate to different spatiotemporal scales and have differential effects on biodiversity. Second, organismal movements can also mediate coexistence in communities, through ‘equalizing’ and ‘stabilizing’ mechanisms. This novel integrated framework provides a conceptual starting point for a better understanding of biodiversity dynamics in light of individual movement and space-use behavior across spatiotemporal scales. By illustrating this framework with examples, we argue that the integration of movement ecology and biodiversity research will also enhance our ability to conserve diversity at the genetic, species, and ecosystem levels.

Satellite remote sensing is an important tool for monitoring the status of biodiversity and associated environmental parameters, including certain elements of habitats. However, satellite data are currently underused within the biodiversity research and conservation communities. Three factors have significant impact on the utility of remote sensing data for tracking and understanding biodiversity change. They are its continuity, affordability, and access. Data continuity relates to the maintenance of long term satellite data products. Such products promote knowledge of how biodiversity has changed over time and why. Data affordability arises from the cost of the imagery. New data policies promoting free and open access to government satellite imagery are expanding the use of certain imagery but the number of free and open data sets remains too limited. Data access addresses the ability of conservation biologists and bio diversity researchers to discover, retrieve, manipulate, and extract value from satellite imagery as well as link it with other types of information. Tools are rapidly improving access. Still, more cross community interactions are necessary to strengthen ties between the biodiversity and remote sensing communities.

Summary 1.Temporal variability in habitat suitability has important conservation and ecological implications. In grasslands, changes in resource availability can occur at broad spatial scales and enlarge area requirements of ungulate populations, which increases their vulnerability to habitat loss and fragmentation. Understanding and predicting these dynamics, although critical, has received little attention so far. 2. We investigated habitat dynamics for Mongolian gazelles ( Procapra gutturosa Pallas) in the eastern steppes of Mongolia. We quantified the distribution of gazelles at four different time periods and tracked primary productivity using Normalized Difference Vegetation Index (NDVI) data from satellite imagery. 3. A second-order logistic model showed that NDVI was an efficient predictor of gazelle presence. We tested the predictive power of the model with independent data from a gazelle telemetry study: 85% of all relocations were found within the predicted area. 4. Gazelles preferred an intermediate range of vegetation productivity, presumably facing quality quantity trade-offs where areas with low NDVI are limited by low ingestion rates, and areas with high NDVI are limited by the low digestibility of mature forage. 5. Spatiotemporal variation of gazelle habitat areas was high. Only 15% of the study area was consistently gazelle habitat throughout all survey periods, indicating that gazelles need to range over vast areas in search of food. Only 1% of the gazelle habitats were consistently located inside protected areas. 6. Synthesis and applications. Habitat variability in grasslands often leads to area requirements of ungulates that prevent effective conservation within single protected areas. They require landscapelevel management plans, but dynamic habitat predictions to inform such plans are difficult to implement and are often unavailable. We showed that satellite estimates of vegetation productivity can be used successfully to generate dynamic habitat models in landscapes with highly variable resources, and demonstrated that intermediate NDVI values were critical to predict occurrence of Mongolian gazelles.