Abstract. Kernel density estimators are becoming more widely used, particularly as h?I?e range estimators. Despite extensive interest in their theoretical properties, little empmcal research has been done to investigate their performance as home range estimators. We used computer simulations to compare the area and shape of kernel density estimates to the true area and shape of multimodal two-dimensional distributions. The fixed kernel gave area estimates with very little bias when least squares cross validation was used to se_lect the smoothing parameter. The cross-validated fixed kernel also gave surface estimates with the lowest error. The adaptive kernel overestimated the area of the distribution and had higher error associated with its surface estimate.

Global positioning system (GPS) telemetry technology allows us to monitor and to map the details of animal movement, securing vast quantities of such data even for highly cryptic organisms. We envision an exciting synergy between animal ecology and GPS-based radiotelemetry, as for other examples of new technologies stimulating rapid conceptual advances, where research opportunities have been paralleled by technical and analytical challenges. Animal positions provide the elemental unit of movement paths and show where individuals interact with the ecosystems around them. We discuss how knowing where animals go can help scientists in their search for a mechanistic understanding of key concepts of animal ecology, including resource use, home range and dispersal, and population dynamics. It is probable that in the not-so-distant future, intense sampling of movements coupled with detailed information on habitat features at a variety of scales will allow us to represent an animal's cognitive map of its environment, and the intimate relationship between behaviour and fitness. An extended use of these data over long periods of time and over large spatial scales can provide robust inferences for complex, multi-factorial phenomena, such as meta-analyses of the effects of climate change on animal behaviour and distribution.Keywords: global positioning system technology; biotelemetry; animal movement; autocorrelation; mechanistic models; fitnessIn the history of science, rapid conceptual advances have often been stimulated by technological innovations. Such technological milestones included Galileo and Kepler designing and looking into telescopes, finding evidence for Copernicanism (and falsifying the Tolemaic system); Hooke and van Leeuwenhoek peering into microscopes and describing cells, thus laying the basis for modern microbiology; Sanger and Maxam and Gilbert developing DNA sequencing, which spawned molecular biology; and high-speed computers fostering the emergence of nonlinear dynamics. In this Theme Issue we herald an exciting synergy between animal ecology and global positioning system (GPS)-based radiotelemetry. New telemetry technology allows us to monitor and to map the details of animal movement, securing vast quantities of such data even for highly cryptic organisms. Conceptual advances have been made with roots in Lagrangian characterization of the details of movement by individual animals, which ultimately leads to Eulerian characterization of population process. Recent theory uses stochastic movement models to challenge our mechanistic understanding of home range and dispersal. We are beginning to tackle the complexities of memory and perception, and how these behavioural processes yield the individual variation that is so pervasive in nature. It is probable that in the not-so-distant future, intense sampling of movements coupled with detailed information on habitat features at a variety of scales will allow us to represent an animal's cognitive map of its environment. Models of resource selection by anima...

While the mechanistic links between animal movement and population dynamics are ecologically obvious, it is much less clear when knowledge of animal movement is a prerequisite for understanding and predicting population dynamics. GPS and other technologies enable detailed tracking of animal location concurrently with acquisition of landscape data and information on individual physiology. These tools can be used to refine our understanding of the mechanistic links between behaviour and individual condition through 'spatially informed' movement models where time allocation to different behaviours affects individual survival and reproduction. For some species, socially informed models that address the movements and average fitness of differently sized groups and how they are affected by fission -fusion processes at relevant temporal scales are required. Furthermore, as most animals revisit some places and avoid others based on their previous experiences, we foresee the incorporation of long-term memory and intention in movement models. The way animals move has important consequences for the degree of mixing that we expect to find both within a population and between individuals of different species. The mixing rate dictates the level of detail required by models to capture the influence of heterogeneity and the dynamics of intra-and interspecific interaction.

Recent advances in animal tracking and telemetry technology have allowed the collection of location data at an ever-increasing rate and accuracy, and these advances have been accompanied by the development of new methods of data analysis for portraying space use, home ranges and utilization distributions. New statistical approaches include data-intensive techniques such as kriging and nonlinear generalized regression models for habitat use. In addition, mechanistic home-range models, derived from models of animal movement behaviour, promise to offer new insights into how home ranges emerge as the result of specific patterns of movements by individuals in response to their environment. Traditional methods such as kernel density estimators are likely to remain popular because of their ease of use. Large datasets make it possible to apply these methods over relatively short periods of time such as weeks or months, and these estimates may be analysed using mixed effects models, offering another approach to studying temporal variation in space-use patterns.Although new technologies open new avenues in ecological research, our knowledge of why animals use space in the ways we observe will only advance by researchers using these new technologies and asking new and innovative questions about the empirical patterns they observe.

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.

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