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Projects focused on movement behaviour and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. Without these guidelines, designing an animal tracking study to produce reliable estimates of space-use and movement properties (necessary to answer basic movement ecology questions), is often done in an ad hoc manner.We developed ‘movedesign’, a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed and distance travelled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design; that is, the choices regarding data resolution (sampling interval) and battery life (sampling duration).Leveraging the ‘ctmm’ R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated kernel density estimators (AKDEs) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data.This application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS locations collected by the devices, which may result in a compromise between reliably estimating home range or speed and distance. ‘movedesign’ has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the corre

Resource selection functions (RSFs) are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because RSFs assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates.Data thinning, generalized estimating equations and step selection functions (SSFs) have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data and (in the case of SSFs) scale-dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on RSFs.In this study, we demonstrate that this method weights each observed location in an animal's movement track according to its level of non-independence, expanding confidence intervals and reducing bias that can arise when there are missing data in the movement track.Ecologists and conservation biologists can use this method to improve the quality of inferences derived from RSFs. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the ctmm R package.

1. Home-range estimates are a common product of animal tracking data, as each range informs on the area needed by a given individual. Population-level inference on home-range areas—where multiple individual home-ranges are considered to be sampled from a population—is also important to evaluate changes over time, space, or covariates, such as habitat quality or fragmentation, and for comparative analyses of species averages. Population-level home-range parameters have traditionally been estimated by first assuming that the input tracking data were sampled independently when calculating home ranges via conventional kernel density estimation (KDE) or minimal convex polygon (MCP) methods, and then assuming that those individual home ranges were measured exactly when calculating the population-level estimates. This conventional approach does not account for the temporal autocorrelation that is inherent in modern tracking data, nor for the uncertainties of each individual home-range estimate, which are often large and heterogeneous. 2. Here, we introduce a statistically and computationally efficient framework for the population-level analysis of home-range areas, based on autocorrelated kernel density estimation (AKDE), that can account for variable temporal autocorrelation and estimation uncertainty. 3. We apply our method to empirical examples on lowland tapir (Tapirus terrestris), kinkajou (Potos flavus), white-nosed coati (Nasua narica), white-faced capuchin monkey (Cebus capucinus), and spider monkey (Ateles geoffroyi), and quantify differences between species, environments, and sexes. 4. Our approach allows researchers to more accurately compare different populations with different movement behaviors or sampling schedules, while retaining statistical precision and power when individual home-range uncertainties vary. Finally, we emphasize the estimation of effect sizes when comparing populations, rather than mere significance tests.

Ecologists have long been interested in linking individual behavior with higher‐level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors, and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modeling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounter rates while the relationship between individual movement and the spatial locations of encounter events in the environment has remained conspicuously understudied. Here, we bridge this gap by introducing a method for describing the long‐term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open source software, and demonstrate the broad ecological relevance of this distribution.We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation‐based scenarios that occur routinely in biological systems: i) a population of individuals with home ranges that overlap with neighbors; ii) a pair of individuals with a hard territorial border between their home ranges; and iii) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white‐faced capuchins (Cebus capucinus) tracked on Barro Colorado Island, Panama, and sleepy lizards (Tiliqua rugosa) tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the potential for competitive or predatory interactions, and/or identify an

Estimating animal home ranges is a primary purpose of collecting tracking data. Many widely used home range estimators, including conventional kernel density estimators, assume independently‐sampled data. In stark contrast, modern animal tracking datasets are almost always strongly autocorrelated. The incongruence between estimator assumptions and empirical reality often leads to systematically underestimated home ranges. Autocorrelated kernel density estimation (AKDE) directly models the observed autocorrelation structure of tracking data during home range estimation, and has been shown to perform accurately across a broad range of tracking datasets. However, compared to conventional estimators, AKDE requires additional modeling steps and has heretofore only been accessible via the command‐line ctmm R package. Here, we introduce ctmmweb, which provides a point‐and‐click graphical interface to ctmm and streamlines AKDE, its prerequisite autocorrelation modeling steps, and a number of additional movement analyses. We demonstrate ctmmweb's capabilities, including AKDE home range estimation and subsequent home range overlap analysis, on a dataset of four jaguars from the Brazilian Pantanal tracked between 2013 and 2015. We intend ctmmweb to open AKDE and related autocorrelation‐explicit analyses to a wider audience of wildlife and conservation professionals.

Modern tracking devices allow for the collection of high-volume animal tracking data at improved sampling rates over VHF radiotelemetry. Home range estimation is a key output from these tracking datasets, but the inherent properties of animal movement can lead traditional statistical methods to under- or overestimate home range areas. The Autocorrelated Kernel Density Estimation (AKDE) family of estimators were designed to be statistically efficient while explicitly dealing with the complexities of modern movement data: autocorrelation, small sample sizes, and missing or irregularly sampled data. Although each of these estimators has been described in separate technical papers, here we review how these estimators work and provide a user-friendly guide on how they may be combined to reduce multiple biases simultaneously. We describe the magnitude of the improvements offered by these estimators and their impact on home range area estimates, using both empirical case studies and simulations, contrasting their computational costs. Finally, we provide guidelines for researchers to choose among alternative estimators and an R script to facilitate the application and interpretation of AKDE home range estimates.

Statistical models are the traditional choice to test scientific theories when observations, processes or boundary conditions are subject to stochasticity. Many important systems in ecology and biology, however, are difficult to capture with statistical models. Stochastic simulation models offer an alternative, but they were hitherto associated with a major disadvantage: their likelihood functions can usually not be calculated explicitly, and thus it is difficult to couple them to well-established statistical theory such as maximum likelihood and Bayesian statistics. A number of new methods, among them Approximate Bayesian Computing and Pattern-Oriented Modelling, bypass this limitation. These methods share three main principles: aggregation of simulated and observed data via summary statistics, likelihood approximation based on the summary statistics, and efficient sampling. We discuss principles as well as advantages and caveats of these methods, and demonstrate their potential for integrating stochastic simulation models into a unified framework for statistical modelling.

Aim To demonstrate how the interrelations of individual movements form large-scale 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.Methods We 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.Results Moose, 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 conclusions We show how broad-sca

1. Reproductive asynchrony, where individuals in a population are short-lived relative to the population-level reproductive period, has been identified recently as a theoretical mechanism of the Allee effect that could operate in diverse plant and insect species. The degree to which this effect impinges on the growth potential of natural populations is not yet well understood.2. Building on previous models of reproductive timing, we develop a general framework that allows a detailed, quantitative examination of the reproductive potential lost to asynchrony in small natural populations.3. Our framework includes a range of biologically plausible submodels that allow details of mating biology of different species to be incorporated into the basic reproductive timing model.4. We tailor the parameter estimation methods of the full model (basic model plus mating biology submodels) to take full advantage of data from detailed field studies of two species of Parnassius butterflies whose mating status may be assessed easily in the field.5. We demonstrate that for both species, a substantial portion of the female population (6·5-18·6%) is expected to die unmated. These analyses provide the first direct, quantitative evidence of female reproductive failure due to asynchrony in small natural populations, and suggest that reproductive asynchrony exerts a strong and largely unappreciated influence on the population dynamics of these butterflies and other species with similarly asynchronous reproductive phenology.

1. Visitation rate is a measure of the frequency with which animals visit specific locations of interest. This information is crucial for various problems in the conservation and management of animal species, e.g. to determine the risk wildlife poses to human managed resources or the predation risks of nests. An important assessment tool, especially for cryptic animals, is to count indirect signs of presence, such as tracks or faeces.2. Here we propose a maximum likelihood-based method that uses information on the age of tracks or signs, and we show that existing visitation rate/probability estimators are special cases of this more general approach.3. Using simulated data, we compared the performance of the new visitation rate estimator to three other estimators, including the most commonly used estimator. These estimators make use of either fresh, aged or total signs, whereas our approach uses information simultaneously on both fresh and aged signs. The new estimator is, on average, in excess of three times more accurate than the next best estimator. Moreover, the new approach is very flexible and can be applied for sampling regimens with irregular time intervals between sampling.4. We demonstrate the application of the method to field data by estimating the visitation rate of Eurasian otters (Lutra lutra) to a commercial fish pond. To facilitate the use of this method, we provide an easy-to-use Excel workbook and give recommendations on the most efficient sampling regimens.5. Synthesis and applications. Visitation rate is an important quantity that can be estimated by repetitive sampling of indirect signs. We demonstrate the advantage of incorporating information explicitly on the age of signs over existing approaches. The new estimator can be applied to any species for which it is possible to discriminate between aged and new signs or tracks, and should be widely applicable in ecology and conservation biology.