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Many animal social structures are organized hierarchically, with some individuals monopolizing resources. Dominance hierarchies have received great attention from behavioural and evolutionary ecologists. There are many methods for inferring hierarchies from social interactions. Yet, there are no clear guidelines about how many observed dominance interactions (i.e. sampling effort) are necessary for inferring reliable dominance hierarchies, nor are there any established tools for quantifying their uncertainty. We simulate interactions (winners and losers) in scenarios of varying steepness (the probability that a dominant defeats a subordinate based on their difference in rank). Using these data, we (1) quantify how the number of interactions recorded and the steepness of the hierarchy affect the performance of five methods for inferring hierarchies, (2) propose an amendment that improves the performance of a popular method, and (3) suggest two easy procedures to measure uncertainty and steepness in the inferred hierarchy. We find that the ratio of interactions to individuals required to infer reliable hierarchies is surprisingly low, but depends on the steepness of the hierarchy and the method used. We show that David's score and our novel randomized Elo-rating are the best methods when hierarchies are not extremely steep, where the original Elo-rating, the I&SI and the recently described ADAGIO perform less well. In addition, we show that two simple methods can be used to estimate uncertainty at the individual and group level, and that the randomized Elo-rating repeatability provides researchers with a standardized measure valid for comparing the steepness of different hierarchies. We provide several worked examples to guide researchers interested in studying dominance hierarchies. Methods for inferring dominance hierarchies are relatively robust. We recommend that a ratio of observed interactions to individuals of at least 10 (for steep hierarchies), and ideally 20 serves as a good benchmark. Our simple procedures for estimating uncertainty in the observed data will facilitate evaluating whether sufficient data have been collected, while plotting the shape of the hierarchy will provide new insights into the social structure of the study organism., (© 2017 The Authors. Journal of Animal Ecology © 2017 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.)

Movement data provide a window - often our only window - into the cognitive, social and biological processes that underlie the behavioural ecology of animals in the wild. Robust methods for identifying and interpreting distinct modes of movement behaviour are of great importance, but complicated by the fact that movement data are complex, multivariate and dependent. Many different approaches to exploratory analysis of movement have been developed to answer similar questions, and practitioners are often at a loss for how to choose an appropriate tool for a specific question. We apply and compare four methodological approaches: first passage time (FPT), Bayesian partitioning of Markov models (BPMM), behavioural change point analysis (BCPA) and a fitted multistate random walk (MRW) to three simulated tracks and two animal trajectories - a sea lamprey (Petromyzon marinus) tracked for 12 h and a wolf (Canis lupus) tracked for 1 year. The simulations - in which, respectively, velocity, tortuosity and spatial bias change - highlight the sensitivity of all methods to model misspecification. Methods that do not account for autocorrelation in the movement variables lead to spurious change points, while methods that do not account for spatial bias completely miss changes in orientation. When applied to the animal data, the methods broadly agree on the structure of the movement behaviours. Important discrepancies, however, reflect differences in the assumptions and nature of the outputs. Important trade-offs are between the strength of the a priori assumptions (low in BCPA, high in MRW), complexity of output (high in the BCPA, low in the BPMM and MRW) and explanatory potential (highest in the MRW). The animal track analysis suggests some general principles for the exploratory analysis of movement data, including ways to exploit the strengths of the various methods. We argue for close and detailed exploratory analysis of movement before fitting complex movement models., (© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.)

Decreasing rate of migration in several species as a consequence of climate change and anthropic pressure, together with increasing evidence of space-use strategies intermediate between residency and complete migration, are very strong motivations to evaluate migration occurrence and features in animal populations. The main goal of this paper was to perform a relative comparison between methods for identifying and characterizing migration at the individual and population level on the basis of animal location data. We classified 104 yearly individual trajectories from five populations of three deer species as migratory or non-migratory, by means of three methods: seasonal home range overlap, spatio-temporal separation of seasonal clusters and the Net Squared Displacement (NSD) method. For migratory cases, we also measured timing and distance of migration and residence time on the summer range. Finally, we compared the classification in migration cases across methods and populations. All methods consistently identified migration at the population level, that is, they coherently distinguished between complete or almost complete migratory populations and partially migratory populations. However, in the latter case, methods coherently classified only about 50% of the single cases, that is they classified differently at the individual-animal level. We therefore infer that the comparison of methods may help point to 'less-stereotyped' cases in the residency-to-migration continuum. For cases consistently classified by all methods, no significant differences were found in migration distance, or residence time on summer ranges. Timing of migration estimated by NSD was earlier than by the other two methods, both for spring and autumn migrations. We suggest three steps to identify improper inferences from migration data and to enhance understanding of intermediate space-use strategies. We recommend (i) classifying migration behaviours using more than one method, (ii) performing sensitivity analysis on method parameters to identify the extent of the differences and (iii) investigating inconsistently classified cases as these may often be ecologically interesting (i.e. less-stereotyped migratory behaviours)., (© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.)

Inferences drawn from long-term field studies are vulnerable to biases in observability of different classes of individuals, which may lead to biases in the estimates of selection, or fitness. Population surveys that monitor breeding individuals can introduce such biases by not identifying individuals that fail early in their reproductive attempts. Here, we quantify how the standard protocol for detecting breeding females introduces bias in a long-term population study of the great tit, Parus major. We do so by identifying females whose breeding attempts fail before they would normally be censused and explore whether this early failure can be predicted by a number of intrinsic and extrinsic factors. We investigate the effect of these biases on estimates of reproductive performance and selection. We show that females that go undetected by standard censusing because they fail early in their breeding attempt were less likely to have been previously trapped within our study site and were more likely to breed in poor-quality habitats. Furthermore, we demonstrate that this bias sampling had lead previous studies on this population to overestimate the reproductive performance of unringed females, which are likely to be immigrants to the population. Finally, we show that these biases in detectability influence estimates of selection on a key life-history trait. While these conclusions are specific to this study, we suggest that such effects are likely to be widespread and that more attention should be given to whether or not methods for surveying natural populations introduce systematic bias that will influence conclusions about ecological and evolutionary processes., (© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.)

1. Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology. 2. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data. 3. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network. 4. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language. 5. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under-exploited potential of experimental manipulations on social networks to address research questions., (© 2015 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of the British Ecological Society.)

1. The recently developed Brownian bridge movement model (BBMM) has advantages over traditional methods because it quantifies the utilization distribution of an animal based on its movement path rather than individual points and accounts for temporal autocorrelation and high data volumes. However, the BBMM assumes unrealistic homogeneous movement behaviour across all data. 2. Accurate quantification of the utilization distribution is important for identifying the way animals use the landscape. 3. We improve the BBMM by allowing for changes in behaviour, using likelihood statistics to determine change points along the animal's movement path. 4. This novel extension, outperforms the current BBMM as indicated by simulations and examples of a territorial mammal and a migratory bird. The unique ability of our model to work with tracks that are not sampled regularly is especially important for GPS tags that have frequent failed fixes or dynamic sampling schedules. Moreover, our model extension provides a useful one-dimensional measure of behavioural change along animal tracks. 5. This new method provides a more accurate utilization distribution that better describes the space use of realistic, behaviourally heterogeneous tracks., (© 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.)