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Aim: Historically, climate has been a dominant driver of global vegetation patterns. Recently, ecological understanding has been updated to acknowledge the influence of human land use (the dominant driver of biodiversity change) in shaping global vegetation patterns. We test whether Raunkiær's life form, a plant classification system designed to reflect climatic drivers, affects how plants respond to both land use and climate. Location: Forty‐one countries across six continents. Time period: 1990 to 2013. Major taxa studied: Terrestrial plants. Methods: Combining data from the biodiversity and land use database PREDICTS, and plant trait databases TRY and BIEN, we use generalized linear mixed models with weighted effects coding to test whether Raunkiær's life form affects plant response to land use and climate in over 4800 species at over 300 sites globally. Results: We provide evidence that human land use is comparable to climate in influencing life form occurrence and that land use produces divergent outcomes across life forms. Main conclusions: Combined with climatic suitability, land use acts as a filter contracting the realized niche of trees and expanding the realized niche of disturbance‐tolerant species. Our results highlight the fundamental role of human activity in shaping species' distribution. [ABSTRACT FROM AUTHOR]

Dispersal is a central life history trait that affects the ecological and evolutionary dynamics of populations and communities. The recent use of experimental evolution for the study of dispersal is a promising avenue for demonstrating valuable proofs of concept, bringing insight into alternative dispersal strategies and trade‐offs, and testing the repeatability of evolutionary outcomes.Practical constraints restrict experimental evolution studies of dispersal to a set of typically small, short‐lived organisms reared in artificial laboratory conditions. Here, we argue that despite these restrictions, inferences from these studies can reinforce links between theoretical predictions and empirical observations and advance our understanding of the eco‐evolutionary consequences of dispersal.We illustrate how applying an integrative framework of theory, experimental evolution and natural systems can improve our understanding of dispersal evolution under more complex and realistic biological scenarios, such as the role of biotic interactions and complex dispersal syndromes. [ABSTRACT FROM AUTHOR]

Uncovering the mechanisms behind animal space use patterns is of vital importance for predictive ecology, thus conservation and management of ecosystems. Movement is a core driver of those patterns so understanding how movement mechanisms give rise to space use patterns has become an increasingly active area of research.This study focuses on a particular strand of research in this area, based around step selection analysis (SSA). SSA is a popular way of inferring drivers of movement decisions, but, perhaps less well appreciated, it also parametrises a model of animal movement. Of key interest is that this model can be propagated forwards in time to predict the space use patterns over broader spatial and temporal scales than those that pertain to the proximate movement decisions of animals.Here, we provide a guide for understanding and using the various existing techniques for scaling up step selection models to predict broad‐scale space use patterns. We give practical guidance on when to use which technique, as well as specific examples together with code in R and Python.By pulling together various disparate techniques into one place, and providing code and instructions in simple examples, we hope to highlight the importance of these techniques and make them accessible to a wider range of ecologists, ultimately helping expand the usefulness of SSA. [ABSTRACT FROM AUTHOR]

These models encompass all areas of ecology from population and community ecology to landscape and ecosystem ecology. Situating ecology as a big-data science: Current advances, challenges, and solutions. Big-data approaches lead to an increased understanding of the ecology of animal movement. Data Ecology, like environmental sciences and other branches of biology, has entered into an era of big data, with enormous possibilities for a better understanding of environmental state (Runting et al., [44]). [Extracted from the article]

Addressing ongoing biodiversity loss requires collaboration between conservation scientists and practitioners. However, such collaboration has proved challenging. Despite the potential importance of tracking animal movements for conservation, reviews of the tracking literature have identified a gap between the academic discipline of movement ecology and its application to biodiversity conservation. Through structured conversations with movement ecologists and conservation practitioners, we aimed to understand whether the identified gap is also perceived in practice, and if so, what factors hamper collaboration and how these factors can be remediated. We found that both groups are motivated and willing to collaborate. However, because their motivations differ, there is potential for misunderstandings and miscommunications. In addition, external factors such as funder requirements, academic metrics, and journal scopes may limit the applicability of scientific results in a conservation setting. Potential solutions we identified included improved communication and better presentation of results, acknowledging each other's motivations and desired outputs, and adjustment of funder priorities. Addressing gaps between science and implementation can enhance collaboration and support conservation action to address the global biodiversity crisis more effectively. [ABSTRACT FROM AUTHOR]

Animals perceive human activities as risky and generally respond with fear‐induced proactive behaviors to buffer the circadian patterns of lethal and nonlethal disturbances, such as diel migrations (DMs) between risky places during safe nighttime and safer places during risky daytime. However, such responses potentially incur costs through movement or reduced foraging time, so individuals should adjust their tolerance when human activities are harmless, through habituation. Yet this is a challenging cognitive task when lethal and nonlethal risks co‐occur, forming complex landscapes of fear. The consequences of this human‐induced complexity have, however, rarely been assessed. We studied the individual DM dynamics of chamois (Rupicapra rupicapra rupicapra), 89 GPS‐tracked individual‐years, from/to trails in the French Alps in areas with co‐occurring lethal (hunting) and nonlethal (hiking and skiing) disturbances, with different intensities across seasons. We developed a conceptual framework relying on the risk‐disturbance hypothesis and habituation to predict tolerance adjustments of chamois under various disturbance contexts and across contrasted seasonal periods. Based on spatial and statistical analyses combining periodograms and multinomial logistic models, we found that DM in relation to distance to a trail was a consistent response by chamois (~85% of individuals) to avoid human disturbance during daytime, especially during the hiking and hunting periods. Such behavior revealed a low tolerance of most chamois to human activities, although there was considerable interindividual heterogeneity in DM. Interestingly, there was an increased tolerance among the most disturbed diel migrants, potentially through habituation, with chamois performing shorter DMs in areas highly disturbed by hikers. Crucially, chamois that were most human‐habituated during the hiking period remained more tolerant in the subsequent harvesting period, which could increase their risk of being harvested. In contrast, individuals less tolerant to hiking performed longer DMs when hunting risk increased, and compared to hiking, hunting exacerbated the threshold distance to trails triggering DMs. No carryover effect of hunting beyond the hunting period was observed. In conclusion, complex human‐induced landscapes of fear with co‐occurring disturbances by nature‐based tourism and hunting may shape unexpected patterns of tolerance to human activities, whereby animal tolerance could become potentially deleterious for individual survival. [ABSTRACT FROM AUTHOR]

Understanding the role of traits in dispersal is necessary to improve our knowledge of historical biogeography, community assembly processes and predictions of species' future movements. Here we aimed to determine the relationship between three traits (coastal distribution, body size, position on the fast/slow life history continuum) and past dispersal probability on an evolutionary timescale in chameleons (Chamaeleonidae). Using species' distribution data we identified the nine most important biogeographic regions for all included chameleons (181/217 species). We compiled life history trait data and used phylogenetic factor analysis to infer independent body size and fast/slow life history trait axes. Finally, we tested whether traits and trait combinations related to biogeographic dispersal success in the past, using trait‐dependent biogeographic models. All three traits were associated with past biogeographical movements. Lineages having coastal distributions and those with large bodies had higher dispersal probabilities. Interestingly, chameleons with either a very fast or very slow life history were more successful dispersers than species with an intermediate strategy. Together, the three traits – coastal, large‐bodied and extreme life history – form a dispersal syndrome. Traits have played an important role in the biogeographic history of chameleons. While only fast traits have been linked to present‐day invasion success in reptiles, both extremes of the life history spectrum were likely advantageous for dispersal and establishment during past biogeographic movements. Fast‐living species may be less susceptible to stochastic extinction in the first phases of a colonization (due to rapid population growth), and slow‐living species may be less vulnerable to environmental stochasticity (due to low demographic variability). Our results call for broader analyses testing the general influence of life history strategy in biogeographic dispersal success, which would help explain species distribution patterns on Earth. [ABSTRACT FROM AUTHOR]

The ability to predict animal space use patterns is a fundamental concern in changing environments. Such predictions require a detailed understanding of the movement mechanisms from which spatial distributions emerge. However, these are typically complex, multifaceted, and therefore difficult to uncover.Here, we provide a methodological framework for uncovering the movement mechanisms necessary for building predictive models of animal space use. Our procedure begins by parametrising a movement model of each individual in a population using step selection analysis, from which we build an individual‐based model (IBM) of interacting individuals, derive predicted broad‐scale space use patterns from the IBM and then compare the predicted and empirical patterns. Importantly, discrepancies between these predicted and empirical patterns are used to formulate new hypotheses about the drivers of animal movement decisions and thus iteratively improve the model's predictive power. We demonstrate our method on a population of feral pigs in Mississippi, USA.Our technique incorporates both social interactions between individuals and environmental drivers of movement. At each iteration of model construction, we were able to identify missing features to improve model prediction by analysing the IBM output. These include overuse‐avoidance effects of self‐attractive mechanisms (i.e. attraction to previously visited sites becomes repulsion if there have been multiple visits in quick succession), which were vital for ensuring predicted occurrence distributions do not become vanishingly small.Overall, we have provided a general method for iteratively improving the predictive power of step selection models. This will enable future researchers to maximise the information obtained from step selection analyses and to highlight potentially missing data for uncovering the drivers of movement decisions and emergent space use patterns. Ultimately, this provides a fundamental step towards the general aim of constructing predictive models of animal space use. [ABSTRACT FROM AUTHOR]

The energetic gains from foraging and costs of movement are expected to be key drivers of animal decision‐making, as their balance is a large determinant of body condition and survival. This fundamental perspective is often missing from habitat selection studies, which mainly describe correlations between space use and environmental features, rather than the mechanisms behind these correlations.To address this gap, we present a novel parameterisation of step selection functions (SSFs), that we term the energy selection function (ESF). In this model, the likelihood of an animal selecting a movement step depends directly on the corresponding energetic gains and costs, and we can therefore assess how moving animals choose habitat based on energetic considerations.The ESF retains the mathematical convenience and practicality of other SSFs and can be quickly fitted using standard software. In this article, we outline a workflow, from data gathering to statistical analysis, and use a case study of polar bears Ursus maritimus to demonstrate application of the model.We explain how defining gains and costs at the scale of the movement step allows us to include information about resource distribution, landscape resistance and movement patterns. We further demonstrate this process with a case study of polar bears and show how the parameters can be interpreted in terms of selection for energetic gains and against energetic costs.The ESF is a flexible framework that combines the energetic consequences of both movement and resource selection, thus incorporating a key mechanism into habitat selection analysis. Further, because it is based on familiar habitat selection models, the ESF is widely applicable to any study system where energetic gains and costs can be derived, and has immense potential for methodological extensions. [ABSTRACT FROM AUTHOR]

Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well‐studied seabirds. Here, we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots Uria aalge breeding on Skomer Island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high‐quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions. [ABSTRACT FROM AUTHOR]

Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS‐based animal telemetry. Cited thresholds for sufficiency include a number of captured animals M≥30 and as many relocations per animal N as possible. These thresholds render many RSF‐based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations.We provide the first comprehensive solution for RSF sample size by deriving closed‐form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra).Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than M=30 animals.We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies. [ABSTRACT FROM AUTHOR]

The time that animals spend travelling at various speeds and the tortuosity of their movement paths are two of the many things that affect space‐use by animals. In this, high turn rates are predicted to be energetically costly, especially at high travel speeds, which implies that animals should modulate their speed according to path characteristics. When animals move so as to maximize distance and minimize metabolic energy expenditure, they travel most efficiently at the speed that gives them a minimum cost of transport (COTmin), a well‐defined point for animals that move entirely in fluid media. Theoretical considerations show though, that land animals should travel at their maximum speed to minimize COT, which they do not, instead travelling at walking pace. So, to what extent does COTmin depend on speed and turn rate and how might this relate to movement paths? We measured oxygen consumption in humans walking along paths with varied tortuosity at defined speeds to demonstrate that the energetic costs of negotiating these paths increase disproportionately with both speed and angular velocity. This resulted in the COTmin occurring at very low speeds, and these COTmin speeds reduced with increased path tortuosity and angular velocity. Logged movement data from six free‐ranging terrestrial species underpinned this because all individuals turned with greater angular velocity the slower their travel speeds across their full speed range. It seems, therefore, that land animals may strive to achieve minimum movement costs by reducing speed with increasing path variability, providing one of many possible explanations as to why speed is much lower than currently predicted based on lab measurements of mammalian locomotor performance. [ABSTRACT FROM AUTHOR]

Habitat fragmentation is a principal threat to biodiversity and artificial river barriers are a leading cause of the global decline in freshwater biota. Although the impact of barriers on diadromous fish is well established, impacts on river‐resident fish communities remain unclear, especially for low‐head barriers.We examined the movement of five contrasting freshwater fish (topmouth gudgeon, European minnow, stone loach, bullhead and brown trout) in an experimental cascade mesocosm with seven pools separated by small vertical barriers.Passage rates differed significantly among species and increased with body size and sustained swimming speed (Usus), ranging from an average of 0.2 passes/hr in topmouth gudgeon to 3.4 passes/hr in brown trout. A random‐walk simulation indicated that barriers can result in net downstream movement and shifts in community composition.Passage rates in brown trout were leptokurtic, that is, most individuals were relatively sedentary while a small proportion showed frequent movements. Upstream passage rates of brown trout increased with body length and boldness while fish with lower aerobic scope tended to move downstream. Passage rates showed significant individual repeatability in brown trout, independent of body size, indicating the potential for in‐stream barriers to exert selective effects on fish populations.Our results show that barrier effects can be more complex than simply blocking fish passage, and that river‐resident fish can be impacted even by very small barriers. We show that fish passage depends on a wide range of morphological, physiological and behavioural drivers, and that barriers can exert selective effects on these traits and cause shifts in community composition.Policy implications. Barrier mitigation measures need to embrace interspecific and intraspecific variation in fish passage to avoid inadvertent artificial selection on fish communities. Given the high abundance of low‐head structures in river systems worldwide, a paradigm shift is needed to recognise the subtle impacts of small barriers on freshwater biodiversity. Removal of small barriers or nature‐like fishways should allow better passage of the wider fish community compared to widely used salmonid‐centric fish passage options. [ABSTRACT FROM AUTHOR]

Step selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as 'selecting' each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data are increasingly gathered at very high frequencies, often ≥1 Hz, so it may be possible to exploit these data to perform more behaviourally‐meaningful step selection analysis.Here, we present a technique to do this. We first use an existing algorithm to determine the turning‐points in an animal's movement path. We define a 'step' to be a straight‐line movement between successive turning‐points. We then construct a generalised version of integrated SSA (iSSA), called time‐varying iSSA (tiSSA), which deals with the fact that turning‐points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free‐ranging goats Capra aegagrus hircus, comparing our results to those of regular iSSA with locations that are separated by a constant time‐interval.Using (regular) iSSA with constant time‐steps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter.By constructing a step selection technique that works between observed turning‐points of animals, we enable step selection to be used on high‐frequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turning‐points can be viewed as decisions, our method places step selection analysis on a more behaviourally‐meaningful footing compared to previous techniques. [ABSTRACT FROM AUTHOR]

Research Highlight: Bond, M. L., Lee, D. E., Ozgul, A., Farine, D. R., & König, B. (2021). Leaving by staying: Social dispersal in giraffes. Journal of Animal Ecology, https://doi. org/10.1111/1365-2656.13582. Dispersal is a key ecological and evolutionary process, which shows marked variability between and within species. The social and kinship structure of species fundamentally affects the patterns and types of dispersal, but information on how animals with fission-fusion group dynamics disperse is missing. Bond et al. provide novel data on natal dispersal of giraffe calves in relation to their dynamic multilayered social system, showing that individuals from both sexes can disperse socially, by switching association with different social groups, without leaving their natal area. The results highlight that traditional spatial-only measures of dispersal, such as dispersal distance, may be inadequate for social species with overlapping social units. [ABSTRACT FROM AUTHOR]

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In environments that vary unpredictably, many animals are nomadic, moving in an irregular pattern that differs from year to year. Exploring the mechanisms of nomadic movement is needed to understand how animals survive in highly variable environments, and to predict behavioural and population responses to environmental change.We developed a network model to identify plausible mechanisms of nomadic animal movement by comparing the performance of multiple movement rules along a continuum from nomadism to residency. Using simulations and analytical results, we explored how different types of habitat modifications (that augment or decrease resource availability) might affect the abundance and movement rates of animals following each of these rules.Movement rules for which departure from patches depended on resource availability and/or competition performed almost equally well and better than residency or uninformed movement under most conditions, even though animals using each rule moved at substantially different rates. Habitat modifications that stabilized resources, either by resource supplementation or degradation, eroded the benefits of informed nomadic movements, particularly for movements based on resource availability alone.These results suggest that simple movement rules can explain nomadic animal movements and determine species' responses to environmental change. In particular, landscape stabilization and supplementation might be useful strategies for promoting populations of resident animals, but would be less beneficial for managing highly mobile species, many of which are threatened by habitat disruption and changes in climate. [ABSTRACT FROM AUTHOR]

Translocation into a novel environment through common fisheries management practices, such as fish stocking, provides opportunities to study behavioural and fitness impacts of translocations at realistic ecological scales. The process of stocking, as well as the unfamiliarity with novel ecological conditions and the interactions with resident fish may affect translocated individuals, leading to alterations of behaviours and causing fitness impacts.Our objectives were to investigate how aquatic top predators behaviourally establish themselves and compete with resident individuals following introduction in a novel lake environment and to investigate the resulting fitness consequences.Using high‐resolution acoustic telemetry, we conducted whole‐lake experiments and compared the activity, activity‐space size and fate of translocated and resident individuals in two model top predators, the northern pike Esox lucius (n = 160) and European catfish Silurus glanis (n = 33). Additionally, we compared the reproductive success of translocated and resident northern pike. The experiment was conducted with large (adult) individuals of different origins, resilient to predation, but subject to agonistic interactions and competition with resident fish.Over a period of several months, the translocated catfish exhibited consistently larger activity‐space sizes than resident catfish, but did not differ from residents in activity and survival. The pike from one of the two translocated origins we tested also showed elevated space‐use, and both translocated origins revealed higher mortality rates than their resident conspecifics, indicating maladjustment to their novel environment. When non‐resident pike reproduced, they overwhelmingly produced hybrid offspring with resident fish, indicating that introductions fostered gene flow of non‐native genes.Our study indicates that fish introductions result in behavioural and fitness impacts even in large‐bodied top predators that experience low levels of natural predation risk. [ABSTRACT FROM AUTHOR]

Mathematical analysis of partial differential equations (PDEs) has led to many insights regarding the effect of organism movements on spatial population dynamics. However, their use has mainly been confined to the community of mathematical biologists, with less attention from statistical and empirical ecologists. We conjecture that this is principally due to the inherent difficulties in fitting PDEs to data.To help remedy this situation, in the context of movement ecology, we show how the popular technique of step selection analysis (SSA) can be used to parametrize a class of PDEs, called diffusion‐taxis models, from an animal's trajectory. We examine the accuracy of our technique on simulated data, then demonstrate the utility of diffusion‐taxis models in two ways. First, for non‐interacting animals, we derive the steady‐state utilization distribution in a closed analytic form. Second, we give a recipe for deriving spatial pattern formation properties that emerge from interacting animals: specifically, do those interactions cause heterogeneous spatial distributions to emerge and if so, do these distributions oscillate at short times or emerge without oscillations? The second question is applied to data on concurrently tracked bank voles Myodes glareolus.Our results show that SSA can accurately parametrize diffusion‐taxis equations from location data, providing the frequency of the data is not too low. We show that the steady‐state distribution of our diffusion‐taxis model, where it is derived, has an identical functional form to the utilization distribution given by resource selection analysis (RSA), thus formally linking (fine scale) SSA with (broad scale) RSA. For the bank vole data, we show how our SSA‐PDE approach can give predictions regarding the spatial aggregation and segregation of different individuals, which are difficult to predict purely by examining results of SSA.Our methods provide a user‐friendly way into the world of PDEs, via a well‐used statistical technique, which should lead to tighter links between the findings of mathematical ecology and observations from empirical ecology. By providing a non‐speculative link between observed movement behaviours and space use patterns on larger spatio‐temporal scales, our findings will also aid integration of movement ecology into understanding spatial species distributions. [ABSTRACT FROM AUTHOR]

Conservation and population management decisions often rely on population models parameterized using census data. However, the sampling regime, precision, sample size, and methods used to collect census data are usually heterogeneous in time and space. Decisions about how to derive population‐wide estimates from this patchwork of data are complicated and may bias estimated population dynamics, with important implications for subsequent management decisions.Here, we explore the impact of site selection and data aggregation decisions on pup survival estimates, and downstream estimates derived from parameterized matrix population models (MPMs), using a long‐term dataset on grey seal (Halichoerus grypus) pup survival from southwestern Wales. The spatiotemporal and methodological heterogeneity of the data are fairly typical for ecological census data and it is, therefore, a good model to address this topic.Data were collected from 46 sampling locations (sites) over 25 years, and we explore the impact of data handling decisions by varying how years and sampling locations are combined to parameterize pup survival in population‐level MPMs. We focus on pup survival because abundant high‐quality data are available on this developmental stage.We found that survival probability was highly variable with most variation being at the site level, and poorly correlated among sampling sites. This variation could generate marked differences in predicted population dynamics depending on sampling strategy. The sample size required for a confident survival estimate also varied markedly geographically.We conclude that for populations with highly variable vital rates among sub‐populations, site selection and data aggregation methods are important. In particular, including peripheral or less frequently used areas can introduce substantial variation into population estimates. This is likely to be context‐dependent, but these choices, including the use of appropriate weights when summarizing across sampling areas, should be explored to ensure that management actions are successful. [ABSTRACT FROM AUTHOR]

Macrophytes play important functional roles in river ecosystems, providing habitat and food, as well as influencing flow, water chemistry, and sediment dynamics. They also represent an important component of river biodiversity.Artificial river barriers have the potential to disrupt macrophyte dispersal, and compromise their distribution and persistence, but little information is available compared to barrier impacts on fish and macroinvertebrates. Here, we review the mechanisms supporting dispersal of river macrophytes in rivers and evaluate the nature of barrier impacts on macrophytes.Hydrochory (dispersal of propagules by water) is the principal mechanism of downstream dispersal, while zoochory (dispersal of propagules by animals) is likely to be the most important vector of upstream dispersal and inter‐catchment transport.Most studies have focused on the impact of large structures such as dams, and the findings indicate the impact is highly context dependent. Slow‐flowing habitats upstream of dams can act as traps to drifting propagules and thereby interrupt hydrochory. However, the consequences of interrupted hydrochory for downstream populations are unclear. River regulation can result in lower macrophyte diversity, although the lentic habitats associated with reservoirs can also favour an increase in the abundance and richness of macrophyte communities.Instream barriers are unlikely to affect zoochory by birds directly, but barriers are well known to restrict fish movements, so there is considerable potential for barriers to disrupt zoochory by fish, although no empirical study has specifically examined this possibility.There is a paucity of studies examining the impacts of low‐head barriers on macrophyte dispersal. Given the influence of macrophytes on river processes, we call for further research into barrier impacts on macrophyte population dynamics in order to gain a better understanding of the consequences of river fragmentation for fluvial communities and ecosystem functioning. [ABSTRACT FROM AUTHOR]

Animal behavior is elicited, in part, in response to external conditions, but understanding how animals perceive the environment and make the decisions that bring about these behavioral responses is challenging. Animal heads often move during specific behaviors and, additionally, typically have sensory systems (notably vision, smell, and hearing) sampling in defined arcs (normally to the front of their heads). As such, head‐mounted electronic sensors consisting of accelerometers and magnetometers, which can be used to determine the movement and directionality of animal heads (where head "movement" is defined here as changes in heading [azimuth] and/or pitch [elevation angle]), can potentially provide information both on behaviors in general and also clarify which parts of the environment the animals might be prioritizing ("environmental framing"). We propose a new approach to visualize the data of such head‐mounted tags that combines the instantaneous outputs of head heading and pitch in a single intuitive spherical plot. This sphere has magnetic heading denoted by "longitude" position and head pitch by "latitude" on this "orientation sphere" (O‐sphere). We construct the O‐sphere for the head rotations of a number of vertebrates with contrasting body shape and ecology (oryx, sheep, tortoises, and turtles), illustrating various behaviors, including foraging, walking, and environmental scanning. We also propose correcting head orientations for body orientations to highlight specific heading‐independent head rotation, and propose the derivation of O‐sphere‐metrics, such as angular speed across the sphere. This should help identify the functions of various head behaviors. Visualizations of the O‐sphere provide an intuitive representation of animal behavior manifest via head orientation and rotation. This has ramifications for quantifying and understanding behaviors ranging from navigation through vigilance to feeding and, when used in tandem with body movement, should provide an important link between perception of the environment and response to it in free‐ranging animals. [ABSTRACT FROM AUTHOR]

Monitoring the movements and behaviour of wildlife using radio telemetry or GPS devices has been critical to the fields of ecology and conservation over several decades. For many field projects however, commercially available devices can be expensive and may not always be ideally suited for collecting desired data.We present a low‐cost solution of customizable tracking devices based on the open‐source Arduino system. These devices can be custom designed for specific studies and easily programmed to collect desired data.Custom‐built collars with GPS and accelerometer units were trialled on 30 free‐ranging domestic dogs in rural Ethiopia. These collars collected high‐resolution data at a frequency of 10 fixes per hour and accelerometer data at 10 Hz over a 10‐day period, at a cost of approximately £100 ($130 USD) per collar.These devices can be placed on any species that can handle a weight of 15.4 g plus battery and housing. The current configuration weighs approximately 240 g which would be suitable for any animal above 8 kg living in terrestrial environments. [ABSTRACT FROM AUTHOR]

Animal movements towards goals or targets are based upon either maximization of resource acquisition or risk avoidance, and the way animals move can reveal information about their motivation.We use hidden Markov models (HMMs) fitted in a Bayesian framework and hourly Global Positioning System fixes to distinguish animal movements into distinct states and analyse the influence of environmental variables on being in, and switching to, a particular state. Specifically, we apply our models to understand elephant movement decisions around agricultural fields, and crop consumption. As it is unclear what the role of habitat features are on this complex process, we analyse whether elephants target agricultural crops for consumption, or simply pass through them in search of water.Our HMMs separate elephant movements into two states: exploratory movements that are fast and directional, and encamped movements that are slow and meandering. For each elephant, we ran 16 models with each possible combination of selected habitat features (river, elephant corridor, agricultural field, trees), and repeated these analyses including interaction effects with both season and time of day. We used cross‐validation to select the best model.In corridors, exploratory movements are dominant. Elephants mainly showed encamped movements at the river during the dry season, when temporary water sources have dried out and elephants relied on this permanent water source. In fields, males most often exhibited exploratory movements to and from the river, while females showed an increase in the frequency of encamped behaviour during the dry season and at night—the times when most crop consumption and movements through fields occur.Adaptation to risk could explain this behaviour, since foraging in fields is likely less risky under the cover of darkness and during the dry season when farmers are absent. This sex segregation in elephant movement decisions highlights the importance of predation risk in shaping movement patterns, which can result in sex segregation in responses to mitigation methods. The increase in encamped movements in the dry season suggests the importance of agricultural timing, and shows the potential for early ploughing and early‐harvest crop types in order to reduce elephant crop consumption. Taking this into account could increase efficiency of elephant crop consumption mitigation. [ABSTRACT FROM AUTHOR]

Classifying acoustic units is often a key step in studying repertoires and sequence structure in animal communication. Manual classification by eye and ear remains the primary method, but new tools and techniques are urgently needed to expedite the process for large, diverse datasets.Here we introduce Koe, an application for classifying and analysing animal vocalizations. Koe offers bulk‐labelling of units via interactive ordination plots and unit tables, as well as visualization and playback, segmentation, measurement, data filtering/exporting and new tools for analysing repertoire and sequence structure – in an integrated environment.We demonstrate Koe with a real‐world case study of New Zealand bellbird Anthornis melanura songs from an archipelago metapopulation. Having classified 21,500 units in Koe, we compare repertoires and sequence structure between sites and sexes.Koe is web‐based (koe.io.ac.nz.) and easy to use, making it ideal for collaboration, education and citizen science. By enabling large‐scale, high‐resolution classification and analysis of animal vocalizations, Koe expands the possibilities for bioacoustics research. [ABSTRACT FROM AUTHOR]

Keywords: accelerometer; animal movement; behaviour; biologging; GPS; modelling; sensor technology; tracking Technological solutions to record the movements, behaviour and physiology of animals, and associated methodological advancements for analysing the data collected, have revolutionized research in animal ecology and beyond (Brisson-Curadeau, Patterson, Whelan, Lazarus, & Elliott, [18]; Kenward, [56]; Ropert-Coudert, Beaulieu, Hanuise, & Kato, [91]; Ropert-Coudert & Wilson, [92]; Weimerskirch, [107]). The general term for this technological approach to study animals is called Biologging - 'the use of miniaturized animal-attached tags for logging and/or relaying data about an animal's movements, behaviour, physiology, and/or environment' (Rutz & Hays, [94]). Thanks to ever smaller loggers which can record an animal's behaviour for ever longer periods of time, biologging is now allowing researchers to study with unprecedented detail how behaviours develop in slow-maturing animals. While an increasing body of research has shown the impact of consistent individual differences in behavioural phenotypes, called animal personalities or behavioural syndromes (Réale et al., [89]; Sih, Bell, Johnson, & Ziemba, [96]), on foraging behaviour, exploratory movements and other spatial behaviours (Bijleveld et al., [12]; Boon, Réale, & Boutin, [15]; Minderman et al., [75]; van Overveld & Matthysen, [103]; Villegas-Ríos, Réale, Freitas, Moland, & Olsen, [105]; Wilson & McLaughlin, [112]), the important relationship between animal personality and foraging site fidelity has not been studied yet. [Extracted from the article]

It is fundamentally important for many animal ecologists to quantify the costs of animal activities, although it is not straightforward to do so. The recording of triaxial acceleration by animal‐attached devices has been proposed as a way forward for this, with the specific suggestion that dynamic body acceleration (DBA) be used as a proxy for movement‐based power.Dynamic body acceleration has now been validated frequently, both in the laboratory and in the field, although the literature still shows that some aspects of DBA theory and practice are misunderstood. Here, we examine the theory behind DBA and employ modelling approaches to assess factors that affect the link between DBA and energy expenditure, from the deployment of the tag, through to the calibration of DBA with energy use in laboratory and field settings.Using data from a range of species and movement modes, we illustrate that vectorial and additive DBA metrics are proportional to each other. Either can be used as a proxy for energy and summed to estimate total energy expended over a given period, or divided by time to give a proxy for movement‐related metabolic power. Nonetheless, we highlight how the ability of DBA to predict metabolic rate declines as the contribution of non‐movement‐related factors, such as heat production, increases.Overall, DBA seems to be a substantive proxy for movement‐based power but consideration of other movement‐related metrics, such as the static body acceleration and the rate of change of body pitch and roll, may enable researchers to refine movement‐based metabolic costs, particularly in animals where movement is not characterized by marked changes in body acceleration. [ABSTRACT FROM AUTHOR]

GPS‐tracking devices have been used in combination with a wide range of additional sensors to study animal behaviour, physiology and interaction with their environment. Tri‐axial accelerometers allow researchers to remotely infer the behaviour of individuals, at all places and times. Collection of accelerometer data is relatively cheap in terms of energy usage, but the amount of raw data collected generally requires much storage space and is particularly demanding in terms of energy needed for data transmission.Here, we propose compressing the raw accelerometer (ACC) data into summary statistics within the tracking device (before transmission) to reduce data size, as a means to overcome limitations in storage and energy capacity.We explored this type of lossy data compression in the accelerometer data of tagged Bewick's swans Cygnus columbianus bewickii collected in spring 2017. Using software settings in which bouts of 2 s of both raw ACC data and summary statistics were collected in parallel but with different bout intervals to keep total data size comparable, we created the opportunity for a direct comparison of time budgets derived by the two data collection methods.We found that the data compression in our case yielded a six times reduction in data size per bout, and concurrent, similar decreases in storage and energy use of the device. We show that with the same accuracy of the behavioural classification, the freed memory and energy of the device can be used to increase the monitoring effort, resulting in a more detailed representation of the individuals' time budget. Rare and/or short behaviours, such as daily roost flights, were picked up significantly more when collecting summary statistics instead of raw ACC data (but note differences in sampling rate). Such level of detail can be of essential importance, for instance to make a reliable estimate of the energy budgets of individuals.In conclusion, we argue that this type of lossy data compression can be a well‐considered choice in study situations where limitations in energy and storage space of the device pose a problem. Ultimately, these developments can allow for long‐term and nearly continuous remote monitoring of the behaviour of free‐ranging animals. [ABSTRACT FROM AUTHOR]

The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and led to the collection of ever‐increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyse tracking data; however, many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the r software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'.Here, we reviewed and described each tracking package based on a workflow centred around tracking data (i.e. spatio‐temporal locations (x, y, t)), broken down into three stages: pre‐processing, post‐processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioural pattern identification, space use characterization, trajectory simulation and others.Supporting documentation is key to render a package accessible for users. Based on a user survey, we reviewed the quality of packages' documentation and identified 11 packages with good or excellent documentation.Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the r movement‐ecology programming community.Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community. [ABSTRACT FROM AUTHOR]

The paradigm‐changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored.Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF).We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology.We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data.Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high‐frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location‐only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models. [ABSTRACT FROM AUTHOR]

The extent to which prey space use actively minimizes predation risk continues to ignite controversy. Methodological reasons that have hindered consensus include inconsistent measurements of predation risk, biased spatiotemporal scales at which responses are measured and lack of robust null expectations.We addressed all three challenges in a comprehensive analysis of the spatiotemporal responses of adult female elk (Cervus elaphus) to the risk of predation by wolves (Canis lupus) during winter in northern Yellowstone, USA.We quantified spatial overlap between the winter home ranges of GPS‐collared elk and three measures of predation risk: the intensity of wolf space use, the distribution of wolf‐killed elk and vegetation openness. We also assessed whether elk varied their use of areas characterized by more or less predation risk across hours of the day, and estimated encounter rates between simultaneous elk and wolf pack trajectories. We determined whether observed values were significantly lower than expected if elk movements were random with reference to predation risk using a null model approach.Although a small proportion of elk did show a tendency to minimize use of open vegetation at specific times of the day, overall we highlight a notable absence of spatiotemporal response by female elk to the risk of predation posed by wolves in northern Yellowstone.Our results suggest that predator–prey interactions may not always result in strong spatiotemporal patterns of avoidance. [ABSTRACT FROM AUTHOR]

Copyright of Methods in Ecology & Evolution is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Remote sensing is a valuable tool in movement ecology. However, the way it is combined with animal movement data is far from optimal as most studies overlook differences in the spatial, temporal and thematic resolutions between both data types.rsmove uses a pixel‐based approach to link animal tracking and remote sensing data that bridge the gap between these two disciplines while respecting the limitations of the latest. Additionally, if offers standardize methods to pre‐analyse the connection between animal movement and environmental change.The package guides the choice of study sites, satellite data and environmental predictors though data mining and visualization tools. rsmove offers a simple methodology to analyse animal–environment interactions that helps avoid unnecessary, time‐consuming satellite data processing through an informed decision process.The package offers a basis for a better communication between ecologists and remote sensing experts contributing to the definition of clear remote sensing data requirements. Additionally, it provides future studies on animal movement tools for a more critical approach on the selection of environmental data supporting the reproducibility of animal movement studies. [ABSTRACT FROM AUTHOR]

Telemetry, or the remote monitoring of animals with electronic transmitters and receivers, has vastly enhanced our ability to study aquatic animals. Radio telemetry, acoustic telemetry and passive integrated transponders are three common technologies that generate detection data — time‐stamped, tag‐specific records that are logged by receivers.We review current statistical methods and comment on potential future directions for analysing detection data derived from fixed telemetry receiver arrays.To illustrate how different methods may be used to achieve diverse study objectives, we provide a case study dataset collected by an array of 42 acoustic telemetry receivers on 187 bull trout in the Kinbasket Reservoir of British Columbia. To close, we present a decision tree for guiding the selection of a method based on study objectives and sampling design.This paper provides both experienced and novice telemetry researchers with the knowledge and tools to facilitate more comprehensive analysis of detection data and, in so doing, ask a wide variety of ecological questions that will enhance our understanding of aquatic organisms. [ABSTRACT FROM AUTHOR]

Automated movement tracking is essential for high‐throughput quantitative analyses of the behaviour and kinematics of organisms. Automated tracking also improves replicability by avoiding observer bias and allowing reproducible workflows. However, few automated tracking programs exist that are open access, open source and capable of tracking unmarked organisms in noisy environments.Tracktor is an image‐based tracking freeware designed to perform single‐object tracking in noisy environments, or multi‐object tracking in uniform environments while maintaining individual identities. Tracktor is code‐based but requires no coding skills other than the user being able to specify tracking parameters in a designated location, much like in a graphical user interface. The installation and use of the software is fully detailed in a user manual.Through four examples of common tracking problems, we show that Tracktor is able to track a variety of animals in diverse conditions. The main strengths of Tracktor lie in its ability to track single individuals under noisy conditions (e.g. when the object shape is distorted), its robustness to perturbations (e.g. changes in lighting conditions during the experiment), and its capacity to track multiple unmarked individuals while maintaining their identities. Additionally, summary statistics and plots allow measuring and visualising common metrics used in the analysis of animal movement (e.g. cumulative distance, speed, acceleration, activity, time spent in specific areas and distance to conspecific, etc.).Tracktor is a versatile, reliable and easy‐to‐use automated tracking software that is compatible with all operating systems and provides many features not available in other existing freeware. Access Tracktor and the complete user manual here: https://github.com/vivekhsridhar/tracktor [ABSTRACT FROM AUTHOR]

Copyright of Methods in Ecology & Evolution is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High‐throughput environmental sensing technologies are increasingly central to global monitoring of the ecological impacts of human activities. In particular, the recent boom in passive acoustic sensors has provided efficient, noninvasive, and taxonomically broad means to study wildlife populations and communities, and monitor their responses to environmental change. However, until recently, technological costs and constraints have largely confined research in passive acoustic monitoring (PAM) to a handful of taxonomic groups (e.g., bats, cetaceans, birds), often in relatively small‐scale, proof‐of‐concept studies.The arrival of low‐cost, open‐source sensors is now rapidly expanding access to PAM technologies, making it vital to evaluate where these tools can contribute to broader efforts in ecology and biodiversity research. Here, we synthesise and critically assess the current emerging opportunities and challenges for PAM for ecological assessment and monitoring of both species populations and communities.We show that terrestrial and marine PAM applications are advancing rapidly, facilitated by emerging sensor hardware, the application of machine learning innovations to automated wildlife call identification, and work towards developing acoustic biodiversity indicators. However, the broader scope of PAM research remains constrained by limited availability of reference sound libraries and open‐source audio processing tools, especially for the tropics, and lack of clarity around the accuracy, transferability and limitations of many analytical methods.In order to improve possibilities for PAM globally, we emphasise the need for collaborative work to develop standardised survey and analysis protocols, publicly archived sound libraries, multiyear audio datasets, and a more robust theoretical and analytical framework for monitoring vocalising animal communities. [ABSTRACT FROM AUTHOR]

Decomposing the life track of an animal into behavioral segments is a fundamental challenge for movement ecology. The proliferation of high‐resolution data, often collected many times per second, offers much opportunity for understanding animal movement. However, the sheer size of modern data sets means there is an increasing need for rapid, novel computational techniques to make sense of these data. Most existing methods were designed with smaller data sets in mind and can thus be prohibitively slow. Here, we introduce a method for segmenting high‐resolution movement trajectories into sites of interest and transitions between these sites. This builds on a previous algorithm of Benhamou and Riotte‐Lambert (2012). Adapting it for use with high‐resolution data. The data's resolution removed the need to interpolate between successive locations, allowing us to increase the algorithm's speed by approximately two orders of magnitude with essentially no drop in accuracy. Furthermore, we incorporate a color scheme for testing the level of confidence in the algorithm's inference (high = green, medium = amber, low = red). We demonstrate the speed and accuracy of our algorithm with application to both simulated and real data (Alpine cattle at 1 Hz resolution). On simulated data, our algorithm correctly identified the sites of interest for 99% of "high confidence" paths. For the cattle data, the algorithm identified the two known sites of interest: a watering hole and a milking station. It also identified several other sites which can be related to hypothesized environmental drivers (e.g., food). Our algorithm gives an efficient method for turning a long, high‐resolution movement path into a schematic representation of broadscale decisions, allowing a direct link to existing point‐to‐point analysis techniques such as optimal foraging theory. It is encoded into an R package called SitesInterest, so should serve as a valuable tool for making sense of these increasingly large data streams. We have constructed a new method for identifying sites of interest, tailored specifically for use with high resolution data. This allows for a complicated movement trajectory to be simplified into a "Markov chain‐like" description of behavioural choices, highlighting the animal's broadscale decisions. [ABSTRACT FROM AUTHOR]

Animal‐attached technologies can be powerful means to quantify space use and behavior; however, there are also ethical implications associated with capturing and instrumenting animals. Furthermore, tagging approaches are not necessarily well‐suited for examining the movements of multiple individuals within specific, local areas of interest. Here, we assess a method of quantifying animal space use based on a modified theodolite with an inbuilt laser rangefinder. Using a database of >4200 tracks of migrating birds, we show that detection distance increases with bird body mass (range 5 g to >10 kg). The maximum distance recorded to a bird was 5500 m and measurement error was ≤5 m for targets within this distance range: a level comparable to methods such as GPS tagging. We go on to present a case study where this method was used to assess habitat selection in seabirds operating in dynamic coastal waters close to a tidal turbine. Combining positional data with outputs from a hydrographic model revealed that great cormorants (Phalacrocorax carbo) appeared to be highly selective of current characteristics in space and time, exploiting areas where mean current speeds were <0.8 m·s−1 and diving at times when turbulent energy levels were low. These birds also oriented into tidal currents during dives. Taken together, this suggests that collision risks are low for cormorants at this site, as the 2 conditions avoided by cormorants (high mean current speeds and turbulence levels) are associated with operational tidal turbines. Overall, we suggest that this modified theodolite system is well‐suited to the quantification of movement in small areas associated with particular development strategies, including sustainable energy devices. [ABSTRACT FROM AUTHOR]

Abstract: Recent advances in biologging have resulted in animal location data at unprecedentedly high temporal resolutions, sometimes many times per second. However, many current methods for analysing animal movement (e.g. step selection analysis or state‐space modelling) were developed with lower‐resolution data in mind. To make such methods usable with high‐resolution data, we require techniques to identify features within the trajectory where movement deviates from a straight line. We propose that the intricacies of movement paths, and particularly turns, reflect decisions made by animals so that turn points are particularly relevant to behavioural ecologists. As such, we introduce a fast, accurate algorithm for inferring turning‐points in high‐resolution data. For analysing big data, speed and scalability are vitally important. We test our algorithm on simulated data, where varying amounts of noise were added to paths of straight‐line segments interspersed with turns. We also demonstrate our algorithm on data of free‐ranging oryx Oryx leucoryx. We compare our algorithm to existing statistical techniques for break‐point inference. The algorithm scales linearly and can analyse several hundred‐thousand data points in a few seconds on a mid‐range desktop computer. It identified turnpoints in simulated data with complete accuracy when the noise in the headings had a standard deviation of ±8∘, well within the tolerance of many modern biologgers. It has comparable accuracy to the existing algorithms tested, and is up to three orders of magnitude faster. Our algorithm, freely available in R and Python, serves as an initial step in processing ultra high‐resolution animal movement data, resulting in a rarefied path that can be used as an input into many existing step‐and‐turn methods of analysis. The resulting path consists of points where the animal makes a clear turn, and thereby provides valuable data on decisions underlying movement patterns. As such, it provides an important breakthrough required as a starting point for analysing subsecond resolution data. [ABSTRACT FROM AUTHOR]

Abstract: Demographic senescence is increasingly recognised as an important force shaping the dynamics of wild vertebrate populations. However, our understanding of the processes that underpin these declines in survival and fertility in old age remains limited. Evidence for age‐related changes in foraging behaviour and habitat use is emerging from wild vertebrate studies, but the extent to which these are driven by within‐individual changes, and the consequences for fitness, remain unclear. Using longitudinal census observations collected over four decades from two long‐term individual‐based studies of unmanaged ungulates, we demonstrate consistent within‐individual declines in home range area with age in adult females. In both systems, we found that within‐individual decreases in home range area were associated with increased risk of mortality the following year. Our results provide the first evidence from the wild that age‐related changes in space use are predictive of adult mortality. [ABSTRACT FROM AUTHOR]

Abstract: Net displacement is an integral component of numerous ecological processes and is critically dependent on the tortuosity of a movement trajectory and hence on the temporal scale of observation. Numerous attempts have been made to quantitatively describe net displacement while accommodating tortuosity, typically evoking a power law, but scale‐dependency in tortuosity limits the utility of approaches based on power law relationships that must assume scale‐invariant tortuosity. We describe a phenomenological model of net displacement that permits both scale‐variant and scale‐invariant movement. Movement trajectories are divided into pairs of relocations specifying start‐ and end‐points, and net displacements between points are calculated across a vector of time intervals. A bootstrap is implemented to create new datasets that are independent both across and within time intervals, and the model is fitted to the bootstrapped dataset using log–log regression. We apply this model to simulated trajectories and both fine‐grain and coarse‐grain trajectories obtained from an Aldabra giant tortoise Aldabrachelys gigantea, African elephants Loxodonta africana, black‐backed jackals Canis mesomelas and Northern elephant seals Mirounga angustirostris. The model was able to quantify the characteristics of net displacement from simulated movement trajectories corresponding to both scale‐variant (e.g. correlated random walks) and scale‐invariant (e.g. random walk) movement models. Furthermore, the model produced identical outputs across time vectors corresponding to different intervals and absolute ranges of time for scale‐invariant models. The model characterized the tortoise as generally exhibiting long scale‐invariant steps, which was corroborated by visual comparison of model outputs to observed trajectories. Elephants, jackals and seals exhibited movement parameters consistent with their known movement behaviours (nomadism, territoriality and widely ranging searching). We describe how the model may be used to compare movements within and between species, for example by partitioning movement into scale‐variant and scale‐invariant components, and by calculating a unitless net displacement scaled to the basal movement capacities of an animal. We also identify several useful derived quantities and realistic parameter ranges and discuss how the model may be implemented in a variety of ecological studies. [ABSTRACT FROM AUTHOR]

Abstract: Dispersal is a key process governing the dynamics of socially and spatially structured populations and involves three distinct stages: emigration, transience and settlement. At each stage, individuals have to make movement decisions, which are influenced by social, environmental and individual factors. Yet, a comprehensive understanding of the drivers that influence such decisions is still lacking, particularly for the transient stage during which free‐living individuals are inherently difficult to follow. Social circumstances such as the likelihood of encountering conspecifics can be expected to strongly affects decision‐making during dispersal, particularly in territorial species where encounters with resident conspecifics are antagonistic. Here, we analysed the movement trajectories of 47 dispersing coalitions of Kalahari meerkats Suricata suricatta through a landscape occupied by constantly monitored resident groups, while simultaneously taking into account environmental and individual characteristics. We used GPS locations collected on resident groups to create a georeferenced social landscape representing the likelihood of encountering resident groups. We used a step‐selection function to infer the effect of social, environmental and individual covariates on habitat selection during dispersal. Finally, we created a temporal mismatch between the social landscape and the dispersal event of interest to identify the temporal scale at which dispersers perceive the social landscape. Including information about the social landscape considerably improved our representation of the dispersal trajectory compared to analyses that only accounted for environmental variables. The latter were only marginally selected or avoided by dispersers. Before leaving their natal territory, dispersers selected areas frequently used by their natal group. In contrast, after leaving their natal territory, they selectively used areas where they were less likely to encounter unrelated groups. This pattern was particularly marked in larger dispersing coalitions and when unrelated males were part of the dispersing coalition. Our results suggest that, in socially and spatially structured species, dispersers gather and process social information during dispersal, and that reducing risk of aggression from unrelated resident groups outweighs benefits derived from conspecific attraction. Finally, our work underlines the intimate link between the social structure of a population and dispersal, which affect each other reciprocally. [ABSTRACT FROM AUTHOR]

Functional root traits are becoming a key measure in plant ecology, and root length measurements are needed for the calculation of root traits. Several methods are used to estimate the total root length ( TRL) of washed root samples [e.g. modified line intersect ( LI) method, Win RHIZOTM and IJ_Rhizo], but no standardized comparison of methods exists., We used a set of digital images of unstained root samples to compare measurements given by the LI method and automated methods provided by Win RHIZOTM and IJ_Rhizo. Linear regression models were used to detect bias. Both linear regression models and the Bland-Altman`s method of differences were used to evaluate the accuracy of eight methods (1 manual, 2 semi-automated and 5 automated) in comparison with a reference method that avoided root detection errors., Length measurements were highly correlated, but did not exactly agree with each other in 11 of 12 method comparisons. All tested methods tended to underestimate the TRL of unstained root samples. The accuracy of Win RHIZOTM was influenced by the thresholding method and the root length density ( RLD) in the pictures. For the other methods, no linear relationship was found between accuracy and RLD. With Win RHIZOTM (global thresholding + pixel reclassification; RLD = 1 cm cm−2), the Regent's method and the Tennant's method underestimated the TRL by 7·0 ± 6·2% and 4·7 ± 7·9%, respectively. The LI method gave satisfactory results on average (underestimation: 4·2 ± 6·0%), but our results suggest that it can lead to inaccurate estimations for single images. In IJ_Rhizo, the Kimura method was the best and underestimated the TRL by 5·4 ± 6·1%., Our results showed that care must be taken when comparing measurements acquired with different methods because they can lead to different results. When acquiring root images, we advise to (i) increase the contrast between fine roots and background by staining the roots, and (ii) avoid overlapping roots by not exceeding a RLD of 1 cm cm−2. Under these conditions, good length estimates can be obtained with Win RHIZOTM (global thresholding + pixel reclassification). The Kimura method in IJ_Rhizo can be an alternative to Win RHIZOTM. [ABSTRACT FROM AUTHOR]

Context: Tree-related microhabitats (TreMs), such as trunk cavities, peeled bark, cracks or sporophores of lignicolous fungi, are essential to support forest biodiversity because they are used as substrate, foraging, roosting or breeding places by bryophytes, fungi, invertebrates and vertebrates. Biodiversity conservation requires the continuous presence of TreMs in a forest. However, little is known about their dynamics. Moreover, we usually have only cross-sectional TreM data (observations of many trees at a single time), making it difficult to estimate TreM formation rates., Method: This study adapted the methods of survival and reliability analysis to model the rate of TreM formation per unit of diameter increment as a function of tree diameter at breast height ( DBH). We tested three variants of this model: the TreM formation rate independent of, proportional to or increasing nonlinearly with DBH. We calculated the likelihood of the models, considering cross-sectional observations either of TreM presence/absence or TreM number on trees of different sizes. We calibrated the models in six subnatural montane forests dominated by European beech ( Fagus sylvatica) and silver fir ( Abies alba) - in the French Pyrenees. Assuming an annual DBH increment value, the annual formation rate of TreMs was predicted both at the level of the tree and at the level of the forest stand., Results: This method provides a coherent framework to model the probability that a TreM forms on a tree during a unit growth step and produces realistic predictions of TreM accumulation on trees. TreM formation accelerated as trees grew for A. alba but not for F. sylvatica. The TreM formation rate was twice as fast on F. sylvatica as on A. alba. We estimated a formation of 0·82-1·28 TreMs/ha per year and 0·5-0·9 TreM-bearing trees/ha per year in the subnatural forests studied., Synthesis and applications: This method makes rigorous modelling of the formation of TreMs possible during the growth of trees and forest stands. The quantitative evaluation of TreM fluxes will help to design forest biodiversity conservation strategies favouring the development and temporal continuity of TreMs. [ABSTRACT FROM AUTHOR]

The field of acoustic telemetry has evolved rapidly and now permits the remote sensing of animal behaviour, movement, physiology and survival in environments, and species not previously possible. However, an inability to detect when a telemetered animal is consumed by a predator can complicate accurate interpretation of the telemetry data. In this paper, we describe the efforts taken to test the two generations of a novel prototype acoustic telemetry transmitter designed specifically to detect predation., Testing involved either staged predation events where tagged prey (Rainbow Trout Oncorhynchus mykiss and Yellow Perch Perca flavescens) were fed to captive Largemouth Bass Micropterus salmoides, or false-positive testing where prey fish were tagged and held without the risk of predation. Metrics of interest were (i) the rate of correctly identifying the predation events, (ii) signal lag (i.e. the time required to detect a predation event), (iii) tag retention time in the predator's gut, and (iv) the rate of false-positive triggering in both live and dead prey fishes., Staged predation events were successfully identified in 61/65 and 52/55 trials for generation 1 and 2 tags, respectively. Signal lag time was reduced in generation 1 tags (generally between 1 and 9 h) relative to generation 2 (3-29 h); although signal lag was highly variable. A generalized linear mixed model (GLMM) indicated strong evidence that signal lag and tag retention were both negatively correlated with water temperature, but were not affected by prey species and only slightly by individual predator traits. There was preliminary evidence that prey size may be an important determinant of both signal lag and tag retention. False-positives in live fish were absent after 120 days for generation 1 tags ( n = 31), however, the false-positive rates were significantly higher (10/44) after only 66 days for generation 2 tags. False-positives in dead fish showed that 20% of the generation 2 predation tags would falsely trigger 2-3 days post-mortem., Testing of the novel predation tags was encouraging, however, further testing is recommended. Predation tags will be an important contribution to the field of acoustic telemetry, thus, permitting the improved data interpretation and less-subjective estimates of predation rates in biotelemetry studies. [ABSTRACT FROM AUTHOR]

Trait-based approaches represent a promising way to understand how trophic interactions shape animal communities. The approach relies on the identification of the traits that mediate the linkages between adjacent trophic levels, i.e. 'trait-matching'. Yet, how trait-matching explains the abundance and diversity of animal communities has been barely explored. This question may be particularly critical in the context of land-use intensification, currently threatening biodiversity and associated ecosystem services., We collected a large dataset on plant and grasshopper traits from communities living in 204 grasslands, in an intensively managed agricultural landscape. We used a multi-trait approach to quantify the relative contributions of trait-matching and land-use intensification acting at both local and landscape scales on grasshopper functional diversity. We considered two key independent functional traits: incisor strength and body size of grasshopper species. Incisor strength, a resource-acquisition trait, strongly matches grasshopper feeding niche. Body size correlates with mobility traits, and may determine grasshopper dispersal abilities., Plant functional diversity positively impacted the diversity of grasshopper resource-acquisition traits, according to the degree of trait-matching observed between plants and herbivores. However, this positive effect was significantly higher in old grasslands. In addition, the presence of specific habitats in the landscape (i.e. wood and alfalfa) strongly enhanced grasshopper resource-acquisition trait diversity in the focal grassland. Finally, grasshopper body size increased with landscape simplification, although the response was modulated by local factors such as soil depth., Trait-matching between plants and herbivores was an important driver explaining the abundance and diversity of resource-acquisition traits within grasshopper communities. However, the presence of specific habitats in the surrounding landscape had also a strong influence on herbivore functional diversity in grasslands. Our study suggests that also mass effects are a central mechanism promoting higher functional diversity within animal communities in highly disturbed anthropogenic systems., A is available for this article. [ABSTRACT FROM AUTHOR]

Understanding site fidelity is important in animal ecology, but evidence is lacking that this behaviour is due to an animal choosing a specific location. To discern site selection behaviour, it is necessary to consider the spatial distribution of habitats that animals can occupy within a landscape. Tracking animals and defining clear habitat boundaries, however, is often difficult., We use in situ habitat distribution data and animal movement simulations to investigate behavioural choice in site fidelity patterns. We resolved the difficulty of gathering data by working with intertidal rock pool systems, which are of manageable size and where boundaries are easy to define. Movements of the intertidal starfish Parvulastra exigua were quantified to test the hypotheses that (1) this species displays fidelity to a particular rock pool and that (2) rock pool fidelity is due to site selection behaviour. Observed patterns of individuals ( n = 10 starfish) returning to a previously occupied rock pool ( n = 5 pools per location) were tested against an expected null distribution generated through simulations of random movements within their natural patchy environment., Starfish exhibited site selection behaviour at only one location even though site fidelity was high (av. 7·4 starfish out of 10 found in test pools) in two of the three locations. The random chance of a starfish returning to a pool increased 67% for each metre further a rock pool was from the original pool, and 120% for each square metre increase in surface area of an original pool. The decision of returning to an original rock pool was influenced by food availability. When microalgal cover was >60%, there was a c. 50% chance of animals staying faithful to that pool., Our results show the importance to consider spatial distribution of habitats in understanding patterns of animal movement associated with animal choices and site fidelity. Returning to a particular place does not necessarily mean that an animal is homing; it may be the only place to go. [ABSTRACT FROM AUTHOR]

Many animals undertake movements that are longer scaled and more directed than their typical home ranging behaviour. These movements include seasonal migrations (e.g. between breeding and feeding grounds), natal dispersal, nomadic range shifts and responses to local environmental disruptions. While various heuristic tools exist for identifying range shifts and migrations, none explicitly model the movement of the animals within a statistical framework that facilitates quantitative comparisons., We present the mechanistic range shift analysis (MRSA), a method to estimate a suite of range shift parameters: times of initiation, duration of transitions, centroids and areas of respective ranges. The method can take the autocorrelation and irregular sampling that is characteristic of much movement data into account. The mechanistic parameters suggest an intuitive measure, the range shift index, for the extent of a range shift. The likelihood based estimation further allows for statistical tests of several relevant hypotheses, including a range shift test, a stopover test and a site fidelity test. The analysis tools are provided in an R package ( marcher)., We applied the MRSA to a population of GPS tracked roe deer ( Capreolus capreolus) in the Italian Alps between 2005 and 2008. With respect to seasonal migration, this population is extremely variable and difficult to classify. Using the MRSA, we were able to quantify the behaviours across the population and among individuals across years, identifying extents, durations and locations of seasonal range shifts, including cases that would have been ambiguous to detect using existing tools., The strongest patterns were differences across years: many animals simply did not perform a seasonal migration to wintering grounds during the mild winter of 2006-2007, even though some of these same animals did move extensively in other, harsher winters. For seasonal migrants, however, site fidelity across years was extremely high, even after skipping an entire seasonal migration. These results suggest that for roe deer behavioural plasticity and tactical responses to immediate environmental cues are reflected in the decision of whether rather than where to migrate. The MRSA also revealed a trade-off between the probability of migrating and the size of a home range. [ABSTRACT FROM AUTHOR]

Recent advances in animal ecology have enabled identification of certain mechanisms that lead to the emergence of territories and home ranges from movements considered as unbounded. Among them, memory and familiarity have been identified as key parameters in cognitive maps driving animal navigation, but have been only recently used in empirical analyses of animal movements., At the same time, the influence of landscape features on movements of numerous species and on space division in territorial animals has been highlighted. Despite their potential as exocentric information in cognitive maps and as boundaries for home ranges, few studies have investigated their role in the design of home ranges of non-territorial species., Using step selection analyses, we assessed the relative contribution of habitat characteristics, familiarity preferences and linear landscape features in movement step selection of 60 GPS-collared Mediterranean mouflon Ovis gmelini musimon × Ovis sp. monitored in southern France. Then, we evaluated the influence of these movement-impeding landscape features on the design of home ranges by testing for a non-random distribution of these behavioural barriers within sections of space differentially used by mouflon., We reveal that familiarity and landscape features are key determinants of movements, relegating to a lower level certain habitat constraints (e.g. food/cover trade-off) that we had previously identified as important for this species. Mouflon generally avoid crossing both anthropogenic (i.e. roads, tracks and hiking trails) and natural landscape features (i.e. ridges, talwegs and forest edges) while moving in the opposite direction, preferentially toward familiar areas. These specific behaviours largely depend on the relative position of each movement step regarding distance to the landscape features or level of familiarity in the surroundings. We also revealed cascading consequences on the design of home ranges in which most landscape features were excluded from cores and relegated to the peripheral areas., These results provide crucial information on landscape connectivity in a context of marked habitat fragmentation. They also call for more research on the role of landscape features in the emergence of home ranges in non-territorial species using recent methodological developments bridging the gap between movements and space use patterns. [ABSTRACT FROM AUTHOR]