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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]

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]

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]

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]

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]

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]

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]

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]

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: 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]

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]

A resource selection function is a model of the likelihood that an available spatial unit will be used by an animal, given its resource value. But how do we appropriately define availability? Step selection analysis deals with this problem at the scale of the observed positional data, by matching each 'used step' (connecting two consecutive observed positions of the animal) with a set of 'available steps' randomly sampled from a distribution of observed steps or their characteristics., Here we present a simple extension to this approach, termed integrated step selection analysis ( iSSA), which relaxes the implicit assumption that observed movement attributes (i.e. velocities and their temporal autocorrelations) are independent of resource selection. Instead, iSSA relies on simultaneously estimating movement and resource selection parameters, thus allowing simple likelihood-based inference of resource selection within a mechanistic movement model., We provide theoretical underpinning of iSSA, as well as practical guidelines to its implementation. Using computer simulations, we evaluate the inferential and predictive capacity of iSSA compared to currently used methods., Our work demonstrates the utility of iSSA as a general, flexible and user-friendly approach for both evaluating a variety of ecological hypotheses, and predicting future ecological patterns. [ABSTRACT FROM AUTHOR]

1. In field ecological and behavioural studies, it is often necessary to identify specific individuals. In birds, colour rings are frequently used tomark individuals; however, rings are often difficult to observe, especially in small species and dense habitats. Acoustic-based monitoring detecting individuals by their characteristic vocalization is a potentially suitable alternative, but this approach is challenging in species with complex songs. 2. On the example of the Tree Pipit (Anthus trivialis), a small migratory passerine often singing in flight or from perches obscured by foliage, we demonstrate that acoustic monitoring based on the syllable repertoire can be very efficient tool for individual recognition. During a 3-year study, we obtained over 500 recordings from males from one study population (a number of them returning after winter). Males banded with colour rings were repeatedly recorded throughout the seasons, and syllable repertoires were determined from spectrograms for each recording. 3. The repertoire of each unambiguously identified male was distinct and stable within as well as between seasons; and males with similar syllable repertoires differed in syntax. Based on the congruence between identification based solely on spectrogram assessment, and that based on observation of colour rings, we inferred that reliable identification of singing males (including non-ringed ones) was possible in the studied population from assessing a repertoire and song syntax of <5-min recording (containing 20-30 songs). The acoustic-based data: (i) increased the overall estimated number of territorial males at the study locality (from 49 ringed to 61), and improved the estimates of the period of their presence; (ii) revealed dynamic within-season changes in territory occupancy that would otherwise be missed; and (iii) allowed identification of returning birds (including nonringed ones and those actively avoiding approaching humans). 4. Our results suggest that some commonly used methodsmay substantially underestimate return rates of migratory bird species. Individual acoustic monitoring should be applicable on various bird species with complex song and stable repertoires, and may be particularly useful for those living in dense habitat or sensitive to handling. [ABSTRACT FROM AUTHOR]

Understanding how to find targets with very limited information is a topic of interest in many disciplines. In ecology, such research has often focused on the development of two movement models: (i) the Lévy walk and (ii) the composite correlated random walk and its associated area-restricted search behaviour. Although the processes underlying these models differ, they can produce similar movement patterns. Due to this similarity and because of their disparate formulation, current methods cannot reliably differentiate between these two models., Here, we present a method that differentiates between the two models. It consists of likelihood functions, including one for a hidden Markov model, and associated statistical measures that assess the relative support for and absolute fit of each model., Using a simulation study, we show that our method can differentiate between the two search models over a range of parameter values. Using the movement data of two polar bears ( Ursus maritimus), we show that the method can be applied to complex, real-world movement paths., By providing the means to differentiate between the two most prominent search models in the literature, and a framework that could be extended to include other models, we facilitate further research into the strategies animals use to find resources. [ABSTRACT FROM AUTHOR]

For diving animals, animal-borne sensors are used to collect time-depth information for studying behaviour, ranging patterns and foraging ecology. Often, this information needs to be compressed for storage or transmission. Widely used devices called conductivity-temperature-depth satellite relay data loggers ( CTD- SRDLs) sample time and depth at high resolution during a dive and then abstract the time-depth trajectory using a broken-stick model ( BSM). This approximation method can summarize efficiently the curvilinear shape of a dive, using a piecewise linear shape with a small, fixed number of vertices, or break points., We present the process of abstracting dives using the BSM and quantify its performance, by measuring the uncertainty associated with the profiles it produces. We develop a method for obtaining a confidence zone and an index for the goodness-of-fit (dive zone index, DZI) for abstracted dive profiles. We validate our results with a case study using dives from elephant seals ( Mirounga spp.). We use generalized additive models ( GAMs) to determine whether the DZI can be used as a proxy for an absolute measure of fit and investigate the relationship between the DZI and the dive shape., We found a strong correlation between the residual sum of squares ( RSS) for the difference between the detailed and abstracted profiles, and the DZI and maximum residual (R4), for dives resulting from CTD- SRDLs (69% deviance explained). On its own, the DZI explained a lower percentage of deviance which was variable for abstracted dives with different numbers of break points. We also found evidence for systematic differences in the DZI for different dive shapes (65% deviance explained)., Although the proportional loss of information in the abstraction of time-depth dive profiles by BSM is high, what remains is sufficient to infer goodness-of-fit of the abstracted profile by reversing the abstraction process. Our results suggest that together the DZI and R4 can be used as a proxy for the RSS, and we present the method for obtaining these metrics for BSM-abstracted profiles. [ABSTRACT FROM AUTHOR]

Animals of many species demonstrate movement behaviour in which decisions are based on a variety of information. Effects of resources have been studied widely, often under the assumption that the environment is constant over the course of the study. Much less understood is the role of dynamic information about continuously changing resource availability and past experiences. Such information can be acquired during movement bouts and used for future decisions via memory., We present a new class of animal movement models, which incorporates a dynamic interplay of movement and information gain processes. Information is contained in a dynamic cognitive map. As an example, we consider time since last visit to locations and how this interacts with environmental information to shape movement patterns. Our models can be fitted to empirical movement trajectories and are therefore amenable to statistical inference (parameter estimation and model selection)., We tested the functionality of our method using simulated data. Parameter estimates were in accordance with true values used in the simulations, and model selection via Bayesian information criterion (BIC) was able to identify true underlying mechanisms of simulated trajectories. Thus, if time since last visit to locations influences movement decisions, our method is able to detect this mechanism., The use of dynamic information such as the one demonstrated in our example models likely requires cognitive abilities such as spatial memory. Therefore, our method can be used to reveal evidence of spatial memory in empirical movement data. Understanding the components of individual movement decisions and their interactions ultimately helps us to predict how population distribution patterns respond to environmental changes, such as landscape fragmentation and changing climate. [ABSTRACT FROM AUTHOR]

Underwater visual census (UVC) using SCUBA is a commonly used method for assessing reef fish communities. Evidence suggests, however, that fish avoid divers due to the sound of bubbles produced by open-circuit SCUBA, and avoidance behaviour is more pronounced as fishing pressure increases. Despite the potential for producing biased counts and conclusions, these behavioural effects have rarely been quantified, especially when assessing the effectiveness of marine protected areas ( MPAs)., To test the magnitude of avoidance behaviour, we surveyed fish populations inside and outside two MPAs in Guam, using two diving systems: standard open-circuit ( OC) SCUBA and a closed-circuit rebreather ( CCR) that produces no bubbles. Data were collected using a diver-operated stereo-video system (stereo- DOV), which provided counts of relative abundance, measures of fish length and the minimum approach distance of the diver to a fish., Inside MPAs, fish surveys conducted with CCR recorded similar community metrics to fish surveys conducted with conventional OC SCUBA. In contrast, outside the MPAs, the bubble-free diving system recorded 48% more species and up to 260% greater fish abundance. These differences reflected the ability of a diver wearing the silent CCR unit to sample the larger, most heavily targeted species that are shy of divers in fished areas. This difference was also large enough to change some results from 'reject' to 'accept' the null hypothesis of 'no significant differences exist between fished and protected areas'., The use of CCR for fish surveys clearly minimizes behavioural biases associated with fish avoiding open-circuit SCUBA divers. We recommend the use of this bubble-free diving system for surveys assessing reef fish populations, especially in areas where fish are heavily targeted by spearfishing. If fish behaviour is not accounted for, surveys using SCUBA could result in erroneous conclusions when comparing fished and protected areas. While the behaviour of fish towards divers is rarely mentioned in conclusions from studies using UVC, it is an important source of bias that should be acknowledged and minimized where possible. [ABSTRACT FROM AUTHOR]