Researchers hoping to elucidate the behaviour of species that aren’t readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1) Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2) they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN). Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.

The tagging of aquatic and semi-aquatic animals with acoustic transmitters and their detection by passive underwater receivers has gained huge popularity over the past decade. This technology offers researchers the opportunity to monitor the finite- to broad-scale movements of multiple individuals over many years; however, the sheer scale and spatial complexity of these datasets are often beyond the capabilities of routine database and spread-sheet applications. In the present paper, we describe software (V-Track) that greatly facilitates the assimilation, analysis and synthesis of animal-location data collected by underwater passive acoustic receivers. The principal features within V-Track are the behavioural event qualifier (BEQ) and the receiver-distance matrix (RDM) calculator. The BEQ identifies and catalogues horizontal movements from receiver detection data, or vertical movements from transmitter sensor data (depth or temperature). The RDM is generated from the geographical location of the acoustic receivers and is utilised by V-Track to illustrate the behavioural event information in a spatial context. V-Track is a package written within the R-programming language, and a graphical user interface is also provided. Here, we feature two case studies to demonstrate software functionality for defining and quantifying behaviour in acoustically tagged marine and freshwater vertebrates.

Summary: Animal‐borne telemetry has revolutionized our ability to study animal movement, species physiology, demography and social structures, changing environments and the threats that animals are experiencing. While there will always be a need for basic ecological research and discovery, the current conservation crisis demands we look more pragmatically at the data required to make informed management decisions. Here, we define a framework that distinguishes how research using animal telemetry devices can influence conservation. We then discuss two critical questions which aim to directly connect telemetry‐derived data to applied conservation decision‐making: (i) Would my choice of action change if I had more data? (ii) Is the expected gain worth the money and time required to collect more data? Policy implications. To answer questions about integrating telemetry‐derived data with applied conservation, we suggest the use of value of information analysis to quantitatively assess the return‐on‐investment of animal telemetry‐derived data for conservation decision‐making.

Summary: Intertidal habitats provide important feeding areas for migratory shorebirds. Anthropogenic developments along coasts can increase ambient light levels at night across adjacent inter‐tidal zones. Here, we report the effects of elevated nocturnal light levels upon the foraging strategy of a migratory shorebird (common redshank Tringa totanus) overwintering on an industrialised estuary in Northern Europe. To monitor behaviour across the full intertidal area, individuals were located by day and night using VHF transmitters, and foraging behaviour was inferred from inbuilt posture sensors. Natural light was scored using moon‐phase and cloud cover information and nocturnal artificial light levels were obtained using geo‐referenced DMSP/OLS night‐time satellite imagery at a 1‐km resolution. Under high illumination levels, the commonest and apparently preferred foraging behaviour was sight‐based. Conversely, birds feeding in areas with low levels of artificial light had an elevated foraging time and fed by touch, but switched to visual rather than tactile foraging behaviour on bright moonlit nights in the absence of cloud cover. Individuals occupying areas which were illuminated continuously by lighting from a large petrochemical complex invariably exhibited a visually based foraging behaviour independently of lunar phase and cloud cover. We show that ambient light levels affect the timing and distribution of foraging opportunities for redshank. We argue that light emitted from an industrial complex improved nocturnal visibility. This allowed sight‐based foraging in place of tactile foraging, implying both a preference for sight‐feeding and enhanced night‐time foraging opportunities under these conditions. The study highlights the value of integrating remotely sensed data and telemetry techniques to assess the effect of anthropogenic change upon nocturnal behaviour and habitat use.

Over the past 15 years, the integration of localised passive telemetry networks into centralised data repositories has greatly enhanced our ability to monitor the presence and movements of highly mobile and migratory species. These large-scale networks are now generating big data, allowing meta-analyses across multiple species, locations, and temporal scales. Broad-scale comparisons of animal movement metrics are constrained by the use of diverse analytical techniques among researchers. Accordingly, there is a need for a tool-set to assist in calculating animal movement metrics that can be easily applied to datasets from local studies to large-scale cooperative networks. We present a standardised framework and an associated analysis tool-set that facilitates the calculation of a range of activity space and movement metrics for passive telemetry datasets. Application of the tool-set is demonstrated using data from the Integrated Marine Observing System continental-scale network of underwater acoustic receivers. We show how the metrics can: (1) be directly compared among multiple species monitored at multiple sites; (2) be compared among multiple species tagged at a single study site; and (3) assess changes in activity space metrics over time. Establishing a framework and tool-set to analyse data from large-scale networks progresses the field of passive telemetry beyond the traditional individual-, species-, or location-centric approaches to facilitate national- or international-scale outputs that better address important questions in the field of movement ecology.