Acoustic telemetry is a popular and cost-efficient method for tracking the movements of animals in the aquatic ecosystem. But data acquired via acoustic telemetry often contains spurious detections that must be identified and excluded by researchers to ensure valid results. Such data management is difficult as the amount of data collected often surpasses the capabilities of simple spreadsheet applications. is an open-source package programmed in R that allows users to integrate all telemetry data collected into a single file, to conditionally attribute animal data and location data to detections and to filter spurious detections based on customizable rules. Such tool will likely be useful to new researchers in acoustic telemetry and enhance results reproducibility.• compiles telemetry files and identifies and stores all data that was collected outside of your study period (e.g. when your receivers were on land for servicing) elsewhere.•As spurious detections are unlikely to appear sequentially in the data, finds all detections that occurred only once (per receiver or in the whole array) within a user-designated time period and stores them elsewhere.• identifies detections that are impossible given the animals' swimming speeds and the receivers detection range and stores them elsewhere.
In areas with high densities of salmon farming, spillover of the ectoparasitic salmon louse Lepeophtheirus salmonis poses a major threat to wild anadromous salmonids. By combining experimentally salmon louse infestation (mean ± standard deviation = 0.25 ± 0.
There are gaps in our understanding of sturgeon's response to anthropogenic sounds and the spatial scales at which they occur. We measured spatial displacement of Atlantic sturgeon in the St. Lawrence River at various distances of approaching merchant ships.
The study of aquatic animal movements is a rapidly growing field of research, with tracking methodology ever developing and refining. Acoustic telemetry is arguably the most popular method used to study the movements of fish. Despite this method being able to elucidate many aspects of movement behavior, including residency, home range, and migration, among others, one aspect that remains challenging is the study and definition of connectivity, particularly within marine seascapes.
Animal movements are typically influenced by multiple environmental factors simultaneously, and individuals vary in their response to this environmental heterogeneity. Therefore, understanding how environmental aspects, including biotic, abiotic, and anthropogenic factors, influence the movements of wild animals is an important focus of wildlife research and conservation. We apply Exponential Random Graph Models (ERGMs) to analyze movement networks of a bull shark population in a network of acoustic receivers and identify the effects of environmental, social, or other types of covariates on their movements.
