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Current and emerging statistical techniques for aquatic telemetry data: A guide to analysing spatially discrete animal detections
Author(s) -
Whoriskey Kim,
Martins Eduardo G.,
AugerMéthé Marie,
Gutowsky Lee F. G.,
Lennox Robert J.,
Cooke Steven J.,
Power Michael,
Mills Flemming Joanna
Publication year - 2019
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13188
Subject(s) - telemetry , biotelemetry , computer science , sampling (signal processing) , remote sensing , selection (genetic algorithm) , environmental science , telecommunications , geography , machine learning , detector
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.