ArticleThe Journal of animal ecology2022
Using piecewise regression to identify biological phenomena in biotelemetry datasets.
Article in The Journal of animal ecology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
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- Navigating motherhood: biological and landscape factors affecting postpartum movement in white-tailed deer.Movement ecology · 2024Article
- Treed Gaussian processes for animal movement modeling.Ecology and evolution · 2024Article
- Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection.Ecology and evolution · 2024Article
- Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles.Movement ecology · 2024Article
- Change-point models for identifying behavioral transitions in wild animals.Movement ecology · 2023Article
- Accelerometer-based detection of African swine fever infection in wild boar.Proceedings. Biological sciences · 2023Article
- Using piecewise regression to identify biological phenomena in biotelemetry datasets.The Journal of animal ecology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behaviour over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animal's location, movement characteristics, external environmental conditions and internal physiological status. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using multiple biotelemetry data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modelled responses (e.g. their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval. We provide six example applications highlighting a variety of taxonomic species, data streams, timescales and biological phenomena. These examples include a short-term behavioural response (flee and return) by a trumpeter swan Cygnus buccinator following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose Alces alces; a physiological response (spike in heart-rate) in a black bear Ursus americanus to a stressful stimulus (presence of a drone); a mortality event of a trumpeter swan signalled by changes in collar temperature and overall dynamic body acceleration; an unsupervised method for identifying the onset, return, duration and staging use of sandhill crane Antigone canadensis migration; and estimation of the transition between incubation and brood-rearing (i.e. hatching) for a breeding trumpeter swan. We implement analyses using the mcp package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criteria and cross-validation measures. These simple modelling approaches are accessible to a wide audience and offer a straightforward means of assessing a variety of biologically relevant changes in animal behaviour.
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Registered trials
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