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A comparison of techniques for classifying behavior from accelerometers for two species of seabird
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/ece3.1660openalex-snapshot
Interpreting behaviors from accelerometry: a method combining simplicity and objectivity
inferredcites →https://doi.org/10.1002/ece3.1914openalex-snapshot
The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data
inferredcites →https://doi.org/10.1002/ece3.4740openalex-snapshot
A comparison of techniques for classifying behavior from accelerometers for two species of seabird
inferredcites →https://doi.org/10.1006/jmsc.1997.0243openalex-snapshot
Seabirds as monitors of the marine environment
inferredcites →https://doi.org/10.1007/978-0-387-21706-2openalex-snapshot
Modern Applied Statistics with S
inferredcites →https://doi.org/10.1007/s00227-004-1328-4openalex-snapshot
GPS tracking a marine predator: the effects of precision, resolution and sampling rate on foraging tracks of African Penguins
inferredcites →https://doi.org/10.1007/s13253-017-0282-9openalex-snapshot
Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures
inferredcites →https://doi.org/10.1016/0198-0254(87)90303-7openalex-snapshot
Flight of auks (Alcidae) and other northern seabirds compared with southern procellariiformes: ornithodolite observations
inferredcites →https://doi.org/10.1016/j.anbehav.2003.09.010openalex-snapshot
A fine-scale time budget of Cape gannets provides insights into the foraging strategies of coastal seabirds
inferredcites →https://doi.org/10.1016/j.applanim.2009.04.008openalex-snapshot
Use of a tri-axial accelerometer for automated recording and classification of goats’ grazing behaviour
inferredcites →https://doi.org/10.1016/j.fishres.2006.11.017openalex-snapshot
Tourist compliance to a Code of Conduct and the resulting effects on whale shark (Rhincodon typus) behavior in Donsol, Philippines
inferredcites →https://doi.org/10.1016/j.jembe.2011.01.008openalex-snapshot
Use of overall dynamic body acceleration for estimating energy expenditure in cormorants
inferredcites →https://doi.org/10.1073/pnas.1304838110openalex-snapshot
High flight costs, but low dive costs, in auks support the biomechanical hypothesis for flightlessness in penguins
inferredcites →https://doi.org/10.1086/375431openalex-snapshot
Variation in Energy Expenditure among Black‐Legged Kittiwakes: Effects of Activity‐Specific Metabolic Rates and Activity Budgets
inferredcites →https://doi.org/10.1098/rsbl.2015.0358openalex-snapshot
The jellyfish buffet: jellyfish enhance seabird foraging opportunities by concentrating prey
inferredcites →https://doi.org/10.1098/rsif.2011.0713openalex-snapshot
Small actions, big costs: the behavioural energetics of a commercially important invertebrate
inferredcites →https://doi.org/10.1098/rspb.2002.2252openalex-snapshot
Stroke and glide of wing–propelled divers: deep diving seabirds adjust surge frequency to buoyancy change with depth
inferredcites →https://doi.org/10.1098/rstb.2010.0087openalex-snapshot
Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology
inferredcites →https://doi.org/10.1098/rstb.2010.0107openalex-snapshot
Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges
inferredcites →https://doi.org/10.1111/1365-2656.12126openalex-snapshot
Age‐related variation in energy expenditure in a long‐lived bird within the envelope of an energy ceiling
inferredcites →https://doi.org/10.1111/2041-210x.12657openalex-snapshot
Analysis of animal accelerometer data using hidden Markov models
inferredcites →https://doi.org/10.1111/2041-210x.12995openalex-snapshot
momentuHMM: R package for generalized hidden Markov models of animal movement
inferredcites →https://doi.org/10.1126/science.1258732openalex-snapshot
The roller coaster flight strategy of bar-headed geese conserves energy during Himalayan migrations
inferredcites →https://doi.org/10.1126/science.aaf4374openalex-snapshot
Frigate birds track atmospheric conditions over months-long transoceanic flights
inferredcites →https://doi.org/10.1139/z07-012openalex-snapshot
The influence of buoyancy and drag on the dive behaviour of an Arctic seabird, the Thick-billed Murre
inferredcites →https://doi.org/10.1186/2050-3385-1-20openalex-snapshot
Observing the unwatchable through acceleration logging of animal behavior
inferredcites →https://doi.org/10.1186/2051-3933-2-6openalex-snapshot
Optimizing acceleration-based ethograms: the use of variable-time versus fixed-time segmentation
inferredcites →https://doi.org/10.1186/s40317-017-0123-1openalex-snapshot
Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry
inferredcites →https://doi.org/10.1186/s40462-014-0017-2openalex-snapshot
Windscapes shape seabird instantaneous energy costs but adult behavior buffers impact on offspring
inferredcites →https://doi.org/10.1186/s40462-014-0027-0openalex-snapshot
AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements
inferredcites →https://doi.org/10.1186/s40462-016-0088-3openalex-snapshot
A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle
inferredcites →https://doi.org/10.1242/jeb.009811openalex-snapshot
A new method to quantify prey acquisition in diving seabirds using wing stroke frequency
inferredcites →https://doi.org/10.1242/jeb.058602openalex-snapshot
Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures
inferredcites →https://doi.org/10.1242/jeb.128.1.335openalex-snapshot
Flight of Auks (Alcidae) and Other Northern Seabirds Compared with Southern Procellarhformes: Ornithodolite Observations
inferredcites →https://doi.org/10.1242/jeb.136135openalex-snapshot
Using accelerometers to remotely and automatically characterize behavior in small animals
inferredcites →https://doi.org/10.1371/journal.pone.0005379openalex-snapshot
Can Ethograms Be Automatically Generated Using Body Acceleration Data from Free-Ranging Birds?
inferredcites →https://doi.org/10.1371/journal.pone.0037997openalex-snapshot
From Sensor Data to Animal Behaviour: An Oystercatcher Example
inferredcites →https://doi.org/10.1371/journal.pone.0088609openalex-snapshot
Love Thy Neighbour: Automatic Animal Behavioural Classification of Acceleration Data Using the K-Nearest Neighbour Algorithm
inferredcites →https://doi.org/10.1371/journal.pone.0139351openalex-snapshot
Combined Use of GPS and Accelerometry Reveals Fine Scale Three-Dimensional Foraging Behaviour in the Short-Tailed Shearwater
inferredcites →https://doi.org/10.1650/condor-15-66.1openalex-snapshot
Accelerometry reveals an impact of short-term tagging on seabird activity budgets
inferredcites →https://doi.org/10.18637/jss.v069.i01openalex-snapshot
Least-Squares Means: The R Package lsmeans
inferredcites →https://doi.org/10.1890/14-1401.1openalex-snapshot
The golden age of bio‐logging: how animal‐borne sensors are advancing the frontiers of ecology
inferredcites →https://doi.org/10.2307/1937540openalex-snapshot
Activity‐Specific Metabolic Rates of Free‐Living Northern Gannets and Other Seabirds
inferredcites →https://doi.org/10.2307/3808646openalex-snapshot
Cardiac and Behavioral Responses of Mountain Sheep to Human Disturbance
inferredcites →https://doi.org/10.3354/ab00104openalex-snapshot
Derivation of body motion via appropriate smoothing of acceleration data
inferredcites →https://doi.org/10.3354/esr00084openalex-snapshot
Identification of animal movement patterns using tri-axial accelerometry
inferredcites →https://doi.org/10.3354/esr00779openalex-snapshot
Using tri-axial accelerometers to identify wild polar bear behaviors
inferredcites →https://doi.org/10.3354/meps07424openalex-snapshot
Foraging strategy of masked boobies from the largest colony in the world: relationship to environmental conditions and fisheries
inferredcites →https://doi.org/10.3354/meps08203openalex-snapshot
Quantifying habitat use and preferences of pelagic seabirds using individual movement data: a review
inferredcites →https://doi.org/10.3354/meps11845openalex-snapshot
Pronounced inter-colony variation in the foraging ecology of Australasian gannets: influence of habitat differences
inferredcites →https://doi.org/10.3354/meps12052openalex-snapshot
Geographical variation in the foraging behaviour of the pantropical red-footed booby
inferredcites →https://doi.org/10.3354/meps268265openalex-snapshot
Offshore diplomacy or how seabirds mitigate intra-specific competition: a case study based on GPS tracking of Cape gannets from neighbouring colonies
inferredcites →https://doi.org/10.3354/meps306283openalex-snapshot
Increased energy expenditure by a seabird in response to higher food abundance
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.3897/bdj.13.e150787zenodo-depositor-declared
Using GPS and accelerometer data to precisely record egg laying, incubation and chick hatching of Cinereous Vultures (Aegypius monachus) in-situ
inferred← citeshttps://doi.org/10.1002/ece3.10035openalex-snapshot
Supervised versus unsupervised approaches to classification of accelerometry data
inferred← citeshttps://doi.org/10.1002/ece3.11455openalex-snapshot
African dryland antelope trade‐off behaviours in response to heat extremes
inferred← citeshttps://doi.org/10.1002/ece3.70802openalex-snapshot
Tracking the Ghosts of the Himalayas: Snow Leopard Conservation Insights From Satellite Collar Data
inferred← citeshttps://doi.org/10.1002/ece3.71647openalex-snapshot
Fine‐Scale Movement Data Reveal Primarily Surface Foraging and Nocturnal Flight Activity in the Endangered Bermuda Petrel
inferred← citeshttps://doi.org/10.1002/ece3.7308openalex-snapshot
Effects of age, sex, and ENSO phase on foraging and flight performance in Nazca boobies
inferred← citeshttps://doi.org/10.1002/ece3.74053openalex-snapshot
Refining Accelerometer‐Based Animal Behaviour Classifications With Sequence‐Informed Post‐Processing
inferred← citeshttps://doi.org/10.1002/ece3.74079openalex-snapshot
Understanding Sex‐Specific Behavioral States of Bobcats in Response to Highway Proximity in South Texas
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Quantifying song behavior in a free‐living, light‐weight, mobile bird using accelerometers
inferred← citeshttps://doi.org/10.1002/ece3.9579openalex-snapshot
A new biologging approach reveals unique flightless molt strategies of Atlantic puffins
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Warming summers limit reindeer grazing, weakening herbivory pressure in the mountain tundra
inferred← citeshttps://doi.org/10.1007/s00442-022-05191-yopenalex-snapshot
Food supply and individual quality influence seabird energy expenditure and reproductive success
inferred← citeshttps://doi.org/10.1016/j.anbehav.2024.12.001openalex-snapshot
Collar? I barely know her: The utility of accelerometry in measuring personality in situ for a free-ranging wild mammal
inferred← citeshttps://doi.org/10.1016/j.anbehav.2026.123712openalex-snapshot
The consequences of food and flight efficiency for the timing of natal dispersal in golden eagles
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Mercury, legacy and emerging POPs, and endocrine-behavioural linkages: Implications of Arctic change in a diving seabird
inferred← citeshttps://doi.org/10.1016/j.yhbeh.2020.104874openalex-snapshot
The effects of food supply on reproductive hormones and timing of reproduction in an income-breeding seabird
inferred← citeshttps://doi.org/10.1021/acs.est.2c08893openalex-snapshot
Mercury Contamination Challenges the Behavioral Response of a Keystone Species to Arctic Climate Change
inferred← citeshttps://doi.org/10.1038/s41598-020-78025-7openalex-snapshot
Acceleration predicts energy expenditure in a fat, flightless, diving bird
inferred← citeshttps://doi.org/10.1038/s41598-022-22258-1openalex-snapshot
The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets
inferred← citeshttps://doi.org/10.1093/conphys/coae057openalex-snapshot
Influence of sea ice concentration, sex and chick age on foraging flexibility and success in an Arctic seabird
inferred← citeshttps://doi.org/10.1098/rsos.210520openalex-snapshot
A bioenergetics approach to understanding sex differences in the foraging behaviour of a sexually monomorphic species
inferred← citeshttps://doi.org/10.1098/rsos.251772openalex-snapshot
Uncovering hidden behaviours: a GPS-accelerometry approach to behaviour classification in storm-petrels
inferred← citeshttps://doi.org/10.1098/rspb.2025.0774openalex-snapshot
Commuting in crosswinds and foraging in fast winds: the foraging ecology of a flying fish specialist
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Using a Homeogram to Detect Sleep in Free-living Animals
inferred← citeshttps://doi.org/10.1101/2023.11.06.565854openalex-snapshot
Daily time constraints limit behavioural capacity to cope with thermally increased metabolic demands
inferred← citeshttps://doi.org/10.1101/2024.05.08.593164openalex-snapshot
Fine-scale movement data reveal surface foraging and nocturnal flight activity in the endangered Bermuda petrel
inferred← citeshttps://doi.org/10.1101/2025.02.01.636019openalex-snapshot
A simple method to classify behaviours of aerial predators using tri-axial high-frequency accelerometers
inferred← citeshttps://doi.org/10.1101/2025.03.05.641645openalex-snapshot
Remote inferences and direct observations provide complementary insights into foraging behavior
inferred← citeshttps://doi.org/10.1101/2025.05.15.654001openalex-snapshot
Using wingbeat frequency to estimate mass gained by seabirds
inferred← citeshttps://doi.org/10.1111/1365-2656.70054openalex-snapshot
Practical guidelines for validation of supervised machine learning models in accelerometer‐based animal behaviour classification
inferred← citeshttps://doi.org/10.1111/2041-210x.14019openalex-snapshot
A review of supervised learning methods for classifying animal behavioural states from environmental features
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Using wingbeat frequency to estimate mass gain
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Integrating immersion with GPS data improves behavioural classification for wandering albatrosses and shows scavenging behind fishing vessels mirrors natural foraging
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Thanks to repetition, dustbathing detection can be automated combining accelerometry and wavelet analysis
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Evaluation of tag attachment techniques for plunge‐diving terns
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Seasonal activity levels of a farm-island population of striated caracaras (Phalcoboenus australis) in the Falkland Islands
inferred← citeshttps://doi.org/10.1186/s40317-022-00315-wopenalex-snapshot
Prey ingestion rates revealed by back-mounted accelerometers in Eurasian spoonbills
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How to study a predator that only eats a few meals a year: high-frequency accelerometry to quantify feeding behaviours of rattlesnakes (Crotalus spp.)
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Using non-continuous accelerometry to identify cryptic nesting events of Galapagos giant tortoises
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Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species
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An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers
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A benchmark for computational analysis of animal behavior, using animal-borne tags
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Accelerometry predicts muscle ultrastructure and flight capabilities in a wild bird
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Accelerating animal energetics: high dive costs in a small seabird disrupt the dynamic body acceleration–energy expenditure relationship
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Additional file 1 of Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species
inferred← citeshttps://doi.org/10.6084/m9.figshare.14091331openalex-snapshot
Additional file 2 of Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species
inferred← citeshttps://doi.org/10.6084/m9.figshare.14091334openalex-snapshot
Additional file 3 of Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species
inferred← citeshttps://doi.org/10.6084/m9.figshare.14091337openalex-snapshot
Additional file 4 of Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species
inferred← citeshttps://doi.org/10.6084/m9.figshare.14344112openalex-snapshot
Additional file 1 of An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers
inferred← citeshttps://doi.org/10.6084/m9.figshare.22816934openalex-snapshot
Additional file 2 of How to study a predator that only eats a few meals a year: high-frequency accelerometry to quantify feeding behaviours of rattlesnakes (Crotalus spp.)
inferred← citeshttps://doi.org/10.6084/m9.figshare.26565495openalex-snapshot
Additional file 1 of Prey ingestion rates revealed by back-mounted accelerometers in Eurasian spoonbills
inferred← citeshttps://doi.org/10.70675/aaa8708ezbb45z4911za328zb31c17d9a1e7openalex-snapshot
Energetic landscapes of migratory seabirds in the Nort Atlantic Ocean in a context of climate change
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