Hourly GPS Locations, Associated Habitat Condition, and Annotated Life History State for Training Machine Learned Models of Waterfowl Daily Activity
August 19, 2022
These data represent an annotated training data for machine learned life history classification of the daily activity of dabbling ducks (f. Anatidae sf. Anatinae) using hourly GPS data. Each row of data represents a single GPS location for one of 5 species of dabbling duck. Note: the machine learned model was developed to be general across sf. Anatinae and does not include specific reference to the species included. That information is obtainable from the point of contact upon request. Each data record contains unique identifiers for individual bird, individual date of locations for an individual bird, and for individual location. Each record contains spatial, temporal and associated habitat information. Each record also contains an annotated label representing one of 8 life history states or movement patterns exhibited by the collective location obtained for the individual on that date. The 8 classes identified were: Brooding, Dead, Local movements, Migration, Molting, Molt-Like, Nesting, Regional Relocation. Detailed description of the machine learned model and annotation methods are provided in the associated manuscript titled, Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: Application to North American waterfowl.
These data support the following publication:
Overton, C., Casazza, M., Bretz, J., McDuie, F., Matchett, E., Mackell, D., Lorenz, A., Mott, A., Herzog, M. and Ackerman, J., 2022. Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl. Movement Ecology, 10(1), pp.1-13. https://doi.org/10.1186/s40462-022-00324-7.
These data support the following publication:
Overton, C., Casazza, M., Bretz, J., McDuie, F., Matchett, E., Mackell, D., Lorenz, A., Mott, A., Herzog, M. and Ackerman, J., 2022. Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl. Movement Ecology, 10(1), pp.1-13. https://doi.org/10.1186/s40462-022-00324-7.
Citation Information
Publication Year | 2022 |
---|---|
Title | Hourly GPS Locations, Associated Habitat Condition, and Annotated Life History State for Training Machine Learned Models of Waterfowl Daily Activity |
DOI | 10.5066/P9XBZKZ8 |
Authors | Cory T Overton, Michael L Casazza, Joseph B Bretz |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Western Ecological Research Center - Headquarters |
Rights | This work is marked with CC0 1.0 Universal |
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Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: Application to North American waterfowl
BackgroundIdentifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to...
Authors
Cory T. Overton, Michael L. Casazza, Joseph Bretz, Fiona McDuie, Elliott Matchett, Desmond Alexander Mackell, Austen Lorenz, Andrea Lynn Mott, Mark P. Herzog, Josh T. Ackerman
Related
Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: Application to North American waterfowl
BackgroundIdentifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to...
Authors
Cory T. Overton, Michael L. Casazza, Joseph Bretz, Fiona McDuie, Elliott Matchett, Desmond Alexander Mackell, Austen Lorenz, Andrea Lynn Mott, Mark P. Herzog, Josh T. Ackerman