State-space framework for estimating measurement error from double-tagging telemetry experiments

Arliss J. Winship*, Salvador J. Jorgensen, Scott A. Shaffer, Ian D. Jonsen, Patrick W. Robinson, Daniel P. Costa, Barbara A. Block

*Corresponding author for this work

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

1.Double-tagging experiments are invaluable for determining the accuracy and precision of location data provided by different telemetry technologies used with free-ranging animals. 2.We developed a state-space modelling framework for estimating the precision of telemetry location data based on double-tagging experiments. The model is simultaneously fitted to multiple data types with different temporal resolutions while including errors in all data. 3.We used the model to estimate the precision of a specific geolocation method based on light and sea surface temperature applied to a large marine telemetry dataset. Data were available from double-tagging experiments on 111 animals representing seven marine species including 4 sharks, 2 birds and 1 pinniped. Study animals carried electronic tags that provided geolocation estimates as well as more precise satellite-based location data (Argos and Global Positioning System). 4.Estimates of the precision of geolocations were similar to previous findings. The overall estimated SD of geolocation errors for each species ranged from 0·5 to 3·9° for longitude and 0·8 to 3·6° for latitude. 5.While these results are specific to this particular type of location estimation method, the state-space framework presented here is a robust approach to estimating the precision of various types of telemetry location data from double-tagging experiments. The model simultaneously allows for appropriate inferences about true animal locations and movement.

Original languageEnglish
Pages (from-to)291-302
Number of pages12
JournalMethods in Ecology and Evolution
Volume3
Issue number2
DOIs
Publication statusPublished - Apr 2012
Externally publishedYes

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