Mind the gap—optimizing satellite tag settings for time series analysis of foraging dives in Cuvier’s beaked whales (Ziphius cavirostris) View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Nicola J. Quick, William R. Cioffi, Jeanne Shearer, Andrew J. Read

ABSTRACT

Studies of deep-diving beaked whales using Argos satellite-linked location-depth tags frequently return data with large gaps in the diving record. We document the steps taken to eliminate these data gaps and collect weeks of continuous time series data for a behavioral response study that took place in 2017. We used baseline data collected from 2014 to 2016 to analyze message diagnostics, and assess our current programming schedule using a multiple criteria decision making matrix (MCDM), as a robust way to develop a new sampling regime. The MCDM approach suggested animal behavior and the quantity of data collected were the main causes of gaps in our baseline tag records. We implemented a new sampling regime to sample only long-duration, presumed foraging dives, simultaneously increasing temporal coverage of each individual message and reducing the number of messages by 50%. The reduction of gaps increased the data available for continuous time series analysis from an average of just over 2 days and 13.5 sequential presumed foraging dives in our baseline tags to just over 19 days and 118 sequential presumed foraging dives in tags deployed during the 2017 behavioral response study. We demonstrate that a critical approach, based on analysis of baseline data and question-driven weighted criteria, enabled the reduction and even elimination of gaps in the diving records of these tags. This approach enabled us to develop specific settings for our tags to ensure that our data collection was optimized for statistical analysis of the specific hypotheses we were testing. More... »

PAGES

5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40317-019-0167-5

DOI

http://dx.doi.org/10.1186/s40317-019-0167-5

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https://app.dimensions.ai/details/publication/pub.1112779598


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