The importance of sample size in marine megafauna tagging studies. Issue 6 (16th July 2019)

• Record Type: Journal Article
• Authors: Sequeira, A. M. M.; Heupel, M. R.; Lea, M.‐A.; Eguíluz, V. M.; Duarte, C. M.; Meekan, M. G.; Thums, M.; Calich, H. J.; Carmichael, R. H.; Costa, D. P.; Ferreira, L. C.; Fernandéz‐Gracia, J.; Harcourt, R.; Harrison, A.‐L.; Jonsen, I.; McMahon, C. R.; Sims, D. W.; Wilson, R. P.; Hays, G. C.
• Is Part Of: Ecological applications. Volume 29:Issue 6(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-16
• Subjects: animal welfare -- key questions -- movement behavior -- number of tags -- telemetry studies -- tracking data; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.1947
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: Telemetry is a key, widely used tool to understand marine megafauna distribution, habitat use, behavior, and physiology; however, a critical question remains: "How many animals should be tracked to acquire meaningful data sets?" This question has wide‐ranging implications including considerations of statistical power, animal ethics, logistics, and cost. While power analyses can inform sample sizes needed for statistical significance, they require some initial data inputs that are often unavailable. To inform the planning of telemetry and biologging studies of marine megafauna where few or no data are available or where resources are limited, we reviewed the types of information that have been obtained in previously published studies using different sample sizes. We considered sample sizes from one to >100 individuals and synthesized empirical findings, detailing the information that can be gathered with increasing sample sizes. We complement this review with simulations, using real data, to show the impact of sample size when trying to address various research questions in movement ecology of marine megafauna. We also highlight the value of collaborative, synthetic studies to enhance sample sizes and broaden the range, scale, and scope of questions that can be answered.