AUTHOR=Omeyer Lucy C. M. , McKinley Trevelyan J. , Bréheret Nathalie , Bal Gaëlle , Petchell Balchin George , Bitsindou Abdon , Chauvet Eva , Collins Tim , Curran Bryan K. , Formia Angela , Girard Alexandre , Girondot Marc , Godley Brendan J. , Mavoungou Jean-Gabriel , Poli Laurène , Tilley Dominic , VanLeeuwe Hilde , Metcalfe Kristian TITLE=Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete Sampling JOURNAL=Frontiers in Marine Science VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2022.817014 DOI=10.3389/fmars.2022.817014 ISSN=2296-7745 ABSTRACT=
Monitoring how populations respond to sustained conservation measures is essential to detect changes in their population status and determine the effectiveness of any interventions. In the case of sea turtles, their populations are difficult to assess because of their complicated life histories. Ground-derived clutch counts are most often used as an index of population size for sea turtles; however, data are often incomplete with varying sampling intensity within and among sites and seasons. To address these issues, we: (1) develop a Bayesian statistical modelling framework that can be used to account for sampling uncertainties in a robust probabilistic manner within a given site and season; and (2) apply this to a previously unpublished long-term sea turtle dataset (n = 17 years) collated for the Republic of the Congo, which hosts two sympatrically nesting species of sea turtle (leatherback turtle [