Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete Sampling
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. Gro...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.817014/full |
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| author | Lucy C. M. Omeyer Trevelyan J. McKinley Nathalie Bréheret Gaëlle Bal George Petchell Balchin Abdon Bitsindou Eva Chauvet Tim Collins Bryan K. Curran Bryan K. Curran Angela Formia Alexandre Girard Alexandre Girard Marc Girondot Brendan J. Godley Jean-Gabriel Mavoungou Laurène Poli Dominic Tilley Hilde VanLeeuwe Kristian Metcalfe |
| author_facet | Lucy C. M. Omeyer Trevelyan J. McKinley Nathalie Bréheret Gaëlle Bal George Petchell Balchin Abdon Bitsindou Eva Chauvet Tim Collins Bryan K. Curran Bryan K. Curran Angela Formia Alexandre Girard Alexandre Girard Marc Girondot Brendan J. Godley Jean-Gabriel Mavoungou Laurène Poli Dominic Tilley Hilde VanLeeuwe Kristian Metcalfe |
| author_sort | Lucy C. M. Omeyer |
| collection | DOAJ |
| description | 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uncertainties in a robust probabilistic manner within a given site and season; and (2) apply this to a previously unpublished long-term sea turtle dataset (n = 17 years) collated for the Republic of the Congo, which hosts two sympatrically nesting species of sea turtle (leatherback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 [Dermochelys coriacea] and olive ridley turtle [Lepidochelys olivacea]). The results of this analysis suggest that leatherback turtle nesting levels dropped initially and then settled into quasi-cyclical levels of interannual variability, with an average of 573 (mean, 95% prediction interval: 554–626) clutches laid annually between 2012 and 2017. In contrast, nesting abundance for olive ridley turtles has increased more recently, with an average of 1,087 (mean, 95% prediction interval: 1,057–1,153) clutches laid annually between 2012 and 2017. These findings highlight the regional and global importance of this rookery with the Republic of the Congo, hosting the second largest documented populations of olive ridley and the third largest for leatherback turtles in Central Africa; and the fourth largest non-arribada olive ridley rookery globally. Furthermore, whilst the results show that Congo’s single marine and coastal national park provides protection for over half of sea turtle clutches laid in the country, there is scope for further protection along the coast. Although large parts of the African coastline remain to be adequately monitored, the modelling approach used here will be invaluable to inform future status assessments for sea turtles given that most datasets are temporally and spatially fragmented. |
| first_indexed | 2024-04-13T21:54:31Z |
| format | Article |
| id | doaj.art-587206dc8ff2498c8fa8dc59fffeb90a |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-04-13T21:54:31Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-587206dc8ff2498c8fa8dc59fffeb90a2022-12-22T02:28:17ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-06-01910.3389/fmars.2022.817014817014Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete SamplingLucy C. M. Omeyer0Trevelyan J. McKinley1Nathalie Bréheret2Gaëlle Bal3George Petchell Balchin4Abdon Bitsindou5Eva Chauvet6Tim Collins7Bryan K. Curran8Bryan K. Curran9Angela Formia10Alexandre Girard11Alexandre Girard12Marc Girondot13Brendan J. Godley14Jean-Gabriel Mavoungou15Laurène Poli16Dominic Tilley17Hilde VanLeeuwe18Kristian Metcalfe19Centre for Ecology and Conservation, College of Life and Environmental Sciences, University of Exeter, Penryn, United KingdomCollege of Medicine and Health, University of Exeter, Exeter, United KingdomAssociation Renatura Congo, Ecocentre, Pointe Noire, Republic of CongoAssociation Renatura Congo, Ecocentre, Pointe Noire, Republic of CongoDepartment of Evolution, Behaviour and Environment, School of Life Sciences, University of Sussex, Brighton, United KingdomCongo Program, Wildlife Conservation Society (WCS), Brazzaville, Republic of CongoAssociation Renatura Congo, Ecocentre, Pointe Noire, Republic of CongoMarine Program, Wildlife Conservation Society (WCS), Bronx, NY, United StatesCongo Program, Wildlife Conservation Society (WCS), Brazzaville, Republic of CongoRainforest Trust, Warrenton, VA, United StatesGulf of Guinea Sea Turtle Program, Wildlife Conservation Society, Libreville, GabonCentral African Sea Turtle Conservation Network, Rastoma, Paris, France0Laboratoire Ecologie, Systématique et Evolution, Equipe de Processus Ecologiques et Pressions Anthropiques, UMR 8079 Centre National de la Recherche Scientifique (CNRS), AgroParisTech et Université Paris-Saclay, Orsay Cedex, France0Laboratoire Ecologie, Systématique et Evolution, Equipe de Processus Ecologiques et Pressions Anthropiques, UMR 8079 Centre National de la Recherche Scientifique (CNRS), AgroParisTech et Université Paris-Saclay, Orsay Cedex, FranceCentre for Ecology and Conservation, College of Life and Environmental Sciences, University of Exeter, Penryn, United KingdomAssociation Renatura Congo, Ecocentre, Pointe Noire, Republic of CongoAssociation Renatura Congo, Ecocentre, Pointe Noire, Republic of CongoCentre for Ecology and Conservation, College of Life and Environmental Sciences, University of Exeter, Penryn, United KingdomCongo Program, Wildlife Conservation Society (WCS), Brazzaville, Republic of CongoCentre for Ecology and Conservation, College of Life and Environmental Sciences, University of Exeter, Penryn, United KingdomMonitoring 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 [Dermochelys coriacea] and olive ridley turtle [Lepidochelys olivacea]). The results of this analysis suggest that leatherback turtle nesting levels dropped initially and then settled into quasi-cyclical levels of interannual variability, with an average of 573 (mean, 95% prediction interval: 554–626) clutches laid annually between 2012 and 2017. In contrast, nesting abundance for olive ridley turtles has increased more recently, with an average of 1,087 (mean, 95% prediction interval: 1,057–1,153) clutches laid annually between 2012 and 2017. These findings highlight the regional and global importance of this rookery with the Republic of the Congo, hosting the second largest documented populations of olive ridley and the third largest for leatherback turtles in Central Africa; and the fourth largest non-arribada olive ridley rookery globally. Furthermore, whilst the results show that Congo’s single marine and coastal national park provides protection for over half of sea turtle clutches laid in the country, there is scope for further protection along the coast. Although large parts of the African coastline remain to be adequately monitored, the modelling approach used here will be invaluable to inform future status assessments for sea turtles given that most datasets are temporally and spatially fragmented.https://www.frontiersin.org/articles/10.3389/fmars.2022.817014/fullAfricaleatherback turtlesea turtlesolive ridley turtleBayesian hierarchical modellingpopulation status |
| spellingShingle | Lucy C. M. Omeyer Trevelyan J. McKinley Nathalie Bréheret Gaëlle Bal George Petchell Balchin Abdon Bitsindou Eva Chauvet Tim Collins Bryan K. Curran Bryan K. Curran Angela Formia Alexandre Girard Alexandre Girard Marc Girondot Brendan J. Godley Jean-Gabriel Mavoungou Laurène Poli Dominic Tilley Hilde VanLeeuwe Kristian Metcalfe Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete Sampling Frontiers in Marine Science Africa leatherback turtle sea turtles olive ridley turtle Bayesian hierarchical modelling population status |
| title | Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete Sampling |
| title_full | Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete Sampling |
| title_fullStr | Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete Sampling |
| title_full_unstemmed | Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete Sampling |
| title_short | Missing Data in Sea Turtle Population Monitoring: A Bayesian Statistical Framework Accounting for Incomplete Sampling |
| title_sort | missing data in sea turtle population monitoring a bayesian statistical framework accounting for incomplete sampling |
| topic | Africa leatherback turtle sea turtles olive ridley turtle Bayesian hierarchical modelling population status |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.817014/full |
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