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Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments
Although sea turtles face significant pressure from human activities, some populations are recovering due to conservation programs, bans on the trade of turtle products, and reductions in bycatch. While these trends are encouraging, the status of many populations remains unknown and scientific monit...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735099/ https://www.ncbi.nlm.nih.gov/pubmed/29255157 http://dx.doi.org/10.1038/s41598-017-17719-x |
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author | Sykora-Bodie, Seth T. Bezy, Vanessa Johnston, David W. Newton, Everette Lohmann, Kenneth J. |
author_facet | Sykora-Bodie, Seth T. Bezy, Vanessa Johnston, David W. Newton, Everette Lohmann, Kenneth J. |
author_sort | Sykora-Bodie, Seth T. |
collection | PubMed |
description | Although sea turtles face significant pressure from human activities, some populations are recovering due to conservation programs, bans on the trade of turtle products, and reductions in bycatch. While these trends are encouraging, the status of many populations remains unknown and scientific monitoring is needed to inform conservation and management decisions. To address these gaps, this study presents methods for using unmanned aerial systems (UAS) to conduct population assessments. Using a fixed-wing UAS and a modified strip-transect method, we conducted aerial surveys along a three-kilometer track line at Ostional, Costa Rica during a mass-nesting event of olive ridley turtles (Lepidochelys olivacea). We visually assessed images collected during six transects for sea turtle presence, resulting in 682 certain detections. A cumulative total of 1091 certain and probable turtles were detected in the collected imagery. Using these data, we calculate estimates of sea turtle density (km(−2)) in nearshore waters. After adjusting for both availability and perception biases, we developed a low-end estimate of 1299 ± 458 and a high-end estimate of 2086 ± 803 turtles km(−2). This pilot study illustrates how UAS can be used to conduct robust, safe, and cost-effective population assessments of sea turtle populations in coastal marine ecosystems. |
format | Online Article Text |
id | pubmed-5735099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57350992017-12-21 Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments Sykora-Bodie, Seth T. Bezy, Vanessa Johnston, David W. Newton, Everette Lohmann, Kenneth J. Sci Rep Article Although sea turtles face significant pressure from human activities, some populations are recovering due to conservation programs, bans on the trade of turtle products, and reductions in bycatch. While these trends are encouraging, the status of many populations remains unknown and scientific monitoring is needed to inform conservation and management decisions. To address these gaps, this study presents methods for using unmanned aerial systems (UAS) to conduct population assessments. Using a fixed-wing UAS and a modified strip-transect method, we conducted aerial surveys along a three-kilometer track line at Ostional, Costa Rica during a mass-nesting event of olive ridley turtles (Lepidochelys olivacea). We visually assessed images collected during six transects for sea turtle presence, resulting in 682 certain detections. A cumulative total of 1091 certain and probable turtles were detected in the collected imagery. Using these data, we calculate estimates of sea turtle density (km(−2)) in nearshore waters. After adjusting for both availability and perception biases, we developed a low-end estimate of 1299 ± 458 and a high-end estimate of 2086 ± 803 turtles km(−2). This pilot study illustrates how UAS can be used to conduct robust, safe, and cost-effective population assessments of sea turtle populations in coastal marine ecosystems. Nature Publishing Group UK 2017-12-18 /pmc/articles/PMC5735099/ /pubmed/29255157 http://dx.doi.org/10.1038/s41598-017-17719-x Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Sykora-Bodie, Seth T. Bezy, Vanessa Johnston, David W. Newton, Everette Lohmann, Kenneth J. Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments |
title | Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments |
title_full | Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments |
title_fullStr | Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments |
title_full_unstemmed | Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments |
title_short | Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments |
title_sort | quantifying nearshore sea turtle densities: applications of unmanned aerial systems for population assessments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735099/ https://www.ncbi.nlm.nih.gov/pubmed/29255157 http://dx.doi.org/10.1038/s41598-017-17719-x |
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