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Accuracy, precision, and error in age estimation of Florida manatees using growth layer groups in earbones
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    Journal of Mammalogy, Volume 100, Issue 4, 27 July 2019, Pages 1350–1363,
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  • Description:
    Ages of Florida manatees (Trichechus manatus latirostris) can be estimated by counting annual growth layer groups (GLGs) in the periotic dome portion of the tympanoperiotic complex of their earbones. The Florida Fish and Wildlife Conservation Commission manages an archive of more than 8,700 Florida manatee earbones collected from salvaged carcasses from 1989 to 2017. Our goal was to comprehensively evaluate techniques used to estimate age, given this large sample size and changes to processing protocols and earbone readers over time. We developed new standards for estimating ages from earbones, involving two independent readers to obtain measurements of within- and between-reader precision. To quantify accuracy, precision, and error, 111 earbones from manatees with approximately known ages (first known as calves: KAC) and 69 earbones from manatees with minimum known ages (MKA, based on photo-identification sighting histories) were processed, and their ages were estimated. There was greater precision within readers (coefficient of variation, CV: 2.4-8.5%) than between readers (CV: 13.1-13.3%). The median of age estimates fell within the true age range for 63.1% of KAC cases and was at least the sighting duration for 75.0% of MKA cases. Age estimates were generally unbiased, as indicated by an average raw error +/- SD of -0.05 +/- 3.05 years for the KAC group. The absolute error (i.e., absolute value of raw error) of the KAC data set averaged 1.75 +/- 2.50 years. Accuracy decreased and error increased with increasing known age, especially for animals over 15 years old, whose ages were mostly underestimated due to increasing levels of resorption (the process of bone turnover that obscures GLGs). Understanding the degree of uncertainty in age estimates will help us assess the utility of age data in manatee population models. We emphasize the importance of standardizing and routinely reviewing age estimation and processing protocols to ensure that age data remain consistent and reliable.
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