Effect of formic acid treatment on carbon and nitrogen stable isotope ratios in sperm whale teeth dentine
2023
Stukonytė, Laura | Borrell Thió, Assumpció | Drago, Massimiliano | Lockyer, Christina | Víkingsson, Gísli | Aguilar, Àlex
Rationale: Stable isotope analysis of growth layers in sperm whale teeth dentine can provide valuable insight into individual long-distance displacements and diet. Although treating teeth half-sections with formic acid and rubbing their surface with a graphite pencil improves growth layer visibility and reduces sampling error, previous studies mostly used untreated half-sections as the effect that this treatment may have on stable isotope ratios in dentine is unknown. The present study investigates the treatment effect on stable C and N isotope ratios in sperm whale teeth dentine. Methods: In the teeth of 30 sperm whales, we analysed and compared samples of powdered dentine obtained from (a) untreated half-sections, (b) half-sections etched with formic acid and rubbed with a graphite pencil and (c) half-sections etched with formic acid from which the graphite pencil rubbing had been cleansed off. δ13 C and δ15 N values were compared between the three sample groups. Results: We found significant differences in values of both elements between untreated and etched samples, with a mean increase of 0.2% in δ13C and δ15N values in the etched samples. No significant differences were found between etched samples with graphite rubbing versus those without it. Significant linear regression models were calculated to predict untreated δ13C and δ15N values from the values obtained in the etched half-sections with limited precision. Conclusions: We show for the first time that formic acid etching has a clear effect on C and N values in sperm whale teeth dentine. The developed models permit the estimation of untreated values from etched half-sections, thus enabling the use of the latter in stable isotope analysis. However, as treatment procedures may vary between studies, it is advisable that similar predictive models are developed case-by-case to ensure comparability of results.
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