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Haematological and biochemical reference intervals for wild green turtles (Chelonia mydas): a Bayesian approach for small sample sizes.
Kophamel, Sara; Rudd, Donna; Ward, Leigh C; Shum, Edith; Ariel, Ellen; Mendez, Diana; Starling, Jemma; Mellers, Renee; Burchell, Richard K; Munns, Suzanne L.
Affiliation
  • Kophamel S; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, 4811, Australia.
  • Rudd D; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, 4811, Australia.
  • Ward LC; School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, 4072, Australia.
  • Shum E; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, 4811, Australia.
  • Ariel E; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, 4811, Australia.
  • Mendez D; Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland, 4811, Australia.
  • Starling J; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, 4811, Australia.
  • Mellers R; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, 4811, Australia.
  • Burchell RK; North Coast Veterinary Specialist and Referral Centre, Sunshine Coast, Queensland, 4556, Australia.
  • Munns SL; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, 4811, Australia.
Conserv Physiol ; 10(1): coac043, 2022.
Article in En | MEDLINE | ID: mdl-36937701
ABSTRACT
Animal health is directly linked to population viability, which may be impacted by anthropogenic disturbances and diseases. Reference intervals (RIs) for haematology and blood biochemistry are essential tools for the assessment of animal health. However, establishing and interpreting robust RIs for threatened species is often challenged by small sample sizes. Bayesian predictive modelling is well suited to sample size limitations, accounting for individual variation and interactions between influencing variables. We aimed to derive baseline RIs for green turtles (Chelonia mydas) across two foraging aggregations in North Queensland, Australia, using Bayesian generalized linear mixed-effects models (n = 97). The predicted RIs were contained within previously published values and had narrower credible intervals. Most analytes did not vary significantly with foraging ground (76%, 22/29), body mass (86%, 25/29) or curved carapace length (83%, 24/29). Length and body mass effects were found for eosinophils, heterophillymphocyte ratio, alkaline phosphatase, aspartate transaminase and urea. Significant differences between foraging grounds were found for albumin, cholesterol, potassium, total protein, triglycerides, uric acid and calciumphosphorus ratio. We provide derived RIs for foraging green turtles, which will be helpful in future population health assessments and conservation efforts. Future RI studies on threatened species would benefit from adapting established veterinary and biomedical standards.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Conserv Physiol Year: 2022 Document type: Article Affiliation country: Australia

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Conserv Physiol Year: 2022 Document type: Article Affiliation country: Australia