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Comparison of energy balance between two different-sized groups of Japanese macaques (Macaca fuscata yakui).

Yosuke KuriharaGoro Hanya
Published in: Primates; journal of primatology (2017)
Quantifying the energy balance is essential for testing socio-ecological models. To reveal costs and benefits of group living in Japanese macaques from the perspective of feeding competition, Kurihara and Hanya (Am J Primatol 77:986-1000, 2015) previously compared feeding behavior between two different-sized groups of macaques (larger group 30-35 individuals; smaller group 13-15 individuals) in the coastal forest of Yakushima, Japan. The results suggested that the larger group exhibited greater feeding effort because of intragroup scramble competition and that the smaller group suffered from higher travel costs, possibly owing to intergroup contest competition. However, it remained unclear whether the behavioral differences affected their energy budgets. The present study examined energetic consequences of the different feeding behaviors in the two groups. Using behavioral data from 10 to 13 adult females and nutritional composition of food items, we compared ingestion rates, energetic/nutritional content of diet, and energy budgets between the two groups. Ingestion rates and energetic/nutritional content of diet did not differ between the two groups. Despite the higher feeding effort of the larger group, energy intake did not differ between the two groups. Energy expenditure did not differ between the two groups because higher travel costs were negated by lower feeding effort in the smaller group. Consequently, the energy balance did not differ between the two groups. We demonstrated that the behavioral measures of feeding competition were not translated into their energetic condition; moreover, our findings re-emphasize the importance of quantifying behavioral and fitness measures for interpreting variation in feeding behavior properly.
Keyphrases
  • climate change
  • gene expression
  • machine learning
  • electronic health record
  • deep learning
  • big data
  • dna methylation
  • artificial intelligence
  • weight gain
  • clinical evaluation
  • water quality