The 85th Annual Meeting of the American Association of Physical Anthropologists (2016)

Longitudinal body mass variation in wild primate populations: are individuals or populations more variable?


Evolutionary Anthropology, Duke University

April 16, 2016 , Atrium Ballroom A/B Add to calendar

Since body mass covaries with many ecological aspects of a species, its estimation is a frequent objective for paleontologists. Body mass is frequently predicted using linear regressions of observed predictor variables and body masses of extant species. Individuals with associated body masses are strongly preferred for reference samples. However, body mass can fluctuate dramatically over an individual’s adult lifetime. If individuals are relatively more variable than populations, associated body masses may decrease the reliability of body mass prediction equations.

To compare individual longitudinal body mass variation to cross-sectional variation within a population, we calculated coefficients of variation (CV=standard deviation/mean*100) for 18 adult Alouatta palliata from La Pacifica, Costa Rica and 29 adult Pan troglodytes schweinfurthii from Gombe National Park, Tanzania. All individuals had at least eight recorded body masses. Separate male and female cross-sectional CVs were calculated by randomly sampling an individual’s body mass during a calendar year.

Longitudinal CVs for Alouatta individuals ranged from 5.0 to 12.0 (mean=8.5, sd=2.1). Longitudinal CVs for Pan individuals ranged from 4.2 to 17.1 (mean=7.5, sd=2.7). No significant differences were found between species, but females were significantly more variable than males (whether or not measurements taken during pregnancy were included). Longitudinal CVs were negatively correlated with the number of observations, but were not correlated with the temporal range. Cross-sectional CVs ranged from 7.9 to 19.1 (mean=13.0, sd=3.4), and were significantly greater than individual CVs. These results indicate that using associated masses for reference samples should improve the reliability of body mass prediction equations.

This research was supported by NSF BCS 1540421 to GSY and DMB.