The mountain gorilla (Gorilla beringei beringei) lives in two geographically separated populations, Bwindi Impenetrable National Park, Uganda and in three national parks spanning the Virunga mountain region in Rwanda, Democratic Republic of Congo, and Uganda. The altitude, climate and plant composition of these habitats differ. Our goal was to compare the diets of gorillas living in each of these habitats. The nutrients in staple foods and in the diets of individuals in a group of gorillas in Bwindi (N = 12 individuals) and a group in the Virungas (N = 7 individuals) were compared to determine if differences in dietary composition affected concentrations of nutrients in their diets. At both sites gorilla diets consisted primarily of herbaceous leaves, but the diet of Bwindi gorillas contained more tree leaves, fruit, pith and dry wood, and fewer stems. Despite differences in habitat and dietary composition, the nutrient concentrations in both gorilla diets were remarkably similar. On a dry matter basis, the diets and staple foods of Bwindi and Virunga gorillas contained similar concentrations of crude protein (CP), fibre (NDF) and non-structural carbohydrates (TNC). Bwindi gorillas ate diets containing 18% CP, 43% NDF and 19% TNC on a dry-matter basis, while the diets of the Virunga gorillas contained 17% CP, 41% NDF and 18% TNC. Our results demonstrate that gorillas consume diets that differ by plant species and part, but contain similar concentrations of nutrients. This suggests that classifying animals by broad dietary strategy (e.g. frugivory and folivory) does not provide a reliable indicator of the nutritional quality of their diet, and that our previous assumptions about these categories should be re-evaluated.

Dietary studies are often conducted as longitudinal intervention or crossover trials using multiple days of measurement on each subject during each of several measurement periods, and determining the required numbers of days and subjects is important in designing these studies. Linear mixed statistical models were used to derive equations for precision, statistical power and sample size (number of days and number of subjects) and to obtain estimates of between-subject, period-to-period, and day-to-day variation needed to apply the equations. Two cohorts of an on-going exercise intervention study, and a crossover study of Olestra, each with 14 d of measurement/subject per period, were used to obtain estimates of variability for energy and macronutrient intake. Numerical examples illustrate how the equations for calculating the number of days or number of subjects are applied in typical situations, and sample SAS code is given. It was found that between-subject, period-to-period, and day-to-day variation all contributed significantly to the variation in energy and macronutrient intake. The ratio of period-to-period and day-to-day standard deviations controls the trade-off between the number of days and the number of subjects, and this remained relatively stable across studies and energy and macronutrient intake variables. The greatest gains in precision were seen over the first few measurement days. Greater precision and fewer required days were noted in the study (Olestra) that exerted greater control over the subjects and diets during the feeding protocol.