Results
Model output data were never normally distributed (Shapiro-Wilk –
Diversity: W = 0.928, P < 0.001; Evenness:W = 0.781, P < 0.001) nor homoscedastic
(Bartlett – Diversity: K2 = 105.69, df = 8,P < 0.001; Evenness: K2 =
651-64, df = 8, P < 0.001); thus nonparametric tests
determined the effect of diet breadth on Shannon Diversity and Evenness.
Shannon Diversity increased with diet breadth (Kruskal-Wallis:X2 = 974.05, df = 8, P <
0.001; Figure 1a) but Shannon Evenness was similar for every diet,
regardless of number of food types (Kruskal-Wallis:X2 = 13.99, df = 8, P = 0.08; Figure
1b). One interesting observation was that variation in values of
Evenness decreased with increasing diet breadth. Variability (measured
as the coefficient of variation) decreased with increasing diet breadth
(linear regression: F1,7 = 13.72, P =
0.008, r2 = 0.614). However, the relationship
between variability in J ’ and diet breadth is non-linear: a
reciprocal function provides a better fit between expected and observed
values (F1,7 = 118.8, P < 0.001,r2 = 0.936). Another interesting observation
from the null model analysis is that the value of J ’ under the
assumption of equal preferences is constant (0.434294) regardless of the
number of food types. Thus, the results can be more clearly visualized
by using the standardized value (observed value/equal preferences
value).