3.1 Quantification of specialization metrics
Over the course of four non-sequential years, at five different locations, we quantified the diet of 1,520 scat samples. We successfully determined the sex of the depositor for 1,145 of those scats (75% success rate) (Voelker et al. 2018). The number of scat with successful sex determination varied by location and month (Table 1). Samples with successful sex determination were then binned by the factors Sex, Location, Month, and Year to form unique groups for analysis (Table S1). After eliminating samples without sex determination and with small sample sizes (< 5 samples), we were left with 1,083 samples in 89 groups. Only these 1,083 samples were used in further analyses.\(\text{PS}_{i}\) was calculated relative to the samples in a specific group, and the average \(\text{PS}_{i}\) across all samples and groups was 0.399 (95% CI = 0.026, R = 100,000). The \(\text{PS}_{i}\) values of the 1,083 samples were not normally distributed (kurtosis = 2.66, skewness = 0.65, Figure S2). Therefore, a logit transformation was used to adjust the variance distribution (kurtosis = 5.21, skewness = 1.01, Figure S2). These transformed \(\text{PS}_{i}\) values were used to run the GLMMs. Additionally, the range of theoretical minima across the 89 groups was 0.027 – 0.2 (average = 0.103, median = .091); there was also a correlation between average \(\text{PS}_{i}\) and theoretical minimum\(\text{PS}_{i}\) (rho = -0.231, p = 0.03). This potential bias is addressed in the discussion.