Data Collection
Core unit associations were recorded during behavioural follows
conducted over 21 months between August 28th, 2017 and
May 13th, 2019 (243 days) by two trained field
assistants (E. Mujjuzi and H. Kakeeto). From August
28th, 2017 to Aug. 22nd, 2018, 12
core units were sampled and from Aug. 29th, 2018 to
May 13th, 2019, 13 units were sampled because it
became obvious that an all-male unit (AMU) had formed by the splitting
off of seven adult males from the largest core unit (Lovoa), which
subsequently became an OMU that was markedly less cohesive than the
other units. We deemed 21 continuous months of data as sufficient to
answer our questions because four rainy seasons were covered and clans
had sufficient time to change in core unit membership. One focal unit
was followed each day between 8:00 h -16:00 h and all individuals were
identified based on physical characteristics (e.g., broken fingers, tail
shape, nipple colouration). Scan samples on core unit association were
taken every two-hours, where the number and identity of core units
within a 50 m radius of the focal core unit was recorded along with the
time and date (overall N = 907 scans). We chose a two-hour
interval between scans to help ensure their independence. We reasoned
that two hours was enough time for core units to shift their position
relative to one another (Stead & Teichroeb, 2019). Our data collection
regime led to a relatively even distribution of focal days among core
units during the study (mean N days/unit = 20.17, range: 14-25;
mean N scans/unit =75.25, range: 56-90). Dispersals of
individuals within the study band were recorded on notice of occurrence
and a date range during which the dispersal occurred was generated based
on the last time an individual was noted in their original core unit.
The month of dispersal was determined to be the month with the most
potential dates within that range.
To examine the seasonality of association patterns, we considered three
ecological variables: rainfall, the availability of young leaves, and
the availability of fruits. Rainfall data (mm per month) was obtained
from
https://www.worldweatheronline.com/masaka-weather-history/masaka/ug.aspx
for the nearby town of Masaka (12.5 km away). We considered the
availability of young leaves and fruits as these food items comprise the
majority of the C. a. ruwenzorii diet at this field site (96%,
JAT, unpubl. data). Food availability indices were calculated for each
of these plant parts, for each month of the study period. We used a
line-transect survey to estimate tree species abundance (i.e., number of
trees and their basal area) within the home range of the C. a.
ruwenzorii band. Thirty-two parallel transects set 100 m apart were cut
throughout a 140 ha section of the forest and all trees >10
cm DBH within 5 m of either side of the transect were identified and
measured (covering 9.702 ha) (Teichroeb et al., 2019). The seasonal
availability of these plant parts was estimated using monthly phenology
surveys of 126 trees of 44 species that were known to be consumed byC. a. ruwenzorii. During phenology surveys, observers indexed the
percent canopy cover of mature vs. young leaves, ripe and unripe fruit,
ripe and unripe seed pods, and buds vs. flowers in at least three sample
trees of each species. We calculated the food availability index for
both young leaves and fruits separately by multiplying the mean monthly
phenology score for each plant part in each of the 44 species by the
total basal area of that species, and summing these values for all the
tree species consumed (Dasilva, 1994; Fashing, 2001; Saj & Sicotte,
2007).