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Niche Differentiation Data

Cite this dataset

Webb, Shasta; Melin, Amanda; Williamson, Rachel (2021). Niche Differentiation Data [Dataset]. Dryad.


Understanding variation in social grouping patterns among animal taxa is an enduring goal of ethologists, who seek to evaluate the selective pressures shaping the evolution of sociality. Cohesive association with conspecifics increases intragroup feeding competition and is posited as an important pressure affecting grouping patterns. Furthermore, in sexually dimorphic species, males and females may have different nutritional requirements, which may lead to suboptimal foraging in mixed-sex groups. How do animals living in permanent social groups mitigate these foraging costs? Niche differentiation is often hypothesized as a mechanism, but rigorous and detailed tests of the extent and context of differences in diet and habitat use, key tenets of this hypothesis, are rare. We investigated the potential for niche differentiation in foraging activity budget and environment use in a population of wild white-faced capuchin monkeys ( Cebus imitator ) in northwestern Costa Rica. Using a robust dataset of 15 879 foraging scan samples collected from 4 groups over 13 months, we found that smaller individuals - e.g. juveniles and females - forage more often on smaller branches. We additionally found clear evidence of predator sensitive foraging wherein the smallest individuals spend less time on the ground during invertebrate foraging. Importantly, niche differentiation was far more evident overall during invertebrate foraging, likely due to spatial constraints and environmental homogeneity imposed by fruit patches. In sum, we found considerable variation in habitat use across age and sex classes, likely attributable to differences in size and relative predation risk. These variables likely reduce intraspecific feeding competition by promoting differential diet and habitat use. Our results also provide insight into the limits of niche differentiation as a strategy for competition reduction and may shed light on the evolution of fission- fusion dynamics in highly frugivorous species.


We collected behavioural data from April 2016 – September 2016 and from January 2017 – July 2017. The study population comprised 113 individually identifiable white-faced capuchin monkeys from 4 social groups, which ranged in size from 15-30 individuals in 2016 and from 18-33 individuals in 2017. We observed each group during at least one “rotation” per month, where rotations comprised 2-5 consecutive observation days from dawn (04:45-05:30) until dusk (18:00-19:00). We recorded behavioural data using Samsung Galaxy Tab 4 tablets loaded with PrimateLogger (developed by Scott Johnson for Android devices), which recorded a date/time stamp and UTM coordinates for each line of text entered. During group follows, we conducted instantaneous scans (Altmann, 1974) every 30 minutes on each hour and half hour mark (e.g. 05:00, 05:30, 06:00, etc.). We identified as many individual monkeys as possible within 10 minutes and recorded their behavioural states (see supplementary material). As each individual and its behavioural state was identified, we also recorded its relative height in the canopy, the support substrate type, and the support substrate angle it was using at the moment the individual was first observed. During fruit foraging we recorded the species of fruit; during invertebrate foraging we recorded whether the behaviour was visual searching/gleaning or extractive in nature. To minimize biasing data collection in favor of central individuals, we sought to begin scans with a different monkey each time and moved around the group. Daily maximum temperature (degrees Celsius) is recorded daily as this site using a Kestrel thermometer. Daily rainfall (cm) is recorded with a cylindrical rain gauge. Monthly fruit biomass (kg/ha) is estimated based on monthly phenological survey. 

Usage notes

FruitDifficultyScansXDays_Dryad.csv ; CSV file in which rows are all scans per animal per day; file includes fruit difficulty variable

IndividHabitatFruitScansXDays_Dryad.csv ; CSV file in which rows are all fruit scans per animal per day

IndividHabitatInvertebrateScansXD_Days_Dryad.csv ; CSV file in which rows are all invertebrate scans per animal per day

IndividScanStatesXDays_Dryad.csv ; CSV file in which rows are all scans per animal per day

MonthlyFruitBiomass_Dryad.csv ; CSV file in which rows are esimated fruit biomass (kg/ha) per month

RotationsREW_Dryad.csv ; CSV file in which rows are IDs of each data collection rotation

rotationDate16_17_Dryad.csv ; CSV file in which rows are IDs of each data collection rotation