Study site and subjects
This study was conducted with a population of wild geladas frequenting the Kundi plateau, in the Wof-Washa area (Ethiopia, Amhara region, N9°40.402’ E39°45.060’; altitude (min–max): 3370–3592 m). We followed the subjects from January to May 2019 and from December 2019 to February 2020, spanning the dry and the beginning of the small rainy season (for further information see Appendix S1), on a daily basis, five days per week (excluding days with heavy rain or mist), from around 9:30 to 17:00 (for a total of 94 full days and a total of 658 h). We considered that the small rainy season (cf. Yazezew et al. 2020) had started when the rain set in for three consecutive days. The late dry and early wet periods—often including the post-harvesting phase—can be key periods of nutritional need, possibly associated with crop raiding by geladas searching for crop food remains and seeds (Hirvonen et al. 2016; Dunbar 1977).
Surrounded by cliffs, the Kundi plateau (26 ha) is characterized by crop (about 12 ha) and pasture areas (about 14 ha), which have the same visibility conditions (Fig. S1). In this study, we defined “crop area” as the agriculture fields (including human settlements) and the zone within 300 linear meters from the closest house or cultivated land. This criterion allowed for cultivated land, houses, domestic animal shelters, and passage zones from crop to crop or from crop to houses to be included in the “crop area.” We defined “pasture area” as the grassland without human settlements and cultivated fields, where livestock (horses, goats, sheep, donkeys, and cows) grazed during the day, led by shepherds. During the study period, animals spent 77.083 ± 14.360 (mean ± SE) and 276.458 ± 23.500 (mean ± SE) non-consecutive minutes per day in the crop and pasture areas, respectively. Gelada groups were free to move down the cliffs from the plateau. Further information on the study is available in Appendix S1.
In the first month of the study, a subset of groups frequenting the Kundi plateau were habituated and surveyed by four to six researchers (EP, IN, MaC, AZ, CD, AG). Group size, sex ratio, age ratio, and natural markers of the central male and/or other individuals (as detailed below) were used to identify gelada groups (one-male unit; OMU/all-male unit; AMU), based on Dunbar and Dunbar (1975) criteria. This process required around one month and was facilitated by video-recording of the groups. We were able to survey 14 OMUs and two AMUs and counted 27 adult males, 79 adult females, 60 subadult individuals, 35 juveniles, and 65 infants (31 late, 21 early, 13 black; further information on the population is available in Appendix S1). The number of groups present on the plateau on a daily basis was 8.706 ± SE 0.950 (mean ± SE).
Individual discrimination was achieved for 140 subjects (excluding infants) by considering long-lasting distinctive features (including sex, size, permanent scars, deformations, and particular shapes of the red chest area in adults; Dunbar and Dunbar 1975). Such features were identified during field observations or via video recordings during and after the field data collection.
Field data collection
Each day four observers (MaC, AZ, CD, AG) went on the Kundi plateau and split into two groups to search for the gelada groups toward the top and the bottom of the plateau, respectively. The group composition of observers changed every week, following a rotation schedule. One observer (videographer) recorded the videos and the other assisted the videographer by vocally recording the ongoing activities and the subjects involved in the behavior. Not all of the identified gelada groups were present on the highland every day. Thus, on each day (after the end of the habituation period) data were collected on the visible and recognizable groups, giving priority to the less commonly observed groups when multiple groups were present to reduce observation imbalance and ensure sufficient data collection for all groups.
We conducted scan sampling (Altmann 1974) live (not on video) at 10-min intervals on the recognized, visible groups present on the plateau each day. We gathered a mean of 304.357 ± SE 43.879 scans per group covering the whole daily observation period. Multiple groups could be present in a scan. Whenever possible, we recorded for the purpose of this study (i) group identity, (ii) GPS position based on the central male position (Garmin GPS Map 64), and (iii) the percentage of individuals foraging.
Data on direct human–gelada interactions (e.g. chasing animals, throwing stones, sticks; see table S1 for a detailed description, video MPEG-1) were collected via an all-occurrences sampling method (Altmann 1974) to gather data on each possible episode.
On the recognizable groups, we also collected data via two video cameras (Panasonic HC-V180, full-HD, 50 fps, optical zoom 50x) for a total of 120 h of videos. We gathered a mean of 8.071 ± SE 1.336 video hours per group and a mean of 2.128 ± SE 0.198 video hours per subject, spreading the observational effort across morning and afternoon.
Grooming videos were collected via 10-min focal sampling (Altmann 1974), with the focal subject being selected on the basis of the criteria explained above (giving priority to visible, recognizable, and less observed subjects). If the grooming continued, the recording went on until the end of the grooming session to allow analyses on grooming duration. This rule was applied to all dyads, and extra video duration (after 10 min) was considered only to calculate grooming duration (normalized as explained in the behavioral data section). The videos including grooming lasted on average 11.502 ± SE 0.686 min and involved 22 adult males (belonging to both OMUs and AMUs), 30 adult females, 5 immature males, and 2 immature females.
Owing to the tolerant nature of the study species, aggressive encounters are known to be infrequent (Bergman 2010; Dunbar 2014). Hence, data on aggressive events were collected via all-occurrences sampling (Altmann 1974). Cameras were always kept on, on the clearly visible groups. While the videographer recorded the scene, the assistant would describe the aggressive event aloud to also gather data on what happened off-screen if necessary. At least three aggressive events per group were recorded, involving 23 adult males, 61 adult females, 29 immature males, and 10 immature females. The observed aggressions occurred to displace individuals from a foraging spot.
Health and disturbance data, and operational definitions
We calculated how frequently the OMUs + AMUs (N = 16) were present in the crop area by considering the number of scans in which each group was inside the crop area normalized over the total scans per group. The group position was assessed via GPS coordinates, referring to the alpha-males. We then separated the groups into two categories (“frequent crop users” and “infrequent crop users”), depending on whether the frequencies fell above or below the median frequency of the proportion of scans per group recorded in crops (median = 0.189; range = 0.020–0.340; Table S2) (Fig. S1).
Then, we considered the number of events of direct human disturbance (e.g. humans chasing geladas using stones, dogs, sticks, shooting; Table S1, Figure S2, video MPEG-1) for frequent and infrequent crop users, normalized over the total scans per group in each area (i.e. crop vs. pasture).
On the basis of photos and videos, the individuals (adults and immatures) were considered as bearing external signs of pathology when they showed at least one of the following external signs: abnormal swelling on trunk, limbs, and/or neck, probably related to Taenia serialis infection, as it has been found in other gelada populations (Ohsawa and Dunbar 1984; Nguyen et al. 2015; Schneider-Crease et al. 2017); and alopecia, defined as hair loss either diffuse or patchy, in areas where the loss could not be caused by infant clinging (Fig. 1). The external signs of pathologies were considered for males and two categories of females (lactating and non-lactating) due to the effect that lactation can have on the immune system (Wang 2016). Depending on the group they belonged to, individuals were assigned to either frequent or infrequent crop user groups. Descriptive statistics on the external signs of pathology are included in Appendix S1.
Behavioral data and operational definitions
We determined the daily frequency of foraging in the pasture and crop areas by considering the number of scans in which at least 10% of animals were foraging in either area normalized on the total number of daily scans per area.
Data on grooming were extracted from videos using the focal animal sampling (Altmann 1974). To calculate grooming duration, we considered a grooming session as started when one of the two individuals began cleaning the fur of the other, and as finished when grooming was interrupted for at least 10 s (Mancini and Palagi 2009). We recorded (i) groomer and grooming receiver identities, (ii) age class of both individuals (adult or immature), (iii) sex class (male or female), (iv) time spent grooming, and (v) area where grooming took place (pasture or crop). Because the observation time varied across dyads, for each dyad we divided the daily time spent grooming by the focal daily observation time of that dyad (normalized data).
The aggressive events were extracted from video- and audio-recorded information, following an all-occurrences method (Altmann 1974) on the observable groups. For each aggressive event, we recorded the following data: (i) the identity of the aggressor (individual that initiated the first agonistic pattern) and the identity of the recipient (the individual that received the first aggressive pattern), (ii) age class (adult or immature), (iii) sex class (male or female), (iv) intensity of aggression, i.e. mild (chasing or chasing attempt without contact between opponents) or strong (chasing with contact between opponents; video MPEG-2), (v) whether aggression was intra- or inter-group, and (vi) the area where the aggression took place (pasture or crop). We recorded a total of 114 aggressive events, with a minimum of three aggressive events per group. All videos were analyzed via the free software VLC 3.0.6 (©VideoLAN) by MaC and AG (Cohen’s value for inter-observer reliability calculated on 10% of the total grooming/aggressive events ≥ 0.75).
Fecal sample collection and parasitological analyses
We collected 48 fresh fecal samples (preserved in 10% formalin) from 48 unique individuals during observations and identified the samples as from individuals in the frequent or infrequent crop user group. The number of gastrointestinal parasitic elements (egg/larva/oocyst/cyst)/g of feces was determined using the FLOTAC pellet dual technique (Cringoli et al. 2010). This protocol is a multivalent, quali/quantitative copromicroscopic method for detecting parasitic elements (eggs, larvae, oocysts, and cysts) in animal fecal samples, with an analytical sensitivity of one parasitic element per gram of feces (EPG/LPG/OPG/CPG). The pellet technique is performed for samples with unknown fecal material weight, so the weight of the fecal material can be obtained after weighing the sediment in the tube (pellet) after filtration and centrifugation of the fecal sample. These steps are very important for discriminating between parasites and pseudoparasites, considering that the identification of parasites in fecal samples is often complicated by the high fiber content of the animal diet, as well as the common presence of pollen, plant tissue, flowers, and invertebrate fragments (accidentally ingested with the plants), all of which can be misclassified as parasitic structures (Alvarado-Villalobos et al. 2017).
Two different flotation solutions were used to detect the gastrointestinal parasites: FS2 (sodium chloride solution, specific gravity = 1200) and FS7 (zinc sulfate solution, specific gravity = 1350). Different magnifications were used, ×100 and ×400, respectively, for the study of egg/larvae of helminths and cysts/oocysts of protozoa.
The diagnostic technique described above does not allow the identification at the species/assemblage level, so it was not possible to measure the specific richness.
Statistical analyses
Because of the small sample size (N < 10: Nfrequent_OMU_crop_users = 8, Ninfrequent_OMU_crop_users = 5; not testable for normality), we employed a nonparametric Mann–Whitney test (SPSS 20.0) to compare the frequencies of direct human disturbance (Table S1) to primates between frequent and infrequent crop users. We included in the analyses the groups that underwent at least two disturbance events (Table S1, Fig. S2, video MPEG-1). We excluded three groups not meeting this condition. Exact values were selected following Mundry and Fischer (1998).
Owing to non-normal variable distribution (Kolmogorov–Smirnov test: Ndays = 48; P < 0.05), we used the nonparametric paired Wilcoxon signed-rank test (Siegel and Castellan 1988) to compare the daily frequency of foraging in crop and pasture areas. We applied a Monte Carlo randomization (10,000 permutations) (Bros and Cowell 1987) to account for possible data pseudoreplication (same individuals present on different days).
We ran three generalized linear mixed models (GLMM) with three different target (dependent) variables, on three different aspects: presence of external signs of pathology (GLMM1), grooming duration (GLMM2), and aggression intensity (GLMM3).
GLMM1 was run to explore what individual features could affect the presence of external signs of pathology. We included in the model the occurrence of external signs of pathology as a dependent, binomial variable (factorial; presence/absence). We included four predictors as fixed factors: age class (factorial; adult/immature, excluding infants), sex class according to the presence of infants under lactation (factorial; non-lactating females; lactating females; males), group category based on the level of frequenting the crop area (factorial; frequent and infrequent crop users), and the group size (numeric). The group identity was included as a random factor.
To compare the parasite load (number of parasitic elements/g of feces) between frequent and infrequent crop users, we applied the exact Mann–Whitney nonparametric test (Mundry and Fischer 1998; Siegel and Castellan 1988; non-normal distributions; Kolmogorov–Smirnov test: N = 48, Ancylostomatidae P = 0.001; Chilomastix spp. P < 0.001; Entamoeba histolytica/dispar P < 0.001; Endolimax nana P = 0.007; Giardia intestinalis P < 0.001). The level of probability was adjusted according to the Bonferroni correction (α = 0.010).
GLMM2 was run to test the effect of area (crop/pasture) on the daily time spent grooming by dyads. We included the following predictors (factorial fixed factors): area where grooming took place (pasture/crop), season (dry/small rainy), age class of the two subjects involved in the grooming (adult/immature), sex class (male/female), crop use frequency (frequent/infrequent crop users), and group type (OMU/AMU). The grooming dyad and the unit identity were included as random factors.
Finally, GLMM3 was run to investigate what variables could affect the intensity of aggression. Due to the small number of aggressive events involving AMU (N = 2), for this analysis we considered only aggressive events involving OMUs. The model included the intensity of aggression as a binomial, dependent variable (mild/strong). We included the following fixed factors: area where the aggression took place (pasture/crop), season (dry/small rainy), dyad age class (same/different), dyad sex class (same/different), dyad group (inter-/intra-group aggression), and crop use frequency of both aggressor and recipient (frequent/infrequent crop users). The aggressor–recipient dyad and the OMU membership of individuals were included as random factors.
We fit all three models in R (R Core Team 2018; version 3.5.1) using the function “glmer” (in the case of binomial, dependent variable) of the R package lme4 (Bates et al. 2015). We established the significance of the full model by comparison to a null model comprising only the random effects (Forstmeier and Schielzeth 2011). We used a likelihood ratio test (Dobson 2002) to test this significance (ANOVA with argument “Chisq”). We calculated the p values for the individual predictors based on likelihood ratio tests between the full and the null model using the R function “drop1” (Barr et al. 2013). For GLMM1 and GLMM3, the response variable was binary; hence we used a binomial error distribution. For GLMM2, we log10-transformed the daily proportion of time spent grooming to reach a normal distribution after verifying the distribution and homogeneity of the residuals by the visual inspection of the qqplot and plotting the residuals against the fitted values (Estienne et al. 2017). For multinomial predictors with a significant main effect, we used a multiple contrast package (multcomp) to perform all pairwise comparisons for each bonding level with the Tukey test (Bretz et al. 2010). In this case, the level of probability was adjusted according to the Bonferroni correction. The effect size was calculated via the package “effects”.