Social dynamics of vervet monkeys are dependent upon group identity

Traditions are widespread across the animal realm. Here, we investigated inter-group variability of social dynamics in wild vervet monkeys (Chlorocebus pygerythrus). We analyzed 84,704 social interactions involving 247 individuals collected over nine years in three neighboring groups of wild vervet monkeys. We found that in one group - Ankhase - individuals had a higher propensity to be affiliative (i.e., sociality) and grooming interactions were more reciprocal. Despite yearly fluctuations in sociality, differences between groups remained stable over time. Moreover, our statistical model predictions confirmed that these findings were maintained for similar sex ratios, age distributions, and group sizes. Strikingly, our results suggested that dispersing males adapted their sociality to the sociality of the group they integrated with. As a whole, our study sheds light on the existence of stable social dynamics dependent upon group identity in wild vervet monkeys and suggests that at least part of this variability is socially mediated.

Activity budget and seasonal activity shifts in sympatric lemurs: Increased lean season effort in a cathemeral frugivore contrasts with energy conservation in a diurnal folivore

One of the most fundamental aspects of a species' behavioral strategy is its activity budget; for primates this generally involves the allocation of available time among resting, feeding, traveling, and social behavior. Comparisons between species, populations, or individuals can reveal divergences in adaptive strategies and current stressors, and reflect responses to such diverse pressures as predation, thermoregulation, nutrition, and social needs. Further, variation across seasons is an important part of behavioral strategies to survive food scarcity; this can involve increasing or decreasing effort. We documented activity over the 24-h cycle for the cathemeral, frugivorous Eulemur fulvus and the diurnal, folivorous Propithecus diadema across 13-18 months at Tsinjoarivo, Madagascar. Their activity budgets were dominated by resting (E. fulvus: 74.1%; P. diadema: 85.2%), followed by feeding (15.8%, 12.4%), traveling (9.31%, 1.74%) and social activities (0.76%, 0.70%), respectively. The lower feeding and higher resting in P. diadema likely reflect slower gastrointestinal transit and higher reliance on microbial fermentation to extract energy from fibrous food. The two species showed opposite lean season strategies. E. fulvus increased activity, with more feeding but less travel time, consistent with a shift to less-profitable fruits, and some leaves and flowers, while increasing feeding effort to compensate ("energy maximizing"). P. diadema showed less variation across months, but the lean season still evoked reduced effort across the board (feeding, travel, and social behavior), consistent with a "time minimizing" strategy prioritizing energy conservation and microbe-assisted digestion. Understanding these divergent shifts is key to understanding natural behavior and the extent of behavioral flexibility under stressful conditions. Finally, the complex patterns of fruit availability (intra- and interannually) and the species' behavioral responses across months underscore the need to move beyond simplistic "lean/abundant season" and "fruit/leaf" dichotomies in understanding underlying energetic strategies, and species' vulnerability to habitat change.

Quantifying allo-grooming in wild chacma baboons (Papio ursinus) using tri-axial acceleration data and machine learning

Quantification of activity budgets is pivotal for understanding how animals respond to changes in their environment. Social grooming is a key activity that underpins various social processes with consequences for health and fitness. Traditional methods use direct (focal) observations to calculate grooming rates, providing systematic but sparse data. Accelerometers, in contrast, can quantify activity budgets continuously but have not been used to quantify social grooming. We test whether grooming can be accurately identified using machine learning (random forest model) trained on labelled acceleration data from wild chacma baboons (Papio ursinus). We successfully identified giving and receiving grooming with high precision (81% and 91%) and recall (87% and 79%). Giving grooming was associated with a distinct rhythmical signal along the surge axis. Receiving grooming had similar acceleration signals to resting, and thus was more difficult to assign. We applied our machine learning model to n = 680 collar data days from n = 12 baboons and found that grooming rates obtained from accelerometers were significantly and positively correlated with direct observation rates for giving but not receiving grooming. The ability to collect continuous grooming data in wild populations will allow researchers to re-examine and expand upon long-standing questions regarding the formation and function of grooming bonds.

Personality as a Predictor of Time-Activity Budget in Lion-Tailed Macaques (Macaca silenus)

Simple Summary

Time-activity budgets describe how animals divide their day into various behaviours and activities, e.g., time spent foraging or resting. Activity budgets can serve as crucial indicators of energy intake and expenditure, providing better knowledge of a species’ lifestyle. The conventional trend has been to explore group-level time-activity budgets; however, individuals may also vary in their time-activity budgets (e.g., one individual foraging more than another), with the influencing mechanisms still poorly understood. We propose that animal personality, a behavioural and cognitive profile that makes one individual different from another, may explain why individuals vary in their time-activity budgets. We used a multi-method approach comprised of behavioural observations and experiments to assess the personality traits of lion-tailed macaques. The observed traits were used to predict individual time-activity budgets, broadly categorised into food-related, active, and resting behaviours. We then discuss the significance of this novel approach in light of lion-tailed macaque ecology, conservation, and welfare.

Abstract

Time-activity budget, i.e., how a population or an individual divides their day into various behaviours and activities, is an important ecological aspect. Existing research primarily focused on group-level time-activity budgets, while individual variations have only been reported recently. However, little is known about how consistent inter-individual differences or personalities influence time-activity budgets. We examined the personalities of lion-tailed macaques (Macaca silenus) and investigated their influence on individual time-activity budgets. The resulting personality traits, namely persistence, sociability, affiliation, and anxiety, were used to predict the three broad categories of the time-activity budget—food-related, active, and resting behaviours. We found that persistence and sociability positively predicted the time spent being active. Food-related behaviours were positively predicted by persistence, while anxiety was found to influence them negatively. The time spent resting was negatively predicted by persistence. We did not find an effect of affiliation on the time-activity budgets. We discuss these findings in light of the ecology of lion-tailed macaques. Our study highlights the importance of a novel approach that uses animal personality traits as predictors of individual time-activity budgets and offers insights regarding the use of personality assessments in conservation and welfare activities.

The consistency of individual centrality across time and networks in wild vervet monkeys

Previous primate social network studies largely limited their focus to grooming and/or aggression networks, particularly among adult females. In addition, the consistency of individuals' network centrality across time and/or different networks has received little attention, despite this being critical for a global understanding of dynamic social structure. Here, we analyzed the grooming, aggression, and play social networks of a group of 26-28 wild vervet monkeys (Chlorocebus pygerythrus), including adults and juveniles, over two periods of 6 months. We collected data on grooming, play, and aggression using focal animal sampling with instantaneous recording and ad libitum sampling. We examined whether individuals' network centralities were consistent over the two periods and across networks, as well as the effect of age, sex, and dominance rank on three individual centrality metrics in each network and within each study period. We found that individuals were quite consistent in their network position from 1 year to the next despite changes in group composition. However, their network centralities were not correlated across networks, except for Strength and weighted Eigenvector centrality between grooming and aggression networks. We also found that in the aggression network, high-rankers showed the highest centrality in most network metrics (e.g., Degree, Strength, and Eigenvector centrality) and compared to males, females were most central in 2017 but not in 2018. In the grooming network, high-ranking females had the highest Eigenvector centrality, whereas in the play network, juvenile males had the highest Eigenvector centrality. Our findings corroborate previous findings on vervet monkeys. In addition, they show that individuals' network centralities may vary among networks and over time; thus highlighting the effect of sociodemographics and behaviors' functions on the group level dynamics of social behavior.

Field data confirm the ability of a biophysical model to predict wild primate body temperature

In the face of climate change there is an urgent need to understand how animal performance is affected by environmental conditions. Biophysical models that use principles of heat and mass transfer can be used to explore how an animal's morphology, physiology, and behavior interact with its environment in terms of energy, mass and water balances to affect fitness and performance. We used Niche Mapper™ (NM) to build a vervet monkey (Chlorocebus pygerythrus) biophysical model and tested the model's ability to predict core body temperature (T_b) variation and thermal stress against T_b and behavioral data collected from wild vervets in South Africa. The mean observed T_b in both males and females was within 0.5 °C of NM's predicted T_bs for 91% of hours over the five-year study period. This is the first time that NM's T_b predictions have been validated against field data from a wild endotherm. Overall, these results provide confidence that NM can accurately predict thermal stress and can be used to provide insight into the thermoregulatory consequences of morphological (e.g., body size, shape, fur depth), physiological (e.g. T_b plasticity) and behavioral (e.g., huddling, resting, shade seeking) adaptations. Such an approach allows users to test hypotheses about how animals adapt to thermoregulatory challenges and make informed predictions about potential responses to environmental change such as climate change or habitat conversion. Importantly, NM's animal submodel is a general model that can be adapted to other species, requiring only basic information on an animal's morphology, physiology and behavior.