Design
We introduced different food types to groups of wild golden lion tamarins in their natural environment over two time periods (thereupon “phases”) to evaluate which factors would affect the transfer of food between individuals, particularly between adults and subadults (thereupon adults) and juveniles, and understand the role of transfers in future food choices. At the time of first exposure (January–February 2014), this provided the opportunity for adults to transfer food to juveniles and for juveniles to learn about the different food types, both independently, and from social interactions (first phase). This first phase was conducted during the second half of the wet season (Dietz et al. 1994). Seven months later (August–September 2014), just before the start of the wet season (Dietz et al. 1994), we assessed how previous experience with the different food types influenced juveniles’ food choice once they were independent foragers (second phase). During both phases, the experiment took place in times of food abundance.
Subjects
We studied six readily accessible groups of wild golden lion tamarins that were habituated to regular human contact and consistently monitored, in Silva Jardim municipality, Rio de Janeiro, Brazil. Three groups were at the Poço das Antas Biological Reserve, and three groups were in a fragment of Atlantic forest at the Fazenda Afetiva-Jorge, Imbaú region. At the start of the experiment, 42 individuals from those six different groups participated in the experiment, including ten juveniles between 4 and 5 months old. Each study group had one or two juveniles (golden lion tamarins often give birth to twins). This age range was chosen because juveniles are still dependent on adults for provisioning, and is in line with previous captive studies (Price and Feistner 1993; Rapaport 1999). Group AF2 lost both juveniles during the first phase of the experiment (after four valid trials), and before the start of the second phase of the experiment group Alone lost one juvenile. Thus, although the analysis regarding food-transfer patterns in the first phase of the experiment includes both juveniles of group AF2 and group Alone (N = 10) as well as all the adults present, the analysis regarding learning in the second phase of the experiment does not include the three juveniles that disappeared and was carried out on N = 7 juveniles (in five groups). The juveniles’ choices in the second phase of the experiment were assessed when the juveniles had reached an age of 11–12 months and were no longer reliant on adults for foraging. More information on the subjects and study site can be found in Table S1 of the Electronic Supplementary Materials (ESM), and in Troisi et al. (2018).
Apparatus
Limited amounts of each food type were presented in separate, clear, plastic pots that were attached to a platform or to branches at human chest level (Fig. 1). The pots were approximately 7 cm in diameter and 5.5 cm in depth.
Procedure
First phase
In the first phase, each group was exposed to five food options at the same time. These were: apple, banana, cricket, grape, and mealworm (see Table S2). Food options were arranged semi-randomly to ensure that most of the time the insect types were not adjacent to each other, and that when the trial did not occur on a platform (where the pots could be arranged in a circle) the familiar food had a fairly central place. Both insects and fruits were used to replicate the golden lion tamarins’ natural diet. Two types of food were provided: familiar and novel food. Familiar foods are food types that the golden lion tamarins in this population will have previously eaten prior to the start of this experiment, while novel foods are food types that the golden lion tamarins in this population have not eaten previously to the start of the experiment. Banana was a familiar food for all golden lion tamarins, while the other fruit options were novel. The novel foods were chosen based on the food used in captive studies with Callitrichids (Brown et al. 2005; Rapaport 1998; Vitale and Queyras 1997; Voelkl et al. 2006). The fruits were cut into small pieces (< 2 cm), to fill the pots, and insects were small enough so that several insects could fill the pots. Individuals had no access to those novel foods outside of the experimental context. Despite using food types regularly used in captivity, the dehydrated insects were rarely eaten in our experiment, and were classified as novel foods. We provide more information about our choices of food in the ESM.
Each trial was conducted on a different day (Table S3). Groups were tested on their own, but trials were considered invalid if no juveniles were present, or if individuals were present on the foraging platform for less than 80 s in total. Trials were repeated until five valid trials had been completed per group so that each group would have approximately the same opportunities, and all trials (valid and invalid) were filmed and used for later analysis. Trials continued until all individuals had left (average length of trial for both phases: 11 min 10 s, standard deviation: 9 min 54 s). The dates of all valid trials can be found in Table S3 of the ESM.
Second phase
For the second phase, five trials were conducted for each group deploying the same criteria as in the first phase. This time, two new novel foods were added to the experiment (papaya and pear) bringing the total food options to seven. We added two food types the juveniles had no prior experience with to allow us to test for an effect of individual experience on foraging choice in the second phase. In group AF3, for one of the valid trials, the camera was covered with dew, so we were unable to extract from the video recording most of the data for that trial. We therefore conducted an extra trial for that group and included all trials in the analysis. The dates of all valid trials can be found in Table S3 of the ESM.
Video analysis
We extracted data from videos using the software package VideoLAN Client (VLC). We recorded behavior patterns (Table 1) as states in Microsoft Excel but treated them as discrete events in the analysis. Ten percent of the data were double-coded and the inter-observer reliability was found to be high (r = 0.95, p < 2.2e−16).
Table 1 Definitions of the dependent variables used for the analysis looking at the patterns of food transfers and their consequences on juvenile golden lion tamarins’ foraging choices There is a wide range of food transfer types that have been recognized in callitrichids, from a donor actively sharing food, passively sharing it, food being eaten out of the hand of the donor or food being stolen (Feistner and Price 1990; Hoage 1982; Rapaport 1998). Previous studies have also distinguished different types of food transfers but analyzed them together. Because of the rarity of food transfers where the donor actively transferred food to the receiver in our dataset, we first describe findings with those active “giving” transfers before statistically analyzing all types of food transfers and looking at more subtle behavioral cues such as juveniles’ attempts and adults’ resistance to transfers.
Statistical analysis
We carried out all analysis using R version 3.6.1 (R Core Team 2019). In order to determine the relative importance of the predictor variables in each model, we used an information-theoretic approach with model averaging as described in Grueber et al. (2011) using the dredge function from the MuMIn package (Barton 2019). We calculated the relative degree of support for each variable using the Akaike Information Criterion corrected for small sample sizes (AICc) (Burnham and Anderson 2002). The Results section reports the model-averaged parameter estimates, their unconditional standard errors (incorporating model selection uncertainty), and their 95% confidence intervals. We also report the corresponding back-transformed effect on odds and their 95% confidence intervals (Galipaud et al. 2014). See the ESM section “4. Statistical analysis: model averaging methods” for more details on model averaging procedures.
First criterion: modification of behavior
Probability of success of a food transfer
To analyze the probability of success in a food transfer, we specified a global model using a generalized linear mixed model (GLMM) with a binomial error structure using the lme4 package (Bates et al. 2015). We included both receiver and donor individual as random effects. See the ESM section “5. Statistical analysis: treatment of the random effects” for more details on the random effects in our models. We tested the dataset to ensure that the assumptions were not violated. We checked for overdispersion using the dispersion_glmer function in the blmeco package (Korner-Nievergelt et al. 2015).
Four main explanatory variables were used. The first three variables were dependent on the food option, F, involved in a given food transfer. We were first and foremost interested in whether the type of food (novel or familiar) would impact the probability of success, and thus looked at the effect of food familiarity, defined as whether F was familiar (banana), or not, to the tamarins prior to the experiment’s start (binary variable). We were also interested in whether individuals updated their knowledge on the food types during the course of the experiment. Accordingly, we included an option-specific success variable for both the receiver and donor individuals, where ‘option’ refers to the different food options available to the golden lion tamarins. Option-specific success calculates the number of each food item previously ingested at any given time for any given individual. Donor option-specific success was the amount of F (number of food items) the donor individual had consumed during the experiment prior to the food transfer in question, whereas receiver option-specific success was the equivalent variable for the potential receiver. These variables were included to test whether there was a possible familiarization with the food items as the experiment went on. We also included variables giving characteristics of individuals: donor age and receiver age were binary variables representing whether the donor and potential receiver respectively were a juvenile or not, and, donor sex and receiver sex gave the sex of each individual involved in the food transfer.
We then refit the set of models replacing the continuous variables donor option-specific success and receiver option-specific success with corresponding binary variables, indicating whether donor option-specific success > 0 and whether receiver option-specific success > 0. This was to allow for the possibility (suggested by data exploration) that consuming a single food item of type F may be sufficient for the food to become familiar to a tamarin, or that individuals are neophobic and might require at least some experience with the experimental setup before adopting their usual behavior.
We want to highlight that food familiarity was determined before the experiment, and therefore novel foods (apples and grapes) were considered novel to all, while familiar foods (bananas) were considered familiar to all. Option-specific success on the other hand relates to the number of ingestions of a particular food type to each individual, which changes throughout the experiment, and is therefore dependent on each individual’s experience.
Probability of attempting a food transfer
We then examined whether the patterns of food transfers observed were mainly due to the receivers, and particularly whether wild juveniles attempted to obtain more novel food than familiar food. To analyze the probability of juveniles attempting a food transfer from adults, we used a GLMM with a binomial error structure using the lme4 package (Bates et al. 2015).
For each combination of potential receiver, potential donor, food option, and receiver option-specific success, we calculated the number of opportunities for attempting a food transfer, defined as an event in which a potential donor was ingesting a food item and the potential receiver was present at the time of the event. We then calculated the number of these events in which a food transfer was attempted to obtain the dependent variable for the analysis. There were no opportunities of food transfers between adults and juveniles for mealworms, hence its absence as a food type in this analysis. There were also only five opportunities for crickets, and no food transfers, so we excluded them from the analysis. In this analysis, we included receiver option-specific success as a binary variable, since we found no effect of receiver option-specific success as a continuous variable in the previous analysis. Similarly, we included the variable of food option rather than food familiarity, as we found no effect of food familiarity as a binary variable in the previous analysis. Random effects were included as above (potential receiver and potential donor individual).
Probability of resistance (during a transfer)
We then examined the involvement of the donor in determining the probability that a food transfer would be successful. As a proxy of the donor’s preference for keeping versus giving up food items, we used resistance during a food transfer. To analyze the probability of resistance, we specified a global model using a GLMM with a binomial error structure using the lme4 package (Bates et al. 2015). Similar to the previous analysis, data were restricted to transfers in which potential receivers were juveniles and potential donors were adults. For three food transfers, the presence of resistance was unknown, so we excluded those cases from the analysis. The presence of resistance in a transfer was modeled as a function of food option, previous receiver, and donor option-specific success (as binary) and sex of both the donor and receiver. Random effects were included as above (receiver and donor individual).
Third criterion: learning
The final aspect of teaching behavior that we wanted to explore in a food transfer context was whether or not juveniles learn about the transferred food as a result of the adult’s modified behavior (third criterion of teaching definition). We modeled juveniles’ food choices in the second phase of the experiment (when they were independent foragers ~ 11 months old) as a function of their prior social and asocial experience (during the first phase, when juveniles were ~ 4 months old). The dependent variable was the number of times each food item was ingested independently (not eating a food after obtaining it from a transfer), by each juvenile, for each food type, during the second phase of the experiment. The independent variables were the number of times during the first phase of the experiment where each food type had been eaten following a food transfer (social eating), which was the main factor of interest, as well as the number of times each food type have been eaten independently (individual eating) and exploration. Individual was included as a random effect.
For two food types (papaya and pear), there was no previous experience, and for two other food types (cricket and mealworms), there was little previous experience, leading to the possibility of zero-inflated data. We therefore compared candidate models with different error structure, and with and without accounting for zero-inflation, based on their overdispersion parameter, and their AIC (see Table S4 in the ESM, for AICs and overdispersion parameters of candidate models, and Figure S1 for the parameter estimates of each model). Each model was fitted using the glmmadmb function in the glmmADMB package (Fournier et al. 2012; Skaug et al. 2016). The best global model that showed no overdispersion was a negative binomial zero-inflated model (family = “nbinom”, log link) and was used for further analysis.
Data availability
The datasets analyzed during the current study and the R Code used to analyze them are available on the Open Science Framework repository: https://osf.io/cpkvy/ (DOI: https://doi.org/10.17605/OSF.IO/CPKVY).