Social network centrality predicts dietary decisions in wild great tits
Cite this dataset
Mc Mahon, Keith (2021). Social network centrality predicts dietary decisions in wild great tits [Dataset]. Dryad. https://doi.org/10.5061/dryad.3tx95x6fw
Foraging in groups provides many benefits but also carries costs, such as competition. Social individuals can potentially alleviate competition by broadening their dietary niches through incorporating new foods. However, individuals have less information about the nutritional quality, and safety, of novel foods compared to familiar-foods. Individuals experiencing the most competitive social environments might be expected to be most likely to respond by incorporating novel foods, but it has previously been challenging to test directly how sociality relates to dietary decisions in natural populations. Here, we present RFID-tracked wild great tits (Parus major) with novel food, and use social network analysis to test how sociality predicts individuals’ foraging choices. We show that socially-central individuals with more social links have a higher propensity to use novel food compared to socially-peripheral individuals, and that this relationship is unrelated to aversion to novel feeders, number of feeding observations, and demographic factors. We demonstrate how our findings indicate sociable individuals can offset the costs of highly competitive social environments by foraging more broadly. Finally, we discuss how competition may drive behavioural change in natural populations, and the implications for understanding the causes and consequences of social strategies and dietary decisions.
ReadMe Data Description For Sociality and Diet Objects (note this description is also included in the .RData file) contains 2 Primary objects:
Object 1 = 'inds.data': This is general information on all the individuals take took place in the groups, along with info on their foraging activities, and their social network positions in all 3 aspects of the network. The following columns need some further description. recs.tot is total records, recs.pre is records before exp began, recs.exp is records over the experimental period, recs.novel1.out is number of records when the first novel feeder was out, recs.novel2.out is same but when second novel feeder was out (note that this '1' and '2' theme carries on with the columns), recs.exp.novelfood.1 is the number of recs ON THE FIRST EXPERIMENTAL FEEDER (i.e. recs on the novel food feeder) when the first novel feeder was out, recs.exp.novelfood.2 is the number of recs ON THE SECOND EXPERIMENTAL FEEDER (i.e. recs on the novel food feeder) when the second novel feeder was out, the next set of columns give info about the number of days the bird visited the feeder at the different time points etc etc, site.exppart.ninds is the number of individuals present in this local network, site.exppart.ninds is the number of flocking events this local network was derived from, locations is the locations this bird was recorded at during this network , the rest of the columns are metrics for the individual within that local network such as ngroups is the nubmer of groups the ind was observed in for this local network, avggroup is their average group size, deg is their unweighted network degree, wdeg is their weighted network degree (or 'strength'), avedge is the average strength of their non-zero bonds within the network, wevc is their eigenvector centrality.
Object 2 = 'recs.data': This is raw data in terms of the datastream from the RFID detections of great tits at the feeders, the columns are described in the column headings.
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