Biology subjects

Farine, D.

Publications and source records attributed to Farine, D..

3 recordsLinked to original sources

bioRxiv2018

Nepotism masks evidence for reciprocity in cooperation networks

Nepotism and reciprocity are not mutually exclusive explanations for cooperation, because helping decisions can depend on both kinship cues and past reciprocal help. The importance of these two factors can therefore be difficult to disentangle using observational data. We developed a resampling procedure for inferring the statistical power to detect observational evidence of nepotism and reciprocity. We first applied this procedure to simulated datasets resulting from perfect reciprocity, where the probability and duration of helping events from individual A to B equaled that from B to A. We then assessed how the probability of detecting correlational evidence of reciprocity was influenced by (1) the number of helping observations and (2) varying degrees of simultaneous nepotism. Last, we applied the same analysis to empirical data on food sharing in vampire bats and allogrooming in mandrills and Japanese macaques. We show that at smaller sample sizes, the effect of kinship was easier to detect and the relative role of kinship was overestimated compared to the effect of reciprocal help in both simulated and empirical data, even with data simulating perfect reciprocity and imperfect nepotism. We explain the causes and consequences of this difference in power for detecting the roles of kinship versus reciprocal help. To compare the relative importance of genetic and social relationships, we therefore suggest that researchers measure the relative reliability of both coefficients in the model by plotting these coefficients and their detection probability as a function of sampling effort. We provide R scripts to allow others to do this power analysis with their own datasets.

animal behavior and cognition↗

bioRxiv2017

An automated barcode tracking system for behavioural studies in birds

O_LIRecent advances in technology allow researchers to automate the measurement of animal behaviour. These methods have multiple advantages over direct observations and manual data input as they reduce bias related to human perception and fatigue, and deliver more extensive and complete data sets that enhance statistical power. One major challenge that automation can overcome is the observation of many individuals at once, enabling whole-group or whole-population tracking.\nC_LIO_LIWe provide a detailed description for implementing an automated system for tracking birds. Our system uses printed, machine-readable codes mounted on backpacks. This simple, yet robust, tagging system can be used simultaneously on multiple individuals to provide data on bird identity, position and directionality. Further, because our codes and backpacks are printed on paper, they are very lightweight.\nC_LIO_LIWe describe the implementation of this automated system on two flocks of zebra finches. We test different camera options, and describe their advantages and disadvantages. We show that our method is reliable, relatively easy to implement and monitor, and with proper handling, has proved to be safe for the birds over long periods of time. Further, we highlight how using single-board computers to control the frequency and duration of image capture makes this system affordable, flexible, and adaptable to a range of study systems.\nC_LIO_LIThe ability to automate the measurement of individual positions has the potential to significantly increase the power of both observational and experimental studies. The system can capture both detailed interactions (using video recordings) and repeated observations (e.g. once per second for the entire day) of individuals over long timescales (months or potentially years). This approach opens the door to tracking life-long relationships among individuals, while also capturing fine-scale differences in behaviour.\nC_LI

animal behavior and cognition↗

bioRxiv2017

Association Indices For Quantifying Social Relationships: How To Deal With Missing Observations Of Individuals Or Groups

Social network analysis has provided important insight into many population processes in wild animals. Constructing social networks requires quantifying the relationship between each pair of individuals in the population. Researchers often use association indices to convert observations into a measure of propensity for individuals to be seen together. At its simplest, this measure is just the probability of observing both individuals together given that one has been seen (the simple ratio index). However, this probability becomes more challenging to calculate if the detection rate for individuals is imperfect. We first evaluate the performance of existing association indices at estimating true association rates under scenarios where (i) only a proportion of all groups are observed (group location errors), (ii) not all individuals are observed despite being present (individual location errors), and (iii) a combination of the two. Commonly-used methods aimed at dealing with incomplete observations perform poorly because they are based on arbitrary observation probabilities. We then derive complete indices that can be calibrated for the different types of observation probabilities to generate accurate estimates of association rates. These are provided in an R package that readily interfaces with existing routines. We conclude that using calibration data is an important step when constructing animal social networks, and that in their absence, researchers should use a simple estimator and explicitly consider the impact of this on their findings.

animal behavior and cognition↗