DOI: 10.5061/dryad.ttdz08m59
title: Data for: Genetic relatedness shapes social dynamics in a threatened finch: Implications for population assessment

description: Tropical granivorous finches often form large flocks around resources. The
 composition of these flocks, whether they are random groups of individuals
 or comprise related birds travelling together, is currently unknown.
 Understanding this distinction would aid in assessing the accuracy of
 population counts. To bridge this knowledge gap, we combined
 high-frequency location tracking with comprehensive genetic sequencing to
 investigate the relationship between pairwise association strength and
 genetic relatedness in Gouldian finches (Erythrura gouldiae). Our study
 revealed that birds captured near each other were more inclined to travel
 together, and their relatedness was significantly linked to the strength
 of their association. These findings suggest that within-flock
 associations are influenced by genetic relatedness, contributing to the
 stability of the flock size. We propose that juvenile kin associations
 play a pivotal role in this dynamic, potentially enhancing survival rates
 by forming sibling subgroups. The consistent flock sizes of Gouldian
 finches during movement have implications for estimating population sizes
 from waterhole counts, allowing flocks to be considered as distinct units
 for concurrent counts at multiple waterholes. This approach would offer a
 reasonably accurate method for estimating local populations, and
 conducting repeated counts on consecutive days could provide reliable and
 replicable results.
description: Seventy-six Gouldian finches were captured across three capture
 periods (sessions) in May, July, and October 2019. Birds were trapped at
 waterholes using mist nets, and each finch was uniquely banded, and a 70
 µm blood sample for genetic analyses was extracted. Tracking data were
 stored and can be accessed via the Motus Wildlife Tracking System (Motus
 project #241). <strong>Social
 associations</strong> Raw detection data was
 downloaded from the Motus platform (www.motus.org) and
 processed to remove detections within the first 24 hours post-release to
 allow for a post-tagging acclimation period. Detections with run lengths
 (number of consecutive uniquely coded detections by a receiver) of
 &lt; 4 were also removed to exclude observations with a higher
 probability of false detection.  We identified
 associations between tagged conspecifics following a ‘gambit of the group’
 approach, with individuals considered to be associating if they were
 observed co-occurring at fixed receivers simultaneously. We used the data
 from the first 40 days after the last sampling date to maximize the number
 of active tags after excluding the 24-hour acclimation period. This left a
 total of 57 active tags. As individuals could be detected at multiple
 receivers simultaneously due to the overlapping detection ranges,
 individuals were assigned to the receiver with the strongest signal
 strength (<em>sig</em>) every 10 minutes. We then counted the
 number of co-occurrences per pair of tagged conspecifics in a time window
 of one hour (i.e., the number of times the strongest
 <em>sig</em> was detected at the same tower within an hour).
 We then used the Simple Ratio Index (SRI) to determine the association
 strength between each pair of tagged conspecifics. Because most
 individuals were not present throughout all three sampling sessions, we
 calculated SRI for each tracking session separately.
 <strong>Pairwise genetic distance</strong>
 DNA extraction and SNP (single-nucleotide polymorphism)
 genotyping were carried out by Diversity Arrays Technology (https://www.diversityarrays.com/) using DArTseq™ protocols (Schouten et al., 2012). We filtered the data using the ‘dartR’ package in R version 4.2.2 according to repeatability (repeatability &gt; 0.95), removing monomorphs and custom filtering to remove SNPs with a mean read depth of less than 15 and a mean ratio of sequence depth between alleles of greater than two. Using the filtered dataset, we calculated the pairwise genetic distance between all pairs of individuals using the individual genetic distance measure of Smouse and Peakall (1999) in the ‘PopGenReport’ package in R.
description: # Data for: **Genetic Relatedness Shapes Social Dynamics in a Threatened
 Finch: Implications for Population Assessment**
 [https://doi.org/10.5061/dryad.ttdz08m59](https://doi.org/10.5061/dryad.ttdz08m59) This dataset contains the data from 37 Gouldian finches (*Erythrura gouldiae*) individuals used to investigate the correlation between pairwise association strength (Simple Ratio Index; SRI) and pairwise genetic distance. We used Linear Mixed-effects (LME) models in the ‘*nlme*’ package in R, together with the ‘corMLPE’ package (provided at [https://github.com/nspope/corMLPE](https://github.com/nspope/corMLPE)). This package implements a correlation structure to account for a pairwise data structure (or correlation structure for “Clarke’s maximum likelihood population effects model”). We fitted the pairwise SRI measures as the response variable and the pairwise genetic distance as the predictor. We also tested the effect of different covariates on the SRI measures, including geographic distance (Near: 0-2 km, Far: 8-11 km), age (Adult or Juvenile), and sex (Male, Female, or Juvenile). To account for the data structure, we also tested the inclusion of sample location (seven levels with combinations of four locations: A, B, C, and D) and tracking session (three levels: 1, 2, and 3) as random and fixed effects, respectively. ## Description of the data and file structure The data is contained in a .csv file structured in the following columns: * Bird_ID1: ID of bird 1. * Bird_ID1: ID of bird 2. * SRI: pairwise Simple Ratio Index measure. * Genetic.dist: pairwise genetic distance measure (Smouse and Peakall, 1999). * Age: Age for ID1 + Age for ID2. Two categories: Adult or Juvenile. * Sex: Sex for ID1+ Sex for ID2. Three categories: Male (M), Female (F), or Juvenile (J). * Sample.loc: Sample location for ID1 + Sample location for ID2. Four categories: A, B, C or D. * Date: Sample date for ID1 + Sample date for ID2. Nine categories: 2019-05-04, 2019-05-06, 2019-05-07, 2019-05-09, 2019-07-17, 2019-10-02, 2019-10-03, 2019-10-07, 2019-10-10. * Session: tracking session (only pairs tracked in the same session were included). Three categories: 1, 2 and 3. * Geographic.dist: geographic distance between pairwise sample locations. Two categories: Near (0-2 km), Far (8-11 km). ## Sharing/Access information Raw automated radio-tracking detection data was downloaded from the Motus Wildlife Tracking System platform ([www.motus.org](http://www.motus.org)). ## Code/Software For the correlation analyses, we used Linear Mixed-effects (LME) models in the ‘*nlme*’ package (Pinheiro et al., 2023), together with the ‘corMLPE’ package (provided at [https://github.com/nspope/corMLPE).](https://github.com/nspope/corMLPE\).) All analyses were carried out using R version 4.2.2 (R Core Team, 2022).