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Collective behavior evolves independently of benthic-limnetic divergence in stickleback

Cite this dataset

Neumann, Kevin et al. (2024). Collective behavior evolves independently of benthic-limnetic divergence in stickleback [Dataset]. Dryad.


​Comparing populations across replicate environments or habitat types can help us understand the role of ecology in evolutionary processes. If similar phenotypes are favored in similar environments, parallel evolution may occur. Collective behavior, including collective movement (e.g., schooling, flocking) and social networks, can play a key role in the adaptation by animals to different environments. However, studies exploring the parallelism of collective behavior are limited, with research traditionally focusing on morphological traits. Here, we asked if collective behavior has evolved in parallel across replicate populations of benthic and limnetic three-spined stickleback (Gasterosteus aculeatus). There were repeatable, population-level differences in collective behavior in a common garden, with some populations forming groups that were more cohesive and with higher strength and clustering coefficients. This suggests that collective behavior can evolve. However, these differences were not predicted by ecotype (benthic vs. limnetic). We found no evidence that boldness or morphological traits – both of which are known to be associated with benthic-limnetic divergence – were correlated with collective behavior. Together, these results suggest that while collective behavior evolves in this system, it does not co-evolve with divergence along the benthic-limnetic axis.

README: Data for article - Collective behavior evolves independently of benthic-limnetic divergence in stickleback

Authors: Kevin M. Neumann, Lucas Eckert, Damaris Miranda, Andrew Kemp, Alison M. Bell\
Date created: 06/21/24

Article is currently in review in the Journal of Animal Ecology.

Data is from a study conducted in December 2022 - April 2023 at the University of Illinois, Urbana-Champaign. This is a laboratory study exploring the boldness, collective behavior, and morphology of three-spined stickleback fish. Upon publication, methodological details will be available in the manuscript. Until then, contact Kevin M. Neumann for details via email -

Description of the data and file structure

There are 4 data files and 1 code file. all_cb*_*means.csv is all behavior data, with the values as means of the two observations for a given individual. all_cb.csv is the same structure as the previous, but with two values per individual (trial 1 and trial 2). Variable definitions for these two files are as follows: 

id = fish ID; lake = population fish came from; type = type of lake (benthic vs. limnetic); group = experimental group fish was in; family = family of origin for that fish; std_len_mm = standard length of fish; strength_seconds = strength, in seconds, for that individual; LocalCC = clustering coefficient for each individual (see MS for definition of strength and clustering); cohesion_mm = distance from centroid of group, in mm, for that individual; activity_m = distance swam by fish; boldness = latency to emerge from shelter, in seconds

morph.csv contains all morphological data. Variables in this data not yet defined are:

snout_len = length of snout, head_len = length of head, caudal_ped_depth = depth of caudal peduncle, body_depth = depth of body 

Finally, pairwise.csv contains pairwise interaction rates between each possible pair of individuals (see MS for detail). Variables in this data not yet defined are:

source = one of two fish in the interaction, target = other fish in the interaction, pair, = unique identifier for each possible pair across groups, weight = interaction rate between this pair (see MS for detail).


Analysis was all conducted in R Version 4.4.0. We used the following packages: lme4, car, rptR, lmerTest, emmeans, glmmTMB, ppcor, factoextra, tidyverse, Hmisc, and boot. 


Dataset is social behavior collected from stickleback using idTracker ( and morpohlogical data collected from photos. Data was processed and analyzed using python and R. 


National Science Foundation, Award: DGE 21-46756