Habitat structural complexity predicts cognitive performance and behavior in western mosquitofish
Data files
Jun 03, 2024 version files 48.79 KB
Abstract
Urbanization and stream order alter freshwater habitat complexity (defined as the degree of variation in physical habitat structure). More complex habitats have more variation in habitat structure. Habitat complexity affects species composition and shapes animal ecology, behavior, and cognition. We used a delayed detour test to measure whether motor self-regulation and behavior of Western mosquitofish, Gambusia affinis, varied with habitat structural complexity that was quantified for nine populations. We predicted that motor self-regulation, motivation, and risk-taking behavior would increase with increasing habitat complexity, yet we found the opposite relationship. Lower complexity habitats offer less refuge which could increase predation pressure and select for greater risk-taking by fish with greater motor self-regulation. Our findings provide insight into how habitat complexity is related to cognitive processes and behavioral outcomes, and provide an explanation for why some species have a higher tolerance for urbanized environments.
README: Habitat structural complexity predicts cognitive performance and behavior in western mosquitofish
https://doi.org/10.5061/dryad.1g1jwsv3c
Female Gambia affinis from nine populations were tested in a delayed detour test to measure behavioral and cognitive differences across habitats representing gradients of varying structural complexity and urbanization. The data set includes the scores the fish received for exit time (time (s) to leave the starting chamber), motivation (time (s) for reach to reach barrier), and motor self-regulation (time (s) for fish to swim around barrier and reach social reward). We analyzed this data in R studio using Coxme proportional hazards test. We found that fish from low complexity habitats had faster exit times and faster motor self-regulation than fish from medium or high complexity environments.
Description of the data and file structure
Irwin_Biology_Letters_Data.csv - CSV file of data
IRWINbiology_letters.R - R script used to perform analysis (uploaded to Zenodo)
Column descriptions:
Population - the name of the population associated with each collection site
Date - the actual date the fish was tested
SL (MM)- standard length in millimeters of each fish
#fish - Fish identification number
Day - the day the fish was tested in our range of all testing dates
Exit (s)- the number of seconds until the fish left the starting chamber
Reachbarrier (s)- the number of seconds it took the fish to touch the barrier after exiting the starting chamber
Aroundbarrier (s)- the number of seconds it took the fish to swim around the barrier after reaching the barrier
Reach female (s)- the number of seconds it took the fish to reach the social reward after swimming around the barrier
Total task(s)- sum of Exit (s)+ Reachbarrier (s)+ Aroundbarrier (s) + Reach female (s)
Solutionspeed (s)- sum of Reachbarrier (s)+ Aroundbarrier (s) + Reach female (s)
Exitornot 1- a censor for fish that did not exit. Fish that exited the starting chamber received a 1, fish that did not exit received a 0
CENSOR- a censor for fish that exited but did not solve the test. Fish that solved the test received a 1, fish that did not solve received a 0
H- a score for shannon diversity index associated with each collection site
Complexity- categorical measure of habitat structural complexity. Habitats were characterized as low, medium, or high complexity based on the Complexnorm score
Complexnorm- a numerical value quantifying normalized habitat complexity of each collection site
%Dev- the percent of impervious cover of the sub watershed associated with each collection site
Latitude- Latitude of collection site
Longitude- Longitude of collection site
Sharing/Access information
If you would like help accessing data please contact Kyndal Irwin at kbi4@txstate.edu
Code/Software
We used a Coxme proportional hazards test in R studio (coxme) package to analyze potential differences in exit time (s), motivation (s), and motor self-regulation (s).