Code and data for: The difficulty of predicting evolutionary change in response to novel ecological interactions: a field experiment with Anolis lizards

Data files

Apr 21, 2026 version files 770.24 MB

anolis_pub.zip

770.24 MB
README.md

3.74 KB

Abstract

Determining whether and how evolution is predictable is an important goal, particularly as anthropogenic disturbances lead to novel species interactions that could modify selective pressures. Here, we use a multi-generation field experiment with brown anole lizards (Anolis sagrei) to test hypotheses about the predictability of evolution. We manipulated the presence and absence of predators and competitors across 16 islands in the Bahamas with preexisting brown anole populations. Before the experiment and again after roughly five generations, we measured traits related to locomotory performance and habitat use of brown anoles and used ddRAD sequencing to estimate genome-wide changes in allele frequencies. Although previous work showed that predators and competitors had consistent effects on brown anole behavior, diet, and population sizes, we found that evolutionary change at both phenotypic and genomic levels was difficult to forecast. Phenotypic changes were contingent on sex and habitat use, whereas genetic change was unpredictable and not measurably correlated with phenotypic changes, experimental treatments, or other environmental factors. Our work shows how differences in ecological context can alter evolutionary outcomes over short timescales and underscores the difficulty of forecasting evolutionary responses to multi-species interactions in natural conditions, even in a well-studied system with ample ecological information.

Authors:

Timothy J. Thurman^1,2,3, Todd M. Palmer⁴, Jason J. Kolbe⁵, Arash M. Askary¹, Kiyoko M. Gotanda^1,6,7, Oriol Lapiedra⁸, Tyler R. Kartzinel^9,10, Naomi Man in't Veld¹¹, Liam J. Revell^12,13, Johanna E. Wegener⁵, Thomas W. Schoener¹⁴, David A. Spiller¹⁴, Jonathan B. Losos¹⁵, Robert M. Pringle¹⁶, Rowan D. H. Barrett¹

Affiliations:

¹ Redpath Museum and Department of Biology, McGill University. Montréal, QC, Canada

² Smithsonian Tropical Research Institute. Panamá, República de Panamá

³ Current address: Division of Biological Sciences, University of Montana, Missoula, MT, USA

⁴ Department of Biology, University of Florida, Gainesville, FL, USA

⁵ Department of Biological Sciences, University of Rhode Island, Kingston, RI, USA

⁶ Department of Zoology, University of Cambridge. Cambridge, United Kingdom.

⁷ Current address: Department of Biological Sciences, Brock University, St. Catharines, ON, Canada

⁸ Centre for Ecological Research and Applied Forestries, Cerdanyola del Vallès, Catalonia 08193, Spain

⁹ Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, RI, USA

¹⁰ Institute at Brown for Environment and Society, Brown University, Providence, RI, USA

¹¹ Unaffiliated

¹² Department of Biology, University of Massachusetts at Boston, Boston, MA, USA

¹³ Facultad de Ciencias, Universidad Católica de la Santísima Concepción, Concepción, Chile

¹⁴ Department of Evolution and Ecology, University of California, Davis, CA, USA

¹⁵ Department of Biology and Living Earth Collaborative, Washington University, St. Louis, MO, USA

¹⁶ Department of Ecology & Evolutionary Biology, Princeton University, Princeton, NJ, USA

Corresponding author: Timothy Thurman. Email: timothy.j.thurman@gmail.com

All code authored by Timothy Thurman unless otherwise noted.

File Organization

This repository contains a number of top-level files and folders. Most subfolders contain additional README files with further information.

processed_data- Data files that have been processed and are ready to use. Generally, these files are the result of running data processing scripts on the src folder on raw data in the raw_data folder.
raw_data- Raw data files, including output from analysis programs (e.g., imageJ outputs), and spreadsheets of raw data.
results- Statistical results in (often in .Rdata files), tables, and figures.
src- Code written to process, analyze, and plot data.
src_hpc- This folder contains code written as a pipeline to process raw sequencing data on a HPC cluster. Folders of submission scripts used for data processing are named according to the order in which the pipeline runs. See README within.

Computing environment

Statistical analyses were performed using the R programming language. We used the renv R package to manage our computing environment. See the renv.lock file for a full description of the R packages used in this project, including the version numbers and the source from which we obtained the packages. Non-HPC analyses were performed on a Mac running macOS Catalina 10.15.7. HPC analyses were performed on the Compute Canada clusters Guillimin, Beluga, and Narval.

Other data

Code, and some data, are also available on GitHub at (https://github.com/tjthurman/anolis_pub). Raw sequencing reads are deposited in the NCBI Short Read Archive under BioProject Accession number PRJNA874464.