Rapid environmental change poses unprecedented challenges to species persistence. To understand the extent that continued change could have, genomic offset methods have been used to forecast maladaptation of natural populations to future environmental change. However, while their use has become increasingly common, little is known regarding their predictive performance across a wide array of realistic and challenging scenarios. Here, we evaluate the performance of currently available offset methods (gradientForest, the Risk-Of-Non-Adaptedness, redundancy analysis with and without structure correction, and LFMM2) using an extensive set of simulated datasets that vary demography, adaptive architecture, and the number and spatial patterns of adaptive environments. For each dataset, we train models using either all, adaptive, or neutral marker sets and evaluate performance using in silico common gardens by correlating known fitness with projected offset. Using over 4,850,000 of such evaluations, we find that 1) method performance is largely due to the degree of local adaptation across the metapopulation (LA), 2) adaptive marker sets provide minimal performance advantages, 3) performance within the species range is variable across gardens and declines when offset models are trained using additional non-adaptive environments, and 4) despite (1), performance declines more rapidly in globally novel climates (i.e., a climate without an analog within the species range) for metapopulations with greater LA than lesser LA. We discuss the implications of these results for management, assisted gene flow, and assisted migration.
If you use or are inspired by code in this repository, please cite the manuscript (citations will be updated after acceptance, check GitHub for link):
Lind BM, KE Lotterhos. 2024. The limits of predicting maladaptation to future environments with genomic data. DOI: https://doi.org/10.1101/2024.01.30.577973
and/or the code's archive (which mirrors this repository):
Lind BM. 2024. GitHub.com/ModelValidationProgram/MVP-offsets: Revision release (v1.0.1). Zenodo. DOI: https://doi.org/10.5281/zenodo.11209812
This research was funded by NSF-2043905 (KEL) and Northeastern University.
Various Anaconda environments are used across scripts. Anaconda (not miniconda) was used for coding environments.
1. Python environment
- this python (v3.8.5) environment is used to run all python scripts (.py) and notebooks (.ipynb) in the repository.
- most python scripts depend on cloning pythonimports from Brandon Lind. After cloning, the path of the cloned repository will need to be exported to the PYTHONPATH within
$HOME/.bashrc:
export PYTHONPATH="${PYTHONPATH}:/path/to/pythonimports"
- the following will also need to be added to
$HOME/.bashrc:
export PYTHONPATH="${PYTHONPATH}:/path/to/MVP-offsets/01_src"
- create the mvp_env environment (
conda create -n mvp_env -f /path/to/MVP-offsets/mvp_env.yml) - activate the mvp_env environment (
conda activate mvp_env), then:conda install -c conda-forge scikit-allel
2. Gradient Forests environment
- this R (v3.5) environment is used to run the GradientForests package v0.1-18
- create this environment with the following command:
conda create -n r35 -f /path/to/MVP-offsets/r35.yml - activate the gf_env environment (
conda activate r35) then install GradientForests:R CMD INSTALL /path/to/MVP-offsets/01_src/gradientForest_0.1-18.tar.gz - open R, then:
install.packages(data.table)install.packages(rgeos)install.packages(raster)
3. LFMM2/LEA + RDA environment
- this R (v4.0.3) environment is used to run lfmm2 from the LEA2 package, as well as redundancy analysis (RDA)
- create this environment with the following command (updating path):
conda create -n MVP_env_R4.0.3 -f /path/to/MVP-offsets/MVP_env_R4.0.3.yml
These two directories hold scripts (01_src) and jupyter notebooks (02_analysis) used for processing and analyzing data. Notebooks in 02_analysis are best viewed in a jupyter session (for collapsing and scrolling cell outputs) but are hyperlinked for viewing on nbviewer.org within each directory's README.
- executable scripts, see directory's README for more information
- folders containing jupyter notebooks, see directory's README for more information