Multiomics inform invasion risks under global climate change

Global climate change is exacerbating biological invasions; however, the roles of genomic and epigenomic variations and their interactions in future climate adaptation remain underexplored. Using the model invasive ascidian Botryllus schlosseri across the Northern Hemisphere, we investigated genomic and epigenomic responses to future climates and developed a framework to assess future invasion risks. We employed generalized dissimilarity modeling and gradient forest analyses to assess genomic and epigenomic offsets under climate change. Our results showed that populations with genomic maladaptation did not geographically overlap with those experiencing epigenomic maladaptation, suggesting that genomic and epigenomic variations play complementary roles in adaptation to future climate conditions. By integrating genomic and epigenomic offsets into the genome-epigenomic index, we predicted that populations with lower index values were less maladapted, indicating a higher risk of future inva..., , , # Multiomics inform invasion risks under global climate change

[https://doi.org/10.5061/dryad.rxwdbrvkd](https://doi.org/10.5061/dryad.rxwdbrvkd)

## Description of the data and file structure

The table provides comprehensive information on all sampling sites for Botryllus schlosseri. The initial two columns delineate the full names of the sampling sites and the respective countries where they are situated. The \"Code\" column signifies the unique identifier for each sampling site, while \"Latitude\" and \"Longitude\" columns denote the geographical coordinates of these sites. Lastly, \"N\" indicates the total number of samples collected at each respective sampling location.
### Files and variables

File: sampling_site_infromation.csv

**Description:** 

##### Variables

* Sampling site:
* Country:
* Code:
* Latitude:
* Longitude:
* N:

## Code/software

1\. R_script for \_Genomic offset.txt:

This script used for computing genomic offset (go) using the Generalised Dissimilarity Modelling (G...