The work is led by @weiweibio.bsky.social, a talented postdoc looking for faculty positions now and conceptualized both computational prediction and in lab validation. Computational implementation is a collaboration with Xing Wu, postdoc with @mexpositoalonso.bsky.social
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In A. thaliana and other plant systems, local adaptation is stronger closer to the equatorial edge and load is highest farther from the equator, which highlights a critical vulnerability: under strong selection, these high mutation load and low local adaptation populations may be the most at risk.

We monitored these populations for 3 years, and found that population growth rate decreased as the treatments became more stressful. Further, "high risk" populations (either high load or low local adaptation scores) had a higher stochasticity for recovery after a single year of water stress.

Crucially, we found a significant interaction between load and local adaptation, which grew stronger as the environment became more stressful. This suggests a synergistic decay of fitness resulting from locally adaptive variants, globally maladaptive load, and high water stress environments.

We found that populations with a low number of local adaptation alleles or high mutation load had decreased fitness in our highest drought stress environments.

By using data from other A. thaliana common gardens and genomic prediction tools, we created synthetic experimental populations of A. thaliana that varied in their abundance of locally adaptive alleles and also in their mutational load.

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