EcoLocator: deep learning predicts geographic origin and climate from genotypes
pastramimachine · x · 2026-09-20
Researchers at the University of Oregon (Jordan Rodriguez, Andrew Kern, et al.) released a bioRxiv preprint introducing EcoLocator, a supervised deep neural network that jointly predicts geographic location and climate-of-origin from genetic variation data.
Key points:
- Motivation: climate change is disrupting locally adapted populations, and gold-standard common-garden/provenance trials are too slow and costly to scale.
- Method: EcoLocator combines genotype-environment association ideas with supervised deep learning, claimed as state of the art for this joint prediction task.
- Impact: offers a cheap complementary tool for conservation and restoration planning under climate change; code and data are public.
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