Lemmon-Kishi, M. | Search bioRxiv

bioRxiv2026

Molecular Clock Dating of Ancient Environmental DNA Reveals Damage Beyond Deamination

Ancient environmental DNA (aeDNA) from permafrost, lake, cave, and marine sediments provides a rich source of genetic data that captures broad perspectives of past biodiversity. Accurate dating is crucial for discovering ecologically relevant patterns from aeDNA, and molecular clock dating would allow for sample ages to be estimated from the recovered genetic material itself instead of the geological components. However, the fragmented and damaged nature of short-read ancient DNA (aDNA) from multiple taxonomic sources poses significant challenges and has limited this dating approach for aeDNA. Here we developed ratePlacer, a phylogeny-based method for analyzing aeDNA that can combine information from many short reads in a sample while accounting for DNA damage to provide maximum likelihood estimates of sample ages. Simulations demonstrate that ratePlacer accurately dates samples even under the fragmented, damaged conditions characteristic of aeDNA and outperforms Bayesian tip-dating approaches for taxonomically mixed samples commonly found in aeDNA. Yet age estimates from re-dating Kap Kobenhavn varied across taxa, highlighting the difficulty of molecular clock dating in aeDNA. This dating also revealed elevated G [->] T and C [->] A mismatches consistent with oxidative damage. These patterns reveal aDNA damage beyond deamination and that remains understudied, suggesting that aeDNA should be carefully evaluated in genomic and evolutionary analyses. The new dating method, ratePlacer, extends molecular clock dating of aDNA from single-specimen to pooled environmental DNA data, where traditional methods struggle.

bioinformatics↗

bioRxiv2026

STEM-LM: Spatio-Temporal Ecological Modeling via Masked Language Model for Joint Species Distribution

Joint species distribution models (JSDMs) are central to biodiversity forecasting and conservation decision-making. As ecological datasets grow in size, dimensionality, and spatio-temporal resolution, there is a need for flexible yet scalable JSDMs tailored to large-scale species observation data. Recent advances in masked language modeling for text and genomics suggest a natural alternative: by treating each species presence or absence as a token, and a sites species assemblage together with its spatio-temporal and ecological covariates as a sentence, we can learn joint co-occurrence structure by reconstructing masked species from their neighboring sites. We propose STEM-LM1, a Transformer-based JSDM that frames joint species distribution modeling as masked language modeling. By varying the masking rate during training, a single trained model supports both purely spatiotemporal/ecological prediction and conditioning on arbitrary subsets of observed species for joint co-occurrence inference at a given site. On a North American butterfly and a global plant distribution dataset, STEM-LM performs better or on par with other statistical and deep-learning based methods in terms of discriminative ranking, while producing substantially better rank-calibrated occurrence probabilities. Utilizing partial species observations at the same site greatly enhances prediction performance.

ecology↗