Thermal stratification and fish thermal preference explain vertical eDNA distributions in lakes
Bibliographic record
Abstract
Significant advances have been made towards surveying animal and plant communities using DNA isolated from environmental samples. Despite rapid progress, we lack a comprehensive understanding of the "ecology" of environmental DNA (eDNA), particularly its temporal and spatial distribution and how this is shaped by abiotic and biotic processes. Here, we tested how seasonal variation in thermal stratification and animal habitat preferences influences the distribution of eDNA in lakes. We sampled eDNA depth profiles of five dimictic lakes during both summer stratification and autumn turnover, each containing warm- and cool-water fishes as well as the cold-water stenotherm, lake trout (Salvelinus namaycush). Habitat use by S. namaycush was validated by acoustic telemetry and was significantly related to eDNA distribution during stratification. Fish eDNA became "stratified" into layers during summer months, reflecting lake stratification and the thermal niches of the species. During summer months, S. namaycush, which rarely ventured into shallow waters, could only be detected at the deepest layers of the lakes, whereas the eDNA of warm-water fishes was much more abundant above the thermocline. By contrast, during autumn lake turnover, the fish species assemblage as detected by eDNA was homogenous throughout the water column. These findings contribute to our overall understanding of the "ecology" of eDNA within lake ecosystems, illustrating how the strong interaction between seasonal thermal structure in lakes and thermal niches of species on very localized spatial scales influences our ability to detect species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".