The quality of organic matter shapes the functional biogeography of bacterioplankton across boreal freshwater ecosystems
Bibliographic record
Abstract
Abstract Aim The need to go beyond taxonomy to understand patterns in microbial function has led to an increased use of trait‐based approaches, yet we know little about how microbial functional traits vary across large‐scale environmental gradients in natural ecosystems. Here, we apply a trait‐based approach to explore the large‐scale variability in the trait structure underlying the processing of dissolved organic matter ( DOM ) by boreal bacterioplankton communities, as well as its regulation and links to taxonomic composition. Location Samples were collected from 296 rivers and lakes across five regions in northern Q uebec ( C anada), which span large gradients in environmental, climatic and geographical properties typical of the boreal zone. Methods We used the metabolic profiles obtained with B iolog E co P lates® as an imprint of the trait structure underlying bacterial processing of DOM , and I llumina sequencing of the 16SrRNA gene to characterize the taxonomic composition of these bacterial assemblages. The resulting spatial patterns were compared with an array of climatic, landscape and limnological properties varying at the landscape scale. Results Despite a clear regional segregation of the sampled sites based on environmental variables, the trait structure of boreal bacteria did not show any regional or ecosystem‐specific patterns, but rather was linked to a gradient of quality of DOM . Community trait configurations diverged progressively with decreasing terrestrial influence, probably due to local processes that transform and diversify the available pool of DOM . This DOM quality gradient did not explain the taxonomic biogeography of these communities, which was controlled by a different set of environmental factors. Main conclusions The functional biogeography of boreal bacterioplankton is driven by the nature of the DOM pool, and particularly by the influence of terrestrial DOM . The lack of coherence between functional and taxonomic biogeographies implies that the environmental controls of freshwater bacterial performance cannot be directly inferred from spatial patterns in taxonomic composition.
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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 | 0.001 | 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 | 0.002 |
| 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".