Microbial abundance and transport in glacial near-surface meltwater
Bibliographic record
Abstract
Glacier surfaces are active microbial ecosystems which contribute to melt feedback cycles and biogeochemical processes. Despite this recognition, there is a lack of knowledge regarding the transport dynamics and residence time of microbes in this supraglacial habitat. Throughout the ablation season, meltwater is generated across a glacier’s surface and flows through the porous near-surface weathering crust before entering the channelised supraglacial network. Within the weathering crust, solar radiation provides a “photic zone” which, combined with nutrient availability, is conducive for microbial activity. The water flow through this porous near-surface layer provides a transport mechanism for these microbes. However, the nature of controls upon this phenomenon remain unexplored, despite the relevance for cellular export to downstream ecosystems, glacier surface albedo and biogeochemical cycling. To determine potential controls on microbial transport in the weathering crust, we present 913 measurements of microbial cell abundance in supraglacial meltwaters from 11 glaciers across the northern hemisphere. Each measurement is coupled with weathering crust hydraulic conductivity or stream discharge. These data reveal a mean microbial abundance of 2.2 × 104 cells mL-1 (with a range of 103 – 106) in supraglacial meltwaters within the weathering crust and stream channels. Modal microbe size was 1 – 2 μm (56 % of microbes), with 89 % of microbes smaller than 4 μm. No substantiated difference in size distributions between weathering crust and stream meltwaters were observed. No correlation between microbial abundance and near-surface hydraulic conductivity or stream discharge were observed, either across the entire dataset or when considered on a glacier-by-glacier basis. At three glaciers, water temperature and electrical conductivity (a proxy measure for ionic load) were also recorded; but we observe no correlation between these two variables and microbial abundance. Our data suggests weathering crust microbe abundance is consistent across differing glacial environments, and the concentrations entrained in the near-surface equal those seen in supraglacial streams. As such, despite the low transfer rate of meltwater, there appears to be limited evidence for substantial storage or accumulation of biomass in the near-surface weathering crust. Moreover, microbe entrainment does not appear to be driven by primary hydrological controls. Assuming that once liberated within the weathering crust entrained microbes reach channelised supraglacial networks, we estimate a delivery of 1.1 × 109 kg C a-1 to downstream environments globally (excluding Antarctica) to 2100, using existing discharge forecasts. This study represents a crucial first step in examining microbial abundance within, and transport across glacier surfaces and their potential role in biogeochemical process-feedbacks and the inoculation of downstream environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".