Aquatic ecosystem metabolism and carbon biogeochemistry as indicators of stream health in the Deadman watershed, British Columbia, Canada
Bibliographic record
Abstract
Stream health refers to the ability of a stream to maintain ecological integrity and support human values, and is an important concept guiding environmental assessment. Dissolved organic matter (DOM) quantity and composition, along with ecosystem metabolism parameters, are valuable indicators of stream health given their ability to represent the interdependence of ecosystem structure and function, and their sensitivity to disturbance. Despite increasing knowledge of how wildfires impact DOM, there are inconsistencies in our current understanding due to complex interactions between wildfire and watershed characteristics. Additionally, there are limited studies which meaningfully integrate DOM and metabolism, and none that contextualize their interrelatedness with respect to wildfire disturbance and stream health. In this thesis, I evaluated DOM and metabolism to characterize stream health in the Deadman watershed in British Columbia, Canada, which was recently impacted by severe wildfire. I conducted a field-based observational study, where DOM quantity and composition from spring snowmelt to baseflow in the fall was characterized through discrete sampling. Daily stream metabolic rates were estimated using hourly measurements of dissolved oxygen, water temperature, light, and streamflow. I found that seasonality of DOM was characterized by a large shunt of terrestrially derived, highly aromatic DOM during spring snowmelt, indicating high terrestrial-aquatic ecosystem connectivity. Spatial variability in DOM was highly related to the presence or absence of mid-reach lakes. Levels of specific ultraviolet absorbance at 254 nm, representative of DOM aromaticity, were among the highest reported when compared to comprehensive studies, and are likely indicative of the influence of wildfire. The aseasonality of gross primary productivity suggests constraints on metabolism related to streamflow and light. Ecosystem respiration (ER) was relatively low across most study sites, and the fraction of autotrophic respiration was high compared to other studies, indicating that ER at most study sites was primarily supported by autochthonous organic matter production. Organic carbon mineralization rates were low when compared to other studies, indicating low uptake of terrestrial organic matter. Within the context of stream health values – water quality, aquatic habitat, and support of productive fisheries – I found that DOM and metabolism dynamics indicate potential limitations to stream food web productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".