Association Between Subcatchment Land Cover and Ecological Stoichiometry Along a Human Modified Stream Network
Bibliographic record
Abstract
The intersection between land cover and topography at the subcatchment scale can generate spatial heterogeneity in carbon (C), nitrogen (N), and phosphorus (P) supplies and alter ecological stoichiometry within river networks. Recognition of spatial patterns in the balance of energy and elements at the stream network scale is needed to increase our comprehension of the importance that watershed physiography has on stream functionality. Here we assessed the influence of topographically weighted land cover on the ecological stoichiometry of stream water, periphyton, and benthic macroinvertebrates among 18 stream segments within the Beaver River watershed (Ontario, Canada). Natural and anthropogenic land cover classes were topographically weighted within each subcatchment by increasing the weight of land cover located in hydrologically connected areas of the landscape (i.e., proximity to stream network, flow distance to sampling location, and flow accumulation). Univariate regression models were used to evaluate the influence of topographically weighted land cover and land use on the C, N, and P content and ratios of stream water, periphyton, and benthic macroinvertebrate consumers while accounting for spatial autocorrelation within the river network. We found that topographically weighted developed land was largely associated with the balance and concentrations of nutrients in stream water and primary consumer nutrient ratios, whereas topographically weighted agriculture inversely correlated with natural and wetland cover was associated with periphyton stoichiometry in addition to that of stream water and primary consumers. Spatial patterns in primary consumer elemental composition coincided with that of stream water and periphyton indicating a potential intermediary effect of land use on resource quality. Our results suggest that heterogeneity in the spatial arrangement of land cover within river networks can influence the ecological stoichiometry of stream water, periphyton, and benthic macroinvertebrates at the subcatchment scale.
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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 | 0.000 | 0.001 |
| 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".