Long‐lived organisms provide an integrative footprint of agricultural land use
Bibliographic record
Abstract
Nitrogen (N) fertilizer runoff into rivers is linked to nutrient enrichment, hydrologic alteration, habitat degradation and loss, and declines in biotic integrity in streams. Nitrogen runoff from agriculture is expected to increase with population growth, so tracking these sources is vital to enhancing biomonitoring and management actions. Unionid mussels are large, long-lived, sedentary, primary consumers that transfer particulate material and nutrients from the water column to the sediments through their filter feeding. Because of these traits, mussels may provide a temporal integration of nitrogen inputs into watersheds. Our goals were to (1) establish a baseline delta15N signature for unionid mussels in watersheds not heavily influenced by agriculture for use in comparative analyses and (2) determine if mussels provide an integrative measure of N sources in watersheds with varying percentages of agriculture across large spatial scales. We compiled tissue delta15N data for 20 species of mussels from seven geographic areas, including 23 watersheds and 42 sample sites that spanned varying degrees of agricultural intensification across the eastern United States and Canada. We used GIS to determine land cover within the study basins, and we estimated net anthropogenic nitrogen inputs (NANI) entering these systems. We then determined the relationship between mussel tissue delta15N and percentage of land in agriculture (%AG) and net anthropogenic N loading. The delta15N of mussel tissue could be predicted from both %AG and net anthropogenic N loading, and one component of NANI, the amount of N fertilizer applied, was strongly related to the delta15N of mussel tissue. Based on our results, mussels occupying a system not affected by agricultural land use would have a baseline delta15N signature of approximately 2.0 pe thousand, whereas mussels in basins with heavy agriculture had delta15N signatures of 13.6 per thousand. Our results demonstrate that mussels integrate anthropogenic N input into rivers at a watershed scale and could be a good bioassessment tool for tracking agriculture N sources.
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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 | 0.001 |
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".