The prevalence of nonlinearity and detection of ecological breakpoints across a land use gradient in streams
Bibliographic record
Abstract
Human activities can alter aquatic ecosystems through the input of nutrients and carbon, but there is increasing evidence that these pressures induce nonlinear ecological responses. Nonlinear relationships can contain breakpoints where there is an unexpected change in an ecological response to an environmental driver, which may result in ecological regime shifts. We investigated the occurrence of nonlinearity and breakpoints in relationships between total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), and total dissolved carbon (DOC) concentrations and ecological responses in streams with varying land uses. We calculated breakpoints using piecewise regression, two dimensional Kolmogorov-Smirnov (2DKS), and significant zero crossings (SiZer) methods. We found nonlinearity was common, occurring in half of all analyses, with some evidence of multiple breakpoints. Linearity, by contrast, occurred in less than 14% of cases, on average. Breakpoints were related to land use gradients, with 34-43% agricultural cover associated with DOC and TDN breakpoints, and 15% wetland and 9.5% urban land associated with DOC and nutrient breakpoints, respectively. While these breakpoints are likely specific to our study area, our study contributes to the growing literature of the prevalence and location of ecological breakpoints in streams, providing watershed managers potential criteria for catchment land use thresholds.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".