Using a space‐for‐time substitution approach to predict the effects of climate change on nutrient cycling in tropical island stream ecosystems
Bibliographic record
Abstract
Abstract Climate change is expected to alter precipitation patterns worldwide, which can have direct effects on streamflow dynamics. In many tropical regions, climate‐driven changes in rainfall are predicted to decrease streamflow and increase flash flooding, but the implications of these changes for stream ecosystem function are poorly understood. We used a rainfall gradient on Hawaii Island that mimics projected changes in rainfall and streamflow in order to estimate how climate change affects nutrient recycling. We measured per‐capita excretion (nitrogen, phosphorus) and egestion rates of three dominant taxa (shrimp, caddisfly, midge) in eight streams along the gradient for 3 years. We scaled these rates to the population and community levels and measured nitrogen and phosphorus demand of the ecosystem to estimate if the relative contribution of nutrients supplied by invertebrates changes along the gradient. Across all three taxa, population egestion and excretion rates declined by 10‐fold in drier streams. These declines were driven by lower population density, rather than differences in per‐capita rates. Under the current climate scenario, community excretion supplied up to 70% of the nitrogen demand, which was 10‐fold lower with projected changes in rainfall. Conversely, community excretion supplied up to 5% of the phosphorus demand, which did not vary across the rainfall gradient. This difference indicates that climate change may exacerbate nitrogen limitations in tropical island streams, and change the balance of nitrogen and phosphorus dynamics. Our study also demonstrates that space‐for‐time substitutions are a valuable tool to examine implications of climate change on ecosystem function in freshwater systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".