Trophic‐level dependent effects on CO<sub>2</sub> emissions from experimental stream ecosystems
Bibliographic record
Abstract
Concern over accelerating rates of species invasions and losses have initiated investigations into how local and global changes to predator abundance mediate trophic cascades that influence CO2 fluxes of aquatic ecosystems. However, to date, no studies have investigated how species additions or losses at other consumer trophic levels influence the CO2 flux of aquatic ecosystems. In this study, we added a large predatory stonefly, detritivorous stonefly, or grazer tadpole to experimental stream food webs and over a 70-day period quantified their effects on community composition, leaf litter decomposition, chlorophyll-a concentrations, and stream CO2 emissions. In general, streams where the large grazer or large detritivore were added showed no change in total invertebrate biomass, leaf litter loss, chlorophyll-a concentrations, or stream CO2 emissions compared with controls; although we did observe a spike in CO2 emissions in the large grazer treatment following a substantial reduction in chlorophyll-a concentrations on day 28. However, the large grazer and large detritivore altered the community composition of streams by reducing the densities of other grazer and detritivore taxa, respectively, compared with controls. Conversely, the addition of the large predator created trophic cascades that reduced total invertebrate biomass and increased primary producer biomass. The cascading effects of the predator additions on the food web ultimately led to decreased CO2 emissions from stream channels by up to 95%. Our results suggest that stream ecosystem processes were more influenced by changes in large predator abundance than large grazer or detritivore abundance, because of a lack of functionally similar large predators. Our study demonstrates that the presence/absence of species with unique functional roles may have consequences for the exchange of CO2 between the ecosystem and the atmosphere.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".