Dispersal transiently modifies the temperature dependence of ecosystem productivity after an extreme thermal fluctuation
Bibliographic record
Abstract
Abstract Effects of warming on ecosystem productivity are typically summarized over broad time scales, yet they emerge from communities that can reorganize in a matter of days. Temperature accelerates ecosystem productivity through predictable effects on metabolic rates, but dispersal across thermally heterogeneous metacommunities may modify this effect by redistributing communities and their underlying thermal phenotypes. Using multi-trophic freshwater mesocosm communities in which warming and dispersal were manipulated, we tested the hypothesis that increasing dispersal modifies the thermal sensitivity of gross primary productivity (GPP) through the redistribution of phytoplankton biomass, size spectra, and thermal phenotype composition along spatial thermal gradients. High dispersal temporarily weakened the thermal sensitivity of GPP after an unplanned heatwave. This effect was caused by reduced mass-specific GPP in the warmest mesocosms accompanied by regional homogenization of phytoplankton communities and the spread of poorly adapted thermal phenotypes under the highest dispersal treatments. Except immediately post-heatwave, the thermal sensitivity of GPP was robust to dispersal, despite evidence of metacommunity dynamics. These findings suggest that the temperature dependence of ecosystem metabolism can be briefly modified by dispersal after a perturbation, but remains robust under steady-state conditions.
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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 | 0.000 | 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".