The transient response of ecosystems to climate change is amplified by trophic interactions
Bibliographic record
Abstract
Studies of ecosystem responses to climate change often focus on potential equilibria in species or community distributions, overlooking the transitions to new equilibrium states. Transient phases can however last for decades or centuries, during which both demography and interspecific interactions are expected to play a crucial role. Here, we investigate the response of vegetation to climate warming at high latitudes involving a shift from open vegetation to either boreal (mainly coniferous) or temperate (mainly deciduous) forests. We specifically address how interactions among browsers and vegetation could affect the shift in dominance of vegetation type after climate warming. We characterize the transient dynamics using five measures: 1) asymptotic resilience, i.e. the rate at which equilibrium is restored, 2) initial resilience, the short‐term rate of change of the ecosystem after climate change, 3) ecosystem exposure, i.e. the shift of the equilibrium due to climate change, 4) sensitivity, or the time to recover equilibrium, and 5) vulnerability, measured as the cumulative amount of changes in vegetation states during the transient phase. We find that plant–herbivore interactions usually extend the length of the transient period and induce more cumulative changes in vegetation types. This result implies that the consideration of multiple interacting species is necessary to provide robust scenarios of how ecosystems will respond to global changes. We furthermore show that plant–herbivore interactions disrupt the correlation between the five measures characterizing the transient dynamics, highlighting the need for a full multidimensional characterization of transients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".