Large potential impacts of marine heatwaves on ecosystem functioning
Bibliographic record
Abstract
Ocean warming is driving significant changes in the structure and functioning of marine ecosystems, shifting species' biogeography and phenology, changing body size and biomass and altering the trophodynamics of the system. Particularly, extreme temperature events such as marine heatwaves (MHWs) have been increasing in intensity, duration and frequency. MHWs are causing large-scale impacts on marine ecosystems, such as coral bleaching, mass mortality of seagrass meadows and declines in fish stocks and other marine organisms in recent decades. In this study, we developed and applied a dynamic version of the EcoTroph trophodynamic modelling approach to study the cascading effects of individual MHW on marine ecosystem functioning. We simulated theoretical user-controlled ecosystems and explored the consequences of various assumptions of marine species mortality along the food web, associated with different MHW intensities. We show that an MHW can lead to a significant biomass reduction of all consumers, with the severity of the declines being dependent on species trophic levels (TLs) and biomes, in addition to the characteristics of MHWs. Biomass of higher TLs declines more than lower TLs under an MHW, leading to changes in ecosystem structure. While tropical ecosystems are projected to be sensitive to low-intensity MHWs, polar and temperate ecosystems are expected to be impacted by more intense MHWs. The estimated time to recover from MHW impacts is twice as long for polar ecosystems and one-third longer for temperate biomes compared with tropical biomes. This study highlights the importance of considering extreme weather events in assessing the effects of climate change on the structures and functions of marine ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".