Bibliographic record
Abstract
Abstract. Marine heatwaves (MHWs) are becoming longer, more frequent and more intense in recent decades. MHWs have caused large-scale ecological impacts, such as coral bleaching, mass mortality of seagrass, fishes and invertebrates, and shifts in abundance and distribution of marine species. However, the implications of these MHW-induced impacts on marine species for the structure and functioning of marine food webs are not clearly understood. In this study, we use the EcoTroph-Dyn ecosystem modelling approach to examine the impacts of MHWs occurring during the year's warmest month on the trophodynamics of marine ecosystems. EcoTroph-Dyn represents marine ecosystem dynamics at a spatial resolution of 1° longitude by 1° latitude and a temporal resolution of 14 days. We applied the model to simulate changes in trophodynamic processes, energy transfer and ecosystem biomass using daily temperature and monthly net primary production (NPP) that were derived from satellite observation from 1998 to 2021. We compared and contrasted the simulated changes in biomass by trophic levels with results generated from temperature and NPP time series that had been filtered to remove MHWs. Our results show a significant decline in biomass between 1998 and 2021 specifically caused by MHWs. For example, in the Northeast Pacific Ocean, our model simulated a specific MHW decline in biomass of 8.7 % ± 1.0 (standard error) in the region from 2013 to 2016. Overall, MHW-induced biomass declines are more pronounced in the northern hemisphere and Pacific Ocean. Moreover, the MHW-induced declines in high trophic level biomass were larger than lower trophic levels and lasted longer post-MHW. Finally, this study highlights the need to integrate MHWs into modelling the effects of climate change on marine ecosystems. It shows that the EcoTroph approach, and especially its new dynamic version, provides a framework to understand more comprehensively the implications of climate change for marine ecosystem structure and functioning.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.456 | 0.278 |
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".