Advancing Global Ecological Modeling Capabilities to Simulate Future Trajectories of Change in Marine Ecosystems
Bibliographic record
Abstract
Considerable effort is being deployed to predict the impacts of climate change and anthropogenic activities on the ocean’s biophysical environment, biodiversity, and natural resources to better understand how marine ecosystems and provided services to humans are likely to change and explore alternative pathways and options. We present an updated version of EcoOcean (v2), a spatial-temporal ecosystem modelling complex of the global ocean that spans food-web dynamics from primary producers to top predators. Advancements include an enhanced ability to reproduce spatial-temporal ecosystem dynamics by linking species productivity, distributions, and trophic interactions to the impacts of climate change and worldwide fisheries. The updated modelling platform is used to simulate past and future scenarios of change, where we quantify the impacts of alternative configurations of the ecological model, responses to climate-change scenarios, and the additional impacts of fishing. Climate-change scenarios are obtained from two Earth-System Models (ESMs, GFDL-ESM2M and IPSL-CMA5-LR) and two contrasting emission pathways (RCPs 2.6 and 8.5) for historical (1950-2005) and future (2006-2100) periods. Standardized ecological indicators and biomasses of selected species groups are used to compare simulations. Results show how future ecological trajectories are sensitive to alternative configurations of EcoOcean, and yield moderate differences when looking at ecological indicators and larger differences for biomasses of species groups. Ecological trajectories are also sensitive to environmental drivers from alternative ESM outputs and RCPs, and show spatial variability and more severe changes when IPSL and RCP 8.5 are used. Under a non-fishing configuration, larger organisms show decreasing trends, while smaller organisms show mixed or increasing results. Fishing intensifies the negative effects predicted by climate change, again stronger under IPSL and RCP 8.5, which results in stronger biomass declines for species already losing under climate change, or dampened positive impacts for those increasing. Several species groups that win under climate change become losers under combined impacts, while only a few (small benthopelagic fish and cephalopods) species are projected to show positive biomass changes under cumulative impacts. EcoOcean v2 can contribute to the quantification of cumulative impact assessments of multiple stressors and of plausible ocean-based solutions to prevent, mitigate and adapt to global change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".