Same mesozooplankton functional groups, different functions in three Arctic marine ecosystems
Bibliographic record
Abstract
Abstract The trophic relationships interconnecting marine organisms together into a dynamic trophic network drive the structure and the functioning of the entire ecosystem. Since the flow of carbon within trophic networks is controlled by a variety of functional traits related to food acquisition and individual survival, it is crucial to understand how functional diversity relates to marine ecosystems properties such as the resistance and resilience against perturbations. In the Arctic, marine ecosystems are facing stronger and faster environmental changes than anywhere on Earth, leading to profound perturbations in the planktonic assemblages at the base of the trophic networks. While it is known that mesozooplankton plays a crucial role of matter and energy hub within marine Arctic food web, the precise role of the diverse mesozooplankton functional groups in carbon circulation and in marine ecosystems functioning remains poorly known. We coupled a trait‐based approach of mesozooplankton diversity to an ecological network analysis approach to test whether similar mesozooplankton functional groups played similar ecological roles in three Arctic ecosystems during the summer period. We formed nine mesozooplankton functional groups by gathering different species according to their feeding strategies. Then we implemented those into inverse food web models (linear inverse modelling) describing three contrasted Arctic ecosystems. In each ecosystem, we performed sensitivity analysis experiments where each mesozooplankton functional group was removed one at a time. Our results showed that, although the same main functional groups composed the three ecosystems, the few outstanding changes observed in the carbon circulation within the food web were strongly controlled by both the initial whole‐network properties and productivity of the ecosystem. The various roles played by a given mesozooplankton functional group in the ecosystem depend on its impact on carbon flows through the food web it belongs to. As a result, identifying which functional groups could be threatened, and which carbon flows could be altered by climate change is critical information to predict future ecosystems functioning. Read the free Plain Language Summary for this article on the Journal blog.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.156 | 0.001 |
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; both teacher heads agree on what is shown here.
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".