Co‐Extinctions and Co‐Compensatory Species Responses to Climate Change Moderate Ecosystem Futures
Bibliographic record
Abstract
Consensus has been reached that the sequential loss of biodiversity leads to a non-linear and accelerating decline in ecosystem properties. The form of this relationship, however, is based on theory and empirically derived observations that do not include species co-extinctions. Here, we use data from marine benthic invertebrate communities to parameterise trait-based extinction models that adjust the probability of species extirpation and compensation by including the dependencies between different species across a gradient of climate-driven environmental change. Our simulations reveal that the inclusion of static co-extinctions leads to more pronounced declines in the trajectories of sediment bioturbation-a process of great importance to the functioning of marine ecosystems-than those observed with sequential losses of single species. Compensatory mechanisms and the allowance of the formation of new interactions derived from local and regional species pools moderate the compounding influence of co-extinction but introduce additional variability in community response depending on the composition and functional role of incoming and outgoing species. Our observations emphasise the importance of accounting for local and regional community dynamics, especially in highly connected systems that are prone to extinction cascades when projecting the ecosystem consequences of altered biodiversity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".