Marine communities do not follow the paradigm of increasing similarity through time
Bibliographic record
Abstract
Humans have transformed ecosystems through habitat modification, harvesting, species introduction, and climate change. Changes in species distribution and composition are often thought to induce biotic homogenization, defined as an increase in the spatial similarity of species compositions through time. However, it is unclear whether homogenization is common in ocean ecosystems and if changes in similarity exhibit linear or more complex dynamics. Here, we assessed patterns of homogenization or its converse (differentiation) across more than 175,000 samples of 2,006 demersal fish species from 34 regions spanning six decades and 20% of the planet’s continental shelf area. While ten regions (29%) recorded significant homogenization, eleven (32%) recorded significant differentiation. Non-monotonic temporal fluctuations in species composition occurred in 15 regions, highlighting complex dynamics missed by before-and-after snapshots that can drive spurious conclusions about trends in similarity. Fishing pressure and temperature helped explain variance in similarity across years and regions. However, the strength and direction of these effects differed by region. Here we showed that, despite intense anthropogenic impacts on the oceans, the majority of demersal marine fish communities do not follow the global homogenization paradigm common in other realms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".