Common processes drive metacommunity structure in freshwater fish
Bibliographic record
Abstract
Abstract Aim Environmental change affects metacommunity structure both directly—via abiotic factors and dispersal that affect species occurrence—and indirectly—via complex interactions among co‐occurring species. We examined how the three main metacommunity factors—environmental conditions, spatial processes and species associations—affect metacommunity structure and whether responses are predictable in real‐world systems by using novel methods to disentangle the drivers. Location Eastern Asia, northern Europe and central North America. Time period Contemporary. Major taxa studied Freshwater fish. Methods We used a dataset of freshwater fish species occurrences in temperate lakes in three countries in different biogeographic regions. We analysed co‐occurrence patterns by using a joint species distribution model. Results We demonstrated that environmental processes are the main drivers of species' distribution and diversity, suggesting that future climate change (anthropogenic alteration of abiotic factors) will heavily influence the structure of metacommunities. We also showed that spatial processes and species interactions mediated the influence of environmental processes, especially at the lake level. Main conclusions Our results indicate that ongoing changes in metacommunity structure are modulated not only by the direct impacts of shifting abiotic factors but also by indirect effects of species interactions. Our global analysis indicates that even under the current high rate of environmental change, an identifiable set of underlying processes can be used to predict impacts of this change on metacommunity structure.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".