Changes in species composition and community structure during plant–pollinator community assembly
Bibliographic record
Abstract
The assembly of plant-pollinator communities has traditionally been explored from the perspective of species composition, often overlooking how interaction structure and the roles species play in their communities can change even when species composition remains constant. Here, we use 10 years of data to investigate the assembly of plant-pollinator networks in an intensively managed agricultural landscape. We compare the characteristics of assembling communities to those of mature and unrestored communities to explore if and how changes are reflected in species composition, network structure, and species' roles therein. Unexpectedly, we found that although species' composition of mature communities became increasingly dissimilar over time, the overall community structure and individual species' roles in assembling communities remained unchanged. Yet, the network structure of assembling communities gradually converged toward that of mature communities. Our results suggest that even when traditional diversity measures remain relatively invariant, network structure can uncover the dynamic nature of ecological communities, rendering interaction networks an important component of community assembly studies. Our findings advance the understanding of essential ecological processes underlying community assembly and provide insights into the mechanisms shaping species' roles within ecological networks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".