Geographic variation in the robustness of pollination networks is mediated by modularity
Bibliographic record
Abstract
Abstract Aim Extinctions and coextinctions seriously threaten global plant–pollinator assemblies, and thus a better understanding of the geographic variability in their robustness is urgently required. Although the geographic patterns of species extinction rates are frequently explored, it remains largely unknown how the subsequent coextinction risk of species varies across environments. We hypothesize that the geographic variation of network robustness to extinctions is mediated by modularity – the tendency of a network to be organized in modules of strongly interacting species – because modularity buffers perturbations and varies across environments. Location Global. Time period Current. Major taxa studied Flowering plants and their animal pollinators. Methods Using 79 pollination networks, we first explored the variation of network robustness across geographic and climatic gradients and, second, analysed the role of modularity in explaining the association between robustness and those environmental gradients. We quantified the robustness of taxonomic, functional and phylogenetic diversity of pollinators under simulated coextinctions triggered by specialist‐first, generalist‐first, and random plant removals. Results Only the robustness of phylogenetic diversity under specialist‐first removals showed a global latitudinal trend by which robustness increased towards the tropics on mainlands but increased towards the poles on islands. Generally, robustness was strongly promoted by modularity, and also directly dampened by insularity and precipitation seasonality (PS). Through the mediation of modularity, robustness was indirectly increased by actual evapotranspiration and PS, and decreased by the interaction between PS and insularity. Besides, network size and sampling area affected robustness but did not influence modularity. Main conclusions The indirect environmental effect on robustness via modularity was prevalent, which supports our hypothesis and reveals the importance of network structure in mediating the geographic variation of network robustness. The global pattern of robustness indicates the phylogenetic diversity of pollinators is relatively vulnerable to the loss of specialist plants in tropical islands and high‐latitude mainland compared to other regions.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".