Comparison of grassland plant-pollinator networks on dairy farms in three contrasting French landscapes
Bibliographic record
Abstract
Temperate grasslands provide both habitats and flower resources for pollinators in agricultural landscapes. Plant-pollinator networks change according to local and landscape variables, which are important to identify to help conserve pollinators in grasslands. We analysed plant-pollinator networks in 18 grasslands on experimental dairy farms located in three French regions contrasting by their climate, altitude, landscape or management. We combined visual surveys and pollen DNA barcoding. Our objectives were to determine which environmental factors influence pollinator taxa abundance and diversity and differences among the visual plant-pollinator networks in the three farming regions. Flower-visiting insects were trapped in six grasslands per farm during three sessions from mid-April to mid-July along fixed 400 m2 transects. Insects were identified individually to the lowest taxonomic rank possible. Pollen carried by insects was identified using nuclear ribosomal ITS2 sequences belonging to the NCBI nucleotide database. The size and diversity of plant-pollinator networks were much larger and higher in permanent grasslands at the two farms located in lowlands (Mirecourt) and mountains (Marcenat) than those at the farm with temporary grasslands and a crop landscape (Lusignan), but the degree of specialisation (H2′) was relatively similar and low (mean of 0.46). Diptera, especially Empididae and Syrphidae, represented most plant-pollinator interactions in Mirecourt and Marcenat, while Hymenoptera were more abundant at Lusignan. The percentage of semi-natural habitats in 500 m buffers and vegetation height explained 23% of the variance in pollinator abundance. Ranunculus sp. Knautia arvensis, Centaurea jacea and Trifolium repens were key plant species in the networks. DNA metabarcoding of pollen loads identified 114 genera in addition to those identified by visual observations (+34–42 per site), reflecting insects’ floral pathways and differences in the immediate landscape among farms. This study highlighted the importance of Diptera in plant-pollinator networks and the need to conserve permanent grassland diversity to conserve pollinators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".