Forecasting pollination declines through DNA barcoding: the potential contributions of macroecological and macroevolutionary scales of inquiry
Bibliographic record
Abstract
While pollinators are widely acknowledged as important contributors to seed production in plant communities, we do not yet have a good understanding of the importance of pollinator specialists for this ecosystem service. Determination of the prevalence of pollinator specialists is often hindered by the occurrence of cryptic species and the limitations of observational data on pollinator visitation rates, two areas where DNA barcoding of pollinators and pollen can be useful. Further, the demonstrated adequacy of pollen DNA barcoding from historical records offers opportunities to observe the effects of pollinator loss over longer timescales, and phylogenetic approaches can elucidate the historical rates of extinction of specialist lineages. In this Viewpoint article, we review how advances in DNA barcoding and metabarcoding of plants and pollinators have brought important developments to our understanding of specialization in plant-pollinator interactions. We then put forth several lines of inquiry that we feel are especially promising for providing insight on changes in plant-pollinator interactions over space and time. Obtaining estimates of the effects of reductions in specialists will contribute to forecasting the loss of ecosystem services that will accompany the erosion of plant and pollinator diversity.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".