ArticleThe Journal of animal ecology2026
Optimising daily sampling of plant-bee interaction networks.
Article in The Journal of animal ecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Optimising daily sampling of plant-bee interaction networks.The Journal of animal ecology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Plant-pollinator interactions are dynamic not only across seasons but also over hourly time scales, even beyond the day-night contrast. However, capturing the daily temporal variability of pollination networks during field data collection is challenging. Consequently, our understanding of daily variation in network composition and structure remains incomplete due to limited sampling coverage. Moreover, inaccurate sampling protocols can result in missing links that impair the identification of underlying processes and emerging patterns. We address this gap by studying the fine-scale temporal organisation of the diverse diurnal plant-bee relationships and examining how the interaction composition and network structure fluctuate throughout the day. We extensively sampled plant-bee interactions in a Brazilian Cerrado over two flowering seasons, encompassing six 2-h sampling periods throughout the day. We compared interaction β-diversity and network structural properties across sampling periods. A factorial approach was used to merge interactions among all combinations of sampling periods to assess how the composition and structure of the partial-day networks deviated from those exhibited by the full-day network (i.e. consolidating all interactions). Plant-bee interactions exhibited notable variations in network metrics across daily sampling periods. From dawn to dusk, plant-bee networks showed reduced modularity and specialisation but greater nestedness. Interaction β-diversity between sampling periods increased progressively with wider temporal intervals, mainly explained by interaction rewiring among shared species. Combining four 2-h sampling periods recovered up to ~80% of all pairwise interactions and ~ 82% of interaction frequencies, and generated partial-day networks that most closely resembled the structure of full-day networks. These best-performing combinations consistently drew from daily central hours, whereas early-morning and late-afternoon periods contributed fewer but unique interactions. Our study demonstrates that plant-bee interaction networks are time-sensitive at the intraday scale and that interaction completeness is not necessarily improved by increasing effort within a single time window, but rather by distributing sampling across, or focusing on, the most informative periods of the day. We provide a general framework to evaluate how temporal sampling design affects network representativeness in systems where interactions depend on time-of-day.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.