📖 Dive into the full paper for details on methods, stats, and ecological implications! onlinelibrary.wiley.com/doi/10.1002/...

🧩 Conclusions: Flower eDNA is a promising non-invasive tool for capturing arthropod–plant interactions in forest ecosystems. However, detection rates are patchy—likely influenced by flower traits, visitor behavior, and methodological nuances.

🔍 Method matters: pooling flowers before DNA extraction increased species detection. This is a key methodological insight for future eDNA sampling.

❓ Unexpectedly, microclimate (temperature) showed no significant effect on arthropod detection for either plant species. Forest structure mattered more.

🌳 For F. vesca, arthropod richness was higher in open, broadleaf-dominated plots—likely due to more light boosting pollinator activity. Forest density had a negative effect.

🔬 Using COI metabarcoding on washed flower samples, we detected 92 arthropod taxa—34 on F. vesca, 64 on T. pratense. Notably, T. pratense hosted nearly double the richness. Still, many taxa appeared only once per plot.

🌸 We translocated Fragaria vesca (wild strawberry) and Trifolium pratense (red clover) into 40 forest plots varying in density and tree composition. Each plant came paired: one accessible to pollinators, one covered as a control.

🙏 Huge thanks to all the volunteers contributing to biodiversity platforms. Your observations matter, immensely.
We hope this work helps make your data even more valuable.
📘 Full paper: doi.org/10.1007/s135...
📂 Code & data: zenodo.org/record/15252...

Citizen science is powerful, but not perfect.
Ignoring temporal bias can lead to flawed conclusions about how pollinators respond to climate change, risking misguided conservation efforts.
We can do better. And we must. 🐝🌍

So, what can we do?
✅ Include weekday/weekend in statistical models
✅ Be transparent about biases
✅ Combine citizen science with other datasets
✅ Promote standardized protocols

Some species, like Bombus hypnorum, are especially prone to weekend bias, likely because they nest in vertical structures and often enter buildings. Their urban, visible habits make them easier to spot.
Bias isn’t just temporal: it’s tied to species traits too. 🐝🏙️

When we modeled the emergence date of Bombus hortorum without adjusting for weekend bias, we found a significant advance over time.
After adjusting for the day of the week?
🚫 The trend disappeared.
📉 Bias can send us down the wrong path.

We compared 2M+ citizen science records and 340k museum specimens.
🎯 Citizen science peaked on weekends.
🏛️ Museum data? The opposite—more records on weekdays.
Two biases, two directions. Both matter for how we interpret trends.

One-third (33%) of bumblebee records from citizen science platforms come from weekends. This is well above what random sampling would predict (!!!)

Why does this matter? Because weekend bias can distort our understanding of how pollinators respond to climate change.

As rising temperatures shift the timing of plant flowering and pollinator emergence, accurate phenological data is crucial, but hard to collect. Citizen science helps fill the gap, especially for tracking hibernating pollinators. 🐝🌸📅
But there's a catch...

Yo diría que es del género Pemphredon 🙂

Thanks so much! Definitely agree - photo surveys are great for spotting those rare pollinators. It’s wild that no Mutilid wasps were found in 50k visits. Just goes to show how many other interactions out there we may still be missing out on!