# Why do queen bumblebees emerge from hibernation during weekends?

Joan Díaz-Calafat, Jaime Luna-Santamaría

► To cite this version:

Joan Díaz-Calafat, Jaime Luna-Santamaría. Why do queen bumblebees emerge from hibernation during weekends?. Apidologie, 2025, 56 (4), pp.66. 10.1007/s13592-025-01192-x. hal-05160426

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# Why do queen bumblebees emerge from hibernation during weekends?

Joan DÍAZ-CALAFAT<fnref n="1" />,<fnref n="2" />✉, and Jaime LUNA-SANTAMARÍA<fnref n="1" />✉

Received 26 December 2024 – Revised 20 April 2025 – Accepted 4 June 2025

**Abstract** – Climate-driven shifts in bumblebee emergence and flowering time can disrupt plant reproduction and affect pollinator health. To study such mismatches, accurate phenological data is crucial but challenging to collect, especially for pollinator hibernation emergence. Citizen science is increasingly being used to expand the spatial and temporal scope of data collection in research. Nevertheless, these data can be biased due to different reasons. We found that 33.14% of records in Europe and 32.47% in North America were gathered on weekends – exceeding what would be expected by random chance and showing opposite patterns to bumblebee museum specimen records. Bias also affected queen emergence date and varied by species, suggesting that species-specific traits may mediate the bias extent. We also present a case study showing how adjusting for day-of-the-week effects can change the statistical significance of temporal trends in bumblebee emergence dates. We thus recommend including the day of the week in statistical models to account for temporal biases. Our findings highlight the importance of correcting temporal biases in citizen science data to ensure accurate evaluations of ecological responses to climate change.

citizen science / climate change / global warming / phenology / temporal bias

## 1. INTRODUCTION

In response to the increase in global temperatures caused by climate change, winter emergence of certain bumblebees in North America has advanced by approximately 10 days in the past 130 years (Bartomeus et al. 2011), and by 5 days over the last 20 years in European regions like Sweden (Blasi et al. 2023). Additionally, warmer springs have caused flowering times to advance by an average of 2.4 days per degree Celsius of warming in north-central

North America (Calinger et al. 2013) and by 3.6 days per degree Celsius in Europe (Willems et al. 2022). Despite both pollinators and plants adjusting their emergence and flowering times to warmer temperatures, phenological mismatch can take place when these responses occur at different intensities or are not sufficiently synchronized. For instance, if a plant species blooms earlier than usual due to warmer temperatures but its pollinators do not adjust their timing accordingly, the plant may experience reduced pollination (Kudo and Cooper 2019). This phenological mismatch can interfere with the coexistence of interacting species, compromising their biotic relationships and survival, ultimately affecting the fitness of both plants and pollinators. For

<footnote n="1">¹ Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box 190, 234 22 Lomma, Sweden</footnote>

<footnote n="2">² MUCBO | Museu Balear de Ciències Naturals (FJBS-MBCN), Carretera Palma-Port de Sóller, km 30,5, 07100 Sóller, Illes Balears, Spain</footnote>

<footnote>Corresponding author: J. Díaz-Calafat, joandiazcalafat@gmail.com joan.diaz.calafat@slu.se</footnote>

<footnote>Manuscript editor: Mathieu Lihoreau</footnote>
example, near the Rocky Mountain Biological Laboratory in Colorado, USA, the reduced synchrony between flowers and bumblebees has apparently resulted in a reduction of bumblebee abundance (Pyke et al. 2016).

Bumblebee emergence date is crucial for phenology studies, particularly in the context of climate change (Blasi et al. 2023; Kudo and Cooper 2019; Miller-Struttmann et al. 2022). When temperatures start to rise in early spring, queen bumblebees emerge from hibernation and search for a suitable nesting site, often finding refuge in old rodent burrows or grassy areas (Kells and Goulson 2003). This is the only moment in which queens will be foraging, as they need to collect pollen and nectar to create a food supply and lay their first batch of eggs. The quantity and quality of resources gathered during this stage determines the size of the colony, or even whether the colony will establish at all (Becher et al. 2024). Therefore, it is important that floral resources be available and that flowering time is synchronized with queen bumblebee emergence. Once workers develop from the first batch of eggs, they will be the ones in charge of collecting pollen and nectar, taking over the foraging and nest maintenance duties, and allowing the queen to focus solely on egg-laying. The colony then expands rapidly throughout mid to late summer, as more workers are produced to bring back nectar and pollen to sustain the growing nest. As late summer approaches, the queen begins to lay eggs that will develop into new queens and males. These new queens leave the nest to mate, with males dying shortly after. The fertilized new queens then seek hibernation sites for the winter, while the old queen and the remaining colony members perish as the weather gets colder. The new queens will emerge from hibernation the following spring, beginning the cycle again. However, not all bumblebee populations across climatic zones experience winter diapause. In warmer climates, colonies can develop year-round, and bivoltinism can take place in some populations (Hart et al. 2021; Stelzer et al. 2010). Additionally, in very hot and arid climates, bumblebees may undergo summer aestivation instead of winter diapause (Gurel et al. 2008).

Gathering comprehensive data on the timing of queen bumblebee winter emergence can present a challenge. Traditional methods are often labor-intensive, time-consuming, and logistically demanding, limiting the scope and scale of studies. Recognizing these challenges, research has increasingly turned to citizen science as a valuable tool for data collection (e.g., Blasi et al. 2023). Citizen science offers the opportunity to mobilize the collective efforts of volunteers, thereby significantly expanding the spatial and temporal coverage of data collection efforts. By engaging citizens in observing and recording bumblebee emergence events, researchers can harness a vast network of observers to gather valuable data across diverse habitats and geographical regions. Nevertheless, these data can be biased based on different factors such as regional accessibility, variability in reporting consistency, temporal coverage, observer taxonomic preference or observer experience and skill level (Díaz-Calafat et al. 2024; Rosário et al. 2024). In this article, we use bumblebee records from citizen science data across Europe and North America to assess temporal bias on bumblebee data in general and on queen bumblebee winter emergence in particular. Specifically, we focus on the weekend bias, which refers to a distortion in data collection caused by observations or recordings being more frequently made on weekends than on weekdays. In citizen science projects in particular, this bias can appear when volunteers are more likely to engage in data collection during their leisure time on weekends, leading to an overrepresentation of weekend data. As a result, the data may not accurately reflect temporal events or patterns that occur uniformly throughout time (Courier et al. 2013). To specifically assess weekend bias on citizen science collected data concerning bumblebees, we asked: (1) What is the extent of weekend recording in bumblebee phenology? (2) Does weekend bias change over time? (3) Is there weekend bias in bumblebee recording and queen emergence data? (4) Is winter emergence time of some bumblebee species more associated with weekend recording than others? Moreover, we present a case study on queen bumblebee
emergence timing where we assess the differ-
ences between the same statistical analysis con-
ducted without accounting for weekend bias and
with weekend bias correction.

## 2. METHODS

**2.1. Data collection**

Bumblebee occurrence data was downloaded
from the Global Biodiversity Information Facil-
ity (GBIF). We selected only georeferenced
records from Europe and North America, cover-
ing the period from 1999 to June 2024. Citizen
science data was downloaded by filtering “human
observations” in the *BasisOfRecord* variable of
the Darwin Archive Core, as these data typically
belong to citizen science platforms. This resulted
in a total of 2,065,696 data points. Similarly, we
downloaded data from museum specimens using
the same parameters and by filtering “preserved
specimens” from the GBIF data. This resulted
in 343,914 records. The citizen science dataset
is available from GBIF.org (2024; https://doi.
org/10.15468/dl.372w4a), and the museum data
from GBIF.org (2025; https://doi.org/10.15468/
dl.23svan).

**2.2. Data cleaning**

Citizen science observations with no species
data were removed, as well as duplicates based
on species, date and geographical coordinates.
Moreover, observations from the naturgucker.
de platform were excluded, as many bumblebee
identification errors were found. Then, occur-
rence data was cleaned using the 'Coordinate-
Cleaner' package in the R software (R Core
Team 2024; Zizka et al. 2019). This R pack-
age enables users to conduct various tests on
a collection of coordinate records, identifying
and eliminating any problematic entries. Coor-
dinates falling in a different country than that
one specified in the metadata were removed, as
well as those falling onto the sea or missing the
country in their metadata. Moreover, as poorly

geo-referenced occurrence records are often
erroneously assigned to centroids, we removed
coordinates within a 1 km radius of country capi-
tals and country centroids. For the same reason,
records located within 100 m of a list of biodi-
versity institutions included in the 'Coordinate-
Cleaner' package were also excluded. In addi-
tion to the previously mentioned cleaning steps,
occurrence records with low coordinate accuracy
(> 25 km) were removed. Coordinates lacking
accuracy data were assumed to be as precise as
indicated by their decimal places. This resulted
in a total of 1,211,937 clean bumblebee occur-
rence records. Museum specimen data was not
cleaned. For details on the data cleaning process,
see Table S1 in Supplementary Information.

**2.3. Statistical analyses**

All statistical analyses were conducted using
R 4.3.3 (R Core Team 2024).

(1) What is the extent of weekend recording in bumblebee phenology?

To visualize weekend bias, we calcu-
lated the number of records for each day of
the week, separately for Europe and North
America, across different years and bum-
blebee species. We then compared these
counts to the number of observations that
would be expected if they were evenly dis-
tributed throughout the week. We did this
for both citizen science data and museum
specimens. Then, to estimate the probability
of an observation occurring on a weekend,
we calculated the day of the week (Monday-
Sunday) for each bumblebee recording date.
We then created a binomial variable, coded
as 1 for weekends (i.e., Saturday and Sun-
day) and 0 for weekdays (i.e., Monday to
Friday). To adjust for the relative frequency
of weekends and weekdays within a week,
we applied weights to each observation
based on their inverse frequency: 1/2 for
weekends and 1/5 for weekdays. We used
logistic Generalized Linear Models (GLMs)
with a logit link to assess weekend bias. In these models, the binary weekend variable was the response and the only predictor was the intercept. This provides an estimate of the overall proportion of weekend observations, with the intercept representing the log-odds of an observation being made on a weekend. This model was run separately for citizen science data and museum specimens, as well as for Europe and North America.

(2) Does weekend bias change over time?

To assess any temporal tendencies of weekend bias in citizen science data, we ran logistic GLMs with a logit link. In these, weekend was used as a binary response and the observations' recording year as a predictor. These models were run for all bumblebees together as well as separately for Europe and North America.

(3) Is there weekend bias in bumblebee record-
ing and queen emergence data?

For this analysis, we restricted the data to
the occurrences belonging to the ten most
common bumblebee species for Europe and
North America (Figure 1). We sorted these
records in different climatic Köppen-Geiger
climate zones using the 'kgc' R package
(Bryant et al. 2017). These climatic zones
divide regions of the earth by their relative
heat and humidity through the year. Since
temperature is an important driver of bum-
blebee emergence from hibernation (Paw-
likowski et al. 2020), we selected the first
record for each bumblebee species in each
year (1999-2024) and Köppen-Geiger cli-
mate zone in order to increase our number
of data points. This record was considered
as the queen's emergence date from hiber-
nation. Records for *Bombus terrestris* (L.),
*B. lucorum* (L.), *B. magnus* Vogt, 1911 and
*B. cryptarum* (Fabricius, 1775) were joined
under the same taxon, as they belong to
cryptic species that are difficult to tell apart
morphologically in the field (Bossert 2015).
Then, we ran the same models as in *question

1 using this new dataset. Only citizen sci-
ence data was used to answer this question.

(4) Is winter emergence time of some bumblebee species more associated with weekend recording than others?

To assess differences in weekend record-
ing patterns across various bumblebee spe-
cies, we analysed the frequency of recording
of the ten most recorded bumblebee species
in Europe and North America in citizen sci-
ence data. For this, we ran logistic GLMs
with a logit link for each species separately.
Weekend was used as a binary response, and
the intercept as the only predictor. Two dif-
ferent datasets per species were used: one
including all occurrence data throughout
the flying season of each bumblebee, and
one with only the earliest record per species,
year and Köppen-Geiger climatic zone.

2.4. Case study

We conducted a case study using queen emer-
gence data from the species *Bombus hortorum*
(L.), a common bumblebee found throughout
most of Europe with a range that extends from
southern Iberia to northern Norway and spans
across the entire Siberian region (Rasmont
2016). In this case study, the objective was to
determine whether the winter emergence date of
this species has advanced over time. To do this,
we compared three statistical models: one that
ignored whether observations were recorded on
weekends and two that accounted for potential
temporal recording bias in different ways. We
ran Poisson GLMs with a log link for the same
dataset, using the 'glmmTMB' function in the
package of the same name (Brooks et al. 2017).
In these, the response variable was the recording
date as a Julian day (i.e., 1-365), and the record-
ing year was the only predictor. To address the
temporal structure of the data, the two models
that accounted for temporal bias included either
the day of the week (i.e., Monday-Sunday) or
the weekend status (i.e., weekend-weekday) as
a random effect. AIC was used to compare the
performance between models.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Citizen science bumblebee occurrence locations used in this study. For a clearer visualization, only occurrence data from the ten most common species are depicted. Different colors correspond to different Köppen-Geiger climatic zones: Af (tropical rainforest climate), Am (tropical monsoon climate), As (tropical Savanna with dry summers), Aw (tropical Savanna climate with dry winters), BSh (hot semi-arid climate), BSk (cold semi-arid climate), BWh (hot desert climate), BWk (cold desert climate), Cfa (humid subtropical climate), Cfb (temperate oceanic climate), Cfc (subpolar oceanic climate), Csa (hot-summer Mediterranean climate), Csb (warm-summer Mediterranean climate), Cwa (monsoon-influenced humid tropical climate), Cwb (monsoon-influenced temperate oceanic climate or subtropical highland climate), Dfa (hot-summer humid continental climate), Dfb (warm-summer humid continental climate), Dfc (subarctic climate), Dsc (Mediterranean-influenced subarctic climate), Dsb (Mediterranean-influenced warm-summer humid continental climate), Dwa (monsoon-influenced hot-summer humid continental climate) and ET (Tundra). The ten most reported species per Europe (top) and North America (bottom) are illustrated next to each map. European species from left to right and top to bottom: *Bombus pascuorum*, *B. terrestris*, *B. lapidarius* (all CC BY-NC Joan Díaz Calafat), *B. pratorum* (CC BY-NC "lastovka"), *B. hypnorum*, *B. hortorum* (both CC-BY-NC Joan Díaz Calafat), *B. bohemicus* (CC BY-NC Irina Butorina), *B. sylvarum* (CC BY-NC Miroslav Marić), *B. vestalis* (CC0 Rod Gibson), *B. jonellus* (CC-BY-NC J. Tyllinen), North American species from left to right and top to bottom: *B. impatiens* (CC0 Erik Schiff), *B. griseocollis* (CC-BY-NC Chase Bonanno), *B. bimaculatus* (CC-BY-NC Kurt Herr), *B. pensylvanicus* (CC-BY-NC Tami Reed), *B. ternarius* (CC-BY-NC Joe Bartok), *B. vosnesenskii* (CC-BY-NC Daniel Fitzgerald), *B. melanopygus* (CC-BY-NC Kris Ethington), *B. rufocinctus* (CC-BY-NC Kirsten Bovee), *B. sonorus* (CC-BY-NC Eric Carpenter), *B. perplexus* (CC-BY Bernie Paquette).</figcaption></figure>
### 3. RESULTS

We obtained a total of 957,585 citizen science bumblebee occurrence records from 62 different species in Europe, and 251,314 records from 58 species in North America. These records originated from 84 different participatory platforms in the European dataset, and only from three in the North American dataset (See Table S2 in Supplementary Information). The ten most common bumblebee species in Europe were: *Bombus pascuorum* (Scopoli, 1763; *n* = 244,206), *Bombus terr/luc. complex* (*n* = 239,691), *Bombus lapidarius* (L.; *n* = 131,493), *Bombus pratorum* (L.; *n* = 93,400), *Bombus hypnorum* (L.; *n* = 77,809), *Bombus hortorum* (*n* = 46,219), *Bombus bohemicus* Seidl, 1838 (*n* = 10,872), *Bombus sylvarum* (L.; *n* = 9,831), *Bombus vestalis* (Geoffroy, 1785; *n* = 9,780) and *Bombus jonellus* (Kirby, 1802; *n* = 9,568). For North America, these were: *Bombus impatiens* Cresson, 1863 (*n* = 84,270), *Bombus griseocollis* (De Geer, 1773; *n* = 33,411), *Bombus bimaculatus* Cresson, 1863 (*n* = 21,950), *Bombus pensylvanicus* (De Geer, 1773; *n* = 18,007), *Bombus ternarius* Say, 1837 (*n* = 11,318), *Bombus vosnesenskii* Radoszkowski, 1862 (*n* = 10,024), *Bombus melanopygus* Nylander, 1848 (*n* = 8,601), *Bombus rufocinctus* Cresson, 1863 (*n* = 7,018), *Bombus sonorus* Say, 1837 (*n* = 5,067) and *Bombus perplexus* Cresson, 1863 (*n* = 4,951). Regarding museum specimen data, we obtained 81,821 records for Europe and 262,093 for North America.

#### (1) What is the extent of weekend recording in bumblebee phenology?

We found that 33.14% of European citizen science bumblebee records were reported over weekends, while this was 32.47% for North America. In both cases, this was higher than what is expected by random chance (i.e., 28.57%, or 2 out of 7; Europe: GLM, $z = 57.14, p < 0.001$ and North America: GLM, $z = 25.05, p < 0.001$; Figure 2A). In contrast, only 22.28% of European museum specimen data was collected during weekends, compared to 15.11% in North America – both significantly lower than expected by random chance and showing the opposite pattern

to citizen science data (Europe: GLM, $z = -24.27, p < 0.001$ and North America: GLM, $z = -94.79, p < 0.001$; Figure 2B). Despite temporal recording patterns across continents being similar, citizen science reports on Fridays in North America were slightly higher than expected. In contrast, reports on Fridays in Europe were comparable to those on other weekdays.

#### (2) Does weekend bias change over time?

Weekend bias significantly changed over time, but in opposite directions for each continent (Figure 3). In Europe, the models show an increase in weekend reporting frequency with increasing recording year (GLM, $z = 11.54, p < 0.001$), whilst in North America there is a decrease (GLM, $z = -6.724, p < 0.001$).

#### (3) Is there weekend bias in bumblebee recording and queen emergence data?

When considering only the earliest record per year and Köppen-Geiger climatic zone for each of the ten most common bumblebee species in Europe and North America, weekend records were significantly higher than expected for both continents (GLM, $z = 2.5, p < 0.05$ and GLM, $z = 2.96, p < 0.005$, respectively).

#### (4) Is winter emergence time of some bumblebee species more associated with weekend recording than others?

Although weekend bias was observed across most single-species bumblebee datasets, it was only significant in *Bombus hypnorum* when focusing exclusively on winter emergence times (Table I). Moreover, our models identified two additional cases with p-values close to significance, suggesting a potential trend towards weekend bias for *B. sonorous* (*p* = 0.078) and *B. vosnesenskii* (*p* = 0.052).

### 3.1. Case study: *Bombus hortorum*

The earliest record date for *Bombus hortorum* significantly advanced (i.e., the Julian day
A Citizen science data

<figure><img src="image_3.png" /></figure>

B Museum data

<figure><img src="image_4.png" /></figure>

**Figure 2.** A. Percentage of citizen science-based observations from bumblebees reported on each day of the week in Europe and North America. B. Percentage of museum bumblebee records captured on each day of the week in Europe and North America. In both cases, the horizontal reference line indicates the percentage of observations expected at random (i.e., 14.3%).

decreased) with increasing recording year (GLM, z = -2.08, *p* = 0.037). However, when adding a weekend binary variable as a random factor, the effect of year ceased being statistically significant (GLM, z = 0.623, *p* = 0.533). Moreover, accounting for each day of the week separately as a random factor decreased such significance

even more (GLM, z = -556, *p* = 0.578). When comparing these three models, the AIC values were the lowest for the model that corrected for each day of the week separately (7064.7), followed by the model that did not have any random effect (7093.6) and the model that only corrected for weekends (8774.6).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Predicted weekend recording probability for Europe (blue) and North America (orange) based on the recording year.</figcaption></figure>

## 4. DISCUSSION

Our study reveals a clear weekend bias in bumblebee records submitted to citizen science platforms across both Europe and North America. While this temporal bias has been increasing over time for Europe, it is decreasing for North America. A similar bias is evident in museum specimen data, but in the opposite direction: fewer bumblebees are being collected on weekends. Importantly, we demonstrate that weekend bias influences the recording of phenological patterns, such as bumblebee winter emergence, although its impact may vary among species. Statistical models that disregard this temporal structure of citizen science data produced divergent results compared to models that incorporate the day of the week in their terms, potentially leading to the misinterpretation of the results. This underscores the need to account for such temporal biases in phenological studies, and particularly in those addressing climate change impacts. To our knowledge, we are the first to report this type of temporal bias in crowdsourced insect phenology data.

Weekend bias is a well-known artifact in data collected by volunteers, and has primarily been studied in bird-related data. Previous research has highlighted this bias in various contexts, such as clutch initiation dates of common birds (Cooper 2014), observations of rare bird species (Żmihorski et al. 2012) and the timing of bird migratory arrivals (Courier et al. 2013). The proportion of observations made by volunteers during weekends for bumblebee data (33.14%) is similar to that one reported for migratory bird arrivals (32%-33.7%) in previous studies (Courier et al. 2013), but not as high as that one reported for rare birds (up to 54.9%) by Żmihorski et al. (2012). Overall, this suggests that citizen science efforts are disproportionately concentrated on weekends, regardless of the project focus or taxa studied, likely due to the availability of volunteers.

The temporal patterns of museum specimen data have received relatively less attention. In this study, we show that bumblebees housed in museum collections have been predominantly collected on weekdays, likely because researchers and collectors typically conduct fieldwork

<figure><img src="image_6.png" /></figure>
<table><caption>**Table I** Model output for individual logistic GLMs assessing weekend bias for the ten most common bumblebee species in Europe and North America. In these models, weekend bias was assessed as a binary variable, and recording year was the only predictor used. Two different datasets were used: one with all records available for each bumblebee species throughout the flying season ("all records"), and one with only the earliest record per species, year and Koeppen-Geiger climatic zone ("earliest records"). Note that the model concerning the earliest records of *Bombus bimaculatus* was excluded because there were not enough data points available. Significant p-values appear in bold</caption><thead><tr><th rowspan="2">Continent</th><th rowspan="2">Species</th><th colspan="2">All records</th><th colspan="2">Earliest records</th></tr><tr><th>Z score</th><th>P value</th><th>Z score</th><th>P value</th></tr></thead><tbody><tr><td rowspan="8">Europe</td><td><i>B. pascuorum</i></td><td>33.81</td><td><strong>&lt; 0.001</strong></td><td>0.964</td><td>0.335</td></tr><tr><td><i>B. terr/luc complex</i></td><td>30.69</td><td><strong>&lt; 0.001</strong></td><td>-0.534</td><td>0.593</td></tr><tr><td><i>B. lapidarius</i></td><td>19.89</td><td><strong>&lt; 0.001</strong></td><td>0.991</td><td>0.321</td></tr><tr><td><i>B. pratorum</i></td><td>20.15</td><td><strong>&lt; 0.001</strong></td><td>0.697</td><td>0.486</td></tr><tr><td><i>B. hypnorum</i></td><td>21.35</td><td><strong>&lt; 0.001</strong></td><td>1.989</td><td><strong>0.047</strong></td></tr><tr><td><i>B. hortorum</i></td><td>12.41</td><td><strong>&lt; 0.001</strong></td><td>0.416</td><td>0.677</td></tr><tr><td><i>B. bohemicus</i></td><td>0.847</td><td>0.397</td><td>0.436</td><td>0.663</td></tr><tr><td><i>B. sylvarum</i></td><td>0.356</td><td>0.722</td><td>0.798</td><td>0.425</td></tr><tr><td rowspan="8">North America</td><td><i>B. vestalis</i></td><td>4.846</td><td><strong>&lt; 0.001</strong></td><td>1.290</td><td>0.197</td></tr><tr><td><i>B. jonellus</i></td><td>2.448</td><td><strong>0.014</strong></td><td>1.146</td><td>0.252</td></tr><tr><td><i>B. impatiens</i></td><td>11.21</td><td><strong>&lt; 0.001</strong></td><td>1.146</td><td>0.252</td></tr><tr><td><i>B. griseocollis</i></td><td>6.662</td><td><strong>&lt; 0.001</strong></td><td>0.960</td><td>0.337</td></tr><tr><td><i>B. bimaculatus</i></td><td>7.067</td><td><strong>&lt; 0.001</strong></td><td>-</td><td>-</td></tr><tr><td><i>B. pensylvanicus</i></td><td>11.64</td><td><strong>&lt; 0.001</strong></td><td>0.705</td><td>0.481</td></tr><tr><td><i>B. ternarius</i></td><td>7.262</td><td><strong>&lt; 0.001</strong></td><td>0.834</td><td>0.404</td></tr><tr><td><i>B. vosnesenskii</i></td><td>7.588</td><td><strong>&lt; 0.001</strong></td><td>1.940</td><td>0.052</td></tr><tr><td></td><td><i>B. melanopygus</i></td><td>4.628</td><td><strong>&lt; 0.001</strong></td><td>1.466</td><td>0.143</td></tr><tr><td></td><td><i>B. rufocinctus</i></td><td>2.336</td><td><strong>0.020</strong></td><td>-0.144</td><td>0.886</td></tr><tr><td></td><td><i>B. sonorus</i></td><td>5.034</td><td><strong>&lt; 0.001</strong></td><td>1.765</td><td>0.078</td></tr><tr><td></td><td><i>B. perplexus</i></td><td>3.737</td><td><strong>&lt; 0.001</strong></td><td>0.148</td><td>0.882</td></tr></tbody></table>

within institutional or academic settings that follow weekday schedules, with limited activity on weekends. The similarity in weekend bias between the two continents highlights a global trend in data collection practices. However, despite this, temporal trends in citizen science seem to diverge. In Europe, weekend reporting has increased over time, while North America shows the opposite pattern. Courter et al. (2013) already showed a decrease over time of weekend reporting on bird migration arrival over time in North America. The increase in weekend reporting for Europe could be probably linked to the growing popularity of citizen science, as well as

to the proliferation of region-specific projects and the larger number of different participatory platforms available compared to North America. However, the reasons behind these divergent trends will have to be explored in future studies. Understanding regional differences in public engagement with citizen science projects – and how these differences contribute to bias – is vital for accurate data interpretation. This is especially relevant in contexts where data collection may involve varying levels of bias across regions, or where regional differences influence phenology. For example, the impacts of climate change on phenology can vary significantly between

<figure><img src="image_7.png" /></figure>
regions (Stemkovski et al. 2023), making it cru-
cial to disentangle the varying levels of bias in
citizen-science data from differences in phenol-
ogy themselves. Moreover, both temperature and
citizen science engagement are higher in urban
areas (Díaz-Calafat et al. 2024; Heisler and
Brazel 2010), meaning that urban records may
disproportionately reflect earlier phenological
events or extended activity periods, particularly
for species sensitive to temperature cues such as
bumblebees (Merckx et al. 2021; Stelzer et al.
2010).

We also show the impact of weekend bias on the recording of phenological data in both Europe and North America, particularly regarding the timing of bumblebee queen emergence from hibernation. Weekend bias on winter emergence time was evident when all bumblebee species were analysed together, yet when assessing species individually it became inconspicuous for certain species, illustrating that species-specific traits, such as detectability or habitat preferences, may mediate the extent of bias. For example, observations of winter emergence in *B. hypnorum* were notably more frequent on weekends. This species nests in vertical structures such as trees (hence its English common name: tree bumblebee), but has become quite abundant in urban areas. Urban environments provide numerous well-insulated and well-protected aboveground nesting sites, including within buildings (Prŷs-Jones 2019). We hypothesize that these nesting habits make *B. hypnorum* easier to detect indoors by observers during weekends, as volunteers typically work away from home during weekdays and may spend more time at home during weekends. These findings suggest that such biases might disproportionately affect species with specific ecological traits that align with human activity patterns, which can distort interpretations of climate change impacts on phenology shifts of such species, potentially leading to the implementation of inaccurate policies and the mislocation of conservation priorities, efforts and resources.

Researchers should consider accounting
for weekend bias when analysing long-term

phenological datasets to avoid over- or under-
estimating shifts in species activity. In our
case study, the winter emergence date for *B. hortorum* advanced over time, but this trend
lost statistical significance when accounting
for weekend bias and daily recording patterns,
illustrating the importance of disentangling
true phenological shifts from recording biases.
Based on this, we recommend the inclusion
of the day of the week in phenology models,
as already suggested by Courter et al. (2013).
This seems to improve the performance of the
models by accounting for the noise produced
by temporal patterns in data structure. In our
case study, including the day of the week in the
phenology model resulted in a lower AIC value
compared to including a binary weekend vari-
able, implying that there may be other tempo-
ral patterns affecting data collection beyond the
weekend effect (e.g., Díaz-Calafat et al. 2024).
In particular, our findings show a higher-than-
expected proportion of observations on Fridays,
at least in North America, further supporting
this idea.

In conclusion, while citizen science offers
invaluable opportunities for expanding pheno-
logical datasets across large spatial and tempo-
ral scales, we call for the careful consideration
of the biases inherent to such datasets. Not all
ecological questions may be equally suited for
unsupervised data collection. Projects relying
on precise timing or identification, such as
tracking pollinator emergence, may be particu-
larly sensitive to observer biases unless stand-
ardized protocols are in place. Implementing
rigid data collection guidelines – such as spec-
ifying observation timing, effort, and identifi-
cation criteria – and providing proper training
or validation tools (e.g., image recognition,
expert verification) can significantly improve
data reliability. In the studies where unsuper-
vised participation takes place, incorporat-
ing statistical corrections for known biases
becomes essential to enhance the reliability of
such datasets. Our findings demonstrate how
weekend bias can distort interpretations of
phenological changes in bumblebee activity.
Thus, the success of citizen science depends
not only on volunteer engagement but also on
thoughtful project design that anticipates and
corrects for potential sources of error. Future
research should continue to explore these
biases to enhance the reliability of citizen sci-
ence data in tracking species responses to cli-
mate change, incorporating insights from both
volunteer behaviour and species-specific traits.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material
available at https://doi.org/10.1007/s13592-025-01192-x.

ACKNOWLEDGEMENTS

We are grateful to all volunteers that contribute their time
and observations to participatory platforms, as well as
to those people that make such platforms and projects
possible for the monitoring of biodiversity. We also genu-
inely appreciate everyone who generously allowed us to
use their bumblebee pictures, including: "lastovka", Irina
Butorina, Miroslav Marić, Rod Gibson, J. Tyllinen Erik
Schiff, Chase Bonanno, Kurt Herr, Tami Reed, Joe Bar-
tok, Daniel Fitzgerald, Kris Ethington, Kirsten Bovee,
Eric Carpenter and Bernie Paquette.

AUTHOR CONTRIBUTIONS

JDC: Conceptualization, Data curation, Formal Analy-
sis, and Software. Both authors: Methodology, Valida-
tion, Visualization, Writing – original draft, and Writing
– review & editing.

FUNDING

Open access funding provided by Swedish University of Agricultural Sciences. This research has received no funding.

DATA AVAILABILITY

The data supporting this article can be downloaded from
GBIF at https://doi.org/10.15468/dl.372w4a and https://
doi.org/10.15468/dl.23svan. The code used for analy-
ses can be found in Zenodo at: https://doi.org/10.5281/
zenodo.15252776.

DECLARATIONS

**Competing interests** The authors have no relevant finan-
cial or non-financial interests to disclose.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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