# Buzzy bees, quantifying bee activity within sunflower fields using acoustic monitoring and deep learning

Ludovic Crochard, Colin Fontaine, Mathilde Baude, Maxime Ragué, Didier

Bas, Sabrina Gaba, Vincent Bretagnolle, Romain Julliard, Yves Bas

► To cite this version:

Ludovic Crochard, Colin Fontaine, Mathilde Baude, Maxime Ragué, Didier Bas, et al. Buzzy bees, quantifying bee activity within sunflower fields using acoustic monitoring and deep learning. 2024. [hal-04819327v1](https://hal.science/hal-04819327v1)

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Preprint submitted on 4 Dec 2024 (v1), last revised 15 Jan 2026 (v2)

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<figure><img src="image_1.png" /></figure>
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Buzzy bees, quantifying bee activity within
sunflower fields using acoustic monitoring
and deep learning

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Ludovic Crochard<sup>a,*</sup>, Colin Fontaine<sup>a</sup>, Mathilde Baude<sup>b,c</sup>, Maxime Ragué<sup>d,e</sup>, Didier Bas<sup>f</sup>, Sabrina Gaba<sup>d,e</sup>, Vincent Bretagnolle<sup>g,e</sup>, Romain Julliard<sup>a</sup>, Yves Bas<sup>a,h</sup>

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<sup>a</sup> Centre d'Ecologie et des Sciences de la Conservation, UMR 7204 MNHN-CNRS-Sorbonne Université,
Muséum national d'Histoire naturelle, F-75005, Paris, France

<sup>b</sup> Université d'Orléans, Orléans, France

<sup>c</sup> Sorbonne Université, UPEC, Université Paris Cité, CNRS, IRD, INRAE, Institut d'Ecologie et des Sciences
de l'Environnement (iEES-Paris), Paris, France

<sup>d</sup> INRAE, CNRS, Université de La Rochelle, USC 1339, Centre d'Etudes Biologiques de Chizé, F-79360
Villiers-en-Bois, France

<sup>e</sup> LTSER "Zone Atelier Plaine & Val de Sèvre", CNRS, Beauvoir-sur-Niort, France

<sup>f</sup> none (volunteer), France

<sup>g</sup> CNRS, Université de La Rochelle, UMR 7372, Centre d'Etudes Biologiques de Chizé, F-79360 Beauvoir-sur-Niort, France

<sup>h</sup> CEFE, Université de Montpellier, CNRS, EPHE, IRD, Montpellier, France

* Corresponding author

Ludovic Crochard

43 rue Buffon - Campus Buffon

3 allée des Crapauds - Bât 135

CESCO - UMR 7204

75005 Paris

Correspondence: ludovic.crochard1@mnhn.fr
ABSTRACT

Since 70% of the world's crops depend on pollinators for production and concerns are growing regarding insect decline, it is essential to implement robust and efficient monitoring of pollinator activity. However, traditional methods of pollinator monitoring are generally time-consuming and destructive. With the rise of technology, passive methods are being developed using computer vision or acoustic recording coupled with machine learning, and are offering the possibility to increase the temporal and spatial coverage of biodiversity and ecosystem functions monitoring. Passive acoustic monitoring is a promising method for tracking pollinators. However, it has rarely been implemented, and has mostly used relatively old machine learning methods. Deep learning methods, originally, developed for image analysis are beginning to be used for acoustic monitoring of various taxa, including flying insects. Here we proposed a method for quantifying pollinator activity in sunflower fields, based on the automatic identification of sounds produced by their beating wings. We tested a random forest and a deep learning algorithm on acoustic recordings using a new open access software dedicated to acoustic biodiversity monitoring, named TadariDeep. We found a higher performance of deep learning compared to random forest algorithms for the classification of pollinator flight sounds. The comparison of the acoustic monitoring of insects with pollinator activity estimated from a common protocol based on visual observations validates this method. We found that acoustic monitoring coupled with deep learning sound recognition provides a more realistic view of pollinator activity than visual observations, thanks to continuous monitoring. Acoustic monitoring of pollinators using deep learning, therefore, appears as a reliable method to quantify pollinator activity and might be used to monitor insect pollination over large spatial and temporal scales. Further improvements are however still needed for the species identification of pollinators.

**Keywords:** Convolutional neural networks, Data augmentation, Passive acoustic monitoring, Pollination services
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Animal pollination is an essential ecological function involved in the reproduction of 90% of flowering plants, often through insects (Ollerton et al., 2011). In agricultural landscapes, 70% of major global food crops benefit from insect pollination, representing approximately 35% of the annual global food production (Klein et al., 2007). This makes insect pollination a major ecosystem service that is, however, being threatened by pollinator decline (Biesmeijer et al., 2006; Potts et al., 2010; Zattara and Aizen, 2021). Because pollinator communities exhibit significant spatial and temporal variability (Gay et al., 2024; Reverté et al., 2019), pollinator monitoring is important to quantify pollinator activity over space and time and indirectly assess pollination services.

Several methods are used to monitor insect pollinators. The most common involve capturing insects using pan traps or sweep nets along transects (O'Connor et al., 2019; Westphal et al., 2008). These are often used to make spatial comparisons of the pollinator diversity according to the landscape features and/or agricultural practices, to identify the interactions they establish with flowering plants (e.g. Crochard et al., 2022) and to study the contribution of insect-pollination to crop yield (e.g. Perrot et al., 2019). These methods usually involve the killing of captured individuals and are highly labor-intensive and time-consuming since they require a significant amount of time in the field to catch pollinators and in the laboratory to identify them (Montgomery et al., 2021). This high time cost tends to limit the spatial and temporal coverage of pollination studies.

Thanks to technological developments, passive and non-destructive methods of biodiversity monitoring are being developed (van Klink et al., 2022). In particular, bio-acoustic methods based on automatic sound classification with machine learning algorithms (Gibb et al., 2019), are already used to study birds (e.g., Kahl et al., 2021; Metcalf et al., 2022), bats (e.g., Kerbiriou et al., 2019; Roemer et al., 2021), and marine mammals (e.g., Shiu et al., 2020; Van Uffelen et al., 2017). Regarding insects, acoustic monitoring has already been developed to study the impact of anthropogenic pressures on Orthopteran communities (Jeliazkov et al., 2016; Penone et al., 2013) but also for aquatic insects (Desjonquères et al., 2020; Gottesman et al., 2020). In the case of flying insects such as pollinators, they can be detected through the sound they make beating their wings in flight (Kawakita and Ichikawa, 2019). Early studies on pollinator acoustics focused mainly on bumblebees. They studied colony dynamics (Heise et al., 2020), the link between their characteristic sound frequencies and morphological traits known to play a role in pollination efficiency (Miller-Struttmann et al., 2017) or the specific identification of a limited number of bumblebee species (Gradišek et al. 2017) or bee and hornet species (Kawakita and Ichikawa 2019). Nevertheless, these studies rely on rather potentially outdated machine learning methods, such as support-vector machine or random forest, to identify and classify the sounds emitted by pollinators. Since 2016, a machine learning method relying on convolutional neural networks has become increasingly popular in bioacoustics (Stowell, 2022). This class of methods achieved breakthroughs in automatic image classification (Hicks et al., 2021; Mohanty et al., 2016; Weinstein, 2018) and can analyze the spectrograms of recorded sounds analogously. This method has been widely used because it is more efficient than classical machine learning methods, making fewer errors in sound classification, and it can be used for the monitoring of various taxa, such as birds, marine mammals, bats, fish (Stowell, 2022) and most recently flying insects (Folliot et al., 2022).
In this context, we aimed to develop a convolutional neural network for automatically recognizing and classifying sounds emitted by pollinators during their flight to quantify pollinator activity in agricultural fields. Sunflower is the most important oil crop in terms of cultivated area in Europe (FAOSTAT, 2020), and the yield increase due to insect pollination is estimated to be between 18% and 100% (i.e. doubling the yield) (Carvalheiro et al., 2011; Garibaldi et al., 2016; Greenleaf and Kremen, 2006; Perrot et al., 2019). Sunflower is mostly visited and pollinated by honeybees, which account for between 72 and 97.8% of pollinator visits (Bartual et al., 2018; Carvalheiro et al., 2011; Greenleaf and Kremen, 2006). This makes it a good candidate for monitoring the dynamics of pollinator activity, without necessarily knowing their species identity (Rader et al., 2016). Here, we first compared the performance of a deep learning method based on convolutional neural networks trained on spectrograms using the newly developed TadariDeep software (https://github.com/YvesBas/TadariDeep) with a random forest based method trained on sound events features using the Tadarida software. Then, to test the reliability of acoustic monitoring, we investigated whether the estimate of pollinator activity from continuous acoustic monitoring reflects those estimated by a classical method, i.e. replicated sweep net sampling along transects. We studied the relationship between the number of buzzes detected and the number of honeybees sampled along the transects both on the days of transect sampling and throughout the flowering period of the sunflower fields.

# Material & Methods

## A) Site description

In 2020, we selected 30 sunflower fields from the Long-Term Social-Ecological Research site "Zone Atelier Plaine & Val de Sèvre" in South-West France. This research site is 435 km² and composed of 87% of cultivated areas (Bretagnolle et al., 2018 a,b), dominated by cereals and sunflowers, representing roughly 55% and 13% of the cultivated area, respectively. To promote heterogeneity in pollinator activity among sunflower fields, fields were selected to cover the landscape gradients of semi-natural habitats, sunflowers, and organic fields. Selected sunflower fields had an average area of 6 ha (± 4.6 SD). The flowering of these fields ranged from early July to mid-August, and lasted between 2 and 3 weeks per field.

## B) Acoustic records

### Recording device and records pretreatment

We used AudioMoth recorders, which are low-cost, small, and low-energy (Hill et al., 2018) to perform audio recording (see SI.A for the description and parameters). In each field, approximately 20m from the field edge, we placed an Audiomoth 10 to 20 cm from a randomly selected sunflower head. A windscreen covered the Audiomoth to reduce the wind sound. We made continuous audio recordings between 6 am and 10 pm, at 16 kHz sampling rate, to cover the period between sunrise and sunset at our study site during July and August.

We aimed to compare the performance of random forest and convolutional neural networks for pollinator buzz detection. This requires a training dataset, but to our knowledge there is no a database of annotated pollinator buzzes. To build our training dataset we proceeded in two
steps. First, we manually annotated a restricted number of audio records for which we knew that pollinators were present (see "Step 1: Building the training datasets"). By annotating 60 minutes of records, we obtained 225 pollinator sounds and 1627 other sounds. Second, to increase the size of our training dataset without spending too much time listening to recordings to manually annotate pollinator buzzes, we trained a random forest classifier on this initial training dataset and used it to identify pollinator buzzes in more than 400,000 audio recordings. Thanks to a stratified sampling of the results that were listened to and manually annotated, we obtained 12 644 extra pollinator sounds and 9 565 extra other sounds.

These steps were achieved using the Tadarida toolbox (Bas et al., 2017) designed to analyze the recordings and build classifiers of sound events. To optimize the use of this toolbox, it was necessary to pre-process our recordings. We cut them into sub-records of 10 seconds thanks to Kaleidoscope (https://www.wildlifeacoustics.com/products/kaleidoscope-pro) software and thus obtained 4 014 996 10-second records. Then, Tadarida-L, a component of Tadarida toolbox, was used to detect sound events on spectrograms of all of these records (Table S1). Fast Fourier Transformation window was set to a size of 1024 and an overlap of 75 % so that the time resolution is 16 ms, and the frequency resolution is 7.8 Hz. To train Tadarida-C classifiers to recognize flying pollinators from other sound events, we built several training datasets. For this, we proceeded in several steps.

## Step 1: Building the training datasets

### - Training dataset A

Machine learning classification methods need a training dataset from which the algorithm learns to classify sound events into predefined categories. Such training datasets are made of labeled sound events, the labels corresponding to the classes into which sounds should be classified *a posteriori*. To ensure that our training dataset included pollinator sound events, we randomly selected the recordings from 12 out of 291 5-minute long observation sessions, representing 375 10-second long recordings, for which we knew that pollinators were present (see Supporting Information B). Sound events that Tadarida-L detected within these 375 records were listened to and labeled using eight classes still using Tadarida-L. Three of the classes corresponded to pollinators "*Apis mellifera*", "*Bombus sp.*", and "Unidentified pollinator", which we could differentiate thanks to the visual observations made. Table S2 presents the full list of classes and the number of sound events labeled correspondingly that constitute the training dataset A, comprising a total of 225 pollinator sound events. Then from this training dataset, we used Tadarida-C to build the first classifier based on a random forest algorithm, hereafter named "RF-0" (Fig. 1 and see the "Step 2: Classifier training" section for details).

### - Training dataset B

To ensure that all possible sound conditions were represented in our training dataset, 10% of all records not coupled to the visual observations were randomly selected. Based on their acoustic features, we classified all sound events detected in these with our classifier "random forest-0" and obtained for each recording a confidence score for the "pollinator" class, which corresponds to the sum of the confidence scores of "*Apis mellifera*", "*Bombus sp.*" and "Unidentified pollinator" classes. The higher the score, the more likely it is that the record contains a pollinator sound. We defined six pollinator score groups based on the confidence
score of the "pollinator" class (i.e. 1: 0-0.5; 2: >0.5-0.6; 3: >0.6-0.7; 4: >0.7-0.8; 5: >0.8-0.9; 6: >0.9).
To improve the training dataset, we wanted to increase the number of sound events in the
"pollinator" class and correct the main errors made by the classifier "random forest-0" (not a
pollinator but a high confidence score of the "pollinator" class). For this, we conducted a
sampling of the recordings stratified by sunflower field and "pollinator" confidence score
group, with the number of recordings sampled increasing with confidence score groups. All of
these recordings were listened to and all sound events were labelled with Tadarida-L. We
obtained a training dataset B with 17 classes of sounds and a total of 12 869 sound events
corresponding to pollinators (Fig. 1 and Table S3 to see the list of classes and the number of
sound events we had labeled per class).

192 - Training datasets C & D

Data augmentation consists of artificially increasing the size of the training dataset by
sampling data to which a small modification is applied (Stowell, 2022). Our preliminary tests
showed that the human voice could be the source of many classification errors. To improve the
performance of our classifier in differentiating between pollinator sounds and human voice, we
increased the class of sounds corresponding to the human voice. To do so, we collected human
voice events from the Common Voice Corpus 9.0 database
(https://commonvoice.mozilla.org/fr/datasets) and mixed these events with the events from
the training dataset B, with an amplitude ratio randomly selected between 1% and 99%. A
training dataset C was then created by adding to the training dataset B, 1700 of these events
mixing human voice and sound events from our recordings in sunflower fields (Fig. 1c). In the
same way, we created training dataset D by adding 3300 sound events mixing human voice
mixing events and sound events from our recordings to the training dataset C (Fig. 1d). In the
following, we only use the training datasets B, C, and D.

206 *Step 2: Classifier training*

We compared two different machine learning methods, i.e., random forest and convolutional neural networks, to classify sound events as pollinator flight sounds versus other sounds emitted in the same frequency band. To do so we used two softwares: Tadarida-C, a component of Tadarida toolbox (Bas et al., 2017) and TadariDeep (https://github.com/YvesBas/TadariDeep). The first allowed us to extract 269 acoustic features for each labelled sound event and train a classifier using a random forest classification. The second, based on computer vision, using Tensorflow 2.0.0 framework and transfer learning (MobileNet architecture), allowed to train a classifier based on convolutional neural networks. For this last method, we optimized the batch size and the number of iterations (nbepochs). For both methods, we used the training datasets B, C, and D, previously described, leading to a total of 15 classifiers (see Fig.1, and Table S4 for the characteristics of each classifier).

It is also important to note that the detection process is the same for both machine learning
methods. Nevertheless, with TadariDeep used for the convolutional neural networks, a filter
was applied to limit short sound events with a small amount of information. Thus, only sound
events with a duration of 90 ms or more were taken into account for this method.

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<figure><img src="image_2.png" /><figcaption>Figure 1: Overview of the building of training datasets and classifiers. The orange boxes represent the original datasets used to build the different training and test datasets. The arrows indicate the different operations performed during the building of the datasets. The green circles represent the random forest classifiers built with Taradida-C. The blue circles represent the convolutional neural networks classifiers built with TadariDeep.</figcaption></figure>

### Step 3: Classifiers testing

We tested the effectiveness of the random forest and convolutional neural network classifiers using a test dataset of 100 recordings per pollinator score group (as defined in the part “Training dataset B”). These recordings were randomly selected from the set of 8709 unlabelled 10-second recordings for which we also had visual observation of the pollinator run (see section C of Materials and Methods below) after all sound events in these recordings had been classified with the classifier RF-1 (see Table S4).

Thanks to the test dataset, we assessed the efficiency of our 15 classifiers to identify pollinators against other sounds with receiver operator characteristics (ROC) curves. These curves are generated based on the probability (confidence score) that the classifier classifies a pollinator sound event as a “pollinator” accounting for the specificity (inverse of false positive rate) and sensitivity (inverse of false negative rate). We made a ROC curve for each classifier we created. We calculated the area under the curve (AUC) which provides a summary of the classifier's performance.

## C) Reliability tests of continuous acoustic monitoring compared with a traditional monitoring method to estimate pollinator activity

### Comparing acoustic monitoring with counts of pollinators in field transects
We compared acoustic monitoring with pollinator counts along transects within sunflower fields. To do so, in each field, we counted the number of visits of honeybees to sunflowers along transects. The transects were 5-meter wide and 20-meter long, starting 10 meters from the edge of the field and ending 30 meters away, and they were not timed. The position of the transects was chosen so that they were within a few meters of the sunflower plant being acoustically monitored. For each field, transects were repeated 4 to 6 times during a period that encompasses the sunflower bloom, with some counts conducted before, during, and after the flowering (Supplementary Figure S1).

For this analysis, only fields with little or no failure of the recording equipment during the flowering period were used, i.e. 20 fields (Fig.S1). Only days with at least 14 hours of recording out of the theoretical 16 hours were retained. We considered all sounds with a pollinator confidence score, obtained with convolutional neural networks greater than or equal to 78.1 to be pollinator sounds. This confidence score maximizes both sensitivity and specificity (see the red dot in Fig. 2). With this confidence threshold, the average number of pollinator buzzes per field throughout the season was 4880 (± 2588 SD) and the average number of pollinator buzzes per day was 197 (± 259 SD).

We first tested for a relationship between the number of honeybees counted per transect, and the number of flying insects detected on acoustic recordings on the same day. To do so, we modeled the number of buzzes detected during the days of transect sampling, with the number of honeybees counted per transect as explanatory variable. Second, to explore the same relationship but over the entire flowering period of the sunflower fields, we tested for a relationship between the average number of flying insects detected per day of acoustic monitoring and the average number of honeybees counted per transect performed in the same fields. For both tests, we used generalized mixed linear models with negative binomial distribution and field identity as a random effect on the intercept.

All statistical analyses were performed using R (R Core Team, 2022). The residuals of all models were visually inspected with the R package DHarma (Hartig and Lohse, 2022).

## Results

### Comparison of convolutional neural networks and random forest classifiers for pollinator acoustic monitoring

Random forest and Convolutional Neural Network methods involved different sound detection algorithms. Tadarida-L detected sound events in 99.3% of the records of the test dataset (596 out of 600 records) while TadariDeep detected them in 95% of the records (570 out of 600 records). This difference between the two software was explained by the filter applied to the sound events in TadariDeep (minimum duration 90). In the 600 records, composing the test dataset, we counted 94 records that contained at least one pollinator sound. Tadarida-L detected sound events for all of them but for the same reasons as above, TadarariDeep detected sound events corresponding to pollinator sound for only 91 of these recordings.

As comparison of classifier performance is only possible when considering the same detection lists, to compare the classification accuracy of the two methods, we considered only
291 the recordings for which both methods detected sound events, i.e. 95% of the recordings in the
test dataset.
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293 ROC curves and their AUC showed that, for both the random forest and the convolutional
294 neural networks method, the best performance was obtained using the training dataset D, the
295 one composed of the labeled sound events from sunflower fields, augmented with 3300 mixed
296 human voice sound events. The Area Under the Curve was equal to 0.878 for Random Forests,
297 and 0.957 for convolutional neural networks, with a batchsize of 16 and 160 iterations (Fig. 2).
298 Without data augmentation, the area under the curve of the best classifiers was equal to 0.856
299 for Random Forests and 0.953 for convolutional neural networks (Fig. 2). While both methods
300 performed better with data augmentation, the benefit was greater for Random Forest method
301 with an AUC increase of 2.6% while convolutional neural networks only increased by 0.4% (Fig.
302 2). Whatever the training dataset used, the convolutional neural networks outperformed the
303 random forest. By calculating the average error rate according to the different selection
304 thresholds (1-AUC), we were able to determine that the error rate of the convolutional neural
305 networks method was three times lower than that of the random forest method for the
306 classification of pollinator flight sounds.
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<figure><img src="image_3.png" /><figcaption>Figure.2: Receiver operating characteristic (ROC) curves between the confidence score of the false-positive rate (FPR) and the true-positive rate (TPR) for the best classifiers of the random forest method (grey curves) and the best classifiers of the convolutional neural network method (black curves). Each point on the ROC curve corresponds to a confidence score for the "pollinator" class. For each of them, the sensitivity, corresponding to the frequency that a sound emitted by a pollinator is correctly classified in the "pollinator" class by the classifier, and 1-Specifity (the false positive rate), corresponding to the frequency that a sound not emitted by a pollinator is classified in the "pollinator" class, are calculated. Continuous lines correspond to the best classifiers trained with data augmentation, based on training dataset D, and dashed lines without data augmentation, based on training dataset B. The red point corresponds to the threshold (78.1) selected for the other parts of the study concerning the "pollinator" confidence.</figcaption></figure>

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Comparing acoustic monitoring with counts of pollinators in field transects

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When acoustic monitoring was restricted to the days of visual counts along transects, we found a significant positive relationship between the number of buzzes recorded during the day of an observation session, obtained with the convolutional neural networks method, and the number of honeybees counted along this transect (Chisq=14.485, p-value < 0.001, Fig. 3e). When acoustic detections and transect counts were averaged per day over the entire flowering season, we found no relationship between the average number of buzzes detected per day and the number of honeybees counted per transect averaged over the season (χ²=1.1577, p-value=0.2819, Fig. 3f).

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

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Figure 3 – Relationships between the number of buzzes detected by acoustic monitoring and the number of pollinators counted along transects. a, b, c, d: Number of buzzes detected per day along the flowering season for four examples from four monitored fields. The days on which transects were performed in each field are represented by colored dots and bars. e: Relationship between the number of honeybees counted per transect and the number of buzzes detected by acoustic monitoring on the same day. The black line corresponds to the prediction from the generalized linear model with the associated standard error symbolized as a grey ribbon. f: Relationship between the number of honeybees per transect averaged per field and the average number of buzzes per day detected by acoustic monitoring during the entire flowering season. The colored points in e match the colored bars and points used in a-d. Colored points in f match with fields presented in a-d. To prevent overlapping points on panel e and f, the coordinates of points were jitted.
Convolutional neural networks outperform random forest classifiers for acoustic monitoring of pollinator activity

Our study shows that monitoring pollinator activity in sunflower fields using acoustic recordings is feasible and promising. We further demonstrate that convolutional neural networks are more powerful than random forests for such a task, being three times more reliable than the random forest method to classify the sounds emitted by flying pollinators from other sounds in agricultural landscapes. Such better performance of convolutional neural networks over other machine learning algorithms is not always the rule (Garcia et al., 2020), but has also been found in singing birds (Knight et al., 2017; Marchal et al., 2022).

During the training of our different classifiers, we noticed that the human voice was often misclassified as bee buzz, reaching high confidence scores for pollinator sound classes, and this was despite the data augmentation we carried out (see Fig. 2 and SI. B). This kind of problem with the human voice has already been noted in bird detection (Stowell et al., 2019). Here, the human voices recordings are due to the voices of the experimenters who were close to the sound recorders during the observation sessions in the sunflower fields. The human voice data that we used for data augmentation came from recordings of people speaking in front of their computers without background noise. Although we mixed it with field recordings to add some background noises, we could not get rid of all the false positives and the performance of the classifiers was only slightly better than that of classifiers without data augmentation. This suggests that the human voice recordings used for data augmentation were quite different from the human voice recorded in the sunflower fields, or that the Signal-to-Noise Ratio was too low. This highlights the importance of using recordings from the study sites for training and testing machine learning algorithms as well as the need to create large, diversified, and open annotated reference datasets.

Comparing the number of buzzes detected by acoustic monitoring coupled with convolutional neural networks with the number of honeybees counted along sunflower field transects, we found contrasting results depending on the temporal window used. When restricting acoustic data to the days of transect sampling, we found that these two methods provided concordant information on pollinator activity. However, when considering acoustic data from all days during the flowering season, the relationship between the two proxies of pollinator activity vanished. This stems from the fact that, although replicated 5 or 6 times per field throughout the flowering season, the transects remain punctual observations that may not be representative of the entire flowering season, because fieldwork organization constraints make it very difficult to sample different fields or locations evenly during the peak of pollinator activity. Indeed, matching days of transect sampling with peaks of pollinator activity is difficult (Figure 3 and S1) as activity depends on weather, notably the temperature (Blažytė-Čereškienė et al., 2010; Woyke et al., 2003), and on the phenology of floral resources available in the landscape (Polatto et al., 2014; Guezen & Forrest, 2021) with pollinators optimizing their energy consumption (Stabentheiner & Kovac 2014). Albeit issues related the functioning of the recording device, acoustic monitoring allows monitoring several sampling sites simultaneously and continuously making the estimation of pollinator activity for the entire flowering season more representative than the more commonly used transects. Yet, we
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might expect that proxies for pollinator activity at these broad temporal scales should best reflect pollination services (i.e. fruit production from successive visits throughout the flowering season), unlike the punctual monitoring traditionally used.

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## Perspectives and limits to overcome

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Passive acoustic monitoring of pollinators method is thus a promising method to estimate pollination services (van Klink et al., 2022). For crops such as sunflower, where the effect of pollinators on yield appears to depend mainly on honeybee abundance (Altayeb and Nagi, 2015; Aslan and Yavuksuz, 2010; Perrot et al., 2019), a simple quantification of flying insect sounds, as we did, could give a good estimate of insect-pollination benefit to yield. However, some methodological developments for species identification are needed for other crops visited by a larger diversity of pollinators, such as oilseed rape (Garibaldi et al., 2011; Jauker et al., 2012; Kremen et al., 2002; Rader et al., 2016), for which the effect of pollinator diversity on yield has been demonstrated (Bartomeus et al., 2014; Perrot et al., 2018; Zou et al., 2017). Such pollinator identification from buzz sounds has already been achieved for a limited number of species: three bee and one hornet species (Kawakita and Ichikawa 2019), and 12 bumblebee species (Gradišek et al. 2017). We can expect improvements in identifying pollinator species from the sound of their wings beats as more and more annotated pollinator sounds become available. Nevertheless, detecting small pollinators that emit lower sound levels than larger ones, such as honeybees or bumblebees, remains challenging.

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Better knowledge of the detection range of the pollinators around the sound recorder is also needed to assess pollination services within an agricultural field. Compared to other methods such as transects, which are conducted over a large area, our method measures pollinator activity at a fixed point in the field and close to a plant. Several recorders per field might be needed to accurately account for hedge effects, where the activity of pollinators decrease towards the center of the agricultural fields (Hevia et al., 2016).

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Our results suggest that passive acoustic monitoring is at least as effective as traditional methods for monitoring pollinator activity, as long as the diversity of pollinators is not of interest. Continuous monitoring further avoids the biases related to the choice of sampling dates that occur with most other methods. Assessing methods for species identification from acoustic pollinator flight sounds as well as relating acoustic activity to pollination success appear to be the next step to propose pollination services indicators based on passive acoustic monitoring.

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## Acknowledgements

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We would like to express our thanks to CC-IN2P3 for allowing us to perform our deep learning analysis on their platform.

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## Funding

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This research was funded through the ANR IMAGHO (ANR-18-CE32-0002).
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Conflict of interest disclosure

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The authors declare that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.

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Data, scripts, code, and supplementary information availability

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Data, script, and supplementary materials to this article can be found online at :
https://doi.org/10.5281/zenodo.14276099

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