Alexandre Folliot

Sylvain Haupert

Manon Ducrettet

Frédéric Sèbe

Jérôme Sueur
email: sueur@mnhn.fr

Using acoustics and artificial intelligence to monitor pollination by insects and tree use by woodpeckers

Keywords: ecological functions, ecoacoustics, artificial intelligence, forest ecosystem, pollinators, woodpeckers

The collection and interpretation of field data is a prerequisite for informed conservation in protected environments. Although several techniques, including camera trapping and passive acoustic monitoring, have been developed to estimate the presence of animal species, very few attempts have been made to monitor ecological functions. Pollination by insects and wood use, including tree related foraging and intraspecific communication, by woodpeckers

are key functions that need to be assessed in order to better understand and preserve forest ecosystems within the context of climate change. Here, we developed and applied for the first time an acoustic survey to monitor pollination by insects and wood use by woodpeckers in a protected Alpine forest in France. We deployed four autonomous recorders over a year, resulting in 2,285 hours of recordings. We trained a convolutional neural network (CNN) on spectrographic images to automatically detect the sounds of flying insects' buzzing and woodpeckers' drumming as they forage and call. We used the output of the CNN to estimate the seasonality, diel pattern, climatic breadth and distribution of both functions and their relationships with weather parameters. Our method showed that insects were flying (therefore potentially pollinating flowers) in bright, warm and dry conditions, after dawn and before dusk during spring and summer. Woodpeckers were mainly drumming around March at the time of pair formation in cool and wet conditions. Having considered the role of weather parameters, climate change might have contrasting effects on insect buzzing and woodpecker drumming, with an increase in temperature being favorable to pollination by insects but not to wood use by woodpeckers, and a concomitant increase in relative humidity being favorable to wood use but not to pollination. This study reveals that a systemic facet of biodiversity can be tracked using sound, and that acoustics provide valuable information for the environment description.

INTRODUCTION

Biodiversity conservation requires the continuous collection of data in order to inform and predict the changes that affect ecosystem populations and communities [START_REF] Yoccoz | Monitoring of biological diversity in space and time[END_REF][START_REF] Pereira | Towards the global monitoring of biodiversity change[END_REF][START_REF] Magurran | Biological diversity in a changing world[END_REF]. The need to develop reliable indicators in order to track biodiversity over time has been recognized by all international authorities involved in preserving nature and defining international conservation targets [START_REF] Pereira | Towards the global monitoring of biodiversity change[END_REF][START_REF] Petrou | Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets[END_REF]. Biodiversity monitoring is therefore a prerequisite for making appropriate decisions regarding the preservation of ecosystems that face local and short-term (e.g., fires, exploitation) and global and long-term (e.g., climate change, land-use change, invasive species) threats, which are acknowledged as having significant immediate and delayed consequences alike at all scales (Vanbergen and Insect Pollinators Initiative 2013[START_REF] Segan | A global assessment of current and future biodiversity vulnerability to habitat loss-climate change interactions[END_REF][START_REF] O'connor | Monitoring global changes in biodiversity and climate essential as ecological crisis intensifies[END_REF][START_REF] Weiskopf | Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States[END_REF]. In particular, climate change is affecting animal behavior [START_REF] Buchholz | Behavioural research priorities for the study of animal response to climate change[END_REF], population distribution [START_REF] Chen | Rapid range shifts of species associated with high levels of climate warming[END_REF], and phenology [START_REF] Cohen | A global synthesis of animal phenological responses to climate change[END_REF]).

Primary information sources on biodiversity can be classified into field observations collected in situ within the ecosystem in question, and remote sensing observations provided ex situ by satellites or airborne imagery [START_REF] Proença | Global biodiversity monitoring: from data sources to essential biodiversity variables[END_REF]. Although the ex situ option can provide extensive and meaningful data [START_REF] Anderson | Biodiversity monitoring, earth observations and the ecology of scale[END_REF], in situ data still tend to be prioritized of prime importance, because field information (amongst others) is essential for assessing assemblage composition, documenting functional traits, and understanding finescale ecological patterns and processes [START_REF] Ríos-Saldaña | Are fieldwork studies being relegated to second place in conservation science?[END_REF]. Field studies mostly work at the species level, hence in situ biodiversity monitoring mainly aims to describe temporal and spatial population properties and estimate species richness and evenness [START_REF] Carmel | Trends in Ecological Research during the Last Three Decades -A Systematic Review[END_REF]. Several in situ monitoring tools, including those based on citizen science programs, have been developed to identify species through direct observations, as well as indirect genetic, image or sound information [START_REF] Chandler | Contribution of citizen science towards international biodiversity monitoring[END_REF][START_REF] Kühl | Effective Biodiversity Monitoring Needs a Culture of Integration[END_REF], Stephensen et al. 2020). However, biodiversity monitoring cannot be restricted to species occurrence; it should also include the functions or roles that species play in the ecosystem to which they belong. Indeed, functional diversity is another key facet of biodiversity which has received increasing attention for more than 30 years [START_REF] Calow | Towards a Definition of Functional Ecology[END_REF][START_REF] Irschick | Functional ecology: integrative research in the modern age of ecology[END_REF]. Analyses of functional diversity are necessary to understand how ecosystem functions and services are maintained and to guide conservation efforts [START_REF] Schwartz | Linking biodiversity to ecosystem function: implications for conservation ecology[END_REF][START_REF] Laureto | Functional diversity: an overview of its history and applicability[END_REF].

Ecoacoustics is a new discipline that aims to tackle ecological questions through the recording and analysis of the sounds emanating from all or certain parts of ecosystems [START_REF] Sueur | Ecoacoustics: the Ecological Investigation and Interpretation of Environmental Sound[END_REF]. Within this field, several new techniques have been proposed to automatically detect the presence of soniferous species and to globally parametrize the acoustic diversity of a population, a community, an ecosystem or a landscape [START_REF] Stowell | Ecoacoustics: acoustic sensing for biodiversity monitoring at scale[END_REF]). If ecoacoustics is able to identify species by listening to them, could it also track an ecological function in both time and space? So far, ecoacoustic efforts have been directed to assess species diversity, but very rarely to follow ecological functions, except the indirect tracking of tropical birds' seed dispersion function [START_REF] Ducrettet | Monitoring canopy bird activity in disturbed landscapes with automatic recorders: A case study in the tropics[END_REF], foraging by bats [START_REF] Kolkert | Insectivorous bats foraging in cotton crop interiors is driven by moon illumination and insect abundance, but diversity benefits from woody vegetation cover[END_REF], and crucial coral reef functions [START_REF] Elise | Assessing key ecosystem functions through soundscapes: a new perspective from coral reefs[END_REF].

Two particular sounds can reveal an important aspect of terrestrial ecosystem functioning: pollination buzzing by flying insects and wood drumming by woodpeckers. The pollination of flowering plants is a significant ecological function of terrestrial ecosystems, as it ensures plant reproduction (Ollerton 2012[START_REF] Breeze | Economic measures of pollination services: shortcomings and future directions[END_REF]. Pollinator populations, which are regarded as indicators of good ecosystem conditions, are declining [START_REF] Potts | Global pollinator declines: trends, impacts and drivers[END_REF]. Climate change is also causing the phenological decoupling of plant-pollinator interactions [START_REF] Hegland | How does climate warming affect plant-pollinator interactions?[END_REF][START_REF] Settele | Climate change impacts on pollination[END_REF]. Tracking pollination therefore appears to be a major challenge for future biodiversity conservation. Pollination is mainly ensured by flying insects, including Hymenoptera, Diptera, and Coleoptera species, which visit flowers to collect pollen or nectar. Flying insects generate incidental sounds when beating their wings and pollinating [START_REF] Vallejo-Marín | Buzz pollination: studying bee vibrations on flowers[END_REF]. These sounds, or buzzing, can be recorded with appropriate recording tools. Woodpeckers are another major ecological provider in forest ecosystems [START_REF] Virkkala | Why study woodpeckers? The significance of woodpeckers in forest ecosystems[END_REF]. By drilling holes in dead tree trunks both to forage and to nest, and by drumming in order to defend their territories, they provide nesting cavities for other bird species [START_REF] Cockle | Woodpeckers, decay, and the future of cavity-nesting vertebrate communities worldwide[END_REF], disperse wood-rotting fungi [START_REF] Farris | The role of foraging woodpeckers in the decomposition of Ponderosa pine snags[END_REF], and help regulate spruce bark beetles (e.g., Dendroctonus spp., Ips spp.), which attack healthy trees [START_REF] Fayt | Regulation of spruce bark beetles by woodpeckers -a literature review[END_REF]. Woodpeckers are therefore considered excellent indicators of forest biodiversity and, more specifically, of bird species richness (Mikusiński et al. 2011).

Here, we hypothesized that these two functions-pollination by flying insects, and wood use by woodpeckers-can be automatically tracked using ecoacoustic methods, enabling information on a forest's ecological state, functions and services to be inferred through listening to their buzzing and drumming. To this end, we used data collected via a long-term ecoacoustic monitoring program in an Alpine protected forest in France. We then applied artificial intelligence (AI) to automatically estimate the seasonality, diel pattern climatic breadth, and spatial distribution of these two important ecological functions, and inferred predictions related to climate change.

MATERIAL AND METHODS

Study site and recording

The study site is an Alpine forest called the Risoux, which is mainly composed of a mix of European spruce (Picea abies), European beech (Fagus sylvatica), and European silver fir (Abies alba). At its center, the site is dominated by European spruce. The forest is located in the Parc Naturel Régional du Haut-Jura, a protected area in eastern France, close to the border with Switzerland (46°32'10''N, 6°52'21'' E, 1,230 m a.s.l.). The climate is semicontinental, with cold temperatures (annual average temperature 5.5°C), large snowfalls (> 2 m), an extended winter period, and a very narrow period between April and October when the forest floor and understory are green. Climate change impacts bird physiology, biotic interactions, conservation and distribution [START_REF] Scridel | A review and meta-analysis of the effects of climate change on Holarctic mountain and upland bird populations[END_REF]. In the specific case of the Alpine Risoux mountain forest, a decline of specialist bird species, an increase of generalist bird species, and shifts in migration and nesting dates has been reported already [START_REF] Joveniaux | Haut-Jura, l'énergie du territoire. Mise en place d'un suivi intégré de la flore, de l'entomofaune et de l'avifaune. Suivi temporel de l'avifaune nicheuse du Risoux[END_REF]. The loss and the fragmentation of habitats due to anthropogenic pressure have driven the Parc Naturel Régional du Haut-Jura's conservation plans, including the implementation of long-term biodiversity monitoring using acoustics

Recording

The soundscape of the Risoux was recorded using four automatic recorders (SongMeter 4, Wildlife Acoustics Inc, Concord, MA, USA) installed along a West-East axis in the central zone of the forest. The distance between the recorders was 1.00 ± 0.10 km [START_REF] Grinfeder | Soundscape dynamics of a cold protected forest: dominance of aircraft noise[END_REF]. The positioning of the recorders was chosen to (1) cover the forest area, (2) sample a single habitat defined by the dominance of European spruce, (3) avoid pseudo-replication between neighboring recorders, and (4) avoid anthropogenic noise due to car traffic and local activities (hiking, hunting, skiing). The recorders were attached to trees at a height of 2.50 m, and oriented on a 45-315° axis so that the microphones would be parallel to the main West-East circulation axes [START_REF] Grinfeder | Soundscape dynamics of a cold protected forest: dominance of aircraft noise[END_REF]. The recorders were programmed to record 1 min every 15 min (1' on, 14' off, 96 recordings per day) all year round, with operational maintenance in October and July. The sampling period started on August 1, 2018, and ended on July 31, 2019. Sounds were saved in lossless .wav format with a 44.1 kHz sampling frequency and 16-bit digitization depth. This resulted in a data set of 140,160 sound files ( 96x 365 days x 4 sites) for a total of 2,336 hours of recordings. However, due to some recording failures, the effective database contained 137,087 sound files for a total of 2,285 hours of recordings.

Environmental data

Temperature (°C), solar irradiance (lx) and relative humidity (%) were monitored with HOBO MX 2202 and MX 2301 sensors (Onset Computer Corporation, Bourne, MA, USA) using the same spatial and temporal sampling as the audio recorders. Solar irradiance was used to refer to the light intensity per unit area. For data visualization, illuminance was log10 transformed and split into three classes, namely night (< 1 lx, i.e., 0 on log10 scale), twilight (between 1 and 400 lx, i.e., between 0 and 2.6 on log10 scale), and day (more than 400 lx, i.e., more than 2.6 on log10 scale). Local sunrise and sunset time were estimated using sunrise-and-sunset.com.

Supervised sound classification

Dataset organization. The complete dataset contained 137,087 one-minute files. All files were split into successive five-second frames with neither overlap nor tapering leading to 1,645,044 frames. The classification process was trained, validated and tested on a subset of the complete data set containing 1,401 one-minute files, i.e., 1% of the complete recording data set. Given that the sounds of interest were expected to be rare, the 1% subsampling consisted in randomly selecting files within the period between April 5, 2019 and July 5, 2019, between 7 am and 7 pm, when woodpeckers and insects are known to be most active.

The subset data set was split into three sub-subset data sets: the training data set used to train the classification model (60%), the validation data set used to evaluate the model and refine its hyperparameters (20%), and the test data set used as a reference to assess the model's performance (20%).

Dictionary. The dictionary data set included the reference sounds used to train the classification process. The dictionary covered three distinctive sound classes: "buzzing," "drumming," and "background." The "buzzing" class included all flight sounds produced by insects, including Hymenoptera (e.g., Vespidae, Apidae) and Diptera (e.g., Muscidae, Syrphidae). The "drumming" class included the sounds produced by woodpeckers (black woodpecker Dryocopus martius, great spotted woodpecker Dendrocopos major, three-toed woodpecker Picoides tridactylus) when striking a tree trunk with their beak, when either nesting, foraging, defending a territory, or calling to attract a mate [START_REF] Garcia | Evolution of communication signals and information during species radiation[END_REF]. The "background" class included all other sounds coming from other animals or other sources, such as weather sounds (geophony) and anthropogenic sounds (anthropophony). In order to minimize false detections, the "background" class also comprised sounds whose acoustic characteristics are close to the sounds of interest and therefore risked being confused with the latter. These included chainsaw sounds (being close to insects' "buzzing" sounds) and falling tree branches (being close to woodpeckers' "drumming" sounds). The three classes were built with 1,000 files from internal and external data that were similarly split into successive fivesecond frames with neither overlap nor tapering. The internal data were derived from records taken in the Risoux, while the external data came from sound libraries. The sounds of interest were identified by one of the authors (AF) carefully listening and visualizing spectrograms with a custom built Matlab (MathWorks, Natick, MA, USA) program. This annotation process dictionary included 783 "drumming" frames (68 internal, 715 external), 956 "buzzing" frames (406 internal, 550 external), and 2,866 "background" frames (2,866 internal, 0 external) (Table 1).

Filtering. All files were filtered out in the frequency domain to remove low-frequency noise due to wind and high-frequency sound due to sources other than flying insects and woodpeckers. The filter was a finite impulse response (FIR) bandpass filter between 400 and 2200 Hz.

Image generation and processing. Each sound frame was transformed into a spectrographic image using the short-term Fourier transform (STFT) based on a Fourier window comprising 2,048 samples (0.046 s) with a Hamming tapering function and an overlap of 441 samples (= 0.01 s). This resulted in a time precision of 0.036 s and a frequency precision of 21.53 Hz.

The initial image obtained with the STFT was then processed to reduce background noise due to distant sound sources and thereby enhance the contrast of the image. A Gaussian filtering (sigma = 1.2) and heuristic thresholding according to Stein's unbiased risk estimate (SURE thresholding) were used to bring out the contour of the foreground sounds [START_REF] Stander | Using Stein's estimator to correct the bound on the entropic uncertainty principle for more than two measurements[END_REF].

To avoid overlearning, that is, to reduce the sensitivity of the neural network differences between the learning and test data sets, each frame of the learning data set was altered by translations and size changes in time and frequency [START_REF] Ko | A Study on data augmentation of reverberant speech for robust speech recognition[END_REF]. The result of the preprocessing was an image of 86x498 pixels, hereafter called the enhanced spectrogram image.

Convolutional neural network.

A convolutional neural network (CNN) is a deep learning method which is extensively used in image recognition [START_REF] Lecun | Deep learning[END_REF]. A CNN consists in stacking layers of artificial neurons (also called kernels or filters within the CNN framework) which perform on images a mathematical operation called convolution. Here, a CNN was used as a supervised method to classify the enhanced spectrogram image corresponding to the three sound classes, i.e., "buzzing," "drumming," and "background" sounds. This classification was carried out with a single output class. Where "buzzing" and "drumming" both occurred in a five-second frame, the class with the highest output probability was selected. In order to limit the computing power and time required to train the CNN, we chose to build a custom built CNN architecture made of a few successive layers:

(1) four convolutional layers with zero padding, one pixel stride and different numbers of convolution kernels with different sizes (layer 1 with ten kernels of size 4x4 pixels, layer 2 with five kernels of size 8x8, layer 3 with five kernels of size 16x16, and layer 4 with three kernels of size 24x24); (2) four correction layers with the Rectified Linear Unit (ReLU) activation function after each convolution to introduce non-linearities to the decision function; (3) four layers of maximum pooling, after each ReLU layer, allowing the information to be compressed by halving the size of the intermediate image while preventing overfitting; (4) a fully connected layer, i.e., where the neurons have connections to all the outputs of the previous layer; and (5) a softmax layer associating a probability to each output class. The decision probability threshold for the final classification into one of the three categories was set to 0.95.

Evaluation. The quality of the CNN output was evaluated on the validation data set ("buzzing," "drumming," and "background") by computing the overall accuracy and the average prediction. The overall accuracy was obtained by computing (TP + TN)/(TP + TN + FP + FN), with true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). The average prediction approximated the area under the curve (AUC) of the precision-recall curve, precision being TP/(TP + FP) and recall being TP/(TP + FN) [START_REF] Boyd | Area under the precision-recall curve: point estimates and confidence intervals in Machine learning and knowledge discovery in databases[END_REF].

Optimization. A first application of the CNN on the complete data set revealed 304 (0.18 ‰) unexpected detections of "buzzing" sounds at night or during the winter. These false-positive detections were transferred to the "background" class. There were no unexpected detections for "drumming" sounds.

Implementation. All the supervised classification processes were implemented using the Deep Learning Toolbox of Matlab 2019a on a personal laptop (Intel Core i7 CPU).

Statistics. The medians and the distributions of detections were compared with the medians and the distributions of environmental variables using normalized histograms and nonparametric tests: the Wilcoxon rank test for unpaired samples and the Kolmogorov-Smirnov test, respectively. The proportion of detections according to the four sites was tested using a χ 2 test of conformity. All tests were run with a type I error of 5%.

RESULTS

Annual and diel phenology

The best model obtained with our single CNN had an overall accuracy of 93.9% and an average prediction of 90.5%. "Buzzing" and "drumming" sounds were extremely rare, with only 8,196 frames containing the former (0.51%) and 1,767 frames containing the latter (0.11%). Most of the "buzzing" sounds were detected between the end of May and the end of October (88% of detections), during the day (86% of detections), with peaks after sunrise and before sunset (Figure 1). The annual and diel phenology of "drumming" sounds was more dispersed. Nonetheless, most of the activity occurred between the beginning of February and the end of April (43% of detections), during the day (74% of detections), and more specifically just before sunrise and during the subsequent four hours (47% of detections) (Figure 1).

Temperature

The median of "buzzing" detections was 17.29°C, significantly different from the median annual temperature of 7.55°C (Wilcoxon test for unpaired samples, W=291067117, p < 2.2 10 -16 ), with 85.64% of the detections found above the latter. The distribution of "buzzing" detections according to temperature was significantly different from that of the annual temperature (Kolmogorov-Smirnov test, D=0.41, p < 2.2 10 -16 ). The median of "drumming" detections was 4.16°C, and thus closer but still significantly different from the median annual temperature (Wilcoxon test, W=32708884, p < 2.2 10 -16 ), with 35% of the detections found above the latter. The distribution of "drumming" detections according to temperature was also significantly different from the distribution of the annual temperature, but the distance between the distributions was less than for "buzzing" sounds (Kolmogorov-Smirnov test, D = 0.16, p-value < 2.2 10 -16 ), indicating a stronger match with local temperature. Overall, "buzzing" sounds occurred at high temperatures which were rare, whereas "drumming" sounds occurred at low temperatures which were abundant (Figure 2).

Solar irradiance

The median of "buzzing" detections was observed at 1826.6 lx (eq. 3.26 log10(lx)), significantly different from the median annual solar irradiance of 1.9 lx (eq. 0.29 log10(lx)) (Wilcoxon test for unpaired samples, W=194466998, p < 2.2 10 -16 ), with 92.08% of the detections found above the latter. The distribution of "buzzing" sounds according to solar irradiance was significantly different from that of the annual solar irradiance (Kolmogorov-Smirnov test, D=0.92, p < 2.2 10 -16 ). The median of "drumming" detections was observed at 510.72 lx (eq. 2.7 log10(lx)), significantly different from the median annual solar irradiance (Wilcoxon test for unpaired samples, W=34677502, p < 2.2 10 -16 ), with 76.23% of the detections found above the latter. The distribution of "drumming" sounds according to solar irradiance was also significantly different from the distribution of the annual solar irradiance, but the distance between the distributions was less than for "buzzing" sounds (D = 0.75, pvalue < 2.2e-16), indicating a stronger match with local solar irradiance. Overall, "buzzing" sounds were mostly detected in high light conditions during the day, whereas "drumming" sounds mainly occurred in low light conditions, at twilight or in daylight when overcast (Figure 3).

A heatmap of the number of detections according to a color scale in relation to the two environmental variables of temperature and solar irradiance shows that "buzzing" sounds appeared to be restricted to a smaller temperature and light dispersion space, with 75% of the detections found in a smaller rectangle (from 6°C to 29°C; from 2.1 log10 lx to 4.1 log10 lx) than was true of "drumming" sounds (from -9°C to 19°C; from 0 lx to 4 log10 lx) (Figure 4).

Relative humidity

The median of "buzzing" detections was observed at 62.09%, significantly different from the median annual relative humidity of 90% (Wilcoxon test for unpaired samples, W=37836196, p < 2.2 10 -16 ), with 7.36% of the detections found above the latter. The distribution of "buzzing" sounds according to relative humidity was significantly different from that of the annual relative humidity (Kolmogorov-Smirnov test, D=0.39, p < 2.2 10 -16 ). The median of "drumming" detections was observed at 93.5%, significantly different from the median annual relative humidity (Wilcoxon test for unpaired samples, W=32064592, p < 2.2 10 -16 ), with 50.08% of the detections found above the latter. The distribution of "drumming" detections was also significantly different from that of the annual relative humidity, but the distance between the distributions was less than for "buzzing" sounds (D = 0.15121, p < 2.2 10 -16 ), indicating a stronger match with local relative humidity. Overall, "buzzing" sounds were recorded in dry but uncommon local conditions, whereas "drumming" sounds were recorded in wet conditions (Figure 5).

Spatial heterogeneity

The number of detections was unevenly distributed among the four recording sites for both "buzzing" (χ 2 test of conformity, χ 2 = 1377.3, d.f. = 3, p < 2.2 10 -16 ) and "drumming" sounds (χ 2 test of conformity, χ 2 = 162.22, d.f. = 3, p < 2.2 10 -16 ). The ratios between the maximum and minimum numbers of detections were 2.6 for "buzzing" and 2.1 for "drumming" sounds, respectively (Figure 6).

Discussion

Ecoacoustics is a new discipline that aims to monitor, describe and decipher biodiversity using non-invasive and automatic acoustic methods [START_REF] Sueur | Ecoacoustics: the Ecological Investigation and Interpretation of Environmental Sound[END_REF]. CNNs have been used in a wide range of ecoacoustic applications [START_REF] Ruff | Workflow and convolutional neural network for automated identification of animal sounds[END_REF], especially to track marine mammals [START_REF] Lu | Detection and classification of marine mammal sounds using AlexNet with transfer learning[END_REF], birds [START_REF] Kahl | BirdNET: A deep learning solution for avian diversity monitoring[END_REF]amphibians (LeBien et al. 2020). In this investigation, we demonstrate the novel application of a CNN to track two ecological functions within an Alpine forest which harbors remarkable biodiversity, rather than simply detect the presence of particular species. As a result,we were able to estimate the seasonality, diel pattern, climatic breadth and spatio-temporal distribution of pollination by flying insects and wood use by woodpeckers.

The five-layer CNN selected was trained with only 1,401 reference sounds so that the annotation process, as necessary for such a supervised technique, would not be time consuming (indeed, this process took 20 hours to complete), and so that the model could be trained on a personal laptop computer with a standard CPU card without any access to specialized computing facilities. The CNN model was then successfully applied to 1,645,044 spectrogram frames with good levels of accuracy (93.9%) and mean prediction (90.5%). In addition, the CNN efficiently detected rare events, as the detection rate was only 0.51% for "buzzing" and 0.11% for "drumming" sounds. All these results suggest that the CNN technique can be used with confidence and without major difficulties for other audio data sets, in particular for those that will be collected over the coming years in the same forest.

This also opens the possibility to detect scarce but key acoustic events such as rapid ecological events (e.g., falling tree sounds), sudden threats (e.g., hunters' gunshot blasts) and rare species vocalizations (e.g., gray wolf Canis lupus, Eurasian lynx Lynx lynx).

The CNN was able to detect the sounds produced by flying insects, including Hymenoptera and Diptera, most of which pollinate flowers. The technique could be transferred to a large number of other applications, including honeybee monitoring, and pollinator activity in crop fields and other habitats. A special case could be the monitoring of hoverflies, a group of Diptera (Syrphidae) which produce distinguishable sound when flying.

Hoverflies are regarded as key ecological species, not only as pollinators, but also because their larvae inhabit a large diversity of habitats and microhabitats, from plant roots to tree canopies, and occupy several trophic levels, including saprophagy, microphagy, mycophagy, phytophagy, and entomophagy [START_REF] Wotton | Mass seasonal migrations of hoverflies provide extensive pollination and crop protection services[END_REF]). In the Alpine forest of the Jura Mountains studied here, flying insects were active and were therefore potentially pollinating flowers in bright, warm and dry conditions. These conditions are not very surprising, as pollinators are known to require a minimal ambient temperature for flight muscle activity [START_REF] Corbet | Temperature and the pollinating activity of social bees[END_REF], and tend to prefer warm flowers (Rands and Whitney 2008). Such environmental conditions restrict the occurrence of buzzing to a period running from May to the end of October, corresponding to spring and summer, again in agreement with currently known pollinator phenology [START_REF] Duchenne | Phenological shifts alter the seasonal structure of pollinator assemblages in Europe[END_REF]). More specifically, our method revealed two peaks of activity (after dawn and before dusk), a diel pattern that was also apparent when monitoring flower visits by bumble bees in Norway using image-based techniques [START_REF] Steen | Diel activity, frequency and visit duration of pollinators in focal plants: in situ automatic camera monitoring and data processing[END_REF]).

Although differences may exist among pollinator species, the general pattern revealed by our monitoring program can still be useful to test for potential shifts in the future.

Following the +1.5°C IPPC climate model [START_REF] Masson-Delmotte | Climate Change 2021: The physical science basis.contribution of working group i to the sixth assessment report of the intergovernmental panel on climate change[END_REF], pollinators might forage over a longer diel period and a longer season, potentially increasing the pollination rate as a result. However, an increase in temperature may be coupled with increases in rainfall and relative humidity, a prediction which holds for the Jura Mountains [START_REF] Beniston | Climatic change in mountain regions: a review of possible impacts[END_REF]. Such climatic changes can have detrimental effects on plant-pollinator interactions at individual and population scales, involving, among others, thermoregulatory expense, body overweight, olfactory signal obfuscation for insects, nectar dilution, pollen degradation, and volatile reduction for flowers [START_REF] Lawson | The effects of rainfall on plant-pollinator interactions[END_REF]. The balance between the possible gain due to increased temperatures and costs due to increased rainfall is difficult to predict in the specific case of the Risoux, although large-scale predictions under a climate change scenario anticipate pollinator declines (Soroye et al. 2020) and possible mismatches between pollinator and plant phenologies [START_REF] Kudo | Early onset of spring increases the phenological mismatch between plants and pollinators[END_REF]Ida 2013, Renner et al. 2018). The number of automatic detections also revealed differences in the case of "buzzing" sounds between the four sites. This uneven spatial distribution, in particular for the recorder placed on the East side of the forest, may be due to differences in local resources, that is, differences in the number of foraging sites, or micro-climatic differences leading to different pollinator communities associated with different plant communities. Despite the similarity of habitats (i.e., European spruce forest), passive acoustic monitoring also revealed an unanticipated level of diversity within the forest, suggesting the occurrence of local habitat variations.

Pollination is intrinsically linked to flower resources, which can differ according to microclimatic and landscape variations. Experts could disentangle these local differences using traditional floristic and entomological surveys.

Woodpeckers are excellent biodiversity indicators of forest ecosystems because their presence indicates complex forests which harbor considerable diversity in terms of forest composition, structure, disturbance and management [START_REF] Mikusiński | Woodpeckers as indicators of forest bird diversity[END_REF]. Consequently, woodpeckers have also been deemed good potential proxies of climate change [START_REF] Walsh | Climate change, woodpeckers, and forests: current trends and future modeling needs[END_REF]. Here, the drumming of the complete woodpecker guild was associated with cool and wet conditions. Thus, a 1.5°C increase in the ambient temperature may negatively affect woodpeckers, as they would be facing warmer conditions than before. However, at the same time, increased rainfall and relative humidity may favor their populations. Meanwhile, with respect to pollinators, temperature and humidity may have the reverse effect, the former being a negative factor and the latter being a positive factor. As this was the case for the North American woodpecker species, whose populations can expand or contract according to climate change [START_REF] Walsh | Climate change, woodpeckers, and forests: current trends and future modeling needs[END_REF], the future population density and phenology of the woodpeckers inhabiting the Risoux forest are also difficult to predict. Future population dynamics will be dependent not only on climate change but also on forest use and management.

We were also able to determine a peak of general woodpecker drumming activity around March at the time of pair formation. Similar seasonality has been estimated for the North American pileated woodpecker (Dryocopus pileatus) by experts listening to semiautomated recordings [START_REF] Tremain | Seasonal variation in acoustic signals of Pileated Woodpeckers[END_REF]. Most of the drumming occurs after sunrise, corresponding to the dawn chorus [START_REF] Gil | The bird dawn chorus revisited[END_REF]. Given that in our study the CNN was not trained to distinguish between territorial and feeding drumming, some detections were found outside of the usual reproduction period. Nevertheless, both types of drumming events revealed the presence of woodpeckers and hence their key ecological function in the forest ecosystem. Differences between the four recording sites might have been due to local population dynamics, discrepancies in resource distribution, or variations in species' nesting preferences.

Similar to our results for pollinators, our monitoring system could not distinguish between the three woodpecker species present in the forest, thus masking potential species differences. Recent studies have shown that there is enough species information encoded in woodpecker drumming [START_REF] Garcia | Evolution of communication signals and information during species radiation[END_REF] to allow the automatic identification of species [START_REF] Florentin | Identification of European woodpecker species in audio recordings from their drumming rolls[END_REF][START_REF] Florentin | Detection and identification of European woodpeckers with deep convolutional neural networks[END_REF]. In this investigation, we aimed to monitor an ecological function, but a specific assessment of woodpecker populations could be run as a second step if a proper training data set were built for each species inhabiting the forest.

Concluding remarks

Through an in situ monitoring study based on ecoacoustic recording methods, signal analysis, and AI algorithms, we were able to detect and track two key ecological functions within a forest ecosystem. The two functions showed strikingly different temporal and environmental responses, in part because the species responsible for these functions have markedly different ecological and thermal niches. In particular, flying insects and birds are poikilothermic and homeothermic organisms, respectively, and therefore manifest contrasting degrees of dependence on ambient temperature. The tracking of functions without species identification can be highly valuable for forest monitoring and conservation, but also for other habitats, including crop fields, where pollination is crucial. Moreover, the acoustic monitoring of ecological functions provides a holistic perspective and so it may include some redundant information where species have similar life history traits, or hide heterogeneity where species have distinct biological characteristics.

These results underline the potential of ecoacoustics for not only in monitoring soundscapes or particular vocalizing species, but also in tracking ecological functions through appropriate automatic classification models. The data revealed both the seasonality and the diel pattern of each ecological function, suggesting different (and sometimes opposite) sets of climate pressures in the future. Tracking ecological functions in the long term (i.e., over a period of years) should show how an ecosystem reacts to climatic change. In addition, the monitoring highlighted an unanticipated level of spatial heterogeneity between the recording sites that should be considered for conservation priorities. Table 1. Fig. 6. Spatial distribution of "buzzing" and "drumming" sounds. Relative frequency of 'drumming' (red) and "buzzing" (blue) sounds at each recording site.

Tables

FiguresFig. 1 .

 1 Figures

Fig. 2 .

 2 Fig. 2. Temperature breadth of "buzzing" and "drumming" sounds. Histograms of the number

Fig. 3 .

 3 Fig. 3. Light breadth of "buzzing" and "drumming" sounds. Histograms of the number of

Fig. 4 .

 4 Fig. 4. Temperature and light breadth of "buzzing" and "drumming" sounds. The number of

Fig. 5 .

 5 Fig. 5. Relative humidity breadth of "buzzing" and "drumming" sounds. Histograms of the

Acknowledgments

This work was supported by the Parc Naturel Régional du Haut-Jura, which received funding from the Région Bourgogne-Franche-Comté, the Région Auvergne-Rhône-Alpes and the DREAL Bourgogne-Franche-Comté. We warmly thank Marie-Pierre Reynet and Julien Barlet for their enthusiastic contributions to this long-term project. We also greatly thank Fernand Deroussen for providing sounds included in the training data set. We are grateful to two anonymous reviewers for their very helpful comments.

Authors contribution

Conceptualization and data collection: Jérôme Sueur, Sylvain Haupert, Frédéric Sèbe Data Analysis : Alexandre Folliot, Manon Ducrettet Writing : all authors

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.