Félix Michaud
email: felix.michaud@mnhn.fr

Jérôme Sueur

Maxime Le Cesne

Sylvain Haupert

Unsupervised classification to improve the quality of a bird song recording dataset

Keywords: Bioacoustics, ecoacoustics, data-centric, bird song, clustering, deep learning, labelling function, label noise

Highlights

• Open audio databases provide unique information for ecological sciences • However, most of them are weakly labelled with a risk of signal confusion • A labelling function based on segmentation, featurization, and clustering is proposed • A test on the Xeno-Canto database revealed a significant reduction of label error • Unsupervised labelling can foster the use of audio archives

Introduction

Bird song has been the center of most bioacoustic studies for about 70 years and is currently a key element of soundscape ecology and ecoacoustic research in terrestrial environments [START_REF] Xie | Data-driven analysis of global research trends in bioacoustics and ecoacoustics from 1991 to 2018[END_REF]. The sounds produced by birds, essentially their territorial songs, are commonly recorded using parabolic or directional microphones so that a single individual representing a single species appears as the sound of highest intensity. Such recordings, known as focal recordings, are deposited and shared in public and private sound libraries whose sizes are increasing continuously [START_REF] Toledo | The value of audiovisual archives[END_REF].

Xeno-Canto is the most used and diversified focal bird song database (Xeno-canto, 2021). This community project aims at gathering and curating bird sounds from expert and nonexpert contributors with a total of around 678,000 audio recordings of 10,300 species from all major world areas. Xeno-Canto can be used for taxonomy, behavior, biogeography and ecology research [START_REF] Planqué | Xeno-canto: a 21st-century way to appreciate Neotropical bird song[END_REF]. In recent years, Xeno-Canto has been the major source of machine and deep learning challenges for the automatic identification of bird species through acoustics [START_REF] Joly | LifeCLEF 2015: multimedia life species identification challenges[END_REF]. Algorithms to monitor bird species [START_REF] Denton | Improving Bird Classification with Unsupervised Sound Separation[END_REF][START_REF] Dufourq | Passive acoustic monitoring of animal populations with transfer learning[END_REF][START_REF] Nolasco | Few-shot bioacoustic event detection at the DCASE 2022 challenge[END_REF][START_REF] Stowell | Computational bioacoustics with deep learning: a review and roadmap[END_REF], study dialects [START_REF] Cohen | Automated annotation of birdsong with a neural network that segments spectrograms[END_REF], or identify individuals [START_REF] Martin | Rookognise: Acoustic detection and identification of individual rooks in field recordings using multi-task neural networks 41[END_REF] are also growing quickly. Among them, the convolutional neural network BirdNET currently appears as the most promising opensource and user-friendly solution for automatic bird identification [START_REF] Kahl | BirdNET: A deep learning solution for avian diversity monitoring[END_REF].

Xeno-Canto is extremely useful but is weakly labelled: a species name is attributed to each recording without timestamps to provide temporal localization of bird song sequences. Three main methods have been used to reduce this inaccuracy by temporally localizing the sounds of interest. The first option consists in asking bird experts to manually annotate the recordings. By listening to and visualizing spectrographic representation of the audio, the experts select the beginning and the end of each sound of interest, sometimes manually drawing bounding boxes on the spectrogram. This procedure ensures high quality data but requires rare expertise, is time-consuming, and can be complexified by possible inconsistencies between labels added by different experts [START_REF] Donmez | Proactive learning: cost-sensitive active learning with multiple imperfect oracles[END_REF][START_REF] Gil-González | Regularized chained deep neural network classifier for multiple annotators[END_REF][START_REF] Sheng | Get another label? improving data quality and data mining using multiple, noisy labelers[END_REF]. The second method consists in semiautomatic annotation, combining automatic clustering and manual annotation. This approach has already been applied for a wide range of animal vocalizations, including soundscape analysis [START_REF] Arthur | Songexplorer: a deep learning workflow for discovery and segmentation of animal acoustic communication signals. bioRxiv. Assent, I[END_REF][START_REF] Fukuzawa | Koe: Web-based software to classify acoustic units and analyse sequence structure in animal vocalizations[END_REF][START_REF] Phillips | Revealing the ecological content of longduration audio-recordings of the environment through clustering and visualisation[END_REF][START_REF] Ruff | Workflow and convolutional neural network for automated identification of animal sounds[END_REF][START_REF] Steinfath | Fast and accurate annotation of acoustic signals with deep neural networks[END_REF][START_REF] Zhong | Acoustic detection of regionally rare bird species through deep convolutional neural networks[END_REF]. In this work, we focus on a third method that requires no direct involvement of human experts but instead develops a program that automatically assigns a label to each sound unit. This type of program is designated as a labelling function [START_REF] Ratner | Data programming: Creating large training sets, quickly[END_REF][START_REF] Zhang | A survey on programmatic weak supervision[END_REF].

In the context of building a custom bird song dataset from open databases, most of the labelling functions consist of automatically localizing the salient sounds by applying segmentation methods, from simple energy threshold [START_REF] Fodor | The ninth annual MLSP competition first place[END_REF][START_REF] Lasseck | Bird song classification in field recordings: Winning solution for NIPS4B 2013 competition[END_REF][START_REF] Potamitis | Automatic Classification of a Taxon-Rich Community Recorded in the Wild[END_REF] to more complex signal and image processing [START_REF] Sprengel | Audio based bird species identification using deep learning techniques[END_REF].

Then, the weak label of the identified bird is assigned automatically to all the segmented sounds. However, other sounds than the one of interest might be segmented. Such wrong label assignment, known as label noise, has been pointed out as one of the main limitations in the process of developing a robust bird sound detection algorithm [START_REF] Kahl | BirdNET: A deep learning solution for avian diversity monitoring[END_REF].

Indeed, the presence of label noise in datasets can impair classification tasks [START_REF] Frenay | Classification in the Presence of Label Noise: A Survey[END_REF] by degrading the learning performance of the algorithm or even preventing the convergence of the model [START_REF] Arpit | A closer look at memorization in deep networks[END_REF][START_REF] Chen | Understanding and utilizing deep neural networks trained with noisy labels[END_REF][START_REF] Karimi | Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis[END_REF][START_REF] Sheng | Get another label? improving data quality and data mining using multiple, noisy labelers[END_REF], and can also induce class imbalance problems. Three solutions were proposed to reduce label noise [START_REF] Frenay | Classification in the Presence of Label Noise: A Survey[END_REF]. The first solution relies on the implicit capacity of certain algorithms to be robust to noise during the training session. As an example, it was shown that very large artificial neural networks are able to fit a dataset with label noise up to 90% [START_REF] Rolnick | Deep learning is robust to massive label noise[END_REF]. However, this modelcentric option requires a large amount of data and computer power, lacks easy deployment capacity, and increases computing time, energy consumption, and CO 2 emissions [START_REF] Schwartz | Green ai[END_REF][START_REF] Strubell | Energy and policy considerations for deep learning in NLP[END_REF]. This solution was largely chosen for bird sound detection and classification [START_REF] Joly | Overview of LifeCLEF 2021: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction[END_REF][START_REF] Stowell | Computational bioacoustics with deep learning: a review and roadmap[END_REF]. The second solution consists of the explicit handling of label noise by an algorithm [START_REF] Algan | Image classification with deep learning in the presence of noisy labels: A survey[END_REF][START_REF] Frenay | Classification in the Presence of Label Noise: A Survey[END_REF]. Such a solution is denoted as a noise-tolerant or noise model-based method and requires a good prior knowledge of the label noise to integrate a representation of the noise in the model. Here, we focus on a third solution that aims at addressing label noise at the source with an upstream filter that detects and removes mislabeled data from the dataset [START_REF] Brodley | Identifying mislabeled training data[END_REF]. This way, by directly removing label noise at the source, the learning process can be facilitated [START_REF] Sambasivan | Everyone wants to do the model work, not the data work[END_REF], which is in line with the data-centric approach that advocates for cleaner and smaller datasets to create good conditions for lighter training processes [START_REF] Motamedi | A Data-Centric Approach for Training Deep Neural Networks with Less Data[END_REF][START_REF] Ng | Data-Centric AI Competition[END_REF].

To address this issue and reduce label noise in large bird song datasets, we introduce a data-centric novel labelling function composed of three successive steps: 1) time-frequency sound unit segmentation, 2) feature computation for each sound unit, and 3) classification of each sound unit as bird song or noise with either an unsupervised DBSCAN algorithm or the supervised BirdNET neural network. The labelling function was optimised, validated, and tested on the songs of 44 West-Palearctic common bird species.

Material and methods

An overview of the complete workflow to design the labelling function is presented Figure 1.

Audio database

The audio database included West-Palearctic common bird species of the Passeriformes, Columbiformes, Piciformes, Cuculiformes, and Strigiformes orders. The monitoring of these species is crucial for biodiversity assessment as testified by the Pan-European Common Bird Monitoring Scheme (PECBMS, 2021). The audio database was split into three sets: a training set, a validation set, and a test set. The training set and the validation set referred to the same 22 bird species (Table A.1.). The selection of the species followed a previous study conducted in the Parc Naturel Régional of Vallée de Chevreuse (24,300 ha), a protected area located 40 km southwest of Paris, France [START_REF] Depraetere | Monitoring animal diversity using acoustic indices: Implementation in a temperate woodland[END_REF]. The third set included 22 other common species that were selected from the Global Biodiversity Information Facility (GBIF, 2021) database (Table A.2.).

All recordings were automatically retrieved from Xeno-Canto. This international online archive gathers focal bird song recordings. To obtain a representative dataset of bird sounds within a reasonable annotation time, the first step was to retrieve a maximum of 100 files for each species with the following metadata conditions: (1) song type, (2) high quality rate (A or B), and (3) a duration between 20 s and 180 s. Twenty files were then randomly chosen for each species. Finally, 20 s of each file were selected at the start of the first song encountered in the file and annotated.

Automatic segmentation of the regions of interest (ROIs)

Pre-processing

The mp3 audio files were loaded with a sampling rate of 44.1 kHz. A band-pass filter from 100 Hz to 18,000 Hz was applied to remove low-frequency background noise as well as high-frequency artefacts due to mp3 compression. The short-term fourier transform (STFT) was calculated with a Fourier window of 2,048 samples, an overlap of 1,024 samples and a Hanning window (Fig. 2a). The resulting spectrogram was then resized in time and frequency so that closely related pixels were merged together (Fig. 2b). Background noise was partly removed by estimating the stationary noise directly on the spectrogram. For each frequency band, the average amplitude along the time axis was estimated to obtain the raw noise profile along the frequency axis. Then, the noise profile was smoothed using a running mean over 25 frequency bands. Eventually, the noise profile was subtracted to the overall resized spectrogram [START_REF] Towsey | Estimating animal acoustic diversity in tropical environments using unsupervised multiresolution analysis[END_REF] and the amplitude values of the resulting spectrogram were finally turned into dB (Fig. 2c).

Figure 2. Spectrogram pre-processing and segmentation. Audio files were first converted into spectrograms (a), spectrograms were then resized in time and frequency (b). After that, noise profiles were subtracted from spectrograms and the values were turned into dB (c). Furthermore, spectrograms were binarized with the hysteresis thresholding binarization method (d). The last step is the delimitation of the bounding boxes (e).

Segmentation

Segmentation was processed on the spectrogram by delimiting time-frequency rectangular bounding boxes around salient sounds. The delimitation of these regions of interest (ROIs) was based on a region growing segmentation method known as hysteresis thresholding binarization [START_REF] Ulloa | scikit-maad: An open-source and modular toolbox for quantitative soundscape analysis in Python[END_REF]. The method is based on two dB thresholds: the high threshold selects high intensity pixels while the low threshold groups neighbor pixels (Fig. 2d). The bounding boxes that were separated in time by less than 0.24 s and separated in frequency by less than 170 Hz, were merged. The bounding boxes that lasted less than 0.36 s were not considered as they are too short to be a bird song unit (Fig. 2e).

Optimization and metrics

The pre-processing and segmentation processes depended on four parameters: the time and frequency scaling factor of the spectrogram and the two thresholds of the hysteresis binarization. The optimal combination of these four parameters was estimated on a selection of files. Manual delimitation of bounding boxes was conducted on these files using the Audacity time-frequency annotation tool on the spectrogram view (Audacity v3.1.3, 2021).

Then, the automatic extraction workflow was applied for each species on the specific annotated 20 s for a list of combinations of the four parameters. The resizing factor in time ranged from 6 to 18, the resizing factor in frequency ranged from 4 to 30, the upper threshold ranged from 10 to 40 dB, and the lower threshold ranged from 7 to 37 dB.

The distance between manual and automatic delimitations was measured with the Intersection Over Union (IOU), a metric that measures area overlap between two boxes [START_REF] Zou | Object Detection in 20 Years: A Survey[END_REF]. The median of the IOUs was calculated for each parameter combination and each file and then for all the files for a species. The best IOU score for all species was then selected to obtain the best combination of parameters.

Once the best combination for the four parameters was found, all the ROIs present in the 20 s audio segments for the 20 files for all species were automatically extracted based on the bounding boxes' limits. The ROIs were delineated in time by the vertical limits and in frequency by the horizontal limits. The sound was then extracted first by trimming the audio recording around the beginning and end of the ROI in time and then by applying a fifteenth order bandpass butterworth filter with the lower and higher cutoff frequencies corresponding to the lower and upper spectral limits of the ROIs.

Automatic classification of the regions of interest

Features

The unsupervised classification was performed on the spectrogram. However, the spectrogram resolution and the bandpass filter differ from those used for the ROI segmentation. The sample rate was set to 24 kHz, the Fourier window took 512 samples, and the bandpass filter range was between 250 Hz and 11 kHz. The unsupervised classification was based on two signal descriptors. The first descriptor was a vector of values resulting from a two-dimensional wavelet analysis (Ulloa et al., 2018). The regions of interest were convolved with a series of scale Gabor filters to extract spectro-temporal characteristics according to the selected resolution. It was composed of 48 Gabor filters with two vertical filters, two horizontal filters, and four diagonal filters with two angles at 45° and 135° at six different scales. The second descriptor was based on a Fourier analysis that consisted in measuring the spectral centroid of each region of interest. The feature vector to describe a ROI therefore had dimensions of 49 by 1. A single vector is obtained for every ROI.

Clustering

The classification of bird songs and noise was achieved with the unsupervised method called Density-based spatial clustering of applications with noise (DBSCAN) [START_REF] Ester | A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise 6[END_REF]. We opted for an unsupervised method for the workflow to be applied on any bird species with enough audio recordings without the need for a learning stage. Referring to a density-based definition of clusters, DBSCAN does not require an initial number of clusters and works for non-spherical clusters. In addition, DBSCAN allows the identification of outliers that can be considered as noise, a prerequisite of labelling functions. DBSCAN searches for core samples by starting with an arbitrary point g and retrieves all points that are distant by less than an Euclidean distance (Eps) and have a minimum number of points in their neighborhood (MinPts). Samples are defined as noise if they are outside any cluster.

There are no ideal Eps and MinPts values so their setting is not trivial [START_REF] Schubert | DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN[END_REF]. Eps was estimated individually for each species using a K-dist graph, which plots the average distance between each point and its k nearest neighbors in an ascending order. The inflection point, or "knee", indicates the limit between the maximum distance of the core points of the cluster (signal) and the outlier points of the cluster (noise). This point was automatically detected with a knee locator algorithm called Kneed [START_REF] Satopaa | Finding a "Kneedle" in a Haystack: Detecting Knee Points in System Behavior[END_REF].

MinPts was determined by running the clustering algorithm of each species on the training set with MinPts values ranging from 1% to 50% of the number of ROIs. The macro average precision over all species was calculated for each percentage. This led to MinPts of 10% for all sets.

To evaluate the result of the classification, the points belonging to the cluster were annotated as positive samples and the outliers as negative samples. In cases where there were more than one cluster, we kept only the largest cluster as we formed the hypothesis that the other smaller clusters were bird songs due to other species, anthropogenic noise such as car traffic, or unusual bird songs that could belong to the species of interest. Knowing the false and true positives and negatives, we applied the three performance metrics commonly used in machine learning to evaluate the automatic process: (1) macro-average of precision (number of true positives over the sum of true positives and true negatives), ( 2) macro-average of recall (number of true positives over the sum of true positives and false negatives), and (3) macro-average of F1 score (combination of the precision and recall).

Finally, we introduced two other metrics. The initial percentage of noise corresponds to the number of ROIs labelled as noise over the total number of ROIs. The final percentage of noise corresponds to the number of false positives over the sum of the true and false positives, equivalent to 1-precision.

Manual annotation

The 5,315 regions of interest returned by the automatic segmentation were manually annotated by one of us (MLC) as 'signal' (part or complete bird song) or 'noise' (not a bird song or a bird song overlapping with other sounds or a portion of a bird song that is too small to be recognizable by a human expert) using the Audacity spectrographic view.

Supervised classification with BirdNET

To compare the DBSCAN output with a supervised method, the classification of bird song was operated with the convolutional neural network (CNN) BirdNET-Lite [START_REF] Kahl | BirdNET: A deep learning solution for avian diversity monitoring[END_REF].

BirdNET is designed for bird sound classification of 984 bird species around the world and is considered state-of-the-art bird audio identification. BirdNET was geographically conditioned to France during the spring season (fifteenth week of the Julian calendar). We kept the default parameters which were a sensitivity of 1 and a minimum of confidence of 0.1. The ROIs of different lengths were padded with zeros to have the same length. Then the ROIs were passed through the CNN for classification.

Implementation

Both methods could be run on a personal computer or on a virtual machine. In the latter case, with a single core with a clock of 2.2 GHz and 13 GB of RAM, the computation time to The complete process was written in Python language with the packages scikit-maad v1.3.0 [START_REF] Ulloa | scikit-maad: An open-source and modular toolbox for quantitative soundscape analysis in Python[END_REF], scikit-learn v0.23.2 [START_REF] Pedregosa | Scikit-learn: Machine Learning in Python[END_REF]), librosa v0.8.0 (McFee et al., 2015), and kneed v0.8.1 [START_REF] Satopaa | Finding a "Kneedle" in a Haystack: Detecting Knee Points in System Behavior[END_REF]. The full code is available as a Python package on Github (https://github.com/ear-team/bam).

Results

Audio files

A total of 880 audio files of 20 s (20 audio files * 44 species) were retrieved per species from Xeno-Canto, resulting in 4h53'20" of recordings.

Regions of interest (ROIs)

The best combination of the four parameters for the automatic segmentation of the ROIs was 10 and 15 for the time and the frequency downscaling factor, respectively, of the spectrogram, and 37 dB and 33 dB for the first and the second threshold, respectively, of the hysteresis binarization.

For the training set, a total of 1,855 bounding boxes were automatically extracted. The average number of ROIs per species was 84 with a minimum of 41 for the tawny owl (Strix aluco) and a maximum of 134 for the mistle thrush (Turdus viscivorus). The median percentage of noise per species estimated by the labelling function was 36.5% with a great disparity between species (

Clustering

In the case of the training set, a reduction in the median noise was observed for all species (Fig. 3a), from 36.5% to 12.5%. A similar reduction was observed for the validation set, from 35.5% to 13.5% (Fig. 3b, Table 1). The dispersion of the final noise was higher for the train set than for the validation set as indicated by a broader interquartile range after denoising. and long-tailed tit [Aegithalos caudatus]) that were fully denoised as well. One species, the song thrush (T. philomelos), remained with a high final noise with values between 76% in the training dataset and 77% in the validation dataset. This species was also the species with the highest initial quantity of noise (78% in the training set and 83% in the validation set).

In the case of the test set, which was composed of 22 bird species that were not in the training and validation sets, a reduction of the noise was also observed, from 48% to 33.5% (Fig. 3c, Table 1). The slighter reduction of the noise compared with the training and validation sets was associated with a wider dispersion of the final noise. After the DBSCAN clustering, only the noise included in the recordings of the meadow pipit (Anthus pratensis) could be reduced to zero. An increase in noise was observed for the little owl (Athene noctua) from 58% to 75% and for the goldcrest (Regulus regulus) from 44% to 53%. The Eurasian blue tit (C. caeruleus), the common reed bunting (Emberiza schoeniclus), and the Eurasian bullfinch (Pyrrhula pyrrhula) showed high final noise scores of 79%, 64%, and 70%, respectively.

The macro precision, which estimated the degree of cleanness of the dataset, was above 0.81 for the training dataset and above 0.82 for the validation dataset, while it dropped for the test dataset to 0.67 (Table 1). The macro recall was below 0.8 for all the three datasets, with values ranging between 0.65 for the training set and 0.72 for the validation set (Table 1). The macro F1 score had values between 0.65 for the test set and 0.73 for the validation set. To understand the influence of the initial quantity of noise on the clustering quality, the initial quantity of noise was represented against the final quantity of noise (Fig. 4a). Species that had more than 40% of initial noise had a low reduction of noise, especially for the clustering labelling function. For the species with less than 40% initial noise, DBSCAN clustering was more efficient than BirdNET as more points were close to 0% final noise. All species that reached 0% noise after clustering had an initial quantity of noise under 40%.

The polynomial regression applied to the clustering results (Fig. 4a) revealed that for the species with an initial quantity of noise between 10% and 35%, the denoising completed by the clustering algorithm was particularly efficient as all these species had a final quantity of noise around 5%. Beyond 35%, the denoising was less effective as shown by the polynomial approximation which was closer to the y = x line.

BirdNET

The median final noise values obtained with BirdNET were higher than for the DBSCAN clustering algorithm with values of 14% and 14.5% for the training and validation sets, respectively, while the median final noise was lower with a value of 23% for the test set (Fig. 3d-f, Table 1). Only two species were completely denoised in the three sets [European green woodpecker (P. viridis] and common starling [Sturnus vulgaris]). Contrary to DBSCAN clustering, BirdNET classification efficiency is expected to be independent of the initial quantity of noise per species as confirmed by the quasi-linear relationship between the initial and final noise (Fig. 4b).

The macro precision was above 0.8 for the training and validation sets, which was lower than for the DBSCAN clustering. The macro precision was 0.75 for the test, which was higher than for the DBSCAN clustering (Table 1). The macro recall values ranged between 0.74 for the test set and 0.84 for the validation set, which was higher than for the DBSCAN clustering. BirdNET had a higher recall than DBSCAN clustering.

Clustering and BirdNET

The final noises obtained with both methods were compared for all the sets (Fig. 5). The performance of DBSCAN clustering was higher when the initial noise was below 20%. Both methods behaved in the same way when the initial noise was between 20% and 40%. The BirdNET labelling function obtained better results when being applied on species with greater than 40% initial noise.

Discussion

In this work, we designed a specific labelling function to improve the quality of the Xeno-Canto bird song dataset by minimizing label noise. This labelling function followed a threestep process: 1) spectrographic time-frequency segmentation, 2) feature computation, and

3) noise or signal classification with either an unsupervised (DBSCAN) or a supervised (BirdNET) algorithm. We will comment on the performance of the new labelling function, discuss the advantages and drawbacks of each step of the workflow, and finally propose possible improvements.

Performance of the labelling function

Through manual annotation of focal bird recordings, we could assess label noise generated by automated labelling for the first time. The proportion of segmented sounds that do not correspond to the species of interest varied between 10% as observed for the black redstart (P. ochruros) and 80% such as for the song thrush (T. philomelos), the Eurasian bullfinch (P.

pyrrhula), and the Eurasian blue tit (C. caeruleus). Over all sets, 16 of the 44 species were affected by more than 50% label noise. Such a level of label noise can be explained by 1) the inherent variability induced by different audio equipment ranging from hyper focal to omnidirectional microphones, 2) different background noise levels due to other sound sources including other bird species, or 3) the diversity of the time-frequency bird song structure [START_REF] Kershenbaum | Acoustic sequences in nonhuman animals: a tutorial review and prospectus[END_REF].

The labelling function could reduce the median label noise by up to a factor three with a large disparity between the species and the dataset. When considering all datasets, 25 of 44 species had a final label noise below 25% using the unsupervised method (DBSCAN), while this proportion slightly increased to 31 of 44 species using the supervised method (BirdNET).

The performance of the labelling function was significantly lower for the test set than for the training and validation sets regardless of the classifying method. The difference in performance might be explained by the segmentation that generated a higher initial label noise level in the test set than in the training and validation sets. As the segmentation was optimized on the bird songs of the species in the training set, the hyperparameters found after tuning might be too specific and, hence, overfit the process.

The DBSCAN clustering success varied depending on the initial label noise. In particular, DBSCAN outperformed BirdNET when the initial label noise was below 20% but underperformed when it was above 40%. Contrary to BirdNET, the performance of DBSCAN is dependent on the proportion of label noise in the dataset. If the initial label noise level was above a critical value, the adaptive selection of MinPts and Eps might be biased by the presence of a critical amount of sounds that is not representative of the sound of interest [START_REF] Latifi-Pakdehi | DBHC: A DBSCAN-based hierarchical clustering algorithm[END_REF]. A large initial label noise level might also increase the chance to aggregate similar audio samples into a cluster that do not belong to the species of interest.

Signal segmentation and classification of bird songs is challenging due to the high temporal and frequency diversity of acoustic units [START_REF] Kershenbaum | Acoustic sequences in nonhuman animals: a tutorial review and prospectus[END_REF][START_REF] Priyadarshani | Automated birdsong recognition in complex acoustic environments: a review[END_REF]. Here, the level of label noise was particularly important for high frequency bird songs such as those of the blue tit (C. caeruleus) or the kinglet (Regulus regulus). In that case, the signal-to-noise ratio, due to strong attenuation was expected to be lower and so the regions of interest were less salient and more difficult to delimit. The results were also low for species emitting brief sounds like the bullfinch (P. pyrrhula) or long sequences of notes like the song thrush (T. philomelos). Acoustic reverberation and attenuation due to outdoor propagation can also blur the temporal architecture of the song, making the detection of fast events particularly challenging. The relative success between species suggests that the acoustic and clustering hyperparameters of our labelling function might need to be tuned for use in other contexts, including other bird species but also possibly mammal, amphibian, or insect sound productions.

A modular labelling function

Our labelling function was designed to be modular. Each part, from segmentation to clustering, is independent and can be adapted to other needs and contexts. Sound segmentation has usually been operated with a single threshold on the time wave [START_REF] Fodor | The ninth annual MLSP competition first place[END_REF][START_REF] Lasseck | Bird song classification in field recordings: Winning solution for NIPS4B 2013 competition[END_REF][START_REF] Potamitis | Automatic Classification of a Taxon-Rich Community Recorded in the Wild[END_REF]. Our segmentation method is based on a double threshold process applied to a time-frequency representation of sound. The double threshold process allows the selection of sounds that are not necessarily the most salient ones so that middle-distance sounds can be still detected. Compared to the detector designed by [START_REF] Sprengel | Audio based bird species identification using deep learning techniques[END_REF], which is widely used to build bird song datasets to train deep learning models such as BirdNET [START_REF] Kahl | BirdNET: A deep learning solution for avian diversity monitoring[END_REF], our method segments the signal of interest in time but also in frequency so that the timber can be considered. This strategy opens the possibility of identifying two sounds of interest when they overlap in time but not in frequency. Our segmentation is based on conventional image processing, but deep learning solutions for images have been intensively developed in recent years. These attractive options include temporal convolutional networks (TCNs) [START_REF] Bai | An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling[END_REF][START_REF] Steinfath | Fast and accurate annotation of acoustic signals with deep neural networks[END_REF], image object detector architectures such as Faster R-CNN or YOLO [START_REF] Coffey | DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations[END_REF][START_REF] Kong | Joint detection and classification convolutional neural network on weakly labelled bird audio detection[END_REF][START_REF] Narasimhan | Simultaneous segmentation and classification of bird song using CNN[END_REF][START_REF] Shrestha | Bird Audio Diarization with Faster R-CNN[END_REF][START_REF] Venkatesh | You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection[END_REF][START_REF] Wu | SILIC: A cross database framework for automatically extracting robust biodiversity information from soundscape recordings based on object detection and a tiny training dataset[END_REF][START_REF] Zsebők | Automatic bird song and syllable segmentation with an open-source deep-learning object detection methoda case study in the Collared Flycatcher (Ficedula albicollis)[END_REF] and audio source separation such as speaker diarization [START_REF] Denton | Improving Bird Classification with Unsupervised Sound Separation[END_REF][START_REF] Morfi | Few-shot bioacoustic event detection: a new task at the DCASE 2021 challenge[END_REF]. However, segmentation methods all require a very large amount of labelling data as well as high computational resources, two main constraints at a time when naturalist expertise is disappearing and energy consumption must be limited.

To describe the segmented sounds, we opted for hand-crafted features based on the 2D wavelet analysis as proposed by Ulloa et al. (2018). The wavelet transform has the advantage not to be human-centric, contrary to the Mel-frequency cepstral coefficients (MFCC) which has increasing popularity for describing animal vocalizations [START_REF] Zottesso | Bird species identification using spectrogram and dissimilarity approach[END_REF]. In addition, 2D wavelet kernels that are not learned show a striking similarity with convolutional kernels learned by the first-layers of CNN [START_REF] Meng | Energy-Efficient Gabor Kernels in Neural Networks with Genetic Algorithm Training Method[END_REF][START_REF] Yosinski | How transferable are features in deep neural networks?[END_REF].

Instead of using hand-crafted features, another approach consists in using learned features, the so-called deep audio embeddings coming from deep learning models already trained on animal vocalizations [START_REF] Goffinet | Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires[END_REF][START_REF] Stoumpou | Analysis of Mouse Vocal Communication (AMVOC): a deep, unsupervised method for rapid detection, analysis and classification of ultrasonic vocalisations[END_REF]. Following this idea, a recent study successfully tested several methods to learn a latent representation of audio samples to separate the song of the barred owl (Strix varia), the common crane (Grus grus), and the common loon (Gavia immer) from noise [START_REF] Tolkova | Parsing Birdsong with Deep Audio Embeddings[END_REF]. The methods include a convolutional autoencoder and two pre-trained networks: the VGGish model [START_REF] Hershey | CNN architectures for large-scale audio classification[END_REF] and the Wavegram-Logmel-CNN16 architecture [START_REF] Kong | PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition[END_REF]. Both CNN were pre-trained on the Google Audioset dataset, a large collection of annotated sounds from YouTube videos [START_REF] Gemmeke | Audio Set: An ontology and human-labeled dataset for audio events[END_REF] with more than 600 classes of sounds, but a unique class "birds". Another solution would have been to use the 512-dimensional embedding provided by BirdNET. In any case, special attention is required to adapt the dimension of the embedding vector to the number of audio samples per species to avoid facing the curse of dimensionality (Assent, 2012). Deep audio embeddings sound promising, but they lack interpretability and still need to be improved by enriching the training dataset with animal signals and soundscapes.

Finally, recent studies proposed using non-linear unsupervised dimensionality reduction such as the Uniform Manifold Approximation and Projection (UMAP) [START_REF] Mcinnes | UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction[END_REF] to generate latent space representation directly from the spectrogram. Clustering is then performed directly on the embedding generated with UMAP [START_REF] Sainburg | Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires[END_REF][START_REF] Thomas | A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations[END_REF]. Although this approach seems very straightforward, it still requires very precise onset detection between calls/songs to avoid intraclass dissimilarity [START_REF] Thomas | A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations[END_REF].

The last module of the labelling function consisted in separating the sound of interest and noise using DBSCAN clustering. DBSCAN and other density-based clustering nonparametric algorithms have already been used successfully but for different tasks on marine mammals such as separating blue whale (Balaenoptera musculus) calls from noise [START_REF] Cuevas | Unsupervised blue whale call detection using multiple time-frequency features[END_REF], classifying killer whale (Orcinus orca) vocalizations without supervision [START_REF] Poupard | Large-scale unsupervised clustering of Orca vocalizations: a model for describing Orca communication systems[END_REF], and identifying the coda repertoire of sperm whales [START_REF] Amorim | Coda repertoire and vocal clans of sperm whales in the western Atlantic Ocean[END_REF][START_REF] Gero | Individual, unit and vocal clan level identity cues in sperm whale codas[END_REF]. Very recently, HDBSCAN, a hierarchical variant of DBSCAN [START_REF] Mcinnes | hdbscan: Hierarchical density based clustering[END_REF], has been used to create the vocal repertoire of the meerkat (Suricata suricatta) [START_REF] Thomas | A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations[END_REF], harpy eagles (Harpia harpyja), and giant otters (Pteronura brasiliensis) [START_REF] Schneider | Introducing the Software CASE (Cluster and Analyze Sound Events) by Comparing Different Clustering Methods and Audio Transformation Techniques Using Animal Vocalizations 26[END_REF]. So far, only three density-based clustering nonparametric algorithms have been tested (DBSCAN, HDBSCAN and OPTICS [START_REF] Ankerst | OPTICS: ordering points to identify the clustering structure[END_REF]) on animal vocalizations, but many other variants have recently been designed (Latifi-Pakdehi and Daneshpour, 2021) and might be adapted to design more efficient labelling functions.

Perspectives and conclusion

The growing success of both bioacoustics and ecoacoustics for understanding animal behavior and ecology is mainly based on the collection and automatic analysis of large scale recording archives. If field recording is now made possible with personal recorders and autonomous recording units, the automatic retrieval of sounds of interest for further

Figure 1 .

 1 Figure 1. From weak to strong labels: workflow of the labelling function. After collecting N weakly labelled audio recordings of a focal species from the Xeno-Canto database, the labelling function workflow consists of (1) segmenting each audio recording into time-frequency sound units, (2) calculating 49 features (2D wavelet coefficients (n = 48) + frequency centroid (n = 1)) of each sound unit, (3) clustering all sound units into signal

  process the validation dataset (20 audio files of 22 species) took 7 minutes to download the files from xeno-canto, 6 minutes to extract the ROIs, 13 minutes for the ROIs classification with DBSCAN (feature extraction and clustering), and 25 minutes for the ROIs classification with BirdNET.

Figure 3 .

 3 Figure 3. Initial and final quantity of noise for the three datasets when applying the DBSCAN unsupervised clustering (a, b, c) or the supervised BirdNET classification (d, e, f). The initial percentage of noise corresponds to the number of ROIs labelled as noise over the total number of ROIs. The final percentage of noise correspondsto the number of false positives (i.e., the ROIs that were automatically assigned as signal but were noise) over the sum of the true and false positives. It can also be expressed as 100%precision (i.e., the number of true positives over the sum of true and false positives). Each box represents the median (central line) and the 25% (Q1) and 75% quartiles (Q3) (box limits), while the whiskers represent the last sample point below and above 1.5 times the interquartile range (Q3-Q1) for the lower and upper whiskers, respectively. For an exact representation of the distribution, each recording is shown with a point and lines connecting the treatments.

Figure 4 .

 4 Figure 4. Comparison of initial noise with final noise with the unsupervised DBSCAN clustering method (a) and with the BirdNET algorithm (b). Each point represents one species. The y = x blue line indicates when the final quantity of noise is equal to the initial quantity of noise. The black line is a polynomial regression of degree 2 indicating the general trend.

Figure 5 .

 5 Figure 5. Diminution of noise compared to the DBSCAN clustering and BirdNET algorithms in regard to the initial quantity of noise. DBSCAN clustering was more performant for species lying below the y = x blue line. BirdNET was more performant for species lying above the y = x blue line.

Table 1 ,

 1 Table A.1.). The song thrush (Turdus philomelos) was the species with the highest initial quantity of noise (78%) and the tawny owl (Strix aluco) had the lowest (15%). The validation set contained 1,732 ROIs extracted from the same 22 species as the training set. The European green woodpecker (Picus viridis) had the lowest number of ROIs (40) and the common cuckoo (Cuculus canorus) had the highest (162). The median percentage of noise per species was 35.5% (Table 1, Table A.1.). The song thrush (Turdus philomelos)

	had the largest initial quantity of noise (83%) when the European greenfinch (Chloris chloris)
	had the lowest (13%).
	The test set, which was composed of 22 other species, contained 1,728 ROIs. The Cetti's
	warbler (Cettia cetti) had the lowest number of ROIs (48) and the Eurasian blue tit
	(Cyanistes caeruleus) had the largest (123). The median percentage of noise per species
	set was 48% (

Table 1 ,

 1 Table A.2.). The Eurasian bullfinch (Pyrrhula pyrrhula) had the largest initial quantity of noise (82%) while the black redstart (Phoenicurus ochruros) had the lowest

	(10%).

Table 1

 1

			Training	Validation	Test
		Median Initial noise 36.5%	35.5%	48.0%
		Median Final noise	12.5%	13.5%	33.5%
	DBSCAN	Macro precision	0.81	0.82	0.67
	Clustering	Macro recall	0.65	0.72	0.68
		Macro F1 score	0.67	0.73	0.65
		Median Final noise	14.0%	14.5%	23.0%
		Macro precision	0.81	0.81	0.75
	BirdNET
		Macro recall	0.82	0.83	0.74
		Macro F1 score	0.81	0.81	0.71

. Global scores per labelling function for the three datasets. Noise levels are expressed in percentage when quality metrics vary between 0 and 1. After applying the DBSCAN clustering, the training set contained two species (common cuckoo [C. canorus] and tawny owl [S. aluco]) that were completely denoised and the validation set contained three species (common cuckoo [C. canorus], tawny owl [S. aluco],

Table A .

 A description is still challenging. Here, taking advantage of the largest bird song audio data set, we showed that a labelling function based on time-frequency analysis and clustering methods can help select appropriate signals and thus improve the quality of the data collected. Further developments are still required to adapt such facilities to soundscape recordings where the sounds of interest of several species are mixed. 2. Scores of the test set for the DBSCAN clustering and the BirdNET methods. IN: initial noise, FN: final noise, R: recall

	Fieldfare	TRAINING DATASET			VALIDATION DATASET
	(Turdus pilaris)	57	48	79	28	50
			DBSCAN	BirdNET		DBSCAN	BirdNET
		IN	FN	R	FN	R	IN	FN	R	FN	R
	Long-tailed Tit (Aegithalos caudatus)	55	26	52	38	91	33	0	32	18	96
	Eurasian Skylark (Alauda arvensis)	40	4	71	13	87	48	12	92	12	88
	Tree Pipit (Anthus trivialis)	24	7	90	5	85	23	14	69	15	94
	Short-toed Treecreeper (Certhia brachydactyla)	23	2	98	13	100	35	23	92	26	98
	European Greenfinch (Chloris chloris)	55	35	34	31	77	13	4	31	6	75
	Common Wood Pigeon (Columba palumbus)	23	8	28	12	90	69	15	100	29	100
	Common Cuckoo (Cuculus canorus)	18	0	12	10	99	25	0	36	9	83
	Yellowhammer (Emberiza citrinella)	37	5	47	21	88	47	17	69	21	90
	European Robin (Erithacus rubecula)	42	19	67	28	60	36	12	87	13	64
	Common Chaffinch (Fringilla coelebs)	27	2	94	10	88	21	13	80	9	98
	Common Nightingale (Luscinia megarhynchos)	39	20	90	2	69	40	32	89	22	57
	Great Tit (Parus major)	54	42	68	25	71	71	38	50	46	100
	Common Redstart (Phoenicurus phoenicurus)	71	35	56	57	96	40	18	85	14	90
	European Green Woodpecker (Picus viridis)	36	4	90	0	76	59	8	71	13	76
	Dunnock (Prunella modularis)	23	14	83	16	96	26	6	56	14	92
	Eurasian Nuthatch (Sitta europaea)	39	32	31	31	70	35	24	43	18	82
	Tawny Owl (Strix aluco)	15	0	74	6	97	36	0	56	15	91
	Eurasian Blackcap (Sylvia atricapilla)	42	30	100	14	69	42	28	100	23	79
	Eurasian Wren (Troglodytes troglodytes)	33	11	77	12	85	27	14	86	10	75
	Common Blackbird (Turdus merula)	30	10	57	14	82	31	4	97	4	75
	Song Thrush (Turdus philomelos)	78	76	88	46	54	83	77	95	52	48
	Mistle Thrush (Turdus viscivorus)	27	15	17	16	89	23	10	61	9	80

Funding

This work was realized with the support of the Sorbonne Center for Artificial Intelligence (SCAI) of Sorbonne University (IDEX SUPER 11-IDEX-0004).

Appendices


Agustina Ferrando

Julieta Sturla Lompré

Emilia Gonzalez

Marcos Franco

Marta Commendatore

Marina Nievas

Cécile Militon

Georges Stora

José Luis Esteves

Philippe Cuny

Franck Gilbert

Seasonal composition and activity of the intertidal macrobenthic community of Caleta Valdés (Patagonia, Argentina) applying in situ and ex situ experimental protocols

Keywords: Biodiversity, bioturbation, sedimentary matrix, soft-bottom macrofauna, protected areas

Introduction

The Patagonian coast is about 3000 kilometers long with a high value in terms of biodiversity [START_REF] Esteves | Análisis de indicadores de contaminación en la zona costera patagónica[END_REF]. This coast is used by migratory birds and marine mammals to rest, feeding and mating [START_REF] Yorio | Marine protected areas, spatial scales, and governance: implications for the conservation of breeding seabirds[END_REF]. Moreover, it includes zones of reproduction and breeding of fish, crustaceans, and mollusks sustaining one of the most productive temperate marine ecosystems in the world [START_REF] Vázquez | Biodiversidad costero marina en la Patagonia: características, conservación e importancia[END_REF]. As in other marine coastal environments, the food webs are mainly sustained by phytoplankton [START_REF] Smetacek | Diatoms and the ocean carbon cycle[END_REF]. Particularly, in these coasts, upwelling phenomena increase primary production [START_REF] Acha | Marine fronts at the continental shelves of austral South America: Physical and ecological processes[END_REF]. Due to its remarkable biodiversity and ecosystemic richness, big areas of this coast have a special protection status (e.g. Península de Valdés was established as Humanity Mundial Patrimony in 1999, North Zone San Jorge gulf was defined as Patagonian Austral Inter-jurisdictional Marine Coastal Park in 2007 and Punta Tombo as Natural Protected Area in 1979, among others). Despite these protection measures, there are diverse and growing anthropogenic activities constituting a direct threat to the ecosystem. Therefore, studies about the seasonal dynamics of the macrobenthic communities constituting a particular interest at ecological level but also for integrated coastal zone management purposes.

Indeed, the soft-bottom benthic organisms play a key role in nutrient cycles, pollutant metabolism, dispersion, and burial of the organic matter and secondary production processes [START_REF] Hopkinson | Benthic metabolism and nutrient cycling along an estuarine salinity gradient[END_REF][START_REF] Snelgrove | The biodiversity of macrofaunal organisms in marine sediments[END_REF]. These organisms have reduced mobility [START_REF] Pearson | Macrobenthic succession in relation to organic enrichment and pollution of the marine environment[END_REF][START_REF] Teixeira | Thermal impact of a nuclear power plant in a coastal area in Southeastern Brazil: Effects of heating and physical structure on benthic cover and fish communities[END_REF] and therefore they are highly sensitive to the physical and chemical changes of the sediment matrix [START_REF] Dauvin | Benthic indicators: From subjectivity to objectivity -Where is the line?[END_REF][START_REF] Muniz | Testing the applicability of a Marine Biotic Index (AMBI) to assessing the ecological quality of soft-bottom benthic communities, in the South America Atlantic region[END_REF]. For this reason, this group of organisms is useful as an indicator of ecological state in impacted sites and/or in monitoring programs (e.g. [START_REF] Teixeira | Thermal impact of a nuclear power plant in a coastal area in Southeastern Brazil: Effects of heating and physical structure on benthic cover and fish communities[END_REF]. Through the excavation, burrow ventilation, and/or mud and organic matter ingestion [START_REF] Taghon | Utilization of deposited and suspended particulate matter by benthic "interface" feeders[END_REF], they induce the transport of particles (sediment reworking) and fluids also known as bioturbation [START_REF] Kristensen | What is bioturbation? The need for a precise definition for fauna in aquatic sciences[END_REF].

Community bioturbation is a good integrator of macrofaunal functioning as it incorporates various aspects of behavior (e.g. feeding, locomotion, burrow building) and may vary depending on the community structure and local heterogeneity [START_REF] Solan | Worldwide measurements of bioturbation intensity, ventilation rate, and the mixing depth of marine sediments[END_REF]. Differences in bioturbation over time may also reflect the abiotic and/or biotic changes of not only the benthic but also the pelagic ecosystem. Particularly, bioturbation affects processes such denitrification [START_REF] Gilbert | 2-D optical quantification of particle reworking activities in marine surface sediments[END_REF], nitrification (Aller et al., 1998), sulfate reduction [START_REF] Canavan | Organic matter mineralization in sediment of a coastal freshwater lake and response to salinization[END_REF], benthic fluxes (Aller and Aller, 1998;[START_REF] Mermillod-Blondin | Do tubificid worms influence the fate of organic matter and pollutants in stormwater sediments?[END_REF][START_REF] Michaud | The functional group approach to bioturbation: The effects of biodiffusers and gallery-diffusers of the Macoma balthica community on sediment oxygen uptake[END_REF][START_REF] Mortimer | The effect of macrofauna on porewater profiles and nutrient fluxes in the intertidal zone of the humber estuary[END_REF] and microbe dynamics [START_REF] Goñi-Urriza | Effect of macrofaunal bioturbation on bacterial distribution in marine sandy sediments, with special reference to sulphur-oxidising bacteria[END_REF][START_REF] Papaspyrou | Microbial reaction rates and bacterial communities in sediment surrounding burrows of two nereidid polychaetes (Nereis diversicolor and N. virens)[END_REF].

Reworking organisms can be classified into functional groups based on the different ways in which the organisms behave and on the resulting transport of particles within the sedimentary column. According to [START_REF] Kristensen | What is bioturbation? The need for a precise definition for fauna in aquatic sciences[END_REF], these organisms can be differentiated into biodiffusors, upwards and downward conveyors, and regenerators. Biodiffusors are species whose activities produce a local, constant, and random mixture of sediment over short distances. Upwards and downward conveyors are species vertically distributed that respectively feed head down and deposit its dregs in the surface of the sediment or inversely, and finally the regenerators, excavator species that transfer the sediment from the bottom to the surface. In ecosystems, the complex communities are then able to generate various patterns and intensities of sediment reworking depending on their functional composition (e.g. [START_REF] Duport | Benthic macrofauna and sediment reworking quantification in contrasted environments in the Thau Lagoon[END_REF]. Moreover, it can be modulated by the environmental conditions via changes in organisms behavior.

For example, the burrowing polychaetes Hediste diversicolor can switch from deposit-feeding to filter-feeding if the phytoplankton concentration is high enough [START_REF] Riisgård | Filter-feeding in the polychaete Nereis diversicolor: a review[END_REF] and Alitta virens has been shown to have a temperature dependence of sediment reworking activity [START_REF] Ouellette | Effects of temperature on in vitro sediment reworking processes by a gallery biodiffusor, the polychaete Neanthes virens[END_REF].

The goals of this study were (a) to evaluate for the first time the seasonal variation of the structure and the activity (i.e., sediment reworking) of the intertidal macrobenthic communities of Caleta Valdés and, (b) to validate an ex situ experimental protocol for future ecotoxicological studies.

Materials and methods

Study area

Caleta Valdés (CV) is a north-south-oriented coastal lagoon located at the eastern side of Península Valdés. This is 30 km long and has its mouth at the southern end where the water exchange is produced at two and four nudes of velocity. A gravel bank limits the lagoon, generating a channel of 200 m width and a mean depth of 5 m. The enclosed north zone presents a marsh that cover a 25% of the total surficial of the system [START_REF] Esteves | Jornadas Nacionales de Ciencias del Mar. CENPAT. Actas[END_REF]. The tidal regimen is semidiurnal with a mean high of 5 m. The sampling site for the experimentation was chosen in the muddy north continental zone of CV (42°15′53″ S, 63°40′50″ W; Figure 1) due to its macrofauna richness and the absence of anthropogenic pollution. Currently, this natural reserve is protected of human activities so that strict control is carried out on touristic and smaller productive activities, which are developed in the area.

Sampling

Sediment cores were sampled by hand in autumn (April 2011), winter (July 2011), spring (November 2011), and summer (February 2012) using 10 cm diameter and 25 cm length PVC cores, in agreement with previous bioturbation studies [START_REF] Ferrando | Oil spill effects on macrofaunal communities and bioturbation of pristine marine sediments (Caleta Valdés, Patagonia, Argentina): Experimental evidence of low resistance capacities of benthic systems without history of pollution[END_REF][START_REF] Hedman | Particle reworking and solute transport by the sediment-living polychaetes Marenzelleria neglecta and Hediste diversicolor[END_REF][START_REF] Quintana | Simultaneous study of particle reworking, irrigation transport and reaction rates in sediment bioturbated by the polychaetes Heteromastus and Marenzelleria[END_REF][START_REF] Timmermann | Modeling of advective solute transport in sandy sediments inhabited by the lugworm Arenicola marina[END_REF]. At each season, at low tide, eight cores were vertically and randomly pushed down to 20 cm sediment depth approximately. Then, for ex situ experiments, four of them were immediately collected by hand and transported to the laboratory. The other four cores were embedded in the sediments for in situ assessments. In addition to sediment sampling, 60 L of seawater were also collected in plastic containers to be used for the ex situ experiments. This procedure was applied for each studied season.

Incubation conditions

In situ experiments

In order to assess biological reworking activity, in each sediment core, 4 g of luminophores (inert fluorescent particles, [START_REF] Duport | Benthic macrofauna and sediment reworking quantification in contrasted environments in the Thau Lagoon[END_REF] of two sizes and colors (pink: 65-125 μm; green: 125-355 μm) were homogeneously spread at the sediment surface at initial time (T=0 day). Luminophores sizes were selected according to the two major size distribution modes of sedimentary particles in the studied site [START_REF] Sturla Lompré | Fate of petroleum hydrocarbons in bioturbated pristine sediments from Caleta Valdés (Patagonia Argentina): An ex situ bioassay[END_REF]. After 13 days of in situ incubation, the cores were withdrawn and carried out to the laboratory. Then, the sediment cores were sliced to provide 0.5 cm thick sediment layers from the surface to 2 cm depth and 1 cm thick sediment layers from 2 to 17 cm depth. Each sediment slice was separated in four equal parts that were randomly distributed to perform the different analyses.

Ex situ experiments

Similarly, for each season, the four cores containing the sediments sampled at CV were distributed in a 56 L tank that was then filled until the cores were totally submerged with the seawater collected the same day in the same sampling station. To assess biological reworking activity in a no water flow system where no hydrodynamic loss of tracers could occur, in each sediment column, 2 g of the two types of luminophores (pink: 65-125 μm and green: 125-355 μm) were homogeneously spread at the sediment surface at the initial time (T=0 day). The system was kept continuously aerated (air bubbling). Sediment cores were incubated for 13 days at a similar ambient temperature to the in situ assessment.

Then, they were manually collected and processed identically as for the in situ experiments.

Analytical procedures

Sediment characterization

A quarter of each sediment slice was used to determine sediment granulometry (only in autumn 2011) by laser beam diffraction (Partica LA-950; HORIBA Instruments, Inc.), and for the four seasons, water content and organic matter by oven drying at 105 °C during 24 h and muffle furnace ignition at 450 °C during 4 h, respectively. Eight granulometry fractions were obtained: fine silt < 6 µm, medium silt 6-20 µm, coarse silt 20-60 µm, very fine sand 60-125 µm, fine sand 125-200 µm, medium sand 200-600 µm, coarse sand 600-1000 µm, and very coarse sand 1000-2000 µm.

Macrobenthic communities

The remaining three quarters of each sediment slice were fixed and dyed with a neutralized 4% formaldehyde solution and Rose Bengal colorant, respectively.

Then, the samples were sieved, first with a 500 μm mesh and then with a 44 μm mesh. The sediment retained in the first mesh was preserved in ethanol 70% for the identification and counting of the macrobenthic organisms. The sorting of organisms (for the four seasons) was done to the lowest possible taxonomic level using stereoscopic and optic microscopes in the laboratory with reference material and dichotomous keys [START_REF] Banse | Benthic errantiate polychaetes of British Columbia and Washington[END_REF][START_REF] Blake | Polychaeta[END_REF][START_REF] Hartman | Atlas of the Errantiate Polychaetous Annelids from California[END_REF][START_REF] Hartman | Atlas of the Sedentariate Polychaetous Annelids from California[END_REF]Orensanz et al., unpublished data, among others). Only the whole organisms or anterior fragments of each taxon were recorded.

Bioturbation

The sediments retained in the 44 μm mesh were used (see above) to quantify sediment reworking. The sediments were homogenized and subsampled to quantify the luminophores percentage using a microplate reader (Biotek, Synergy Mx) at λex/λem: 460/500 and 565/602 nm for the pink and green luminophores, respectively [START_REF] Majdi | Quantification of sediment reworking by the Asiatic clam Corbicula fluminea Müller, 1774[END_REF]. For each sediment column at the four seasons, we obtained the vertical luminophores profile at the experimental final time (13 days) from the percentage of luminophores found in each layer concerning the total amount in the core. The quantification of sediment reworking was then evaluated from the distributions of luminophores by the gallery-diffusor model [START_REF] François | Functional approach to sediment reworking by gallery-forming macrobenthic organisms: modeling and application with the polychaete Nereis diversicolor[END_REF]. This model allows describing both the biodiffusion-like transport (Db coefficient) due to the continuous displacement of the tracers and the nonlocal advective displacement of the tracers (r coefficient). The best fit between observed and modeled tracer distribution with depth (i.e., producing the best Db and r coefficients couple) was estimated by the least-squares method [START_REF] Gilbert | Sediment reworking by marine benthic species from the Gullmar Fjord (Western Sweden): Importance of faunal biovolume[END_REF].

Statistical data analysis

Variation between seasons (autumn, winter, spring and summer) and experimental conditions (I: in situ and E: ex situ) of water content and organic matter (OM) were evaluated by core through two-way ANOVA considering seasons and conditions as fixed factors (n=32) (Statistica, version 7). A square root transformation was applied to satisfy the homogeneity of variances assumption. The differences between pairs of seasons and conditions were tested through Tukey test for multiple mean comparisons (Statistica, version 7).

A comparison of the mean abundance by core of dominant taxa between seasons and I and E experimental conditions were evaluated through two-way ANOVA considering seasons and conditions as fixed factors (n=32) (Statistica, version 7).

A square root transformation was applied to satisfy the homogeneity of variances assumption. The differences between pairs of seasons and conditions were tested through Tukey test for multiple mean comparisons (Statistica, version 7).

Biological data analyses were performed using the software package PRIMER 7.0 (Plymouth Marine Laboratory, UK). The sorting using the total abundance of each taxa by core recorded in the I and E experiments at the four seasons was carried out with a nonmetric multidimensional scaling (nMDS) analysis (Bray-Curtis index; group average link; square root). In addition, a Permutational Multivariate Analysis of Variance (PERMANOVA) was applied to assess the effect of the seasons, I and E experimental conditions, and the interaction between both factors (Bray-Curtis index; fourth root and 9999 permutations) (PRIMER v7). A Pair-wise PERMANOVA was applied to analyze the significant differences between seasons and conditions (Bray-Curtis index; fourth root and 9999 permutations) (Anderson et al. 2008). Moreover, Specific richness (S), total abundance (N), and the Shannon diversity index (H′) were calculated by layer and core (I and E) at the four seasons. Variation of each biological parameter between seasons and conditions was evaluated through two-way ANOVA considering seasons and conditions as fixed factors (n=32) (Statistica, version 7).

The differences between pairs of seasons and conditions were tested through Tukey test for multiple mean comparisons.

A comparison of the bio-diffusion (Db) and bio-advection (r) coefficients calculated for each experimental condition (I and E) was performed through the nonparametric Kruskal-Wallis one-way analysis of variance by ranks [START_REF] Kruskal | Use of Ranks in One-Criterion Variance Analysis[END_REF] (Statistica,version 7). Moreover, a one-way ANOVA was applied to test the differences between seasons (n=16). The differences between pairs of seasons were tested through Fisher LSD test for multiple mean comparisons (Statistica, version 7). A Kruskal-Wallis and Pair-wise Multiple Comparisons analysis were carried out to test the differences as a non-parametric alternative due to lack of homogeneity of variances of data in some cases.

Results

Characterization of the sediment

The granulometry profiles were similar for the in situ and the ex situ sediments showing a predominance of medium sand (200-600 µm) (Figure 2). Moreover, the fine fractions (< 200 µm) were highest at the first two centimeters of the sedimentary columns. Regards in situ sediments, the maximum percentages of water content (58.5 ± 8.2%) and organic matter (OM) (1.7 ± 0.1%) were recorded in the first layer (0-0.5 cm) in winter and autumn, respectively (Online Resource 1). In spring, the maximum percentages of water content (48.5 ± 19.6%) and OM (1.3 ± 0.2%) were found in the fourth layer (1.5-2 cm) and the first layer (0-0.5 cm), respectively. In summer, the maximum percentages of water content (33.3 ± 0.8%) and OM (0.9 ± 0.0%) were recorded in the second (0.5-1 cm) and second and third layers (0.5-1.5 cm), respectively. These patterns were similar to those recorded in ex situ conditions (Online Resource 1). Comparing seasons and experimental conditions (I and E), percentages of water content did not show significant effect of the interaction of these factors (Two-way ANOVA, p = 0.15, n = 32) but highly significant effect of each factor (p < 0.01, n = 16). Percentages of water content recorded in I and E conditions for winter and summer were similar (Tukey test p = 0.23 and p = 0.06; n = 8, respectively). Contrary, in autumn and spring, this parameter was significantly higher I than E (Tukey test p < 0.01; n = 8) (Figure 3). Moreover, in I conditions, water content showed not significant differences (Tukey test p > 0.05; n = 16) among autumn, winter and spring, but did (Tukey test p < 0.01; n = 16) in summer compared to other seasons. By contrast, in E conditions, in winter water content was significantly higher (Tukey test p < 0.01; n = 16) than in other seasons (Figure 3). Percentages of OM showed significant effect of the interaction season and experimental condition (Two-way ANOVA, p = 0.01, n = 32) and just significant effect of season (p < 0.01, n = 16). Only significant differences between I and E conditions were recorded in spring (p = 0.026, n = 8). Comparing seasons, at both conditions, the highest values were recorded in autumn with significant differences (Tukey test p < 0.01; n = 16) from the rest seasons. At I conditions, OM significant differences (p < 0.01; n = 16) were between winter and spring from summer; conversely, E conditions presented significant differences (p < 0.01, n = 16) between winter from spring and summer (Figure 3).

Macrobenthic communities

A total of 27 taxa (I and E conditions) were found in CV including sixteen taxa of crustaceans, nine taxa of polychaetes, and one taxon of oligochaetes and nemerteans (Table 1). Mean abundance of the six most dominant taxa (> 4.8%) are shown in Figure 4. All taxa except Axiothella sp. were more abundant in the spring-summer period. This was pronounced in summer for the polychaete Exogone molesta (Syllidae), the most abundant species (37.4% of total macroinfauna). In addition, the two-way ANOVA showed only for Scoloplos sp. and E. molesta highly significant (p = 0.00, n = 32) and significant (p = 0.02, n = 32) effect of the interaction (season and experimental condition) and I and E factor, respectively. On the contrary, all the dominant taxa showed a highly significant effect (p = 0.00, n = 32) of the season factor. Particularly, the differences between pairs of seasons showed not significant differences (Tukey test p > 0.05; n = 32) for Axiothella sp. and between autumn and winter all the cases (Tukey test p > 0.05; n = 32). Moreover, abundance recorded in these seasons showed highly significant differences (Tukey test p < 0.01; n = 32) with spring and summer, which were similar in most cases (Figure 4). Finally, only for E. molesta in spring were detected significant differences (Tukey test p < 0.05; n = 32) between experimental conditions.

The nMDS showed a good representation (stress = 0.15) and grouped spring and summer samples, which were located on the opposite side of winter samples.

Autumn samples were in an intermediate position close to spring-summer it. In addition, I and E faunistic composition were similar all the seasons (Figure 5).

Moreover, the interaction between the factors season and experimental condition (I and E) were not significant (PERMANOVA, p > 0.01) meanwhile the effect of each factor was it (p < 0.01) (Table 2). The Pair-wise PERMANOVA showed highly significant differences (p < 0.01) between all the seasons in both experimental conditions (I and E) meanwhile the differences between I and E were highly significant (p < 0.01) only in spring.

Vertical profiles of the community parameters (S, N and H´) showed a similar trend for the four seasons with maximum values in the first five centimeters of the sedimentary columns and a decrease with depth (Figure 6). The two-way ANOVA for each community parameter showed only significant effect of the interaction (season and experimental condition) for H´, meanwhile the effect of both factors were significant for S and H´ (p < 0.01, n = 32) and just factor season for N (p < 0.11, n = 32). Regarding the differences between experimental conditions (I and E), significantly higher values I than E were recorded in spring H´ (Tukey test p = 0.023, n = 8, respectively). Particularly, at I condition, the highest S and N values were recorded in spring and summer while H´ values were relatively constant between seasons (Figure 7). Moreover, S and N showed strong differences between autumn and winter versus spring and summer (Tukey test p = 0.00 n =16), and were no differences between autumn versus winter (Tukey test p = 0.00; n = 18). Similarly, N showed strong differences between all the seasons (Tukey test p = 0.00; n = 16) except between autumn versus winter (Tukey test p = 0.15; n = 16). Regard to E condition, S had no significant differences among autumn, winter and spring (Tukey test p > 0.05, n = 16) but did between winter and summer (Tukey test p = 0.029, n = 16). Total abundance were similar between autumn versus winter (Tukey test p = 0.17, n = 16) and spring (Tukey test p = 0.06, n = 16), and in summer was significantly higher than the other season (Tukey test p < 0.01, n = 16). Likewise, H´ in autumn, spring and summer were similar (Tukey test p = 0.99, n = 16), but in winter showed differences compared to spring and autumn (Tukey test p = 0.006 and p = 0.016; n = 16, respectively).

Overall, the macrobenthic community was functionally composed of biodiffusors (surficial biodiffusors, epifaunal biodiffusors and gallery-biodiffusors), regenerators, and upward conveyors (Table 1). Upward conveyors predominated in autumn (61%) and winter (76%). Spring showed more balanced repartition between upward conveyors and biodiffusors, and this latter finally represented 81% of the functional groups in summer (Figure 8).

Bioturbation

The depth distributions of the luminophores for the two particles range sizes (pink and green) were similar (Kruskal test p > 0.05). Although the main bioturbation activity was recorded until 2 cm depth, both tracers were found buried down to 13 to 17 cm depth within the different sediment cores and seasons (Figure 9). The shapes of the tracer's distribution were also the same whatever the tracers and seasons showing a combination between a biodiffusive-like distribution from the surface completed by the presence of tracers deeper down. Thus, all tracers (pink and green) data were pooled together and the gallery-diffusor model [START_REF] François | Functional approach to sediment reworking by gallery-forming macrobenthic organisms: modeling and application with the polychaete Nereis diversicolor[END_REF] was used to calculate the apparent bio-diffusion (Db) and bio-advection (r) coefficients within each core. Like previously observed for the majority of physical, chemical, and biological parameters, whatever the season the calculated coefficients did not show differences between the sediments incubated in situ and ex situ conditions (Kruskal-Wallis test p > 0.05; n = 16). In I conditions, nor Db nor r showed season effect (Kruskal-Wallis test p = 0.5 and one-way ANOVA p = 0.05; n = 16, respectively). Contrary, in E conditions, highly significant differences were found for the Db coefficient between autumn versus spring and summer versus autumn and winter (Fisher LSD test p < 0.01; n = 16).

Moreover, the r coefficient showed significant differences between spring versus winter (Multiple comparisons test p < 0.01; n = 16) (Figure 10). Particularly, the Db highest average values were found in autumn and winter (1.71 ± 0.14 and 1.49 ± 0.31 cm2 year-1, respectively). Nevertheless, the results were highly variable in spring and summer. Finally, maximum values of r were recorded in spring and summer (Figure 10).

Discussion

The seasonal composition and functioning (sediment reworking activity) of the intertidal macrobenthic community of Caleta Valdés (CV) applying an in situ and ex situ experimental protocol were evaluated. The macrofaunal invertebrates (> 0.5 mm) occupy almost every trophic level in marine ecosystems and influence the physical, chemical, and biological structure surroundings [START_REF] Lenihan | Soft-sediment communities[END_REF]. Moreover, it is recognized that soft-sediment ecosystems are driven by complex interactions between water column processes, organic matter inputs and their utilization by benthic populations, and the hydrodynamic and sedimentary conditions [START_REF] Barry | Physical Heterogeneity and the Organization of Marine Communities[END_REF][START_REF] Snelgrove | Animal Sediment Relationships Revisited: Cause Versus Effect[END_REF].

The coastal sediments of shallow environments play a key role in nutrient recycling because they can provide even greater fluxes to the water column than those from the continent [START_REF] Clavero | Seasonal fluxes of phosphate and ammonium across the sediment-water interface in a shallow small estuary (Palmones River, southern Spain)[END_REF][START_REF] Niencheski | Benthic respiration and inorganic nutrient fluxes in the Estuarine Region of Patos Lagoon (Brazil)[END_REF].

Moreover, numerous studies highlight the importance of physical processes such as the velocity and intensity currents as the main environmental factors that influence the benthic systems (Pastor de [START_REF] Pastor De Ward | Polychaete Assemblages in the San José Gulf (Chubut, Argentina), in relation to Abiotic and Biotic Factors[END_REF]. These factors determine the granulometry of the sediment [START_REF] Brown | The Ecology of Sandy Shores[END_REF][START_REF] Dauvin | Spatial structure of a subtidal macrobenthic community in the Bay of Veys (western Bay of Seine, English Channel)[END_REF]Muniz and Pires, 2000, among others) and food availability [START_REF] Brown | The Ecology of Sandy Shores[END_REF][START_REF] Mclachlan | Global patterns in sandy beach macrobenthic communities[END_REF], modifying the composition of the communities. In the present study, the superficial layers (0-2 cm) were enriched in fine-grained particles with a relatively high percentage of water content and organic matter, meanwhile in the sandy deeper sediment, these parameters decreased to lower and almost uniform values. Similar results were recorded in sediments from Caleta Sara (San Jorge gulf, Patagonia Argentina), applying a similar methodology [START_REF] Romanut | Respuesta de los ensambles de poliquetos y crustáceos de fondo blando ante el agregado de petróleo crudo a los sedimentos (Caleta Sara, Patagonia Argentina)[END_REF]. Particularly, the benthic organisms are able to change the local geochemical conditions through the reworking of sediment particles and irrigation during feeding, excretion, and locomotion within and through different sediment layers [START_REF] Flach | Seasonal variations in faunal distribution and activity across the continental slope of the Goban Spur area (NE Atlantic)[END_REF][START_REF] Kristensen | Decomposition of plant materials in marine sediment exposed to different electron acceptors (O2, NO3and SO4 2-), with emphasis on substrate origin,degradation kinetics, and the role of bioturbation[END_REF][START_REF] Venturini | Polychaete response to fresh food supply at organically enriched coastal sites: Repercussion on bioturbation potential and trophic structure[END_REF]. Therefore, it is not surprising that in our study the bioturbation activity was concentrated mainly in the first centimeters of the sedimentary columns, i.e., 50% of the luminophores were finally retained [START_REF] Gambi | Polycliaete distribution, diversity and seasonality related to seagrass cover in shallow soft bottoms of the Tyrrhenian Sea (Italy)[END_REF][START_REF] Gambi | Composition, abundance and stratification of soft-bottom macrobenthos from selected areas of the Ross Sea shelf (Antarctica)[END_REF][START_REF] Jorein | Distribuição do macrozoobentos na coluna vertical dos sedimentos da região estuarina de Cananéia (SP)[END_REF][START_REF] Venturini | Polychaete response to fresh food supply at organically enriched coastal sites: Repercussion on bioturbation potential and trophic structure[END_REF] where the higher densities of macroorganisms were observed. In fact, the water-sediment interface, considered as a large sink of organic matter and oxygen, is an area of intense biological activity and remineralization [START_REF] Nixon | Remineralization and Nutrient Cycling in Coastal Marine Ecosystems[END_REF]. Particularly, the combined effect of particulate and fluid transport on sediment biogeochemical processes is reflected in the vertical color transition (from brown to olive green/black) of the sediment profile [START_REF] Lyle | The brown-green color transition in marine sediments: A marker of the Fe(III)-Fe(II) redox boundary[END_REF]. This color transition is dictated by the change from iron (oxyhydr)oxides at the surface to black sulphidic phases at depth [START_REF] Statham | Extending the applications of sediment profile imaging to geochemical interpretations using colour[END_REF] correlating with a variety of environmental drivers [START_REF] Solan | Worldwide measurements of bioturbation intensity, ventilation rate, and the mixing depth of marine sediments[END_REF].

Moreover, depending on the biogeochemical transformations developed in this zone by the benthic communities, the sediment may be source of some nutrients and sink for others [START_REF] Cabrita | Seasonal variation in denitrification and dissolved nitrogen fluxes in intertidal sediments of the Tagus estuary, Portugal[END_REF].

Macrobenthic fauna is a key element of many marine and estuarine monitoring programs but those usually do not (or cannot) take into account distribution patterns at different spatial-temporal scales [START_REF] Ysebaert | Spatial and temporal variation in benthic macrofauna and relationships with environmental variables in an estuarine, intertidal soft-sediment environment[END_REF]. In the present study, the abundance of the dominant taxa and the bioturbation activity (especially in depth) were globally higher in spring-summer (Db = 1.04 ± 0.59 cm-2 y -1 and r = 12.60 ± 3.22 y -1 ) than in the rest of the seasons (Db = 1.59 ± 0.26 cm-2 y -1 and r = 4.81 ± 5.85 y -1 ). The Db and r values recorded at CV were in the range of those measured in Mediterranean Sea lagoons, the Thau lagoon, and the Carteau Cove by [START_REF] Duport | Benthic macrofauna and sediment reworking quantification in contrasted environments in the Thau Lagoon[END_REF] and [START_REF] Gilbert | Functional response of an adapted subtidal macrobenthic community to an oil spill: macrobenthic structure and bioturbation activity over time throughout an 18-month field experiment[END_REF], respectively. Nevertheless, we can go no further in the comparison between sites because the sediment mixing intensity is mainly driven by population biomass [START_REF] Matisoff | Mathematical Models of Bioturbation[END_REF][START_REF] Reible | Contaminant fluxes from sediment due to Tubificid oligochaete bioturbation[END_REF], density [START_REF] Duport | Effects of population density on the sediment mixing induced by the gallery-diffusor Hediste (Nereis) diversicolor[END_REF][START_REF] Ingalls | The influence of depositfeeding on chlorophyll-a degradation in coastal marine sediments[END_REF][START_REF] Mermillod-Blondin | Testing the functional redundancy of Limnodrilus and Tubifex (Oligochaeta, Tubificidae) in hyporheic sediments: An experimental study in microcosms[END_REF][START_REF] Sun | Enhanced degradation of algal lipids by benthic macrofaunal activity: Effect of Yoldia limatula[END_REF], and interspecific variability within functional groups [START_REF] François | Variabilité spécifique à l'intérieur des groupes fonctionnels: étude du remaniement sédimentaire de deux bivalves Veneridae, Ruditapes decussatus et Venerupis aurea[END_REF]. Moreover, it can be modulated by environmental conditions (e.g. [START_REF] Gilbert | Sediment reworking by the burrowing polychaete Hediste diversicolor modulated by environmental and biological factors across the temperate North Atlantic. A tribute to Gaston Desrosiers[END_REF][START_REF] Hollertz | Burrowing behaviour and sediment reworking in the heart urchin Brissopsis lyrifera Forbes (Spatangoida)[END_REF][START_REF] Maire | A comparison of sediment reworking rates by the surface deposit-feeding bivalve Abra ovata during summertime and wintertime, with a comparison between two models of sediment reworking[END_REF][START_REF] Nogaro | Food supply impacts sediment reworking by Nereis diversicolor[END_REF][START_REF] Orvain | Environmental and behavioural factors affecting activity in the intertidal gastropod Hydrobia ulvae[END_REF][START_REF] Ouellette | Effects of temperature on in vitro sediment reworking processes by a gallery biodiffusor, the polychaete Neanthes virens[END_REF].

The maximums of macrofauna abundance recorded in spring-summer in this study were similar to those found at Cerro Avanzado (Golfo Nuevo, Patagonia Argentina) which was associated with higher juvenile occurrence and primary productivity (Lizarralde et al., unpublished data). This increase could be also influenced by the closeness to the Península Valdés frontal system, which is one of the best-known tidal fronts on the northern Patagonian continental shelf [START_REF] Derisio | Influence of a tidal front on zooplankton abundance, assemblages and life histories in Península Valdés, Argentina[END_REF]. These fronts are generated usually within the same water mass where the boundary between stratified (offshore) and coastal vertically mixed water is found [START_REF] Sabatini | Mesozooplankton features in a frontal area off northern Patagonia (Argentina) during spring 1995 and 1998[END_REF]. In temperate climates, seasonal thermoclines are established near the surface during late spring and summer. This structure is maintained until autumn when stratification breaks down [START_REF] Acha | Marine fronts at the continental shelves of austral South America: Physical and ecological processes[END_REF][START_REF] Carreto | Toxic red-tide in the Argentine Sea. Phytoplankton distribution and survival of the toxic dinoflagellate Gonyaulax excavata in a frontal area[END_REF][START_REF] Glorioso | Temperature distribution related to shelf-sea fronts on the Patagonian Shelf[END_REF]. The fronts are zones where the lateral and vertical mixes are increased producing an increase of the primary and secondary production [START_REF] Mann | Dynamics of Marine Ecosystems[END_REF][START_REF] Olson | The concentrating of organisms at fronts: a cold-water fish and a warm-core Gulf Stream ring[END_REF][START_REF] Yamamoto | Middle Layer Intrusion as an Important Factor Supporting Phytoplankton Productivity at a Tidal Front in Iyo Nada, the Seto Inland Sea, Japan[END_REF]. The vertical circulation not only promotes the fertilization by nutrients in the surface [START_REF] Mann | Dynamics of Marine Ecosystems[END_REF] but also the sinking water with the exportation of particles and organisms to the deepest strata facilitating the persistence of big invertebrates and vertebrates benthic populations [START_REF] Sournia | Pelagic biogeography and fronts[END_REF][START_REF] Turley | Pyrithiones as antifoulants: Environmental chemistry and preliminary risk assessment[END_REF]. The highest concentrations of Zygochlamys patagonica found in the region have also been related to the formation of this front [START_REF] Bogazzi | Spatial correspondence between areas of concentration of Patagonian scallop (Zygochlamys patagonica) and frontal systems in the southwestern Atlantic[END_REF]. [START_REF] Viñas | Gut analysis of first-feeding anchovy larvae from the Patagonian spawning areas in relation to food availability[END_REF] have reported previously eggs and nauplii of copepods peaking in frontal waters off Península Valdés at highest chlorophyll "a" concentration zones. The importance of the fronts to the benthic communities lies not only on the high primary production but also by vertical fluxes that transport food particles to the bottom and by the weakening of the vertical stratification that allowing increasing the particulate material sink rate. This material rich in labile OM is consumed and incorporated into the benthic biomass. Particularly, all dominant taxa recorded in CV except Axiothella sp. were more abundant in the spring-summer period. The (Oligochaeta), and Leuroleberis poulseni (Crustacea). Nevertheless, the members of the studied taxa generally are identifying as carnivores, filters, selective or no selective sediment feeders and scavengers, respectively. Therefore, these taxa could tend to move from the surface to the most depth zone of the sedimentary column, producing an increase of the bioturbation activity in the whole column at this period.

From a functional point of view, the overall high activity of the spring-summer community (i.e., increased Db and r coefficients) produced a higher deep repartition of particles than those recorded in autumn and winter, even when in summer the community was dominated by biodiffusors meanwhile in autumn and winter predominated upwards conveyors. This clearly demonstrates that the repartition of particles within sediments is strongly dependent of the interactions between the different functional groups rather than the presence of a dominant group and that we cannot simply assume that the dominance of one functional group automatically produces a dominant sediment reworking process within a community. In spring-summer period in CV by example, the increase of subsurface biodiffusive transport may have feed the deeper advective process resulting in a global deeper burying of surface particles by the community.

Regarding in situ and ex situ comparing, in general terms, the community parameters profiles did not differ between the sediment incubated in field conditions versus those incubated in the laboratory. Moreover, there was a high similarity in the faunistic composition in both experiments. Consistently, there were no differences in the bioturbation activity of macrobenthic organisms for both experimental conditions. Thus allowing validation of an ex situ experimental protocol for macrobenthic community and functioning studies. To the best of our knowledge, there are no previous studies allowing such protocol to be validated.

These results are particularly useful as a reliable alternative for ecotoxicological studies in protected environments where in situ addition of pollutants to the sedimentary matrix won't be feasible (see [START_REF] Ferrando | Oil spill effects on macrofaunal communities and bioturbation of pristine marine sediments (Caleta Valdés, Patagonia, Argentina): Experimental evidence of low resistance capacities of benthic systems without history of pollution[END_REF][START_REF] Ferrando | Response of spionid polychaetes under different concentrations of crude oil in an ex situ experience (Patagonia, Argentina)[END_REF][START_REF] Sturla Lompré | Fate of petroleum hydrocarbons in bioturbated pristine sediments from Caleta Valdés (Patagonia Argentina): An ex situ bioassay[END_REF][START_REF] Romanut | Respuesta de los ensambles de poliquetos y crustáceos de fondo blando ante el agregado de petróleo crudo a los sedimentos (Caleta Sara, Patagonia Argentina)[END_REF]among others).

Conclusions

The seasonal composition and functioning of the intertidal macrobenthic community of CV were reported for the first time, contributing to the knowledge of this vulnerable ecosystem threatened by anthropogenic activities. In addition, ex situ and in situ measurements demonstrated similar results, thus allowing validation of an ex situ experimental protocol for macrobenthic community and functioning studies. The information generated will allow assessing pollutant effects on macrobenthic communities inhabiting sediments from a natural environment without disturbing the ecosystem. In addition, these results provide a first baseline of benthic information on CV that will be helpful to monitor the effects of potential pollution in Patagonian coastal systems.

Author contributions

Acknowledgements

Tables Table 1 Macrobenthic For each season, significant differences (p < 0.05) between in situ and ex situ are represented with an asterisk

  feeding modes are not defined specifically for Exogone molesta (Syllidae), Cerathocephale sp. (Nereididae), Scoloplos sp. (Orbiniidae), Oligochaeta sp.

Agustina Ferrando :

 Ferrando Conceptualization, Methodology, Investigation, Writing -Original Draft, Writing -Review & Editing, Julieta Sturla Lompré: Investigation, Writing -Review & Editing, Emilia Gonzalez: Investigation, Marcos Franco: Investigation, Marta Commendatore: Methodology, Investigation, Writing -Original Draft, Funding acquisition, Marina Nievas: Investigation, Writing -Review & Editing, Funding acquisition, Cécile Militon: Investigation, Georges Stora: Supervision, Funding acquisition, José Luis Esteves: Conceptualization, Methodology, Investigation, Writing -Original Draft, Project administration, Funding acquisition, Philippe Cuny: Investigation, Writing -Review & Editing, Project administration, Funding acquisition, Franck Gilbert: Conceptualization, Methodology, Investigation, Writing -Review & Editing, Project administration, Funding acquisition.

Figures captions Figure 1 .

 captions1 Figures captions

Figure 2 .Figure 3 .

 23 Figure 2. Mean contribution percentage of each granulometry fraction with depth

Figure 4 .Figure 5 .Figure 6 .

 456 Figure 4. Abundance of the dominant species by core (mean + SD; n = 4) in

Figure 7 .Figure 8 .Figure 9 .Figure 10 .

 78910 Figure 7. Macrobenthic community parameters by core (mean + SD; n = 4) in

Fig. 1

 1 Fig.1

Fig. 2

 2 Fig.2

Fig. 3

 3 Fig. 3

Fig. 4

 4 Fig. 4

  Fig. 5

Fig. 7

 7 Fig. 7

Fig. 8

 8 Fig. 8

Fig. 9

 9 Fig. 9

Fig. 10

 10 Fig. 10

  species mean abundance (n=4) in in situ and ex situ sediments for the four seasons. I: In situ; E: Ex situ

	Caprella scaura	SB	0.5	0.5	1.0
	Pseudocumatidae sp.	SB				0.8		1.0
	Cyrtograpsus
	angulatus	R	0.8			0.3
	Cirolanidae sp.	SB			0.3		0.3	0.3	0.3
	Gammaridae sp. 2	SB		0.5		0.3
	Corophioidea sp.	SB							0.5
	Leptostraca sp.	EB			0.3
	Anthuroidea sp.	SB						0.3
	Nemertea
	Nemertea sp.	GB	Autumn	Winter 1.5	Spring 0.5	Summer
		F.G. a	I	E	I	E	I	E	I	E
	Polychaeta
	Exogone molesta	GB	13.8 14.0 3.3	0.3	47.5 1.8 210.5 198.0
	Ceratocephale sp.	GB	1.8	1.5			41.5 19.5 34.3 29.3
	Axiothella sp.	UC	16.3 21.8 13.5 17.3 18.8 19.0 6.5	9.0
	Scoloplos sp.	GB	0.5	2.3	0.3		31.0 19.8 4.8	5.3
	Levinsenia gracilis	SB	1.5	5.8	0.3		0.8			0.3
	Pionosyllis sp.	GB					2.3		4.8	1.5
	Capitella "capitata"	UC			2.5	5.8
	Chone mollis	SB	0.8	2.5			1.3	0.3	0.8	0.8
	Brania sp.	GB	0.3
	Oligochaeta
	Oligochaeta sp.	GB	15.5 16.0 1.0	6.5	28.5 32.5 37.5 58.8
	Crustacea
	Leuroleberis poulseni	SB	8.3 10.8 0.8	0.5	31.5 23.0 26.5 34.5
	Ostracoda sp. 1	SB	2.8	2.5	17.8 8.0	15.5 8.0	6.0	5.3
	Phoxocephalopsis sp.	SB	1.3	2.8	4.8	2.3		1.5	0.8	0.8
	Ostracoda sp. 2	SB			5.8	2.5		0.5	0.5	4.0
	Anacalliax
	argentinensis	GB	1.5	2.8	3.0	0.8	0.8	0.3	2.0	0.8
	Paranthura sp.	SB	0.5	0.8					2.0	2.5
	Gammaridae sp. 1	SB	3.3		0.3			0.3	0.3
	Paranebalia sp.	GB			1.8	0.8				1.3

a F.G.: Functional group; Biodiffusors species presented in three sub-groups as EB: Epifaunal biodiffusors; SB: Surficial biodiffusors and GB: Gallery biodiffusors; R: Regenerators; UC: Upward conveyors. For a complete description of the different functional groups, see

[START_REF] Kristensen | What is bioturbation? The need for a precise definition for fauna in aquatic sciences[END_REF]

.

Table 2

 2 Summary PERMANOVA. Res: Residuals; df: Degrees of freedom; SS:

	Sum of squares; MS: Middle squares; mc: Monte Carlo simulation; ** p (mc) <
	0.01
	Source	df	SS	MS Pseudo-F p (mc)
	Experimental condition	1	2013.5 2013.5 5.9166	0.001**
	Season	3	15226 5075.4 14.914 0.0001**
	Experimental condition x Season	3	1240.5 413.51 1.2151	0.2721
	Res	24	8167.5 340.31
	Total	31	26648

This work was funded by the Evaluation -orientation de la Coopération Scientifique Sud -Ministerio de Ciencia, Tecnología e Innovación Productiva (ECOS Sud -MINCYT) collaboration program (Proyecto Caleta; action no A10U02) and the French Agence Nationale de la Recherche (project DECAPAGE; ANR CESA-2011-006 01) and by the National Agency for Scientific and Technological Promotion (ANPCyT) Project PICT 0441. The authors are grateful to Horacio Ocariz and Elvio Monteros for their assistance during fieldwork and the design of the images, respectively.