Loïc Lozac'h

Hassan Bazzi

Nicolas Baghdadi

Mohammad El Hajj

Mehrez Zribi

Rémi Cresson

SENTINEL-1/SENTINEL-2-DERIVED SOIL MOISTURE PRODUCT AT PLOT SCALE (S 2 MP)

Keywords: Soil Moisture, Sentinel-1, Sentinel-2, Orfeo Toolbox

The objective of this paper is to present an operational approach for mapping soil moisture at high spatial resolution over agricultural areas with vegetation cover. The developed approach uses the neural network (NN) technique based on coupling Sentinel-1 radar data and Sentinel-2 optical data. The neural networks were developed and validated using synthetic and real databases. To operationally map the soil moisture, the developed NN uses the C-band SAR signal in VV polarization, SAR incidence angle, and the Normalized Differential Vegetation Index "NDVI" as the inputs. To optimize the automatic production of soil moisture maps a pipeline using the Orfeo Toolbox is implemented.

INTRODUCTION

Frequent and accurate estimation of soil moisture at high spatial resolution is required for the prediction of the water cycle behavior and agricultural management such as irrigation and water requirements of agricultural areas [START_REF] Tebbs | Emerging and Potential Future Applications of Satellite-Based Soil Moisture Products[END_REF]. The arrival of Sentinel-1 (S1) satellite have encouraged the development of an operational algorithm for soil moisture mapping over agricultural areas with high revisit time (up to 6 days) and high spatial resolution (up to plot scale). The S1 mission from the European Space Agency (ESA) is a constellation of two polar orbiting SAR satellites (Sentinel-1A and Sentinel-1B) operating in the C-band (~5.4 GHz). Recently, several studies tend to use the neural network (NN) technique to invert backscattering models and estimate soil moisture. To estimate soil moisture and roughness from the C-band polarimetric radar data, Baghdadi et al. [START_REF] Baghdadi | Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks[END_REF] trained NN using the synthetic database backscattering coefficients generated for a wide range of bare soil conditions (Hrms between 0.3 and 3.6 cm and mv between 5 and 45 vol.%) using the Integral Equation Model IEM. The validation of the trained neural network with real database shows an accuracy of 7 vol.% for soil moisture estimation when considering priori information about soil moisture and surface roughness.

Recently, El Hajj et al. [START_REF] Hajj | Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas[END_REF] developed an operational method to map soil moisture at the plot scale over agricultural areas based on coupling S1-SAR data and Sentinel-2 (S2) optical data using the neural network technique (S²MP). The Sentinel-1/Sentinel-2 derived soil moisture product at plot scale (S 2 MP) provides soil moisture estimation at plot scale based on the synergic use of radar (Sentinel-1) and optical (Sentinel-2) data. The aim of this paper is to present the practical operational mapping of soil moisture by coupling Sentinel-1 and Sentinel-2 data. In this paper, section 2 describes the proposed methodology for soil moisture estimation using the neural network technique. Section 3 then describes the optimization of the soil moisture estimation algorithm for operational automatic mapping of soil moisture at plot scale and Section 4 presents the main conclusions.

S1-SSM ALGORITHM

To estimate soil moisture over vegetated areas, the Water Cloud Model (WCM) developed by Attema and Ulaby [START_REF] Attema | Vegetation modeled as a water cloud[END_REF] is commonly used. In the WCM the radar backscattering signal is the sum of the direct vegetation contribution and the soil contribution multiplied by the attenuation factor. Thus, to estimate soil moisture values, the radar backscattering signal and a descriptor for vegetation are required.

To invert the radar backscattering signal and estimate soil moisture values the approach uses the neural networks (NN) technique [START_REF] Hajj | Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas[END_REF]. To obtain the final NN that allows the estimation of soil moisture values, several steps are previously performed. In fact the development of the inversion method consists of the following steps:  Generate a synthetic database of backscattering coefficients using a parameterized WCM [START_REF] Attema | Vegetation modeled as a water cloud[END_REF] combined with the modified IEM [START_REF] Baghdadi | Semiempirical Calibration of the Integral Equation Model for SAR Data in C-Band and Cross Polarization Using Radar Images and Field Measurements[END_REF];  Noise the synthetic SAR C-band database and NDVI values to obtain synthetic database close to the real SAR and NDVI data;

 Train and validate the neural networks using the synthetic dataset;  Finally, apply the trained neural networks to S1 and S2 images to map the soil moisture.

Incidence angle, noisy radar signal at VV polarization, and noisy NDVI are the inputs of the NN. The synthetic database was simulated using Hrms (Height root mean square) values between 0.5 and 3.8 cm. The NN was trained using the Levenberg-Marquardt algorithm with two hidden layers. The number of neurons for each hidden layer is 20. Linear and tangent-sigmoid transfer functions were associated with the first and second hidden layers, respectively.

A priori knowledge on the soil moisture state is introduced in order to improve the soil moisture estimates. Indeed, using weather forecasts such as precipitation and temperature, it is easy to define if the state of the soil being either dry to slightly wet (no precipitation for many days before SAR acquisition) or very wet (heavy rainfall preceding SAR acquisition). In fact, the integration of a priori information about the soil state leads to a better estimation of the soil moisture because it constrains the range of possible soil moisture parameter values estimated by the neural networks. Therefore, two cases could be obtained:

 Dry to Slightly Wet Soil: The soil is supposed to be dry to slightly wet according to expertise based mainly on precipitation and temperature data. In this case, soil moisture values are assumed to vary between 2 and 25 vol. %.  Very Wet Conditions: The soil is supposed to be very wet according to the expertise of the meteorological data (precipitation and temperature). The mv values are assumed to vary between 25 and 40 vol. %. For a priori information that the soil is dry to slightly wet (case 1), the NN was developed using the training database elements with the mv between 2 and 30 vol. %. For a priori information that soil is very wet (case 2), the NN was developed using the training database elements with the mv between 20 and 40 vol. %. An overlap of 10 vol. % on the mv is considered between the databases used for the training of the dry (case 1) and the wet (case 2) neural networks.

Finally, to operationally map the soil moisture, the developed NN requires the C-band SAR signal in VV polarization, SAR incidence angle, and the Normalized Differential Vegetation Index "NDVI" as the inputs. While the SAR signal and incidence angle are derived from the S1 data, the NDVI value is derived from S2 images. Therefore, over a given study site and for a given Sentinel-1 acquisition, the soil moisture estimation could be obtained using the Sentinel-1 image accompanied with one or a mosaic of Sentinel-2 images (depending on the area of the study site) acquired on date near the date of the S1 acquisition. In this case, the three input parameters (VV SAR signal, incidence angle, and NDVI) required by the NN are available and a soil moisture map could be generated.

To assess the accuracy of the soil moisture estimation, the soil moisture values estimated have been compared to in situ measurements over a study site located near Montpellier, France. The root mean square error (RMSE) of the comparison between estimated and in-situ soil moisture values reaches 5 vol.% [START_REF] Hajj | Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas[END_REF]. Moreover, for better assessment of the S 2 MP product, the S 2 MP was also compared to similar soil moisture products such as SMOS (Soil Moisture and Ocean Salinity), SMAP (Soil Moisture Active Passive), and ASCAT (Advanced Scatterometer) [START_REF] Hajj | Evaluation of SMOS, SMAP, ASCAT and Sentinel-1 Soil Moisture Products at Sites in Southwestern France[END_REF]. The results show that the S 2 MP provides the most accurate SSM estimation when compared to a wide range of in situ measurements. The S 2 MP was also compared to the recent Copernicus surface soil moisture (C-SSM) provided by the European space agency. The comparison performed over a wide region of south France [START_REF] Bazzi | A Comparison of Two Soil Moisture Products S 2 MP and Copernicus-SSM Over Southern France[END_REF] revealed that the S 2 MP provides more accurate SSM estimations than the C-SSM estimations when compared to in-situ measurements. The S 2 MP was also compared with the precipitation records obtained from the Global Precipitation Mission (GPM) showing high consistency between the SSM estimations derived from the S 2 MP and the precipitation records at 0.1° x 0.1° grid scale [START_REF] Bazzi | Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France[END_REF].

OPTIMIZATION FOR OPERATIONAL MAPPING OF SOIL MOISTURE

The optimization project was accomplished using iterative software development process framework. It starts with the study of the functional needs of the end users, analyses of the technologies that will be used, and then building a prototype. Eventually we obtain a processing pipeline that fully meets the end-users needs.

Functionalities and used technologies

The functionalities retained by the study of end-users needs are summarized in table 1.

Functionality

Optimizing the Neural Network model inversion Creation of agricultural plot scale mask Segmentation of NDVI Rasterization of vectorized agricultural plot scale Automatic calibration of Sentinel 1&2 imagery Temporal series processing Automatic download of Sentinel 1&2 imagery Portability with Docker Table 1: Pipeline functionality offered by the study

The technologies used in this project are the Orfeo Toolbox (OTB) to optimize image processing, the TensorFlow (TF) for the neural network inversion, Python scripting for processing pipeline connections and Docker for portability. OTB is open-source project for processing remote-sensing images at large scale. It is developed in C++ programming language using the Integration Tool Kit Application Programming Interface (ITK-API) originally created for medical imaging. The great originality of OTB applications is that it uses a streaming technology that allows processing an image by regions lowering computational time and memory usage. It also allows piping multiple applications without creating temporary files.

TensorFlow is an open-source platform for machine learning. It has stable API release in Python and C++, and allows development on multiple kind of platform based on Central or Graphic Processing Units. OTBTF (OTB TensorFlow) is a module developed by Cresson [START_REF] Cresson | A Framework for Remote Sensing Images Processing Using Deep Learning Techniques[END_REF] to bind OTB capabilities with TensorFlow deep machine learning resources. OTB also contains a Python API, thus, batch processing and files manipulation have been scripted in Python. Docker is a tool that can wrap applications and their dependencies in an isolated container and it can be deployed on any server. The other benefit of Docker is that the container uses images of operational system, which are versioned.

Workflow

Downloading Sentinel-1/2 time series

Based on an Area of Interest (AOI), a request is performed on Sentinel data provider website (Sentinel1 https://scihub.copernicus.eu/, Sentinel2 https://theia.cnes.fr/atdistrib/rocket/#/home). An xml file containing the links to download the requested images is obtained and downloaded with a simple script.

Sentinel Image calibration

The calibration of the Sentinel-1 and Sentinel-2 images is performed by ESA Sentinel Application software (SNAP) (http://step.esa.int/main/toolboxes/snap/). Both radiometric and geometric calibrations are performed on the S1 images. The radiometric calibration aims to convert the digital number values of the S1 images into backscattering coefficients (σ°) in linear units. The aim of the geometric correction is to ortho-rectify the S1 images using the 1 arcsecond SRTM (Shuttle Radar Topography Mission) which is automatically downloaded by SNAP. A Docker image has been created containing this application with a batch script that processes all the images.

In the case where Sentinel-2 images are only available in level-1, the Sentinel-2 images are calibrated into level-2 images using the sen2cor extension of the SNAP software. However, for different AOI worldwide, the French land data center (Theia) provides level-3 images corresponding to a monthly synthesis from weighted mean of clear sky observations. In this case, these level-3 synthesis images are used.

NDVI calculation

From level 2 or level 3 Sentinel-2 images, a python script was developed using OTB applications to calculate the NDVI values. This script computes the NDVI, applies the cloud mask to the NDVI, manages the nodata values and finally creates an unsigned 8 bit (uint8) NDVI image. The temporal gap filling (cloud filling) for time series images, proposed by the Centre d'Etudes Spatiales de la Biosphère (CESBIO), is used to create monthly spread NDVI over the whole period.

Segmentation and agricultural mask

In order to consider only agricultural plots for soil moisture estimation, two options are available. First, if the precise limits of agricultural plot are available in vector format, then this vector shapefile is rasterized to obtain a pseudo-segmented image. The soil moisture estimation is then performed for each agricultural plot. Otherwise, if the precise limits of the plots are not available, the second option uses a stack of monthly NDVIs to perform segmentation in order to obtain plots with homogeneous NDVI values. The segmentation is very high machine resource consuming, so depending of the AOI size, you may need to deport this computation on a high RAM cluster. In the case of default segmentation, an agricultural mask is required to remove non-agricultural segments.

Soil moisture inversion and batch time series

Figure 2 shows the processing details of OTB application used to invert the SAR signal into soil moisture values (SoilMoistureInvertSARModel). A python script will perform the soil moisture estimation at each SAR date using the S1-VV and incidence angle images, the NDVI image corresponding to the S1 acquisition date (e.g. NDVI of October 2017 is used for all SAR images acquired in October 2017), a masked labels image obtained from the segmentation, and a slope image from SRTM. A batch processing is applied over the whole time series.

Portability with Docker

The entire pipeline is enclosed in two Docker images. One Docker image is with OTBTensorFlow and the other is with SNAP application from ESA. Thus, the pipeline can be easily installed on any server. While Linux platform is highly recommended, the pipeline could be also installed on Mac or Windows.

CONCLUSION

The present study aimed to present an operational approach for soil moisture mapping in agricultural areas at a high spatial resolution (up to plot scale). Our approach is based on a synergic use of Sentinel-1 and Sentinel-2 images. Our approach for soil moisture estimates used the neural networks technique to invert the radar signal and estimate the soil moisture.

For operational production of soil moisture maps, we designed an optimized soil moisture pipeline using Orfeo Toolbox. This optimization of soil moisture mapping significantly reduced the time required for obtaining soil moisture maps. For the core application of inversion, an image used to be processed in 30 min, requires now less than 5 min using the proposed pipeline. A time series for a given AOI over one year used to be processed in 2 weeks now takes between 3 to 4 days.

Figure 1 :

 1 Figure 1 : Processing details of the Segmentation OTB application (SoilMoistureSegmentationFiltering)

Figure 2 :

 2 Figure 2 : Processing details of the SAR inversion OTB application (SoilMoistureInvertSARModel)


Mehrez Zribi

Mireille Huc

Thierry Pellarin

Nicolas Baghdadi

Nazzareno Pierdicca

Soil moisture retrieval using GNSS-R data

Keywords: CYGNSS, GNSS-R, soil moisture, change detection

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Soil moisture is a key state parameter, related to the land surface, and influences the manner in which rainwater is shared between evapotranspiration, infiltration, and runoff [START_REF] Koster | Regions of Strong Coupling Between Soil Moisture and Precipitation[END_REF][START_REF] Saux-Picart | Water and Energy budgets simulation over the Niger super site spatially constrained with remote sensing data[END_REF][START_REF] Gao | Irrigation Mapping Using Sentinel-1 Time Series at Field Scale[END_REF]. In the case of semi-arid regions, it is crucial for irrigation management and drought monitoring. Considerable efforts have thus been devoted to improve the evaluation of soil moisture and evapotranspiration, and to understand their relationship with the vegetation cover and soil water content. Most of this research has been based on remote sensing, which is highly convenient for the estimation of spatio -temporal variations in soil moisture. Over the last 20 years, microwave remote sensing has convincingly demonstrated its ability to enable the monitoring of soil moisture. Through the use of radiometers, scatterometers and Synthetic Aperture Radars (SARs), the soil moisture can be monitored at various spatial resolutions, ranging from local field scales to approximately 50 km. More recently, the use of GNSS reflectometry (GNSS-R) has become a mature technique for the remote sensing of land surfaces [START_REF] Egido | Airborne GNSS-R Polarimetric Measurements for Soil Moisture and Above-Ground Biomass Estimation[END_REF][START_REF] Ruf | A New Paradigm in Earth Environmental Monitoring with the CYGNSS Small Satellite Constellation[END_REF][START_REF] Katzberg | Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: Results from SMEX02[END_REF][START_REF] Masters | Initial results of landreflected GPS bistatic radar measurements in SMEX02[END_REF][START_REF] Sánchez | On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation[END_REF][START_REF] Zribi | Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign[END_REF][START_REF] Motte | A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring[END_REF] With several spaceborne missions, including the Techdemosat-1 demonstrator, and the recently launched dedicated CYGNSS constellation [START_REF] Ruf | New ocean winds satellite mission to probe hurricanes and tropical convection[END_REF], currently flying and able to measure GNSS-R signals reflected by the Earth's Surface, the field is currently opening up to the systematic use of global observations [START_REF] Camps | Sensitivity of TDS-1 GNSS-R Reflectivity to Soil Moisture: Global and Regional Differences and Impact of Different Spatial Scales[END_REF][START_REF] Chew | Demonstrating soil moisture remote sensing with observations from the UK TechDemoSat-1 satellite mission[END_REF][START_REF] Chew | Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture[END_REF][START_REF] Clarizia | Analysis of CYGNSS Data for Soil Moisture Retrieval[END_REF].

With the advent of these new satellite missions, combined with a detailed understanding of their technical and scientific capabilities, the present study proposes an inversion algorithm for the retrieval of surface soil moisture, based on surface reflectivity derived from CYGNSS data. Section 2 describes the satellite data, the study sites, the database used for ground validation and the retrieval methodology. Section 3 describes the results and the validation of soil moisture products. These results are discussed in section 4, and our conclusions are presented in section 5.

DATABASE AND METHODS

Studied sites and ground measurements

In the following, three local sites are considered for more detailed analyses, and the verification of soil moisture estimations derived from CYGNSS signals (Figure 1) by comparison with in-situ data. As these sites are located in three climatically different regions, our analysis of the data has allowed the quality and limitations of moisture estimations to be discussed.

CYGNSS data

NASA's CYGNSS constellation of eight satellites was launched in December 2016, and was designed to measure GNSS reflectivity over the tropics, at latitudes ranging between ± 37°, with the main purpose to measure wind in tropical cyclones. A land reflectivity observable  is derived from the peak value of the L1 DDMs, to which a calibration factor is applied. The latter is computed using the radar equation for a coherent signal, such that the received power reflected by a surface Prs is given by:

(

) 1
where Pr is the peak value of the L1 DDM power, N is the estimated noise floor, Pt is the transmitted power, Gt and Gr are the transmitter and receiver antenna gains, respectively, and Rt and Rr are the distances between the transmitter and the specular point, and between the receiver and the specular point, respectively. By inverting (1), the reflectivity can thus be computed as:

(2)

All of the parameters in this expression can be retrieved from the CYGNSS NetCDF files.

In the case of a smooth reflecting surface, the theoretical footprint of a reflected GNSS signal is determined by the first Fresnel zone, corresponding to approximately 0.5 × 0.5 km in the case of a spaceborne receiver.

Proposed inversion approach

Five processing steps were implemented, prior to application of the inversion algorithm:

Step1: In order to eliminate noisy measurements, which would lead to a high uncertainty in the reflectivity estimations, the data were filtered by retaining only these measurements having a high Signal-to-Noise-Ratio (SNR >3 dB). In addition, only data recorded at incidence angles lower than 35° were considered, since a strong decrease in SNR was encountered with measurements recorded at higher angles.

Step2: The CYGNSS datapoints were then restricted to measurements for which the corresponding NDVI (estimated from PROBA-V data) lay between 0.1 and 0.8. The aim of this filter was to discard water bodies (low NDVI), which can generate soil moisture estimations with high errors, due to the very high reflectivity of flat specular water surfaces, as well as dense forests (very high NDVI), for which soil moisture estimations are often unreliable, as a consequence of the limited penetration of electromagnetic waves.

Step3: The nature of the local terrain can have a very strong influence on GNSS-R reflectivity, leading to inaccurate soil moisture estimations. For this reason, only data recorded over low altitude areas were considered, by applying a cutoff at threshold of 600 m as taken by Chew et al., 2018 [START_REF] Chew | Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture[END_REF].

Step 4: The CYGNSS measurements retained, after the filtering described in steps 1, 2 and 3, was then normalized to a reference incidence angle equal to 20°, using locally a simple linear correction.

Step 5: The CYGNSS observations were aggregated over a grid with 0.5° × 0.5° pixel size, by averaging data over a daily interval. It must be noted that the spatio-temporal sampling of the available database has some limitations. On any given day, only a part of each 0.5° × 0.5° pixel is covered by CYGNSS reflectivity measurements, and this partial coverage varies from one day to the next one. A change detection algorithm is applied to the data over a 0.5° × 0.5° grid, allowing the soil moisture to be determined for the full duration of the study (about 21 months). It is assumed that the highest value of reflectivity (max) corresponds to the highest level of observed soil moisture, and that the lowest value (min) corresponds to the driest conditions. As this study makes use of a large database, we make the assumption that max corresponds to soil saturation and min corresponds to the driest possible conditions. An index ranging between 0 and 100% is then estimated, according to expression (4), which we hereafter refer to as the soil moisture index (Ind). In the case of the ASCAT moisture products, 0% corresponds to the driest soil (very low soil moisture, close to 0 vol.% at the studied sites), and 100% corresponds to the wettest conditions (generally considered to correspond to soil saturation).

(4)

RESULTS AND DISCUSSIONS

In Figure 3 we compare the ground measurements with the GNSS-R products for the three study areas in Tunisia, Niger and Benin, showing that the surface soil moisture (at a depth of 5 cm) derived from the GNSS-R data is well correlated with the in situ measurements. For these three sites, the GNSS-R products are found to track the temporal dynamics of soil moisture with a good accuracy during the rainy season. During the dry season, the CYGNSS data are highly variable, despite the absence of temporal and/or spatial variations in soil moisture. This could be due to uncertainties in the retrieval of low values of reflectivity, and to surface heterogeneities at the 0.5°x0.5° grid scale.

The satellite products converted in volumetric moisture are found to be only moderately correlated with the groundtruth measurements: the RMSE is equal to 6.7 vol.%, 1.8 vol.% and 5.1 vol.% respectively, for the Merguellil, Basso Dallo and Ouémé areas. When the dry period is excluded from the proposed comparison, the results improve and are characterized by more accurate soil moisture estimations.

CONCLUSIONS

The aim of this study is to develop an inversion algorithm for the estimation of surface soil moisture. CYGNSS data were processed and combined with other auxiliary satellite products from PROBA-V, and a DTM from SRTM. A change detection method was then applied to the daily reflectivity product, tuned on a 21-month sequence of CYGNSS acquisitions, leading to a calibrated soil moisture index ranging between 0 and 100. The proposed methodology has been validated by comparing the CYGNSS product with ground measurements, recorded at three different sites (Merguellil, Basso Dallo and Ouémé), leading to an RMSE equal to 6.7 vol.%, 1.8 vol.% and 5.1 vol.%, respectively.

Figure 1 .

 1 Figure 1. Illustration of the three studied sites (Merguellil, Dallo Basso and Ouémé)

Figure 2

 2 provides a low-resolution (0.5 × 0.5° grid) reflectivity map of West Africa, computed for the month of March, 2017. This is a dry month, during which the soil moisture in the studied region is very low, and has virtually no influence on ground reflectivity. The highest values are retrieved in regions with flat surfaces and almost no vegetation cover, in the north of the studied region associated, a priori, with a high coherent component of the scattered signal.

Figure 2 .

 2 Figure 2. Reflectivity map of West Africa, computed from CYGNSS data recorded in March 2017

Figure 3 .

 3 Figure 3. Comparison between CYGNSS moisture products and ground measurements. Upper panel: Merguellil site mid panel: Dallo Basso site; lower panel: Ouémé site


Nicolas Baghdadi

Hassan Bazzi

Mohammad El Hajj

Mehrez Zribi

COMPARISON BETWEEN THEIA AND COPERNICUS SURFACE SOIL MOISTURE PRODUCTS OVER SOUTHERN FRANCE

Keywords: Soil Moisture, Theia, S 2 MP, Copernicus SSM, Sentinel-1, South France

A comparison between the Sentinel-1/Sentinel-2-derived Soil Moisture Product at plot scale (S 2 MP) and the new Copernicus Surface Soil Moisture "C-SSM" product at 1-km scale over a wide region in southern France was carried out. Both products show generally good agreement with in situ measurements. The results show that using in situ measurements collected over agricultural areas and grasslands, the accuracy of the C-SSM is good (RMSD = 6.0 vol.%, ubRMSD = 6.0 vol.% and R=0.48) but less accurate than the S 2 MP (RMSD = 4.0 vol.%, ubRMSD = 3.9 vol.% and R=0.77). The intercomparison between the two SSM products shows that both products are highly correlated over areas mainly used for cereals (R value between 0.5 and 0.9 and RMSE between 4 vol.% and 6 vol.%). Over areas containing forests and vineyards, the C-SSM values tend to overestimate the S 2 MP values (beyond 5 vol.%). In the case of well-developed vegetation cover the S 2 MP doesn't provide SSM estimations while C-SSM sometimes provides underestimated SSM values.

INTRODUCTION

Soil moisture data at high spatial resolution is essential for agricultural management, such as monitoring of irrigation practices and water requirements of agricultural areas [START_REF] Gao | Irrigation Mapping Using Sentinel-1 Time Series at Field Scale[END_REF]- [START_REF] Bousbih | Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data[END_REF] Soil moisture values are mainly obtained through the inversion of either physical [START_REF] Fung | Microwave scattering and emission models and their applications[END_REF] or statistical models [START_REF] Baghdadi | A new empirical model for radar scattering from bare soil surfaces[END_REF]- [START_REF] Gorrab | Retrieval of both soil moisture and texture using TerraSAR-X images[END_REF]. While statistical models require site calibration, physical models can always be used to simulate the radar backscattering from radar configuration. Among the physical models, the Integral Equation Model (IEM) [START_REF] Fung | Microwave scattering and emission models and their applications[END_REF] is the most commonly used to estimate soil moisture over bare soil or soil with little vegetation cover. Baghdadi et al. [START_REF] Baghdadi | Calibration of the Integral Equation Model for SAR data in C-band and HH and VV polarizations[END_REF], [START_REF] Baghdadi | Semiempirical Calibration of the Integral Equation Model for SAR Data in C-Band and Cross Polarization Using Radar Images and Field Measurements[END_REF] proposed a semi-empirical calibration of the IEM to compensate between the large difference observed between IEM simulation and real SAR data. To estimate soil moisture over vegetated areas, the Water Cloud Model (WCM) developed by Attema and Ulaby [START_REF] Attema | Vegetation modeled as a water cloud[END_REF] is commonly used. In the WCM the radar backscattering signal is modeled as a sum of the direct vegetation contribution and the soil contribution multiplied by the attenuation factor. Several studies have parametrized the WCM for different SAR band configurations and for several crop types [START_REF] Hajj | Soil moisture retrieval over irrigated grassland using X-band SAR data[END_REF]. In addition to the inversion of statistical or physical models for soil moisture retrieval, recent studies have started reporting the estimation of soil moisture using the change detection method [START_REF] Gao | Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution[END_REF].

Recently, the arrival of the Sentinel-1 (S1) SAR satellite provided users with free open access SAR data at a high spatial resolution (10 m x 10 m) and high revisit time (six days over Europe). The S1 mission from the European Space Agency (ESA) is a constellation of two polar orbiting SAR satellites (Sentinel-1A and Sentinel-1B) operating in the Cband (~5.4 GHz). The SAR data of the S1 mission at high spatial and temporal resolutions have encouraged mapping soil moisture in an operational mode. Paloscia et al. [START_REF] Paloscia | Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation[END_REF] used the neural network technique to invert the Sentinel-1 SAR Cband and estimate the SSM values. Recently, El Hajj et al. [START_REF] Hajj | Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas[END_REF] developed an operational method to map soil moisture at the plot scale over agricultural areas based on coupling S1-SAR data and Sentinel-2 (S2) optical data using the neural network technique (S²MP). The French Land data center Theia (https://www.theia-land.fr/en/) use the algorithm developed by El Hajj et al. [START_REF] Hajj | Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas[END_REF] for the provision of soil moisture maps at the plot scale for several sites over the world (South France, Italy, Spain, Morocco, Lebanon, etc.). Most recently, the Copernicus Global Land Service has started providing SSM of the soil's topmost 5 cm over the European continent from Sentinel-1 sensors at 1 km x 1 km spatial resolution using the change detection method [START_REF] Bauer-Marschallinger | Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles[END_REF] The comparison between the Copernicus product C-SSM and the Theia product S 2 MP was carried out over a wide region in south France (Figure 1). Both products are first evaluated against in situ measurements of soil moisture. The new Copernicus SSM product was then intercompared with the SSM estimations from the S 2 MP. Section 2 presents the study site, the SSM products used, and the in situ SSM measurements. The results and discussions are presented in Section 3. Finally, Section 4 presents the main conclusions.

MATERIALS 2.1. Study Site

Our study site corresponds to the Occitanie region of southern France (2 °30 ′ E and 43 °30 ′ N, Figure 1). The region has a variety of landscape and is mainly covered by agricultural areas in the middle and western parts. The northern regions are generally covered with a mix of forests, grassland and agricultural crop land. The eastern region is generally covered with a mix of agricultural crop land, grassland and vineyards. The climate in the eastern part of the region is considered Mediterranean (700 mm average annual precipitation), whereas that of the western part is more humid and oceanic (1200 mm average annual precipitation).

S 2 MP Product

The S 2 MP product is obtained by coupling Sentinel-1 SAR data and Sentinel-2 optical data. The S 2 MP product provides SSM estimates over the agricultural areas at plot scale with 6 days revisit time [START_REF] Hajj | Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas[END_REF]. To estimate the SSM values, El Hajj et al. [START_REF] Hajj | Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas[END_REF] inverted the WCM parameterized by Baghdadi et al. [START_REF] Baghdadi | Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands[END_REF] for C-band combined with the IEM, as modified by Baghdadi et al. [START_REF] Baghdadi | Calibration of the Integral Equation Model for SAR data in C-band and HH and VV polarizations[END_REF]. The inversion approach uses the neural network (NN) technique to invert the radar signal into SSM value. To operationally map the soil moisture, the developed NN uses the C-band SAR signal in VV polarization, SAR incidence angle, and the Normalized Differential Vegetation Index "NDVI" as the inputs. While the SAR signal and incidence angle are derived from the S1 data, the NDVI value is derived from Sentinel-2 images. The S 2 MP maps are produced for agricultural areas (it is not applied to vineyards and orchards). Forest and urban areas are masked using the land cover map of Inglada et al. [START_REF] Inglada | Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series[END_REF]. Additionally, areas with slope greater than 20% are masked in the soil moisture product (calculated from SRTM DEM at 30 m spatial resolution). The S 2 MP are available in free open access mode via the Theia French Land Data Center (http://www.theialand.fr/en/thematic-products). The S 2 MP maps were derived mainly using the ascending acquisition mode of the S1 sensors (time ~17:40 UT) to avoid the presence of frozen soil when using the descending mode that passes by our zone at 6:00 UT.

Copernicus SSM Product

Recently, Copernicus Global Land Service began to distribute the first soil moisture estimations over the European continent at a 1-km spatial resolution using S1 data in C-band [START_REF] Bauer-Marschallinger | Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles[END_REF]. The SSM retrieval algorithm is based on the TU (University of Technology) Wien Change Detection Model already applied for the ASCAT data and adapted for Sentinel-1 data. In the applied model, the changes observed in the SAR backscattered coefficient (𝜎 0 ) are interpreted as changes in the soil moisture values, whereas other surface properties, such as the geometry, the surface roughness and the vegetation cover, are interpreted as static parameters. The C-SSM products at 1-km spatial resolution are retrieved from the Sentinel-1 radar backscattering images acquired in IW mode and VV polarization. The product delivers relative SSM values in percentages ranging between 0% and 100%. The C-SSM product values were converted to a volumetric unit (vol.%) in order to compare the C-SSM estimations produced in relative units (%), with measured soil moisture being given in absolute volumetric unit (vol.%) [START_REF] Bazzi | A Comparison of Two Soil Moisture Products S2MP and Copernicus-SSM over Southern France[END_REF]

In situ Soil Moisture Measurements

The SSM values were measured on 23 reference plots (10 grassland and 13 wheat) during the period between 1 January 2017 and 31 May 2017 [START_REF] Hajj | Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas[END_REF]. The SSM values were measured within 2 hours of the S1 acquisition time in 22 different campaigns at different dates of S1 acquisition. For each plot, 25 to 30 volumetric soil moisture measurements were taken from the top 5 cm layer using a calibrated TDR (Time Delay Reflectometer). Then, within each plot, the SSM measurements were averaged to obtain a mean value for each plot. The measured soil moisture values varied between 7.0 vol.% and 36.3 vol.%.

RESULTS AND DISCUSSIONS

The SSM values of each product were evaluated using the in situ SSM measurements during the period between January 2017 and May 2017. Figure 2 shows the comparison between the SSM products and the in situ SSM measurements. The S 2 MP shows a higher R value (0.78) than that of the Copernicus SSM (0.48). Additionally, the RMSD is lower for the S 2 MP (4.0 vol.%) than for the Copernicus SSM (6.0 vol.%). Notably, the p-value of the comparison for both products is lower than 0.01, which indicates that the correlation is significant. Figure 3 represents the maps of statistical metrics calculated from the comparison between the SSM from the S 2 MP and C-SSM products during the period between October 2016 and October 2017 (the correlation coefficient R between the two products, the root mean square difference values "RMSD", and the bias values (C-SSM -S 2 MP). Moderate to high correlation values between the compared products are observed, mainly in the western, middle and eastern part of Occitanie (R between 0.5 and 0.9). Figure 3b shows that the RMSD values for these parts are approximately homogeneous and ranging between 4 and 6 vol.%. Between these parts, only the eastern part shows high positive bias values (between 2 and 4 vol.%). The correlation map (Figure 3a) shows low values mainly in the northern part of Occitanie, where R is less than 0.3 with moderate RMSD values (between 6 and 8 vol.%). High RMSD values (more than 8 vol.%) are observed for the northeastern part. The bias map for the northern area shows high positive values (> 6 vol.%) (Figure 3c).

The statistical metrics obtained from the comparison of both SSM products are analyzed as a function of the land cover (Figure 1). The S 2 MP product provides soil moisture estimation over agricultural crop land only grown in summer and winter (urban, forest, grassland and vineyards are masked), which should be considered before analyzing the effect of the land cover. Thus, we suppose that the soil moisture calculated from the S²MP product is the same for the whole cell of 1 km × 1 km (not composed only of crops). The results reveal that the western and middle parts show the highest R values with smallest bias and RMSD values. In the land cover map, this area shows a majority of summer and winter crops (more than 65% of the area). The eastern part that shows high correlation values but moderate to high bias values is composed of a mix of vineyards (30%), grassland (30%) and summer-winter crops (14%). The northern part, with the lowest R and moderate RMSD and bias values, is generally occupied with a mix of grassland (60% of the area) and forests (23% of the area). This part comprises no more than 9% agricultural areas. Finally, the northeastern part (high RMSD and bias values) is mostly covered by a mix of forests (40%) and grassland (40%). The analysis of the SSM products' quality according to land cover type reveals that over areas mainly used for cereals and market gardening (the case of the middle and western parts), the correlation between the SSM products is high, indicating high consistency in soil moisture estimation between the products. When the area becomes more occupied by grassland (the case in the northern part), the correlation decreases but keeps moderate RMSD and bias values. However, when the forested area dominates and the land cover becomes more heterogeneous (forests, grassland, agricultural…) (the case in the northeastern part) an overestimation of C-SSM with respect to S 2 MP is observed. Moreover, when the vineyards are dominant in the land cover (the case of the eastern part) overestimation of C-SSM with respect to S 2 MP is observed. The limitation observed over heterogeneous land cover for the C-SSM product could be related to the spatial resolution because the product is produced at a 1-km pixel size and not at a fine scale.

CONCLUSION

The aim of this study was to assess the accuracy of Surface Soil Moisture (SSM) in southern France estimated by the Sentinel-1/Sentinel-2-derived Soil Moisture Product at plot scale (S 2 MP) and the new Copernicus Surface Soil Moisture "C-SSM" product at 1-km scale. The results reveal that both products show good reliability with in situ measurements. However, S 2 MP shows higher accuracy (RMSD = 4.0 vol.%, R = 0.77) than the C-SSM (RMSD = 6.0 vol.%, R=0.48) when compared to in situ measurements over agricultural areas and grasslands. The analysis of the intercomparison between the products as a function of the land cover shows that the S 2 MP and C-SSM are highly correlated over dense agricultural areas (mainly cereals of winter and summer). When the land cover becomes more heterogeneous (mix of forests, grassland and vineyards with agricultural areas) the correlation between the products decreases. The C-SSM tends to overestimate SSM values over cells containing vineyards and forests. In addition, the C-SSM sometimes shows underestimated SSM values when the vegetation is well developed. However, the validation of the new soil moisture product of Copernicus needs additional experimental sites to allow stronger conclusions.

Figure 1 .

 1 Figure 1. Land cover map of Occitanie region (Fr).

Figure 2 :

 2 SSM products against in situ measurements. (a) S 2 MP-1 km, (b) Copernicus SSM-1 km.

Figure 3 :

 3 Figure 3: Statistical metrics maps at 1-km grid generated from the comparison between the C-SSM and S2MP. (a) Correlation coefficient R map, (b) RMSD map, (c) Bias map.

ACKNOWLEDGMENT

This research was supported by IRSTEA (National Research Institute of Science and Technology for Environment and Agriculture) and the French Space Study Center (CNES, DAR 2019 TOSCA). The authors wish to thank the European Commission and the European Space Agency for providing the S1 images, and the French land data service center Theia for providing the level 2A S2 data.


Nicolas Deguines
email: nicodeguines@gmail.com

Romain Lorrillière

Anne Dozières
email: anne.dozieres@mnhn.fr

Carmen Bessa-Gomes
email: carmen.bessa-gomes@universite-paris-saclay.fr

Romain Lorrilliere
email: lorrilliere@mnhn.fr

François Chiron
email: francois.chiron@universite-paris-saclay.fr

Any despot at my table? Competition among native and introduced bird species at garden birdfeeders in winter

Keywords: Citizen Scientists, France, Interference competition, Pica pica, Psittacula krameri, Serious game, Streptopelia decaocto

INTRODUCTION

Evidence that anthropogenic resource subsidies to wildlife can have major effects on ecosystems functioning traces back eight centuries ago [START_REF] Douglas | Prehistoric Inuit whalers affected Arctic freshwater ecosystems[END_REF], and this probably occurred to some extent since millennia [START_REF] Oro | Ecological and evolutionary implications of food subsidies from humans[END_REF]. Resources associated to human activities and made available to wildlife, voluntarily or not, can improve the physiological conditions of individuals able to consume these subsidies [START_REF] Yom-Tov | Body sizes of carnivores commensal with humans have increased over the past 50 years[END_REF], increase breeding success (Robb et al., 2008b) and lead to population increases [START_REF] Galbraith | Supplementary feeding restructures urban bird communities[END_REF][START_REF] Oro | Ecological and evolutionary implications of food subsidies from humans[END_REF][START_REF] Plummer | The composition of British bird communities is associated with long-term garden bird feeding[END_REF]. In turn, in both aquatic and terrestrial realms, food web dynamics can be affected following modification in ecosystem production [START_REF] Singer | Anthropogenic subsidies alter stream consumer-resource stoichiometry, biodiversity, and food chains[END_REF] or species interactions [START_REF] Arcos | Competition between the yellow-legged gull Larus cachinnans and Audouin's gull Larus audouinii associated with commercial fishing vessels: The influence of season and fishing fleet[END_REF][START_REF] Cortés-Avizanda | Resource unpredictability promotes species diversity and coexistence in an avian scavenger guild: a field experiment[END_REF][START_REF] Newsome | The ecological effects of providing resource subsidies to predators[END_REF][START_REF] Osterback | Longterm shifts in anthropogenic subsidies to gulls and implications for an imperiled fish[END_REF][START_REF] Rodewald | Anthropogenic resource subsidies decouple predator-prey relationships[END_REF]. In recent decades, garden bird feeding strongly increased, involving millions of households worldwide and constituting a theoretical supply of food for millions of birds (Robb et al., 2008a). How this release of a key limiting factor of bird populations [START_REF] Newton | Population limitation in birds[END_REF][START_REF] Siriwardena | The effect of supplementary winter seed food on breeding populations of farmland birds: evidence from two large-scale experiments[END_REF], especially during winter, affect bird communities has yet to be fully comprehended.

From a conservation perspective, garden bird feeding may have pros and cons. In urban areas, where bird diversity has been reduced [START_REF] Chace | Urban effects on native avifauna: A review[END_REF][START_REF] Marzluff | Worldwide urbanization and its effects on birds[END_REF] and population declines may be particularly worrying [START_REF] Cbs | Decline in 13 out of 20 urban bird populations[END_REF]SDES and AFB, 2018;[START_REF] Sullivan | Using habitat-specific population trends to evaluate the consistency of the effect of species traits on bird population change[END_REF], garden bird feeding is generally considered beneficial. In species using feeders, with no change for others, thus resulting in more diverse communities.

However, potential detrimental effects are known: nutritionally unbalanced food items can reduce egg production [START_REF] Plummer | Fat provisioning in winter impairs egg production during the following spring: a landscapescale study of blue tits[END_REF], birds' enhanced proximity may increase probability of disease transmission [START_REF] Galbraith | Experimental feeding regime influences urban bird disease dynamics[END_REF][START_REF] Lawson | Health hazards to wild birds and risk factors associated with anthropogenic food provisioning[END_REF], and predation risks could be elevated [START_REF] Tryjanowski | Who started first? Bird species visiting novel birdfeeders[END_REF]. A major additional concern is the possibility that this provision of readily available food resources could favour the establishment of exotic species. For example, experimental work from the North Island of New Zealand showed that supplementary feeding restructured bird communities to the benefit of exotic species, that were mostly granivorous [START_REF] Galbraith | Supplementary feeding restructures urban bird communities[END_REF]. In Europe, the introduced Rose-ringed parakeet (Psittacula krameri) thrives in cities and has become the most abundant exotic bird species [START_REF] Pârâu | Rose-ringed parakeet populations and numbers in Europe: a complete overview[END_REF]. Garden bird feeding may provide a staple enhancing its survival and its colonisation in temperate climates [START_REF] Clergeau | Bird feeders may sustain feral Rose-ringed parakeets Psittacula krameri in temperate Europe[END_REF].

So far, the Rose-ringed parakeet literature focused on competition for nesting sites [see [START_REF] White | Assessing the ecological and societal impacts of alien parrots in Europe using a transparent and inclusive evidence-mapping scheme[END_REF] for a review], whereas this species commonly use birdfeeders [START_REF] Clergeau | Bird feeders may sustain feral Rose-ringed parakeets Psittacula krameri in temperate Europe[END_REF] where it may display the most agonistic behaviours [START_REF] Le Louarn | Interference competition between an invasive parakeet and native bird species at feeding sites[END_REF]. Body mass is another important factor determining species' abilities to compete for supplementary feeding at birdfeeders [START_REF] Francis | Effects of supplementary feeding on interspecific dominance hierarchies in garden birds[END_REF]. To our knowledge, only one study compared potential effects of the Rose-ringed parakeet to a similarly large and dominant native species (the Great spotted woodpecker) at the community-level: presence of a caged bird of either of the latter two species reduced bird visits and feeding, but the Rose-ringed parakeet's effects were stronger [START_REF] Peck | Experimental evidence of impacts of an invasive parakeet on foraging behavior of native birds[END_REF]. However, Peck & colleagues' (2014) work was a year-round experiment, and competition outcomes could markedly differ in wintering bird communities exposed to a variety of abiotic and biotic stresses, i.e. under cold conditions and depleted resources [START_REF] Pravosudov | Energy management in passerine birds during the nonbreeding season[END_REF]Robb et al., 2008b), and when birdfeeders may represent a critical source of food in urban areas. Additionally, there is a need to investigate these questions while controlling for the influence that other large species could have, and with data of bird interactions between free-living individuals.

Our study aims to investigate competition in wintering bird communities at garden birdfeeders, and to evaluate whether an introduced species hampers access to resources. We use data from the Citizen Science scheme BirdLab [START_REF] Lorrilliere | Winter bird survey in French gardens, two complementary schemes for a wide array of questions[END_REF] to carry out a large-scale assessment encompassing different French populations of the Rose-ringed parakeet and various environmental contexts (e.g. urban landscapes, climates), strengthening the representativity of our analysis [START_REF] Devictor | Beyond scarcity: citizen science programmes as useful tools for conservation biogeography[END_REF]. Coupling a serious game and observations of free-living bird individuals, BirdLab engages volunteers in collecting bird cooccurrences at birdfeeders following a standardised protocol. As bird species differ in their use of birdfeeders and their ability to cope with novelty [START_REF] Tryjanowski | Urbanization affects neophilia and risk-taking at bird-feeders[END_REF][START_REF] Tryjanowski | Who started first? Bird species visiting novel birdfeeders[END_REF], we assess responses at both community-and species-level, expecting species may fare differently in competing for resources. Most importantly, we evaluate whether effects of the Rose-ringed parakeet vary from those of two similarly large and potentially aggressive species: the Eurasian magpie (Pica pica) and the Eurasian collared-dove (Streptopelia decaocto). In Winter, seeds -and potentially other plant materials -constitute the diet of these three large species [START_REF] Clergeau | Bird feeders may sustain feral Rose-ringed parakeets Psittacula krameri in temperate Europe[END_REF][START_REF] Poling | Dietary overlap and foraging competition between Mourning doves and Eurasian collared-doves[END_REF][START_REF] Reebs | Regurgitated pellets and late winter diet of Black-billed magpies, Pica pica, in Central Alberta[END_REF][START_REF] Shiels | Diets of Kauai's invasive rose-ringed parakeet (Psittacula krameri): evidence of seed predation and dispersal in a human-altered landscape[END_REF][START_REF] Tatner | The diet of urban Magpies Pica Pica[END_REF]. While the collared-dove does not spur as much debate as the Rose-ringed parakeet, both arrived in Europe recently (in particular in France, in the early 1950s and 1970s respectively), in contrast with the Eurasian magpie present in Europe by the Middle Pleistocene [START_REF] Jánossy | Die mittelpleistozäne fauna der höhle des hungária-berges bei Dorog (Gerecse Gebirge, Ungarn)[END_REF]. Specifically, we tested whether community cumulative presence time and richness at birdfeeders varied between four treatments: control (absence of any large species) and presence of either the Eurasian magpie, the Eurasian collared-dove, or the Rose-ringed parakeet. To better understand these community-level patterns, we then estimated the time the Rose-ringed parakeet occupied exclusively (i.e., as the only species present) birdfeeders and tested whether it differs from other species' exclusive occupation of birdfeeders. Finally, we investigated whether the presence of either the Eurasian magpie, the Eurasian collared-dove, or the Rose-ringed parakeet influenced the probability of presence of six small species at birdfeeders. Following previous research (Le Louarn et al., 2016;[START_REF] Peck | Experimental evidence of impacts of an invasive parakeet on foraging behavior of native birds[END_REF], we hypothesised the Rose-ringed parakeet may hamper birds' access to feeders more than the other two large birds, the Eurasian magpie and the Eurasian collared-dove.

MATERIALS AND METHODS

DATA COLLECTION

BirdLab (the Feederwatch citizen science project database for France) is a citizen science program in which volunteers follow a standardised protocol to record birds visiting birdfeeders during winter [START_REF] Lorrilliere | Winter bird survey in French gardens, two complementary schemes for a wide array of questions[END_REF]. To participate, volunteers set up a pair of identical birdfeeders (hereafter referred to as a 'station'), distant from 1-2 m from each other.

Stations can be private (e.g., in private gardens) or shared (e.g., at a university campus or in a public garden). Each birdfeeder is provided with sunflower seeds, the most preferred food supplement in various human-dominated landscapes [START_REF] Tryjanowski | Food preferences by birds using bird-feeders in winter: a large-scale experiment[END_REF]. Volunteers participate by using a user-friendly application (developed for tablet and smartphone on Android or iOS) in which the two birdfeeders are represented: during a five-minute session, icons of bird species are dragged and dropped to feeders, mimicking movements of the freeliving bird individuals visiting the station (Fig. 1). Volunteers thus record in real-time bird arrivals (Fig. 1b), shifts between birdfeeders (Fig. 1c) and departures (Fig. 1d). Each fiveminutes session starts when the volunteer drags in the first bird icon on a feeder. The application includes 27 species commonly seen at birdfeeders in France [START_REF] Lorrilliere | Winter bird survey in French gardens, two complementary schemes for a wide array of questions[END_REF]. All 27 species are easily recognized. Nonetheless, the BirdLab application uses timed quizzes to test both bird recognition (made intuitive by relying on icons instead of names) and the speed of dragging-dropping bird icons. Passing the first quiz is mandatory to participate at the 'beginner' level (i.e., 14 bird species are available); succeeding in subsequent quizzes (i.e., being able to recognize among more species timely) allows reaching three next levels: 'Amateur' (17 species), 'Confirmed' (26 species) and 'Expert' (27 species). Participation in BirdLab is possible in winter (from November 15 th to March 31 st ) since 2014.

For each session, the geographical coordinates of the station are obtained using the GPS of the volunteer's device (tablet or smartphone), along with the date and time of the observation. From each BirdLab session, for each bird, we obtain its species, and when exactly it was present on which birdfeeder. Individuals are not marked and we thus cannot know the actual number of unique individuals per species that were present during a session.

For the purpose of the present study, the information we retrieved was, for a given species and session, when exactly it was present and how many simultaneous individuals there were at a station, i.e. combining birdfeeders and allowing to compute a cumulative presence time per species and per session.

DATA SELECTION

We applied several filters relative to data quality and data specificity needed for the biological questions asked in the present paper.

A few sessions included an unlikely high number of species present simultaneously at the station (e.g., reaching 23 out of 27 species), and this most probably indicated that the protocol had not been followed. We determined which stations produced any of the top 1% of sessions in terms of the number of species simultaneously present at birdfeeders, and removed all sessions from these stations. Remaining sessions had from 0 to 7 species present simultaneously, which is in agreement with our experience of the BirdLab protocol. We removed stations where more than ten volunteer accounts recorded sessions, in order to reduce the variability due to multiple volunteers using a station. Additionally, we kept data from experienced volunteers only by excluding i) the first and second sessions recorded at each station, and ii) sessions recorded at the 'beginner' level (see 2.3 for the inclusion of this variable in our statistical models). Sessions carried out in an insufficient level of daylight were removed: we kept those from 30 mn before sunrise to 30 mn after sunset [given the location and date of the sessions, and using the sunrise function of the maptools package [START_REF] Bivand | maptools: Tools for handling spatial objects[END_REF] of R version 3.5.2. (R Core Team, 2018)]. Finally, when two sessions from a station had been recorded less than 30 minutes apart, we randomly kept only one to avoid potential temporal auto-correlation between samplings.

As the Rose-ringed parakeet was added to the BirdLab application on 15 th November 2017, our dataset spans winters 2017/2018 and 2018/2019. We kept sessions recorded in December, January or February, excluding November and March for which the limited number of sessions precluded their inclusion as levels of the categorical variable 'month' in our models. We selected sessions from stations where the Rose-ringed parakeet had been recorded at least once, and further included stations present within 10 km. While maintaining the landscape context (i.e., dominated by urban land-use; Fig. 2b) and the bird communities (i.e., common species found in urban gardens) similar across stations, this allowed us to obtain a dataset fitting our scientific objective: assessing how the presence of large species (European magpie, Eurasian collared-dove, and Rose-ringed parakeet) may affect bird communities at birdfeeders compared to when all large species were absent. To do so, we selected sessions in which none or only one of the three large species studied had been recorded. Importantly, the sessions kept never included other large species present in the Journal Pre-proof J o u r n a l P r e -p r o o f corone, the Rock dove Columba livia, the Common woodpigeon Columba palumbus, and the Great spotted woodpecker Dendrocopos major), for which the lack of sufficient data prevented their inclusion in the present study. Most volunteers installed birdfeeders of the platform type, and data from stations with birdfeeders of the hanging type were too scarce to be included in the present analysis.

In the end, the total dataset included 818 sessions from 33 stations (Fig. 2), totalling over 68 hours of observations. Among the 27 species considered in the BirdLab program, 21 were included in our final dataset (Table 1).

STATISTICAL ANALYSES

We relied on mixed-effects modelling to investigate how the three large species could affect winter bird communities at the stations included in our dataset. All statistical analyses were performed using R version 3.5.2 (R Core Team, 2018) and mixed-effects models were performed with the lme4 package [START_REF] Bates | Fitting linear mixed-effects models using lme4[END_REF]. Below, we first describe the two models investigating bird responses at the community-level (cumulative presence time and richness).

Second, we present the model estimating how long each species is the only species present per session. Finally, we describe seven models testing for large species effect on the presence/absence of small species.

2.3.1.Community-level

For each session, we computed cumulative presence time and richness of the community (i.e., the 18 small species in Table 1). We used these as the response variables of two separate linear mixed-effects models. To obtain the cumulative presence time, we summed the time for each individual of a species if multiple individuals were present.

Journal Pre-proof J o u r n a l P r e -p r o o f 10 Cumulative presence time was log-transformed to meet the assumption of normality of model residuals (see below). Sessions recorded at the same station are not independent of each other, and we thus included 'Station_ID' as a random intercept effect in both models.

The same set of explanatory variables were included in the two models. A large species treatment variable was included (categorical with four levels: 'Absence', 'European magpie present', 'Eurasian collared-dove present', 'Rose-ringed parakeet present'), with 'Absence' of large species being the reference level (i.e., the models tested whether the presence of either of the three large species differed from when all were absent). Other explanatory variables were: winter (categorical variable with two levels: '2017/2018', '2018/2019'), month (categorical variable with three levels: 'December', 'January', 'February'), volunteer expertise (categorical variable with three levels: 'Amateur', 'Confirmed', 'Expert') and the longitude and latitude of stations (continuous variables) to account for potential spatial auto-correlation in our dataset. We computed variance inflation factors [START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF] of explanatory variables and found no evidence of collinearity (all VIF values < 2).

We graphically checked that the residuals of each model met the assumptions of homoscedasticity and normality [START_REF] Faraway | Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models[END_REF][START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF]. For each model, we graphically confirmed the spatial independence of the residuals by computing a spline correlogram [START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF] using the ncf R package [START_REF] Bjornstad | ncf: spatial covariance functions[END_REF]. Both models had a sample size of 818 (i.e., number of sessions).

Species exclusive occupancy of a station

To better understand how large species may affect others, we ran a linear mixed-effect model estimating how much time per session each species occupied a station as the only species present, and testing whether it differed from the time the Rose-ringed parakeet occupied a station as the only species present. The recorded times of exclusive occupancy of a station per session were not independent of each other: there were multiple times measured per session (one for each species present), and multiple sessions were recorded per station.

We modelled this structuration in the data by including 'Session_ID' nested within 'Station_ID' as a random intercept effect in the model.

The response variable was the species cumulative presence time occupying a station as the only species present per session. A session begins when a bird icon is dragged on a feeder, and the first step for the volunteer is to add the birds that may already be present at either of the two birdfeeders. To account for this design of the protocol that could slightly bias the time a species occupies a station as the only species present, we excluded initial and short sequences of time (i.e., less than 5 seconds within the first 30 seconds) that a species was recorded at the station as the only species present in a session. The resulting cumulative time occupying a station as the only species present per species and session was log-transformed (after adding 1 to each value, to cope with 0'soccurring for a species present in a session but never occupying a station as the only species present) to meet the assumption of normality of model residuals.

In this model, we included 'Species' as an explanatory variable (categorical with 17 levels, corresponding to the 17 species which occurred in at least 20 sessions; Table 1), with 'Rose-ringed parakeet' being the reference level (i.e., the model tested whether the cumulative time occupying a station as the only species present per session of a given species differed from the Rose-ringed parakeet value). An additional explanatory variable was the cumulative time of all other species present during the given session, as this could influence the time that a given species occupied the station as the only species present during the session. Other explanatory variables were winter, month, volunteer expertise, and the longitude and latitude of stations. Variance inflation factors [START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF] of explanatory variables indicated no worrisome collinearity (all VIF values < 2).

We graphically confirmed that model residuals met the assumptions of homoscedasticity, normality and spatial independence [START_REF] Faraway | Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models[END_REF][START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF]. The sample size was 2780 (i.e., number of times species were present in the 818 sessions). We controlled the risk of type I error due to multiple testing in this model (in particular with the 17 levels of the explanatory variable Species) by correcting P values using the false discovery rate control [START_REF] Verhoeven | Implementing false discovery rate control: increasing your power[END_REF].

Small species probability of presence

We performed separate generalised linear mixed-effects models to test whether the presence of small species differed between sessions with no large species or with one of either the three large species present. Six species had sufficient records in our dataset to be modelled separately (listed as 'Commonly recorded small species' in Table 1), and we pooled the remaining 12 species to model them as a group (listed as 'Other small species' in Table 1).

To account for the lack of independence between sessions recorded at the same station, we included 'Station_ID' as a random intercept effect in the seven models.

In each model, the response variable was binary, corresponding to a species (or the group of other species) being present (1) or absent (0) in a session, and it was therefore modelled with a binomial family and a logit link function.

The same set of explanatory variables were included in the seven models. Again, the large species treatment variable was included, with 'Absence' of large species being the reference level. Additionally, we added as an explanatory variable the cumulative presence time of all other species present (except the large one, if present) during the given session, as this could influence the probability of a species to be present in a session. Finally, each model also included as explanatory variables winter, month, volunteer expertise, and the longitude and latitude of stations. Computing variance inflation factors [START_REF] Zuur | Mixed effects models and extensions in ecology with R, Statistics for Biology and Health[END_REF] of explanatory variables, we found no evidence of collinearity (all VIF values < 2).

Each model used a subset of the total dataset, tailored to modeling the presence and absence of each species (or the group of 12 species). For each model, we determined in each winter the dates of first and last records of the modelled species and restrained the dataset to sessions within these dates. Then, we only kept sessions from stations where the modelled species had been recorded at least once. In the resulting dataset, we followed [START_REF] Austin | Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models[END_REF] and checked that there were at least 20 sessions per continuous variable and level of categorical variables. This requirement was met for the six species modelled separately, although one level of a categorical variable had to be dropped in the House sparrow model (effect of the Rose-ringed parakeet presence could not be tested). We had to group the 12 remaining species to verify this condition, unmet when kept separated. The final sample sizes for these seven models are provided in Figure 5. In each model, we checked there was no over/under-dispersion and no zero-inflation using the R package DHARMa [START_REF] Hartig | DHARMa: Residual diagnostics for hierarchical (multi-Level / mixed) regression models[END_REF]; we also followed [START_REF] Faraway | Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models[END_REF] and graphically checked model residuals using the R package arm [START_REF] Gelman | arm: data analysis using regression and multilevel/hierarchical models[END_REF]. The assumption of spatial independence of model residuals was graphically confirmed.

RESULTS

In presenting the results of our models, statistically significant relationships were considered for p-values lower than 0.05.

COMMUNITY-LEVEL

The cumulative presence time of small species was significantly lower in the presence of either the Eurasian collared-dove (94.4 s, i.e. -33%) or the Rose-ringed parakeet (80.5 s, i.e. -43%) in comparison with sessions free of large species (Fig. 3a, Table 2). Presence of the Eurasian magpie was not significantly associated with a change in the cumulative presence time of small species. On average, volunteers of the highest level of expertise ('Expert') recorded significantly longer cumulative presence time of small species per session than 'Amateur' volunteers (Table 2).

Compared to sessions without large species, we only detected a significant difference in small species richness in the presence of the Eurasian collared-dove (Fig. 3b, Table 2), which was associated to 0.46 fewer species in average (i.e. -13%). In contrast, the presence of the Eurasian magpie or the Rose-ringed parakeet were not significantly associated with changes in the richness of small species in a session. On average per session, both 'Confirmed' and 'Expert' volunteers recorded significantly higher small species richness than 'Amateur' volunteers (Table 2).

SPECIES EXCLUSIVE OCCUPANCY OF A STATION

Over the 41 sessions in which the Rose-ringed parakeet was recorded, it occupied a station as the only species present during 26.3 s per session on average (Fig. 4). Out of the 16 other species for which such an estimation could be computed, only the European goldfinch and the Eurasian collared-dove tended to occupy a station as the only species present for a longer period of time, although the difference with the Rose-ringed parakeet was not significant (Table 3). The Eurasian magpie (with 5.5 s) was among the 11 species that occupied stations as the only species present for a significantly shorter duration than the Roseringed parakeet. No significant effect of volunteer expertise was detected (Table 3).

SMALL SPECIES PROBABILITY OF PRESENCE

Among the six small species whose probability of presence could be modelled separately, the presence of four species was significantly associated with the presence of at least one large species (Fig. 5, Table 4). Presence of the Rose-ringed parakeet was significantly associated with a substantial reduction in the probability of presence of the European robin. Presence of the Eurasian magpie and the Eurasian collared-dove were associated with significantly reduced probability of presence of the Eurasian blue tit, and the Eurasian collared-dove was also associated with a decreased probability of presence of the Great tit. Interestingly, there were some positive associations between the presence of a large species and the probability of presence of some small species, although this pattern was only significant between the Eurasian collared-dove and the House sparrow (Fig. 5, Table 4).

As a group, the probability of presence of the 12 small species that could not be modelled separately was not significantly associated with the presence of any of the three large species.

Compared to sessions from 'Amateur' volunteers, sessions from 'Confirmed' volunteers were more likely to include the European robin and sessions from 'Expert' volunteers were more likely to include the Great tit (Table 4). Additionally, the probability of presence of the group of 12 small species ('Other small species') was significantly higher in sessions from both 'Confirmed' and 'Expert' volunteers.

DISCUSSION

Within urban environments where garden bird feeding in winter represents a novel and increasing anthropogenic food supplement, we examined the competition within bird communities and evaluated the competitive roles of large and/or novel species. Using data from a nation-wide and innovative citizen science scheme recording bird attendance and cooccurrences at birdfeeders, we found that the introduced Rose-ringed parakeet was among the most preemptive competitor species (i.e., occupying a pair of birdfeeders exclusively) and that this led to reduced community-level presence time at birdfeeders. However, delving into cooccurrences of species, the probability of presence of only one species (the European robin) out of six was significantly reduced in the presence of the Rose-ringed parakeet. Most importantly, we compared the effects of the Rose-ringed parakeet with two other large species and our findings suggest that while the competition for food resources differ from the other two large species at the species-level, it does not have a larger effect at the community-level.

Our findings, with complementary work on the Rose-ringed parakeet's competition for nesting sites [START_REF] Strubbe | Assessing the potential impact of invasive ring-necked parakeets Psittacula krameri on native nuthatches Sitta europeae in Belgium[END_REF], are in agreement with a theoretical and empirical body of knowledge suggesting that competition from introduced to native species is not expected to have large effects [START_REF] Davis | Biotic globalization: does competition from introduced species threaten biodiversity?[END_REF], in birds in particular [START_REF] Martin-Albarracin | Impact of nonnative birds on native ecosystems: a global analysis[END_REF].

We are aware of only two studies that assessed competition for food resources from the Rose-ringed parakeet, and the analysis of our large dataset (68.2 hours of observations) of free-living birds at birdfeeders complement and expand our previous understanding of the Rose-ringed parakeet's potential effect. Our results at the community-level empirically confirm the experimental work of [START_REF] Peck | Experimental evidence of impacts of an invasive parakeet on foraging behavior of native birds[END_REF] who had found that a caged Rose-ringed parakeet or its calls reduced feeding rate. From four hours of video recordings of birds at birdfeeders, Le Louarn et al. ( 2016) suggested that the Rose-ringed parakeet displayed the most antagonistic behaviours among eight species, including the Eurasian collared-dove. However, we found that the two species similarly reduced community cumulative presence time and that only the dove was associated with lower community richness. This discrepancy highlights that predicting community-level responses from behaviours between individual pairs is not straightforward. Indeed, out of six small species, we found that three displayed a probability of presence at birdfeeders negatively associated with one or more large species, and that one species (the House sparrow) was positively associated to one of the large species (Fig. 5). These various patterns of co-occurrences at feeding sites may result from direct and indirect effects of competitive interactions within bird communities [START_REF] Wootton | The nature and consequences of indirect effects in ecological communities[END_REF], that may buffer detrimental effects of antagonistic encounters. Further examination of cooccurrences among and between small and large species at birdfeeders will be critical to untangle the net ecological effects of any species in the community.

In common with previous work studying bird competition in urban areas (Le Louarn et al., 2016;[START_REF] Peck | Experimental evidence of impacts of an invasive parakeet on foraging behavior of native birds[END_REF][START_REF] Tryjanowski | Food preferences by birds using bird-feeders in winter: a large-scale experiment[END_REF], the extent with which relying on birdfeeders as food resources may represent competition for natural resources is uncertain.

Food resources for wintering birds are arguably less spatially aggregated in natural or agricultural settings compared to seeds on birdfeeders, and preemptive competition for wild resources may thus be less important than we observed with feeders. However, given the prominence of garden bird feeding in urban areas (Robb et al., 2008a) and how these anthropogenic food subsidies affect the temporal dynamics of bird communities [START_REF] Plummer | The composition of British bird communities is associated with long-term garden bird feeding[END_REF], competition at birdfeeders may represent a substantial part of competition for food resources in urban landscapes, especially in winter. Additionally, the BirdLab protocol is to use sunflower seeds, but birds have dietary preferences [START_REF] Francis | Effects of supplementary feeding on interspecific dominance hierarchies in garden birds[END_REF][START_REF] Poling | Dietary overlap and foraging competition between Mourning doves and Eurasian collared-doves[END_REF][START_REF] Tryjanowski | Food preferences by birds using bird-feeders in winter: a large-scale experiment[END_REF] that can influence investment in foraging and/or competition. In the future, the BirdLab protocol could thus open up to different food items for volunteers to use at birdfeeders, in order to investigate how resource identity affects competition within bird communities. This perspective could also provide insights into why, out of the three large species, only the Eurasian magpie stayed short periods on birdfeeders (Table 1), whereas all three large species rely on seeds and other plant materials during winter [START_REF] Clergeau | Bird feeders may sustain feral Rose-ringed parakeets Psittacula krameri in temperate Europe[END_REF][START_REF] Poling | Dietary overlap and foraging competition between Mourning doves and Eurasian collared-doves[END_REF][START_REF] Tatner | The diet of urban Magpies Pica Pica[END_REF].

Considerable research efforts are devoted to understanding how exotic species may affect native species [START_REF] Godet | What conservation does[END_REF][START_REF] Simberloff | Impacts of biological invasions: what's what and the way forward[END_REF]. However, evaluating the effects of introduced species may be difficult when lacking a comparison with functionally equivalent native species. In this paper, we included not only a native equivalent (the Eurasian magpie) of the Rose-ringed parakeet but also a neonative species [the Eurasian collared-dove, which naturally colonised the country; following the definition of Essl and colleagues (2019)]. Because the neonative species has now widely spread in France since its colonisation about two decades before the introduced Rose-ringed parakeet, the native bird communities could be more habituated to its competitive pressure than that of the Rose-ringed parakeet. However, interestingly, our results at both community-and species-level suggest the neonative species to be a greater competitor for these anthropogenic food subsidies than the Rose-ringed parakeet. Additionally, the large native and exotic species did not affect the same species at birdfeeders. Therefore, to better understand, predict and manage the effects that any species -regardless its origin -may have on ecosystems, we emphasise that ecologists should focus on species traits [START_REF] Davis | Don't judge species on their origins[END_REF][START_REF] Fournier | Predicting future invaders and future invasions[END_REF], for example, body mass, trophic and nesting preferences, or phenology [START_REF] Orchan | The complex interaction network among multiple invasive bird species in a cavity-nesting community[END_REF].

Relying on citizen science -"the involvement of volunteers in scientific research" [START_REF] Dickinson | Citizen science as an ecological research tool: challenges and benefits[END_REF] was essential to this study. Citizen science has proven highly valuable to advance research in a wide range of disciplines [START_REF] Cooper | The invisible prevalence of citizen science in global research: migratory birds and climate change[END_REF][START_REF] Mckinley | Citizen science can improve conservation science, natural resource management, and environmental protection[END_REF][START_REF] Theobald | Global change and local solutions: Tapping the unrealized potential of citizen science for biodiversity research[END_REF]. While data quality or sampling bias may raise analytical challenges, appropriate project design (e.g. standardised protocol, methods to engage volunteers), post-collection filtering and/or validation of data, and statistical solutions can overcome most existing limitations [START_REF] Bird | Statistical solutions for error and bias in global citizen science datasets[END_REF][START_REF] Follett | An analysis of citizen science based research: usage and publication patterns[END_REF][START_REF] Serret | Data quality and participant engagement in citizen science: comparing two approaches for monitoring pollinators in France and South Korea[END_REF]. We here acknowledge that a limitation of the BirdLab dataset is the impossibility to validate observations a posteriori and to rule out identification errors. However, we argue that our results can be trusted for three reasons. First, we only kept data from experienced volunteers and further adjusted for volunteers' expertise in all statistical models. Second, the BirdLab protocol is limited to 27 very common and easily recognizable species, and this is particularly true of the three large species (especially the Rose-ringed parakeet) and the six small passerines which were analyzed separately (Fig. 5). Last but not least, identification errors from any volunteers should be largely independent of the variable of interest in the present analysis, i.e. presence or absence of a large species, and therefore have very little qualitative impact on our findings. The fact that we detected significant effects of volunteer' expertise in several of our models highlights that accounting for volunteer experience when analysing citizen science data may be essential to ensure that effects of ecological variables of interest are as unbiased as possible.

Within only two years, the BirdLab scheme gathered a sufficiently large dataset of standardised observations to assess competition from the Rose-ringed parakeet, still a spatially restricted species in France despite being the most abundant exotic bird in Europe [START_REF] Pârâu | Rose-ringed parakeet populations and numbers in Europe: a complete overview[END_REF]. Effects from the two novel species (Rose-ringed parakeet and Eurasian collared-dove) on the attendance at birdfeeders would be better estimated if we had data prior arrivals. These data are lacking so far; however, with expanding Rose-ringed parakeet populations in France [START_REF] Clergeau | Dynamique de population de la perruche à collier Psittacula krameri introduite en Île-de-France[END_REF], the BirdLab scheme will be in a unique position to determine how an exotic bird integrate and potentially modify community interactions in winter. Documenting such community interactions changes at birdfeeders may be critical to understanding the mechanisms behind temporal species abundance trends revealed by longterm monitoring, emphasizing the interest of coupling complementary citizen science scheme [START_REF] Lorrilliere | Winter bird survey in French gardens, two complementary schemes for a wide array of questions[END_REF]. For example, this could help disentangle effects and causes of concomitant native and exotic populations temporal trends [START_REF] Colléony | When the winners are the losers: invasive alien bird species outcompete the native winners in the biotic homogenization process[END_REF]. This illustrates how valuable citizen science can be to advance our knowledge of biological Journal Pre-proof J o u r n a l P r e -p r o o f 20 invasions [START_REF] Roy | Increasing understanding of alien species through citizen science (Alien-CSI)[END_REF], beyond other ecological goals and educational benefits [START_REF] Deguines | Assessing the emergence of probiodiversity practices in citizen scientists of a backyard butterfly survey[END_REF][START_REF] Deguines | Fostering close encounters of the entomological kind[END_REF][START_REF] Mckinley | Citizen science can improve conservation science, natural resource management, and environmental protection[END_REF].

CONCLUSIONS

Garden bird feeding is an increasingly popular activity that provides food supplement to birds and can restructure entire communities [START_REF] Galbraith | Supplementary feeding restructures urban bird communities[END_REF][START_REF] Plummer | The composition of British bird communities is associated with long-term garden bird feeding[END_REF].

In this study, we sought to examine how wintering birds compete for this novel resource and cope with a large and putatively competitive exotic species. We directly compared the introduced Rose-ringed parakeet with two similarly large and potentially aggressive (neo)native species and, thanks to an innovative semi-experimental citizen science scheme, investigated this issue with national-scale data of free-living birds attending birdfeeders. At the community-level, we found that the Rose-ringed parakeet and the Eurasian collared-dove reduced community cumulative presence time at birdfeeders. However, only the dove also reduced community richness. This is in line with our findings that the Rose-ringed parakeet and the Eurasian collared-dove were the strongest preemptive competitor (i.e., monopolising birdfeeders), in contrast with the Eurasian magpie. Interestingly, at the species-level, responses to the three large species varied in strength and direction (positive or negative associations), calling for further considerations of interspecific direct and indirect interactions.

For example, antagonistic encounters among large species, native or introduced, could have ripple effects on smaller passerines and key ecosystem functions (e.g. seed dispersion, insect predation). This, and the opportunity in future years to monitor how the most abundant exotic bird in Europe integrate local communities, constitute critical fundamental and applied research perspectives which the BirdLab citizen science program should be able to tackle thanks to the involvement of volunteers.

Table 1. List of the 21 species included in the study. 'Large species', 'Commonly recorded small species' (species which response to large species presence could be separately estimated, see Methods and Fig. 5) and 'Other small species' (species that were pooled before estimating an overall response to large species presence, see Methods and Fig. 5) are ordered by decreasing number of sessions they were recorded in. The mean body mass was retrieved from Demongin and colleagues (2020). 2) from the 'Absent' treatment. Bars are 95% confidence intervals estimated from the models, using R package ciTools [START_REF] Haman | ciTools: confidence or prediction intervals, quantiles, and probabilities for statistical models[END_REF]. 3). Bars are 95% confidence intervals estimated from the model, using R package ciTools [START_REF] Haman | ciTools: confidence or prediction intervals, quantiles, and probabilities for statistical models[END_REF]. respectively; Table 4). The six separate small species are ordered from left to right by the median time of presence during a session (Table 1), and sample sizes (n) are provided below each species name. Bars are 95% confidence intervals estimated from the models, using R package ciTools [START_REF] Haman | ciTools: confidence or prediction intervals, quantiles, and probabilities for statistical models[END_REF]. Effect of the Rose-ringed parakeet on the House sparrow could not be estimated (see 2.3.3).

Figure 1 .

 1 Figure 1. BirdLab protocol. Panel (a) shows how the station is represented in the BirdLab

Figure 2 .

 2 Figure 2. Localisation of the 33 BirdLab stations. In (a), crosses correspond to the stations.

Figure 3 .

 3 Figure 3. Community-wide changes in the presence of large species. Average community

Figure 4 .

 4 Figure 4. Species exclusive occupancy of a station per session. Squares are estimates (from

Figure 5 .

 5 Figure 5. Associations between small species and large species presence. Probability of

ACKNOWLEDGEMENTS

We warmly thank all volunteers of the BirdLab scheme. We are grateful to the NGO LPO and to the Vigie-Nature team for help in managing BirdLab. We thank the editor and two anonymous reviewers for providing helpful and constructive comments. ND and RL were funded by the BiodivERsA3-2015-104 (BIOVEINS) grant. The funding source had no involvement in study design or execution.

AUTHORS' CONTRIBUTIONS

ND, RL, FC and CBG conceptualized the ideas and designed the methodology; ND analysed the data, designed the figures, and led the writing of the manuscript; RL curated the data; AD, RL, FC and CBG managed the BirdLab scheme. All authors contributed critically to the drafts and gave final approval for publication.

Table 2. Results of the community-level mixed effects models. Predictors' estimates are shown for the linear mixed-effects models (Cumulative presence time, Richness), along with their associated standard errors (SE), and P-values (P). 'EM', 'ECD' and 'RRP' respectively stand for the 'European magpie', the 'Eurasian collared-dove', and the 'Rose-ringed parakeet'. When P<0.05, statistics are shown in bold.

Declaration of competing interests

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

Graphical abstract

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HIGHLIGHTS

-Citizen scientists record birds at birdfeeders through a serious game -Community effects of Rose-ringed parakeet were compared with two similar species -Bird community use of feeding resources was hampered by two of three large species -Each large species differently affected other species' use of birdfeeders -Garden bird feeding may not greatly favor introduced over native bird species