# Environmental factors governing spatio-temporal series of aquatic vegetation in the Bagnas lagoon

Adam Bernard, Samuel Alleaume, Xavier Fortuny, François Munoz, Estelle

Pitard

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Adam Bernard, Samuel Alleaume, Xavier Fortuny, François Munoz, Estelle Pitard. Environmental factors governing spatio-temporal series of aquatic vegetation in the Bagnas lagoon. Estuarine, Coastal and Shelf Science, 2025, 319, pp.109261. 10.1016/j.ecss.2025.109261. hal-05050636

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# Environmental factors governing spatio-temporal series of aquatic vegetation in the Bagnas lagoon

A. Bernard<sup>a, *</sup>, S. Alleaume<sup>c</sup>, X. Fortuny<sup>d</sup>, F. Munoz<sup>b, d</sup>, E. Pitard<sup>a, d</sup>

## ABSTRACT

Macrophytes are a key component of lagoon ecosystems, and the spatio-temporal variation of their biomass reflects the effect of environmental drivers. Understanding these dynamics is essential for managing, preserving and anticipating the fate of such fragile ecosystems. First, using both satellite data and field observations, we showcase here a machine learning approach to accurately detect submerged vegetation in a preserved lagoon in South France, the Bagnas lagoon. Second, we investigate the statistical relationship between the surface area of *Stuckenia pectinata* and abiotic factors known to influence the life history of this aquatic plant. We thus identify and evaluate the factors that most impact *Stuckenia pectinata* population dynamics within the lagoon. We find that water temperature, salinity, and depth are significantly related to submerged aquatic vegetation (SAV) coverage. We show that the SAV surface area reaches a peak during summer, which can vary across years. Furthermore, we examine the lifetime distribution of SAV during each year cycle across the pond. We show that the average annual lifespan differs significantly from one year to the next, which can be explained by annual differences in exposure time to sufficiently high water levels.

## 1. Introduction

Aquatic macrophytes represent an essential component of wetland ecosystems, by contributing significantly to their ecological integrity and functionality (Chambers et al. [2007]). They constitute a fundamental food resource for herbivorous birds (Kantrud [1990]), and provide habitat for a diverse range of aquatic invertebrates, serving as both shelter and food resource. The primary consumers inhabiting the macrophyte vegetation, in turn, become prey for various insectivorous birds. They also contribute to nutrient cycling (OHare et al. [2017]), act in stabilizing sediment, hence on carbon sequestration, thereby contributing to the overall physico-chemical stability of the environment (Macreadie et al. [2014]). A few studies have addressed how abiotic parameters shape the distribution of macrophytes in various ecosystems (Bornette and Puijalon [2010]). However, we still lack studies on closed lagunary ecosystems, despite their specific dynamics and the multiple ecosystem services they provide. In particular, *Stuckenia pectinata* (also known as sago pondweed) is a nearly cosmopolitan macrophyte (Kantrud [1990]). Although it is a perennial plant, *S. pectinata* shows seasonal development in temperate climate, i.e. the turions start developing in early spring, grow until mid summer, when they can flower, and senescence occurs in autumn and leads to decreasing photosynthetic biomass.

It has been shown that the development of *Stuckenia* depends on various abiotic parameters within the environment (Bornette and Puijalon [2010]). Temperature is known to be a crucial factor, both in field surveys (Wersal et al. [2006], Rodrigo and Calero [2019]), and in experimental setting where the optimal temperature for tuber sprouting was shown to be around 20–25 °C (Ganie [2017]). Water saturation of the sediment (Ganie [2017]), and light availability (Wersal et al. [2006]) have proved also important. Water salinity is an other significant driver. Even though *Stuckenia* is able to tolerate a wide range of salinity levels, some studies found a slightly negative effect of salinity on the fertility of this plant (Calero, Sara et al. [2015]). The reproduction of *Stuckenia* is a broad topic: It can be asexual, as the turions contribute to vegetative development, while sexual reproduction further contributes to the dissemination by seeds (Kantrud [1990]). Sometimes, even whole fragments of shoots can be ripped from the initial plant, driven by the current or moved by birds, and establish at an other place.

In this study, we chose the Bagnas National Natural Reserve's main pond as our study site. *Stuckenia pectinata* is almost the only macrophyte occupying the lagoon. Covering around 150 ha, the pond is considered a permanent water body, although during hot years, drastic droughts can entail partial drying (with low remaining water depth), or even total drying. In wetter periods, the pond can reach up to one meter of water depth at the central deepest point.

<footnote><sup>a</sup> Laboratoire Charles Coulombs (L2C), Montpellier, France</footnote>

<footnote><sup>b</sup> Laboratoire de Biométrie et Biologie Evolutive (LBBE), Lyon, France</footnote>

<footnote><sup>c</sup> UMR TETIS, INRAE, AgroParisTech, CIRAD, CNRS, Université de Montpellier, Montpellier, France</footnote>

<footnote><sup>d</sup> ADENA, Réserve Naturelle du Bagnas, Agde, France</footnote>

<footnote>* Corresponding author.
E-mail address: adam.bernard@umontpellier.fr (A. Bernard).</footnote>
Since this lagoon is protected for conservation purposes, the managers need to regularly check the conservation status of the reserve by assessing ecosystem health. The area of submerged macrophytes is one of the indicators (Lepareur et al., 2018). Classical field sampling approaches with, e.g., regular sampling over a grid, can alter the fragile ecosystem. To minimize human impact of this activity, while still precisely assessing submerged aquatic vegetation (SAV hereafter) occurrence and dynamics, remote sensing approaches can be used and provide broad-scale and repeatable observations of the whole ecosystem.

Remote sensing applied to SAV detection is a quickly evolving topic, and it can be undertaken using a wide variety of approaches (Veettil et al. [2020]). Here, we will focus on how using satellite imagery for SAV detection.

Remote sensing data used to monitor macrophyte distributions across wide territories can be satellite images obtained with both active and passive sensors (Traganos et al. [2022]). The monitoring can support identification of priority sites for conservation (Houngnandan et al. [2020]). Remote sensing methods applied to submerged aquatic vegetation can further fulfil many objectives, e.g., estimation of spatial coverage, species recognition and distribution, estimation of biomass, analysis of growth and senescence patterns, detection of epiphytes etc. More specifically, satellite imagery has already been used to assess SAV coverage in the Bagnas reserve (Menu et al. [2021]). At that time, Sentinel-2 was in service for a year, making possible the survey of *Stuckenia pectinata* in the Bagnas pond from 2017 to 2018, with an objective of quantifying the seasonality of the SAV.

Here we investigate the validity of using satellite imagery, specifically from Sentinel-2, to assess dynamics of *Stuckenia pectinata* in the Grand Bagnas lagoon over a 6-year time series. The approach provides valuable data over a large extent, at a high spatial resolution, and at high temporal revisit frequency.

The purpose of this paper is (1) to assess and quantify the temporal development of *S. pectinata* vegetation by providing aggregated metrics such as the total area of SAV, and (2) to identify the main abiotic drivers of the development. The questions we will answer in this paper are:

1. How does the SAV grow and retract in the pond in a seasonal manner? At which moments of the year? How does SAV coverage over the whole site vary in time?

2. Which of the abiotic drivers best explain the occupancy of the SAV in the pond over time?

3. Are there any inter-annual variations of the duration of SAV coverage over the whole time series? Which factors can explain such variability between years?

For these aims, we applied a robust machine learning approach to analyze remote sensing data based on Sentinel-2 imagery of the Bagnas central pond from January 2017 to December 2022. We used a dataset of photo-interpreted points for calibration of the random forest and another for validation. In addition, we compared the detected extent of *S. pectinata* with actual ground truth measurements at some dates. We thus defined two different ways to validate the remote sensing detection. We then modelled the total area of detected SAV as a function of abiotic parameters. We also quantified the inter-annual differences of detected SAV area over the whole time series. Then, we characterized the influence of abiotic drivers as well as their hypothesized positive or negative effects on development.

## 2. Materials and methods

### 2.1. Study site

The Bagnas National Natural Reserve was established in 1983. It is located at the south-west end of the Thau's lagoon, between the cities of Marseillan and Agde, in the south of France (Fig. 1). The reserve is spread across 561 ha and is managed by the ADENA organization. In the

early 2000s, the site was granted the Natura 2000 European status (Bensettiti et al., 2004), recognizing the ecological importance of the ecosystem and targeting its conservation. The reserve is composed of both permanent and temporary ponds.

Here, we focus on the main permanent water body of the reserve (the “Grand étang du Bagnas”), which extends over approximately 150 ha. This lagoon is mostly composed of shallow water, very rarely exceeding 1m of depth at the center. The topography is relatively homogeneous, as is the water temperature gradient over the whole pond. However, the SAV distribution appears to be heterogeneous in space and time, with alternating growing and decreasing phases and spatially disconnected sites for growth. The SAV found in the pond is mostly monospecific, composed of *Stuckenia pectinata*, although a very small amount of Rupilia maritima has been observed some years (Papuga and Andrieu, 2018).

### 2.2. Sentinel-2 images and time-series

To identify the spatial distribution of submerged vegetation in the lagoon, we used Sentinel-2 multi-spectral bands at the finest spatial resolution available: blue, green, red and near infra-red (respectively corresponding to the bands B2, B3, B4 and B8 at 10 m spatial resolution). We used the Level 2A product downloaded from the Theia platform, which is corrected for most undesirable atmospheric effects and provides time series images.

Thanks to both Sentinel-2 satellites (Sentinel-2A and Sentinel-2B) the temporal resolution of the time-series can be of 5 days. However, not every image is exploitable, so that the temporal resolution of our time-series was 5 days in summer (clear weather) and between 2 weeks and a month in winter. We manually selected images which, despite the correction, had no visible clouds close to the site, and showed no obvious instrumental effects.

Our final time-series extended from January 2017 to December 2022, for a total of 187 images over the Sentinel-2 tile T31TEJ (We provide in Table 1 a summary presentation of all the images included in our time-series.).

The pond is surrounded by a dense vegetation of reed-beds. Therefore, we designed a mask to exclude the terrestrial and helophytic vegetation from our study area. The variations of reed-beds locations are negligible over this 6-year time period, so we used the same mask over the whole time-series. The mask encompassed all the pixels with MSAVI2 spectra index values above 0.15 on the 12th of June 2017 image (Menu et al. [2021]). We chose this image and index value because it was previously used in earlier studies conducted on the same site with satellite data (Menu et al. [2021]), as this index's values show the biggest differences between reed-beds and the rest of the ecosystem.

In fact, during mid-summer months like June, the reed-beds vegetation surrounding the pond is well-developed, producing a clear radiometric signal with the MSAVI2 index. This makes it easier to create an accurate mask using a value threshold (Menu et al. [2021]).

The calculation of the MSAVI2 index (Qi et al., 1994) is done as follows:

$$MSAVI2 = ((2 \times \text{NIR} + 1 - ((((2 \times \text{NIR} + 1)\hat{2} - 8 \times (\text{NIR} - \text{R}))(1/\hat{2}))) / 2)$$

### 2.3. Random Forest classification

The Random Forest classification is a robust and efficient approach (Breiman [2001]) to analyze remote sensing data. In the context of image analysis, where data can be intricate and subject to variations in light, weather, and water conditions, the Random Forest approach has the potential to effectively model the relationship between the pixel features and the presence of SAV. We used the “randomForest” package in R (Liaw and Wiener [2001]) to train the algorithm and we used the “varImpPlot” function to examine the relative importance of predictors
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Localisation of the study site. Localisation of the Bagnas Natural Reserve, near Agde, south of France. The main pond of the reserve is highlighted in dashed points, while the total area of the reserve is inside the solid line. The coordinates are provided in the Lambert93 projection, using meters as the unit of measurement.</figcaption></figure>

for the detection.

The features of the Random Forest model were the reflectance value of each of the 10m resolution spectrum bands of Sentinel 2: B2 (blue), B3 (green), B4 (red), and B8 (near infra-red). The trained Random Forest provided a measure of the relative predictor importance, indicating which band contributed most to the classification. This determined which characteristics of the images contributed most to SAV detection. Moreover, the SAV signature may not be a clear and linear combination

of spectral components, and the Random Forest approach could handle this complexity effectively, since such algorithm can detect complex non-linear effects.

To train the Random Forest algorithm, we built a dataset by extracting pixels within photo-interpreted polygons (called polygon dataset from here onwards). The polygons were defined following a photo-interpretation based on a hybrid method considering the MSAVI2 index values, the variability of MSAVI2 values, false color Sentinel-2
<caption>**Table 1**</caption>

Summary table of all the image available in our time-series from January 2017 to December 2022. All the images used are from the T31TEJ Sentinel-2 tile, and from the L2A product from Theia.

<table><thead><tr><th>2017</th><th>2018</th><th>2019</th><th>2020</th><th>2021</th><th>2022</th></tr></thead><tbody><tr><td>2017/01/03</td><td>2018/01/03</td><td>2019/01/03</td><td>2020/01/08</td><td>2021/01/27</td><td>2022/01/12</td></tr><tr><td>2017/01/13</td><td>2018/01/23</td><td>2019/02/12</td><td>2020/02/22</td><td>2021/02/11</td><td>2022/01/17</td></tr><tr><td>2017/02/22</td><td>2018/01/28</td><td>2019/02/22</td><td>2020/03/08</td><td>2021/02/26</td><td>2022/01/22</td></tr><tr><td>2017/03/14</td><td>2018/02/02</td><td>2019/02/27</td><td>2020/03/13</td><td>2021/03/08</td><td>2022/01/27</td></tr><tr><td>2017/04/03</td><td>2018/02/27</td><td>2019/03/04</td><td>2020/03/18</td><td>2021/03/13</td><td>2022/02/01</td></tr><tr><td>2017/04/23</td><td>2018/03/14</td><td>2019/03/09</td><td>2020/03/28</td><td>2021/03/23</td><td>2022/02/06</td></tr><tr><td>2017/05/23</td><td>2018/03/19</td><td>2019/03/24</td><td>2020/04/02</td><td>2021/03/28</td><td>2022/02/11</td></tr><tr><td>2017/06/02</td><td>2018/04/18</td><td>2019/03/29</td><td>2020/04/07</td><td>2021/04/07</td><td>2022/02/21</td></tr><tr><td>2017/06/12</td><td>2018/05/03</td><td>2019/04/13</td><td>2020/05/07</td><td>2021/04/12</td><td>2022/02/26</td></tr><tr><td>2017/06/22</td><td>2018/05/18</td><td>2019/05/13</td><td>2020/05/12</td><td>2021/04/22</td><td>2022/03/03</td></tr><tr><td>2017/07/07</td><td>2018/05/23</td><td>2019/05/23</td><td>2020/05/17</td><td>2021/05/27</td><td>2022/03/23</td></tr><tr><td>2017/07/12</td><td>2018/06/27</td><td>2019/06/02</td><td>2020/05/22</td><td>2021/06/21</td><td>2022/03/28</td></tr><tr><td>2017/07/17</td><td>2018/07/02</td><td>2019/06/17</td><td>2020/05/27</td><td>2021/06/26</td><td>2022/04/17</td></tr><tr><td>2017/07/22</td><td>2018/07/07</td><td>2019/06/22</td><td>2020/06/06</td><td>2021/07/01</td><td>2022/04/22</td></tr><tr><td>2017/08/06</td><td>2018/07/17</td><td>2019/06/27</td><td>2020/06/16</td><td>2021/07/11</td><td>2022/04/27</td></tr><tr><td>2017/08/21</td><td>2018/07/27</td><td>2019/07/07</td><td>2020/06/21</td><td>2021/07/16</td><td>2022/05/17</td></tr><tr><td>2017/09/05</td><td>2018/08/01</td><td>2019/07/12</td><td>2020/07/06</td><td>2021/07/21</td><td>2022/05/27</td></tr><tr><td>2017/09/10</td><td>2018/08/06</td><td>2019/07/17</td><td>2020/07/11</td><td>2021/07/26</td><td>2022/06/06</td></tr><tr><td>2017/09/20</td><td>2018/08/11</td><td>2019/07/22</td><td>2020/07/16</td><td>2021/07/31</td><td>2022/06/11</td></tr><tr><td>2017/10/05</td><td>2018/08/16</td><td>2019/08/01</td><td>2020/07/26</td><td>2021/08/10</td><td>2022/07/01</td></tr><tr><td>2017/10/10</td><td>2018/08/21</td><td>2019/08/11</td><td>2020/07/31</td><td>2021/08/20</td><td>2022/07/11</td></tr><tr><td>2017/10/25</td><td>2018/08/26</td><td>2019/08/16</td><td>2020/08/05</td><td>2021/08/25</td><td>2022/07/16</td></tr><tr><td>2017/10/30</td><td>2018/09/05</td><td>2019/08/31</td><td>2020/08/15</td><td>2021/08/30</td><td>2022/07/21</td></tr><tr><td>2017/11/14</td><td>2018/09/20</td><td>2019/09/05</td><td>2020/08/25</td><td>2021/09/19</td><td>2022/07/26</td></tr><tr><td>2017/11/19</td><td>2018/09/25</td><td>2019/09/15</td><td>2020/09/04</td><td>2021/09/24</td><td>2022/08/05</td></tr><tr><td>2017/11/29</td><td>2018/09/30</td><td>2019/10/15</td><td>2020/09/14</td><td>2021/09/29</td><td>2022/08/10</td></tr><tr><td>2017/12/09</td><td>2018/10/05</td><td>2019/10/25</td><td>2020/10/24</td><td>2021/10/09</td><td>2022/08/15</td></tr><tr><td>2017/12/19</td><td>2018/10/20</td><td>2019/11/04</td><td>2020/11/18</td><td>2021/10/14</td><td>2022/08/20</td></tr><tr><td>2017/12/24</td><td>2018/10/25</td><td>2019/12/29</td><td>2020/11/23</td><td>2021/11/18</td><td>2022/09/04</td></tr><tr><td>-</td><td>2018/11/24</td><td>-</td><td>2020/12/03</td><td>2021/11/28</td><td>2022/09/19</td></tr><tr><td>-</td><td>2018/12/04</td><td>-</td><td>2020/12/28</td><td>2021/12/18</td><td>2022/10/04</td></tr><tr><td>-</td><td>2018/12/14</td><td>-</td><td>-</td><td>-</td><td>2022/10/09</td></tr><tr><td>-</td><td>2018/12/24</td><td>-</td><td>-</td><td>-</td><td>2022/12/23</td></tr><tr><td>-</td><td>2018/12/29</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

images, and field expertise from the reserve managers (method detailed in Menu et al. [2021]).

We built the polygon dataset with a total of 12 images, 6 in 2017 and 6 in 2023, taken during the growing period (from February to August). The first step involved calculating the MSAVI2 index from the images, followed by computing the variance of the index for each pixel. Polygons were then delineated on the MSAVI2 variance images based on the following criteria: pixels with low MSAVI2 values and low variance were identified as water, while pixels with high MSAVI2 values and high variance were identified as being occupied by SAV. Once this process was completed, the reserve managers reviewed the polygons using their field expertise to identify any discrepancies. When uncertainties remained, field visits were conducted to verify the accuracy of the polygons. Additionally, false-color images were utilized to visually aid in polygon delineation.

The polygon dataset is composed of pixel features such as the band value in blue, green, red, and near infrared (B2, B3, B4, B8 bands), and the binary presence/absence of SAV, for a total of 7112 pixels.

We have divided the polygon dataset into two parts: 80 % of the dataset as a training part, and 20 % as a validation part.

We trained the random forest with the training dataset, for predicting presence or absence of SAV. We then validated the random forest prediction on the validation dataset (see Fig. 2 & the results section).

We also validated the model with a second, independent ground truth dataset. This dataset included a measure of local SAV coverage (between 0 and 100 %) at sampling points. We considered the best accuracy of our binary classification algorithm on the field observation dataset, for an approximated threshold of coverage above which the ground truth coverage of SAV would be classified as a presence by our algorithm. How the ground truth dataset was built is described in the next section.

## 2.4. Ground truth submerged aquatic vegetation (SAV) coverage survey

We undertook ground truth field surveys of the SAV coverage at 5 different dates (one in 2021 and 4 in 2023), of which 3 dates were informative enough to be used as ground truth validation points: 14th of June 2021, 23rd of May 2023, and 22th of June 2023. During these surveys, we estimated the coverage of SAV at 196 sampling points throughout the pond. We estimated the coverage semi quantitatively in 6 categories: 0 % (No SAV), 0–5 % (very little SAV), 5–25 % (small coverage), 25–50 % (intermediate coverage), 50–75 % (high coverage), and 75–100 % (saturated). To account for localisation uncertainty on GPS data during field surveys, we considered the majority class inside a 20m radius circle around each GPS position, as the precision of our GPS was 10m. Such buffers are regularly considered in other studies ground surveys (I-Sea, 2023). The ground truth sampling maps showing all the points at each date are provided in Fig. S1.

We then compared the coverage of SAV on these ground truth surveys to the occurrence of detected SAV on the classified satellite maps at the nearest date to the survey. Since the ground truth surveys data were semi-quantitative, while the classified maps were binary (presence/absence), we evaluated the accuracy of the random forest prediction for different thresholds of SAV coverage, and then we plotted the receiver operating characteristic curves (ROC) to determine the most accurate threshold to be used. A ROC curve is a graphical plot that illustrates the performance of a binary classifier model at varying threshold values. We simply selected the threshold that had the highest accuracy on the classified maps, with regard to the ROC curves results (See Results section).

## 2.5. Regular hydrology field survey

For conservation and monitoring purposes, reserve managers are
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Flowchart of the method. Flowchart of the data treatment, random forest training and validation, and the mapping of SAV based on Sentinel-2 data.</figcaption></figure>

conducting hydrological surveys every two weeks across the entire reserve, including entry and exit points for water. Some of these surveys are conducted at a central location within the pond (TB5-Centre). The parameters recorded included water temperature (°C), water level (in m on the NGF scale [IGN, 2023]), i.e. the average altitude compared to the mean sea level), salinity (in g/L), conductivity (mS/cm2), oxygen levels (dissolved oxygen in mg/L, saturation in %) (Fortuny [2022]). After conducting a Variance Inflation Factor analysis (VIF) described in the next section, some of these abiotic parameters were retained to be used as explanatory variables for statistical modelling of the dynamics of the SAV. Fig. 3 represents the full timeseries of abiotic parameters used in the models.

## 2.6. Statistical analysis

To model the effect of the abiotic parameters on the total area of SAV in the pond, we designed Generalized Linear Mixed Models (GLMM) with the response family beta. We used the beta family since our explained variable (the SAV area, expressed as a percentage of available pond area) does not follow a Gaussian distribution, but rather follows a continuous distribution varying between 0 and 1, with higher density on both extremes. The data distribution we have is comparable to a beta law with parameters $α < 1$ and $β < 1$ (Maclachlan [2011]).

The model represents the percentage of detected macrophyte area over the water area (the explained variable), as a function of the abiotic parameters described hereafter. The year was additionally included as a random effect variable.

We tested the co-linearity of the predictors by performing a Variation Inflation Factor (VIF) analysis (Fox and Monette [1992]). This consists in estimating how much the variance of an estimated regression coefficient is increased because of collinearity of the predictors. The values of VIF starts at 1 and has no upper limit. A value of 1 indicates there is no correlation between a given predictor variable and any other predictor variables in the model. A value > 5 indicates potentially misleading correlations among predictor variables in the model, suggesting to use only one among the correlated variables.

We then selected as predictors in the GLMM the variables with lowest VIF values, namely: water temperature, water salinity, water depth, and water oxygen percentage (Fig. S2). The predictors we excluded were water conductivity (highly correlated with the salinity) and the water oxygen saturation (highly correlated with the oxygen percentage).

To identify whether each explanatory variable should be integrated as a linear effect and/or as a quadratic effect, we compared the Akaike's Information Criterion (AIC) values of models for any combination of predictors and their squared values.

The Akaike's Information Criterion (AIC) is a metric used to estimate
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Timeseries of the abiotic parameters along with the timeseries of the detected SAV area. This is the timeseries of the abiotic parameters considered in the GLMM models, which are sampled every two weeks from January 2017 to December 2022 at the TB5-centre point by the reserve managers. The top graph represents the detected coverage of SAV. We can see that for most times where the water levels are high, the SAV coverage is still low, while when the water level decreases, the SAV coverage is reaching a peak.</figcaption></figure>

prediction error and assess the relative quality of statistical models for a given dataset (Burnham and Anderson, 2004). By comparing a collection of models, AIC evaluates the quality of each model, making it a valuable tool for model comparison and selection. The lower the AIC value is, the better the model fits the data with a parcimonious enough number of parameters. We hence selected two models, M1 and M2.

We call M1 the model with lowest AIC value, which included water temperature as a linear effect, both water depth and water salinity as a quadratic effect, and oxygen level as a linear effect (AIC = -225.30, lower bold line in Table 2).

The second model which we decided to keep (M2) was more parsimonious, considering all the variables above, with the only difference being the water level taken as a linear effect instead of a quadratic effect. It had an AIC value (AIC = -223.94, upper bold line in Table 2)) close to the best model (Burnham and Anderson, 2004).

There were clear inter-annual variations in the surface area of SAV (Fig. 3), which could be the consequence of environmental variations across years unexplained by the selected predictors. We therefore considered the year as a random effect in the statistical model.

We also tested the effect of each predictor independently from the others by making simple models in which we considered only one predictor at a time Table 3.

We performed all calculations in R. Raster manipulation was performed using the “rgdal” (Keitt et al. [2010]) and “raster” (Hijmans

<table><caption>**Table 2**
AIC scores comparison of the different GLMM models.</caption><thead><tr><th>Models</th><th>Df</th><th>AIC Score</th></tr></thead><tbody><tr><td>Water Temperature + Water Depth + Water Salinity + Water Oxygen + Year</td><td>7</td><td>-213.85</td></tr><tr><td>Water Temperature + Water Depth + (Water Salinity)<sup>2</sup> + Water Oxygen + Year</td><td>8</td><td>-223.94</td></tr><tr><td>Water Temperature + (Water Depth)<sup>2</sup> + Water Salinity + Water Oxygen + Year</td><td>8</td><td>-212.59</td></tr><tr><td>(Water Temperature)<sup>2</sup> + Water Depth + Water Salinity + Water Oxygen + Year</td><td>8</td><td>-211.86</td></tr><tr><td>Water Temperature + (Water Depth)<sup>2</sup> + (Water Salinity)<sup>2</sup> + Water Oxygen + Year</td><td>9</td><td>-225.30</td></tr><tr><td>(Water Temperature)<sup>2</sup> + Water Depth + (Water Salinity)<sup>2</sup> + Water Oxygen + Year</td><td>9</td><td>-224.19</td></tr><tr><td>(Water Temperature)<sup>2</sup> + (Water Depth)<sup>2</sup> + Water Salinity + Water Oxygen + Year</td><td>9</td><td>-210.67</td></tr><tr><td>(Water Temperature)<sup>2</sup> + (Water Depth)<sup>2</sup> + (Water Salinity)<sup>2</sup> + Water Oxygen + Year</td><td>10</td><td>-224.82</td></tr></tbody></table>

[2024]) packages. The “glmmTMB” package (Brooks et al. [2017]) was used to perform beta regression modeling with random effects. Statistical analyses were done using the base “stats” package in R, but also the “car” package for anova analysis.
<caption>**Table 3**</caption>

Fixed effects of abiotic parameters, each of which was considered independently in a simple model.

<table><thead><tr><th>Model</th><th>Estimate</th><th>Std.error</th><th>z value</th><th>p-value</th><th></th></tr></thead><tbody><tr><td>Only Water Temperature</td><td>0.15776</td><td>0.01689</td><td>9.342</td><td>&lt;2e-16</td><td>***</td></tr><tr><td>Only Water Depth</td><td>-2.5056</td><td>0.4654</td><td>-5.384</td><td>7.3e-08</td><td>***</td></tr><tr><td>Only Water Depth2</td><td>-8.0125</td><td>1.7698</td><td>-4.527</td><td>5.97e-06</td><td>***</td></tr><tr><td>Only Water Salinity2</td><td>-0.006512</td><td>0.001991</td><td>-3.270</td><td>0.001074</td><td>**</td></tr><tr><td>Only Water Oxygen</td><td>-0.20299</td><td>0.03848</td><td>-5.275</td><td>1.33e-07</td><td>***</td></tr></tbody></table>

## 2.7. Computation of SAV lifetimes on pixels

The lifetimes of SAV in local pixels over a year provided complementary information on local persistence dynamics.

The lifetime of a pixel during a given year was the longest consecutive period (in days) during which we detected SAV at this point, from March to March of the next year (to match the biological cycle of *Stuckenia*). We computed this value for any pixel of the pond and for every year. It gives crucial information on whether there are long-living or short-living SAV yearly in a pixel. This information is useful, as it provides an additional insight to quantify the inter-annual variability of the SAV, as well as the proportion of pixels with a given lifetime.

We analyzed the inter-annual variations in mean SAV lifetimes, using the Kruskall-Wallis nonparametric test. To statistically identify which year differed from others, we performed pairwise comparisons of all the years using the post hoc Dunn's test.

## 2.8. Time of exposure at a minimal water depth

To discuss the effect of the time exposure at certain conditions in water depth on the mean lifetime of the SAV, we computed the time duration in days during which the water level was over a threshold at the GB5 sampling point each year. We selected four water depth thresholds for analysis: 20, 25, 30, and 35 cm (NGF scale). These thresholds were chosen arbitrarily for exploratory purposes. A minimum depth of 35 cm was considered a suitable upper threshold, as field observations from the site managers indicate that the plant begins to struggle under such conditions. Similarly, a minimum depth of 20 cm was considered a reasonable lower threshold to explore. For each threshold modality, we calculated the number of days the water depth was over the threshold (explanatory variable) and correlated it to the mean lifetimes of the SAV.

# 3. Results

## 3.1. Random Forest validation (photointerpretated polygon data and ground truth data)

First, the Random Forest model was validated using the 1400 pixels contained in the validation part of the polygon dataset. The Random Forest algorithm had an accuracy of 99.29 %, with a [0.9869, 0.9966] 95 % confidence interval (Confusion matrix in (Table 4)).

Secondly, for the ground truth validation data, we obtained ~78 % accuracy when considering the 50–75 % coverage threshold (Fig. S3) and the ROC curve point corresponding to this threshold was also very distant from the diagonal line (Fig. S4).

We therefore could consider that our model had the best accuracy for this threshold. This means that a pixel classified as a presence on an

<caption>**Table 4**</caption>

Confusion matrix of the Random Forest algorithm on test data. Accuracy is 99.29 %.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Model</th></tr><tr><th>Absence</th><th>Présence</th></tr></thead><tbody><tr><td rowspan="2">Référence</td><td>Absence</td><td>947</td><td>7</td></tr><tr><td>Présence</td><td>3</td><td>443</td></tr></tbody></table>

image most likely corresponds to an above 50 %–75 % SAV coverage on the field.

## 3.2. Random Forest feature importance and classified time series

When computing the relative importance of features in the Random Forest, we observed major importance of NIR signal in the detection, followed by the red signal. Green and blue signals were of a relatively smaller importance [Fig. S5].

By applying the trained Random Forest on our complete time series of satellite images, we obtained a total of 187 classified maps of SAV. An example of a classified map at a given date is given in Fig. 4A (namely, May 27th, 2020). The frequency of occupancy of SAV in each pixel from January 2017 to December 2021 is shown in Fig. 4B. This second map gives the information of the regions in the pond where SAV is more often detected.

## 3.3. Submerged aquatic vegetation cover time series

Fig. 3 shows the time series of the area of detected SAV from the 1st of January 2017 to the 31st of December 2022. It shows the seasonal variation in total SAV area, with minimum values in winter and maximum values in late spring and across all summer, reflecting phenological variation in vegetative development of the perennial *Stuckenia pectinata*.

We found lower peak heights of *Stuckenia* cover at some years, which reflected a lower maximum occupancy area. We also visually observed differences in the month at which the SAV starts to be detected (mid-year in 2017, earlier in 2019). It is clear that some years could also differ by the duration of the maximum coverage, with long occupancy seasons of ~6–7 months in 2017 and 2018, vs. shorter seasons of ~4 months in 2019 and 2021.

## 3.4. Abiotic drivers of submerged aquatic vegetation cover variation

As mentioned in the Material and Methods, we decided to keep the two best GLMM models, namely M1 and M2, because their AIC were too close to conclude on a best model. We plotted the SAV coverage as a function of time together with the M1 prediction in Fig. 5.

For both models, we tested the influence of each fixed effect in the model by using the summary function in R, which performs a Student's test of the effect of each predictor.

In both models, we found a significant positive effect of water temperature on SAV coverage (M1: Estimate = 0.19237, p-value <0.05; M2: Estimate = 0.218276, p-value <0.05).

We also found a significant effect of water salinity with a quadratic response in the two models (M1: p-value <0.05; M2: p-value <0.05).

In M1, surprisingly, water depth effect on SAV coverage did not seem to be significant (M1: p-value >0.05). But when it was considered as a linear effect in M2, it showed a significant negative effect (M2: Estimate = -4.074181, p-value <0.05).

In both models, we found no significant effect of water oxygenation on the SAV coverage (M1: p-value >0.05; M2: p-value >0.05). Results of the models M1 and M2 are summarized respectively in Tables 5 and 6.

To test for the random effect of the year in each model, we compared the likelihoods of the models with (GLMM) and without the year as a
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Random forest classified maps of the detected SAV. (A) This is a classified single image of the main Bagnas pond, taken the 27th of May 2020. Areas occupied by seagrass are represented in green, areas with no detected seagrass are in white. Grey pixels are a mask to represent out of water vegetation (reed-beds), these pixels are not classified by the detection algorithm. (B) This is a cumulated image of the seagrass detection from January 2017 to December 2021. 2022 was excluded due to the different nature of the year (dry period). It gives a visual representation of the preferential locations where the SAV is growing over this time period. The red scale represent the number of images where SAV was detected for each pixel. The coordinates are provided in the Lambert93 projection, using meters as the unit of measurement.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Timeseries of the detected SAV coverage along with the prediction from the M1 GLMM model. The whole timeseries is represented, from Jan 2017 to Dec 2022. The detected SAV is plotted in green, while the prediction by the M1 model is represented by the cyan points. The cyan curve is a smoothed curve drawn from the predicted points.</figcaption></figure>

<table><caption>**Table 5**
Fixed effects of abiotic parameters in the M1 model.</caption><thead><tr><th>Variable</th><th>Estimate</th><th>Std.error</th><th>z value</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept)</td><td>-4.774258</td><td>1.421816</td><td>-3.358</td><td>0.000785 ***</td></tr><tr><td>Water Temperature</td><td>0.219579</td><td>0.021593</td><td>10.169</td><td>&lt;2e-16 ***</td></tr><tr><td>Water Depth</td><td>1.859025</td><td>3.453848</td><td>0.538</td><td>0.590406</td></tr><tr><td>Water Depth2</td><td>-6.968838</td><td>3.854147</td><td>-1.808</td><td>0.070585 .</td></tr><tr><td>Water Salinity</td><td>0.274470</td><td>0.113998</td><td>2.408</td><td>0.016055 *</td></tr><tr><td>Water Salinity2</td><td>-0.008618</td><td>0.002432</td><td>-3.544</td><td>0.000394 ***</td></tr><tr><td>Water Oxygen</td><td>0.056936</td><td>0.043040</td><td>1.323</td><td>0.185878</td></tr></tbody></table>

random effect (GLM). M1 and M2 were different from their no random effect counterparts (M1: p-value <0.05; M2: p-value <0.05), and both models had a better explanatory power when considering the random

<table><caption>**Table 6**
Fixed effects of abiotic parameters in the M2 model.</caption><thead><tr><th>Variable</th><th>Estimate</th><th>Std.error</th><th>z value</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept)</td><td>-3.021188</td><td>1.000081</td><td>-3.021</td><td>0.00252 **</td></tr><tr><td>Water Temperature</td><td>0.218276</td><td>0.021671</td><td>10.072</td><td>&lt;2e-16 ***</td></tr><tr><td>Water Depth</td><td>-4.074181</td><td>1.005815</td><td>-4.051</td><td>5.11e-05 ***</td></tr><tr><td>Water Salinity</td><td>0.153673</td><td>0.089916</td><td>1.709</td><td>0.08744 .</td></tr><tr><td>Water Salinity2</td><td>-0.007715</td><td>0.002385</td><td>-3.234</td><td>0.00122 **</td></tr><tr><td>Water Oxygen</td><td>0.033762</td><td>0.041399</td><td>0.816</td><td>0.41477</td></tr></tbody></table>

effect of the year (M1 with no random effect: AIC = -154.12, with random effect: AIC = -213.85; M2 with no random effect: AIC = -165.29, with random effect: AIC = -223.94) (Table 7). Each predictor had a significant effect on SAV coverage when taken separately in the
<table><caption>**Table 7**
Comparison of models with and without the random effect of the year.</caption><thead><tr><th>Model</th><th>Df</th><th>AIC</th><th>BIC</th><th>logLik</th><th>deviance</th><th>Chisq</th><th>p-value</th></tr></thead><tbody><tr><td>M1 without random effect</td><td>8</td><td>-166.07</td><td>-142.77</td><td>91.035</td><td>-182.07</td><td></td><td></td></tr><tr><td>M1 with random effect</td><td>9</td><td>-225.30</td><td>-199.09</td><td>121.650</td><td>-243.30</td><td>61.23</td><td>5.077e-15 ***</td></tr><tr><td>M2 without random effect</td><td>8</td><td>-165.29</td><td>-144.91</td><td>89.647</td><td>-179.29</td><td></td><td></td></tr><tr><td>M2 with random effect</td><td>9</td><td>-223.94</td><td>-200.64</td><td>119.972</td><td>-239.94</td><td>60.649</td><td>6.821e-15 ***</td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Boxplot of the SAV lifetime distribution for each year. This plot compares the distribution of SAV lifetime duration for every year. All the years represented have their mean lifetimes significantly different. Sample sizes are given on top of each column, and outliers are represented by the dots.</figcaption></figure>

model, even those that were not significant in M1 and M2. All the estimates and p-values of those simple models can be found in Table 3. This means that, although they aren't co-linear, some predictors could have some confounding effect when considered together.

### 3.5. Variation in SAV lifetimes between years

The violin plot in Fig. 6 represents the distribution in SAV lifetimes values across years. The thickest parts of the violins are the values of lifetimes we got most often. For each year, the mean lifetime is represented by a red cross. We tested for the annual differences in mean lifetime. All the years were significantly different from each other according to the Kruskal-Wallis non-parametric test (chi-squared = 43698,

p-value <0.05), followed by the post-hoc Dunn's test, in which all the p-values were <0.05. We summarized the results of the Dunn's test showing the differences between the years in Table 8. We also represented the lifetimes of the SAV for every pixel and every year, forming a synthetic map of the pond occupancy over every year (in Fig. S6). These maps are informative to understand visually if there are any visible suitability regions for SAV development in the pond. For example in 2020–2021, we observed darker green spots on the north part of the pond, indicating patches of longer lifetime there. For this period, although there were regions where the SAV did not develop at all (indicated by arrows), there was a clear lifetime difference between the north part and the south part of the pond.

<table><caption>**Table 8**
Results of the Dunn's test used to do a pairwise comparison of every year.</caption><thead><tr><th></th><th>2017–2018</th><th>2018–2019</th><th>2019–2020</th><th>2020–2021</th><th>2021–2022</th></tr></thead><tbody><tr><td>2018–2019</td><td>Z = 25.729<br>p < 2.2e-16</td><td></td><td></td><td></td><td></td></tr><tr><td>2019–2020</td><td>Z = 79.148<br>p < 2.2e-16</td><td>Z = 53.87<br>p < 2.2e-16</td><td></td><td></td><td></td></tr><tr><td>2020–2021</td><td>Z = 69.896<br>p < 2.2e-16</td><td>Z = 44.515<br>p < 2.2e-16</td><td>Z = -9.409<br>p < 2.2e-16</td><td></td><td></td></tr><tr><td>2021–2022</td><td>Z = 101.475<br>p < 2.2e-16</td><td>Z = 76.457<br>p < 2.2e-16</td><td>Z = 22.747<br>p < 2.2e-16</td><td>Z = 32.152<br>p < 2.2e-16</td><td></td></tr><tr><td>2022</td><td>Z = 189.074<br>p < 2.2e-16</td><td>Z = 164.753<br>p < 2.2e-16</td><td>Z = 110.9856<br>p < 2.2e-16</td><td>Z = 120.487<br>p < 2.2e-16</td><td>Z = 87.955<br>p < 2.2e-16</td></tr></tbody></table>
# 4. Discussion

The estimation and monitoring of SAV coverage are major objectives for conservation purposes. In this respect, study in shallow natural lagoon ecosystems such as the Bagnas reserve give the opportunity to have an in-depth understanding of their behaviour and variations. We have successfully investigated the variability of SAV coverage and found that there are variations both within and between years.

In this section, we discuss the reliability of our detection method and its validation, especially in relation with the specificities of the Bagnas ecosystem, and we discuss the relations found between SAV occupancy and abiotic factors. We also provide an overview of the perspectives that such studies can convey to better understand and manage shallow lagunary ecosystems.

## 4.1. Remote sensing detection and validation

The specificity of our study is that we used both validation on polygons, and on field (ground truth) data, to test the reliability of our classification procedure. In fact, the RF could be nicely calibrated on the polygon dataset (validation of 99 % accuracy), but proved less precise when validated on ground truth data (~78 % accuracy).

These accuracies need to be interpreted in a different manner. The random forest validation accuracy of 99 % suggest that our classification algorithm works very well on data extracted from polygons. It consists of an occurrence data to occurrence data comparison (binary to binary). We believe that this extremely good validation result is due to the photointerpreted nature of both the training data and the validation.

In the other hand, the ground truth dataset validation had two goals: The first goal was to find out the threshold of ground truth coverage of SAV for which the detection goes from negative to positive. It consists of a coverage data to occurrence data comparison (semi-quantitative to binary). We found that this threshold was most likely between 50 % and 75 % of ground truth SAV coverage. The second goal was to assess the accuracy at this threshold, and it was up to 78 %. We find a 78 % accuracy quite satisfying, since other studies using remote sensing to assess macrophytes distribution found similar or lower accuracy when comparing their classification to ground truth data (Traganos et al. [2022]).

In our case, this accuracy might be higher if we had available images at the exact date of the ground truth surveys. We have endeavored to use the available image closest to the survey date. However, even with the best possible correspondance, there could still be a time gap of up to 8 days between the ground truth survey and the image used. While gap-filling methods could have been applied to minimize this delay, these approaches can introduce additional challenges, such as selecting an appropriate interpolation model. The main difficulty in detecting SAV is their submerged nature at the start of the season, and for some macrophytes other than *Stuckenia*, this can persist throughout the entire season (Duffy et al. [2018]). High turbidity in the water column can further impede detection, particularly when the water column is large relative to the size of the SAV, making early-season detection especially challenging. We chose not to take into account the turbidity in the classification scheme of satellite data. In fact, in our case, it is difficult to identify and isolate this effect, considering that turbidity is very variable in space and can be caused by various factors (can be caused by bird foraging for example).

## 4.2. Limitations in the field measurements

This study has been made possible by the field measurement of several abiotic factors of primary importance, discarding other possible abiotic factors. In addition, the measurements were made generally at a few points in space. Though this is in contrast with the high spatial resolution of SAV data that can be collected through satellite image classification, we believe that our study of environmental driver effects

provides a first important step.

More specifically, we expected that water temperature and water depth are spatially homogeneous within the pond. Also, temperature is impacted by sun exposure, which is the same over the whole pond (no high vegetation close to the pond). Temperature can also be impacted by the presence of SAV, the more SAV coverage there is, the cooler the water temperature is. This phenomenon is likely to happen when *Stuckenia* shoots are floating and create a kind of “vegetal curtain” over the water surface, absorbing and reflecting the sunlight in the stages of greatest development, but variations in temperature are likely to be small. Water depth was also relatively homogeneous, given the flat topography of the Bagnas site. Our data for turbidity, consisting in a survey at only one point of the Bagnas is probably not informative enough, and cannot reflect the overall turbidity of the pond.

In contrast, salinity can be fairly heterogeneous in the pond, depending on the season, with variations of ± 4 g/L from west to east in the pond (assessed when we did the ground truth SAV coverage surveys). In this paper, we made the assumption that the salinity surveyed at the central point of the Bagnas pond was representative for the whole salinity in the pond, yet this could be improved by investigating the spatial variation of the abiotic parameters and their impact in greater details.

We did not measure nutrient concentrations in our study, although they can impact macrophyte development. In the Bagnas pond, the nutrients are likely to arrive from the north west, where the freshwater canal flows into the pond. Visually, we found that, indeed, the north-west of the pond seems to be a very suitable spot for the SAV to grow early in the season, and to maintain itself sometimes during the whole season, suggesting the importance of nutrients for suitability. It may be measured and tested in further studies. This north-west freshwater canal also brings water for longer periods in times of droughts, which also increase the suitability of this spot of the pond.

In addition, the nature and composition of sediment, although not studied in our case, can play a significant role in the distribution of *Stuckenia*. In some studies it is generally found that S. prefers muddy substrates to sandy substrates. On site we found muddy substrate over most of the pond, and a sandy substrate in the compartments to the west, where SAV growth was in fact more limited. The quantity and availability of nutrients in the sediments and the historical structures of the lagoon resulting from salt production (salt tables, rock formations, etc.) may also partly explain the spatial distribution and dynamics of the SAV.

## 4.3. Environmental drivers of SAV development in shallow lagunary ecosystems

Our study shows that SAV development increases linearly with water temperature. This suggests that in the conditions studied here the maximal physiological limit that impedes SAV development was not attained yet.

Conversely, water salinity has a significant quadratic effect in both studied models, with a peak of SAV cover at 16 g/L in M1 and at 10 g/L in M2. We know that *Stuckenia* tolerates a wide range of salinity concentration in the water (typically between 0 and 10 g/L). Here, we find an optimum in both cases. Note that salinity is also correlated to water level, which implies that the lower the water level, the higher the salt concentration in the water.

The water depth alone was not a significant predictor when considered as a quadratic effect (in M1), however it had a significant negative effect when considered linear (in M2). This negative effect could seem counterintuitive, since in shallow waters, the plant needs a minimum amount of water depth to grow. These results show that SAV coverage is indeed more important when water is low, corresponding to times of the year with high development of the SAV, hot temperatures, and lower water level. Hence, the underlying mechanism relating water level and SAV coverage is more indirect. The SAV starts growing earlier in the year, at times where temperatures are not very high and where water
level is higher. In other words, the SAV early growth is delayed compared to the water height. This becomes evident when examining Fig. 3: the water height increases early in the season, yet no SAV is detected during this period. It is only after the water height reaches its peak that SAV begins to increase.

With this result in mind, identifying the factors influencing SAV lifetimes becomes a rather subtle task. It may be beneficial to analyze water levels in a more temporal context. For instance, a metric such as the duration for which the SAV remains submerged below a certain water height threshold each year could potentially provide a more meaningful measure.

By using a single value of water exposure per year, we shifted our focus from the full distribution of SAV lifetimes to their average annual lifetime. We explored potential correlations between these quantities and found significant correlations between the mean lifetimes of SAV and the 30 cm and 35 cm water depth thresholds (Figure S7 and Figure S8). This would suggest that maintaining a water depth greater than 30 cm or 35 cm for extended periods positively impacts SAV lifetimes in the Bagnas (both p-values <0.05). In contrast, no significant correlations were observed for the 20 cm and 25 cm thresholds.

However, with only six data points, interpreting the correlation remains difficult, even if it is significant for two water exposure thresholds. The correlation coefficient may be heavily influenced by the low mean lifetime observed in 2022, so we refrain from drawing any definitive conclusions.

If this result could be replicated in future studies or across additional years, it would suggest that *Stuckenia* requires a critical water depth for a specific duration to thrive. This period of submersion likely varies between years, partially influencing the lifetimes of SAV in the pond.

Concerning the effect of oxygen, it was not significant in the models considered. The oxygen rate is more likely to be an indicator of the development phase of the SAV, however with a temporal shift with SAV development. The photosynthetic activity of *Stuckenia* makes the water oxygen level rise until mid-late season, and then the organic matter decomposition takes the oxygen levels down again (oxydation of organic matter). So the oxygen levels measured in the water is more likely to be considered as a consequence of the photosynthetic activity of the SAV (Silva et al. [2009]). Furthermore, oxygen concentration is largely dependent on other factors such as water temperature (related to the season), the wind, eutrophication level (conditioning phytoplankton development, but also the development of light competitors like microalgea (Waycott et al. [2009]). This makes the understanding of oxygen rates with respect to SAV coverage a very complex issue.

An other important point related to *Stuckenia* and the modeling of its dynamic is that the plant seems to be sensitive to early season parameters for rapid development (Spencer [1986]). This can be observed in the coverage profiles, oscillating quickly between two states: either the pond is detected mostly empty of SAV, or almost full. This is the main reason why we choose to use the beta law in the GLMM models, which can depict such data distribution. Although in this work, we considered the impact of the parameters to be the same across the whole season, a complementary approach would be to have two separate models: one for the early season growing period, and one for the mid season to the declining phase.

In a rather speculative way, one can also think about the possible relation between turbidity and SAV coverage. An interesting direct effect turbidity can have on *Stuckenia* is that, in some cases where the turbidity rises, the light exposure decreases, and the plant reacts by allocating more resources in shoot elongation as an adaptive response (Spencer [1986]). In such cases, the detection of SAV might increase faster than expected, depending on the strength of the adaptive response from the plant. On the classified time series, we observed that there could be a very rapid increase in detected SAV. Sometimes the SAV coverage went from intermediate value to full coverage from one image to the next during the growth period. Alternatively, this rapid increase could be more likely explained by the fact that *Stuckenia* can show very

high productivity under favourable conditions. In fact, some studies have described a productivity up to 16g/m2/day of organic matter in some environments (HowardWilliams et al. [1986], Purohit and Singh [1987]). Therefore, within the period between two images -sometimes 15 to 30 days-, many pixels would likely reach the detection threshold.

## 5. Conclusion and perspectives

Understanding the seasonal variability of SAV coverage is crucial for effective management of the Bagnas lagoon ecosystem.

In this paper we studied the relationship between the detected SAV and the abiotic factors most likely impacting the SAV distribution and we have analyzed the statistical determinants for the spatio-temporal variation of the occupation of *Stuckenia pectinata* in the Bagnas pond. The collection of this data via remote sensing detection and classification over 6 years has allowed us to answer the questions addressed in the Introduction. We studied which abiotic parameters had a significant effect on the SAV coverage in such shallow lagoon ecosystems. We found that temperature, salinity and water depth are the main determinants of SAV growth in the Bagnas pond.

We saw that SAV coverage seasonal variation closely follows seasonal variation of the environmental parameters. Perturbation of these seasonal parameters might impact the SAV drastically, as observed in 2022, when a significant heatwave event occurred in southern France, causing the pond to dry up unusually early in the season. Despite *Stuckenia*'s well-documented rapid growth capabilities, the SAV did not have sufficient time to fully colonize the pond that year. Finally, we detected clear inter-annual variation in the SAV mean lifetimes.

Several lines of research to further use and improve our detection and modelling approach can be proposed. The first one would be to apply on other wetland types where *Stuckenia* populations are generally found, keeping in mind that different depth and turbidity in other contexts are known to be significant issues. If water depth is too large in such other wetland types, the method needs to be adapted by, at least, estimating and taking into account the water attenuation coefficient (Dekker et al., 2006).

This method could also be generalized and applied to the detection of other macrophytes. A potential limitation here is the re-calibration of the Random Forest algorithm for the detection of the new target species, as the spectral signature of other macrophyte species will likely be different from *Stuckenia*'s.

As a consequence of the influence of abiotic factors on SAV distribution, some macrophytes are known to be bio-indicators of the ecosystem's health. With the model presented in this paper, we have the possibility to predict the SAV coverage, based on the significant parameters we identified. The water depth is directly regulated by the reserve managers, making this parameter the easiest to control. The managers also have an hydrological model to predict the water salinity. Water temperature could be predicted using the air temperature, hours of enlightenment, and wind related parameters. With a simultaneous estimate of these 3 parameters at any date, we could predict the associated SAV total area over the pond, allowing improved monitoring and management of the reserve.

Another complementary approach would be to make assumptions on the actual biological and ecological processes underlying the dynamics of the SAV in such an environment, which would be responsible for the spatio-temporal distribution of *Stuckenia* and the patterns we observe on the time series. In further studies we could use a combination of some already existing models to create a more complex, hybrid, mechanistic model. We could use cellular automata, which might be relevant to the grid nature of images. To model both small distance propagation and long distance colonization, we could use diffusion models taken from physics (Ruiz-Reynés et al. [2020]). Such mechanistic models would allow us to understand the SAV dynamics based on causal factors rather than solely statistical effects.
CRediT authorship contribution statement

**A. Bernard:** Writing – original draft, Methodology, Investigation, Data curation. **S. Alleaume:** Methodology. **X. Fortuny:** Investigation. **F. Munoz:** Writing – original draft, Methodology, Conceptualization. **E. Pitard:** Writing – original draft, Methodology, Conceptualization.

Declaration of competing interest

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

Acknowledgments

This project was supported by the LabEx NUMEV (ANR-10-LABX-0020) within the I-Site MUSE (A.B, F.M., E.P.) and by Region Occitanie (A.B.). Guillaume Papuga and Francesca Rossi are acknowledged for fruitful discussions.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecss.2025.109261.

Data availability

Data will be made available on request.

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