Elodie Puybareau
email: elodie.puybareau@esiee.fr

Diane Genest
email: dgenest@rd.loreal.com

Emilie Barbeau

Marc Léonard

Hugues Talbot

An automated assay for the assessment of cardiac arrest in fish embryo

Keywords: Toxicology, medaka, segmentation, image stabilization, change detection, connected filtering

Studies on fish embryo models are widely developed in research. They are used in several research field such as drug discovery or environmental toxicology.

In this article, we propose an entirely automated assay to detect cardiac arrest in Medaka (Oryzias latipes) based on image analysis. We propose a multi-scale pipeline based on mathematical morphology. Starting from video sequences of entire wells in 24-well plates, we focus on the embryo, detect its heart, and ascertain whether or not the heart is beating based on intensity variation analysis.

Our image analysis pipeline only uses commonly available operators. It has a low computational cost, allowing analysis at the same rate as acquisition. From an initial dataset of 3,192 videos, 660 were discarded as unusable (20.7%), 655 of them correctly so (99.25%) and only 5 incorrectly so (0.75%). The 2,532 remaining videos were used for our test. On these, 45 errors were made, leading to a success rate of 98.23%.

Introduction

The use of fish embryo model as a potential alternative to laboratory mammalian species is increasing in a variety of physiological applications [START_REF] Embry | The fish embryo toxicity test as an animal alternative method in hazard and risk assessment and scientific research[END_REF] [START_REF] Nagel | Dart -the embryotest with the zebrafish danio rerio -a general model in ecotoxicology and toxicology[END_REF].

In compliance with international animal welfare regulations, fish embryo models provide ethically acceptable systems having the complexity of a complete organism. More precisely, embryonic development in fish continues after hatching through the eleutheroembryo stage. At this stage, the energetic supply to the developing organism is provided by the yolk sac. The transition to the larval stage starts with the onset of exogenous feeding. [START_REF] Balon | Types of feeding in the ontogeny of fishes and the life history model[END_REF][START_REF] Belanger | Natural thresholds in the early ontogeny of fishes related to definitions of protected and non-protected life intervals[END_REF]. Eggs (before hatching) and eleutheroembryos do not meet the European regulatory definition of animals used for scientific purposes [5] and are therefore considered an alternative to (adult) animal testing [START_REF] Embry | The fish embryo toxicity test as an animal alternative method in hazard and risk assessment and scientific research[END_REF][START_REF] Halder | Regulatory aspects on the use of fish embryos in environmental toxicology[END_REF][START_REF] Strähle | Zebrafish embryos as an alternative to animal experiments -a commentary on the definition of the onset of protected life stages in animal welfare regulations[END_REF]. However, evaluating effects of chemical compounds requires many measurements. Hundreds of measurements have to be conducted for each compound, with thousands of these chemicals being evaluated every year in the chemical, pharmaceutical, and cosmetic industries. These measurements had to be processed manually. Some can be done semi-automatically, but these approaches are not satisfying [START_REF] Schindelin | Fiji: an open-source platform for biological-image analysis[END_REF].

Image processing has been widely used in conjunction with fish studies, for instance for sizing and aging analysis [START_REF] Ruff | Fish sizing and monitoring using a stereo image analysis system applied to fish farming[END_REF][START_REF] Morison | An integrated system for production fish aging: image analysis and quality assurance[END_REF], species recognition [START_REF] Strachan | Fish species recognition by shape analysis of images[END_REF], automated counting [START_REF] Toh | Automated fish counting using image processing[END_REF], behavior assessment [START_REF] Cahill | Circadian rhythmicity in the locomotor activity of larval zebrafish[END_REF][START_REF] Kato | A computer image processing system for quantification of zebrafish behavior[END_REF], and more recently for enabling micro-injections in fish embryo [START_REF] Wang | A fully automated robotic system for microinjection of zebrafish embryos[END_REF]. Recent progress was achieved in automating image procedures analysis on Medaka embryos [START_REF] Oxendine | Adapting the medaka embryo assay to a high-throughput approach for developmental toxicity testing[END_REF] and more commonly with Zebrafish embryos [START_REF] Liu | Computerized image analysis for quantitative neuronal phenotyping in zebrafish[END_REF][START_REF] Xia | Computational techniques in zebrafish image processing and analysis[END_REF][START_REF] Pylatiuk | Automatic zebrafish heartbeat detection and analysis for zebrafish embryos[END_REF].

Fish embryos (Medaka and Zebrafish) are transparent and their cardiovascular system is readily visible. At these early stages of development, cardiac arrest does not induce an immediate death due to the blood gas exchanges that occur through skin diffusion [START_REF] Jacob | Influence of hypoxia and of hypoxemia on the development of cardiac activity in zebrafish larvae[END_REF][START_REF]Gills are needed for ionoregulation before they are needed for o2 uptake in developing zebrafish, danio rerio[END_REF]. Nonetheless, we will refer to our cardiac arrest detection assay as a mortality test, since it can be considered as a prediction of mortality. Here we describe an automated image-processing pipeline to detect a beating heart with minimal human interaction, maximum speed, and reliability. The proposed procedure improves on a previous feasibility study [START_REF] Puybareau | An automated assay for the evaluation of mortality in fish embryo[END_REF], which had some limitations. In particular, it required a significant workload involving gel preparation and the manual positioning of embryos on the support gel. In addition, the number of plates used was limited due to a moving platform. In the procedure we will be describing in this article, freshly spawned eggs are placed in a liquid incubation medium, where they remain for 9 days, with automatic medium renewal every 3 days. This means having to conduct an image analysis of the eggs both unhatched and hatched in a liquid medium. This procedure involves using a fixed platform and a moving camera.

The image analysis pipeline that we propose is based on mathematical morphology [START_REF] Serra | Image Analysis and Mathematical Morphology -Volume II : Theoretical Advances[END_REF][START_REF] Soille | Morphological Image Analysis, principles and applications[END_REF][START_REF]Mathematical Morphology: from theory to applications[END_REF]. In the next section, we report the difficulties and challenges encountered in this study and their proposed solutions.

Problem statement and challenges

Our procedure is part of a complex process for detecting morphological and functional abnormalities in fish embryos. This endeavor imposes some experimental constraints: we have to deal with both eggs and hatched eggs at the analysis level, and the embryos need to be anaesthetized during morphological analysis. Because this process is intended to be fully automated, a differentiating procedure between eggs and hatched eggs must be developed. We refer to the "hatched eggs" as alevins. We use the designation embryo to refer to eggs and alevins simultaneously. Medaka hearts normally beat at a frequency of around 130 beats per minute (bpm) i.e 2.2Hz [START_REF] Puybareau | Automated heart rate estimation in fish embryo[END_REF]. However, this can vary by between 0 to 300 bpm, i.e 0-5Hz, in extreme cases. To avoid incorrect measurements, our recordings must be made using a frame rate that is high enough for our purposes. Our current camera records one second long videos at 30 Hz, corresponding to a Nyquist cutoff frequency of 15Hz, which is sufficient. Recorded videos are 1500×1500 pixels in size, covering the whole well.

Because the incubation medium is liquid, undesirable motion may be present during acquisition. Embryos may also slide to the edge of each well, render-ing them partially or totally invisible. To minimize this, the platform is fixed and we use a moving camera. The quantity of liquid in the well is also carefully adjusted, so that the embryo moves as infrequently as possible once it is placed in the well. Incomplete or otherwise corrupted videos may occasionally be acquired. Shadows and undesirable objects are also a risk. These unusable sequences must be identified at the start of processing.

Even with the above precautions and even if all of the embryos were to remain immobile under anesthesia, some residual movement is still possible. Such motion may be caused by involuntary reflex swimming, or it may be induced by vibrations and shocks in the lab environment, which are easily transmitted by the liquid in the well and on the embryo, inducing false positives if not corrected on dead embryos. In particular, the eyes can cause significant difficulties as they appear as the darkest part of the embryos' bodies and are not transparent . Consequently, we chose variance measures in the embryo's body excluding the eyes to detect heartbeats. However, artifactual motion caused by even the slightest uncompensated frame motion may induce artifacts of high variance and generate false positives. Because the heart is close to the eyes, this detection needs to be carefully handled. Moreover, in egg form, embryos are wounded and the eyes can superimpose with the heart, making the detection of a heartbeat impossible. More generally, eggs have different optical characteristics compared to alevins. This is why it is important to determine whether a well contains an egg or an alevin so that the processing procedures can be adjusted accordingly.

During the nine-day incubation period in the chemical compound under study, the medium is regularly changed. At the nine-day mark, a fixed quantity of medium is removed from the well so that only 0.5mL of liquid remains during image acquisition. The medium may still contain impurities or the chorion if the egg hatched during incubation. The real embryo must be carefully distinguished from these impurities during image processing.

Materials and Methods

Organism

Medaka (Oryzias latipes) develops quickly (hatching normally occurs between 7 and 9 days post fertilization). In our study, embryos were procured from Amagen (UMS 3504 CNRS / UMS 1364 INRA). The eggs are incubated for 9 days at a temperature of 28 • C.

Computer and software

We used the Python 2.7 environment under Windows 7 (64 bits) in an HP computer with a 3.60GHz Intel R Core TM i7-4790 CPU and 32 GB of RAM.

We used Numpy, Scipy, and Scikit-image [START_REF] Van Der Walt | scikit-image: image processing in python[END_REF], Python Imaging Library (PIL), Pink [START_REF] Couprie | The pink image processing library[END_REF], and Open CV for Python [START_REF] Bradski | The opencv library[END_REF]. Platform control and image data acquisition were performed using FEI Visilog 7.

Hardware

During the incubation period, medium replacements were performed with an Hamilton MICROLAB STAR automated device. All videos were acquired using a monochrome camera (reference Basler Camera monochrome CCD 1" 2330*1750p 31 images/s Camera Link Mount C) and an acquisition platform from FEI.

Acquisition protocol

Fish eggs are manually placed in a 24-well plate, one embryo per well, immediately after spawning on day 0. Each well contains 2mL of incubation medium, together with a predetermined concentration of the water-soluble compound under study. Embryos are incubated in this medium in their respective well until day 9, when image acquisition is performed. Medium replacements are automatically performed on days 2, 5 and 7. On day 9, we remove 1.5mL of the incubation medium from each well and the fish embryos are anesthetized with 70 µL of tricaine (1 g/L) in a total volume of 0.57mL. The final tricaine concentration is 0.1g/L. The plate is placed under acquisition platform and recording is automatically performed under the control of a Visilog Visual Basic script.

For each well, 30 uncompressed video frames at a resolution of 1500×1500 pixels are recorded over a duration of 1 s.

Due to storage constraints, sequence analysis cannot be performed offline. This means that it must take place in parallel to sequence acquisition. This imposes a maximum computation time of about 10 seconds. This limits the image analysis pipeline to simple, fast yet robust operators, preferably welltested and found in off-the-shelf image processing software packages.. On Fig. 1 we depict the flowchart of our assay. It consists of two steps: a stabilization and denoising phase, followed by a processing phase. The latter detects periodic changes in embryos associated with a beating heart. Videos are read as raw data interpreted as grey-level values.

Image analysis solution

In this section, we present our notations and image processing operators, mostly from mathematical morphology [START_REF] Serra | Image analysis and mathematical morphology[END_REF]. Table 1 summarizes these. Let I be a M × N pixels grey level image, taking 8-bit discrete values, i.e. I :

[1, N ] × [1, M ] → Z ∩ [0, 255]. B i is a disk structuring element of radius i;
ε Bi (I) is the erosion of I by the structuring element B i . δ Bi (I) is the adjunct dilation [START_REF]Mathematical Morphology: from theory to applications[END_REF], γ Bi (I) and ϕ Bi (I) are respectively the morphological opening and closing [START_REF] Heijmans | Morphological image operators[END_REF]. We denote γ α λ (X) the area opening of X with parameter λ [START_REF] Vincent | Morphological area openings and closings for grey-scale images[END_REF].

We also denote (I) ≥θ the binary, thresholded image of I above value θ, i.e.:

x

∈ [1, N ] × [1, M ], (I) ≥θ =    1 if I(x) ≥ θ 0 if I(x) < θ (1)
G σ (I) Zero-mean Gaussian of variance σ G σ (p) = 1 σ √ 2π exp -p 2 2σ 2 DoG σ (I) Derivative of Gaussian at variance σ ∇ G σ (I) [36] v 2 (Euclidean) norm of vector v = (v x , v y ) v 2 x + v 2 y
We now present the notion of radial opening [START_REF] Soille | Directional morphological filtering[END_REF]. Let ρ τ ϑ be a line segment of length τ and orientation ϑ. The radial opening γ ρ τ is the algebraic opening obtained by taking the supremum (i.e. the pointwise maximum) of all the openings γ ρ τ ϑ using ρ τ ϑ as structuring element, with ϑ varying between 0 and π.

γ ρ τ (I) = ϑ∈[0,π] γ ρ τ ϑ (I) (2)
Intuitively, this opening preserves all structures in the image that can contain a segment of length τ in any direction. When I is a binary image, the supremum operator reduces to the set union ∪.

We introduce the notation S l to refer to a sequence l of n images. We note 140 S l i the frame i of this sequence. For clarity reasons, we write F l = S l 0 the first frame of the sequence S l .

Video quality control and detection of unusable videos

We begin by determining which videos present important and undesirable changes during the sequence. These changes may be due to the presence of black frames, shadows or large uncontrolled motion. For this, on each difference d i between two successive frames of the video sequence S 0 , we compute the statistical variance (V i ):

∀i ∈ [1, 29], d i = S 0 i -S 0 i+1 ( 3
)
V i = variance(d i ) (4)
In the case of a correctly recorded video, two successive frames should be very similar, and their pixelwise difference yields a near-zero output, so its 145 variance remains small. On the contrary, if a large motion appears on a frame, the difference will show a high contrast. If at least one of all computed variances is higher than the experimentally determined threshold (set to 30), the video sequence is deemed unusable.

Segmentation of the well and selection of a region of interest 150

Embryo segmentation is crucial for several reasons. For speed and reduced memory usage, we crop the area of interest to a small window centered on the embryo. During this step, we also detect sequences where the embryo is not fully visible, i.e. too close to the edges of the well. Moreover, motion stabilization must be performed on the embryo itself, and not on other elements of the field of view. We first isolate the region of interest by finding the disk area corresponding to the inner part of the well. This step also removes all objects connected to the edges of the well. The procedure for finding the area of this disk is as follows: edges of the disk appear dark, so we first compute a so-called bottom-hat filter: see Fig. 2(a) and Eq. ( 5) for a definition. In this equation, B 20 is chosen to remove small artifacts in the well. This yields image A 0 (Fig. 2(b)), which we binarize via an automated thresholding operation to obtain image A 1 in Fig. 2(c). The automated thresholding procedure is as follow: we determine the optimal threshold following the Otsu criterion, i.e. the one that maximizes the inter-class variance between two classes, foreground and background [START_REF] Otsu | A threshold selection method from gray-level histograms[END_REF]), with the added constraint that the area of the foreground must be in the interval [20,000; 40,000] pixels. This interval is experimentally determined to guarantee that the edges of the well are present in the foreground. This constraint is convex and so easily implemented: we consider all thresholds in order from the highest to the lowest in order. The foreground area necessarily increases during this process. In the acceptable foreground area interval for the area of the foreground, we select the threshold with the highest Otsu criterion. We call this threshold θ co (for constrained-Otsu).

A 0 = ϕ B20 (F 0 ) -F 0 (5) A 1 = (A 0 ) ≥θco (6)
We remove small components from the well with an area opening γ α 100 of parameter λ = 100, followed by a closing with a ball B 40 to reconstruct fragmented edges of the well. Then a radial opening γ ρ 100 with linear element ρ of length τ = 100 is applied to remove short artifacts from the well, while retaining the thin well borders [START_REF] Soille | Directional morphological filtering[END_REF]. This yields image A 2 :

(a) (b) (c)
A 2 = γ ρ 100 (ϕ B40 (γ α 100 (A 1 ))) (7)
From this result shown on Fig. 3(a), we only want to keep the internal ring that represents the separation between the interior of the well and its edges.

For this, we use a well-established morphological approach to segmentation, based on the Watershed transform. This operator is intuitively defined as in hydrology, in a grayscale image whose intensity can be assimilated to a 3D 155 terrain, as delimiting the borders between adjacent catchment basins [START_REF] Meyer | Morphological segmentation[END_REF]. We compute the magnitude of the Derivative of the Derivative of Gaussian filter (DoG): DoG σ = ∇ G σ using the Deriche recursive implementation of the gradient operator for speed [START_REF] Deriche | Using canny's criteria to derive a recursively implemented optimal edge detector[END_REF] with parameter σ = 10 and we use a markersbased Watershed algorithm [START_REF] Cousty | Watershed cuts: minimum spanning forests and the drop of water principle[END_REF] on the magnitude of this gradient. A disk at 160 the center of the frame is taken as internal marker m 1 int and the frame corners are the external marker m 1 ext . We write:

A 3 = watershed( DoG 10 (A 2 ) , m 1 int , m 1 ext ) (8)
The resulting contour is shown on Fig. 3(b). The result may be incorrect if the embryo is too close to the edge of the well. To avoid this, we expand the

D = (x, y), (x -a) 2 + (y -b) 2 ≤ R 2 (9)
with (a, b) = barycenter(G 4 ) and R = max(width(G 4 ), height(G 4 )) 2

Localization of the embryo in the well

Our image analysis procedure is intended to work for both alevins and eggs, but some eggs do not develop at all and differ markedly from healthy eggs and alevins (see Fig. 4(a) and (c)). They feature low contrast, which makes them look like empty chorions or impurities that can develop in the wells. An early pipeline challenge is to reliably detect and identify the embryo in each well.

To achieve this, we begin by performing an initial segmentation adapted to all components in the well, whatever their level of intensity or variance. In the previously calculated bottom-hat image A 0 (see Fig. 2(b)), all components of interest are easy to classify as connected components located strictly inside the segmented well. We call h the contrast significance, understood as the intensity variation that connected components must have to be considered significant [START_REF] Soille | Morphological Image Analysis, principles and applications[END_REF].

The value h is experimentally set to ignore the irrelevant intensity variations due to noise, while still detecting the dimmest components that cannot be ignored (i.e. the undeveloped eggs). We call p i peak the local maximum of intensity in the neighborhood of the pixel p i . Image B 1 contains the so-called h-maxima of A 0 ∩ D, defined as follows:

B 1 =    p i ∈ A 0 ∩ D with p i =   0 if (p i peak -p i ) > h p i if (p i peak -p i ) ≤ h    (10)
The image B 1 of the h-maxima can be efficiently computed using a morphological reconstruction operator, as explained in [START_REF] Vincent | Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms[END_REF]. Several components can be detected in the resulting frame B 1 . These components may be embryos, empty chorions, or some type of impurity. To identify the embryo, we use several criteria: presence of eyes, minimal and average intensities, variance, and circularity. A component is considered to have an eye if an extremely dark spot, representing less than a quarter of its total area, is present. Since impurities are generally homogeneous, this procedure filters out dark impurities, that are uniformly dark, and light impurities and chorions that are evenly bright. However, it can also filter out under-developed eggs. To avoid this problem, we add further classification criteria: a high average intensity or a low variance. We also verify circularity to differentiate between undeveloped eggs, chorions, and bright impurities. Finally, this process enables us to classify components as either "under-developed eggs," "other embryos," or "impurities and chorions." We delete components identified as impurities or chorions. There must only be one embryo per well. Therefore, if several components classified as "under-developped eggs" remain after this step, only the largest is kept. If sev-eral components of the other classes remain, we only keep the largest component among those from the "other embryo" class. Indeed, we have experimentally found that it is more difficult to distinguish under-developed eggs from chorions than other embryos from impurities. Thus the probability of making a mistake from the "undeveloped egg" class is higher. The result M 0 is a binary mask (with values in {0,1}) containing only one component expected to locate the embryo in the well (see its red contour in Figure 4(a,b)). An empty result occurs when an embryo intersects the edges of the well. In this case, the corresponding sequence cannot be analyzed correctly. If an embryo is found, we crop the mask M 0 and the sequence by defining a bounding box around the segmentation dilated by B 20 , a disk of diameter 20 pixel. This results in a new sequence denoted S 1 centered on the embryo (see Fig. 4(c)). However, because of contrast variations and the large variability of grey levels between embryos, the mask M 0 is only approximate. In particular, for alevins, it delimits a rough area with the tail included (see Figure 4(b)) and potentially contains some shadows and impurities if they are too close to the embryo. For the purpose of heartbeat detection, we need to restrict the search field to the thorax region.

Differentiation between eggs and hatched embryos

Because they have different optical properties, it is necessary to identify the embryo type for further processing. The differentiation step is based on the morphological analysis of the embryo contours previously detected. The previous segmentation provides a reliable localization of the embryo, but only a rough approximation of its contours (see Fig. 5(a) and (b)), so these need to be refined. For this, we consider the first frame F 1 of the cropped sequence S 1 . In particular, it is crucial to weed out potential shadows and impurities, which may have been segmented with the embryo, while retaining the tail segmentation for the alevins. We apply the same bottom-hat procedure as in Eq. (5) of section 4.2 to eliminate the background. Then we experimentally define an adaptive threshold slightly above the average pixel intensity near the border of the cropped frame. For our images, with a 8-bit depth, an increment of 5 was experimentally determined as appropriate: θ = average(F 1 ) + 5. We obtain a binary image, whose small components are filtered out with an area opening with parameter λ = 5. We apply the morphological gradient of an image I defined as grad M (I) ≡ δ B1 (I) -ε B1 (I) to the resulting image, to obtain image

C 1 . C 1 = grad M (γ α 5 ((ϕ B40 (F 1 ) -F 1 ) ≥θ )) (11)
In order to properly extract the contours of the embryo without confusing them with residual artifacts that may still be present in the background, we again use a markers-based watershed methodology on the image C 1 , as follows:

m 2 int = skeleton(γ B8 (ε B15 (M 0 ))) (12)
M 1 = watershed(C 1 , m 2 int , m 2 ext ) (13)
The image's outline is set as the external marker m 2 ext and the ultimate binary skeleton [START_REF] Kresch | Skeleton-based morphological coding of binary images[END_REF] of the eroded and opened mask M 0 is set as the internal marker m 2 int . The erosion and the opening are respectively performed with a radius-15 and a radius-8 disk, in order to remove potential thin impurities linked to the previous embryo segmentation.

The outline of the resulting binary mask M 1 is shown in Fig. 5(c) and(d).

We now use the shape of M 1 , to differentiate between the eggs and the alevins. Eggs are highly circular, so we can use the classical circularity attribute:

circ = 4πA P 2 ( 14
)
Where A is the area and P is the perimeter of the binary shape under study.

This ratio is at most equal to 1 for a disk, and decreases as the elongation becomes more pronounced. However, it is still possible for some alevins to be so tightly wound that their associated binary shape presents a high circularity.

Eggs also possess hairs on their chorion that may reduce the circular aspect of their associated mask. To correctly differentiate between both cases, we also consider two other criteria. We have experimentally determined that a healthy well-segmented egg has a radius of around 60 pixels. Therefore, allowing for some margin of error, we apply a morphological opening γ B40 that deletes the alevins' mask, as alevins are much thinner than eggs. If the component under study is filtered out during this step, it is considered to be an alevin. If it is not, we determine the minimum enclosing disk of the mask and we calculate the area difference d between this disk and the mask M 1 . Indeed, since the eggs' hairs are uniformly distributed on the chorion, the difference between the mask area and the area of its minimum enclosing disk is higher for hatched alevins than for eggs. Below an experimentally determined threshold of 3,000 pixels, we consider the component under study to be an egg. Otherwise, we conclude that it is an alevin.

Segmentation of the thorax of hatched embryos

For the purpose of heartbeat detection, it is essential to restrict the region of interest to the thorax, in order to minimize the probability of false detection due to electronic noise or blood flow in the bright tail regions. Therefore, after the differentiation step, we refine the embryo segmentation in the case of alevins. Alevins are darker than the background and their eyes, in particular, are very dark. They are fairly easy to segment as a large connected component associated with the darkest minima in the thorax region. We apply the same threshold process as in Eq. ( 6). Because we have experimentally determined that the minimal area of an alevin's thoracic region is approximately 600 square 265 pixels, we apply a morphological area opening using the criterion λ = 100 to eliminate small components. Moreover, we limit the thorax region to the M 0 , by computing the intersection.

M 2 = M 0 ∩ (γ α 100 ((F 0 ) ≤θoc )) (15)
The result M 2 is a new binary mask representing the alevin's thorax (see Fig. 6(b)). We crop the sequence and obtain a new sequence S 2 centered on 270 this area (Fig. 6(c)). These remained unchanged for egg sequences and M 2 and S 2 are respectively equivalent to M 1 and S 1 .

Registration

Even when anesthetized and subject to vibration isolation, embryos may still move slightly during acquisition. In order to eliminate false positives, all sequences need to be stabilized. For efficiency, we chose a keypoint based methodology, specifically using SIFT [START_REF] Lowe | Object recognition from local scale-invariant features[END_REF]. SIFT detects and matches pairs of significant points P 1 and P 2 between pairs of frames. This allows us to solve the equation for rigid transformations:

P 1 = P 2 × R + T. ( 16
)
Here T = (d x , d y ) is the translation vector and R is the rotation matrix.

Since embryos do not deform significantly, it is sufficient to consider this class of transforms. Our model can select between translation-rotations and translationonly transformations. Model selection is a useful feature, because simpler models are usually more robust. Here we distinguish between pure translation (where R is the identity matrix) and translation-rotation by computing the sum of square difference between the two model outputs. If they do not differ significantly, we choose the simpler model. This latter outcome is the more frequent in our experiments. Pure rotation never occured in our experiments so we do not consider that model. Since impurities may be present in the well, movement is often visible around the embryo. Thus, in order to stabilize the sequence with respect to the embryo and not the other moving components in the well, we ensure that key-points in the embryo only are selected, restricting key-points to the mask M 2 . Taking the first frame of the sequence as reference, the selected model transform is applied to all the following frames. In order that the whole stabilized sequence be of constant width and height, we consider the bounding box of the sequence, and crop it by the maximum displacement in both x and y, which are respectively max(|d x |) and max(|d y |). We call S 3 the stabilized and cropped sequence of the embryo.

Denoising

Depending on the illumination conditions, sequence S 3 may be more or less degraded by noise. We use a bilateral filter [START_REF] Tomasi | Bilateral filtering for gray and color images[END_REF] in the 2D+t domain to reduce noise. We can interpret the bilateral filter as a neighborhood-dependent convolution. At each pixel (i, j) belonging to a window W , the filtered frame I D is given by:

I D (i, j, t) = 1 (k,l,m)∈W w(i, j, t, k, l, m) (k,l,m)∈W I(k, l, m) × w(i, j, t, k, l, m) (17)
Where

w(i, j, t, k, l, m) = exp(- ((i -k) 2 + (j -l) 2 + (t -m) 2 ) 2σ 2 d - I(i, j, t) -I(k, l, m) 2 ) 2σ 2 r ) (18)
In this formulat, I is the original input image. Depending on parameters, this filtering could be too strong and could cause the heartbeat to become un-295 detectable. Experimentally, the best parameters for removing the noise without altering the heartbeat are: window size = 3×3×3, σ r = 0.5 , σ d = 0.6. The outcome of this process is a restored sequence S 4 . Because the bilateral filter is not as effective on the borders of the sequence, is it preferable to remove them. This implies that we lose the first and the last frames, and so S 4 is only 28-frames 300 long.

Segmentation of the inner parts of the embryo

To ascertain the presence of a heartbeat in the thorax region, we look for cyclic motion in this region only. To prevent false motion detection in unrelated areas, we compute a mask M 3 corresponding to the region of interest. This eliminates areas most subject to noise, such that the eyes. We define D 1 as the sequential average image of the sequence S 4 (see Table 1):

D 1 = seq average(S 4 ) ( 19
)
Blood causes the heart and vessel to appear darker, so they are easy to segment as a large connected component associated with the darkest minima, simultaneously maximizing the inter-class variance.

D 2 = (D 1 ) ≤(θo+θc) (20)
Here θ o is obtained using the Otsu criterion. Because the heart and vessels are thin compared with the rest of the body, we need to bias the threshold to encompass a larger region, so we add a constant θ c to the Ostu threshold, experimentally optimized to 20. Here D 2 is a binary mask, corresponding to the body of the alevin, including the eyes. We detect the eyes in D 2 fairly simply, as they are large, obvious minima inside this region. We cannot assume that they eyes are separated but we do know their minimum size. The equation yielding M 3 is:

(a) (b) (c) (d)
M 3 = ε B1 (γ B3 ((D 2 .D 1 ) ≥(θo-θ d ) )) (21)
In this equation, θ o is again the Otsu optimal threshold, which depends on the distribution of grey-levels within D 2 . Because we want to bias the threshold nearer to the eyes, which are very dark, we subtract an experimentally optimized constant θ d , which turns out to be equal to 20 as well, from θ o . The outline of the resulting mask M 3 in alevins is exemplified in Fig. 7(a,b). This procedure is used only on alevins in order to restrict the region of interest to detect heartbeats. It is not suitable for eggs, due to the folded aspect of the embryo. For these, we compute M 3 using the same procedure for segmenting the eyes but considering M 2 as the geodesic mask. (Fig. 7(c,d)).

Elimination of spurious, non-cyclic motion

So far we have assumed that heartbeats can be associated with significant variations of grey-levels in the thorax region of the alevin. For this, we estimate a time-wise, grey-level variance at every location in this region. However, a significant variance during a sequence may be also due to a single, large, spurious motion instead of a regular, periodic heartbeat. To distinguish between these two cases, we split S 4 into four equal length sub-sequences. Each subsequence is 7-frames long, which is enough to record a typical, single heartbeat.

(a) (b) (c) (d)
The sub-sequences are called S 4 i , i ∈ {1 . . . 4}. We now consider the sequential variance image V i = seq variance(S 4 i ), i ∈ {1 . . . 4} and the sequential median V = seq variance {V i }. We see if that a single, large, spurious motion occurs during the entire sequence, then only one of the V i will record high values. The median V of the V i will still be low. In contrast, if a regular, signicant variations occurs in the majority of the V i , then V will have high values that we assume to be due to periodic motion. The result is shown on figure 8.

S 4 = S 4 1 ∪ S 4 2 ∪ S 4 3 ∪ S 4 4 (22)
∀i ∈ [START_REF] Embry | The fish embryo toxicity test as an animal alternative method in hazard and risk assessment and scientific research[END_REF][START_REF] Belanger | Natural thresholds in the early ontogeny of fishes related to definitions of protected and non-protected life intervals[END_REF],

V i = seq variance(S 4 i ) (23)
E 1 = M 3 . seq median({V i , i ∈ [1, 4]}) (24)

Segmentation of cyclic motion areas

We obtain a binary image of E 1 via a small closing, a threshold by the scalar median value µ of all strictly positive variances present in E 1 , and a small area opening.

(a) µ = median(E 1 ) ( 25)

(b) (c) (d)
H 1 = γ α 4 (ϕ B1 (E 1 ) ≥µ ) (26)
Because the optical properties of eggs are not the same as those of alevins, we notice more residual cyclic motion due to noise in the case of eggs. Therefore, we add an area opening with λ=8 only for eggs. If H 1 contains only zero-valued pixels, the embryo is considered dead. Otherwise it is assumed to be alive (see 335 Fig. 9).

Detection of cyclic motion associated with the mouth

Some alevins have no detectable heartbeat in the sequence due to significant pigmentation in the thorax region. However, sometimes cyclic mouth motion induced by natural reflex demonstrates that the alevin is alive. To allow for these specific cases, we recommend the detection of cyclic mouth motion. Once again, we apply our cyclic motion detection and segmentation based on variance, but this time on the inverted mask M 3 . If an area of cyclic motion is detected (M mouth ), we estimate the distance between each component in this area and the alevin's eyes (M eyes ), by superposing both corresponding masks:

G 1 =
M eyes ∪ M mouth , then performing a dilation of G 1 by a radius-14 ball:

G 2 = δ B14 (G 1
). If a single area remains, the area of cyclic motion is close enough to the eyes to be the mouth. In terms of implementation, we keep the component of G 2 that contains the eyes, which we call G 3 , and check to see if it also contains a component exhibiting cyclic motion (see Fig. 10).

(a) (b) (c) (d)
G 4 = G 3 .G 1 (27)
If the number of components in G 4 is higher than the number of eye components, we consider it a cyclic mouth motion area. An alevin with no heartbeat detected but mouth motion present is considered to be alive.

Validation and Discussion

In this section, we present the results obtained using a total of 3,192 videos, 2,532 of which were actually usable to test for heartbeat detection. We begin describing the establishment of ground truth in part 5.1, before moving on to discuss processing. Our results are presented in part 5.3. We tackle the problem of remaining limitations in part 5.4 and finally discuss about the quality of our validation method in part 5.5.

Ground truth establishment

In the context of our test validation, two possible types of ground truth exist:

observations under a microscope and those directly acquired on videos. Each present different advantages and drawbacks. The strongest way of assessing the quality of the complete embryo analysis process, including plate preparation, data acquisition, and data treatment, is to compare our results to the observations of embryos under a microscope. On the other hand, the automated method we developed works on video sequences whose information may be much different from the observations made under microscope. Several aspects linked to the experimental protocol or the acquisition method can explain this fact. (i)

Video quality is such that some weak heart beats may be undetectable on video even if they are visible under a microscope. (ii) Observations made while using a microscope also depend on operator fatigue and subjectivity. (iii) Because there is a time gap between the observations made under microscope and videos acquisition, an embryo may also die during the interval. (iv) Observations made under microscope facilitate scrutiny of the heart since embryos can be moved to a favorable position, whereas in videos the embryo's posture may obscure the heart. For these reasons, it appears that the most relevant way to assess the program's quality (one of the goals of this article) is through direct video observation.

In the context of our study, both possible ground truths were considered. In the table 2 expert (named "expert 0") originally observed the embryos under microscope before the total set of 3,192 videos were acquired. We refer to these observations as "Dataset 0". The expert identified each case as a living or dead embryo by checking for the presence of a heartbeat. After that, another expert ("expert 1 ") analyzed the resulting 3,192 acquired videos ("Dataset 1"). He began manually assessing the usability of the videos, by checking that they were complete, wellrecorded, that the well was not empty, and that the embryo was not too close to the well boundary. In this manner, 655 unusable videos were identified.

Then, for the remaining 2,537 usable sequences only, this expert determined if the embryo was alive or dead. However, whereas determining videos usability is easy and thus reliable, detecting a beating heart is sometimes difficult and therefore subject to errors. For this reason, we selected a subset of only 200 usable videos ("Dataset 2") so that the health status of the embryo could be reassessed by two other independent observers ("experts 2 and 3"). In the end, three different observers were involved in analyzing the 200 usable data subset.

Because the experts' observations pertaining to Dataset 2 were not always identical, a consensus was then reached between these three observers concerning the videos that they assessed differently. The 200 data resulting from this consensus represent the ground truth we use to validate our automated method, as explained in the following section 5.3.

Processing

Our heartbeat detection method returns three possible results: "unusable," "alive," or "dead." This method processes a sequence in approximately 10 seconds, in accordance with our initial constraints. All parameters were hand optimized using a training sample of 100 sequences.

Validation of the method

Using the 200 Dataset 2 videos, we can now compare the results of our program with the previously established consensus data. We consider the program to be erroneous if it detects a dead embryo that was identified as alive according to expert consensus (false negative) or if, on the other hand, it detects a living embryo that was identified as dead by expert consensus (false positive). We detected 3 errors made by the program for a corresponding error rate of 1.5%.

(see Table 3). Moreover, this error rate only corresponds to false negatives. In toxicity tests, this is a more acceptable type of error since it does not provide a false sense of security with regard to the molecule tested. In Table 3(

Limitations and further optimization

In some cases, even embryos that are dead may appear to move. This may be caused by movement in the water, fluttering, shadows or embryo rotation inside the well (see Fig. 11 for an illustration). In dark areas, acquisition noise is proportionnally more troublesome [START_REF] Talbot | Efficient poisson denoising for photography[END_REF], and may be confused with cyclic motion. Artefact motion may be present if we do not correctly stabilize the initial sequence. This may happen if motion in the original sequence is too large or too rapid or both, which is infrequent. Finally, the heart may beat too slowly or weakly. In these cases, it would be difficult for a human to detect it also.

Conversely, because embryo health status is assessed under the microscope before videos acquisition, an embryo may be identified as alive during microscopic analysis and die during the time gap. To avoid this, we plan to assess the embryo twice in our future studies: both before and after video analysis so that heart failure can be detected.

Qualitative assessment of the validation method

We tested our procedure on a total set of 3,192 videos acquired over several experimental runs. This is a significant number of videos containing healthy as well as diseased embryos: some with edemas and other malformations. Thus, this set reflects production usage and allows us to validate the robustness of our protocol (see Fig. 12). The purpose of this article is to present and assess an image analysis pipeline for detecting a beating heart on 1 second long videos of fish embryos. Our results on 2,532 usable sequences show an acceptably low error rate, near 1.5% overall. This proves the efficacy and reliability of our image analysis method.

However, when considering its integration within the entire system of embryos preparation, image acquisition, and processing, several points remain to be discussed, especially with respect to the validation phase and the establishment of ground truth.

Since we are discussing living organisms, establishing ground truth is not always easy. We rely on multiple visual observations of a subset of video sequences, which were not always consistent: expert observers did not always come to same conclusion. Indeed, we noticed 6 differences between them for 200 assessed videos, a rate of 3%. A second viewing of these videos was consequently performed with all observers present to achieve a consensus. With respect to this consensus, each observer had made between 3 and 5 errors, a rate between 1.5% and 2.5%. We note that our program had also made 3 errors. We conclude that the rate of assessment subjectivity is near 1.5%. Such a rate is acceptable, because experiments may easily be repeated three times.

Assuming errors are random and independent, the final error rate on repeated experiments is negligible (2.25 × 10 -4 ).

However, this low error rate is based on video-based ground truth and is only representative of the quality of the program itself. To assess the reliability of the entire procedure, including preparation, acquisition, and treatment, we need to establish ground truth by observing embryos under a microscope. Nevertheless, as we explained in part 5.1, we still face video quality and accuracy issues with the current acquisition procedure. For example, when comparing the manual health status determination under microscope on Dataset 0 and the videos on Dataset 1, we noticed a discrepancy in 282 cases, for a rate of 11%. To improve upon this, we are investigating the use of the VAST system: http://www.unionbio.com/vast/, which currently is only available for Zebrafish, not for Medaka.

Many sequences (20.5%) are correctly detected by the program as unusable.

Some of them are due to an empty well, and so are not an issue, but the majority are due to embryos being too close to the well boundary. This represents an actual problem for the efficiency of the global procedure. To solve it, we are currently experimenting with wells that have a rounded, rather than flat, bottom. These are not widely available commercially and are not compatible with our Hamilton MICROLAB automated filling system. However, they are compatible with our acquisition device, and would solve the problem of embryos that are too close to the edges of the well. We hope that with some experimental protocol adjustments, we will be able to use them in production.

In spite of these issues, our current pipeline is used in production at L'Oreal.

We are confident that we will eventually solve all of the experimental problems discussed and reach an error rate comparable to that of human observers using a microscope under optimal conditions.

Conclusion

In this article, we have detailed an image analysis pipeline for detecting the living status of a Medaka embryo. This is achieved by locating the area around its heart and determining if significant, periodic grey-level variations in this area, which are assumed to correspond to a heartbeat. We have optimized the parameters of the proposed procedure on a small subset of 100 sequences. A large set of 2,537 videos was used as a test set, with these parameters fixed. By comparison to multiple human observers, we have round that the error rate of the video analysis is only 1.77%, which is similar to the error rate human observers have between themselves. This error rate is sufficiently low for production use.

Plans for future work include improvement of the experimental protocol so that the optimal viewing position for automated image analysis is attained and experimental errors are avoided. Our proposed pipeline is in production usage at L'Oreal.

Figure 2 :

 2 Figure 2: Bottom-hat application. (a) is the frame F 0 before bottom-hat, (b) is the result A 0 and (c) is the subsequent thresholded image A 1 .

Figure 3 :

 3 Figure 3: Segmentation of the inner part of the well. (a) is before watershed algorithm application (image A 2 ); (b) is the convex hull result (image A 3 ); (c) is the result of convex hull (image A 4 ) and (d) is the outline of final result D superimposed on F 0 .

Figure 4 :

 4 Figure 4: Segmentation of the well and location of the embryo. (a) and (b) show the outlines of D and M 0 on the initial frame F 0 . (c) and (d) show the first frames of cropped sequences S 1 .

Figure 5 :

 5 Figure 5: Segmentation of the embryo. (a,b) are the outline of the mask M 0 (before adjustment) ; (c,d) are those of M 1 (after adjustment).

Figure 6 :

 6 Figure 6: Segmentation of the initial frame to locate the thorax of the alevin. (a) is the initial frame F 0 , (b) the thorax mask M 2 and (c) the first frame of S 2

Figure 7 :

 7 Figure 7: Segmentation of the inner parts of two alevins and two eggs. (a) and (c) are alive whereas (b) and (d) are dead.

Figure 8 :

 8 Figure 8: False color rending of the temporal variance. (a) and (c) are for the living embryos in Fig.7(a,c); (b) and (d) for the dead ones in Fig.7(b,d).

Figure 9 :

 9 Figure 9: Segmentation of cyclic motion detection in embryos. (a) and (c) are for the living embryos in Fig.8(a,c); (b) and (d) are for the dead embryos in Fig.8(b,d).

Figure 10 :

 10 Figure 10: Distance assessment between areas of cyclic motion and alevin's eyes. (a) represents the superposed eyes and areas of cyclic motion masks G 1 , (b) shows the dilation G 2 , (c) is the component of G 3 which contains Meyes and (d) is the result G 4 .

  b), we present the error rates that were calculated for each expert as compared to the final consensus based on Dataset 2. Expert 1, who processed the 3,192 videos (Dataset 1), has a similarity rate of 98.5% with respect to the consensus data. Consequently, the experts' observations can be considered sufficiently reliable to analyze the results of the entire program. The results of this analysis (program vs. expert 1) are described below. Out of the initial 3,192 Dataset 1 videos, the program correctly flagged 655 as unusable and incorrectly flagged another 5 as unusable due to some error within the program itself. If we consider the entire set, 3,187 videos were correctly

Figure 11 :

 11 Figure 11: Incorrect segmentations due to fluttering.

Figure 12 :

 12 Figure 12: Heart segmentation in the presence of malformations. (a) is in the presence of edema and (b) in the presence of axial malformation.

Table 1 :

 1 Table of mathematical notations.

		Acquisition
		Quality test
		of the video sequence
	Segmentation of the embryo : location in the well	Segmentation of the well	On low image resolution
	Differentiation (egg or alevin)	If alevin : adjustment Segmentation	Pre-processing
	Registration	Frames cropping
	Denoising	Segmentation of the inner parts of the embryo
		Variation estimations in
		the mask of
		the inner parts
		of the embryo
			Cyclic motion
			detection
		Segmentation of cyclic
		motion areas
	If A=0: the embryo is	Calculation of A, the area	If A>0: the embryo is
	considered dead	of the cyclic regions	considered alive
	Figure 1: Flowchart of the embryo mortality image processing assay
		7

  summarizes the establishment of ground truth datasets. Our first

		Dataset 0	Dataset 1	Dataset 2
	Screening method	Microscope	Videos	Videos
	Dataset size	3192	3192	200
	Experts in charge	Expert 0	Expert 1	Experts 1, 2
	of the analysis*			and 3
	Classification	"Alive" or "Dead"	"Unusable",	"Alive" or "Dead"
	labels used		"Alive" or "Dead"

* Each expert screened the entire dataset.

Table 2 :

 2 Establishment of ground truth datasets. For Dataset 2, a consensus was reached between the three experts and a final set of 200 ground truth data was obtained.

Acknowledgment

We thank Noémie De Croze and Mylene Rialland from the L'Oréal environmental research team in Aulnay sous Bois for their expertise, the material provided, and their essential contribution during this project.

flagged, leading to a success rate of 99.85%. The remaining 2,532 videos were used for mortality test validation. There were 45 errors in this set, for an error 415 rate of 1.77%. Such an error rate is low and can be considered satisfactory.

We noticed that 11 of these 45 errors were due to embryos that had died a long time before acquisition and had consequently absorbed the blue marker.

These embryos appear very dark on the video and are therefore more affected by noise, which was incorrectly labeled as periodic motion. This is something 420 we can improve in a future version of our software pipeline.


Charlotte R Dromard
email: charlotte.dromard@univ-antilles.fr

Damien A Devault

Yolande Bouchon-Navaro

Jean-Pierre Allenou

Hélène Budzinski
email: helene.budzinski@u-bordeaux.fr

Sébastien Cordonnier

Nathalie Tapie

Lionel Reynal

Soazig Lemoine

Jean-Pierre Thomé

Jean-Pierre Allénou

Emmanuel Thouard
email: emmanuel.thouard@ifremer.fr

Dominique Monti

Claude Bouchon

Environmental fate of chlordecone in coastal habitats: recent studies conducted in Guadeloupe and Martinique (Lesser Antilles)

Keywords: organochlorine pollution, desorption, allophane, zooplankton, trophic food webs, bioamplification

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The organochlorine pollution by chlordecone, an insecticide spread in the past in banana plantations, is now recognized as a major ecological, economic and social crisis in the French West Indies. Used between 1972 and1993 in fields of Guadeloupe and Martinique, chlordecone (commercialized as Kepone® then Curlone®) has been globally banned by the Stockholm Convention on Persistent Pollutant since 2009. This molecule is responsible for toxicological consequences such as an increased risk of prostate cancer, motor skills delays, an increased risk of preterm birth, endocrine disruptions and reproduction impairments [START_REF] Epstein | Kepone -Hazard evaluation[END_REF][START_REF] Multigner | Chlordecone exposure and risk of prostate cancer[END_REF][START_REF] Boucher | Exposure to an organochlorine pesticide (chlordecone) and development of 18-month-old infants[END_REF][START_REF] Kadhel | Chlordecone exposure, length of gestation, and risk of preterm birth[END_REF][START_REF] Multigner | Chlordecone exposure and adverse effects in French West Indies populations[END_REF].

Due to its physical and chemical properties, this hydrophobic molecule has an affinity with soil organic matter (partition coefficient Koc = 2500 l.kg -1 , Howard et al. 1981). Chlordecone has a low volatility (vapour pressure = 2.5 x 10 -5 mm Hg), presents high thermodynamic stability and resistance to chemical or biological degradation, involving a high persistence in the environment [START_REF] Cabidoche | Long-term pollution by chlordecone of tropical volcanic soils in the French West Indies: A simple leaching model accounts for current residue[END_REF]). Consequently, it remains in the soil before degradation into at least 5b-hydrochlordecone [START_REF] Devault | Natural transformation of chlordecone into 5b-hydrochlordecone in French West Indies soils: statistical evidence for investigating long-term persistence of organic pollutants[END_REF]. The time to reach the depollution level (at the threshold of quantification of 10 μg.kg -1 ) depends on the nature of the soils: a few decades for nitisol, two to three centuries for ferralsol, five to six for andosol [START_REF] Cabidoche | Long-term pollution by chlordecone of tropical volcanic soils in the French West Indies: A simple leaching model accounts for current residue[END_REF].

Andosols contain amorphous clays, called allophanes, issued from the transformation of volcanic materials with very specific properties. Allophanes present drastically different structures and physical properties compared to usual clays, that is a very high poral volume and an important pores surface. Hygroscopic water contributed to allophanes spherical form, leading them to stack in a three-dimensional fractal labyrinth in which chlordecone could be trapped [START_REF] Woignier | Allophane: A natural gel in volcanic soils with interesting environmental properties[END_REF]. Due to its trapping ability, allophanes allow chlordecone to reaches runoff and ground waters, and finally end up in the sea. Nitisols are tropical and subtropical deep (iron oxides dominate), red, well-drained soils with clay content of more than 30%. Clays are more aged than andosols, i.e.

water content is less important and clays present flatten structure compared to andosols. Nitisols are well-drained type of ferralsol, which are commonly low-draining and muddy due to the high clay content, and more aged than andosols. Andosols and nitisols are particularly abundant in the south of Basse Terre (Guadeloupe) and in the north of Martinique, in the areas where banana plantations occur [START_REF] Sierra | Les sols de la Guadeloupe-Génèse, distribution et propriétés[END_REF]. Indeed, in Guadeloupe, approximately 11,400 ha present a high risk of contamination that is 90% of probability to find chlordecone in these areas. This area represents 25% of the land surface used for agriculture. In Martinique, this value reaches 40% of agricultural lands (Direction de l'Alimentation, de l'Agriculture et de la Forêt, data 2018).

Since 2003, several studies conducted in the Lesser Antilles have highlighted the presence of a contamination by chlordecone in soil [START_REF] Cabidoche | Long-term pollution by chlordecone of tropical volcanic soils in the French West Indies: A simple leaching model accounts for current residue[END_REF], vegetables [START_REF] Clostre | Organochlorine (chlordecone) uptake by root vegetables[END_REF], aquatic and marine organisms [START_REF] Coat | Contamination of some aquatic species with the organochlorine pesticide chlordecone in Martinique[END_REF][START_REF] Bertrand | Characterization of the contamination of the halieutic fauna by chlordecone around Guadeloupe (surveys 2008 to 2011)[END_REF][START_REF] Coat | Organochlorine pollution in tropical rivers (Guadeloupe): role of ecological factors in food web bioaccumulation[END_REF][START_REF] Bouchon | Level of contamination by metallic trace elements and organic molecules in the seagrass beds of Guadeloupe Island[END_REF][START_REF] Dromard | Assessment of the contamination of marine fauna by chlordecone in Guadeloupe and Martinique (Lesser Antilles)[END_REF][START_REF] Monti | Contamination of freshwater fauna[END_REF][START_REF] Méndez-Fernandez | From banana fields to the deep blue: assessment of chlordecone contamination of oceanic cetaceans on the eastern Caribbean[END_REF]. However, few studies have been conducted on the fate of chlordecone between agricultural fields and marine ecosystems, especially when it arrives in estuarine environments. The studies on Kepone cycling in aquatic environments has been principally conducted in Virginia, because the original production of Kepone began in Hopewell. In 1975, it was discovered that the Kepone factory had not only exposed workers, but also severely contaminated the James River estuary [START_REF] Nichols | Sedimentologic fate and cycling of Kepone in an estuarine system: example from the James River estuary[END_REF][START_REF] Luellen | Kepone in James River fish: 1976-2002[END_REF].

Chlordecone desorption in the James River estuary has been studied by [START_REF] Nichols | Sedimentologic fate and cycling of Kepone in an estuarine system: example from the James River estuary[END_REF] who showed that chlordecone stayed sorbed to organic particles when arriving in estuarine environment in the range of pH (7)(8) and salinity (0.006 to 19.5). To our knowledge, chlordecone desorption has never been studied in the Lesser Antilles that present a particular context due to their tropical pedoclimatic conditions. Our first hypothesis is that chlordecone could be trapped by allophanes, due to their physical structure, and stayed sorbed during its transfer to the marine environment.

Along the James River and at the mouth of this river, contamination of aquatic fauna by chlordecone has been highlighted, starting with plankton, the first link in the trophic food web. Zooplankton is broadly highly impacted by this organochlorine pollution [START_REF] Bahner | Kepone® Bioconcentration, accumulation, loss and transfer through estuarine food chains[END_REF][START_REF] Luellen | Kepone in James River fish: 1976-2002[END_REF]. Our second hypothesis is that zooplankton could represent a major way of transfer between chlordecone from the water column (adsorbed on terrestrial particles or dissolved) and others organisms from the trophic food-web.

Finally, some studies demonstrated that chlordecone passes through the different levels of the food chain and shows accumulation phenomena, like many other organochlorine pollutants [START_REF] Bahner | Kepone® Bioconcentration, accumulation, loss and transfer through estuarine food chains[END_REF]. In Guadeloupe and Martinique, the degree of contamination of marine organisms depends mostly on their location around the two islands [START_REF] Dromard | Assessment of the contamination of marine fauna by chlordecone in Guadeloupe and Martinique (Lesser Antilles)[END_REF]. Fish and crustacean are generally more contaminated when they are located downstream the contaminated watersheds and when they are close to the coast [START_REF] Dromard | Contamination of marine fauna by chlordecone in Guadeloupe: evidence of a seaward decreasing gradient[END_REF]. At the same time, detrivorous and carnivorous organisms generally display higher levels of chlordecone contamination than other trophic groups [START_REF] Luellen | Kepone in James River fish: 1976-2002[END_REF][START_REF] Dromard | Contamination of marine fauna by chlordecone in Guadeloupe: evidence of a seaward decreasing gradient[END_REF]. Our third hypothesis is that chlordecone concentrates in marine organisms, depending on their location (close or far from the source of pollution) and on their feeding ecology.

The principal objective of the present study is to compile the results of recent studies on the environmental fate of chlordecone from estuaries to the marine ecosystems in the Lesser Antilles, with two main objectives: 1) to study chlordecone desorption as its arrival in the marine environment, 2) to evaluate the transfer of chlordecone in marine trophic food web, from plants to top predators. To do so, three studies recently conducted in Guadeloupe and Martinique are presented in the present work.

Material and Methods

Experimental protocol to measure chlordecone desorption on allophanes

A selected allophanic soil (6.1% w.w.) historically contaminated was sampled in Martinique and sieved on 2 mm-mesh stainless steel sieve. Samples of 1.5 g of soil has been incubated at dark and moderately stirred in glass vial during 24h in artificial sea salted water at 0, 10, 20 and 35 psu. Artificial seawater results from a mixture of Vittel® mineral water and coral reef "Instant ocean" sea salt from Aquarium Systems®. Each experiment was performed in triplicates.

In order to estimate chlordecone adsorption on the glass of the vials, three positive controls were implemented with Vittel water mixed with chlordecone up to a concentration of 0.1 g.l -1 .

After incubation, suspended soils were filtered on Solid Phase Micro-Extraction (SPME) glass fiber filter (PDMS 100 μm Merlin): soil was lyophilized, then extracted in a microwave extractor (4 ml of dichloromethane, 30 watts, 10 minutes) and filtered again on a glass fiber filter in order to collect the organic extract.

The latter was partially evaporated under 70 % agitation and 51 % vacuum (900 mb), using a Rapidvap provided by Labocongo®, then reconditioned and fully evaporated under nitrogen stream. Extracts were reconstituted in 1 ml of acetonitrile and kept frozen until analysis.

Internal 13 C chlordecone was added to filtered water (about 150 ml) then liquid-liquid extracted with 3 x 10 ml of dichloromethane. Extracts, dried on Na 2 SO 4 , were partially evaporated under 70 % agitation and 51 % vacuum (900 mb) then reconditioned and fully evaporated under nitrogen stream. Extracts were reconstituted in 1 ml of acetonitrile and kept frozen until analysis. Extraction recovery was respectively of 106 ± 6 % and 111 %.

For this experiment, solvents (dichloromethane, acetonitrile) were at least of analytical grade and provided by ICS (Belin-Beliet®, France). Certified solid standards for chlordecone were obtained from Cluzeau Info Labo.

Concentrations of chlordecone in water and soil were measured by liquid chromatography (Agilent series 1200) using in tandem a mass spectrometer detector (Agilent® 6410a) that was provided by Agilent (Santa Clara, California, USA).

Samplings protocol to evaluate zooplankton contamination

Samples of zooplankton and seawater were collected at the mouth of two rivers in Guadeloupe (Rivière Grande Anse and Rivière du Grand Carbet) and two rivers in Martinique (Rivière Monsieur and Rivière Rouge) in December 2010. A second sampling campaign was done in Guadeloupe in May 2011. These four rivers have been chosen due to their high level of contamination by chlordecone. Samplings were conducted along three transects oriented from the coast to the open sea, using a 500 m-meshed net for the plankton and glass bottles for seawater. On each site, nine stations were chosen to sample zooplankton and seawater (n = 3 replicates at each station for each compartment). Samples were freeze-dried before analysis.

Quantification of chlordecone in zooplankton and seawater was conducted by the Center for Analytical Research and Technology at Liege University (CART, Belgium). Lipids and chlordecone were extracted with an accelerated solvent extractor using n-hexane-dichloromethane (90:10; v:v). The extracts were then dried under nitrogen flow until a constant weight is obtained. 3 ml n-hexane and a surrogate marker (PCB congener 112), at a concentration of 50 pg.µl -1 , were added. Then, the extracts underwent an acid clean-up carried out with 2 ml of sulphuric acid (98-100 %) in order to eliminate organic compounds (lipids, lipoproteins,...). Finally, the eluates were evaporated under an almost dry nitrogen stream. 100 µl n-hexane and 100 µl PCB 209 congener, used as an internal standard at a concentration of 50 pg.µl -1 , were added to the samples before injection. The analyses were performed by GCMS-MS (ThermoQuest ITQ 1100 ion trap). With this method, the lower limit of quantification (LOQ) was 0.010 µg.kg -1 w.w. for zooplankton and 0.010 g.l -1 for seawater [START_REF] Monti | Contamination of freshwater fauna[END_REF].

Samplings protocol to evaluate trophic-food web contamination

To study the contamination of marine food webs by chlordecone, samplings were conducted at Petit-Bourg in Guadeloupe, in three habitats: mangrove, seagrass beds (located at 500 m from the coast) and coral reefs (4 km from the coast), between January 2014 and February 2015. On each habitat, sediment, Superficial Sediment Layer (SSL), Suspended Organic Matter (SOM), vegetal matter (macroalgae and seagrass) and consumers from different trophic levels (primary consumers, secondary consumers, top predators) were collected. The classification of species into trophic group was done according to the literature (Randall 1967, Froese and[START_REF] Froese | FishBase: World Wide Web electronic publication[END_REF]. The full list of collected species is given in S1 Table . Macroalgae, fishes and crustaceans were collected by hand, spearfishing or using nets in seagrass beds and mangroves. Whenever possible, three replicates were made for each species. Each individual was rinsed, measured (total length (TL) in centimeters) and weighed (w.w. in grams). For each organism, a piece of flesh was collected and frozen (-18°C) until analyses.

To sample the suspended organic matter (SOM), seawater was collected in the three habitats in acid-cleaned plastic drums. Water was then filtered on Whatman® GF/F 47 mm filters. Sediments were recovered using a corer to a depth of about 20 cm. Superficial sediment layer (SSL) was sampled on the first 2 cm of sediment.

Concentrations in chlordecone were quantified by LABOCEA (Plouzané, France) with liquid chromatography coupled to mass spectrometry in tandem (UPLC-MS/MS). The lower LOQ with this method was 1 µg.kg -1 (w.w.) for organic material and 10 µg.kg -1 (w.w.) for sediment.

Results

Chlordecone desorption on allophanes

No adsorption on the glass of the vials has been observed on controls. Initial concentration of chlordecone measured in soil was 957 ± 13.2 µg.kg -1 that is about 1.145 µg of chordecone in each sample of 1.5 g of soil. After 24h of incubation, the maximal desorption of chlordecone was observed in the minimal salinity conditions (0 psu) while the minimal desorption was observed in the maximal salinity condition (35 psu) (Table 1). After 24h of incubation, 78 % of the initial quantity of chlordecone was still adsorbed on soil in the freshwater condition (0 psu) and 90 % for the marine condition (35 psu) (Fig. 1). About 20 % of the initial quantity of chlordecone was desorbed from soil in estuarine conditions (10 and 20 psu).

Fig. 1 Percentages of chlordecone in water (white) and in soil (grey) related to salinity after 24h of incubation.

Contamination of the planktonic compartment

In Martinique, the minimal concentration of chlordecone found in zooplankton was 22 µg.kg -1 (Rivière Rouge) while the maximal concentration was measured at the mouth of Rivière Monsieur (272 µg.kg -1 ). In Guadeloupe, concentrations in chlordecone varied between 40 and 306 µg.kg -1 (Fig. 2). Concentrations of chlordecone in seawater were under the LOQ in 53% of the studied stations. In the other stations, concentrations varied from 0.01 to 0.053 µg.L -1 . No correlation was found between the concentration of chlordecone measured in seawater and zooplankton.

Temporal variations in the level of contamination of zooplankton were tested in Guadeloupe. No statistical significant difference was found on the level of contamination in zooplankton between samples collected at the end of the dry season (May) and those collected at the end of the wet season (December), according to the two sites in Guadeloupe.

Spatial variations in the level of contamination of zooplankton were tested between the four sites (two in Martinique and two in Guadeloupe) sampled in December 2010. Mean concentrations of chlordecone in zooplankton show significant spatial differences (Kruskal-Wallis, X 2 = 13.9, p = 0.002). These spatial The bioconcentration factor (BCFw), calculated as the ratio between concentrations of chlordecone measured in zooplankton and those measured in seawater, varied from 440 and 27200 (Table 2). Mean BCFw was maximal at the mouth of Rivière Monsieur in Martinique. The three other sites reached similar order of magnitude: 4320 at Rivière Rouge, 7422 at Grand Carbet and 5338 at Grande Anse (Fig. 2).

Contamination of the trophic food web

Concentrations in chlordecone were measured in different sources of matter and marine organisms in three marine habitats: mangrove, seagrass beds and coral reefs (Fig. 3).

Fig. 3

Mean concentration of chlordecone  SD (in µg.kg -1 ) measured in sources of carbon (in white, SOM: Suspended Organic Matter; Vegetal matter: algal turf, macroalgae, seagrass), primary consumers (in light grey, Susp. Feeders: suspension feeders, HB: herbivores) and secondary consumers (in dark grey, CA1: invertebrate feeders, PKT: planktivores, CA2: fish and invertebrate feeders, PV: piscivores), in mangrove (A), seagrass beds (B) and coral reefs (C). Mean concentrations per trophic category were calculated in pooling all individuals from similar trophic group.

Concentrations in chlordecone in sediment and superficial sediment layer (SL) were found under the limit of quantification (10 µg.kg -1 ). However, chlordecone was found in SOM and vegetal matter in the different habitat.

SOM exhibited the highest values among the different sources of carbon. Primary consumers, including biofilm feeders, suspension feeders and herbivorous organisms, presented intermediate mean concentrations of chlordecone from 122.3  3.8 µg.Kg -1 in mangrove to 17.0  22.0 µg.Kg -1 in coral reefs. Secondary consumers, that are carnivorous organisms (invertebrates feeders, invertebrates and fish feeders and piscivorous), showed the highest mean concentrations of chlordecone among the studied trophic categories from 232.6  172.3 µg.Kg -1 in mangrove to 91.9  57.6 µg.Kg -1 in coral reefs. In each habitat, significant differences in the mean concentrations of chlordecone were found between the food sources (SOM, vegetal matter) and the trophic categories (primary consumers and secondary consumers) (Table 3). In mangrove, multiple post-hoc comparisons tests indicated that differences of chlordecone concentrations between trophic categories are due to the low value in vegetal matter. In this habitat, SOM, primary and secondary consumers showed similar levels of contamination but were statistically different from the level in vegetal matter. In seagrass beds and coral reefs, the patterns of contamination were similar: SOM, vegetal matter and primary consumers were not statistically different according to their level of contamination. However, these three trophic categories significantly displayed lower concentrations of chlordecone than secondary consumers (Table 3).

Discussion

In the Lesser Antilles, all the environmental compartments that have been in contact with chlordecone present a signal of contamination: agricultural soil, rivers, seawater, zooplankton and fauna from the marine food webs. In the present work, the experiments conducted to study the behavior of chlordecone during its arrival in estuarine and marine environments show that molecules stay strongly linked to the particles of terrestrial organic matter, whatever the salinity (0 to 35 psu). About 20 % of the initial quantity of chlordecone was desorbed from soil in estuarine conditions (10 and 20 psu) and 10 % was released in marine conditions (35 psu). Thus, even if furthers investigations should be conducted to verify this affirmation, marine conditions seem to be the least conducive to desorption. These results are in accordance with those of [START_REF] Nichols | Sedimentologic fate and cycling of Kepone in an estuarine system: example from the James River estuary[END_REF], who demonstrated that once chlordecone is sorbed it stays adsorbed in the range of pH (7-8) and salinity (0.006 to 19.5). [START_REF] Bakir | Transport of persistent organic pollutants by microplastics in estuarine conditions[END_REF] demonstrated that salinity generally does not affect desorption rates of persistent organic pollutants (POP), however their study was conducted on sorption of POP on microplastics. The capacity of POP sorption and desorption on allophanes could be different. In the present study, salinity appears to limit chlordecone desorption because of a process which needs furthers investigations. The present experiments could explain the high concentrations of chlordecone measured in the suspended organic matter (SOM), collected after filtration of seawater in the coastal areas (from 28 to 233 µg.kg -1 ). Chlordecone was not encountered in superficial sediment layers and marine sediments cores (all samples presented concentrations inferior to the limit of quantification: 10 µg.kg -1 ). These results could be due to the fact that some samples of sediments collected in the present study were poor in organic matter (for example, less of 2 % of OM in sediments collected in coral reefs). Indeed, the level of contamination of sediments is generally associated with the nature of sediment: organic-rich sediment has much greater capacity to sorb chlordecone than organic-poor sand or kaolinite [START_REF] O'connor | The effect of concentration of adsorbing solids on the partition coefficient[END_REF]. [START_REF] Bodiguel | Transfert of the chlordecone in the trophic foodwebs of commercial marine species in the Lesser Antilles (Chloretro)[END_REF] and Robert (2012) also found a very low level of contamination for these two compartments in a bay in Martinique (22 samples, all were < 0.5 µg.kg -1 ). In 2005, Bocquené and Franco found a disparity between the concentrations of chlordecone measured in sediment (<10 µg.kg -1 ) and SOM (52 and 22 µg.kg -1 ). Chlordecone can be transferred to estuaries and the marine environment in particulate form [START_REF] Crabit | Soil and river contamination patterns of chlordecone in a tropical volcanic catchment in the French West Indies (Guadeloupe)[END_REF] via SOM to which it is firmly attached. This suspended matter does not seem to settle on the bottom directly when it arrives at sea, since the sediment compartment is very little affected by the contamination.

Several hypothesis could explain these observations: 1) contaminated SOM is constituted by very fine and low density particles which may settle far from estuarine and coastal areas (but this explanation cannot be plausible in semi-enclosed bays), 2) contaminated SOM may be directly consumed in the water column by planktonic compartment upon arrival in the marine environment, 3) contaminated SOM may settle in estuaries and coastal areas but may be rapidly degraded by microorganisms in the sedimentary compartment, 4) samples were collected during the wet season, which can induce intense rainfall and disturbances of estuarine and coastal waters, preventing sedimentation of contaminated SOM [START_REF] Eggleton | A review of factors affecting the release and bioavailability of contaminants during sediment disturbance events[END_REF][START_REF] Noegrohati | Fate and behavior of organochlorine pesticides in the Indonesian tropical climate: a study in the Segara Anakan estuarine ecosystem[END_REF].

Levels of contamination of zooplankton compartment were investigated to verify the second hypothesis on chlordecone pathway (see above). Zooplankton is closely linked and in direct contact to SOM in estuarine and coastal waters. Indeed, previous studies have highlighted the sensibility to this compartment towards organochlorine pollutants, probably due to its lipid content [START_REF] Jordan | An evaluation of the Kepone contamination of the plankton of the James River[END_REF][START_REF] Day | Pesticide residues in freshwater and marine zooplankton: a review[END_REF][START_REF] Nichols | Sedimentologic fate and cycling of Kepone in an estuarine system: example from the James River estuary[END_REF]). [START_REF] Nichols | Sedimentologic fate and cycling of Kepone in an estuarine system: example from the James River estuary[END_REF] measured a mean concentration equal to 4 800 µg.kg -1 in zooplankton from the James River estuary. [START_REF] Coat | Organochlorine pollution in tropical rivers (Guadeloupe): role of ecological factors in food web bioaccumulation[END_REF] reported a mean concentration equal to 5 100 µg.kg -1 in the river mouth and 3 500 µg.kg -1 in the coastal waters of Guadeloupe. In the present study, concentrations of chlordecone measured in zooplankton were considerably lower and varied between 22 and 306 µg.kg -1 . Concentrations of chlordecone in zooplankton also spatially varied. The lowest concentrations were observed in front of Rivière Rouge, probably due to the exposure of the site to swell on this Atlantic coast. No temporal variations of chlordecone concentrations were observed between zooplankton collected at the end of the wet season and those collected at the end of the dry season. However, the number of samples was relatively low and samples were taken in the same day during each campaign. Because the planktonic compartment displays very short turn-over, further sampling effort should highlight temporal variations in the level of chlordecone contamination of zooplankton. Bioconcentration factors (BCFw) were calculated as the ratio between concentrations of chlordecone in zooplankton and those in seawater. The highest BCFw was found at the mouth of Rivière Monsieur, probably due to its high degree of confinement (semi-enclosed bay). Zooplankton is a keystone component of both marine and rivers systems. It represents a link between invertebrates and fish by providing food and by recycling essential nutrients through feeding on living and detrital material. The accumulation of organochlorine pollutants in zooplankton is a severe threat to ecosystems and a potential way of transfer of the molecule along marine and freshwater food webs.

Bioaccumulation phenomena of chlordecone have been demonstrated experimentally [START_REF] Bahner | Kepone® Bioconcentration, accumulation, loss and transfer through estuarine food chains[END_REF] and in the field [START_REF] Dromard | Different transfer pathways of an organochlorine pesticide across marine tropical food webs assessed with stable isotope analysis[END_REF]. Two processes of bioaccumulation were demonstrated in the present study. In mangrove, the ambient level of contamination is high (SOM: 191.3 µg.kg -1 ) and this value from the basis of the trophic food web is not statistically different from the other trophic categories (excepted with vegetal matter).

Indeed, in mangrove, the entire trophic food web exhibits high concentrations of chlordecone, reflecting the contamination of the baseline. This first way of contamination indicates a prevalence of a contamination "by bath", i.e. by contact between organisms (via teguments and gills) and contaminated surrounding waters. In seagrass beds and coral reefs, food sources and primary consumers shared similar level of contamination that were significantly different from those of the secondary consumers. In these two habitats, a phenomenon of bioamplification, i.e. a contamination by trophic way, is highlighted. The introduction of carnivory in fish diet is linked to a significant increase in the chlordecone concentrations of marine organisms, probably due to the higher lipid content in the preys. In the Lesser Antilles, the degree of contamination of marine systems by chlordecone was drastically lower than that measured in the James River estuary, due to the difference of pollution discharge between the two sites. However, the majority of the studied species were impacted by chlordecone and showed concentrations above the maximal residue limit (LMR) authorized by the French food and safety authorities for the consumption and the commercialization of seafood products (LMR = 20 µg.kg -1 ).

The variations among species from similar trophic category were sometimes very high, suggesting that other processes could influence the level of contamination of marine fishes, such as their physiology, their size or their movement among the different habitats. Indeed, [START_REF] Luellen | Kepone in James River fish: 1976-2002[END_REF] indicated that fish ecology could considerably influence the level of contamination of marine fishes, especially for migratory species that spend a period of the year in an estuarine environment.

To conclude, molecules of chlordecone can reach marine ecosystems with two principal processes. Firstly, dissolved molecules of chlordecone can infiltrate ground waters during percolation process and join marine environment with resurgences inside rivers or in the sea [START_REF] Crabit | Soil and river contamination patterns of chlordecone in a tropical volcanic catchment in the French West Indies (Guadeloupe)[END_REF]). In the present study, this pollution was highlighted by the concentrations of chlordecone measured in seawater samples. Secondly, due to its strong affinity with soil particle (allophane or others terrestrial particles), molecules of chlordecone can reach runoff waters, rivers and estuaries by leaching of the contaminated soil throughout the erosion process of the catchment [START_REF] Crabit | Soil and river contamination patterns of chlordecone in a tropical volcanic catchment in the French West Indies (Guadeloupe)[END_REF]. When chlordecone arrive in marine environment, it seems to stay sorbed on allophanes and this fact could be similar with other organic compounds, as salinity generally does not affect desorption rates [START_REF] Bakir | Transport of persistent organic pollutants by microplastics in estuarine conditions[END_REF]. Chlordecone, adsorbed or dissolved, is then integrated in SOM and zooplankton. These two compartments are located at the base of trophic food-web and contribute to the contamination of the entire food chain, via contact or trophic way.

In perspectives, several aspects of chlordecone transfer between terrestrial, rivers and marine ecosystems should be developed in future studies. The low proportion of chlordecone desorbed from allophanes requires further studies on the physical and chemical behavior of allophanes when arriving in marine environments. The potential ways of chlordecone degradation in marine sediment could also be investigated in order to understand the low concentrations of chlordecone found in marine sediment. Measurements of chlordecone metabolites in the sediment could help with the understanding of a potential bacterial degradation in this compartment. Finally, studies on the kinetic of contamination versus decontamination in marine fauna could be useful to explain the high variations in the concentrations of chlordecone between species and individuals.

  due to the lower concentrations of chlordecone measured in zooplankton at the mouth of Rivière Rouge compared to the other sites.

Fig. 2

 2 Fig. 2 Mean concentration of chlordecone measured in zooplankton (in µg.kg -1 w.w.) and seawater (into brackets, in g.l -1 w.w.) in Martinique (A) and in Guadeloupe (B) at the mouth of four rivers (C to F). BCFw: Bioconcentration factor (data from Monti et al. 2012); grey surfaces in A and B panels indicate soils contaminated by chlordecone.

  chlordecone (µg.kg-1) Concentration in chlordecone (µg.kg-1) Concentration in chlordecone (µg.kg-1)

Table 1

 1 Concentrations of chlordecone ([CDC]) measured in soil after 24h of incubation. Initial concentration of chlordecone in soil was 957 ± 13.2 µg.kg -1 . SD: Standard Deviation; RSD: Relative Standard Deviation.

			[CDC]	Mean [CDC]  SD	RSD
	Salinity (psu)	Replicates	µg.kg -1	µg.kg -1	%
		1	729
	0	2	983	788  173.1	22.0
		3	653
		1	905
	10	2	733	799  92.6	11.6
		3	759
		1	821
	20	2	902	837  57.8	6.9
		3	790
		1	674
	35	2	1 002	814  169.2	20.8
		3	766

Table 2

 2 Mean concentrations of chlordecone (min -max) measured in zooplankton (in µg.kg -1 w.w.), seawater (in µg.l -1 ) and mean bioconcentration factors (BCFw) at the mouth of four rivers. WS: end of the wet season (December), DS: end of the dry season (may), n: number of samples, "-" indicates absence of measures.

	Islands	Rivers	Seasons	n	Zooplankton	Seawater	BCFw
	Martinique	Rivière Rouge	WS	9	62.3 (22 -99)	0.028 (0.01 -0.05)	4320 (440 -9900)
	Martinique Rivière Monsieur	WS	9	164.3 (79 -272)	0.01	27 200
	Guadeloupe	Grande Anse	WS	9	104 (40 -294)	0.023 (0.02 -0.027)	5338 (2095 -10889)
	Guadeloupe	Grande Anse	DS	9	167.8 (79.5 -271.8)	-	-
	Guadeloupe	Grand Carbet	WS	9	133.0 (50.9 -231.7)	-	-
	Guadeloupe	Grand Carbet	DS	9	164.7 (100 -306)	0.027 (0.017 -0.053) 7 422 (2509 -14647)

Table 3

 3 Mean concentrations of chlordecone ( SD) in µg.kg -1 measured in food sources (SOM and vegetal matter), primary consumers and secondary consumers. Differences in chlordecone concentrations between the trophic categories were tested with a Kruskal-Wallis test. Letters indicate the results of multiple comparisons conducted with post-hoc tests.

	Trophic categories	Mangrove	Seagrass beds	Coral reefs
	SOM	191.3  38.5 b	31.7  2.9 a	30.3  2.1 a
	Vegetal matter	12.3  6.4 a	6.8  5.6 a	12.3  13.5 a
	Primary consumers	122.3  3.79 b	34.6  25.9 a	17.0  22.0 a
	Secondary consumers	232.6  172.3 b	147.5  66.0 b	91.9  57.6 b
	X 2	15.1	44.1	35.6
	p values	0.002	<0.0001	<0.0001

Acknowledgements

The authors express thanks to the institutions that gave financial supports to realize this study: Office de l'Eau of Guadeloupe and Martinique, ONEMA (AFB), DEAL Guadeloupe and Martinique (for the program CLIPPER and the study on the plankton contamination by chlordecone), Prefecture of Martinique (ChloHal program).