# Promotion of the Development of Sentinel Species in the Water Column: Example Using Body Size and Fecundity of the Egg-Bearing Calanoid Copepod Eurytemora affinis

Sami Souissi, Anissa Souissi

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Sami Souissi, Anissa Souissi. Promotion of the Development of Sentinel Species in the Water Column: Example Using Body Size and Fecundity of the Egg-Bearing Calanoid Copepod Eurytemora affinis. Water, 2021, Advances in Mountain and Mediterranean Wetlands Conservation, 13 (11), pp.1442. 10.3390/w13111442. [https://doi.org/10.3390/w13111442](https://doi.org/10.3390/w13111442)

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# Promotion of the Development of Sentinel Species in the Water Column: Example Using Body Size and Fecundity of the Egg-Bearing Calanoid Copepod *Eurytemora affinis*

Sami Souissi <fnref n="1" />,* and Anissa Souissi <fnref n="2" />

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**Citation:** Souissi, S.; Souissi, A. Promotion of the Development of Sentinel Species in the Water Column: Example Using Body Size and Fecundity of the Egg-Bearing Calanoid Copepod *Eurytemora affinis*. *Water* **2021**, *13*, 1442. https://doi.org/10.3390/w13111442

Academic Editor: Michele Mistri

Received: 31 March 2021

Accepted: 19 May 2021

Published: 21 May 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

**Abstract:** The development of sentinel species in aquatic ecosystems is mostly based on benthic organisms; however, organisms living in water column such as zooplankton have received less attention, except for some cladocerans. In this paper, a new ecological indicator based on simple measurements of the size and fecundity of egg-bearing calanoid copepods is developed. The well-studied estuarine copepod *Eurytemora affinis* is used to illustrate this new framework. A large database obtained from laboratory experiments developed under different conditions is used to define a reference regression line between clutch size (CS) and prosome length (PL). The same database allowed one to confirm that the coefficient of variation (CV) of CS is an adequate estimator of the accumulated stress at population level. The CV of PL shows very little variability in all experimental and field conditions. The values of CS and PL obtained from the Seine, Loire, Gironde, Scheldt and Elbe estuaries in Europe are compared to the reference regression line. A quality index (QI) is calculated as a percentage of difference between the observed and the predicted CS. The QI classified 19 samples collected in the Seine estuary between 2004 and 2010 into four classes according to the physiological condition of the copepod female. A single sampling from June 2004 (5.26%) showed a very good condition, whereas 57.9% of the sampling dates confirmed good conditions. On the other hand, four sampling dates were associated to very bad conditions and three sampling dates indicated bad conditions. Seven additional samples obtained from other European estuaries between 2006 and 2009 were also used. Females showing poor conditions were observed in the early spring of 2005 and 2008 as well as during the month of November. These years were characterized by very strong climatic anomalies with a very cold late winter in 2005 and a warm winter in 2008. Therefore, it seems that the QI perfectly reflected the strong stress caused by the sudden change in hydro-climatic conditions that have certainly affected the physiology of copepod females and probably the availability of food. The new indicator is very simple to calculate and can be generalised to several aquatic ecosystems (fresh water and brackish water) by targeting the dominating egg-bearing calanoid copepods. As in the case of *E. affinis*, the development of sentinel species based on copepods or cladocerans can enrich ecological and ecotoxicological studies given their capacity to integrate the variability of their habitats' quality at the individual and population levels.

**Keywords:** sentinel species; calanoid copepod; *Eurytemora affinis*; body size; fecundity; freshwater; brackish water; ecological indicator

## 1. Introduction

The majority of aquatic ecosystems are facing multiple anthropogenic pressures (e.g., eutrophication, pollution, habitats fragmentation, freshwater demand, riverine and maritime transportation, etc.) combined to climatic fluctuations (e.g., global warming, extreme

<footnote n="1">¹ Université de Lille, CNRS, Université du Littoral Côte d'Opale, UMR 8187 LOG, Laboratoire d'Océanologie et de Géosciences, Station Marine de Wimereux, F-59000 Lille, France</footnote>

<footnote n="2">² Université Littoral Côte d'Opale, UMR 1158 BioEcoAgro, TERRA Viollette, USC Anses, INRAe, Université Lille, Université Artois, Université Picardie Jules Verne, Université Liège, Yncréa, F-62200 Boulogne-sur-Mer, France; anissa.souissi@univ-littoral.fr</footnote>

<footnote>* Correspondence: sami.souissi@univ-lille.fr</footnote>
events, drought seasons, storms, etc.) [1]. Consequently, it is important to develop a link between ecological studies and future management strategies to better understand the functioning of most aquatic ecosystems, evaluate their global services and to adopt the most appropriate management and conservation strategies [2]. To reach this goal, a comprehensive monitoring strategy of the whole ecosystem is required, but unfortunately, this was never achieved for most aquatic ecosystems. The studies comparing different aquatic systems and using adequate modelling tools are scarce [3]. Most often, the heterogeneity of the measured variables as well as the absence of an adequate modelling framework capable of linking individual, population and community levels, limit the development of a holistic approach. Some global initiatives, such as the European Water Framework Directive (WFD; 2000/60/EC) aiming to protect the quality of the water by using an integrated river basin management approach, tried to promote biological indicators at large scale. Although the WFD is important in enhancing a global debate on the ways of developing and harmonizing ecological indicators in the context of water quality, some key components of all aquatic ecosystems, such as zooplankton, were missing [4]. Furthermore, the extension of the WFD to the transition zones, such as estuaries, was extremely limited. The limitations of the WFD for the transition zones including estuaries and the coastal zones were partially completed by the European strategy for marine environment directive (2008/56/EC). In that directive, the zooplankton is retained among the other biological groups (phytoplankton, benthos, fish). From a conceptual point of view, zooplankton is a suitable group to develop different ecological indicators at different levels of organisation (individual, population and community). In fact, zooplankton is a very diverse group of small-sized organisms, with a fast growth rate and short generation length. All these characteristics allowed zooplankton to be a good indicator of natural and/or anthropogenic variabilities [4,5]. Particularly, in the freshwater habitats, cladocerans and mainly *Daphnia* have been intensively used as a good ecological indicator [6]. In addition to the advantages of studying cladocerans, other crustacean groups of zooplankton, such as copepods, are also present in all aquatic ecosystems and their study at community and/or population levels can also be useful to enrich the battery of ecological indicators.

In this paper, we focus on freshwater and brackish water habitats at a basin level and their extension to the estuarine systems. Although there is a low number of ecological studies targeting zooplankton communities from the river source to the estuarine habitat, Le Coz et al. [7] tried to use different ecological concepts originated from either riverine or estuarine ecology to the continuum of the Scheldt river and estuary. Zooplankton communities are also very good indicators to characterize the trophic status of Mediterranean wetlands and to suggest some conservation strategies [8]. The ecological status of some large lakes can be assessed by using several ratios, such as cladoceran/calanoid and cyclopoid/calanoid [9]. The ratios can be based on the size of species; for example, the ratio between large and small rotifers in Lake Geneva can be simultaneously affected by eutrophication and global warming [10]. The body size of zooplankton groups was more useful than taxonomic resolution to rapidly assess the effect of environmental factors on temporal wetlands [11]. Furthermore, the body size of the *Daphnia cucullata* was suggested to be a good ecological indicator of the trophic conditions of lakes [6]. Consequently, the body size of zooplankton species seems to be an important trait to be considered when developing ecological indicators.

However, most freshwater ecosystems experience different sources of anthropogenic pressure with different intensities of climatic variability that challenge the development of ecological indicators. Whereas the studies of zooplankton communities are relatively very common [7,8], ecological indicators can be conceived at either individual or population levels. The link between individual and population levels can be achieved by studying the variability of the life traits of sentinel species [2]. For example, the use of condition indices based on measuring size (or morphometry) and weight of individuals was used in studies targeting fish juveniles [12] or benthic invertebrates [13]. The allometric relationship between weight and size of individuals is also used as a good ecological indicator of
the health status of intertidal mudflats [14]. The same study explored the reproductive status by dissecting mature individuals and observing oocyte development. Although reproductive traits are important, this approach needs specific skills and cannot be easily applied by nonexperts.

Nevertheless, the importance of considering life traits and size of individuals seems to be a good option to enrich the development of ecological indicators, but such an approach received less attention in zooplankton species. In fact, the use of body size alone in a single species of a calanoid copepod [15] or caladoceran [6] allowed one to compare different water bodies with contrasting ecological statuses. However, these studies did not combine body size with other life traits and particularly the fecundity of females. We hypothesise that the simultaneous consideration of body size and fecundity of some zooplankters can enrich the potential of developing new ecological indicators.

In this paper, we target calanoid copepods and especially egg-bearing species to suggest a general framework that can be applied to almost all freshwater and brackish water ecosystems. In order to illustrate this approach, we select the widely distributed species *Eurytemora affinis* and focus on the well-studied population [16–18] from the low salinity zone of the Seine estuary. In several freshwater and estuarine ecosystems, the diversity of calanoid copepods is very low and most often a single species is dominating even the whole zooplankton community [19,20]. Moreover, when adult females carry eggs, the clutch size can be estimated in addition to other morphometric parameters [15]. However, the interpretation of these data remains complicated given their high variability. The first objective of this paper is to demonstrate the way that the use of this relationship between fecundity and female size can help to build ecological indicators. The second objective is to show that inter-individual variability that can be measured simply by the coefficient of variation (CV) provides a reliable indicator of the magnitude of stress experienced by the population.

## 2. Materials and Methods

### 2.1. *The Selected Species*

In this study, we used the calanoid copepod *Eurytemora affinis*, an euryhaline and eurytherm species, that has a large geographical distribution and can be found in the low salinity zone of most estuarine ecosystems in the northern Atlantic as well as several lakes [21]. In Europe, three populations (Baltic, English Channel–North Sea and Atlantic) were identified using a phylogenetic approach [22,23]. Other studies in the Baltic confirmed the co-existence of the native *E. affinis* with other close species recently described as *Eurytemora carolleeae* [24]. Although there are close morphological similarities between these two species, we only focus on the European populations of *E. affinis* and particularly the one from the Seine estuary. In fact, the life cycle properties of this population were studied in the laboratory under controlled natural conditions [25–27] or in the presence of different classes of pollutants [28–32] as well as in the field [16–18]. All these studies allowed for the development of individual-based models [33,34]. Very recently, the whole genome of *E. affinis* from the Seine estuary maintained in laboratory culture was sequenced and compared with other available genomes of three copepods, including *E. carolleeae* [35]. The complete barcoding of the species using the mitochondrial genome [36] as well as the annotation of the transcriptome [37] were realised. This gives a great background on the selected species to test the hypotheses of this study and promote the development of sentinel species.

### 2.2. *Experimental Data*

*Eurytemora affinis* was sampled in the low salinity zone of the Seine estuary and transferred to the laboratory. Then, after manually sorting a large number of late developmental stages, the culture was initiated in a large 25 L aquarium at a temperature of 15 °C and salinity of 15. After two generations of acclimation (~1 month), the batch culture was transferred to 20 °C and a salinity of 15 and maintained during 2 additional generations (~20 days)
for acclimation. Copepods were fed every two days using *Rhodomonas marina* as described in [27]. Then, the experiment started by allowing 40 ovigerous females to lay their nauplii for the first generation (F0) at a temperature of 20 °C and salinity of 15 (T20S15). Then, adult females were removed in order to avoid the formation of a new egg-sac. The produced nauplii were followed in a 2 L beaker until adulthood. The water was changed after one week when copepodite stages appeared. After approximatively two weeks of development, a high number of ovigerous females appeared in the beaker, allowing one to stop running the generation experiment. Therefore, 40 live ovigerous females were randomly sorted and used to initiate the following generation F1. At the same time, 40 additional ovigerous females were sorted and used in another beaker containing a freshly prepared culture medium at the same temperature of 20 °C but a higher salinity of 25 (T20S25). The rest of the population was fixed in buffered formalin with final concentration 4% for subsequent morphometric analyses and clutch size counting. Then, the same procedure was repeated for each generation and for both beakers independently. After 18 generations at T20S15 and 17 generations at T20S25, the follow up of the multigeneration protocol was continued in a new incubator with a higher temperature of 24 °C, leading to two final conditions: T24S15 and T24S25. We should notice that in each generation, between 20 and 40 ovigerous females were sorted randomly to measure prosome length (PL) and estimate the clutch size (CS).

Although in the previous study, the first 11 generations were not used in the analysis (see Figure 1 in [27]), they were included in this analysis to obtain more data on the size and fecundity of *E. affinis*. The objective of using this very long-term experiment is to obtain a reference data on 4 different experimental conditions (T20S15, T20S25, T24S15 and T24S25) to test our hypotheses under controlled conditions. For each generation, the mean values of prosome length and clutch size were calculated. The variability in PL and CS between females in each generation was assessed by computing the coefficient of variation (CV = 100 × SD/mean), where SD is the standard deviation. The CV can be used as an additional indicator of stress within each generation.

## 2.3. Field Data

*E. affinis* samples collected between June 2004 and October 2010 in the same tidal conditions (during the ebb) near the Normandy bridge (mouth of the Seine estuary, as in [16]) were combined in this study to develop the ecological indicator. The dates, numbers of ovigerous females, mean values and CV(%) of PL and CS are provided.

## 2.4. Ecological Indicator Based on Prosome Length–Clutch Size Relationship

The experimental data allowed one to define a reference regression line that is supposed to represent good conditions that copepods can encounter in the field.

$$CS_{pred} = a \times PL_{obs} - b \quad (1)$$

where $CS_{pred}$ is the predicted CS and $PL_{obs}$ is the observed prosome length.

Then, a simple ecological indicator named the quality index (QI) was computed for all data obtained from the field. This QI corresponds to the percentage of deviation of the observed clutch size compared to the predicted clutch size.

$$QI = 100 \times (CS_{obs} - CS_{pred})/CS_{pred} \quad (2)$$

where $CS_{obs}$ is the observed mean clutch size.

To illustrate the principle of this method, 4 classes were suggested (Table 1):

All data obtained from the Seine estuary (19 dates) including the year 2005 (7 dates) characterised by a late winter/early spring unfavourable conditions to *E. affinis* development [17] are used to compute this index. Furthermore, additional samples collected in the Loire estuary near Paimboeuf (the same site as in the study of [22]) between 2006 and 2009 (one sampling per year), as well as three other samples obtained between March and April
2006 (one sample per estuary) in the Gironde, Scheldt and Elbe estuaries were added as a supplementary data.

<table><caption>**Table 1.** Definition of 4 classes of the quality index (QI) values and their associated score/colour.</caption><thead><tr><td>Class Number</td><td>QI Values</td><td>Ecological Status</td><td>Colour</td></tr></thead><tbody><tr><td>1</td><td>QI ≥ +20%</td><td>very good</td><td>blue</td></tr><tr><td>2</td><td>-20% ≤ QI &lt; +20%</td><td>good</td><td>green</td></tr><tr><td>3</td><td>-40% ≤ QI &lt; -20%</td><td>bad</td><td>yellow</td></tr><tr><td>4</td><td>QI &lt; -40%</td><td>very bad</td><td>red</td></tr></tbody></table>

## 2.5. *Data Analysis*

The mean values of prosome length (PL) and clutch size (CS) obtained from each generation were gathered to perform a linear regression (CS = *a* × PL – *b*). In order to assess the robustness of this model, the input data (CS and PL) were resampled using a bootstrap technique available on Matlab Software (Mathworks, Inc., Portola Valley, CA, USA). This allows one to obtain 5000 resampled values of the slope (*a*), correlation coefficient (*r*) and the intercept (*b*/*a* corresponding to CS = 0).

The values of CV(%) for prosome length and clutch size were aggregated in the following categories (Table 2):

<table><caption>**Table 2.** Description of the different categories of data obtained from the field and in the laboratory.</caption><thead><tr><td>Category</td><td>Description</td><td>Sample Size (n)</td></tr></thead><tbody><tr><td>In situ all</td><td>All sampling dates</td><td>19</td></tr><tr><td>In situ good</td><td>Sampling dates with good or very good QI</td><td>12</td></tr><tr><td>In situ bad</td><td>Sampling dates with bad or very bad QI</td><td>7</td></tr><tr><td>T20S15</td><td>generations followed at T = 20 °C and S = 15</td><td>17</td></tr><tr><td>T24S15</td><td>generations transferred from T20S15 to T = 24 °C and S = 15</td><td>5</td></tr><tr><td>T20S25</td><td>generations followed at T = 20 °C and S = 25</td><td>16</td></tr><tr><td>T24S25</td><td>generations transferred from T20S25 to T = 24 °C and S = 25</td><td>5</td></tr></tbody></table>

Then, the nonparametric Wilcoxon rank sum test was applied to compare the three paired data samples (In situ good vs. In situ bad), (T20S15 vs. T24S15) and (T20S25 vs. T24S25).

## 3. Results

Figure 1A shows that the different generations from all experimental conditions follow a clear linear trend between clutch size and prosome length with $r^2 = 0.83$. Excepting two synchronous generations from T20S15 and T25S25, that produced significantly smaller females and clutches due to a unique episode of low food quality (i.e., *Rhodomonas marina* used not at its exponential phase of growth), the increase in temperature reduced both prosome length and clutch size. Figure 1B shows the distribution of the coefficient of correlation (*r*) with a mean value of *r* = 0.91. All reconstructed *r* values by the bootstrapping technique are highly significant (*p* < 0.001), confirming the robustness of the linear relationship between CS and PL. Figure 1C,D shows the distributions of the values of the slope and intercept, respectively. The mean value of the intercept is around 730 µm, confirming that logically, egg-carrying females of *E. affinis* from the Seine population cannot be smaller than that size.

The data obtained from the Seine estuary and calculated in the last column of Table 3 are shown in Figure 2 (circle symbols). In addition, seven data obtained from other European estuaries (Loire, *n* = 4; Gironde, *n* = 1; Scheldt, *n* = 1 and Elbe, *n* = 1) are also used. We notice that the highest value of the QI was observed in June 2004 (24.85%), suggesting very good conditions (Figure 2). Additionally, the largest females with good conditions were observed in February 2009 in the Seine estuary (PL = 1127.42 µm, Table 3).
This contrasted with the smallest females observed in the Seine estuary in November 2007 (PL = 846.29 µm). The size differences between these extreme situations is around 33.22% but the differences in clutch size is much higher (458.76%). In the Seine estuary, 11 sampling dates corresponded to good conditions. The QI index classified 63.16% of the dates sampled in the Seine estuary in good or very good conditions. The other sampling dates belong to bad (n = 3, 15.79%) and very bad (n = 4, 21.05%) conditions. Figure 2 shows that excepting one sampling date of November 2007, with very bad conditions, all other sampling dates with low conditions are observed in the years 2005 and 2008. In particular, the critical months are March, April, November and December (Figure 2). We notice that among the seven samples from the other European estuaries, only the one obtained in Loire in April 2008 showed bad condition. It seems that the early spring and late autumn of the years 2005 and 2008 corresponded to unfavourable conditions for *E. affinis* development. It is also interesting to see that the seasonal pattern of the QI was similar in both years 2005 and 2008. In 2005, the very bad conditions were observed in March and April, followed by four good conditions in May, June, July and September, and ended by a bad condition in December (late November too). In 2008, a very bad condition was observed in March followed by a bad condition in April and two consecutive good conditions in June and July, before again experiencing a bad condition in November.

<figure><img src="image_4.png" /></figure>

**Figure 1.** (A) Experimental relationship between clutch size (CS) and prosome length (PL) of *Eurytemora affinis* from the Seine estuary. The different experimental conditions T20S15, T24S15, T20S25, T24S25 giving in total 43 generations are represented by different symbols. The continuous line corresponds to the linear regression CS = 0.2PL – 145.3. (B). Histogram of the distribution of the 5000 values of coefficient of correlation (r) obtained by sampling the original data using a bootstrap technique. (C). Similar to B. but showing the distribution of values of the slope parameter (a). (D). Similar to A. but showing the distribution of values of the intercept (value corresponding to predicted CS = 0).
<table><caption>**Table 3.** Summary of the sampling dates realised in the lower Seine estuary (salinity gradient zone) and the number of ovigerous females used to calculate the quality index (QI) between 2004 and 2010. Mean values of prosome length (PL; in µm) and clutch size (CS; number of eggs per female) for each date were associated with the coefficient of variation (CV = 100 × SD/mean, where SD is the standard deviation). The predicted clutch size (CS<sub>pred</sub>) for each date is obtained using the regression line of Figure 1 (CS = 0.1988 × PL – 145.3). Colours in the last column of the QI are the same as in Figure 2, and they represent arbitrary 4 classes: very good (blue), QI is greater than 20%; good (green), QI ranged between –20% and 20%; bad (yellow), QI ranged between –40% and –20%, and very bad (red), QI is lower than –40%.</caption><thead><tr><th>Sampling Date (DD/MM/YYYY)</th><th>Number of Females (n)</th><th>Mean PL</th><th>CV (%) of PL</th><th>Mean CS</th><th>CV (%) of CS</th><th>Predicted CS (CS<sub>pred</sub>)</th><th>Quality Index (QI)</th></tr></thead><tbody><tr><td>16/06/2004</td><td>50</td><td>886.4</td><td>5.83</td><td>38.6</td><td>24.13</td><td>30.92</td><td>24.85</td></tr><tr><td>08/03/2005</td><td>70</td><td>1006.76</td><td>6.28</td><td>25.73</td><td>38.98</td><td>54.84</td><td>–53.09</td></tr><tr><td>12/04/2005</td><td>64</td><td>1072.78</td><td>6.21</td><td>32.6</td><td>44.76</td><td>67.97</td><td>–52.04</td></tr><tr><td>21/05/2005</td><td>103</td><td>974.29</td><td>4.05</td><td>41.16</td><td>31.39</td><td>48.39</td><td>–14.94</td></tr><tr><td>10/06/2005</td><td>60</td><td>988.3</td><td>3.55</td><td>57.02</td><td>18.20</td><td>51.17</td><td>11.42</td></tr><tr><td>21/07/2005</td><td>58</td><td>937.7</td><td>3.17</td><td>43.12</td><td>29.26</td><td>41.11</td><td>4.88</td></tr><tr><td>16/09/2005</td><td>28</td><td>940.1</td><td>3.96</td><td>38.64</td><td>28.47</td><td>41.59</td><td>–7.10</td></tr><tr><td>01/12/2005</td><td>29</td><td>981.75</td><td>3.95</td><td>32.52</td><td>25.95</td><td>49.87</td><td>–34.79</td></tr><tr><td>23/05/2006</td><td>20</td><td>949.85</td><td>4.00</td><td>44.8</td><td>12.89</td><td>43.53</td><td>2.92</td></tr><tr><td>20/11/2006</td><td>50</td><td>840.86</td><td>5.47</td><td>18.06</td><td>21.15</td><td>21.86</td><td>–17.39</td></tr><tr><td>05/10/2007</td><td>40</td><td>846.33</td><td>4.35</td><td>25.5</td><td>51.00</td><td>22.95</td><td>11.11</td></tr><tr><td>16/11/2007</td><td>80</td><td>846.29</td><td>4.43</td><td>13.7</td><td>36.07</td><td>22.94</td><td>–40.29</td></tr><tr><td>19/03/2008</td><td>40</td><td>978.96</td><td>3.66</td><td>19.6</td><td>54.29</td><td>49.32</td><td>–60.26</td></tr><tr><td>10/04/2008</td><td>59</td><td>988.16</td><td>5.34</td><td>39.3</td><td>48.45</td><td>51.15</td><td>–23.16</td></tr><tr><td>18/06/2008</td><td>50</td><td>899.84</td><td>3.86</td><td>34.6</td><td>37.98</td><td>33.59</td><td>3.01</td></tr><tr><td>08/07/2008</td><td>86</td><td>879.17</td><td>5.11</td><td>26.8</td><td>67.57</td><td>29.48</td><td>–9.09</td></tr><tr><td>04/11/2008</td><td>50</td><td>831.79</td><td>4.04</td><td>14.9</td><td>52.83</td><td>20.06</td><td>–25.72</td></tr><tr><td>25/02/2009</td><td>20</td><td>1127.42</td><td>3.14</td><td>76.55</td><td>31.64</td><td>78.83</td><td>–2.89</td></tr><tr><td>02/10/2010</td><td>18</td><td>1042.41</td><td>4.99</td><td>52.22</td><td>31.20</td><td>61.93</td><td>–15.68</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Figure 2.** Projection of the mean values of clutch size (CS) and prosome length (PL) of ovigerous females sampled in the Seine estuary (circle symbols), Loire estuary (square symbols) and other (Elbe,</figcaption></figure>
Scheldt and Gironde) European estuaries (triangle symbols). The continuous line corresponds to the reference regression line obtained from Figure 1. The month and year of each sampling date is shown in the figure (black text for the Seine estuary and grey colour and italic for Loire, Gironde, Scheldt and Elbe estuaries). The different colours correspond to the values of the QI computed for each sampling date and indicated in Table 3. Blue colour: very good conditions (QI ≥ +20%), green colour: good conditions (−20% ≤ QI < −20%), yellow colour: bad conditions (−40% ≤ QI < −20%) and red colour: very bad conditions (QI ≤ −40%).

Figure 3A shows that the variability of measures of prosome length (PL) are very low (CV < 5%) with slightly higher values in the field samples compared to the experimental conditions. This confirms that PL is a very robust morphometric measurement that can be used as an independent variable in morphological or trait analyses. Although the CV of PL in bad conditions (in situ), and in the most stressed condition T24S25, are slightly higher than the less stressed conditions, the observed differences are not statistically significant. On the contrary, the values of CV of CS are very high and particularly in field conditions. Figure 3B shows that the variability of CS is on average higher in the field (36.12%) than in the laboratory conditions (20.35%, average of all conditions). In field conditions, the CV of CS when QI indicates good or very good conditions is equal to 27.52%, whereas the CV when QI indicated bad or very bad conditions is equal to 43.05% (Figure 3B). Although this is a big difference in CV between favourable and unfavourable field conditions, it is not statistically significant at *p* = 0.05, and this is most likely due to the low number of samples in our study. Under controlled conditions, the increase in water temperature from 20 °C to 24 °C at each salinity (15 and 25) is associated with a statistically significant increase in the CV of CS (Figure 3B). The value of CV in the condition T24S25 with a double thermal and saline stresses (30.66%) is higher than the value of CV obtained in only thermal stress condition T24S15 (25.86%).
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Mean values of the coefficient of variation (CV) of prosome length (A) and clutch size (B) obtained in field and experimental conditions. Error bars correspond to the standard error. Stars indicate significant difference using Wilcoxon rank sum nonparametric test (p < 0.05).</figcaption></figure>

# 4. Discussion

## 4.1. Assessing the Environmental Quality Using Morphometric Measurements

In aquatic ecology, the individual growth parameters, such as body mass and size are often used to assess the condition of benthic organisms [12,14]. More generally, measuring several morphometric traits can be useful in comparing different sites and populations. This approach was applied to the cladoceran *Daphnia cucullata* [6] and the calanoid copepod *Arctodiaptomus salinus* [15]. In addition to morphometric measurements that can be easily achieved, some reproductive traits (e.g., sexual maturity, fecundity) can be good indicators of water quality and ecosystem health in general. In fact, the reproductive traits of benthic organisms (i.e., sexual maturity and fecundity) are good physiological indicators, very
useful to compare between different sites with contrasted anthropogenic pressure such as pollution [13]. More generally, most sentinel species and the use of biometric measurements are developed for benthic organisms and fishes. On the contrary, the organisms living in the water column such as zooplankters received less attention in the framework of developing sentinel species.

One difficulty in promoting sentinel species based on crustacean zooplankters is certainly related to the diversity of this group, favouring the use of the community level to assess the quality of habitats [8]. Even if several species are models in aquatic ecotoxicology [38], most toxicity studies realised in the laboratory under controlled conditions cannot be easily extrapolated to in situ conditions. Particularly, cladocerans and copepods are fast-growing organisms which allow one to study the acute toxicity of several pollutants [28,30], but also the intergenerational effects of natural factors [27] or pollutants [32,39]. In field conditions, several factors can simultaneously affect body size and life traits. In freshwater ecosystems, other benthic crustaceans such as the *Gammarus fossarum* are used as sentinel species. This status has been favoured by an intensive research program aiming to better understand the sensitivity of the species to different environmental conditions while maintaining a balance between in vivo and in situ studies [40]. In addition to *Daphnia*, this study confirmed that some species of calanoid copepods can be promoted as good candidates for developing sentinel species of freshwater and estuarine ecosystems. Indeed, the diversity within this group is often low, unlike cyclopoids, which favours the work at the level of a single population [16,19,20]. Moreover, the majority of these species carry their eggs, which offers a considerable advantage to easily measure morphological traits (e.g., body size) and fecundity (e.g., clutch size). It is sufficient to focus on ovigerous females to easily and operationally make reliable measurements of prosome length and estimate fecundity by counting eggs in the sac [27]. This will offer a great advantage to assess the fecundity of copepods compared to other benthic organisms that require dissection and histological techniques [14]. For copepods, several ecological studies conducted in estuarine environments [41] and in lakes [42] already measured female prosome length and clutch size. However, the potential of using an allometric relationship between female size and clutch size has not been yet explored to develop indices of individual condition. Nonetheless, an integral indicator based on some morphometric measurements applied to the calanoid copepod *Arctodiaptomus salinus* was developed and used to assess the population state at larger scale [15]. In our study, the potential of using simple allometric relationships between size and fecundity to identify the individual condition was demonstrated by using the well-studied estuarine European species *E. affinis*.

## 4.2. Promoting Certain Calanoid Copepods as Good Sentinels: Case of the Species *Eurytemora Affinis*

The copepod *E. affinis* has a large geographical distribution [22] and can dominate zooplankton in the low salinity zone of several estuaries [16]. However, given the geographical discontinuity of the aquatic habitats of this cryptic species, its strong genetic and morphological heterogeneity [43], the description of a new species *E. carolleeae* [44] which is very close morphologically to *E. affinis*, requires great caution when using data from different in situ populations. In this study, we focused on the population of *E. affinis* from several European estuaries. For example, in the Seine estuary the effects of natural factors, such a temperature and salinity on the dynamics of this population are well described at tidal [16], annual [17] and inter-annual [18] scales. Generally, the populations of *E. affinis* are distributed in the low salinity zones of estuaries near the maximum turbidity zone and can bioaccumulate different pollutants [45,46]. Due to the complexity of aquatic ecosystems, the separation between natural factors and pollution remains difficult, although this challenge is crucial in order to better understand the possible combined effects of global warming and pollution on aquatic ecosystems [1]. Parallel to in situ studies of *E. affinis*, an intensive experimental work has been developed allowing the switch from the acquisition of demographic or ecotoxicological parameters using individual observations [25,26,30] to the use of multi-generation observations at the population level [27,32]. The use of *E.*
*affinis* cultured in the laboratory offers great potential to obtain individuals raised in a standardized way to conduct interdisciplinary research such as the effect of turbulence [47] or pollution [29,48] on swimming behaviour. Furthermore, the kinetics of interaction between heavy metals and *E. affinis* exposed in large volumes confirmed the hypothesis of competition between ions of cadmium in presence of nickel and copper [31]. Recently, the same population of *E. affinis* originated from the Seine estuary was promoted as a good model for molecular ecotoxicological studies using genomic approaches [36,37].

### 4.3. *Development of a Quality Index (QI) Based on the Relationship Body Size-Fecundity*

Due to the large plasticity of copepods, and particularly *E. affinis* living in a highly variable environment, it is difficult to obtain accurate reference values for biomarkers used in ecotoxicology and life traits (i.e., fecundity) measured in the field. The use of experimentation is important to understand the effects of natural parameters such as salinity and temperature on enzymatic activity [49], as well as body size and fecundity changes [27]. In field conditions, a high variability of the prosome length and clutch size of ovigerous females of *E. affinis* was observed. The species is known to have a high plasticity, mainly regarding size and fecundity [27]. In addition, the age of females that may affect the fecundity [25] can be heterogeneous in the field. Consequently, it might be difficult to build a reference relationship between female prosome length and clutch size when all data are gathered together. An average regression line may not be the best solution to obtain the reference physiological relationship between body size and fecundity. As in our study, a database gathering more than 40 generations was available, a first reference line from lab conditions was suggested. These experiments that aimed to test two sources of stress related to an experimentally simulated global warming of +4 °C and salinity increase helped to validate the importance of using the coefficient of variation (CV) as an indicator of stress.

Contrary to the average relationship between CS and PL published in [27], based only on four data points, in this study, all data obtained from each single generation were used. Moreover, the early generations of the acclimation phase at a temperature of 20 °C were also added. This provided a very robust linear regression that defined the reference line to be compared to data from field conditions. In order to illustrate this approach, a simple quality index (QI), based on the normalised difference between the observed and predicted values of clutch size, was calculated. Then, the QI values were classified arbitrarily into four classes (Table 3). We assumed that our experimental conditions based on an optimised feeding protocol [27,32] reflected the good conditions in the field. Generally, the best mono-algal diet for copepods is composed by *Rhodomonas* sp. ([50], our personal observations). This confirms that *E. affinis* in our long-term experiment (>1 year) received good diet excepting during one generation that produced smaller females with a lower clutch size (see Figure 1A). However, this low-quality food episode significantly reduced PL and CS but did not affect the general trend of the CS-PL relationship.

In the field conditions, the lowest values of the QI were observed in March 2005, April 2005 and March 2008. Devreker et al. [17] studied the annual variability of *E. affinis* population in the Seine estuary and all environmental parameters during 2005. They showed that the negative anomaly of temperature that was observed in late winter was responsible for the very bad conditions of the population of *E. affinis*. This extreme climatic event observed at a larger scale in southern Europe [51] negatively affected the *E. affinis* population in the Seine estuary [18], as well as the seasonal succession of phytoplankton communities in the English Channel [52]. The QI clearly identified the critical period of poor conditions in March and April 2005, and the good conditions observed from May to September 2005. In fact, Devreker et al. [18] observed a high mortality in the beginning of the study (March–April 2005) that did not allow one to obtain a high density of the population of *E. affinis* during May and June, as it was expected from previous studies [17]. During 2005, the surviving individuals developed to adult stages in May and encountered favourable conditions. Furthermore, the QI confirmed that all ovigerous
females observed between May and September were in good conditions. Unfortunately, no sampling was realised in October and November 2005, but it seems that the females sampled on 1 December 2005 showed bad conditions, but nevertheless with large females compared to those observed during autumn (October–November) in the years 2006, 2007 and 2008 (see Figure 2). This confirms that the annual cycle of 2005 was extremely disturbed at both population [18] and individual (this study) scales. It also seems that November is a critical month for *E. affinis* development in the Seine estuary. The year 2008 appeared to be unfavourable for the spring development of *E. affinis* in the Seine estuary (March–April) and also in the Loire estuary (April). According to the earth observatory of the NASA, the year 2008 was much warmer in Europe, Russia and the Arctic compared to the Pacific Ocean, which was cooler than usual (https://earthobservatory.nasa.gov/images/36699/2008-global-temperature, accessed on 15 March 2021). This climatic situation, with an unusual warming of winter in Europe observed in 2008, could also disturb the regular development of *E. affinis* population, due to the direct effect on metabolism and/or the indirect effect on food quality. However, this hypothesis suggested by the new QI developed in this study should be explored deeply in the future. Even if the number of data was limited in years 2007 and 2008, it seems that the condition of *E. affinis* using the QI was bad or very bad during spring and autumn (see Figure 2). Both years were characterised by a significant warming during winter. However, other factors related to food quality, as well as other anthropogenic factors, could be involved in such pattern.

The river discharge is an easily accessible global parameter that plays a key role in the dynamics of the estuary [17]. Some studies already confirmed that higher river discharges usually enhance the lower trophic level production in the estuarine environment such as *Eurytemora* species [53] or the benthic sentinel *Hediste* (=Nereis) *diversicolor* [54]. During the period 2004–2010, the hydrological regime in the Seine river was characterised by low annual discharges (419.97 m³/s; SD = 225.85) compared to the previous decade 1994–2003 (mean = 596.97, m³/s; SD = 424.01). This situation of low river discharges could exacerbate the observed effects of extreme climatic events encountered during this period. However, these hypotheses on the link between hydro-climatic situations and the production of the lower trophic components need to be confirmed in the future with more dedicated experimental and/or sampling designs.

### 4.4. Generalisation of the QI to Other Freshwater and Estuarine Ecosystems

Several studies in marine ecology tried to establish a relationship between the re-productive output of copepods, generally expressed in terms of carbon content (e.g., for a clutch of eggs), and the female body weight. By aggregating several published data from different species, a global trend between fecundity and female body weight was observed [55]. This power law relationship (linear in log–log plot) could be useful in biogeochemical modelling but it is not accurate when a single species is considered. How-ever, a better taxonomic resolution, particularly at species level, is required to increase the ecological relevance of the study and provide species-specific relationship.

In this study, the European populations of *E. affinis*, and particularly the population from the Seine estuary, were used to develop our approach. The additional data points from the Elbe, Scheldt, Loire and Gironde estuaries perfectly fitted to the regression line, ex-cepting a single data point of April 2008 showing a bad condition (see Figure 2). This means that the approach suggested in this study can be applied to all *E. affinis* populations at least at European level, although phylogenetically, three lineages were described [22]. However, for other species, even those very close genetically, a new calibration of the reference line, that could be obtained from in situ data, is necessary. For example, Lloyd et al. [56] used field data obtained in the Chesapeake Bay (USA) in 2002 and 2003 and fitted a good relationship between CS and PL of the dominating copepod which was named before *E. affinis* but whose name was corrected later to *E. carolleeae* [57]. This robust relationship (r² = 0.85) obtained during two favourable years, 2002 and 2003, could be used as a refer-ence line for the species *E. carolleeae*. By assuming that 2006 was also a favourable year
for *E. affinis* development in Europe, we performed a simple regression line using five estuaries sampled in the same year. This provided an excellent relationship, explaining 99.35% of the total variability (PL = 0.1931 × CS – 139.32, *r*² = 0.9935), with an intercept value of 721.49 mm. This later value fell within the range of possible values generated by the bootstrap technique (see Figure 1D). Therefore, this relationship obtained from the field data for a single season in 2006, was not statistically different from the relationship established in the lab and can also be used as reference line. The linear regression line obtained in [56] for *E. acrolleeae* in the Chesapeake Bay (CS = 0.229 × PL – 126) was statis- tically different from the relationship obtained in the present study (CS = 0.2 × PL – 145.3). The value of the slope (0.229) projected in the histogram showing the distribution of the slope values produced by bootstrap (Figure 1C) was significantly higher than the mean value of slope (0.2) obtained in this study (*p* = 0.0244). The intercept value of *E. carolleeae* was 550.22 µm, which was much smaller than the lowest value generated by bootstrap in this study 677.07 µm (*p* = 0). It seems that the conditions of *E. carolleeae* in the study of [56] were good, because the in situ data did not show a huge dispersion, allowing one to suggest a linear decreasing relationship between PL and temperature with *r*² = 0.59. Pierson et al. [57] measured the PL and carbon contents of females and males of *E. carolleeae* during winter in 2007 and 2008. They compared their results with the predicted value of PL vs. temperature of [56], and confirmed that their values obtained in 2007 were slightly above the regression line, whereas the values of 2008 were below the regression line (see Figure 4b in [56]). This means that females of *E. carolleeae* in winter 2008 indicated bad conditions. Unfortunately, such observations during winter in 2007 and 2008 were not available in the studied European estuaries. However, the available samples realised in March and April 2008 in the Seine and Loire indicated bad conditions in April (both estuar- ies) and very bad conditions in March (Seine estuary). This means that a meta-analysis can be easily conducted by using the QI in several aquatic ecosystems even if the key sentinel species are different. The inter-comparison between different sites and ecosystems could be conducted in the future with much larger database. We can speculate that large-scale climatic events (e.g., global warming, extreme climatic anomalies) can lead to synchronous observations, such as the examples observed in early 2008 in three transatlantic estuaries (the Seine, Loire and Chesapeake Bay). However, a larger database has to be built in the future to accurately test such hypothesis.

The reference lines obtained from field observations for *E. affinis* in Europe during spring 2006 (this study) and in the Chesapeake Bay during 2002 and 2003 [56] showed very low dispersion of the data, which contrasted with the regular situation when field data are used. For a scatter plot with a high dispersion of the data, it is still possible to identify the reference line by modelling the maximum clutch size (e.g., 95th percentile) instead of fitting the average (or median) response. This approach was applied in *Daphnia* [58], where a large dataset allowed one to use 50µm width of size classes and adjust the maximum of brood size (see Figure 1 in [58]).

The QI suggested in this paper can easily be generalised to other freshwater and brackish water ecosystems. In several aquatic ecosystems, the calanoid copepods, and sometimes the whole zooplankton community, are dominated by a single or very low number of species. For example, the egg-bearing calanoid copepod *Eudiaptomus gracilis* is present along the year in several European lakes [20,59]. Gilbert et al. [60] studied the crustacean zooplankton in 36 Mediterranean wetlands in the south of Spain and confirmed that the number of calanoid copepods in each wetland is very low (1 or 2). For that reason, Jiménez-Melero et al. [61] targeted the dominant egg-bearing copepod *Arctodiaptomus salinus* to study its population dynamics and secondary production in a shallow saline pond. Moreover, the morphometric measurements based on the same species were used to compare 12 different populations [15]. In order to apply the QI, it is important to target a single species present almost along the year and avoid a mixture of species that may have different morphologies and reproductive strategies. This will allow one to have different reference regression lines between CS and PL, as we confirmed above for the very close
species *E. affinis* from Europe and *E. carolleeae* from the Chesapeake Bay. When a single species is used, several sites can be compared. For example, Chow-Fraser and Maly [62] studied the clutch size variability of two *Diaptomus* species in 11 lakes. They found that the CS is positively correlated with chlorophyll *a* and negatively correlated with temperature, which is a very common pattern observed in other marine studies [55]. For the species *Diaptomus oregonensis*, they showed a positive correlation between log-transformed data of both CS and PL, with a high dispersion of the data (see Figure 2a in [62]). Without establishing a reference line of regression between CS and PL, it is difficult to explain the sources of this dispersion and variability in CS within and between lakes.

The QI is calculated by using only the mean values of CS and PL. In order to evaluate the variability of the mean values of CS and PL, the coefficient of variation (CV) was also estimated. The inverse of the CV (1/CV) was used at the community level to estimate ecosystem stability [3]. In this study, the CV can be considered as a standardized estimator of the individual variability between females, and could be an indicator of stress intensity. In fact, in optimal and non-stressful conditions, the individual variability in most life traits was often lower than the one observed during stressful conditions (personal observations). This study confirmed that in controlled conditions, the simulated warming (+4 °C) induced a stressful response by significantly increasing the CV at two different salinities (see Figure 3). In field conditions, the CV was generally higher than that observed in laboratory conditions, and particularly in the sampling dates associated to bad or very bad conditions according the QI classification. This means that the variability associated to the mean values should be considered when developing simple ecological indicators such as the QI. However, this conclusion remains valid for all indices of conditions often based on average values of biometric measurements. The variability around the average values should not be neglected for a better ecological interpretation.

This study mainly focussed on adult females of egg-bearing copepods. For free
spawning copepods and adult males, only morphometric measurements are possible. In
that context, it is possible to use the CV of different morphometric parameters to develop
indicators of variability [15]. In fact, stochastic variation is an important component of
phenotypic variance, at the same level as genotypic variation and phenotypic plasticity,
and usually increases under stress [63]. Without using the fluctuating asymmetry (FA) of
bilateral traits, that may need very accurate morphometric data [63], the most common
traits such as prosome length, prosome width, and urosome length can be easily measured
and used in copepod [15] or in cladoceran studies [6]. This approach can complete the QI
developed in this study. In addition, for free spawning copepods in freshwater ecosystems,
it is possible to obtain a relationship between daily egg production rate (equivalent to CS)
and PL by using the incubation techniques commonly deployed in marine systems [64].

**Author Contributions:** S.S. and A.S. conceived the study and wrote the MS. A.S. completed all morphological measurements and the associated database. S.S. performed statistical analyses. Both authors have read and agreed to the published version of the manuscript.

**Funding:** This research promoting the copepod *Eurytemora affinis* as a sentinel species received funding from the GIP Seine-Aval under several research projects: ZOOSEINE, ZOOGLOBAL and SEN-TINELLES. This work is a contribution to the project CPER 2014–2020 MARCO (funded by Europe FEDER, French government the region Hauts-de-France and IFREMER) and the International Associ-ated Laboratory between Université de Lille and National Taiwan Ocean University (IAL MULTI-FAQUA). This work benefitted from the French GDR “Aquatic Ecotoxicology” framework which aims at fostering stimulating scientific discussions and collaborations for more integrative approaches.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to some rules of the funding agencies.

**Acknowledgments:** We thank all past and present members of S. Souissi's group who helped in keeping micro-algae and copepod cultures for several years and particularly those who helped in initiating the multigeneration experiment. We are grateful to all team members and the research vessel crew that contributed to all sampling campaigns in the Seine and Loire estuaries. We are
grateful to Gesche Winkler for providing the samples collected in Spring 2006 in the Elbe, Scheldt, Seine, Loire and Gironde.

**Conflicts of Interest:** The authors declare no conflict of interest.

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