# Urbanization Rate, Copper Concentration and pH Affect Hydrae Communities' Presence and Abundance

Adrien Frantz, Julia Razzolini, Anouk Pellerin, Emmanuel Aubry

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Adrien Frantz, Julia Razzolini, Anouk Pellerin, Emmanuel Aubry. Urbanization Rate, Copper Concentration and pH Affect Hydrae Communities' Presence and Abundance. Ecohydrology, 2025, 18 (5), 10.1002/eco.70067. hal-05156613

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RESEARCH ARTICLE

OPEN ACCESS

# Urbanization Rate, Copper Concentration and pH Affect Hydrae Communities' Presence and Abundance

Adrien Frantz<fnref n="1" /> | Julia Razzolini<fnref n="1" /> | Anouk Pellerin<fnref n="1" /> | Emmanuel Aubry²

<figure><img src="image_3.png" /></figure>

**Correspondence:** Adrien Frantz (adrien.frantz@sorbonne-universite.fr)

**Received:** 28 January 2025 | **Revised:** 20 June 2025 | **Accepted:** 23 June 2025

**Funding:** This work was supported by Sorbonne Université and the Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation, MESRI.

**Keywords:** aquatic | cnidarian | communities | pH | pollution | trace metal | urbanization

## ABSTRACT

Global change affects all ecosystems worldwide, particularly aquatic communities. Yet, the separate and combined effects of environmental parameters linked to global change remain poorly understood, probably because their effects depend on the precise environmental factors, on their interaction, and on the taxa. Here, we investigated the presence and abundance of 3 Hydra species (*Hydra oligactis*, *Hydra vulgaris* and *Hydra viridissima*) in relation to pH, copper concentration, water and air temperatures, waterbody surface and the proportion of impervious surface around each of 49 sites from 20 ponds sampled in the Parisian region. Not surprisingly, many environmental parameters were correlated. We further expected Hydra presence and/or abundance to vary according to some environmental parameters. In particular, due to the hormetic effect of copper (i.e., beneficial at low and toxic at high concentrations) and to the complex relations between pH, temperature and metal toxicity, we expected an interactive effect of these factors on Hydra presence and/or abundance. Though we did not detect any such interactive effect, Hydra presence decreased with increasing pH and with the proportion of impervious surface, while Hydra abundance increased with increasing copper concentrations, suggesting that these concentrations may fall in the lower range of beneficial effects. Our correlative work underlines the need for extensive studies on the ecological and evolutionary consequences of environmental changes in aquatic ecosystems at the levels of individual, populations and communities in the wild.

## 1 | Introduction

Global change affects all ecosystems worldwide, whether terrestrial or aquatic (Vitousek 1994). While global warming has been subject to special focus, it is only one of several drivers of global change, which include both direct alteration of the fluid envelopes of planet Earth (CO₂ enrichment in atmosphere and oceans, climate warming and altered precipitation and stratospheric ozone reduction) and drivers that are regional in nature but so widespread as to scale to global significance (land transformation, freshwater appropriation and degradation, overexploitation of natural populations, exotic species invasions,

ecosystem eutrophication, pesticide use and tropospheric ozone enrichment; Sage 2020).

Most studies on aquatic ecosystems have focused on marine ecosystems and have shown high impacts of global change on marine biodiversity worldwide (e.g., Priya et al. 2023). Freshwater environments comprise a disproportionate amount of global diversity (Reid et al. 2019): Though they represent only 0.01% of the world's water and 0.8% of the Earth's surface, they support almost 6% of described species (Dudgeon et al. 2006). The biodiversity decline in freshwater is far greater than in most affected terrestrial ecosystems,

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Ecohydrology* published by John Wiley & Sons Ltd.

<footnote n="1">¹Sorbonne Université, Université Paris-Est Créteil, CNRS, INRAE, IRD, Institute of Ecology and Environmental Sciences - Paris, F-75005 Paris, France | ²Sorbonne Université, CNRS, EPHE, UMR METIS, F-75252 Paris, France</footnote>
especially in some tropical latitudes, and particularly affects large fishes and other vertebrates (Dudgeon et al. 2006). This decline is mainly due to overexploitation, water pollution, flow modification (likely exacerbated by global climate change; Vörösmarty et al. 2000), destruction or degradation of habitat and exotic species (Dudgeon et al. 2006). Therefore, there is a growing concern about environmental change and physicochemical changes in water bodies impacting biological communities, stressing the need for studying the consequences of global change on freshwater ecosystems.

Though their emissions have been greatly reduced, trace metals remain of particular concern in ecosystems due to their numerous sources and persistence (Azimi et al. 2005; Hill 2020; Falq et al. 2011). Among them, copper is one of the most abundant and persistent metallic elements in aquatic ecosystems. Copper has opposed biological effects depending on its concentration: beneficial at low, toxic at high concentrations (i.e., ‘hormesis’). Indeed, it is an essential trace element with important functions in physiological processes (Puig and Thiele 2002) including the activity of respiratory proteins (Kropat et al. 2015), but it is acutely and chronically toxic, particularly to aquatic organisms, above high concentrations (e.g., ≥µg. L⁻¹; ANZG 2023; Pascual et al. 2020). Though it is naturally occurring and widely distributed in the earth’s crust (approximately 50ppm; Landner and Reuther 2004), copper has numerous anthropogenic sources (municipal wastewater discharges, mining and mineral processing, metal and electrical manufacturing, fungicide use and stormwater).

As for other pollutants, the bioavailability of copper depends on its form. Freely dissolved (operationally defined as the <0.45 µm filtered fraction) is the most bioavailable copper form, while organic or inorganic complex, or copper associated with particulates are less bioavailable (ANZG 2023). Copper solubility and bioavailability are also modulated by several environmental factors, including water temperature and pH (Dortwegt and Maughan 2001).

Water temperature is a dominant factor in shaping species distributions (e.g., Caradima et al. 2020). Abundance and species richness, however, exhibit distinct responses to temperature variations: in aquatic insects, abundance decreases, while species richness increases with increasing temperatures (Piggott et al. 2015; Baranov et al. 2020; Khaliq et al. 2024). In addition to its direct impacts on aquatic wildlife, water temperature influences metal toxicity due to increased metabolic rates and increased respiratory inflows (Khangarot and Ray 1989; Meyer et al. 2007; Kazmi et al. 2022). Specifically, studies on the influence of temperature on copper toxicity have led to contradictory results (Meyer et al. 2007; e.g., on algae: Silva et al. 2018; Yong et al. 2021; Lambert et al. 2016), likely due to different ranges of testing temperatures (Pascual et al. 2022). High water temperature can also directly influence the pH of solutions (Anderson et al. 2021; Kosten et al. 2012) and accelerate algal growth rate, which in turn increases pH in aquatic ecosystems (Kosten et al. 2012; Visser et al. 2016). Rising water temperatures due to global warming thus likely affect aquatic communities through diverse effects. Additional factors (such as dissolved oxygen concentration in water or land-use-related factors) may interact with temperature, as

suggested by region-specific responses of biological communities (Khaliq et al. 2024).

Potential of hydrogen (pH) affects most chemical and biological processes in water. In addition to its direct effects on aquatic wildlife, a decrease in pH (acidification) may impact the use, transport and dose-response relationship of environmental pollutants (Ribeiro et al. 2002), especially in aquatic ecosystems (Stumm and Morgan 1996). However, the long-held belief that low pH increases metal toxicity (Pynnönen 1995) due to increased bioaccumulation caused by higher solubility (Campbell 1995; Dortwegt and Maughan 2001) has been challenged (e.g., Wang et al. 2016). Indeed, decreasing pH can have contradictory effects on the aquatic toxicity of metals, either increasing (Meyer et al. 2007) or decreasing (De Schamphelaere and Janssen 2004) their toxicity. These patterns seem to further depend on the biological taxon and the metal involved (Wang et al. 2016). The current changes in pH caused by global environmental changes (e.g., acid rain, mining operations, volcanic activities and climate change; Wang et al. 2016) may thus modulate metal toxicity in various ways.

Due to these diverse effects on metal solubility and bioavailability, pH and temperature may thus indirectly affect aquatic ecosystems through their modulation of copper availability. Due to the hormetic effects of copper, the combined effects of copper concentration, pH, and temperature are likely intricate and complex. A decrease in copper bioavailability by rising pH and temperature may have deleterious effects within the concentration range where copper has beneficial effects (copper would become a limiting factor) but may have beneficial effects within toxic ranges. Copper toxicity itself can depend on pH and temperature in a non-monotonous way: an increase in pH can either reduce or increase copper toxicity depending on the temperature, as observed in the green microalgae *Raphidocelis subcapitata* (from 15°C to 18°C, increasing the pH from 6 to 8 units reduces copper toxicity, while from 21°C to 30°C, rinsing the pH from 6 to 8 units increases copper toxicity; Pascual et al. 2022). These contradictory results on the effects of pH on metal toxicity and the lack of knowledge on the separate and combined effects of environmental parameters on aquatic wildlife stress the need for widely investigating the separate and combined effects of metal concentration, temperature and pH on aquatic wildlife in the field.

Within aquatic communities, Hydrae are common sedentary freshwater Cnidariae (a taxon to which corals and jellyfish also belong; e.g., Hoegh-Guldberg et al. 2017), very common in European freshwater aquatic ecosystems (Holdway 2005). In France, four species are found: *Hydra vulgaris*, *Hydra oligactis*, *Hydra viridissima* and *Hydra circumcincta*. Hydrae are regarded as a model organism in aquatic toxicology, for which metal toxicity and temperature have been extensively studied in laboratory conditions (Fatima et al. 2024; Farley et al. 2025), though absent from formalized monitoring programs (Quinn et al. 2012). Laboratory experiments have shown that Hydrae are particularly sensitive to metals, among which copper is the most toxic. Surprisingly, however, we did not find any previous work linking Hydrae presence and/or abundance to environmental parameters in the wild. We thus investigated the separate and interactive effects of copper pollution, pH, water
temperature, air temperature and the proportion of impervious surface (urbanization rate) on the presence and the abundance of freshwater Hydrae communities through a correlational approach in the Parisian region.

# 2 | Materials and Methods

## 2.1 | Study Sites

We sampled water in 49 sites from 20 different ponds in the Île-de-France region between June and September 2021 (Figure 1). As we had no previous knowledge about Hydrae presence in Île-de-France, sites and ponds were chosen randomly with regard to Hydrae presence, with special care to encompass sites with likely diverse urbanization rates and spatial localization (Figure 1).

## 2.2 | Hydra Sampling

Within each site, we collected a volume of 1L and surrounding floating elements such as dead leaves, plant debris and decomposing plants collected with a net in the water. We only sampled surface zones, as Hydra are mainly found near the surface, under aquatic plants, on the edge, in permanent, calm waters (Lenhoff 1983). When no Hydra was observed by visual inspection of the samples during collection in the field, we tended to sample again; consequently, the sampling effort (i.e., the number of times we passed the net in the water) differed between sites and may influence both the probability of sampling Hydra and the number of individuals sampled, either negatively (if

we tended to pass several times the net in the water when we observed no Hydra at first) or positively (if on the contrary we tended to pass additional times the net in the water when we did observe Hydra in the first samples). We therefore recorded this number, hereafter called ‘sampling effort’ to include it as a factor in the statistical analyses. All samples were placed in a cool box to avoid overheating during transport. Immediately back at the laboratory, samples were placed at a controlled temperature of 20°C in a climatic chamber.

## 2.3 | pH, Water and Air Temperatures and Copper Concentration Measurements

In each site, we measured water (approximately 10 cm below surface) and air temperatures with an infrared thermometer (Testo 826-T4) once temperatures stabilized after initial variation.

In each site, we sampled 15-mL water and filtered it immediately through a 0.45-µm mesh with a syringe, thus ensuring that the filtered fraction was composed of freely dissolved metals only (ANZG 2023).

All pH measurements were realized in the laboratory with a calibrated pH-metre within a few hours following sampling.

In the laboratory, samples were stabilized with 0.5-mL nitric acid (HNO₃) and kept at a constant temperature of 4°C. On 16 September 2021, copper concentrations were measured with an Inductively Coupled Plasma Optical Emission spectroscope (ICP-OES) 5100 SVDV Agilent (provided by Alipp6 platform, from the Observatoire des sciences de l'univers [OSU]

<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Maps of metropolitan France (in black and white) and of Île-de-France region (in colours) showing different landscapes features (natural, urban and water) and the sites where Hydra presence and abundance were investigated in this study (circles, coloured by a red gradient of abundance).</figcaption></figure>
Ecce Terra, Sorbonne Université). The wavelength used was 327.395 nm. Calibration was validated using a reference sample (TMDA-64.3, batch 0918, Environmental Matrix Reference Material of the Environment and Climate Change Canada) and repeatability checked during analysis by running a standard at 50 µg·L⁻¹ (relative standard deviation [RSD] < 0.6%).

## 2.4 | Geographical Coordinates and Percentage of Urban Features Within Buffer

Geographical coordinates of all sites were determined using https://www.gps-coordinates.net/. We downloaded georeferenced land cover data of the Île-de-France region from https://www.institutparisregion.fr/mode-doccupation-du-sol-mos/. We then placed a circular buffer of 1000 m around each sample point using QGIS (QGIS Development Team, version 3.36 Maidenhead) and calculated the urbanization rate within this buffer. To do so, we extracted landscape characteristics, distinguishing between non-urban terrestrial features (forests, semi-natural and agricultural habitats and open artificialized areas), water and typical urban features (individual and collective housing, activities, transports, quarries, landfills and building sites) and extracted the respective areas of these three landscape categories. We calculated the percentage of urban features as (sum of areas of all urban features)/(total area).

## 2.5 | Pond Area

From the georeferenced land cover data, we extracted the surface of each pond sampled using QGIS. Five ponds were not referenced in these data, so we estimated their surface from https://www.openstreetmap.org/. Due to two bridges, one lake

appeared artificially divided into three smaller lakes in the georeferenced land cover data, so we corrected it manually by summing their surface to obtain a more reliable estimate of the overall surface.

## 2.6 | Statistical Analyses

First, we checked for potential outliers in the data and investigated potential statistical associations between environmental parameters using Kendall's tau-b correlations (best suited when they are ties in the data). Not surprisingly, many parameters were significantly correlated (Table 1). To avoid collinearity issues in the models, we did not include air temperature in any model since it was correlated with most other environmental parameters and fitted three separate models for each dependent variable, each with non-correlated fixed effects: (i) water temperature, copper concentration, their interaction, sampling effort and sampling date; (ii) pH, copper concentration, their interaction, sampling effort and sampling date; and (iii) proportion of impervious surface and pond area.

To investigate the relation between the presence of Hydra and environmental parameters, we first fitted generalized linear mixed models with multivariate normal random effects using penalized quasi-likelihood, which allows us to deal with a binary distributed dependent variable, together with spatial correlation and random effects. We fitted three separate models with the presence of Hydra as the dependent variable with binomial distribution (presence or absence), pond and site as random effects and added an exponential spatial correlation structure to the model using longitude and latitude (to which we added 0.0001° whenever a site had been sampled several times, so that each sample had a unique latitude):

<table><caption>TABLE 1 | Kendall's tau-b correlations matrix between the six environmental parameters of the 49 sites studied.</caption><thead><tr><th>p values<br>tau-b</th><th>pH</th><th>Water<br>t°</th><th>Air t°</th><th>Urban</th><th>[Cu]</th><th>Area</th><th>Date</th><th>Time</th><th>Effort</th><th>Lat</th><th>Long</th></tr></thead><tbody><tr><td>pH</td><td></td><td>0.023</td><td>0.008</td><td>4e-7</td><td>0.57</td><td>0.61</td><td>0.19</td><td>0.19</td><td>0.59</td><td>0.0002</td><td>0.001</td></tr><tr><td>Water t°</td><td>0.23*</td><td></td><td>0.0003</td><td>0.055</td><td>0.64</td><td>0.30</td><td>0.81</td><td>0.50</td><td>0.15</td><td>0.11</td><td>0.017</td></tr><tr><td>Air t°</td><td>0.27**</td><td>0.37***</td><td></td><td>2e-6</td><td>0.41</td><td>0.22</td><td>0.03</td><td>0.005</td><td>0.0009</td><td>0.001</td><td>0.17</td></tr><tr><td>Urban</td><td>0.51***</td><td>0.19°</td><td>0.49***</td><td></td><td>0.055</td><td>0.10</td><td>0.001</td><td>0.37</td><td>0.011</td><td>&lt;2e-16</td><td>0.53</td></tr><tr><td>[Cu]</td><td>0.06</td><td>0.05</td><td>0.09</td><td>0.20°</td><td></td><td>0.017</td><td>0.09</td><td>0.98</td><td>0.10</td><td>0.062</td><td>0.022</td></tr><tr><td>Area</td><td>0.05</td><td>0.11</td><td>-0.13</td><td>-0.17</td><td>-0.26*</td><td></td><td>0.23</td><td>0.63</td><td>0.88</td><td>0.61</td><td>0.002</td></tr><tr><td>Date</td><td>-0.14</td><td>-0.10</td><td>-0.22*</td><td>-0.34***</td><td>-0.18</td><td>0.13</td><td></td><td>0.049</td><td>&lt;2e-16</td><td>5e-5</td><td>0.06</td></tr><tr><td>Time</td><td>0.14</td><td>0.03</td><td>0.29**</td><td>0.09</td><td>-0.004</td><td>-0.05</td><td>-0.21*</td><td></td><td>0.018</td><td>0.14</td><td>0.012</td></tr><tr><td>Effort</td><td>-0.06</td><td>-0.15</td><td>-0.37***</td><td>-0.27*</td><td>-0.18</td><td>0.02</td><td>0.62</td><td>-0.26*</td><td></td><td>0.004</td><td>0.30</td></tr><tr><td>Lat</td><td>0.37***</td><td>0.16</td><td>0.33**</td><td>0.55***</td><td>0.19°</td><td>-0.05</td><td>-0.42***</td><td>0.15</td><td>-0.31**</td><td></td><td>0.77</td></tr><tr><td>Long</td><td>0.32**</td><td>0.24*</td><td>0.14</td><td>0.06</td><td>-0.24*</td><td>0.03**</td><td>-0.19°</td><td>0.26*</td><td>-0.11</td><td>0.030</td><td></td></tr></tbody></table>

Note: Given are correlation coefficients (below diagonal) and two-sided p values (above diagonal). Significant correlations are indicated by the level of significance (° < 0.10, * < 0.05, ** < 0.01, *** < 0.001). 'Urban' is the degree of urbanization within a 1000-m radius around each site.
* In a first model, fixed factors were water temperature, copper concentration (log-transformed), their interaction, sampling effort and sampling date.

* In a second model, fixed factors were pH, copper concentration, their interaction, sampling effort and sampling date.

* In the third model, fixed factors were the proportion of impervious surface within 1000 m and pond area.

We also aimed at investigating potential differences between species in the relation between their presence and environmental parameters. The low sample size available for each species (see results) did not allow us to include species in interaction with each environmental parameter; instead, we tested whether environmental parameters in ponds differed between species when present by fitting generalized linear mixed models with multivariate normal random effects, using penalized quasi-likelihood, with pond and sites as random effects, and spatial correlation. We used raw pH, log-transformed copper concentrations, water temperature and the proportion of impervious surfaces to improve the normal distribution of the residuals.

To investigate the relation between the number of Hydra individuals collected (i.e., abundance and zero excepted) and environmental parameters of the ponds, we fitted models allowing for count data and to include random effects and spatial correlation structure. We fitted three linear mixed-effects models, with log-transformed abundance as the dependent variable (normally distributed: Shapiro-Wilk normality test W=0.97, p=0.88, whereas raw abundance was not: W=0.66, p=0.0001), pond and sites as random effects and added exponential spatial correlation structure to the model using longitude and latitude (adding 0.0001° as previously explained):

* In a first model, fixed factors were water temperature, copper concentration, their interaction, sampling effort and sampling date. We fitted this model both on the whole dataset and on a subset without two outliers of copper concentrations to check for their effect on the relation found.

* In a second model, fixed factors were pH, copper concentration, their interaction, sampling effort and sampling date.

* In a third model, fixed factors were the proportion of impervious surface within 1000 m and pond area.

Since linear mixed-effects models provide Akaike's Information Criterion (AIC), we compared models with and without spatial correlation structure based on their AIC and retained the best model with the lowest AIC (ΔAIC > 2).

Additionally, to test for differences between species with a small sample size, we fitted Multivariate generalized linear mixed models using Markov chain Monte Carlo techniques for each species separately, with the number of individuals collected as the dependent variable, copper concentration, water temperature, their interaction, sampling date and sampling effort as fixed effects and spatial coordinates as random effects.

Statistical analyses were performed using R (Ver. 4.1) and RStudio (2024.04.2 + 764 'Chocolate Cosmos'), with packages

rstatix (for outlier identification; Kassambara 2023), car (which function Anova allows extracting $\chi^2$ associated with global effects), dplyr (Wickham et al. 2023), ggplot (Wickham 2016), ggeffects (for predicted values in the figures; Lüdecke 2018), MASS (for generalized linear mixed models with multivariate normal random effects using penalized quasi-likelihood) and MCMCglmm (for Multivariate generalized linear mixed models using Markov chain Monte Carlo techniques; Hadfield 2010).

# 3 | Results

## 3.1 | Environmental Variables

Copper concentrations ranged between 4.2 and 9.7 µg·L⁻¹, with an average of 5.9 µg·L⁻¹, among which five sites had outliers of high concentrations (above 7.2 µg·L⁻¹). pH varied between 6.47 and 9.19 (mean 7.77) with no outlier detected. Water temperature varied between 17°C and 27.7°C (mean 21.23°C), with two sites detected as outliers of high temperature. pH appeared significantly associated with environmental parameters (all *p* values < 0.05; Table 1), except copper concentration. Not surprisingly, water and air temperature were statistically associated (Table 1), and the proportion of impervious surface depended on the site.

## 3.2 | Hydra Presence in Relation to Environmental Variables

Among the 49 samples collected, Hydra were present in 14 samples and visually identified as belonging to three species: *H. vulgaris* (present in six samples), *H. oligactis* (in five samples) and *H. viridissima* (in three samples). We did not find any pond with coexisting individuals of different species.

In the first model, the presence of Hydra detected in the ponds was not significantly associated with either copper concentration *water temperature interaction* ($\chi^2_1$=0.000, *p*=1.00), sampling effort ($\chi^2_1$=0.01, *p*=0.92), sampling date ($\chi^2_1$=0.74, *p*=0.39), copper concentration ($\chi^2_1$=0.59, *p*=0.44) nor water temperature ($\chi^2_1$=0.85, *p*=0.36). Removing copper and/or water temperature outliers lead to very similar results. In the second model, Hydra presence was not significantly associated with sampling effort ($\chi^2_1$=0.01, *p*=0.90), sampling date ($\chi^2_1$=9e-4, *p*=0.98), with copper concentration, either in interaction with pH ($\chi^2_1$=2.18, *p*=0.14) or alone ($\chi^2_1$=1.50, *p*=0.22), but appeared negatively associated with pH ($\chi^2_1$=9.18, *p*=0.002; Figure 2). Removing copper outliers lead to very similar results. In the third model, Hydra presence was associated with the proportion of impervious surface ($\chi^2_1$=7.40, *p*=0.007; Figure 3), but not with pond area ($\chi^2_1$=0.21, *p*=0.65).

Among the samples where we detected the presence of Hydra species, none of the environmental parameters measured in our work significantly differed among species (pH: $\chi^2_2$=0.01, *p*=0.99; copper concentration (log-transformed): $\chi^2_2$=2.16, *p*=0.34; water temperature (log-transformed): $\chi^2_2$=0.31, *p*=0.86; proportion of impervious surface (log-transformed): $\chi^2_2$=1.36, *p*=0.51).
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Probability of Hydra presence according to pH. White circles with values 0 (Hydra absent) or 1 (Hydra present) indicate the observed data (circles were slightly displaced vertically to avoid overplotting). The continuous line represents the predicted values from the final model including random effects; dashed lines correspond to lower and higher range of 95% confidence interval.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Probability of Hydra presence according to local proportion of impervious surface (within a buffer of 1000m around each of 49 sites). White circles with values 0 (Hydra absent) or 1 (Hydra present) indicate the observed data (circles were slightly displaced vertically to avoid overplotting). The continuous line represents the predicted values from the final model including random effects; dashed lines correspond to lower and higher range of 95% confidence interval.</figcaption></figure>
## 3.3 | Hydra Abundance in Relation to Environmental Variables

In sites where Hydra were present, the number of individuals collected ranged between 1 and 150 (mean = 26.9). Log-transformed abundance showed no outlier.

Among the models fitted for abundance (log-transformed number of individuals collected) with water temperature, copper concentration, their interaction, sampling effort and sampling date as fixed effects, the model with lowest AIC (53.69; all other models had either higher AIC or ΔAIC < 2) assumed no spatial correlation and included copper concentration ($\chi_1^2$ = 6.43, $p$ = 0.011; Figure 4) and sampling effort ($\chi_1^2$ = 2.80, $p$ = 0.095). The distribution of the residuals of this model did not significantly depart from normality ($W$ = 0.93, $p$ = 0.28). Without outliers, the model with lowest AIC assumed no spatial correlation, with copper concentration ($\chi_1^2$ = 0.36, $p$ = 0.55) and sampling effort ($\chi_1^2$ = 3.74, $p$ = 0.053) as fixed effects. Residuals did not significantly depart from normality ($W$ = 0.93, $p$ = 0.39).

Among the models with pH, copper concentration, their interaction, sampling effort and sampling date as fixed effects, the model with the lowest AIC (55.19; all other models had either higher AIC or ΔAIC < 2) assumed no spatial correlation and included copper concentration *pH interaction ($\chi_1^2$ = 0.10, $p$ = 0.75), pH ($\chi_1^2$ = 0.15, $p$ = 0.70), copper concentration ($\chi_1^2$ = 4.04, $p$ = 0.044) and sampling effort ($\chi_1^2$ = 2.42, $p$ = 0.12). The distribution of the residuals of this model did not significantly depart from normality ($W$ = 0.91, $p$ = 0.14). Without outliers, the model with the lowest AIC (43.20) assumed no spatial correlation, with copper concentration *pH ($\chi_1^2$ = 0.48, $p$ = 0.49), copper concentration ($\chi_1^2$ = 0.34, $p$ = 0.56),

pH ($\chi_1^2$ = 0.04, $p$ = 0.84) and sampling effort ($\chi_1^2$ = 2.58, $p$ = 0.11) as fixed effects. The distribution of the residuals of this model did not significantly depart from normality ($W$ = 0.91, $p$ = 0.19).

Finally, among models with water area and the proportion of impervious surfaces, the model with the lowest AIC (53.67; all other models had either higher AIC or ΔAIC < 2) assumed no spatial correlation and included the proportion of impervious surfaces ($\chi_1^2$ = 0.014, $p$ = 0.91) as a fixed factor. The distribution of the residuals of this model did not significantly depart from normality ($W$ = 0.96, $p$ = 0.78).

In MCMCglmm models (with spatial coordinates as random effects) fitted for the number of individuals collected for each species separately, none of the factors included appeared to have a significant effect for any species (Table S1), probably due to the low statistical power linked with a very small sample size.

# 4 | Discussion

## 4.1 | Environmental Parameters

Not surprisingly, several environmental parameters were correlated in our work, as has often been previously shown (including in the same region; Ringot et al. 2018). Due to these correlations, we had to fit a series of models for both Hydra presence and abundance to avoid collinearity issues.

Contrary to our expectations, we did not find any interaction between copper concentration and pH or temperature, which we predicted from the common assumption that pH and

<figure><img src="image_7.png" /><figcaption>**FIGURE 4** | Number of Hydra individuals collected (log-transformed) according to copper concentration in 14 samples from ponds where Hydra were observed. Circles represent raw data. Continuous line represents the predicted values from the final model (including random effects); dashed lines correspond to lower and higher range of 95% confidence interval.</figcaption></figure>
temperature modulate copper toxicity. We, however, found several statistical associations between Hydra presence or abundance and environmental parameters.

## 4.2 | Hydra Presence Decreases With Increasing pH

First, Hydra presence decreased with increasing pH (Figure 2). pH affects most chemical and biological processes in water. It is one of the most important environmental factors limiting species distributions in aquatic habitats, with optima pH for most aquatic organisms between 6.5 and 8 (https://www.epa.gov/caddis/ph). pH in a waterbody is generally considered to be low for values below 6 or 6.5 (depending on the system) and to be high for values exceeding 9 for prolonged periods or with high frequency. Low and high pH result from increases in $H^+$ or $OH^-$ inputs which can have diverse natural and anthropogenic causes, including human sources (such as agriculture, urbanization, mining and resource extraction and industry), which are acidic more often than basic. The pH measured in our work (ranging from 6.47 to 9.19) is close to the criteria used by US Environmental Protection Agency (EPA) for freshwater life (6.5–9.0; Hill 2020). In our study, Hydra presence seemed to be limited by high, rather than by low pH: while we found Hydra in ponds with pH ranging from 6.47 to 8.31, we did not find any Hydra in ponds with pH higher than 8.3 (Figure 2) nor any pond without Hydra for pH lower than 6.79. Previous studies have shown that Hydra sp. tolerate neutral pH ranging from 5 to 7.5 (Loomis 1954; Lenhoff 1983). However, in our correlative work, we measured pH on a restricted timescale, while temporal pH fluctuations can have adverse effects on ecosystems and wildlife. In areas with low buffering capacity, pH can fluctuate over the day; in particular, high rates of primary production decrease dissolved $CO_2$ and increase pH during the day, while pH decreases overnight. Future studies should thus further investigate Hydra presence in relation to temporal pH variations, to gain insight into their potential consequences on the presence and abundance of aquatic organisms.

## 4.3 | Hydra Presence Decreases With Increasing Proportion of Impervious Surfaces

Second, we found that the probability of Hydra presence decreased with the proportion of impervious surface within a 1000-m radius around each site (Figure 3). The proportion of impervious surface is a classic proxy for classifying urban land-use, measuring urbanization rate (Lu and Weng 2006) and is increasing within urban areas at a global scale (326,000 ha/year; Nowak and Greenfield 2020). The decrease in presence and/or in abundance of organisms with increasing proportion of impervious surface has been shown previously in a variety of taxa (e.g., Geslin et al. 2016), with likely diverse underlying mechanisms depending on the taxon and/or lifestyle. While the proportion of impervious surface can directly reduce the availability of resources and of nesting sites among ground-nesting species (e.g., Geslin et al. 2016), it may also indirectly impact aquatic organisms through other associated environmental factors. Urbanization in general is a process of environmental change across multiple, nonexclusive and often correlated

environmental axes, including sound or light pollution, chemical pollution (including copper, e.g., Table 1; Frazer 2005) or the percentage of impervious surface or canopy cover (Szulkin et al. 2020), which are known to profoundly affect ecosystems. Therefore, identifying the precise environmental factor, directly or indirectly linked with the proportion of impervious surface, responsible for the decrease in any organism presence remains elusive, as is the case in our correlative study. In particular, we could not disentangle the respective effects of pH and the proportion of impervious surface, as they were highly positively correlated (Table 1).

## 4.4 | Hydra Abundance Increases With Copper Concentration

Third, we found that the number of Hydra individuals collected tended to increase with increasing copper concentrations in the ponds (Figure 4). This may seem intriguing, as copper is the most toxic metal investigated in comparative acute toxicity studies in Hydra sp., followed by cadmium and zinc (Beach and Pascoe 1998; Pollino and Holdway 1999; Karntanut and Pascoe 2000; Holdway et al. 2001; Karntanut and Pascoe 2002; Karntanut and Pascoe 2005). This high sensitivity to metals in water likely results from their diploblastic morphology (i.e., two-tissue layer body structure: ectoderm and endoderm with thin acellular mesoglea in between), causing almost all cells to have direct contact with any toxicants in water (Holdway 2005; Quinn et al. 2012), including metals, as no analogue of metal binding proteins (with important role in homeostatic control and sequestration of metal ions) has yet been discovered in Hydra (Karntanut and Pascoe 2002).

However, copper has opposed biological effects depending on its concentration (Stebbing 2003), due to hormetic effects: beneficial at low, but toxic at high concentrations. Copper concentrations measured in our work (mean = 5.88 $\mu g \cdot L^{-1}$) were similar to the one measured in a previous work in the same region (mean = 4.136 $\mu g \cdot L^{-1}$; Ringot et al. 2018), though they fell in a much narrower range (4.2–9.7 $\mu g \cdot L^{-1}$ and standard error = 0.17, vs. 0.04–56.56 $\mu g \cdot L^{-1}$ and standard error = 1.18 in Ringot et al. 2018). Importantly, these concentrations fell below the threshold considered toxic for most organisms. In Hydra, copper has sublethal effects around 100 $\mu g \cdot L^{-1}$ (e.g., Zeeshan et al. 2016), but copper toxicity seems to depend on Hydra species: *H. viridissima* being more sensitive to copper (especially aposymbiotic, i.e., free of their endosymbiotic algae; Karntanut and Pascoe 2005) than *H. vulgaris* and *H. oligactis* (Karntanut and Pascoe 2002); in *Hydra littoralis*, a threshold for growth inhibition by copper has been measured around 4 $\mu g \cdot L^{-1}$ (Stebbing 2003), while in *Hydra attenuata* McKinley et al. (2019) measured 96 h $LC_{50}$ = 22.5 $\mu g \cdot L$, $LOEC$ = 50 $\mu g \cdot L$, $NOEC$ = 5 $\mu g \cdot L$, toxicity threshold = 0.125 $\mu g \cdot L$ ($TT$ = ($NOEC$×$LOEC$)/2), and 100% mortality at 50 $\mu g \cdot L$ (corresponding to 0.1×the environmental concentrations in their study). Lowest observed effect concentrations were 16 $\mu g \cdot L$ for symbiotic and 6.8 $\mu g \cdot L$ for aposymbiotic *H. viridissima* (Karntanut and Pascoe 2005). Overall, $LC_{50}$ of copper ranged between 25 and 180 $\mu g \cdot L$ in three Hydra species (Karntanut and Pascoe 2002). Both the copper concentrations measured (here and previously; Ringot et al. 2018) and the apparent relation between the number of Hydra collected and copper concentrations
may thus suggest that copper concentrations measured in Île-de-France are not high enough to cause toxic effects in *Hydra*. This is somewhat surprising, as the Île-de-France region is one of the most urbanized regions in Europe. Other potential mechanisms underlying the positive association observed between Hydra abundance and copper concentrations include indirect relations, through either negative effects of copper on (more sensitive) predators or competitors, or positive effects on prey or mutualists, or indirect (not mechanistically linked) associations between copper and any environmental factor beneficial to Hydra, or local adaptation to copper in both Hydra and the communities. As we passed our samples through 0.45 µm, we only measured the freely dissolved Cu²⁺, which is the most toxic. We thus did not include other forms of copper, which are less toxic and bioavailable, but which may have adverse effects. Further studies should characterize organic matter content to gain insight into the potential contrasting effects of copper concentrations on hydra communities depending on its form.

Finally, the marginally significant negative relationship between the number of individuals collected and sampling effort suggests that, when present, most Hydra individuals were collected during the first passes, so that it did not bias Hydra abundance (especially since it was included in the model).

## 5 | Conclusion

Interestingly, the probability of Hydra presence and the number of individuals collected were not associated with the same predictors. Substantial differences in the environmental factors that best predict site occupancy and abundance are common and have been previously documented (e.g., Dibner et al. 2017; Jiménez-Valverde et al. 2009) and discussed (Dibner et al. 2017). Notably, occupancy and abundance can be determined by environmental characteristics that operate at different temporal and spatial scales (Orrock et al. 2000) and may be dictated by different processes (Cingolani et al. 2007). The factors influencing occupancy (perhaps governed by regional population dynamics) are likely to operate at a broader scale than abundance, and this difference may well account for discrepancies between occupancy and abundance approaches (He and Gaston 2000). Environmental factors may act as coarse filters to dictate where species can and cannot persist, with more local characteristics governing abundance patterns (Dibner et al. 2017). Dibner et al. (2017) underline that caution is needed in uniting predictions of occupancy and abundance. It is possible that adaptation to high copper concentrations limits success at low copper concentrations (similarly to the hypothesized adaptation to low temperature in lizards; Dibner et al. 2017). We cannot exclude other factors correlated to the environmental parameters measured that could drive or interfere with Hydra occupancy and/or abundance in more complex ways (similarly to lizards in Dibner et al. 2017). For example, one factor could indicate the increased abundance of other species, including predators or competitors. Dibner et al. (2017) underline that the correspondence between occupancy and abundance is not simple or straightforward and that we stand to learn more from separating occupancy and abundance analyses than by considering the two together in a single united analysis.

Our work is correlative and the number of ponds where Hydra species were present remains modest (N=14). The Île-de-France region where our study took place is one of the most urbanized regions in Europe, and the precise range of urbanization obviously impacts its influence on biodiversity (Batáry et al. 2018). Additional studies (including experimental approaches) are needed to gain insight into the respective and combined effects of pH, urbanization, temperature, pollutants (e.g., copper) concentration and other environmental parameters likely to influence pollutant toxicity (i.e., toxicity modifying factors) on aquatic wildlife, from individuals to communities facing unprecedented global change.

## Author Contributions

J.R., A.P. and A.F. conceived the ideas and designed the methodology; J.R. and A.P. collected field samples and laboratory data under the supervision of A.F., with the help of E.B. for copper measurements; A.F. analyzed the data, designed the figures and wrote the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

## Acknowledgements

This work was supported by Sorbonne Université and the Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation, MESRI. We thank Alipp6 plateform from Observatoire des sciences de l'univers (OSU) Ecce Terra (http://ecceterra.sorbonne-universite.fr/fr/les_services_aoc/les Plateformes analytiques 2/plateforme-allip6.html) and an anonymous reviewer for useful comments on the manuscript.

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

The data that support the findings of this study are openly available in Zenodo at https://zenodo.org/records/14537717?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjFjYzk4ZjRiLWEwODktNDhiZC1iZGixLTg0YjI0ODA3ZjZhZiIsImRhdGEiOnt9LCJyYW5kb20iOiI2YW M3ODhiNjA1YjY5ZGFiMjcxNDU0NzM4NjIyOGRkYyJ9.N6SnaNiwRvITXpC37vSxDypAcaImUgm9vf_MuxNXrNDbk_4JQaOAaCci6lN2TPNrPpCUdFMwuUY7DlcEkyCyzw.

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section.