# Efficiency of invertebrate-based bioassessment for evaluating the ecological status of streams along a gradient of flow intermittence

Julie Crabot, Sylvain Dolédec, Maxence Forcellini, T. Datry

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## To cite this version:

Julie Crabot, Sylvain Dolédec, Maxence Forcellini, T. Datry. Efficiency of invertebrate-based bioassessment for evaluating the ecological status of streams along a gradient of flow intermittence. Ecological Indicators, 2021, 133, pp.108440. 10.1016/j.ecolind.2021.108440. hal-03485952

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Submitted on 8 Jan 2024

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# Efficiency of invertebrate-based bioassessment for evaluating the ecological status of streams along a gradient of flow intermittence

Julie Crabot, Sylvain Dolédec, Maxence Forcellini, T. Datry

► To cite this version:

Julie Crabot, Sylvain Dolédec, Maxence Forcellini, T. Datry. Efficiency of invertebrate-based bioassessment for evaluating the ecological status of streams along a gradient of flow intermittence. Ecological Indicators, 2021, 133, pp.108440. 10.1016/j.ecolind.2021.108440. hal-03485952

HAL Id: hal-03485952

https://hal.science/hal-03485952v1

Submitted on 8 Jan 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 Efficiency of invertebrate-based bioassessment for evaluating the ecological status of

2 streams along a gradient of flow intermittence

3

4

5 Julie Crabot¹,², Sylvain Dolédec³, Maxence Forcellini¹, Thibault Datry¹

6

7 ¹ INRAE, UR RiverLy, Centre de Lyon-Villeurbanne, France

8 ² Université Clermont Auvergne, CNRS, GEOLAB, F-63000 Clermont-Ferrand, France

9 ³ Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, F-69622,

10 Villeurbanne, France

11

12 **Corresponding author:** julie.crabot@gmx.fr
1 Efficiency of invertebrate-based bioassessment for evaluating the ecological status of

2 streams along a gradient of flow intermittence

3

4

5 Julie Crabot¹,², Sylvain Dolédec³, Maxence Forcellini¹, Thibault Datry¹

6

7 ¹ INRAE, UR RiverLy, Centre de Lyon-Villeurbanne, France

8 ² Université Clermont Auvergne, CNRS, GEOLAB, F-63000 Clermont-Ferrand, France

9 ³ Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, F-69622,

10 Villeurbanne, France

11

12 **Corresponding author:** julie.crabot@gmx.fr
13 **Abstract**

14 Intermittent rivers and ephemeral streams (IRES) represent the majority of the global river
15 network, support high biodiversity and provide multiple ecosystem services. However, they
16 are being degraded at alarming rates, and it may be questioned whether current invertebrate-
17 based biomonitoring protocols, which were designed for rivers and streams that flow all year
18 round, are also appropriate for assessing the ecological status of IRES. Here, we test for the
19 first time the efficiency of the French invertebrate-based biomonitoring indicators to assess
20 the ecological status of intermittent rivers downstream six sewage treatment plants along a
21 gradient of flow intermittence (i.e. the percentage of a year without flow at a given site). We
22 found that the effects of drying confounded with that of sewage and in most cases, current
23 biomonitoring metrics did not distinguish between the effect of sewage and those of drying.
24 Except for the taxonomic richness of invertebrates resistant to drying, all metrics decreased
25 linearly with increasing flow intermittence. In addition, the efficiency for detecting sewage
26 effects was lower at intermittent reaches compared to perennial reaches. Last, we identified
27 biological traits responding to flow intermittence, but not to sewage effluents, and we further
28 discuss the implication for traits-based monitoring. If current biomonitoring protocols
29 calibrated for perennial rivers could still be applied to reaches prone to weak flow
30 intermittence, alternatives are urgently needed for integrating intermittent rivers into current
31 biomonitoring national programs.

32

33 **Keywords**

34 Traits-based approaches; I2M2; biomonitoring; ecological status; drought; temporary rivers
13 Abstract

14 Intermittent rivers and ephemeral streams (IRES) represent the majority of the global river
15 network, support high biodiversity and provide multiple ecosystem services. However, they
16 are being degraded at alarming rates, and it may be questioned whether current invertebrate-
17 based biomonitoring protocols, which were designed for rivers and streams that flow all year
18 round, are also appropriate for assessing the ecological status of IRES. Here, we test for the
19 first time the efficiency of the French invertebrate-based biomonitoring indicators to assess
20 the ecological status of intermittent rivers downstream six sewage treatment plants along a
21 gradient of flow intermittence (i.e. the percentage of a year without flow at a given site). We
22 found that the effects of drying confounded with that of sewage and in most cases, current
23 biomonitoring metrics did not distinguish between the effect of sewage and those of drying.
24 Except for the taxonomic richness of invertebrates resistant to drying, all metrics decreased
25 linearly with increasing flow intermittence. In addition, the efficiency for detecting sewage
26 effects was lower at intermittent reaches compared to perennial reaches. Last, we identified
27 biological traits responding to flow intermittence, but not to sewage effluents, and we further
28 discuss the implication for traits-based monitoring. If current biomonitoring protocols
29 calibrated for perennial rivers could still be applied to reaches prone to weak flow
30 intermittence, alternatives are urgently needed for integrating intermittent rivers into current
31 biomonitoring national programs.

32

33 Keywords

34 Traits-based approaches; I2M2; biomonitoring; ecological status; drought; temporary rivers
## 1. Introduction

In the Anthropocene, water management relies on biomonitoring approaches to assess the responses of aquatic ecosystems to the multiple anthropogenic stressors they are facing (Reid et al., 2019). Accordingly, international legislations such as the Water Framework Directive in Europe (European Commission, 2000) or the Clean Water Act in the USA (Environmental Law Institute, 2012) call for biomonitoring to assess ecological quality of rivers and streams.

The use of biological signatures to assess human impairment in streams has a long tradition in freshwater ecology (e.g. reviews in Dolédec and Statzner, 2010 and Friberg et al., 2011) and benthic macroinvertebrates are one of the most widely used aquatic organisms for achieving such assessments (see e.g. Bonada et al., 2006; Rosenberg and Resh, 1993). To date, current biomonitoring approaches are based almost exclusively on aquatic communities collected from perennial rivers and streams (Reyjol et al., 2014; Seaman et al., 2016; Stubbington et al., 2018) and suggested alternatives for intermittent and ephemeral rivers remain rare and are not integrated into national efforts (Leigh et al., 2013; Prat et al., 2014; Steward et al., 2018).

Intermittent rivers and ephemeral streams, hereafter IRES, are defined as every rivers and streams that cease to flow and/or dry at some points in space and time (Datry et al., 2014).

Many authors have questioned the suitability of current biomonitoring approaches in IRES (Mazor et al., 2014; Munné and Prat, 2011; Prat et al., 2014; Reyjol et al., 2014; Steward et al., 2018; Stubbington et al., 2018; Wilding et al., 2018). However, only a few studies tested the applicability of the current macroinvertebrate-based biomonitoring approaches for intermittent rivers. For example, Morais et al. (2004) showed that the impact of a sewage treatment plant was not detectable in an intermittent stream in Portugal. In addition, a review by Stubbington et al. (2018) suggested that current biomonitoring approaches needed to be refined to be efficient for IRES. Recently, a global quantification of the prevalence of non-perennial rivers indicates that IRES comprise the majority of the river channels on Earth and
## 1. Introduction

In the Anthropocene, water management relies on biomonitoring approaches to assess the responses of aquatic ecosystems to the multiple anthropogenic stressors they are facing (Reid et al., 2019). Accordingly, international legislations such as the Water Framework Directive in Europe (European Commission, 2000) or the Clean Water Act in the USA (Environmental Law Institute, 2012) call for biomonitoring to assess ecological quality of rivers and streams.

The use of biological signatures to assess human impairment in streams has a long tradition in freshwater ecology (e.g. reviews in Dolédec and Statzner, 2010 and Friberg et al., 2011) and benthic macroinvertebrates are one of the most widely used aquatic organisms for achieving such assessments (see e.g. Bonada et al., 2006; Rosenberg and Resh, 1993). To date, current biomonitoring approaches are based almost exclusively on aquatic communities collected from perennial rivers and streams (Reyjol et al., 2014; Seaman et al., 2016; Stubbington et al., 2018) and suggested alternatives for intermittent and ephemeral rivers remain rare and are not integrated into national efforts (Leigh et al., 2013; Prat et al., 2014; Steward et al., 2018).

Intermittent rivers and ephemeral streams, hereafter IRES, are defined as every rivers and streams that cease to flow and/or dry at some points in space and time (Datry et al., 2014).

Many authors have questioned the suitability of current biomonitoring approaches in IRES (Mazor et al., 2014; Munné and Prat, 2011; Prat et al., 2014; Reyjol et al., 2014; Steward et al., 2018; Stubbington et al., 2018; Wilding et al., 2018). However, only a few studies tested the applicability of the current macroinvertebrate-based biomonitoring approaches for intermittent rivers. For example, Morais et al. (2004) showed that the impact of a sewage treatment plant was not detectable in an intermittent stream in Portugal. In addition, a review by Stubbington et al. (2018) suggested that current biomonitoring approaches needed to be refined to be efficient for IRES. Recently, a global quantification of the prevalence of non-perennial rivers indicates that IRES comprise the majority of the river channels on Earth and
might represent the norm rather than the exception (Messager et al., 2021). Such a shift in paradigm thus calls for assessing urgently whether current biomonitoring approaches can apply to IRES and if not, the limitations in their use or interpretation can be identified and corrected to accurately reflect the ecological status of the river networks facing the Anthropocene.

Both short- and long-term effects of drying on aquatic communities are relatively well understood due to the exponential number of dedicated studies through the past decade (e.g. Busch et al., 2020; Leigh et al., 2016). Across climatic and biogeographic zones, aquatic alpha-diversity (i.e. the local taxonomic richness at a given location) consistently decreases when annual flow intermittence (i.e. the percentage of a year without flow at a given site) increases (Belmar et al., 2019; Datry et al., 2014; England et al., 2019; Gauthier et al., 2020).

Immediately upon drying, taxa without adaptations to cope with desiccation are eliminated locally due to death or migration towards perennial refuges within the landscape (Bogan et al., 2015; Datry et al., 2014). The longer the dry event, the smaller the remaining pool of resistant taxa on the surface (Bogan et al., 2013; Datry, 2012; Pařil et al., 2019) and subsurface (Storey and Quinn, 2013; Stubbington et al., 2016; Stubbington and Datry, 2013) sediments. Upon rewetting, the arrival of species with high dispersal abilities from refuges also promotes community recovery (Bogan et al., 2015; Datry et al., 2014). With increased drying duration, the distance to refuges tends to increase, thus limiting the recolonization of previously dry sites (Cañedo-Argüelles et al., 2015; Crabot et al., 2020; Gauthier et al., 2020; Sarremejane et al., 2017b). As a result, legacy effects of drying occur even a long time after rewetting and local taxonomic diversity is generally much lower than that of adjacent perennial reaches (Crabot et al., 2021b; Datry, 2012; Gauthier et al., 2020; Soria et al., 2017). Previous studies reported that such taxa loss at intermittent sites was associated with a lower functional diversity compared to perennial sites (B-Béres et al., 2019; Schriever et al., 2015). However,
might represent the norm rather than the exception (Messager et al., 2021). Such a shift in paradigm thus calls for assessing urgently whether current biomonitoring approaches can apply to IRES and if not, the limitations in their use or interpretation can be identified and corrected to accurately reflect the ecological status of the river networks facing the Anthropocene.

Both short- and long-term effects of drying on aquatic communities are relatively well understood due to the exponential number of dedicated studies through the past decade (e.g. Busch et al., 2020; Leigh et al., 2016). Across climatic and biogeographic zones, aquatic alpha-diversity (i.e. the local taxonomic richness at a given location) consistently decreases when annual flow intermittence (i.e. the percentage of a year without flow at a given site) increases (Belmar et al., 2019; Datry et al., 2014; England et al., 2019; Gauthier et al., 2020).

Immediately upon drying, taxa without adaptations to cope with desiccation are eliminated locally due to death or migration towards perennial refuges within the landscape (Bogan et al., 2015; Datry et al., 2014). The longer the dry event, the smaller the remaining pool of resistant taxa on the surface (Bogan et al., 2013; Datry, 2012; Pařil et al., 2019) and subsurface (Storey and Quinn, 2013; Stubbington et al., 2016; Stubbington and Datry, 2013) sediments. Upon rewetting, the arrival of species with high dispersal abilities from refuges also promotes community recovery (Bogan et al., 2015; Datry et al., 2014). With increased drying duration, the distance to refuges tends to increase, thus limiting the recolonization of previously dry sites (Cañedo-Argüelles et al., 2015; Crabot et al., 2020; Gauthier et al., 2020; Sarremejane et al., 2017b). As a result, legacy effects of drying occur even a long time after rewetting and local taxonomic diversity is generally much lower than that of adjacent perennial reaches (Crabot et al., 2021b; Datry, 2012; Gauthier et al., 2020; Soria et al., 2017). Previous studies reported that such taxa loss at intermittent sites was associated with a lower functional diversity compared to perennial sites (B-Béres et al., 2019; Schriever et al., 2015). However,
because biological trait combinations can be expressed by many taxa or by only a few taxa in a local community, functional diversity response to flow intermittence might sometimes differ from the taxonomic patterns. As such, flow intermittence might reduce taxonomic diversity between intermittent and perennial reaches more importantly than functional diversity (Crabot et al., 2021a; Vander Vorste et al., 2016).

The effects of water quality degradation may confound with the natural responses of macroinvertebrate communities to drying (Soria et al., 2020). A large majority of the taxa used as indicators, i.e. with high sensitivity to water quality degradation such as Ephemeroptera, Plecoptera, Trichoptera (EPT) are those that are also primarily affected by drying. For example, the reduction of lateral hydrological connectivity associated with drought leads to a decline in EPT richness (Bonada et al., 2006b). Moreover, the number of EPT taxa differs between permanent and intermittent reference streams (Bogan et al., 2013; Datry, 2012; Straka et al., 2019). In contrast, some taxa known to be resistant to desiccation and widespread in IRES, such as Odonata, Coleoptera and Heteroptera (OCH), but also Oligochaeta and Diptera (Bonada et al., 2006b; Datry, 2012) are more tolerant to different types of water quality degradation, including water pollution (Chang et al., 2014). For example, looking at the 38 indicators taxa used in the French IBG-DCE for their sensitivity to water quality impairment, the only Coleoptera considered are Elmidae and are given a sensitivity of 2 (out of a maximal sensitivity of 9; AFNOR, 2004). Neither Heteroptera nor Odonata are considered in this list. Consequently, we expect a poor success of current biomonitoring approaches in the context of flow intermittence (Kalogianni et al., 2017; Soria et al., 2020; Stubbington et al., 2018) since both the number of taxa and taxa showing sensitivity to water quality degradation should decrease with increasing water quality degradation and flow intermittence (Fig. 1a). In turn, the absence of relationships between the measured biological scores and the ecological status may occur in case of flow intermittence,
because biological trait combinations can be expressed by many taxa or by only a few taxa in a local community, functional diversity response to flow intermittence might sometimes differ from the taxonomic patterns. As such, flow intermittence might reduce taxonomic diversity between intermittent and perennial reaches more importantly than functional diversity (Crabot et al., 2021a; Vander Vorste et al., 2016).

The effects of water quality degradation may confound with the natural responses of macroinvertebrate communities to drying (Soria et al., 2020). A large majority of the taxa used as indicators, i.e. with high sensitivity to water quality degradation such as Ephemeroptera, Plecoptera, Trichoptera (EPT) are those that are also primarily affected by drying. For example, the reduction of lateral hydrological connectivity associated with drought leads to a decline in EPT richness (Bonada et al., 2006b). Moreover, the number of EPT taxa differs between permanent and intermittent reference streams (Bogan et al., 2013; Datry, 2012; Straka et al., 2019). In contrast, some taxa known to be resistant to desiccation and widespread in IRES, such as Odonata, Coleoptera and Heteroptera (OCH), but also Oligochaeta and Diptera (Bonada et al., 2006b; Datry, 2012) are more tolerant to different types of water quality degradation, including water pollution (Chang et al., 2014). For example, looking at the 38 indicators taxa used in the French IBG-DCE for their sensitivity to water quality impairment, the only Coleoptera considered are Elmidae and are given a sensitivity of 2 (out of a maximal sensitivity of 9; AFNOR, 2004). Neither Heteroptera nor Odonata are considered in this list. Consequently, we expect a poor success of current biomonitoring approaches in the context of flow intermittence (Kalogianni et al., 2017; Soria et al., 2020; Stubbington et al., 2018) since both the number of taxa and taxa showing sensitivity to water quality degradation should decrease with increasing water quality degradation and flow intermittence (Fig. 1a). In turn, the absence of relationships between the measured biological scores and the ecological status may occur in case of flow intermittence,
contrary to what is observed in perennial rivers (Munné et al., 2021; Soria et al., 2020) (Fig. 1b). However, as the responses to flow intermittence are known to be linear (e.g. Datry et al. 2014), an effective operational space for weakly intermittent rivers and streams could allow using current biomonitoring approaches (Fig. 1b).

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Conceptualized responses of communities to water quality degradation and flow intermittence (a.) and expected biomonitoring scores and performance in case of perennial and intermittent flow regimes (b.). In grey, the area that could represent an operational space along which biomonitoring approaches developed for perennial rivers could be applicable to intermittent rivers.</figcaption></figure>
contrary to what is observed in perennial rivers (Munné et al., 2021; Soria et al., 2020) (Fig. 1b). However, as the responses to flow intermittence are known to be linear (e.g. Datry et al. 2014), an effective operational space for weakly intermittent rivers and streams could allow using current biomonitoring approaches (Fig. 1b).

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Conceptualized responses of communities to water quality degradation and flow intermittence (a.) and expected biomonitoring scores and performance in case of perennial and intermittent flow regimes (b.). In grey, the area that could represent an operational space along which biomonitoring approaches developed for perennial rivers could be applicable to intermittent rivers.</figcaption></figure>
In this study, we quantified the structural and functional community responses to six point-source of sewage along with a gradient of flow intermittence (0 to 60% within a year). We tested the hypothesis that current macroinvertebrate-based biomonitoring protocols were not able to infer the ecological status of naturally intermittent rivers. This is because the effect of drying on communities confounds with those of sewage pollution. Therefore, we first expected that impacted sites (downstream of effluents) located in intermittent reaches would have similar communities to that observed upstream of effluents. Thus, biomonitoring scores would not differ between downstream and upstream sites in the context of flow intermittence, contrary to perennial reaches. Second, we expected the capacity of current macroinvertebrate-based biomonitoring protocols to decrease with increasing flow intermittence, as taxonomic and functional richness are likely to decrease linearly with flow intermittence. Third, we anticipated that some traits should be favored by flow intermittence and some by water quality degradation. To unravel the underlying mechanisms, we identified how biotic and functional traits groups varied with flow intermittence and which one warranted further study over the long-term for establishing robust indicators of intermittent rivers quality.

137

## 2. Materials and methods

### 2.1. Study sites

The Albarine River is naturally intermittent draining a 313km² catchment located in the temperate eastern France (Fig. 2a). The upper section of the Albarine flows for 45 km on a karstic plateau and through gorges, where several reaches are intermittent due to karstic infiltrations, mostly occurring during the summer periods (Gauthier et al., 2020). Then, the Albarine flows over a 15-km long alluvial plain to finally end in the Ain River, a tributary of the upper Rhône. Surface water continuously infiltrates into the underneath aquifer due to its alluvial plain underlain by thick (>36 m) and highly permeable (from 2.10⁻² to 7.10⁻² m s⁻¹)
In this study, we quantified the structural and functional community responses to six point-source of sewage along with a gradient of flow intermittence (0 to 60% within a year). We tested the hypothesis that current macroinvertebrate-based biomonitoring protocols were not able to infer the ecological status of naturally intermittent rivers. This is because the effect of drying on communities confounds with those of sewage pollution. Therefore, we first expected that impacted sites (downstream of effluents) located in intermittent reaches would have similar communities to that observed upstream of effluents. Thus, biomonitoring scores would not differ between downstream and upstream sites in the context of flow intermittence, contrary to perennial reaches. Second, we expected the capacity of current macroinvertebrate-based biomonitoring protocols to decrease with increasing flow intermittence, as taxonomic and functional richness are likely to decrease linearly with flow intermittence. Third, we anticipated that some traits should be favored by flow intermittence and some by water quality degradation. To unravel the underlying mechanisms, we identified how biotic and functional traits groups varied with flow intermittence and which one warranted further study over the long-term for establishing robust indicators of intermittent rivers quality.

137

## 2. Materials and methods

### 2.1. Study sites

The Albarine River is naturally intermittent draining a 313km² catchment located in the temperate eastern France (Fig. 2a). The upper section of the Albarine flows for 45 km on a karstic plateau and through gorges, where several reaches are intermittent due to karstic infiltrations, mostly occurring during the summer periods (Gauthier et al., 2020). Then, the Albarine flows over a 15-km long alluvial plain to finally end in the Ain River, a tributary of the upper Rhône. Surface water continuously infiltrates into the underneath aquifer due to its alluvial plain underlain by thick (>36 m) and highly permeable (from 2.10⁻² to 7.10⁻² m s⁻¹)
sediments and because the river is generally perched 1-14 m above the regional aquifer water table along this section (Datry, 2012; Larned et al., 2011). As a result, this section dries completely during summer, when rainfall cannot compensate for transmission losses into the aquifer (Datry, 2012; Gauthier et al., 2020; Larned et al., 2010). Estimated surface water losses to the aquifer range from 0.85 to 2.5 m³ s⁻¹ (Datry, 2012).

In the upper section, depending on the winter snowpack and spring rainfall, drying can occur throughout spring and summer, following a very patchy spatial pattern with high temporal dynamism. Drying events start downstream of the lower sections during spring, at the confluence with the Ain River, and the drying front progressively moves upstream throughout the summer period (Datry, 2012). Flow resumption along the entire river occurs generally at late autumn/early winter (Datry, 2012; Larned et al., 2011). Consequently, annual flow can range between 0 and 60% along the Albarine River (Datry et al., 2011; Larned et al., 2010).

159

## 2.2. *Sampling design*

We selected 6 reaches along the Albarine course, each under the impact of a sewage treatment plant, whose effluents are directly released in the river, and considering the overall gradient of flow intermittence (hereafter FI) ranging from 0 to 60% (Fig. 2b). At each reach, we selected a site located between 50 and 100m downstream from the effluent release point and an upstream site located between 50 and 200m upstream from the effluent release point (Fig. 2b).

161

162

163

164

165

166
sediments and because the river is generally perched 1-14 m above the regional aquifer water table along this section (Datry, 2012; Larned et al., 2011). As a result, this section dries completely during summer, when rainfall cannot compensate for transmission losses into the aquifer (Datry, 2012; Gauthier et al., 2020; Larned et al., 2010). Estimated surface water losses to the aquifer range from 0.85 to 2.5 m³ s⁻¹ (Datry, 2012).

In the upper section, depending on the winter snowpack and spring rainfall, drying can occur throughout spring and summer, following a very patchy spatial pattern with high temporal dynamism. Drying events start downstream of the lower sections during spring, at the confluence with the Ain River, and the drying front progressively moves upstream throughout the summer period (Datry, 2012). Flow resumption along the entire river occurs generally at late autumn/early winter (Datry, 2012; Larned et al., 2011). Consequently, annual flow can range between 0 and 60% along the Albarine River (Datry et al., 2011; Larned et al., 2010).

159

## 2.2. *Sampling design*

We selected 6 reaches along the Albarine course, each under the impact of a sewage treatment plant, whose effluents are directly released in the river, and considering the overall gradient of flow intermittence (hereafter FI) ranging from 0 to 60% (Fig. 2b). At each reach, we selected a site located between 50 and 100m downstream from the effluent release point and an upstream site located between 50 and 200m upstream from the effluent release point (Fig. 2b).
a.

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

b.

<figure><img src="image_5.png" /></figure>

167

168

169 **Figure 2.** Location of the catchment in France (a), and location of the six sewage treatment plants on the Albarine (b). The color of the circles indicates the flow intermittence at each reach (from dark blue for 0% FI to red for 60% FI) and the small black dots indicate the samplings upstream and downstream of the sewage effluents. Continuous lines represent the perennial sections of the rivers and the dotted lines represent the intermittent sections. Grey section are unknown.

175

176 **2.3. Flow intermittence estimates**

177 Flow intermittence was quantified at the 3 reaches located in the upper section using photo-traps (Bushnell Trophycam®) setup for recording one photo every day at midnight and midday and left in place for 2 years (January 2017 until December 2018; Gauthier et al., 2020). For the 3 reaches selected in the lower section, FI was modelled for each reaches using the ELFMOD model (Larned et al., 2011). This model associated flow measurements at rated water-level recorders at each end of the 45-km alluvial section to flow state (flowing or dry) and flow rate (point source discharge measures) at all points between the recorders. The model was calibrated using data from gauging runs between October 2003 and January 2016,

178

179 midday and left in place for 2 years (January 2017 until December 2018; Gauthier et al.,

180 2020). For the 3 reaches selected in the lower section, FI was modelled for each reaches using

181 the ELFMOD model (Larned et al., 2011). This model associated flow measurements at rated

182 water-level recorders at each end of the 45-km alluvial section to flow state (flowing or dry)

183 and flow rate (point source discharge measures) at all points between the recorders. The

184 model was calibrated using data from gauging runs between October 2003 and January 2016,
a.

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

b.

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

167

168

169 **Figure 2.** Location of the catchment in France (a), and location of the six sewage treatment plants on the Albarine (b). The color of the circles indicates the flow intermittence at each reach (from dark blue for 0% FI to red for 60% FI) and the small black dots indicate the samplings upstream and downstream of the sewage effluents. Continuous lines represent the perennial sections of the rivers and the dotted lines represent the intermittent sections. Grey section are unknown.

175

176 **2.3. Flow intermittence estimates**

177 Flow intermittence was quantified at the 3 reaches located in the upper section using photo-traps (Bushnell Trophycam®) setup for recording one photo every day at midnight and midday and left in place for 2 years (January 2017 until December 2018; Gauthier et al., 2020). For the 3 reaches selected in the lower section, FI was modelled for each reaches using the ELFMOD model (Larned et al., 2011). This model associated flow measurements at rated water-level recorders at each end of the 45-km alluvial section to flow state (flowing or dry) and flow rate (point source discharge measures) at all points between the recorders. The model was calibrated using data from gauging runs between October 2003 and January 2016,

178

179 midday and left in place for 2 years (January 2017 until December 2018; Gauthier et al.,

180 2020). For the 3 reaches selected in the lower section, FI was modelled for each reaches using

181 the ELFMOD model (Larned et al., 2011). This model associated flow measurements at rated

182 water-level recorders at each end of the 45-km alluvial section to flow state (flowing or dry)

183 and flow rate (point source discharge measures) at all points between the recorders. The

184 model was calibrated using data from gauging runs between October 2003 and January 2016,
and validated using data collected outside of the calibration period. The empirical model was
used to reconstruct a 29-yr record of daily flows along the river (Rupp et al., 2008). We
subsequently tested the accuracy and precision of model-based estimates of FI using monthly
observations of flow made between October 2011 and June 2019 at the selected sampling
sites. Modelled FI at each reach was calculated by averaging annual estimates of flow
intermittence over the record. Observed FI was highly correlated with modelled FI (r = 0.95,
p < 0.001). The FI took into account only flowing and dry phases because the Albarine River
does not present phases of isolated pools, as it is often the case for alluvial rivers with porous
streambed sediments (Vander Vorste et al., 2016).

194

## 2.4. *Physical and chemical measurements*

The physical and chemical parameters up and downstream effluent releases were sampled at
the six reaches during spring (17 April 2018) and fall (19 December 2018) i.e., prior to and
after the summer drying period. Water samples and analytical measurements were carried out
by the CARSO laboratory (https://www.groupecarso.com/en/) that routinely measures the
physical and chemical parameters of water bodies within the WFD context. At each location
of each reach, 4L of surface water was collected and brought back to the laboratory within 6h
to measure the following physical and chemical parameters according to the national French
standards (Rodier et al., 2016): water temperature (°C), pH, specific conductivity (µS.cm⁻¹),
dissolved oxygen (mg.L⁻¹), total phosphorus (mg.L⁻¹, NF EN ISO 9308-3), suspended solid
(mg.L⁻¹, NF EN 872), total organic carbon (mg.L⁻¹, NF EN 872), dissolved organic carbon
(mg.L⁻¹, NF EN 1484), biochemical demand in oxygen for 5 days (mg.L⁻¹, NF EN 1899-2),
chemical oxygen demand (mg.L⁻¹, ISO 15705), Kjeldahl nitrogen (mg.L⁻¹, NF EN 25663),
ammonium (mg.L⁻¹, NF T90-015-2), nitrates (mg.L⁻¹, NF EN ISO 10304-1), orthophosphates
(mg.L⁻¹, NF EN ISO 68) and nitrites (mg.L⁻¹, NF EN ISO 13395). In addition, organic

196

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198

199

200

201

202

203

204

205

206

207

208

209
and validated using data collected outside of the calibration period. The empirical model was used to reconstruct a 29-yr record of daily flows along the river (Rupp et al., 2008). We subsequently tested the accuracy and precision of model-based estimates of FI using monthly observations of flow made between October 2011 and June 2019 at the selected sampling sites. Modelled FI at each reach was calculated by averaging annual estimates of flow intermittence over the record. Observed FI was highly correlated with modelled FI (r = 0.95, p < 0.001). The FI took into account only flowing and dry phases because the Albarine River does not present phases of isolated pools, as it is often the case for alluvial rivers with porous streambed sediments (Vander Vorste et al., 2016).

194

## 2.4. *Physical and chemical measurements*

The physical and chemical parameters up and downstream effluent releases were sampled at the six reaches during spring (17 April 2018) and fall (19 December 2018) i.e., prior to and after the summer drying period. Water samples and analytical measurements were carried out by the CARSO laboratory (https://www.groupecarso.com/en/) that routinely measures the physical and chemical parameters of water bodies within the WFD context. At each location of each reach, 4L of surface water was collected and brought back to the laboratory within 6h to measure the following physical and chemical parameters according to the national French standards (Rodier et al., 2016): water temperature (°C), pH, specific conductivity (µS.cm⁻¹), dissolved oxygen (mg.L⁻¹), total phosphorus (mg.L⁻¹, NF EN ISO 9308-3), suspended solid (mg.L⁻¹, NF EN 872), total organic carbon (mg.L⁻¹, NF EN 872), dissolved organic carbon (mg.L⁻¹, NF EN 1484), biochemical demand in oxygen for 5 days (mg.L⁻¹, NF EN 1899-2), chemical oxygen demand (mg.L⁻¹, ISO 15705), Kjeldahl nitrogen (mg.L⁻¹, NF EN 25663), ammonium (mg.L⁻¹, NF T90-015-2), nitrates (mg.L⁻¹, NF EN ISO 10304-1), orthophosphates (mg.L⁻¹, NF EN ISO 68) and nitrites (mg.L⁻¹, NF EN ISO 13395). In addition, organic

196

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200

201

202

203

204

205

206

207

208

209
210

pollution was measured using the density of *Escherichia coli* (MPN.10 ml⁻¹, NF EN ISO

211

9308-3).

212

213

## 2.5. Benthic macroinvertebrates

214

### 2.5.1. Sampling

215

Benthic macroinvertebrates were sampled up and downstream effluent releases by the

216

Eurofins Hydrobiology Consulting Company in spring (16-18th April 2018) and fall (17-19th

217

December 2018). Macroinvertebrate sampling was carried out following the NF T90-333

218

standards (AFNOR, 2016). Briefly, this consisted in the following steps. First, each sampling

219

site was first delineated by estimating the percentage of habitats (e.g. pool, riffles, run),

220

percentage of substrates (e.g., sand, gravel, cobbles) and percentage of velocity ranges (e.g.

221

slow ≤ 0.5 m/s, fast > 0.5 m/s) over a length of approximately 15 times the mean wetted

222

width. This allowed estimating the variety and relative abundance of habitats separated into

223

marginal (≤5% of the overall sampled location area) and dominant (>5% of the overall

224

sampled location area) substrates. Then, 12 samples were collected up and downstream

225

effluent releases at each reach using a Surber sample (1/20 m², 500µm mesh size) and they

226

were separated into 3 phases: 4 samples into the marginal habitats, 4 samples into the

227

dominant habitats taken in decreasing order of carrying capacity (from the more biogenic –

228

bryophytes, to the last biogenic – artificial uniform surfaces), and 4 complementary samples

229

into the dominant habitats considering their relative proportion over the sampling site.

230

Samples were washed in the field with the coarsest material (sediment, wood) cleaned with a

231

brush and put back in the river. All macroinvertebrates were preserved in 96% ethanol and

232

brought back to the laboratory within the day. Finally, all organisms of each sample were

233

counted and identified to the lowest practical taxonomic level. Most insects and all mollusks
210

pollution was measured using the density of *Escherichia coli* (MPN.10 ml⁻¹, NF EN ISO

211

9308-3).

212

213

## 2.5. Benthic macroinvertebrates

214

### 2.5.1. Sampling

215

Benthic macroinvertebrates were sampled up and downstream effluent releases by the

216

Eurofins Hydrobiology Consulting Company in spring (16-18th April 2018) and fall (17-19th

217

December 2018). Macroinvertebrate sampling was carried out following the NF T90-333

218

standards (AFNOR, 2016). Briefly, this consisted in the following steps. First, each sampling

219

site was first delineated by estimating the percentage of habitats (e.g. pool, riffles, run),

220

percentage of substrates (e.g., sand, gravel, cobbles) and percentage of velocity ranges (e.g.

221

slow ≤ 0.5 m/s, fast > 0.5 m/s) over a length of approximately 15 times the mean wetted

222

width. This allowed estimating the variety and relative abundance of habitats separated into

223

marginal (≤5% of the overall sampled location area) and dominant (>5% of the overall

224

sampled location area) substrates. Then, 12 samples were collected up and downstream

225

effluent releases at each reach using a Surber sample (1/20 m², 500µm mesh size) and they

226

were separated into 3 phases: 4 samples into the marginal habitats, 4 samples into the

227

dominant habitats taken in decreasing order of carrying capacity (from the more biogenic –

228

bryophytes, to the last biogenic – artificial uniform surfaces), and 4 complementary samples

229

into the dominant habitats considering their relative proportion over the sampling site.

230

Samples were washed in the field with the coarsest material (sediment, wood) cleaned with a

231

brush and put back in the river. All macroinvertebrates were preserved in 96% ethanol and

232

brought back to the laboratory within the day. Finally, all organisms of each sample were

233

counted and identified to the lowest practical taxonomic level. Most insects and all mollusks
234 were identified to genus or species levels, and crustaceans, annelids and mites to family, order
235 or genus levels following the French standard XP T90-388 (AFNOR, 2010).

236 **2.5.2. Biological indices**

237 Two biotic indexes are currently implemented at the national scale to assess stream
238 impairment in France: the so-called French biotic index (abbreviated IBG-DCE for “Indice
239 Biologique Global Directive Cadre sur l’Eau”; AFNOR, 2016, 2010, 2004) and the French
240 invertebrate multimetric index (abbreviated I2M2 for “Indice Invertébrés Multi-Métrique”;
241 Mondy et al., 2012). The IBG-DCE enables quantifying the impairment of the water
242 chemistry and the hydromorphological characteristics of a reach by considering the taxonomic
243 richness (TR, grouped into classes ranging from 1 to 14) and the sensitivity of 38 specific
244 indicator taxa (distributed into 9 indicator groups or IG) to water quality impairment. IBG-
245 DCE is computed as: TR + IG – 1 (with IBG-DCE >20 set to 20). IBG-DCE values varies
246 between 1 (low quality) and 20 (high quality). The categories of ecological status for the IBG-
247 DCE are bad (note of 0-5), poor (5-9), fair (9-12), good (12-14) and high (14-20). The I2M2
248 is made up of 3 elementary metrics considering the sensitivity to anthropogenic disturbance:
249 Average Score Per Taxon or ASPT, relative abundance of ovoviviparous and multivoltine
250 taxa in the assemblage; and 2 metrics describing the taxonomic assemblage structure:
251 Shannon diversity and taxonomic richness. The I2M2 is compatible with the requirements of
252 the Water Framework Directive, as it considers the stream reach typology, includes the
253 calculation of a deviation from a reference state and is expressed in Ecological Quality Ratio
254 from 1 (assemblage conforming to the reference) to 0 (completely disturbed). According to
255 the classification rules for the Albarine, the categories of ecological status for the I2M2 values
256 are bad (<0.118), poor (0.118-0.236), fair (0.236-0.354), good (0.354-0.605) and high
257 (>0.605). It should be finally noted that IBG-DCE invertebrates uses the family level as the
258 finest level of identification whereas for I2M2 it is the genus.
234 were identified to genus or species levels, and crustaceans, annelids and mites to family, order
235 or genus levels following the French standard XP T90-388 (AFNOR, 2010).

236 **2.5.2. Biological indices**

237 Two biotic indexes are currently implemented at the national scale to assess stream
238 impairment in France: the so-called French biotic index (abbreviated IBG-DCE for “Indice
239 Biologique Global Directive Cadre sur l’Eau”; AFNOR, 2016, 2010, 2004) and the French
240 invertebrate multimetric index (abbreviated I2M2 for “Indice Invertébrés Multi-Métrique”;
241 Mondy et al., 2012). The IBG-DCE enables quantifying the impairment of the water
242 chemistry and the hydromorphological characteristics of a reach by considering the taxonomic
243 richness (TR, grouped into classes ranging from 1 to 14) and the sensitivity of 38 specific
244 indicator taxa (distributed into 9 indicator groups or IG) to water quality impairment. IBG-
245 DCE is computed as: TR + IG – 1 (with IBG-DCE >20 set to 20). IBG-DCE values varies
246 between 1 (low quality) and 20 (high quality). The categories of ecological status for the IBG-
247 DCE are bad (note of 0-5), poor (5-9), fair (9-12), good (12-14) and high (14-20). The I2M2
248 is made up of 3 elementary metrics considering the sensitivity to anthropogenic disturbance:
249 Average Score Per Taxon or ASPT, relative abundance of ovoviviparous and multivoltine
250 taxa in the assemblage; and 2 metrics describing the taxonomic assemblage structure:
251 Shannon diversity and taxonomic richness. The I2M2 is compatible with the requirements of
252 the Water Framework Directive, as it considers the stream reach typology, includes the
253 calculation of a deviation from a reference state and is expressed in Ecological Quality Ratio
254 from 1 (assemblage conforming to the reference) to 0 (completely disturbed). According to
255 the classification rules for the Albarine, the categories of ecological status for the I2M2 values
256 are bad (<0.118), poor (0.118-0.236), fair (0.236-0.354), good (0.354-0.605) and high
257 (>0.605). It should be finally noted that IBG-DCE invertebrates uses the family level as the
258 finest level of identification whereas for I2M2 it is the genus.
259 **2.5.3 Biological traits**

260 The biological traits were extracted from the European freshwater invertebrate database
261 (Tachet et al., 2010). We considered 10 traits: maximal size, life span, number of generations
262 per year, types of reproduction, forms of resistance, types of respiration, dispersal, types of
263 locomotion, food and feeding habits. In this database, traits are divided into categories and are
264 quantified using fuzzy coding (Chevenet et al., 1994), which consists in giving an affinity
265 score to a given trait category, between 0 and 3 (maximal size, life span, number of
266 generations per year, types of reproduction, forms of resistance, types of respiration, dispersal,
267 types of locomotion) or 0 and 5 (food and feeding habits), for each taxon. Fuzzy coding
268 enables the compilation of information from various sources and accounts for within-taxon
269 variations.

270 **2.6. Data analysis**

271 To quantify the effects of the sewage treatment plants, we compared the concentrations in
272 nutrients and associated parameters between downstream and upstream effluent releases. For
273 each physical and chemical parameter, we calculated the difference between the downstream
274 and upstream values at each reach and each season. Positive values indicated a higher
275 degradation caused by sewage effluents. For each physical and chemical parameter, we tested
276 if the mean difference was significantly greater than zero using unilateral paired t-tests. To
277 assess the efficiency of the overall family richness, IBG-DCE and I2M2 at detecting water
278 quality degradation, we used a mixed-effect model with sewage (up or downstream effluent
279 releases), season (spring vs. fall) and their interaction as fixed effects, and reaches as random
280 effects. The significance of mixed-effect models was evaluated by a comparison with its
281 associated null model using likelihood-ratio tests. All mixed-effect models were computed
282 with the function 'lmer' of package lme4 (Bates et al., 2019). We checked whether the
283 random effects were significant using the function 'rand' of package lmerTest (Kuznetsova et
259 **2.5.3 Biological traits**

260 The biological traits were extracted from the European freshwater invertebrate database
261 (Tachet et al., 2010). We considered 10 traits: maximal size, life span, number of generations
262 per year, types of reproduction, forms of resistance, types of respiration, dispersal, types of
263 locomotion, food and feeding habits. In this database, traits are divided into categories and are
264 quantified using fuzzy coding (Chevenet et al., 1994), which consists in giving an affinity
265 score to a given trait category, between 0 and 3 (maximal size, life span, number of
266 generations per year, types of reproduction, forms of resistance, types of respiration, dispersal,
267 types of locomotion) or 0 and 5 (food and feeding habits), for each taxon. Fuzzy coding
268 enables the compilation of information from various sources and accounts for within-taxon
269 variations.

270 **2.6. Data analysis**

271 To quantify the effects of the sewage treatment plants, we compared the concentrations in
272 nutrients and associated parameters between downstream and upstream effluent releases. For
273 each physical and chemical parameter, we calculated the difference between the downstream
274 and upstream values at each reach and each season. Positive values indicated a higher
275 degradation caused by sewage effluents. For each physical and chemical parameter, we tested
276 if the mean difference was significantly greater than zero using unilateral paired t-tests. To
277 assess the efficiency of the overall family richness, IBG-DCE and I2M2 at detecting water
278 quality degradation, we used a mixed-effect model with sewage (up or downstream effluent
279 releases), season (spring vs. fall) and their interaction as fixed effects, and reaches as random
280 effects. The significance of mixed-effect models was evaluated by a comparison with its
281 associated null model using likelihood-ratio tests. All mixed-effect models were computed
282 with the function 'lmer' of package lme4 (Bates et al., 2019). We checked whether the
283 random effects were significant using the function 'rand' of package lmerTest (Kuznetsova et
al., 2020). We performed four separate analyses considering overall, EPT, OCH, and Gastropods, Oligochaeta, and Diptera (GOLD) richness. These groups were chosen because they are known to respond differently to FI (e.g. Bonada et al., 2006b; Datry et al., 2014).

We tested our first hypothesis on the negative influence of FI on family richness, IBG-DCE and I2M2 by performing a multiple linear regression with FI and season (spring vs. fall) as explanatory variables.

To test our second hypothesis that the severity of flow intermittence would impact the efficiency of pollution detection, we ran the same tests only on sites upstream effluent releases. We also computed the differences (hereafter delta) in family richness, IBG-DCE and I2M2 between up and downstream effluent releases at each reach and each sampling date. If sewage effects were detected, we would expect positive delta values for OCH and GOLD taxa richness, but negative delta values for the overall family richness, EPT richness, and for the IBG-DCE and I2M2 metrics. We assessed the effect of FI on these delta values with a multiple linear regression, using FI and season (spring vs. fall) as explanatory variables.

To further analyze how intermittence influenced our ability to detect the effects of sewage on biota, we calculated the absolute difference in abundance between up and downstream effluent releases for each taxon. These delta-abundances were log-transformed to reduce the disparities between taxa. We performed a Principal Component Analysis (PCA) on the log-delta abundance values using the 'dudi.pca' function of package ade4 (Dray et al., 2007; Thioulouse et al., 2018). The effect of flow regime (FR; perennial vs. intermittent) and season on the taxon delta-abundances was assessed by running Monte-Carlo tests on the proportion of variance explained between groups (with groups determined either by FR or by season;
al., 2020). We performed four separate analyses considering overall, EPT, OCH, and Gastropods, Oligochaeta, and Diptera (GOLD) richness. These groups were chosen because they are known to respond differently to FI (e.g. Bonada et al., 2006b; Datry et al., 2014).

We tested our first hypothesis on the negative influence of FI on family richness, IBG-DCE and I2M2 by performing a multiple linear regression with FI and season (spring vs. fall) as explanatory variables.

To test our second hypothesis that the severity of flow intermittence would impact the efficiency of pollution detection, we ran the same tests only on sites upstream effluent releases. We also computed the differences (hereafter delta) in family richness, IBG-DCE and I2M2 between up and downstream effluent releases at each reach and each sampling date. If sewage effects were detected, we would expect positive delta values for OCH and GOLD taxa richness, but negative delta values for the overall family richness, EPT richness, and for the IBG-DCE and I2M2 metrics. We assessed the effect of FI on these delta values with a multiple linear regression, using FI and season (spring vs. fall) as explanatory variables.

To further analyze how intermittence influenced our ability to detect the effects of sewage on biota, we calculated the absolute difference in abundance between up and downstream effluent releases for each taxon. These delta-abundances were log-transformed to reduce the disparities between taxa. We performed a Principal Component Analysis (PCA) on the log-delta abundance values using the 'dudi.pca' function of package ade4 (Dray et al., 2007; Thioulouse et al., 2018). The effect of flow regime (FR; perennial vs. intermittent) and season on the taxon delta-abundances was assessed by running Monte-Carlo tests on the proportion of variance explained between groups (with groups determined either by FR or by season;
309 Dolédec and Chessel, 1987) using the functions 'bca' and 'randtest' of package ade4. These
310 patterns were further tested by a two-way ANOVA on the mean log-delta abundance values
311 of all taxa for each reach and each season, with FR, season and their interaction as
312 explanatory variables.

313

314 Finally, to test our third hypothesis on the influence of sewage and FI on the trait combination
315 in communities, we calculated the Community Weighted Mean (CWM) array using the
316 'functcomp' function of package FD (Laliberté et al., 2014). Then, we computed a two-way
317 ANOVA on the CWM values of each trait category, using sewage (up and downstream
318 effluent releases), FR (intermittent vs. perennial) and their interaction as explanatory
319 variables.

320

All analyses were performed using the R software (R Core Team, 2021).

321

## 3. Results

322

### 3.1. Flow and physical and chemical characteristics of the studied reaches

323 Our dataset comprised two perennial reaches and four intermittent reaches, the latter drying
324 from 30% to 60% of the time within a year (Table 1). The difference between up and
325 downstream effluent releases values for all physical and chemical parameters were strictly
326 positive in 64% of all reaches and seasons. The average difference was positive for total
327 organic carbon, dissolved organic carbon, total phosphorus and orthophosphate (Appendix A).
328 This indicates that the sewage treatment plants altered the water quality of the stream. In
329 contrast, the average differences between up and downstream effluent releases values were
330 not influenced by the flow regime (Appendix B).

331
309 Dolédec and Chessel, 1987) using the functions 'bca' and 'randtest' of package ade4. These
310 patterns were further tested by a two-way ANOVA on the mean log-delta abundance values
311 of all taxa for each reach and each season, with FR, season and their interaction as
312 explanatory variables.

313
314 Finally, to test our third hypothesis on the influence of sewage and FI on the trait combination
315 in communities, we calculated the Community Weighted Mean (CWM) array using the
316 'functcomp' function of package FD (Laliberté et al., 2014). Then, we computed a two-way
317 ANOVA on the CWM values of each trait category, using sewage (up and downstream
318 effluent releases), FR (intermittent vs. perennial) and their interaction as explanatory
319 variables.
320
321 All analyses were performed using the R software (R Core Team, 2021).

321 3. Results

322 3.1. Flow and physical and chemical characteristics of the studied reaches

323 Our dataset comprised two perennial reaches and four intermittent reaches, the latter drying
324 from 30% to 60% of the time within a year (Table 1). The difference between up and
325 downstream effluent releases values for all physical and chemical parameters were strictly
326 positive in 64% of all reaches and seasons. The average difference was positive for total
327 organic carbon, dissolved organic carbon, total phosphorus and orthophosphate (Appendix A).
328 This indicates that the sewage treatment plants altered the water quality of the stream. In
329 contrast, the average differences between up and downstream effluent releases values were
330 not influenced by the flow regime (Appendix B).

331
<table><thead><tr><th>Reaches</th><th>Distance to the source (km)</th><th>FR</th><th>FI (%)</th></tr></thead><tbody><tr><td>Brenod</td><td>7.2</td><td>Perennial</td><td>0</td></tr><tr><td>Champdor</td><td>11.0</td><td>Intermittent</td><td>60</td></tr><tr><td>Hauteville</td><td>19.3</td><td>Intermittent</td><td>50</td></tr><tr><td>Chaley</td><td>23.7</td><td>Perennial</td><td>0</td></tr><tr><td>Bettant</td><td>74.5</td><td>Intermittent</td><td>30</td></tr><tr><td>Amberieu en Bugey</td><td>51.6</td><td>Intermittent</td><td>40</td></tr></tbody></table>

332

333 **Table 1.** Hydrological characteristics of each sampled reach. FR stands for ‘flow regime’, FI 334 stands for ‘flow intermittence’.

335

336 **3.2. Biological indices**

337 A weak difference between up and downstream effluent releases values occurred only for the 338 GOLD richness (about 1 taxa) (Table 2). In contrast, all biological metrics decreased strongly 339 with increasing FI except the OCH and GOLD taxa richness (Table 3, Fig. 3). This holds 340 when considering samples made only upstream effluent releases for GOLD taxa richness, 341 which was also influenced by season (Tables 2, 3). Except for OCH taxa richness, the delta 342 values between up and downstream effluent releases did not respond to the FI gradient (Table 343). We found a higher number of OCH taxa down than upstream effluent releases for the more 344 intermittent reaches. Last, all variables were lower in fall compared to spring (Table 2).

345

346 A two-way ANOVA with FR (intermittent or perennial) and season (spring or fall) as factors 347 indicated that two delta values responded to season (Table 4, Fig. 4). For instance, GOLD 348 taxa were more numerous up than downstream effluent releases during spring (delta value: - 349 1.20±2.17) and it was the opposite during fall (1.67±1.21). The IBG-DCE was slightly higher
<table><thead><tr><th>Reaches</th><th>Distance to the source (km)</th><th>FR</th><th>FI (%)</th></tr></thead><tbody><tr><td>Brenod</td><td>7.2</td><td>Perennial</td><td>0</td></tr><tr><td>Champdor</td><td>11.0</td><td>Intermittent</td><td>60</td></tr><tr><td>Hauteville</td><td>19.3</td><td>Intermittent</td><td>50</td></tr><tr><td>Chaley</td><td>23.7</td><td>Perennial</td><td>0</td></tr><tr><td>Bettant</td><td>74.5</td><td>Intermittent</td><td>30</td></tr><tr><td>Amberieu en Bugey</td><td>51.6</td><td>Intermittent</td><td>40</td></tr></tbody></table>

332

333 **Table 1.** Hydrological characteristics of each sampled reach. FR stands for ‘flow regime’, FI stands for ‘flow intermittence’.

335

336 **3.2. Biological indices**

337 A weak difference between up and downstream effluent releases values occurred only for the
338 GOLD richness (about 1 taxa) (Table 2). In contrast, all biological metrics decreased strongly
339 with increasing FI except the OCH and GOLD taxa richness (Table 3, Fig. 3). This holds
340 when considering samples made only upstream effluent releases for GOLD taxa richness,
341 which was also influenced by season (Tables 2, 3). Except for OCH taxa richness, the delta
342 values between up and downstream effluent releases did not respond to the FI gradient (Table
343 3). We found a higher number of OCH taxa down than upstream effluent releases for the more
344 intermittent reaches. Last, all variables were lower in fall compared to spring (Table 2).

345

346 A two-way ANOVA with FR (intermittent or perennial) and season (spring or fall) as factors
347 indicated that two delta values responded to season (Table 4, Fig. 4). For instance, GOLD
348 taxa were more numerous up than downstream effluent releases during spring (delta value: -
349 1.20±2.17) and it was the opposite during fall (1.67±1.21). The IBG-DCE was slightly higher
<table><thead><tr><th rowspan="2">Variable</th><th colspan="3">Degradation</th><th colspan="5">Season</th><th colspan="3">Interaction</th></tr><tr><th>Mean upstream</th><th>Mean downstream</th><th>df</th><th>Chi2</th><th>p-value</th><th>Mean spring</th><th>Mean fall</th><th>df</th><th>Chi2</th><th>p-value</th><th>df</th><th>Chi2</th><th>p-value</th></tr></thead><tbody><tr><td>Family richness</td><td>25(±8)</td><td>27(±7)</td><td>2</td><td>0.61</td><td>0.738</td><td>30(±6)</td><td>22(±7)</td><td>2</td><td>10.12</td><td>0.006 **</td><td>1</td><td>0.04</td><td>0.838</td></tr><tr><td>EPT richness</td><td>6(±2)</td><td>5(±3)</td><td>2</td><td>4.57</td><td>0.102</td><td>7(±2)</td><td>4(±2)</td><td>2</td><td>15.39</td><td>0.000 ***</td><td>1</td><td>0.63</td><td>0.427</td></tr><tr><td>OCH richness</td><td>4(±2)</td><td>5(±3)</td><td>2</td><td>1.88</td><td>0.391</td><td>5(±3)</td><td>3(±2)</td><td>2</td><td>7.224</td><td>0.027 *</td><td>1</td><td>0.03</td><td>0.861</td></tr><tr><td>GOLD richness</td><td>9(±1)</td><td>9(±2)</td><td>2</td><td>8.21</td><td>0.016 *</td><td>9(±1)</td><td>9(±2)</td><td>2</td><td>7.751</td><td>0.021 *</td><td>1</td><td>7.22</td><td>0.007 **</td></tr><tr><td>IBG-DCE</td><td>13.3(±3.4)</td><td>13.5(±3.3)</td><td>2</td><td>1.22</td><td>0.544</td><td>14.7(±2.2)</td><td>12.0(±3.8)</td><td>2</td><td>10.39</td><td>0.006 **</td><td>1</td><td>1.19</td><td>0.276</td></tr><tr><td>I2M2</td><td>0.50(±0)</td><td>0.57(±0.15)</td><td>2</td><td>5.33</td><td>0.070</td><td>0.58(±0.15)</td><td>0.48(±0.16)</td><td>2</td><td>8.851</td><td>0.012 *</td><td>1</td><td>0.04</td><td>0.838</td></tr></tbody></table>

350

**Table 2.** Results of the mixed-effect models performed on the family richness for all taxa, EPT, OCH and GOLD, and biological indices IBG-DCE and I2M2, as response variables, sewage (up vs. downstream effluent releases), season (spring vs fall) and their interaction as fixed effects, and reaches as random effects. The random effect was significant for all variables except GOLD richness. EPT stands for Ephemeroptera, Plecoptera, Trichoptera; OCH for Odonata, Coleoptera, Heteroptera; GOLD for Gastropods, Oligochaeta and Diptera. ‘df’ stands for degrees of freedom.

351

352

353

354

355
<table><thead><tr><th rowspan="2">Variable</th><th colspan="3">Degradation</th><th colspan="5">Season</th><th colspan="3">Interaction</th></tr><tr><th>Mean upstream</th><th>Mean downstream</th><th>df</th><th>Chi2</th><th>p-value</th><th>Mean spring</th><th>Mean fall</th><th>df</th><th>Chi2</th><th>p-value</th><th>df</th><th>Chi2</th><th>p-value</th></tr></thead><tbody><tr><td>Family richness</td><td>25(±8)</td><td>27(±7)</td><td>2</td><td>0.61</td><td>0.738</td><td>30(±6)</td><td>22(±7)</td><td>2</td><td>10.12</td><td>0.006 **</td><td>1</td><td>0.04</td><td>0.838</td></tr><tr><td>EPT richness</td><td>6(±2)</td><td>5(±3)</td><td>2</td><td>4.57</td><td>0.102</td><td>7(±2)</td><td>4(±2)</td><td>2</td><td>15.39</td><td>0.000 ***</td><td>1</td><td>0.63</td><td>0.427</td></tr><tr><td>OCH richness</td><td>4(±2)</td><td>5(±3)</td><td>2</td><td>1.88</td><td>0.391</td><td>5(±3)</td><td>3(±2)</td><td>2</td><td>7.224</td><td>0.027 *</td><td>1</td><td>0.03</td><td>0.861</td></tr><tr><td>GOLD richness</td><td>9(±1)</td><td>9(±2)</td><td>2</td><td>8.21</td><td>0.016 *</td><td>9(±1)</td><td>9(±2)</td><td>2</td><td>7.751</td><td>0.021 *</td><td>1</td><td>7.22</td><td>0.007 **</td></tr><tr><td>IBG-DCE</td><td>13.3(±3.4)</td><td>13.5(±3.3)</td><td>2</td><td>1.22</td><td>0.544</td><td>14.7(±2.2)</td><td>12.0(±3.8)</td><td>2</td><td>10.39</td><td>0.006 **</td><td>1</td><td>1.19</td><td>0.276</td></tr><tr><td>I2M2</td><td>0.50(±0)</td><td>0.57(±0.15)</td><td>2</td><td>5.33</td><td>0.070</td><td>0.58(±0.15)</td><td>0.48(±0.16)</td><td>2</td><td>8.851</td><td>0.012 *</td><td>1</td><td>0.04</td><td>0.838</td></tr></tbody></table>

350

**Table 2.** Results of the mixed-effect models performed on the family richness for all taxa, EPT, OCH and GOLD, and biological indices IBG-DCE and I2M2, as response variables, sewage (up vs. downstream effluent releases), season (spring vs fall) and their interaction as fixed effects, and reaches as random effects. The random effect was significant for all variables except GOLD richness. EPT stands for Ephemeroptera, Plecoptera, Trichoptera; OCH for Odonata, Coleoptera, Heteroptera; GOLD for Gastropods, Oligochaeta and Diptera. ‘df’ stands for degrees of freedom.

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<table><caption>**Table 3.** Results of the linear regression performed on the taxonomic richness for all taxa, EPT, OCH and GOLD, and indicators IBG-DCE and I2M2 as response variables, FI, and season (spring vs. fall) as explanatory variables. All models were computed using all sites, upstream effluent releases only, and on delta values between up and downstream effluent releases. EPT stands for Ephemeroptera, Plecoptera, Trichoptera; OCH for Odonata, Coleoptera, Heteroptera; GOLD for Gastropods, Oligochaeta and Diptera.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Estimate</th><th colspan="4">All sites</th><th colspan="4">Reference sites only</th><th colspan="4">Delta impacted-reference</th></tr><tr><th>Estimate</th><th>SD</th><th>t value</th><th>p-value</th><th>Estimate</th><th>SD</th><th>t value</th><th>p-value</th><th>Estimate</th><th>SD</th><th>t value</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="3">Family richness</td><td>Intercept</td><td>35</td><td>2</td><td>18.93</td><td>&lt;0.001 ***</td><td>37</td><td>2</td><td>15.75</td><td>&lt;0.001 ***</td><td>-2</td><td>3</td><td>-0.79</td><td>0.447</td></tr><tr><td>FI</td><td>-19</td><td>4</td><td>-4.45</td><td>&lt;0.001 ***</td><td>-26</td><td>5</td><td>-5.02</td><td>0.001 **</td><td>14</td><td>7</td><td>2.06</td><td>0.069</td></tr><tr><td>Fall</td><td>-7</td><td>2</td><td>-3.74</td><td>0.001 **</td><td>-7</td><td>2</td><td>-2.90</td><td>0.018 *</td><td>-2</td><td>3</td><td>-0.52</td><td>0.614</td></tr><tr><td rowspan="3">EPT richness</td><td>Intercept</td><td>9</td><td>1</td><td>14.33</td><td>&lt;0.001 ***</td><td>9</td><td>1</td><td>11.03</td><td>&lt;0.001 ***</td><td>-1</td><td>1</td><td>-0.94</td><td>0.371</td></tr><tr><td>FI</td><td>-7</td><td>1</td><td>-4.70</td><td>&lt;0.001 ***</td><td>-7</td><td>2</td><td>-3.51</td><td>0.007 **</td><td>0</td><td>2</td><td>0.00</td><td>1.000</td></tr><tr><td>Fall</td><td>-2</td><td>1</td><td>-3.74</td><td>0.001 **</td><td>-2</td><td>1</td><td>-2.32</td><td>0.046 *</td><td>-1</td><td>1</td><td>-1.36</td><td>0.208</td></tr><tr><td rowspan="3">OCH richness</td><td>Intercept</td><td>6</td><td>1</td><td>6.98</td><td>&lt;0.001 ***</td><td>6</td><td>1</td><td>9.74</td><td>&lt;0.001 ***</td><td>-1</td><td>1</td><td>-0.67</td><td>0.521</td></tr><tr><td>FI</td><td>-2</td><td>2</td><td>-1.27</td><td>0.219</td><td>-5</td><td>1</td><td>-3.66</td><td>0.005 **</td><td>6</td><td>2</td><td>2.42</td><td>0.039 *</td></tr><tr><td>Fall</td><td>-2</td><td>1</td><td>-2.13</td><td>0.045 *</td><td>-2</td><td>1</td><td>-2.74</td><td>0.023 *</td><td>0</td><td>1</td><td>-0.31</td><td>0.763</td></tr><tr><td rowspan="3">GOLD richness</td><td>Intercept</td><td>9</td><td>1</td><td>14.67</td><td>&lt;0.001 ***</td><td>9</td><td>1</td><td>12.78</td><td>&lt;0.001 ***</td><td>-1</td><td>1</td><td>-0.50</td><td>0.626</td></tr><tr><td>FI</td><td>-1</td><td>1</td><td>-0.97</td><td>0.345</td><td>-1</td><td>2</td><td>-0.57</td><td>0.581</td><td>-1</td><td>2</td><td>-0.48</td><td>0.640</td></tr><tr><td>Fall</td><td>0</td><td>1</td><td>0.62</td><td>0.541</td><td>-1</td><td>1</td><td>-1.32</td><td>0.219</td><td>3</td><td>1</td><td>2.40</td><td>0.040 *</td></tr><tr><td rowspan="3">IBGN</td><td>Intercept</td><td>17.7</td><td>0.7</td><td>24.15</td><td>&lt;0.001 ***</td><td>17.5</td><td>1.3</td><td>13.58</td><td>&lt;0.001 ***</td><td>0.62</td><td>0.88</td><td>0.70</td><td>0.500</td></tr><tr><td>FI</td><td>-10.1</td><td>1.7</td><td>-6.09</td><td>&lt;0.001 ***</td><td>-10.5</td><td>2.9</td><td>-3.61</td><td>0.006 **</td><td>0.16</td><td>1.99</td><td>0.08</td><td>0.939</td></tr><tr><td>Fall</td><td>-2.8</td><td>0.8</td><td>-3.54</td><td>0.002 **</td><td>-2.0</td><td>1.3</td><td>-1.49</td><td>0.170</td><td>-2.17</td><td>0.92</td><td>-2.36</td><td>0.043</td></tr><tr><td rowspan="3">I2M2</td><td>Intercept</td><td>0.72</td><td>0.04</td><td>17.36</td><td>&lt;0.001 ***</td><td>0.79</td><td>0.05</td><td>14.61</td><td>&lt;0.001 ***</td><td>-0.116</td><td>0.054</td><td>-2.13</td><td>0.062</td></tr><tr><td>FI</td><td>-0.46</td><td>0.09</td><td>-4.91</td><td>&lt;0.001 ***</td><td>-0.55</td><td>0.12</td><td>-4.56</td><td>0.001 **</td><td>0.147</td><td>0.122</td><td>1.20</td><td>0.261</td></tr><tr><td>Fall</td><td>-0.10</td><td>0.04</td><td>-2.35</td><td>0.029 *</td><td>-0.10</td><td>0.06</td><td>-1.78</td><td>0.108</td><td>-0.045</td><td>0.057</td><td>-0.80</td><td>0.446</td></tr></tbody></table>

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<table><caption>**Table 3.** Results of the linear regression performed on the taxonomic richness for all taxa, EPT, OCH and GOLD, and indicators IBG-DCE and I2M2 as response variables, FI, and season (spring vs. fall) as explanatory variables. All models were computed using all sites, upstream effluent releases only, and on delta values between up and downstream effluent releases. EPT stands for Ephemeroptera, Plecoptera, Trichoptera; OCH for Odonata, Coleoptera, Heteroptera; GOLD for Gastropods, Oligochaeta and Diptera.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Estimate</th><th colspan="4">All sites</th><th colspan="4">Reference sites only</th><th colspan="4">Delta impacted-reference</th></tr><tr><th>Estimate</th><th>SD</th><th>t value</th><th>p-value</th><th>Estimate</th><th>SD</th><th>t value</th><th>p-value</th><th>Estimate</th><th>SD</th><th>t value</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="3">Family richness</td><td>Intercept</td><td>35</td><td>2</td><td>18.93</td><td>&lt;0.001 ***</td><td>37</td><td>2</td><td>15.75</td><td>&lt;0.001 ***</td><td>-2</td><td>3</td><td>-0.79</td><td>0.447</td></tr><tr><td>FI</td><td>-19</td><td>4</td><td>-4.45</td><td>&lt;0.001 ***</td><td>-26</td><td>5</td><td>-5.02</td><td>0.001 **</td><td>14</td><td>7</td><td>2.06</td><td>0.069</td></tr><tr><td>Fall</td><td>-7</td><td>2</td><td>-3.74</td><td>0.001 **</td><td>-7</td><td>2</td><td>-2.90</td><td>0.018 *</td><td>-2</td><td>3</td><td>-0.52</td><td>0.614</td></tr><tr><td rowspan="3">EPT richness</td><td>Intercept</td><td>9</td><td>1</td><td>14.33</td><td>&lt;0.001 ***</td><td>9</td><td>1</td><td>11.03</td><td>&lt;0.001 ***</td><td>-1</td><td>1</td><td>-0.94</td><td>0.371</td></tr><tr><td>FI</td><td>-7</td><td>1</td><td>-4.70</td><td>&lt;0.001 ***</td><td>-7</td><td>2</td><td>-3.51</td><td>0.007 **</td><td>0</td><td>2</td><td>0.00</td><td>1.000</td></tr><tr><td>Fall</td><td>-2</td><td>1</td><td>-3.74</td><td>0.001 **</td><td>-2</td><td>1</td><td>-2.32</td><td>0.046 *</td><td>-1</td><td>1</td><td>-1.36</td><td>0.208</td></tr><tr><td rowspan="3">OCH richness</td><td>Intercept</td><td>6</td><td>1</td><td>6.98</td><td>&lt;0.001 ***</td><td>6</td><td>1</td><td>9.74</td><td>&lt;0.001 ***</td><td>-1</td><td>1</td><td>-0.67</td><td>0.521</td></tr><tr><td>FI</td><td>-2</td><td>2</td><td>-1.27</td><td>0.219</td><td>-5</td><td>1</td><td>-3.66</td><td>0.005 **</td><td>6</td><td>2</td><td>2.42</td><td>0.039 *</td></tr><tr><td>Fall</td><td>-2</td><td>1</td><td>-2.13</td><td>0.045 *</td><td>-2</td><td>1</td><td>-2.74</td><td>0.023 *</td><td>0</td><td>1</td><td>-0.31</td><td>0.763</td></tr><tr><td rowspan="3">GOLD richness</td><td>Intercept</td><td>9</td><td>1</td><td>14.67</td><td>&lt;0.001 ***</td><td>9</td><td>1</td><td>12.78</td><td>&lt;0.001 ***</td><td>-1</td><td>1</td><td>-0.50</td><td>0.626</td></tr><tr><td>FI</td><td>-1</td><td>1</td><td>-0.97</td><td>0.345</td><td>-1</td><td>2</td><td>-0.57</td><td>0.581</td><td>-1</td><td>2</td><td>-0.48</td><td>0.640</td></tr><tr><td>Fall</td><td>0</td><td>1</td><td>0.62</td><td>0.541</td><td>-1</td><td>1</td><td>-1.32</td><td>0.219</td><td>3</td><td>1</td><td>2.40</td><td>0.040 *</td></tr><tr><td rowspan="3">IBGN</td><td>Intercept</td><td>17.7</td><td>0.7</td><td>24.15</td><td>&lt;0.001 ***</td><td>17.5</td><td>1.3</td><td>13.58</td><td>&lt;0.001 ***</td><td>0.62</td><td>0.88</td><td>0.70</td><td>0.500</td></tr><tr><td>FI</td><td>-10.1</td><td>1.7</td><td>-6.09</td><td>&lt;0.001 ***</td><td>-10.5</td><td>2.9</td><td>-3.61</td><td>0.006 **</td><td>0.16</td><td>1.99</td><td>0.08</td><td>0.939</td></tr><tr><td>Fall</td><td>-2.8</td><td>0.8</td><td>-3.54</td><td>0.002 **</td><td>-2.0</td><td>1.3</td><td>-1.49</td><td>0.170</td><td>-2.17</td><td>0.92</td><td>-2.36</td><td>0.043</td></tr><tr><td rowspan="3">I2M2</td><td>Intercept</td><td>0.72</td><td>0.04</td><td>17.36</td><td>&lt;0.001 ***</td><td>0.79</td><td>0.05</td><td>14.61</td><td>&lt;0.001 ***</td><td>-0.116</td><td>0.054</td><td>-2.13</td><td>0.062</td></tr><tr><td>FI</td><td>-0.46</td><td>0.09</td><td>-4.91</td><td>&lt;0.001 ***</td><td>-0.55</td><td>0.12</td><td>-4.56</td><td>0.001 **</td><td>0.147</td><td>0.122</td><td>1.20</td><td>0.261</td></tr><tr><td>Fall</td><td>-0.10</td><td>0.04</td><td>-2.35</td><td>0.029 *</td><td>-0.10</td><td>0.06</td><td>-1.78</td><td>0.108</td><td>-0.045</td><td>0.057</td><td>-0.80</td><td>0.446</td></tr></tbody></table>

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<figure><img src="image_6.png" /><figcaption>**Figure 3.** Macroinvertebrate family richness for the overall, EPT, OCH and GOLD taxa, and for biological indices (IBG-DCE and I2M2) along a gradient of flow intermittence (noted FI). The lines represent the significant fits of the mixed models. Circles and triangles represent spring and fall samples respectively, and blue and green colors stand for sites upstream and downstream releases respectively. EPT stands for Ephemeroptera, Plecoptera, Trichoptera; OCH for Odonata, Coleoptera, Heteroptera; GOLD for Gastropods, Oligochaeta and Diptera.</figcaption></figure>

up to downstream effluent releases during spring (0.67±1.63) and greatly lower during fall (-1.80±1.30). Delta I2M2 values were the only values that responded to FR, with I2M2 being always higher up than downstream effluent releases. Such a difference was higher at perennial (-0.160±0.052) than intermittent reaches (-0.033±0.067). In addition, the I2M2 values classified the intermittent reaches up and downstream effluent releases in “good” ecological status, whereas perennial reaches were classified as “high”, upstream effluent releases, and “good”, downstream effluent releases.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Macroinvertebrate family richness for the overall, EPT, OCH and GOLD taxa, and for biological indices (IBG-DCE and I2M2) along a gradient of flow intermittence (noted FI). The lines represent the significant fits of the mixed models. Circles and triangles represent spring and fall samples respectively, and blue and green colors stand for sites upstream and downstream releases respectively. EPT stands for Ephemeroptera, Plecoptera, Trichoptera; OCH for Odonata, Coleoptera, Heteroptera; GOLD for Gastropods, Oligochaeta and Diptera.</figcaption></figure>

up to downstream effluent releases during spring (0.67±1.63) and greatly lower during fall (-1.80±1.30). Delta I2M2 values were the only values that responded to FR, with I2M2 being always higher up than downstream effluent releases. Such a difference was higher at perennial (-0.160±0.052) than intermittent reaches (-0.033±0.067). In addition, the I2M2 values classified the intermittent reaches up and downstream effluent releases in “good” ecological status, whereas perennial reaches were classified as “high”, upstream effluent releases, and “good”, downstream effluent releases.
Four out of the 6 delta values did not respond to FI nor to FR: overall family richness, EPT and GOLD family richness, and IBG-DCE (Table 4, Fig. 4). The overall delta family richness equaled ~ zero or was even greater than zero (on average 0±4 families), suggesting that family richness was sometimes greater downstream than upstream effluent releases (Table 4, Fig. 4). The delta EPT values were low (-1±1 taxa), indicating no response of this group to sewage.

<table><thead><tr><th>Delta value</th><th>Effect</th><th>Df</th><th>Mean Sq</th><th>F-value</th><th>P</th></tr></thead><tbody><tr><td rowspan="4">Taxa richness</td><td>FR</td><td>1</td><td>33</td><td>2.52</td><td>0.156</td></tr><tr><td>Season</td><td>1</td><td>3</td><td>0.27</td><td>0.623</td></tr><tr><td>FR:Season</td><td>1</td><td>4</td><td>0.32</td><td>0.589</td></tr><tr><td>Residuals</td><td>7</td><td>13</td><td></td><td></td></tr><tr><td rowspan="4">EPT richness</td><td>FR</td><td>1</td><td>0</td><td>0.00</td><td>0.968</td></tr><tr><td>Season</td><td>1</td><td>2</td><td>1.06</td><td>0.337</td></tr><tr><td>FR:Season</td><td>1</td><td>1</td><td>0.33</td><td>0.585</td></tr><tr><td>Residuals</td><td>7</td><td>2</td><td></td><td></td></tr><tr><td rowspan="4">OCH richness</td><td>FR</td><td>1</td><td>8</td><td>3.92</td><td>0.088</td></tr><tr><td>Season</td><td>1</td><td>1</td><td>0.42</td><td>0.590</td></tr><tr><td>FR:Season</td><td>1</td><td>0</td><td>0.00</td><td>0.964</td></tr><tr><td>Residuals</td><td>7</td><td>2</td><td></td><td></td></tr><tr><td rowspan="4">GOLD richness</td><td>FR</td><td>1</td><td>3</td><td>0.85</td><td>0.388</td></tr><tr><td>Season</td><td>1</td><td>24</td><td>7.86</td><td>0.026 *</td></tr><tr><td>FR:Season</td><td>1</td><td>1</td><td>0.47</td><td>0.514</td></tr><tr><td>Residuals</td><td>7</td><td>21</td><td></td><td></td></tr><tr><td rowspan="4">IBGN</td><td>FR</td><td>1</td><td>0.0</td><td>0.01</td><td>0.948</td></tr><tr><td>Season</td><td>1</td><td>16.6</td><td>5.89</td><td>0.046 *</td></tr><tr><td>FR:Season</td><td>1</td><td>0.4</td><td>0.13</td><td>0.733</td></tr><tr><td>Residuals</td><td>7</td><td>2.8</td><td></td><td></td></tr><tr><td rowspan="4">I2M2</td><td>FR</td><td>1</td><td>0.04</td><td>9.25</td><td>0.019 *</td></tr><tr><td>Season</td><td>1</td><td>0.00</td><td>0.04</td><td>0.851</td></tr><tr><td>FR:Season</td><td>1</td><td>0.00</td><td>0.82</td><td>0.396</td></tr><tr><td>Residuals</td><td>7</td><td>0.00</td><td></td><td></td></tr></tbody></table>

**Table 4.** Two-way ANOVA results showing the effects of season, flow regime (FR) and their interaction on the delta values (difference between up and downstream effluent releases) of the overall, EPT, OCH and GOLD family richness, and biological indices (IBG-DCE and I2M2). EPT stands for Ephemeroptera, Plecoptera, Trichoptera; OCH for Odonata, Coleoptera, Heteroptera; GOLD for Gastropods, Oligochaeta and Diptera.
Four out of the 6 delta values did not respond to FI nor to FR: overall family richness, EPT and GOLD family richness, and IBG-DCE (Table 4, Fig. 4). The overall delta family richness equaled ~ zero or was even greater than zero (on average 0±4 families), suggesting that family richness was sometimes greater downstream than upstream effluent releases (Table 4, Fig. 4). The delta EPT values were low (-1±1 taxa), indicating no response of this group to sewage.

<table><thead><tr><th>Delta value</th><th>Effect</th><th>Df</th><th>Mean Sq</th><th>F-value</th><th>P</th></tr></thead><tbody><tr><td rowspan="4">Taxa richness</td><td>FR</td><td>1</td><td>33</td><td>2.52</td><td>0.156</td></tr><tr><td>Season</td><td>1</td><td>3</td><td>0.27</td><td>0.623</td></tr><tr><td>FR:Season</td><td>1</td><td>4</td><td>0.32</td><td>0.589</td></tr><tr><td>Residuals</td><td>7</td><td>13</td><td></td><td></td></tr><tr><td rowspan="4">EPT richness</td><td>FR</td><td>1</td><td>0</td><td>0.00</td><td>0.968</td></tr><tr><td>Season</td><td>1</td><td>2</td><td>1.06</td><td>0.337</td></tr><tr><td>FR:Season</td><td>1</td><td>1</td><td>0.33</td><td>0.585</td></tr><tr><td>Residuals</td><td>7</td><td>2</td><td></td><td></td></tr><tr><td rowspan="4">OCH richness</td><td>FR</td><td>1</td><td>8</td><td>3.92</td><td>0.088</td></tr><tr><td>Season</td><td>1</td><td>1</td><td>0.42</td><td>0.590</td></tr><tr><td>FR:Season</td><td>1</td><td>0</td><td>0.00</td><td>0.964</td></tr><tr><td>Residuals</td><td>7</td><td>2</td><td></td><td></td></tr><tr><td rowspan="4">GOLD richness</td><td>FR</td><td>1</td><td>3</td><td>0.85</td><td>0.388</td></tr><tr><td>Season</td><td>1</td><td>24</td><td>7.86</td><td>0.026 *</td></tr><tr><td>FR:Season</td><td>1</td><td>1</td><td>0.47</td><td>0.514</td></tr><tr><td>Residuals</td><td>7</td><td>21</td><td></td><td></td></tr><tr><td rowspan="4">IBGN</td><td>FR</td><td>1</td><td>0.0</td><td>0.01</td><td>0.948</td></tr><tr><td>Season</td><td>1</td><td>16.6</td><td>5.89</td><td>0.046 *</td></tr><tr><td>FR:Season</td><td>1</td><td>0.4</td><td>0.13</td><td>0.733</td></tr><tr><td>Residuals</td><td>7</td><td>2.8</td><td></td><td></td></tr><tr><td rowspan="4">I2M2</td><td>FR</td><td>1</td><td>0.04</td><td>9.25</td><td>0.019 *</td></tr><tr><td>Season</td><td>1</td><td>0.00</td><td>0.04</td><td>0.851</td></tr><tr><td>FR:Season</td><td>1</td><td>0.00</td><td>0.82</td><td>0.396</td></tr><tr><td>Residuals</td><td>7</td><td>0.00</td><td></td><td></td></tr></tbody></table>

**Table 4.** Two-way ANOVA results showing the effects of season, flow regime (FR) and their interaction on the delta values (difference between up and downstream effluent releases) of the overall, EPT, OCH and GOLD family richness, and biological indices (IBG-DCE and I2M2). EPT stands for Ephemeroptera, Plecoptera, Trichoptera; OCH for Odonata, Coleoptera, Heteroptera; GOLD for Gastropods, Oligochaeta and Diptera.
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Delta (difference between up and downstream effluent releases) for the overall, EPT, OCH and GOLD family richness and biological indicators (IBG-DCE and I2M2) in intermittent (red) and perennial (blue) reaches. The red dashed line stands for no effect of sewages. EPT stands for Ephemeroptera, Plecoptera, Trichoptera; OCH for Odonata, Coleoptera, Heteroptera; GOLD for Gastropods, Oligochaeta and Diptera.</figcaption></figure>

### 3.3. *Structural responses*

The first-two axes of a PCA performed on the delta log-abundances of taxa explained 55.2% of the total variance. The respective between-groups variance equaled 22.4%, p=0.011 for FR (Fig. 5A), 11.2%, p=0.281 for the season (Fig. 5B) and 40.5%, p=0.049 for their combination (Fig. 5C). The difference in abundances between up and downstream effluent releases were lower intermittent reaches compared to perennial reaches (0.57±0.24 and 1.03±0.22 respectively, Table 5). These differences were also lower in fall compared to spring (0.58±0.34 and 0.87±0.26 respectively, Table 5).
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Delta (difference between up and downstream effluent releases) for the overall, EPT, OCH and GOLD family richness and biological indicators (IBG-DCE and I2M2) in intermittent (red) and perennial (blue) reaches. The red dashed line stands for no effect of sewages. EPT stands for Ephemeroptera, Plecoptera, Trichoptera; OCH for Odonata, Coleoptera, Heteroptera; GOLD for Gastropods, Oligochaeta and Diptera.</figcaption></figure>

### 3.3. *Structural responses*

The first-two axes of a PCA performed on the delta log-abundances of taxa explained 55.2% of the total variance. The respective between-groups variance equaled 22.4%, p=0.011 for FR (Fig. 5A), 11.2%, p=0.281 for the season (Fig. 5B) and 40.5%, p=0.049 for their combination (Fig. 5C). The difference in abundances between up and downstream effluent releases were lower intermittent reaches compared to perennial reaches (0.57±0.24 and 1.03±0.22 respectively, Table 5). These differences were also lower in fall compared to spring (0.58±0.34 and 0.87±0.26 respectively, Table 5).
<table><thead><tr><th></th><th>Df</th><th>Sum Sq</th><th>Mean Sq</th><th>F</th><th>P</th></tr></thead><tbody><tr><td>FR</td><td>1</td><td>0.56</td><td>0.56</td><td>22.34</td><td>0.002 **</td></tr><tr><td>Season</td><td>1</td><td>0.28</td><td>0.28</td><td>11.11</td><td>0.013 *</td></tr><tr><td>FR:Season</td><td>1</td><td>0.03</td><td>0.03</td><td>1.02</td><td>0.347</td></tr><tr><td>Residuals</td><td>7</td><td>0.18</td><td>0.03</td><td></td><td></td></tr></tbody></table>

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<figure><img src="image_7.png" /><figcaption>**Table 5.** Results of a two-way ANOVA performed on the overall log-delta abundances (difference between up and downstream effluent releases).</figcaption></figure>

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<caption>**Figure 5.** Projection of the samples upon the first two axes of a PCA performed on the log-delta abundances (absolute difference between up and downstream effluent releases) grouped by FR (A), season (B), and both (C). I and P stands for “intermittent” and “perennial” respectively, f and s stands for “fall” and “spring” respectively.</caption>

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### 3.4. Functional responses

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There was no difference in affinity for any trait category between up and downstream effluent releases (Appendix C), whereas affinity differed between intermittent and perennial reaches for 11 trait categories belonging to six different traits. Perennial reaches were characterized by higher proportions of organisms with long life span, using ovoviviparity and passive aquatic dispersal. They were also characterized by lower proportions of organisms that were univoltine, with aerial active dispersal, resistance forms, using cemented clutches and feeding on living macrophytes compared to intermittent reaches (Appendix C). The affinity of only one trait category differed between up and downstream effluent releases according to the FR:
at intermittent reaches, the proportion of organisms consuming dead plants (>1mm) was higher upstream than downstream effluent releases at intermittent reaches and the opposite was true at perennial reaches.

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## 4. Discussion

In this study, we quantified the structural and functional responses to point-source sewage effluents along a significant gradient of flow intermittence (FI; from 0 to 60%). As expected, the effect of drying confounded with that of water quality degradation and in most cases, current biomonitoring metrics did not distinguish between the effect of sewage and those linked to drying. As all community metrics decreased linearly with increased FI, our second prediction was partly validated and current biomonitoring approaches could be appropriately applied to reaches prone to weak flow intermittence. Last, we identified six biological traits responding to FI but not to water quality degradation. Confirming previous suggestions (Morais et al., 2004; Soria et al., 2020; Stubbington et al., 2018), these results validate that current biomonitoring approaches are unsuitable for application in intermittent rivers. If intermittent rivers with very short (i.e. < 30 days per year) periods of drying could still be monitored as their perennial counterparts, alternative biomonitoring approaches are needed for intermittent rivers with longer periods of drying (i.e. > 30 days per year), along with additional work to define a safe operational space for the application of a standardized biomonitoring approaches in intermittent rivers (Gallart et al., 2017; Munné et al., 2021).

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### 4.1. *Current macroinvertebrate-based biomonitoring tools are not suitable in intermittent rivers*

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Our overarching hypothesis that the ecological status inferred by macroinvertebrate-based biomonitoring protocols is poorly related to water quality of naturally intermittent rivers was largely supported through several lines of circumstantial evidence. First, taxonomic richness

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at intermittent reaches, the proportion of organisms consuming dead plants (>1mm) was
higher upstream than downstream effluent releases at intermittent reaches and the opposite
was true at perennial reaches.

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4. Discussion

In this study, we quantified the structural and functional responses to point-source sewage effluents along a significant gradient of flow intermittence (FI; from 0 to 60%). As expected, the effect of drying confounded with that of water quality degradation and in most cases, current biomonitoring metrics did not distinguish between the effect of sewage and those linked to drying. As all community metrics decreased linearly with increased FI, our second prediction was partly validated and current biomonitoring approaches could be appropriately applied to reaches prone to weak flow intermittence. Last, we identified six biological traits responding to FI but not to water quality degradation. Confirming previous suggestions (Morais et al., 2004; Soria et al., 2020; Stubbington et al., 2018), these results validate that current biomonitoring approaches are unsuitable for application in intermittent rivers. If intermittent rivers with very short (i.e. < 30 days per year) periods of drying could still be monitored as their perennial counterparts, alternative biomonitoring approaches are needed for intermittent rivers with longer periods of drying (i.e. > 30 days per year), along with additional work to define a safe operational space for the application of a standardized biomonitoring approaches in intermittent rivers (Gallart et al., 2017; Munné et al., 2021).

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4.1. *Current macroinvertebrate-based biomonitoring tools are not suitable in intermittent rivers*

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Our overarching hypothesis that the ecological status inferred by macroinvertebrate-based
biomonitoring protocols is poorly related to water quality of naturally intermittent rivers was
largely supported through several lines of circumstantial evidence. First, taxonomic richness

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453 was not different up and downstream effluent releases, and the difference in abundances
454 between up and downstream effluent releases were lower at intermittent reaches compared to
455 perennial ones. Consequently, and not surprisingly, IBG-DCE did not differ between up and
456 downstream effluent releases at intermittent reaches. The I2M2 scores were slightly lower
457 downstream effluent releases, but this was largely due to the differences detected in perennial
458 reaches, and not intermittent ones.

459
460 We found no differences between up and downstream effluent releases in terms of the relative
461 presence of any trait category tested here. The two trait categories considered in the I2M2,
462 namely multivoltinism and ovoviviparity, were selected for this biomonitoring tool because
463 they allowed a good trade-off between the discrimination efficiency, the specificity and the
464 stability of the metric, and because these trait categories were known to respond to changes in
465 water quality or habitat degradation (Mondy et al., 2012). Indeed, a high number of
466 reproductive cycles is a resilience strategy in ecosystems prone to disturbance (Townsend and
467 Hildrew, 1994). Ovoviviparity might prevent high egg mortality rate as they are protected
468 from harsh conditions (e.g. Dolédec and Statzner, 2008; Piscart et al., 2006), and it has been
469 shown to occur prominently during dry seasons (Bêche et al., 2006). However, for these two
470 trait categories we found no difference in their proportions between up and downstream
471 effluent releases. Voltinism varied between perennial and intermittent reaches but we did not
472 observe higher multivoltinism at intermittent reaches, although it is sometimes considered as
473 a strategy for aquatic macroinvertebrates to persist in the context of flow intermittence
474 (Bonada et al., 2007; Stubbington et al., 2017). The negative relationships between
475 ovoviviparity and FI is likely due to ovoviviparous (or partially ovoviviparous) taxa collected
476 that are highly dependent on flow permanence (e.g. *Gammarus* and *Pisidium*). Hence, using
477 ovoviviparity in biomonitoring methods in intermittent rivers might lead to unexpected
478 patterns. In contrast, water quality degradation and drying had combined effects on the
453 was not different up and downstream effluent releases, and the difference in abundances
454 between up and downstream effluent releases were lower at intermittent reaches compared to
455 perennial ones. Consequently, and not surprisingly, IBG-DCE did not differ between up and
456 downstream effluent releases at intermittent reaches. The I2M2 scores were slightly lower
457 downstream effluent releases, but this was largely due to the differences detected in perennial
458 reaches, and not intermittent ones.

459
460 We found no differences between up and downstream effluent releases in terms of the relative
461 presence of any trait category tested here. The two trait categories considered in the I2M2,
462 namely multivoltinism and ovoviviparity, were selected for this biomonitoring tool because
463 they allowed a good trade-off between the discrimination efficiency, the specificity and the
464 stability of the metric, and because these trait categories were known to respond to changes in
465 water quality or habitat degradation (Mondy et al., 2012). Indeed, a high number of
466 reproductive cycles is a resilience strategy in ecosystems prone to disturbance (Townsend and
467 Hildrew, 1994). Ovoviviparity might prevent high egg mortality rate as they are protected
468 from harsh conditions (e.g. Dolédec and Statzner, 2008; Piscart et al., 2006), and it has been
469 shown to occur prominently during dry seasons (Bêche et al., 2006). However, for these two
470 trait categories we found no difference in their proportions between up and downstream
471 effluent releases. Voltinism varied between perennial and intermittent reaches but we did not
472 observe higher multivoltinism at intermittent reaches, although it is sometimes considered as
473 a strategy for aquatic macroinvertebrates to persist in the context of flow intermittence
474 (Bonada et al., 2007; Stubbington et al., 2017). The negative relationships between
475 ovoviviparity and FI is likely due to ovoviviparous (or partially ovoviviparous) taxa collected
476 that are highly dependent on flow permanence (e.g. *Gammarus* and *Pisidium*). Hence, using
477 ovoviviparity in biomonitoring methods in intermittent rivers might lead to unexpected
478 patterns. In contrast, water quality degradation and drying had combined effects on the
proportion of organisms that feed on dead plants, which were scarce (8 taxa per sample) up and downstream effluent releases of intermittent reaches, but were found in higher proportions upstream effluent releases at perennial reaches (14 taxa per sample). The lack of trait responses to water quality degradation suggests that even when accounting for biological traits, current biomonitoring metrics (i.e. I2M2) are poorly discriminant in intermittent rivers, partly due to a certain degree in the functional redundancy observed in aquatic macroinvertebrate communities from naturally intermittent rivers due to a strong environmental filtering (Crabot et al., 2021a). While previous work highlighted that the I2M2 was more responsive to chemical water conditions compared to hydromorphological alterations (Corneil et al., 2018), our study suggests that drying is an extreme case of flow regime alteration which can hinder the detection of the effect of sewage.

Previous studies have suggested that applying current biomonitoring approaches in a context of flow intermittence can be challenging (e.g. Cid et al., 2020; Datry et al., 2014; Morais et al., 2004; Reyjol et al., 2014; Steward et al., 2012; Stubbington et al., 2018). To our knowledge, this is the first time that a dedicated study confirms this challenging issue using macroinvertebrate-based metrics along a gradient of flow intermittence. More than half of the worldwide river network is prone to flow intermittence (Messager et al., 2021), being as high as 35% in France (Beaufort et al., 2018; Snelder et al., 2013), with obviously profound implications in terms of management in the context of the Water Framework Directive in Europe. Many water bodies can be considered as in poor or mediocre state while they are in fact in a good ecological status. For example, in our study, the abundance of indicator species including species from several EPT families such as Odontoceridae (Trichoptera), Chloroperlidae and Capniidae (Plecoptera) decreased with pollution in perennial reaches but not in intermittent reaches.
proportion of organisms that feed on dead plants, which were scarce (8 taxa per sample) up and downstream effluent releases of intermittent reaches, but were found in higher proportions upstream effluent releases at perennial reaches (14 taxa per sample). The lack of trait responses to water quality degradation suggests that even when accounting for biological traits, current biomonitoring metrics (i.e. I2M2) are poorly discriminant in intermittent rivers, partly due to a certain degree in the functional redundancy observed in aquatic macroinvertebrate communities from naturally intermittent rivers due to a strong environmental filtering (Crabot et al., 2021a). While previous work highlighted that the I2M2 was more responsive to chemical water conditions compared to hydromorphological alterations (Corneil et al., 2018), our study suggests that drying is an extreme case of flow regime alteration which can hinder the detection of the effect of sewage.

Previous studies have suggested that applying current biomonitoring approaches in a context of flow intermittence can be challenging (e.g. Cid et al., 2020; Datry et al., 2014; Morais et al., 2004; Reyjol et al., 2014; Steward et al., 2012; Stubbington et al., 2018). To our knowledge, this is the first time that a dedicated study confirms this challenging issue using macroinvertebrate-based metrics along a gradient of flow intermittence. More than half of the worldwide river network is prone to flow intermittence (Messager et al., 2021), being as high as 35% in France (Beaufort et al., 2018; Snelder et al., 2013), with obviously profound implications in terms of management in the context of the Water Framework Directive in Europe. Many water bodies can be considered as in poor or mediocre state while they are in fact in a good ecological status. For example, in our study, the abundance of indicator species including species from several EPT families such as Odontoceridae (Trichoptera), Chloroperlidae and Capniidae (Plecoptera) decreased with pollution in perennial reaches but not in intermittent reaches.
4.2. Defining an operational spaces for assessing the ecological status of intermittent rivers

Our second prediction was only partly supported, which suggests that identifying thresholds in FI for shifting from current macroinvertebrate-based biomonitoring approaches to some alternative is not straightforward. Confirming previous studies carried out in other intermittent rivers (e.g. Crabot et al., 2021a; Datry et al., 2014; Piano et al., 2020), we found a decrease in taxonomic richness, the number of EPT taxa, and the two French biomonitoring indices with increasing FI. However, such relationships were significant only upstream effluent releases; we found a linear effect as shown by others (e.g. Datry et al., 2014) but our limited number of reaches (n=6) may hinder the identification of tipping-points in those relationships. This indicates thresholds in FI may be difficult to identify and suggests that defining safe operational spaces for applying current macroinvertebrate-based biomonitoring approaches in intermittent rivers may be a difficult task. By the way, downstream effluent releases, the combined effects of sewage and FI hindered these relationships.

We also found a strong temporal variability in community responses to FI and sewage, corroborating the view of intermittent rivers as being highly dynamic (Datry et al., 2016; Sarremejane et al., 2017a), which makes the assessment of the ecological status of these prevalent ecosystems even more complex. The delta values in taxonomic richness, number of OCH taxa and the IBG-DCE were higher in fall, after the dry season compared to the spring pre-drying season, in accordance with previous results showing difference in biomonitoring scores between seasons on intermittent streams in reference conditions (Munné and Prat, 2011). This is most likely due to legacy effects of drying events on aquatic communities and processes, as recovery upon rewetting can take several months when observed (e.g., Datry, 2012; Piano et al., 2020). However, even in spring, before the dry season, the persisting effect of drying was still too strong to allow detection of the effects of sewage effluents.
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## 4.2. Defining an operational spaces for assessing the ecological status of intermittent rivers

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Our second prediction was only partly supported, which suggests that identifying thresholds in FI for shifting from current macroinvertebrate-based biomonitoring approaches to some alternative is not straightforward. Confirming previous studies carried out in other intermittent rivers (e.g. Crabot et al., 2021a; Datry et al., 2014; Piano et al., 2020), we found a decrease in taxonomic richness, the number of EPT taxa, and the two French biomonitoring indices with increasing FI. However, such relationships were significant only upstream effluent releases; we found a linear effect as shown by others (e.g. Datry et al., 2014) but our limited number of reaches (n=6) may hinder the identification of tipping-points in those relationships. This indicates thresholds in FI may be difficult to identify and suggests that defining safe operational spaces for applying current macroinvertebrate-based biomonitoring approaches in intermittent rivers may be a difficult task. By the way, downstream effluent releases, the combined effects of sewage and FI hindered these relationships.

We also found a strong temporal variability in community responses to FI and sewage, corroborating the view of intermittent rivers as being highly dynamic (Datry et al., 2016; Sarremejane et al., 2017a), which makes the assessment of the ecological status of these prevalent ecosystems even more complex. The delta values in taxonomic richness, number of OCH taxa and the IBG-DCE were higher in fall, after the dry season compared to the spring pre-drying season, in accordance with previous results showing difference in biomonitoring scores between seasons on intermittent streams in reference conditions (Munné and Prat, 2011). This is most likely due to legacy effects of drying events on aquatic communities and processes, as recovery upon rewetting can take several months when observed (e.g., Datry, 2012; Piano et al., 2020). However, even in spring, before the dry season, the persisting effect of drying was still too strong to allow detection of the effects of sewage effluents.
Nevertheless, in some cases, the organic pollution was dramatic, with as high as 9 mg/L of DOC and up to 31133 MPN/100mL for E. coli (respectively twice and 26 times higher than the values found upstream effluent releases of the same reach). But even in this case, the responses of community and biomonitoring metrics were not different downstream compared to upstream the effluent release due to the drying regime of the reach. These results suggest that when applied before the drying season, current macroinvertebrate-based biomonitoring approaches are poorly applicable to naturally intermittent rivers, which echoes some recent calls to develop alternative approaches (e.g. Cid et al., 2020; Stubbington et al., 2018), either focusing on aquatic biotic groups adapted to desiccation (e.g. using biological traits, Soria et al., 2020), terrestrial and semi-aquatic organisms (e.g. riparian arthropods, Steward et al., 2018) or including regional processes to set up dynamic references (Cid et al., 2020).

For those bioindicators based on a reference condition such as the I2M2 (Mondy et al., 2012), a first step could be to include new references in the current classification that would be representative of the effect of flow intermittence across ecoregions. Another path would be to better take into account the temporal dynamic of these ecosystems (Cid et al., 2020).

Confirming previous observations, our results highlighted the high temporal variation of communities at reaches prone to drying and its influence on biomonitoring methods (Crabot et al., 2020; Leigh et al., 2019; Munné and Prat, 2011). As such, future models built to describe reference conditions at intermittent reaches should include replicates of biotic and abiotic samples at different seasons to capture the temporal variability of community composition and avoid underestimating the diversity at these locations (Cid et al., 2020; Sarremejane et al., 2017a). This could be especially important in Mediterranean headwater streams with regular phases of stagnant disconnected pools, which greatly differ from flowing and dry phases (Bonada et al., 2020; Gallart et al., 2017). This may be challenging because
Nevertheless, in some cases, the organic pollution was dramatic, with as high as 9 mg/L of DOC and up to 31133 MPN/100mL for E. coli (respectively twice and 26 times higher than the values found upstream effluent releases of the same reach). But even in this case, the responses of community and biomonitoring metrics were not different downstream compared to upstream the effluent release due to the drying regime of the reach. These results suggest that when applied before the drying season, current macroinvertebrate-based biomonitoring approaches are poorly applicable to naturally intermittent rivers, which echoes some recent calls to develop alternative approaches (e.g. Cid et al., 2020; Stubbington et al., 2018), either focusing on aquatic biotic groups adapted to desiccation (e.g. using biological traits, Soria et al., 2020), terrestrial and semi-aquatic organisms (e.g. riparian arthropods, Steward et al., 2018) or including regional processes to set up dynamic references (Cid et al., 2020).

For those bioindicators based on a reference condition such as the I2M2 (Mondy et al., 2012), a first step could be to include new references in the current classification that would be representative of the effect of flow intermittence across ecoregions. Another path would be to better take into account the temporal dynamic of these ecosystems (Cid et al., 2020).

Confirming previous observations, our results highlighted the high temporal variation of communities at reaches prone to drying and its influence on biomonitoring methods (Crabot et al., 2020; Leigh et al., 2019; Munné and Prat, 2011). As such, future models built to describe reference conditions at intermittent reaches should include replicates of biotic and abiotic samples at different seasons to capture the temporal variability of community composition and avoid underestimating the diversity at these locations (Cid et al., 2020; Sarremejane et al., 2017a). This could be especially important in Mediterranean headwater streams with regular phases of stagnant disconnected pools, which greatly differ from flowing and dry phases (Bonada et al., 2020; Gallart et al., 2017). This may be challenging because
the ideal biomonitoring tools should be designed according to 12 major criteria among with
the implementation comprising: low costs for sampling and sorting (field approaches), simple
sampling protocol and low cost for taxa identifications (no specialists in taxonomy required)
(Bonada et al., 2006a). Therefore, assessing the ecological status of intermittent rivers will
surely involve a shift in paradigm.

Many formerly perennial rivers are becoming intermittent (Döll and Schmied, 2012;
Messager et al., 2021), raising the question whether current biomonitoring tools would also
fail in such novel-ecosystems (sensu Hobbs et al., 2013). Whereas we are unaware of
empirical data that could help to address this key question, our results suggest that current
macroinvertebrate-based approaches would be even less adequate in artificially intermittent
rivers, at least during the first times following the change in flow regime. This is because in
such contexts, a dramatic decrease in the number of species is expected, along with cascading
effects on functional traits due to poor functional redundancy (Aspin et al., 2019; Crabot et
al., 2021a). The key question is to determine the trajectory of communities in newly-
intermittent rivers, which remain unknown (Crabot et al., 2021b). If community trajectories
converge gradually towards those from naturally intermittent rivers, our results indicate
substantial improvements will be needed to accurately assess the ecological status of rivers
and streams prone to drying. If these trajectories take a different path, we are today unable to
foresee which biomonitoring tools will be needed and call for a research agenda taking into
account the alterations of flow regimes and appearance of hybrid ecosystems (sensu Hobbs et
al., 2013) in the Anthropocene era.

576
the ideal biomonitoring tools should be designed according to 12 major criteria among with
the implementation comprising: low costs for sampling and sorting (field approaches), simple
sampling protocol and low cost for taxa identifications (no specialists in taxonomy required)
(Bonada et al., 2006a). Therefore, assessing the ecological status of intermittent rivers will
surely involve a shift in paradigm.

Many formerly perennial rivers are becoming intermittent (Döll and Schmied, 2012;
Messager et al., 2021), raising the question whether current biomonitoring tools would also
fail in such novel-ecosystems (sensu Hobbs et al., 2013). Whereas we are unaware of
empirical data that could help to address this key question, our results suggest that current
macroinvertebrate-based approaches would be even less adequate in artificially intermittent
rivers, at least during the first times following the change in flow regime. This is because in
such contexts, a dramatic decrease in the number of species is expected, along with cascading
effects on functional traits due to poor functional redundancy (Aspin et al., 2019; Crabot et
al., 2021a). The key question is to determine the trajectory of communities in newly-
intermittent rivers, which remain unknown (Crabot et al., 2021b). If community trajectories
converge gradually towards those from naturally intermittent rivers, our results indicate
substantial improvements will be needed to accurately assess the ecological status of rivers
and streams prone to drying. If these trajectories take a different path, we are today unable to
foresee which biomonitoring tools will be needed and call for a research agenda taking into
account the alterations of flow regimes and appearance of hybrid ecosystems (sensu Hobbs et
al., 2013) in the Anthropocene era.

576
577 Acknowledgements

578 We thank A. Mustarda for her early analyses of the dataset in the context of her Master
579 degree. This work was performed under the auspice of the EUR H2O'Lyon (ANR-17-EURE-
580 0018) of Université de Lyon (UdL) within the program "Investissements d'Avenir" operated
581 by the French National Research Agency (ANR). We thanks A. Laini and R. Vander Vorste
582 for their meaningful comments on a previous version of the manuscript.

583 Funding

584 This work was funded by the Rhône Mediterranean & Corsica Water Agency through the
585 program "Biomonitoring of intermittent rivers". TD and MF were supported by the H2020
586 European Research and Innovation action Grant Agreement N°869226 (DRYvER).

587 Author contributions

588 **Julie Crabot**: Formal analysis, Writing – Original Draft. **Sylvain Dolédec**: Formal analysis, Writing – Original Draft. **Maxence Forcellini**: Formal analysis. **Thibault Datry**: Conceptualization, Methodology, Resources, Writing – Original Draft.

591 Competing interest

592 The authors have no competing interest to declare.

593 Data availability

594 Data will be available on Dryad at the temporary location:
595 https://doi.org/10.5061/dryad.kprr4xh5w

596 References

597 AFNOR, 2016. Macroinvertébrés : Prélèvement en cours d'eau peu profond (No. NF T90-333).

598 AFNOR.

599 AFNOR, 2010. Qualité de l'eau -Traitement au laboratoire d'échantillons contenant des
600 macroinvertébrés de cours d'eau. (No. XP T90-388). AFNOR.

601 AFNOR, 2004. Qualité de l'eau – Détermination de l'indice biologique global normalisé (IBGN) (No. NF
602 T90-350). AFNOR.

603 Aspin, T.W.H., Khamis, K., Matthews, T.J., Milner, A.M., O'Callaghan, M.J., Trimmer, M., Woodward,
604 G., Ledger, M.E., 2019. Extreme drought pushes stream invertebrate communities over
605 functional thresholds. Global Change Biology 25, 230–244.

606 https://doi.org/10.1111/gcb.14495
577 Acknowledgements

578 We thank A. Mustarda for her early analyses of the dataset in the context of her Master
579 degree. This work was performed under the auspice of the EUR H2O'Lyon (ANR-17-EURE-
580 0018) of Université de Lyon (UdL) within the program "Investissements d'Avenir" operated
581 by the French National Research Agency (ANR). We thanks A. Laini and R. Vander Vorste
582 for their meaningful comments on a previous version of the manuscript.

583 Funding

584 This work was funded by the Rhône Mediterranean & Corsica Water Agency through the
585 program "Biomonitoring of intermittent rivers". TD and MF were supported by the H2020
586 European Research and Innovation action Grant Agreement N°869226 (DRYvER).

587 Author contributions

588 **Julie Crabot**: Formal analysis, Writing – Original Draft. **Sylvain Dolédec**: Formal analysis, Writing – Original Draft. **Maxence Forcellini**: Formal analysis. **Thibault Datry**: Conceptualization, Methodology, Resources, Writing – Original Draft.

591 Competing interest

592 The authors have no competing interest to declare.

593 Data availability

594 Data will be available on Dryad at the temporary location:
595 https://doi.org/10.5061/dryad.kprr4xh5w

596 References

597 AFNOR, 2016. Macroinvertébrés : Prélèvement en cours d'eau peu profond (No. NF T90-333).

598 AFNOR.

599 AFNOR, 2010. Qualité de l'eau -Traitement au laboratoire d'échantillons contenant des
600 macroinvertébrés de cours d'eau. (No. XP T90-388). AFNOR.

601 AFNOR, 2004. Qualité de l'eau – Détermination de l'indice biologique global normalisé (IBGN) (No. NF
602 T90-350). AFNOR.

603 Aspin, T.W.H., Khamis, K., Matthews, T.J., Milner, A.M., O'Callaghan, M.J., Trimmer, M., Woodward,
604 G., Ledger, M.E., 2019. Extreme drought pushes stream invertebrate communities over
605 functional thresholds. Global Change Biology 25, 230–244.

606 https://doi.org/10.1111/gcb.14495
607 Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R.H.B., Singmann, H., Dai, B., Scheipl, F., Grothendieck, G., Green, P., Fox, J., 2019. Ime4: Linear Mixed-Effects Models using "Eigen" and S4.

610 B-Béres, V., Tóthmérész, B., Bácsi, I., Borics, G., Abonyi, A., Tapolczai, K., Rimet, F., Bouchez, Á., Várbíró, G., Török, P., 2019. Autumn drought drives functional diversity of benthic diatom assemblages of continental intermittent streams. Advances in Water Resources 126, 129–136. https://doi.org/10.1016/j.advwatres.2019.02.010

614 Beaufort, A., Lamouroux, N., Pella, H., Datry, T., Sauquet, E., 2018. Extrapolating regional probability of drying of headwater streams using discrete observations and gauging networks. Hydrology and Earth System Sciences 22, 3033–3051. https://doi.org/10.5194/hess-22-3033-2018

617 Bêche, L.A., Mcelravy, E.P., Resh, V.H., 2006. Long-term seasonal variation in the biological traits of benthic-macroinvertebrates in two Mediterranean-climate streams in California, U.S.A. Freshwater Biology 51, 56–75. https://doi.org/10.1111/j.1365-2427.2005.01473.x

620 Belmar, O., Bruno, D., Guareschi, S., Mellado-Díaz, A., Millán, A., Velasco, J., 2019. Functional responses of aquatic macroinvertebrates to flow regulation are shaped by natural flow intermittence in Mediterranean streams. Freshwater Biology 64, 1064–1077. https://doi.org/10.1111/fwb.13289

624 Bogan, M.T., Boersma, K.S., Lytle, D.A., 2015. Resistance and resilience of invertebrate communities to seasonal and supraseasonal drought in arid-land headwater streams. Freshwater Biology 60, 2547–2558. https://doi.org/10.1111/fwb.12522

627 Bogan, M.T., Boersma, K.S., Lytle, D.A., 2013. Flow intermittency alters longitudinal patterns of invertebrate diversity and assemblage composition in an arid-land stream network. Freshwater Biology 58, 1016–1028. https://doi.org/10.1111/fwb.12105

630 Bonada, N., Cañedo-Argüelles, M., Gallart, F., Von Schiller, D., Fortuño, P., Latron, J., Llorens, P., Múrria, C., Soria, M., Vinyoles, D., Cid, N., 2020. Conservation and Management of Isolated Pools in Temporary Rivers. Water 12, 2870. https://doi.org/10.3390/w12102870

633 Bonada, N., Dolédec, S., Statzner, B., 2007. Taxonomic and biological trait differences of stream macroinvertebrate communities between mediterranean and temperate regions: implications for future climatic scenarios. Global Change Biology 13, 1658–1671. https://doi.org/10.1111/j.1365-2486.2007.01375.x

637 Bonada, N., Prat, N., Resh, V.H., Statzner, B., 2006a. DEVELOPMENTS IN AQUATIC INSECT BIOMONITORING: A Comparative Analysis of Recent Approaches. Annu. Rev. Entomol. 51, 495–523. https://doi.org/10.1146/annurev.ento.51.110104.151124

640 Bonada, N., Rieradevall, M., Prat, N., Resh, V.H., 2006b. Benthic macroinvertebrate assemblages and macrohabitat connectivity in Mediterranean-climate streams of northern California. jnbs 25, 32–43. https://doi.org/10.1899/0887-3593(2006)25[32:BMAAMC]2.0.CO;2

643 Busch, M.H., Costigan, K.H., Fritz, K.M., Datry, T., Krabbenhoft, C.A., Hammond, J.C., Zimmer, M., Olden, J.D., Burrows, R.M., Dodds, W.K., Boersma, K.S., Shanafield, M., Kampf, S.K., Mims, M.C., Bogan, M.T., Ward, A.S., Perez Rocha, M., Godsey, S., Allen, G.H., Blaszczak, J.R., Jones, C.N., Allen, D.C., 2020. What’s in a Name? Patterns, Trends, and Suggestions for Defining Non-Perennial Rivers and Streams. Water 12, 1980. https://doi.org/10.3390/w12071980

648 Cañedo-Argüelles, M., Boersma, K.S., Bogan, M.T., Olden, J.D., Phillipsen, I., Schriever, T.A., Lytle, D.A., 2015. Dispersal strength determines meta-community structure in a dendritic riverine network. J. Biogeogr. 42, 778–790. https://doi.org/10.1111/jbi.12457

651 Chang, F.-H., Lawrence, J.E., Rios-Touma, B., Resh, V.H., 2014. Tolerance values of benthic macroinvertebrates for stream biomonitoring: assessment of assumptions underlying scoring systems worldwide. Environ Monit Assess 186, 2135–2149. https://doi.org/10.1007/s10661-013-3523-6

655 Chevenet, F., Dolédec, S., Chessel, D., 1994. A fuzzy coding approach for the analysis of long-term ecological data. Freshwater Biology 31, 295–309. https://doi.org/10.1111/j.1365-2427.1994.tb01742.x
607 Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R.H.B., Singmann, H., Dai, B., Scheipl, F., Grothendieck, G., Green, P., Fox, J., 2019. Ime4: Linear Mixed-Effects Models using "Eigen" and S4.

610 B-Béres, V., Tóthmérész, B., Bácsi, I., Borics, G., Abonyi, A., Tapolczai, K., Rimet, F., Bouchez, Á., Várbíró, G., Török, P., 2019. Autumn drought drives functional diversity of benthic diatom assemblages of continental intermittent streams. Advances in Water Resources 126, 129–136. https://doi.org/10.1016/j.advwatres.2019.02.010

614 Beaufort, A., Lamouroux, N., Pella, H., Datry, T., Sauquet, E., 2018. Extrapolating regional probability of drying of headwater streams using discrete observations and gauging networks. Hydrology and Earth System Sciences 22, 3033–3051. https://doi.org/10.5194/hess-22-3033-2018

617 Bêche, L.A., Mcelravy, E.P., Resh, V.H., 2006. Long-term seasonal variation in the biological traits of benthic-macroinvertebrates in two Mediterranean-climate streams in California, U.S.A. Freshwater Biology 51, 56–75. https://doi.org/10.1111/j.1365-2427.2005.01473.x

620 Belmar, O., Bruno, D., Guareschi, S., Mellado-Díaz, A., Millán, A., Velasco, J., 2019. Functional responses of aquatic macroinvertebrates to flow regulation are shaped by natural flow intermittence in Mediterranean streams. Freshwater Biology 64, 1064–1077. https://doi.org/10.1111/fwb.13289

624 Bogan, M.T., Boersma, K.S., Lytle, D.A., 2015. Resistance and resilience of invertebrate communities to seasonal and supraseasonal drought in arid-land headwater streams. Freshwater Biology 60, 2547–2558. https://doi.org/10.1111/fwb.12522

627 Bogan, M.T., Boersma, K.S., Lytle, D.A., 2013. Flow intermittency alters longitudinal patterns of invertebrate diversity and assemblage composition in an arid-land stream network. Freshwater Biology 58, 1016–1028. https://doi.org/10.1111/fwb.12105

630 Bonada, N., Cañedo-Argüelles, M., Gallart, F., Von Schiller, D., Fortuño, P., Latron, J., Llorens, P., Múrria, C., Soria, M., Vinyoles, D., Cid, N., 2020. Conservation and Management of Isolated Pools in Temporary Rivers. Water 12, 2870. https://doi.org/10.3390/w12102870

633 Bonada, N., Dolédec, S., Statzner, B., 2007. Taxonomic and biological trait differences of stream macroinvertebrate communities between mediterranean and temperate regions: implications for future climatic scenarios. Global Change Biology 13, 1658–1671. https://doi.org/10.1111/j.1365-2486.2007.01375.x

637 Bonada, N., Prat, N., Resh, V.H., Statzner, B., 2006a. DEVELOPMENTS IN AQUATIC INSECT BIOMONITORING: A Comparative Analysis of Recent Approaches. Annu. Rev. Entomol. 51, 495–523. https://doi.org/10.1146/annurev.ento.51.110104.151124

640 Bonada, N., Rieradevall, M., Prat, N., Resh, V.H., 2006b. Benthic macroinvertebrate assemblages and macrohabitat connectivity in Mediterranean-climate streams of northern California. jnbs 25, 32–43. https://doi.org/10.1899/0887-3593(2006)25[32:BMAAMC]2.0.CO;2

643 Busch, M.H., Costigan, K.H., Fritz, K.M., Datry, T., Krabbenhoft, C.A., Hammond, J.C., Zimmer, M., Olden, J.D., Burrows, R.M., Dodds, W.K., Boersma, K.S., Shanafield, M., Kampf, S.K., Mims, M.C., Bogan, M.T., Ward, A.S., Perez Rocha, M., Godsey, S., Allen, G.H., Blaszczak, J.R., Jones, C.N., Allen, D.C., 2020. What’s in a Name? Patterns, Trends, and Suggestions for Defining Non-Perennial Rivers and Streams. Water 12, 1980. https://doi.org/10.3390/w12071980

648 Cañedo-Argüelles, M., Boersma, K.S., Bogan, M.T., Olden, J.D., Phillipsen, I., Schriever, T.A., Lytle, D.A., 2015. Dispersal strength determines meta-community structure in a dendritic riverine network. J. Biogeogr. 42, 778–790. https://doi.org/10.1111/jbi.12457

651 Chang, F.-H., Lawrence, J.E., Rios-Touma, B., Resh, V.H., 2014. Tolerance values of benthic macroinvertebrates for stream biomonitoring: assessment of assumptions underlying scoring systems worldwide. Environ Monit Assess 186, 2135–2149. https://doi.org/10.1007/s10661-013-3523-6

655 Chevenet, F., Dolédec, S., Chessel, D., 1994. A fuzzy coding approach for the analysis of long-term ecological data. Freshwater Biology 31, 295–309. https://doi.org/10.1111/j.1365-2427.1994.tb01742.x
658 Cid, N., Bonada, N., Heino, J., Cañedo-Argüelles, M., Crabot, J., Sarremejane, R., Soininen, J.,
659 Stubbington, R., Datry, T., 2020. A Metacommunity Approach to Improve Biological
660 Assessments in Highly Dynamic Freshwater Ecosystems. BioScience 70, 427–438.
661 https://doi.org/10.1093/biosci/biaa033

662 Corneil, D., Villeneuve, B., Piffady, J., Chandesris, A., Usseglio-Polatera, P., Souchon, Y., 2018.
663 Introducing nested spatial scales in multi-stress models: towards better assessment of
664 human impacts on river ecosystems. Hydrobiologia 806, 347–361.
665 https://doi.org/10.1007/s10750-017-3374-1

666 Crabot, J., Heino, J., Launay, B., Datry, T., 2020. Drying determines the temporal dynamics of stream
667 invertebrate structural and functional beta diversity. Ecography 43, 620–635.
668 https://doi.org/10.1111/ecog.04835

669 Crabot, J., Mondy, C.P., Usseglio-Polatera, P., Fritz, K.M., Wood, P.J., Greenwood, M.J., Bogan, M.T.,
670 Meyer, E.I., Datry, T., 2021a. A global perspective on the functional responses of stream
671 communities to flow intermittence. Ecography na, na. https://doi.org/10.1111/ecog.05697

672 Crabot, J., Polášek, M., Launay, B., Pařil, P., Datry, T., 2021b. Drying in newly intermittent rivers leads
673 to higher variability of invertebrate communities. Freshwater Biology 66, 730–744.
674 https://doi.org/10.1111/fwb.13673

675 Datry, T., 2012. Benthic and hyporheic invertebrate assemblages along a flow intermittence gradient:
676 effects of duration of dry events. Freshwater Biology 57, 563–574.
677 https://doi.org/10.1111/j.1365-2427.2011.02725.x

678 Datry, T., Bonada, N., Heino, J., 2016. Towards understanding the organisation of metacommunities
679 in highly dynamic ecological systems. Oikos 125, 149–159. https://doi.org/10.1111/oik.02922

680 Datry, T., Corti, R., Claret, C., Philippe, M., 2011. Flow intermittence controls leaf litter breakdown in
681 a French temporary alluvial river: the “drying memory.” Aquat Sci 73, 471–483.
682 https://doi.org/10.1007/s00027-011-0193-8

683 Datry, T., Larned, S.T., Fritz, K.M., Bogan, M.T., Wood, P.J., Meyer, E.I., Santos, A.N., 2014. Broad-
684 scale patterns of invertebrate richness and community composition in temporary rivers:
685 effects of flow intermittence. Ecography 37, 94–104. https://doi.org/10.1111/j.1600-
686 0587.2013.00287.x

687 Datry, Thibault, Larned, S.T., Tockner, K., 2014. Intermittent Rivers: A Challenge for Freshwater
688 Ecology. BioScience 64, 229–235. https://doi.org/10.1093/biosci/bit027

689 Dolédec, S., Chessel, D., 1987. Seasonal successions and spatial variables in freshwater environments.
690 I. Description of a complete two-way layout by projection of variables[Rythmes saisonniers et
691 composantes stationnelles en milieu aquatique. I. Description d'un plan d'observation
692 complet par projection de variables]. Acta Oecologica/Oecologia Generalis 8, 403–426.

693 Dolédec, S., Statzner, B., 2010. Responses of freshwater biota to human disturbances: contribution of
694 J-NABS to developments in ecological integrity assessments. Journal of the North American
695 Benthological Society 29, 286–311. https://doi.org/10.1899/08-090.1

696 Dolédec, S., Statzner, B., 2008. Invertebrate traits for the biomonitoring of large European rivers: an
697 assessment of specific types of human impact. Freshwater Biology 53, 617–634.
698 https://doi.org/10.1111/j.1365-2427.2007.01924.x

699 Döll, P., Schmied, H.M., 2012. How is the impact of climate change on river flow regimes related to
700 the impact on mean annual runoff? A global-scale analysis. Environ. Res. Lett. 7, 014037.
701 https://doi.org/10.1088/1748-9326/7/1/014037

702 Dray, S., Dufour, A.B., Chessel, D., 2007. The ade4 package-II: Two-table and K-table methods. R
703 News 7, 47–52.

704 England, J., Stubbington, R., Chadd, R., Dunbar, M., Westwood, C., Sarremejane, R., Leeming, D.,
705 2019. An invertebrate-based index to characterize ecological responses to flow intermittence
706 in rivers. Fundamental and Applied Limnology / Archiv für Hydrobiologie 193.
707 https://doi.org/10.1127/fal/2019/1206

708 Environmental Law Institute, 2012. The Clean Water Act Jurisdictional Handbook, 2nd ed.
709 Environmental Law Institute, Washington, DC.
658 Cid, N., Bonada, N., Heino, J., Cañedo-Argüelles, M., Crabot, J., Sarremejane, R., Soininen, J.,

659 Stubbington, R., Datry, T., 2020. A Metacommunity Approach to Improve Biological

660 Assessments in Highly Dynamic Freshwater Ecosystems. BioScience 70, 427–438.

661 https://doi.org/10.1093/biosci/biaa033

662 Corneil, D., Villeneuve, B., Piffady, J., Chandesris, A., Usseglio-Polatera, P., Souchon, Y., 2018.

663 Introducing nested spatial scales in multi-stress models: towards better assessment of
664 human impacts on river ecosystems. Hydrobiologia 806, 347–361.

665 https://doi.org/10.1007/s10750-017-3374-1

666 Crabot, J., Heino, J., Launay, B., Datry, T., 2020. Drying determines the temporal dynamics of stream

667 invertebrate structural and functional beta diversity. Ecography 43, 620–635.

668 https://doi.org/10.1111/ecog.04835

669 Crabot, J., Mondy, C.P., Usseglio-Polatera, P., Fritz, K.M., Wood, P.J., Greenwood, M.J., Bogan, M.T.,

670 Meyer, E.I., Datry, T., 2021a. A global perspective on the functional responses of stream
671 communities to flow intermittence. Ecography na, na. https://doi.org/10.1111/ecog.05697

672 Crabot, J., Polášek, M., Launay, B., Pařil, P., Datry, T., 2021b. Drying in newly intermittent rivers leads
673 to higher variability of invertebrate communities. Freshwater Biology 66, 730–744.

674 https://doi.org/10.1111/fwb.13673

675 Datry, T., 2012. Benthic and hyporheic invertebrate assemblages along a flow intermittence gradient:

676 effects of duration of dry events. Freshwater Biology 57, 563–574.

677 https://doi.org/10.1111/j.1365-2427.2011.02725.x

678 Datry, T., Bonada, N., Heino, J., 2016. Towards understanding the organisation of metacommunities
679 in highly dynamic ecological systems. Oikos 125, 149–159. https://doi.org/10.1111/oik.02922

680 Datry, T., Corti, R., Claret, C., Philippe, M., 2011. Flow intermittence controls leaf litter breakdown in
681 a French temporary alluvial river: the “drying memory.” Aquat Sci 73, 471–483.

682 https://doi.org/10.1007/s00027-011-0193-8

683 Datry, T., Larned, S.T., Fritz, K.M., Bogan, M.T., Wood, P.J., Meyer, E.I., Santos, A.N., 2014. Broad-
684 scale patterns of invertebrate richness and community composition in temporary rivers:

685 effects of flow intermittence. Ecography 37, 94–104. https://doi.org/10.1111/j.1600-

686 0587.2013.00287.x

687 Datry, Thibault, Larned, S.T., Tockner, K., 2014. Intermittent Rivers: A Challenge for Freshwater

688 Ecology. BioScience 64, 229–235. https://doi.org/10.1093/biosci/bit027

689 Dolédec, S., Chessel, D., 1987. Seasonal successions and spatial variables in freshwater environments.

690 I. Description of a complete two-way layout by projection of variables[Rythmes saisonniers et

691 composantes stationnelles en milieu aquatique. I. Description d'un plan d'observation
692 complet par projection de variables]. Acta Oecologica/Oecologia Generalis 8, 403–426.

693 Dolédec, S., Statzner, B., 2010. Responses of freshwater biota to human disturbances: contribution of

694 J-NABS to developments in ecological integrity assessments. Journal of the North American

695 Benthological Society 29, 286–311. https://doi.org/10.1899/08-090.1

696 Dolédec, S., Statzner, B., 2008. Invertebrate traits for the biomonitoring of large European rivers: an
697 assessment of specific types of human impact. Freshwater Biology 53, 617–634.

698 https://doi.org/10.1111/j.1365-2427.2007.01924.x

699 Döll, P., Schmied, H.M., 2012. How is the impact of climate change on river flow regimes related to
700 the impact on mean annual runoff? A global-scale analysis. Environ. Res. Lett. 7, 014037.

701 https://doi.org/10.1088/1748-9326/7/1/014037

702 Dray, S., Dufour, A.B., Chessel, D., 2007. The ade4 package-II: Two-table and K-table methods. R
703 News 7, 47–52.

704 England, J., Stubbington, R., Chadd, R., Dunbar, M., Westwood, C., Sarremejane, R., Leeming, D.,

705 2019. An invertebrate-based index to characterize ecological responses to flow intermittence
706 in rivers. Fundamental and Applied Limnology / Archiv für Hydrobiologie 193.

707 https://doi.org/10.1127/fal/2019/1206

708 Environmental Law Institute, 2012. The Clean Water Act Jurisdictional Handbook, 2nd ed.

709 Environmental Law Institute, Washington, DC.
710 European Commission, 2000. Directive 2000/60/EC of the European Parliament and of the Council of
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745 research and management of intermittent rivers: an historical review and future directions.
746 Freshw Biol 61, 1181–1199. https://doi.org/10.1111/fwb.12646

747 Leigh, C., Stubbington, R., Sheldon, F., Boulton, A.J., 2013. Hyporheic invertebrates as bioindicators of
748 ecological health in temporary rivers: a meta-analysis. Ecological Indicators 32, 62–73.
749 https://doi.org/10.1016/j.ecolind.2013.03.006

750 Mazor, R.D., Stein, E.D., Ode, P.R., Schiff, K., 2014. Integrating intermittent streams into watershed
751 assessments: applicability of an index of biotic integrity. jnbs.1 33, 459–474.
752 https://doi.org/10.1086/675683

753 Messenger, M.L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K.,
754 Trautmann, T., Watt, C., Datry, T., 2021. Global prevalence of non-perennial rivers and
755 streams. Nature 594, 391–397. https://doi.org/10.1038/s41586-021-03565-5

756 Mondy, C.P., Villeneuve, B., Archaimbault, V., Usseglio-Polatera, P., 2012. A new macroinvertebrate-
based multimetric index (I2M2) to evaluate ecological quality of French wadeable streams
757 fulfilling the WFD demands: A taxonomical and trait approach. Ecol. Indic. 18, 452–467.
758 https://doi.org/10.1016/j.ecolind.2011.12.013
759
710 European Commission, 2000. Directive 2000/60/EC of the European Parliament and of the Council of
711 23 October 2000 Establishing a Framework for Community Action in the Field of Water
712 Policy. Office for Official Publications of the European Communities.

713 Friberg, N., Bonada, N., Bradley, D.C., Dunbar, M.J., Edwards, F.K., Grey, J., Hayes, R.B., Hildrew, A.G.,
714 Lamouroux, N., Trimmer, M., Woodward, G., 2011. Biomonitoring of Human Impacts in
715 Freshwater Ecosystems: The Good, the Bad and the Ugly, in: Woodward, G. (Ed.), Advances
716 in Ecological Research. Academic Press, pp. 1–68. https://doi.org/10.1016/B978-0-12-
717 374794-5.00001-8

718 Gallart, F., Cid, N., Latron, J., Llorens, P., Bonada, N., Jeuffroy, J., Jiménez-Argudo, S.-M., Vega, R.-M.,
719 Solà, C., Soria, M., Bardina, M., Hernández-Casahuga, A.-J., Fidalgo, A., Estrela, T., Munné, A.,
720 Prat, N., 2017. TREHS: An open-access software tool for investigating and evaluating
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721 Total Environment 607–608, 519–540. https://doi.org/10.1016/j.scitotenv.2017.06.209
722

723 Gauthier, M., Launay, B., Goff, G.L., Pella, H., Douady, C.J., Datry, T., 2020. Fragmentation promotes
724 the role of dispersal in determining 10 intermittent headwater stream metacommunities.
725 Freshwater Biology 65, 2169–2185. https://doi.org/10.1111/fwb.13611

726 Hobbs, R.J., Higgs, E.S., Hall, C.M., 2013. Introduction: Why Novel Ecosystems?, in: Novel Ecosystems.
727 John Wiley & Sons, Ltd, pp. 1–8. https://doi.org/10.1002/9781118354186.ch1

728 Kalogianni, E., Vourka, A., Karaouzas, I., Vardakas, L., Laschou, S., Skoulikidis, N.Th., 2017. Combined
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729 Mediterranean intermittent river. Science of The Total Environment 603–604, 639–650.
730 https://doi.org/10.1016/j.scitotenv.2017.06.078
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732 Kuznetsova, A., Brockhoff, P.B., Christensen, R.H.B., Jensen, S.P., 2020. ImerTest: Tests in Linear
733 Mixed Effects Models.

734 Laliberté, E., Legendre, P., Shipley, B., 2014. FD: Measuring functional diversity (FD) from multiple
735 traits, and other tools for functional ecology.

736 Larned, S.T., Datry, T., Arscott, D.B., Tockner, K., 2010. Emerging concepts in temporary-river ecology.
737 Freshwater Biology 55, 717–738. https://doi.org/10.1111/j.1365-2427.2009.02322.x

738 Larned, S.T., Schmidt, J., Datry, T., Konrad, C.P., Dumas, J.K., Diettrich, J.C., 2011. Longitudinal river
739 ecohydrology: flow variation down the lengths of alluvial rivers. Ecohydrology 4, 532–548.
740 https://doi.org/10.1002/eco.126

741 Leigh, C., Aspin, T.W.H., Matthews, T.J., Rolls, R.J., Ledger, M.E., 2019. Drought alters the functional
742 stability of stream invertebrate communities through time. Journal of Biogeography 46,
743 1988–2000. https://doi.org/10.1111/jbi.13638

744 Leigh, C., Boulton, A.J., Courtwright, J.L., Fritz, K., May, C.L., Walker, R.H., Datry, T., 2016. Ecological
745 research and management of intermittent rivers: an historical review and future directions.
746 Freshw Biol 61, 1181–1199. https://doi.org/10.1111/fwb.12646

747 Leigh, C., Stubbington, R., Sheldon, F., Boulton, A.J., 2013. Hyporheic invertebrates as bioindicators of
748 ecological health in temporary rivers: a meta-analysis. Ecological Indicators 32, 62–73.
749 https://doi.org/10.1016/j.ecolind.2013.03.006

750 Mazor, R.D., Stein, E.D., Ode, P.R., Schiff, K., 2014. Integrating intermittent streams into watershed
751 assessments: applicability of an index of biotic integrity. jnbs.1 33, 459–474.
752 https://doi.org/10.1086/675683

753 Messenger, M.L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K.,
754 Trautmann, T., Watt, C., Datry, T., 2021. Global prevalence of non-perennial rivers and
755 streams. Nature 594, 391–397. https://doi.org/10.1038/s41586-021-03565-5

756 Mondy, C.P., Villeneuve, B., Archaimbault, V., Usseglio-Polatera, P., 2012. A new macroinvertebrate-
based multimetric index (I2M2) to evaluate ecological quality of French wadeable streams
757 fulfilling the WFD demands: A taxonomical and trait approach. Ecol. Indic. 18, 452–467.
758 https://doi.org/10.1016/j.ecolind.2011.12.013
759
760 Morais, M., Pinto, P., Guilherme, P., Rosado, J., Antunes, I., 2004. Assessment of temporary streams:

761 the robustness of metric and multimetric indices under different hydrological conditions.

762 Hydrobiologia 516, 229-249. https://doi.org/10.1023/B:HYDR.0000025268.66163.32

763 Munné, A., Bonada, N., Cid, N., Gallart, F., Solà, C., Bardina, M., Rovira, A., Sierra, C., Soria, M.,

764 Fortuño, P., Llorens, P., Latron, J., Estrela, T., Fidalgo, A., Serrano, I., Jiménez, S., Vega, R.,

765 Prat, N., 2021. A Proposal to Classify and Assess Ecological Status in Mediterranean

766 Temporary Rivers: Research Insights to Solve Management Needs. Water 13, 767.

767 https://doi.org/10.3390/w13060767

768 Munné, A., Prat, N., 2011. Effects of Mediterranean climate annual variability on stream biological

769 quality assessment using macroinvertebrate communities. Ecological Indicators 11, 651-662.

770 https://doi.org/10.1016/j.ecolind.2010.09.004

771 Pařil, P., Polášek, M., Loskotová, B., Straka, M., Crabot, J., Datry, T., 2019. An unexpected source of

772 invertebrate community recovery in intermittent streams from a humid continental climate.

773 Freshwater Biology 64, 1971-1983. https://doi.org/10.1111/fwb.13386

774 Piano, E., Doretto, A., Mammola, S., Falasco, E., Fenoglio, S., Bona, F., 2020. Taxonomic and

775 functional homogenisation of macroinvertebrate communities in recently intermittent Alpine

776 watercourses. Freshwater Biology 65. https://doi.org/10.1111/fwb.13611

777 Piscart, C., Usseglio-Polatera, P., Moreteau, J.-C., Beisel, J.-N., 2006. The role of salinity in the

778 selection of biological traits of freshwater invertebrates. Archiv für Hydrobiologie 166, 185-

779 198. https://doi.org/10.1127/0003-9136/2006/0166-0185

780 Prat, N., Gallart, F., Schiller, D.V., Polesello, S., García-Roger, E.M., Latron, J., Rieradevall, M., Llorens,

781 P., Barberá, G.G., Brito, D., Girolamo, A.M.D., Dieter, D., Porto, A.L., Buffagni, A., Erba, S.,

782 Nikolaidis, N.P., Querner, E.P., Tournoud, M.G., Tzoraki, O., Skoulikidis, N., Gómez, R.,

783 Sánchez-Montoya, M.M., Tockner, K., Froebrich, J., 2014. The Mirage Toolbox: An Integrated

784 Assessment Tool for Temporary Streams. River Research and Applications 30, 1318-1334.

785 https://doi.org/10.1002/rra.2757

786 R Core Team, 2021. R: The R Project for Statistical Computing [WWW Document]. URL

787 https://www.r-project.org/

788 Reid, A.J., Carlson, A.K., Creed, I.F., Eliason, E.J., Gell, P.A., Johnson, P.T.J., Kidd, K.A., MacCormack,

789 T.J., Olden, J.D., Ormerod, S.J., Smol, J.P., Taylor, W.W., Tockner, K., Vermaire, J.C., Dudgeon,

790 D., Cooke, S.J., 2019. Emerging threats and persistent conservation challenges for freshwater

791 biodiversity. Biological Reviews 94, 849-873. https://doi.org/10.1111/brv.12480

792 Reyjol, Y., Argillier, C., Bonne, W., Borja, A., Buijse, A.D., Cardoso, A.C., Daufresne, M., Kernan, M.,

793 Ferreira, M.T., Poikane, S., Prat, N., Solheim, A.-L., Stroffek, S., Usseglio-Polatera, P.,

794 Villeneuve, B., van de Bund, W., 2014. Assessing the ecological status in the context of the

795 European Water Framework Directive: Where do we go now? Science of The Total

796 Environment 497-498, 332-344. https://doi.org/10.1016/j.scitotenv.2014.07.119

797 Rodier, J., Legube, B., Merlet, N., Alary, C., Belles, A., 2016. L'analyse de l'eau Contrôle et

798 interprétation. Dunod, p. 1297.

799 Rosenberg, D.M., Resh, V.H. (Eds.), 1993. Freshwater Biomonitoring and Benthic Macroinvertebrates.

800 Springer US.

801 Rupp, D., Larned, S., Arscott, D., Schmidt, J., 2008. Reconstruction of a Daily Flow Record Along a

802 Hydrologically Complex Alluvial River. Journal of Hydrology 359, 88-104.

803 https://doi.org/10.1016/j.jhydrol.2008.06.019

804 Sarremejane, R., Cañedo-Argüelles, M., Prat, N., Mykrä, H., Muotka, T., Bonada, N., 2017a. Do

805 metacommunities vary through time? Intermittent rivers as model systems. Journal of

806 Biogeography 44, 2752-2763. https://doi.org/10.1111/jbi.13077

807 Sarremejane, R., Mykrä, H., Bonada, N., Aroviita, J., Muotka, T., 2017b. Habitat connectivity and

808 dispersal ability drive the assembly mechanisms of macroinvertebrate communities in river

809 networks. Freshwater Biology 62, 1073-1082. https://doi.org/10.1111/fwb.12926
760 Morais, M., Pinto, P., Guilherme, P., Rosado, J., Antunes, I., 2004. Assessment of temporary streams:

761 the robustness of metric and multimetric indices under different hydrological conditions.

762 Hydrobiologia 516, 229–249. https://doi.org/10.1023/B:HYDR.0000025268.66163.32

763 Munné, A., Bonada, N., Cid, N., Gallart, F., Solà, C., Bardina, M., Rovira, A., Sierra, C., Soria, M.,

764 Fortuño, P., Llorens, P., Latron, J., Estrela, T., Fidalgo, A., Serrano, I., Jiménez, S., Vega, R.,

765 Prat, N., 2021. A Proposal to Classify and Assess Ecological Status in Mediterranean

766 Temporary Rivers: Research Insights to Solve Management Needs. Water 13, 767.

767 https://doi.org/10.3390/w13060767

768 Munné, A., Prat, N., 2011. Effects of Mediterranean climate annual variability on stream biological

769 quality assessment using macroinvertebrate communities. Ecological Indicators 11, 651–662.

770 https://doi.org/10.1016/j.ecolind.2010.09.004

771 Pařil, P., Polášek, M., Loskotová, B., Straka, M., Crabot, J., Datry, T., 2019. An unexpected source of

772 invertebrate community recovery in intermittent streams from a humid continental climate.

773 Freshwater Biology 64, 1971–1983. https://doi.org/10.1111/fwb.13386

774 Piano, E., Doretto, A., Mammola, S., Falasco, E., Fenoglio, S., Bona, F., 2020. Taxonomic and

775 functional homogenisation of macroinvertebrate communities in recently intermittent Alpine

776 watercourses. Freshwater Biology 65. https://doi.org/10.1111/fwb.13611

777 Piscart, C., Usseglio-Polatera, P., Moreteau, J.-C., Beisel, J.-N., 2006. The role of salinity in the

778 selection of biological traits of freshwater invertebrates. Archiv für Hydrobiologie 166, 185–

779 198. https://doi.org/10.1127/0003-9136/2006/0166-0185

780 Prat, N., Gallart, F., Schiller, D.V., Polesello, S., García-Roger, E.M., Latron, J., Rieradevall, M., Llorens,

781 P., Barberá, G.G., Brito, D., Girolamo, A.M.D., Dieter, D., Porto, A.L., Buffagni, A., Erba, S.,

782 Nikolaidis, N.P., Querner, E.P., Tournoud, M.G., Tzoraki, O., Skoulikidis, N., Gómez, R.,

783 Sánchez-Montoya, M.M., Tockner, K., Froebrich, J., 2014. The Mirage Toolbox: An Integrated

784 Assessment Tool for Temporary Streams. River Research and Applications 30, 1318–1334.

785 https://doi.org/10.1002/rra.2757

786 R Core Team, 2021. R: The R Project for Statistical Computing [WWW Document]. URL

787 https://www.r-project.org/

788 Reid, A.J., Carlson, A.K., Creed, I.F., Eliason, E.J., Gell, P.A., Johnson, P.T.J., Kidd, K.A., MacCormack,

789 T.J., Olden, J.D., Ormerod, S.J., Smol, J.P., Taylor, W.W., Tockner, K., Vermaire, J.C., Dudgeon,

790 D., Cooke, S.J., 2019. Emerging threats and persistent conservation challenges for freshwater

791 biodiversity. Biological Reviews 94, 849–873. https://doi.org/10.1111/brv.12480

792 Reyjol, Y., Argillier, C., Bonne, W., Borja, A., Buijse, A.D., Cardoso, A.C., Daufresne, M., Kernan, M.,

793 Ferreira, M.T., Poikane, S., Prat, N., Solheim, A.-L., Stroffek, S., Usseglio-Polatera, P.,

794 Villeneuve, B., van de Bund, W., 2014. Assessing the ecological status in the context of the

795 European Water Framework Directive: Where do we go now? Science of The Total

796 Environment 497–498, 332–344. https://doi.org/10.1016/j.scitotenv.2014.07.119

797 Rodier, J., Legube, B., Merlet, N., Alary, C., Belles, A., 2016. L’analyse de l’eau Contrôle et

798 interprétation. Dunod, p. 1297.

799 Rosenberg, D.M., Resh, V.H. (Eds.), 1993. Freshwater Biomonitoring and Benthic Macroinvertebrates.

800 Springer US.

801 Rupp, D., Larned, S., Arscott, D., Schmidt, J., 2008. Reconstruction of a Daily Flow Record Along a

802 Hydrologically Complex Alluvial River. Journal of Hydrology 359, 88–104.

803 https://doi.org/10.1016/j.jhydrol.2008.06.019

804 Sarremejane, R., Cañedo-Argüelles, M., Prat, N., Mykrä, H., Muotka, T., Bonada, N., 2017a. Do

805 metacommunities vary through time? Intermittent rivers as model systems. Journal of

806 Biogeography 44, 2752–2763. https://doi.org/10.1111/jbi.13077

807 Sarremejane, R., Mykrä, H., Bonada, N., Aroviita, J., Muotka, T., 2017b. Habitat connectivity and

808 dispersal ability drive the assembly mechanisms of macroinvertebrate communities in river

809 networks. Freshwater Biology 62, 1073–1082. https://doi.org/10.1111/fwb.12926
810 Schriever, T.A., Bogan, M.T., Boersma, K.S., Cañedo-Argüelles, M., Jaeger, K.L., Olden, J.D., Lytle, D.A.,
811 2015. Hydrology shapes taxonomic and functional structure of desert stream invertebrate
812 communities. Freshwater Science 34, 399–409. https://doi.org/10.1086/680518

813 Seaman, M., Watson, M., Avenant, M., King, J., Joubert, A., Barker, C., Esterhuyse, S., Graham, D.,
814 Kemp, M., Le Roux, P., Prucha, B., Redelinghuys, N., Rossouw, L., Rowntree, K., Sokolic, F.,
815 Rensburg, L., van der Waal, B., Van Tol, J.J., Vos, T., 2016. DRIFT-ARID: A method for assessing
816 environmental water requirements (EWRs) for non-perennial rivers. Water S.A 42.
817 https://doi.org/10.4314/wsa.v42i3.01

818 Smelder, T.H., Datry, T., Lamouroux, N., Larned, S.T., Sauquet, E., Pella, H., Catalogne, C., 2013.
819 Regionalization of patterns of flow intermittence from gauging station records. Hydrology
820 and Earth System Sciences 17, 2685–2699. https://doi.org/10.5194/hess-17-2685-2013

821 Soria, M., Gutiérrez-Cánovas, C., Bonada, N., Acosta, R., Rodríguez-Lozano, P., Fortuño, P., Burgazzi,
822 G., Vinyoles, D., Gallart, F., Latron, J., Llorens, P., Prat, N., Cid, N., 2020. Natural disturbances
can produce misleading bioassessment results: Identifying metrics to detect anthropogenic
823 impacts in intermittent rivers. Journal of Applied Ecology 57, 283–295.
824 https://doi.org/10.1111/1365-2664.13538

825 Soria, M., Leigh, C., Datry, T., Bini, L.M., Bonada, N., 2017. Biodiversity in perennial and intermittent
rivers: a meta-analysis. Oikos 126, 1078–1089. https://doi.org/10.1111/oik.04118

826 Steward, A., Von Schiller, D., Tockner, K., Marshall, J., Bunn, S., 2012. When the river runs dry: Human
827 and ecological values of dry riverbeds. Frontiers in Ecology and the Environment 10, 202–
828 209. https://doi.org/10.1890/110136

829 Steward, A.L., Negus, P., Marshall, J.C., Clifford, S.E., Dent, C., 2018. Assessing the ecological health of
830 rivers when they are dry. Ecological Indicators 85, 537–547.
831 https://doi.org/10.1016/j.ecolind.2017.10.053

832 Storey, R., Quinn, J., 2013. Survival of aquatic invertebrates in dry bed sediments of intermittent
streams: Temperature tolerances and implications for riparian management. Freshwater
833 Science 32, 250–266. https://doi.org/10.1899/12-008.1

834 Straka, M., Polášek, M., Syrovátka, V., Stubbington, R., Zahrádková, S., Němejcová, D., Šikulová, L.,
835 Řezníčková, P., Opatřilová, L., Datry, T., Pařil, P., 2019. Recognition of stream drying based on
836 benthic macroinvertebrates: A new tool in Central Europe. Ecological Indicators 106, 105486.
837 https://doi.org/10.1016/j.ecolind.2019.105486

838 Stubbington, R., Bogan, M.T., Bonada, N., Boulton, A.J., Datry, T., Leigh, C., Vander Vorste, R., 2017.
839 Chapter 4.3 - The Biota of Intermittent Rivers and Ephemeral Streams: Aquatic Invertebrates,
in: Datry, T., Bonada, N., Boulton, A. (Eds.), Intermittent Rivers and Ephemeral Streams.
840 Academic Press, pp. 217–243. https://doi.org/10.1016/B978-0-12-803835-2.00007-3

841 Stubbington, R., Chadd, R., Cid, N., Csabai, Z., Milisa, M., Morais, M., Munne, A., Paril, P., Pesic, V.,
842 Tziortzis, I., Verdonschot, R.C.M., Datry, T., 2018. Biomonitoring of intermittent rivers and
843 ephemeral streams in Europe: Current practice and priorities to enhance ecological status
844 assessments. Sci. Total Environ. 618, 1096–1113.
845 https://doi.org/10.1016/j.scitotenv.2017.09.137

846 Stubbington, R., Datry, T., 2013. The macroinvertebrate seedbank promotes community persistence
847 in temporary rivers across climate zones. Freshwater Biology 58, 1202–1220.
848 https://doi.org/10.1111/fwb.12121

849 Stubbington, R., Gunn, J., Little, S., Worrall, T.P., Wood, P.J., 2016. Macroinvertebrate seedbank
composition in relation to antecedent duration of drying and multiple wet-dry cycles in a
temporary stream. Freshwater Biology 61, 1293–1307. https://doi.org/10.1111/fwb.12770

850 Tachet, H., Philippe, R., Bournaud, M., Usseglio-Polatera, P., 2010. Invertebres D'eau Douce.
851 Systematique, Biologie, Ecologie. CNRS Editions, Paris.

852 Thioulouse, J., Dray, S., Dufour, A.-B., Siberchicot, A., Jombart, T., Pavoine, S., 2018. Multivariate
853 Analysis of Ecological Data with ade4. Springer New York. https://doi.org/10.1007/978-1-4939-8850-1
810 Schriever, T.A., Bogan, M.T., Boersma, K.S., Cañedo-Argüelles, M., Jaeger, K.L., Olden, J.D., Lytle, D.A.,
811 2015. Hydrology shapes taxonomic and functional structure of desert stream invertebrate
812 communities. Freshwater Science 34, 399–409. https://doi.org/10.1086/680518

813 Seaman, M., Watson, M., Avenant, M., King, J., Joubert, A., Barker, C., Esterhuyse, S., Graham, D.,
814 Kemp, M., Le Roux, P., Prucha, B., Redelinghuys, N., Rossouw, L., Rowntree, K., Sokolic, F.,
815 Rensburg, L., van der Waal, B., Van Tol, J.J., Vos, T., 2016. DRIFT-ARID: A method for assessing
816 environmental water requirements (EWRs) for non-perennial rivers. Water S.A 42.
817 https://doi.org/10.4314/wsa.v42i3.01

818 Smelder, T.H., Datry, T., Lamouroux, N., Larned, S.T., Sauquet, E., Pella, H., Catalogne, C., 2013.
819 Regionalization of patterns of flow intermittence from gauging station records. Hydrology
820 and Earth System Sciences 17, 2685–2699. https://doi.org/10.5194/hess-17-2685-2013

821 Soria, M., Gutiérrez-Cánovas, C., Bonada, N., Acosta, R., Rodríguez-Lozano, P., Fortuño, P., Burgazzi,
822 G., Vinyoles, D., Gallart, F., Latron, J., Llorens, P., Prat, N., Cid, N., 2020. Natural disturbances
can produce misleading bioassessment results: Identifying metrics to detect anthropogenic
823 impacts in intermittent rivers. Journal of Applied Ecology 57, 283–295.
824 https://doi.org/10.1111/1365-2664.13538

825 Soria, M., Leigh, C., Datry, T., Bini, L.M., Bonada, N., 2017. Biodiversity in perennial and intermittent
rivers: a meta-analysis. Oikos 126, 1078–1089. https://doi.org/10.1111/oik.04118

826 Steward, A., Von Schiller, D., Tockner, K., Marshall, J., Bunn, S., 2012. When the river runs dry: Human
827 and ecological values of dry riverbeds. Frontiers in Ecology and the Environment 10, 202–
828 209. https://doi.org/10.1890/110136

829 Steward, A.L., Negus, P., Marshall, J.C., Clifford, S.E., Dent, C., 2018. Assessing the ecological health of
830 rivers when they are dry. Ecological Indicators 85, 537–547.
831 https://doi.org/10.1016/j.ecolind.2017.10.053

832 Storey, R., Quinn, J., 2013. Survival of aquatic invertebrates in dry bed sediments of intermittent
streams: Temperature tolerances and implications for riparian management. Freshwater
833 Science 32, 250–266. https://doi.org/10.1899/12-008.1

834 Straka, M., Polášek, M., Syrovátka, V., Stubbington, R., Zahrádková, S., Němejcová, D., Šikulová, L.,
835 Řezníčková, P., Opatřilová, L., Datry, T., Pařil, P., 2019. Recognition of stream drying based on
836 benthic macroinvertebrates: A new tool in Central Europe. Ecological Indicators 106, 105486.
837 https://doi.org/10.1016/j.ecolind.2019.105486

838 Stubbington, R., Bogan, M.T., Bonada, N., Boulton, A.J., Datry, T., Leigh, C., Vander Vorste, R., 2017.
839 Chapter 4.3 - The Biota of Intermittent Rivers and Ephemeral Streams: Aquatic Invertebrates,
in: Datry, T., Bonada, N., Boulton, A. (Eds.), Intermittent Rivers and Ephemeral Streams.
840 Academic Press, pp. 217–243. https://doi.org/10.1016/B978-0-12-803835-2.00007-3

841 Stubbington, R., Chadd, R., Cid, N., Csabai, Z., Milisa, M., Morais, M., Munne, A., Paril, P., Pesic, V.,
842 Tziortzis, I., Verdonschot, R.C.M., Datry, T., 2018. Biomonitoring of intermittent rivers and
843 ephemeral streams in Europe: Current practice and priorities to enhance ecological status
844 assessments. Sci. Total Environ. 618, 1096–1113.
845 https://doi.org/10.1016/j.scitotenv.2017.09.137

846 Stubbington, R., Datry, T., 2013. The macroinvertebrate seedbank promotes community persistence
847 in temporary rivers across climate zones. Freshwater Biology 58, 1202–1220.
848 https://doi.org/10.1111/fwb.12121

849 Stubbington, R., Gunn, J., Little, S., Worrall, T.P., Wood, P.J., 2016. Macroinvertebrate seedbank
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