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Linking diatom sensitivity to herbicide to phylogeny: A step forward for biomonitoring? Floriane Larras, François Keck, Bernard Montuelle, Frédéric Rimet, and Agnes Bouchez Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 06 Jan 2014 Downloaded from http://pubs.acs.org on January 12, 2014

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Linking diatom sensitivity to herbicide to

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phylogeny: a step forward for biomonitoring?

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,‡

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,‡

Floriane Larras 1 , François Keck 1 , Bernard Montuelle 1, Frédéric Rimet 1, Agnès Bouchez 1* 1

4 5

INRA, UMR Carrtel, F-74203 Thonon, France

Université de Savoie, UMR Carrtel, F-73011 Chambéry, France

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KEYWORDS: Phylogeny; sensitivity; diatoms; herbicides; bioassessment

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ABSTRACT

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Phylogeny has not yet been fully accepted in the field of ecotoxicology, despite studies

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demonstrating its potential for developing environmental biomonitoring tools, as it can

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provide an a priori assessment of the sensitivity of several indicator organisms. We therefore

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investigated the relationship between phylogeny and sensitivity to herbicides in freshwater

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diatom species. This study was performed on four photosystem II inhibitor herbicides

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(atrazine, terbutryn, diuron and isoproturon) and 14 diatom species representative of Lake

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Geneva biofilm diversity. Using recent statistical tools provided by phylogenetics, we

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observed a strong phylogenetic signal for diatom sensitivity to herbicides. There was a major

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division in sensitivity to herbicides within the phylogenetic tree. The most sensitive species

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were mainly centrics and araphid diatoms (in this study, Thalassiosirales and Fragilariales),

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whereas the most resistant species were mainly pennates (in this study, Cymbellales,

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Naviculales and Bacillariales). However, there was considerable variability in diatom

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sensitivity within the raphid clade, which could be explained by differences in trophic

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preferences (autotrophy or heterotrophy). These traits appeared to be complementary in

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explaining the differences in sensitivity observed at a refined phylogenetic level. Using

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phylogeny together with complementary traits, as trophic preferences, may help to predict the

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sensitivity of communities with a view to protecting their ecosystem.

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Introduction

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Over the last 40 years, many methods have been developed for monitoring the ecological

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quality of water bodies1–3. Among these methods, bioindicators constitute a powerful tool to

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assess the anthropogenic pressures on biota and ecosystem function. Most bioindicators used

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in freshwaters, such as fish, macroinvertebrates and microalgae, address specific

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biomonitoring needs4. Microalgae, and especially diatoms, are suitable for monitoring the

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overall health of the environment. They are particularly used to assess the quality of rivers in

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terms of organic and nutrient pollution5. Their communities respond rapidly to changes in

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habitat quality6,7. Diatoms are good biomonitors because they are found in most aquatic

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habitats and exhibit a huge diversity in terms of taxonomy (over 30000 species)8,

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morphology9 and ecology10. Despite these advantages, fewer ecological assessments are

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carried out worldwide using diatoms than using macroinvertebrates or fish11–13. The high

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sensitivity of diatoms to photosystem II inhibitor (PSII) herbicides has been widely observed

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at both the single-species14–16 and community levels15,17–19. At these two levels of biological

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organization, some of these herbicides impair diatoms even at concentrations as low as those

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regularly found in the environment16,20,21.

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Ecotoxicological studies, based on toxic exposure in laboratory bioassays, provide the basic

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information required for developing biomonitoring tools. Studies at a higher complexity level, 2 ACS Paragon Plus Environment


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such as microcosms, provide more environmentally realistic data about toxic impacts, but

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identifying the environmental effects attributable to herbicides is a rather complex. As a first

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step, it is important to determine herbicide toxicity under single-species conditions. We

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therefore performed single-species bioassays, which are useful tools for assessing the

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physiological sensitivity of a single diatom species to a single herbicide in which confounding

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factors were excluded and a high degree of reproducibility can be achieved. However,

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sensitivity data obtained from these tests are only available for relatively few species because

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generating these data is time consuming and labor intensive. Because diatoms exhibit such a

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huge diversity, it actually seems inconceivable to assess with bioassays the sensitivity of even

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a small percentage of these species. Thus, developing methods to predict their sensitivity

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represents a challenge for using more species sensitivity data to improve their biomonitoring

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efficiency. The current influx of DNA sequence data allows for establishing robust

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phylogenies to many species. Connecting sensitivity data to the phylogeny of the tested

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species is a key tactic that might provide an a priori prediction of the species’ sensitivity to

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contaminants. Recent studies have investigated the potential of phylogeny to assess the

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sensitivity of amphibians22, microalgae23,24, fish25, macroinvertebrates26, chironomids, and

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mayflies27 to various pollutants. Many authors have focused on the detection of a

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phylogenetic signal, which is the tendency for related species to share similar ecological or

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biological features. For instance, these studies showed that the phylogenetic signals of

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sensitivity varied in strength depending on the pollutant considered. However, the use of a

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phylogenetic framework to improve biomonitoring remains an unexplored field, and no data

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are available for diatoms. Approaches that integrate phylogeny and ecotoxicology could

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supply information for bioassessment tools operating at a larger taxonomic scale and could

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thus increase the effectiveness of biomonitoring. The lack of a quality dataset based on a set

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of species presenting high diversity and a wide range of sensitivities28 has so far prevented


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this type of study. However, new statistical methods integrating phylogenies offer a promising

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avenue for exploring the variability of sensitivity within and between taxonomic levels. For

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example, Blomberg’s K statistic29 and Pagel’s λ30 are both well-established statistical tools

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provided by phylogenetics to detect the phylogenetic signal, to measure its strength, and to

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test its significance. Blomberg’s K statistic has been applied extensively in the context of

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ecotoxicological studies by Buchwalter et al.26 and Carew et al.27, whereas Pagel’s λ performs

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well for complex models of trait evolution31. Phylogenetic principal component analysis

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(pPCA)32 is also a promising new method that allows for working in a multivariate framework

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to explore the phylogenetic signal and to explore possible correlations among the distributions

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of sensitivities.

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This study is a preliminary exploration of the potential use of phylogenetic signals to

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investigate the sensitivity of freshwater diatoms to herbicides. The first aim was to explore the

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sensitivity patterns of diatoms to 4 PSII inhibitor herbicides within the phylogeny. These

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herbicides were chosen for their high level of phytotoxicity and because they are often

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detected in French surface waters33. We performed single-species laboratory bioassays of a set

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of 14 diatom species exposed to four PSII inhibitor herbicides (atrazine, terbutryn, diuron, and

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isoproturon). This species set is representative of the biofilm diversity in Lake Geneva and,

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was developed in the context of biomonitoring this lake. We also provide a phylogeny of

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these 14 diatom species reconstructed from the 18S and rbcL markers.

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Second, we wanted to determine whether the phylogeny of these diatoms was significantly

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linked to their herbicide sensitivities. We explored the phylogenetic signal for herbicide

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sensitivity using Blomberg’s K statistic and Pagel’s λ. We also applied the pPCA to our data

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as a tool to detect biologically meaningful combinations of herbicide sensitivities that are

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phylogenetically structured. Because we had sensitivity data for four different herbicides and

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few possible explanatory factors, the pPCA allowed us to uncover the underlying



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phylogenetic trends and patterns and to thus reveal the ecotoxicological evolutionary

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strategies of diatoms. The ability to explore the phylogenetic signal in a multivariate

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framework is also important in ecotoxicology to consider the multiple environmental

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stressors.

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Finally, the results are discussed in light of diatom ecology and provide useful insights into

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diatom biomonitoring. Traditional and phylogenetically-based regression analyses were used

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to assess the relationships between herbicide sensitivities and commonly used biomonitoring

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indices. We wanted to determine whether the link between phylogeny and sensitivity should

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be considered for inclusion in the development of biomonitoring tools intended to protect

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environmental diatom communities against these herbicides.

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Materials and methods

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Diatom species

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Fourteen freshwater benthic diatom species were selected to represent the diversity found in

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the Lake Geneva biofilm. Each species was maintained in culture and is registered at the

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Thonon Culture Collection (http://www.inra.fr/carrtel-collection). The selected species

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included Achnanthidium minutissimum (Kützing) Czarnecki (TCC-746), Craticula accomoda

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(Hustedt) D.G. Mann (TCC-107), Cyclotella meneghiniana Kützing (TCC-755), Encyonema

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silesiacum (Bleisch) D.G. Mann (TCC-678), Fistulifera saprophila (Lange-Bertalot & Bonik)

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Lange-Bertalot (TCC-535), Fragilaria capucina var. vaucheriae (Grunow) Lange-Bertalot

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(TCC-752), F. crotonensis Kitton (TCC-301), F. rumpens (Kützing) G.W.F. Carlson (TCC-

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666), Gomphonema parvulum (Kützing) Kützing (TCC-653), G. clavatum Ehrenberg (TCC-

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527), Mayamaea fossalis (Krasske) Lange-Bertalot (TCC-366), Nitzschia palea (Kützing) W.

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Smith (TCC-139-2), Sellaphora minima (Grunow) Mann (TCC-524) and Ulnaria ulna (C.L.

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Nitzsch) Compère (TCC-635). Photographs of the strains are available on the barcoding


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website of the INRA institute (http://www.rsyst.inra.fr/?q=fr/content/micro-algues). The

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cultures

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collection_eng/Culture-media/Composition-of-the-culture-media) that had been filtered at

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0.22 µm (Millipore, Germany) and were grown in a 300 mL Erlenmeyer flask at 21±2°C with

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a 16:8 h light:dark cycle at 66 µmol.m-2.s-1.

were

maintained

in

DV

culture

medium

(http://www6.inra.fr/carrtel-

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Herbicide solutions

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We tested four herbicides, namely atrazine, terbutryn, diuron and isoproturon from Sigma-

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Aldrich (St Louis, MO 63103, USA, purity over 99.5%). For the bioassays, stock solutions

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were prepared in DV media. Due to the low solubility of atrazine and diuron, we added 0.05%

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of the solvent dimethyl sulfoxide (DMSO) to the stock solutions and sonicated the mixture for

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30 minutes. Previous research (unpublished data) has confirmed that this concentration has no

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adverse effects on benthic diatoms. Moreover, no interaction between DMSO and

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Photosystem II inhibitor herbicide was observed below 0.5% of DMSO34.

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Sensitivity dataset

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The effective concentrations that reduce the population growth rate by 10 (EC10,

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Supporting information Table S1) and 50% (EC50, Supporting information Table S2) were

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available from Larras et al.35 for Fragilaria. capucina var. vaucheriae, F. rumpens, Ulnaria

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ulna, Craticula accomoda, Mayamaea fossalis, Sellaphora minima, Nitzschia palea,

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Achnanthidium minutissimum, Cyclotella meneghiniana, Encyonema silesiacum and

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Gomphonema parvulum. To increase the diversity of the tested species, we performed

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additional single-species bioassays on G. clavatum, F. saprophila and F. crotonensis for these

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four herbicides as described by Larras et al.35. We worked at two levels of sensitivity so that

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our data could be generalized; EC50 and EC10 are both relevant in the environmental

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regulatory framework36.



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Mixture toxicity prediction

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We predicted the sensitivity of each species at both the EC10 and EC50 levels to equitoxic

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mixtures of 1) all four selected herbicides (EC10-Mix and EC50-Mix); 2) the two herbicides

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belonging to the phenylurea family (EC10-Mixp and EC50-Mixp); 3) the two herbicides

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belonging to the triazine family (EC10-Mixt and EC50-Mixt). The compositions of the

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equitoxic mixtures were determined using the concentration addition model (CA, Eq. 131),

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because many studies have already shown that this model can predict the toxicity of mixtures

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of photosystem II inhibitors37–39.

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Pi −1 ) i =1 ECxi n

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Equation 1:

ECx mix = ( ∑

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where ECX mix is the total concentration of the mixture that elicits a total effect of x%, Pi is

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the relative proportion of each substance within the mixture, and ECxi is the individual

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concentration of each substance that induces the effect x.

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Phylogenetic analyses

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The diatom strains used in this study were genetically characterized by Sanger sequencing

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using two markers (18S and rbcL) as described in Kermarrec et al.40. Genbank accession

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numbers are provided for each sequence as supporting information (Table S3). The sequences

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were aligned using the Muscle algorithm41 provided in the SeaView graphical user interface42.

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The 18S and rbcL markers were first aligned separately and then combined. The resulting

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contig spanned 2457 bp. We used the Maximum Likelihood (ML) and Bayesian Inference

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(BI) methods in parallel to compute and assess the tree topology from the aligned contig.

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Each tree was rooted using Bolidomonas pacifica L.Guillou & M.-J.Chrétiennot-Dinet, an


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algal species closely related to diatoms43. We used the MrAIC software44 to select the best

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substitution model. Because rbcL is a plastid gene and 18S is a nuclear gene, these genes may

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be subject to different evolutionary constraints. We carried out partitioned analyses to

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independently estimate the model parameters for each gene. Moreover, we constructed trees

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for the 18S and the rbcL genes separately to ensure that these trees both reflected the same

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evolutionary history. We used RAxML 7.2.845 to identify the most likely tree topology for the

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ML method. The branch support was assessed by 1000 bootstrap replicates. Finally, we ran an

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analysis for the BI method with MRBAYES 3.1.246. The analysis consisted of two runs, three

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heated chains, 1,000,000 generations and 2000 samplings from the posterior probability

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distribution.

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Sensitivity-phylogeny relationships

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The phylogenetic data (i.e., tree topology with branch lengths) and trait-related data

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(sensitivity to herbicides) were jointly analyzed to assess the phylogenetic signal of diatom

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sensitivity. All of the analyses were performed after a square-root transformation of EC10 and

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EC50 values for single substances and mixtures to stabilize the variances. To ensure that our

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results were not an artifact of a particular sensitivity level, we performed phylogenetic signal

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analyses on both EC10 and EC50. However, only the EC50 values were used for the

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complementary analyses because there is less uncertainty for these values.

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Phylogenetic signal for herbicide sensitivity

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To measure the phylogenetic signal (i.e. the tendency for related species to be similar to

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each other) of the herbicide sensitivity of a species we used the K statistic to quantify the

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strength of the phylogenetic signal for a given trait (EC values for a given herbicide or

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mixture) using randomization-based procedures. We calculated a K value for each trait and



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used a randomization test based on the phylogenetically independent contrast (PIC) method29

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with 10,000 repetitions. Because our sample size was low (14 species), this test is slightly

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underpowered, and we may expect Type II errors to be inflated. As an alternative to the K

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statistic, intercept-only GLS models that assume no covariance among species (i.e., a star

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phylogeny) can be fitted and compared with intercept-only GLS models that assume a Pagel’s

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λ correlation structure30. Pagel’s λ is a coefficient estimated from trait-related data that

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weights the Brownian model correlation structure. The models can be compared directly using

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the Akaike information criterion (AIC)47. As a rule of thumb, if the difference between the

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AIC values of two models exceeds 2 units, then the model with the lowest AIC is considered

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to best fit the data.

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Phylogenetic Principal Component Analysis

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Phylogenetic principal component analysis (pPCA) is a multivariate method constraining

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traditional PCA to exhibit phylogenetic autocorrelation32. This approach reveals the main

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phylogenetic structures of a set of traits. We used pPCA to highlight patterns among pesticide

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sensitivities in the diatom phylogeny. Jombart et al.32 defined two types of phylogenetic

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structures that can occur in biological features. First, global structures are strongly linked to

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the idea of a phylogenetic signal and result from global patterns of sensitivity similarity in

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phylogenetically related taxa. Second, local structures express the overdispersion of

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sensitivity values that occurs for closely related species in specific parts of the phylogenetic

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tree. Both global and local structures are detected and extracted by pPCA in the form of

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synthetic variables known as the global principal component and the local principal

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component, respectively.

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We performed the pPCA on the EC50 values of each species for each herbicide and for

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mixtures of these herbicides, and retained only the first global principal component (GPC1)

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and the first local principal component (LPC1), which correspond to the highest positive and


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the highest negative Eigenvalues, respectively. Screeplot inspection allowed us visually to

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check that the selected components were sufficient to summarize our data.

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Ancestral character estimations

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We estimated ancestral character (sensitivity) values for the EC50-Mix to visualize the

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order of sensitivity of several diatom species to a mixture of herbicides within the

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phylogenetic tree. We estimated the ancestral characters for each node assuming a Brownian

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motion model of evolution for the examined traits using a restricted maximum likelihood

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method47,48. Finally, we transformed our quantitative trait values (i.e., ancestral and current)

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into a categorical variable with two levels, which can be interpreted as a sensitive/resistant

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classification. The threshold between these two levels was selected as the mean of the square

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roots of the current EC50-Mix values (34.78). The values for sensitive species ranged

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between 8.18 and 20.20 (mean = 16.14), while those for resistant species ranged between

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36.46 and 79.27 (mean = 53.42).

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Phylogenetic regressions

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Phylogenetic Generalized Least Squares (PGLS) and traditional regressions (with a star

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phylogeny) were used to assess and rank the explanatory power of two biomonitoring indices

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(VDAM-NH10 and IPSS49, Table 1) for herbicide sensitivities. For each herbicide, we

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compared the set of PGLS and traditional regressions for both indices using AIC as an

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indicator of the relative support. Ecological guilds50 (Table 1) and multiple regressions

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including both VDAM-NH and IPSS as explanatory variables were not tested here because

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these models require much more data. Our dataset would have led to convergence errors or

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misleading results. The PGLS models were fitted by log-likelihood maximization.

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Statistical packages

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All of the statistical analyses were performed using R 3.0.0 software51. The phylogenetic

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data handling, Pagel’s λ correlation structure and ancestral character estimations were



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performed using the ape package52. The PGLS were adjusted using the nlme package53. The

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phylogenetic multivariate analyses were performed using the adephylo package54, and

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Blomberg’s K statistics were computed using the picante package55.

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Results

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Phylogenetic tree analysis

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The two methods (ML and BI) used to reconstruct the phylogeny produced very similar

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topologies. Similarly, using 18S, rbcL or 18S + rbcL contigs produced similar topologies with

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minor variations in the branch lengths. These small topological differences did not affect the

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methods used here (results not shown). Phylogenetic trees computed with every combination

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of methods and markers are provided as supporting information (Figure S4). The topologies

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were well supported overall by bootstraps and Bayesian posterior probabilities. The tree

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computed from the 18S + rbcL contig using the ML method with branch support values for

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each method and its agreement with other references are presented in Figure 1. The diatom

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species clearly fell into three different groups: the centric, araphid, and pennate raphid

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diatoms. The centric diatom (Cyclotella meneghiniana) was placed near to the pennate

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diatoms. The araphid pennate diatoms (Ulnaria ulna and Fragilariales species) were sister

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species to the nine raphid pennate diatom species in our dataset. The raphid diatoms were

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divided into two clades: one clade has a raphe canal (Nitzschia palea) and the other does not

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(Naviculales, Achnanthales and Cymbellales). Subsequent clades included taxa belonging to

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the Cymbellales, freshwater Achnanthales, and Naviculales species, which were well

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supported. The nodes that separate these important groups were robust with Bayesian support

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values close to 100 (Figure 1). The support values computed by ML were lower, especially in

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the case of the node that separates the raphid and araphid diatoms (67). The deepest node of


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Naviculales was poorly supported by ML and was unresolved by BI. This convergent tree was

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used in all of the subsequent phylogenetic analyses.

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Diatom sensitivity to herbicides

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An overview of diatom sensitivity to herbicides is presented in Figure 2. Species

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sensitivities show similar patterns for both the EC10 and EC50 (Figure 2). The complete

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dataset of untransformed EC10 and EC50 values for each herbicide and each species is

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provided as supporting information (Tables S1 and S2). Cyclotella meneghiniana and

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Fragilariales species were the most sensitive to the four herbicides under both single and

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mixture conditions. More resistant species, such as Nitzschia palea and Sellaphora minima,

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showed different patterns depending on the herbicide family tested. Nitzschia palea was more

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resistant to the triazines, while Sellaphora minima was more resistant to the phenylurea

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herbicides, especially at the EC10 level. Fistulifera saprophila tended to be more resistant to

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the triazines, especially at the EC50 level. The opposite was observed for all of the herbicides

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for Gomphonema clavatum. As a general trend, Encyonema silesiacum, Achnanthidium

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minutissimum and Mayamaea fossalis tended to have medium sensitivities compared to those

284

of other species.

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Phylogenetic signal for herbicide sensitivity

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The phylogenetic signal tested with Blomberg’s K was significant (alpha < 5%) at the EC10

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and EC50 levels for atrazine, Mixt and Mix (Table 2). Mixp was found to be significant only at

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the EC10 level. We found that Pagel’s λ was closely correlated to Blomberg’s K (Pearson

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correlation = 0.76, p-value = 0.002) and that both values led to similar conclusions.

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Interestingly, EC10 appeared to reveal a stronger phylogenetic signal than did EC50 for the

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phenylurea compounds, especially for diuron. Blomberg’s K statistic ranged from 0.401 for

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EC10-terbutryn to 0.862 for EC10-Mixt. We found no statistically significant differences in K

293

values between the EC10 or EC50 groups of traits (Wilcoxon paired test; p-value = 0.438).



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To illustrate the phylogenetic signal, we mapped the EC50-Mix values and the estimated

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ancestral character values for EC50-Mix onto the phylogenetic tree (Figure 1). The ancestral

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character estimation showed a clear phylogenetic segregation between the sensitive

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(Thalassiosirales and Fragilariales species) and resistant (Cymbellales, Naviculales and

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Bacillariales species) species. Moreover, the trophic mode index VDAM-NH10, the ecological

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guild50 and the values of the specific pollution sensitivity index of the IPSS biotic diatom

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index49 of each species are given (Figure 1). From traditional and phylogenetic regressions

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(Supporting information Table S4), it was clear that VDAM-NH offers better explanatory

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power for herbicide sensitivities than does IPSS. Terbutryn sensitivity was an exception, as it

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was equally well explained by both IPSS and VDAM-NH. The trophic mode appeared to

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match both the sensitivity and phylogeny, as demonstrated statistically (p-value < 0.05). It is

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also interesting to note that for isoproturon, the Mixp and Mix phylogenetic models performed

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substantially better than did the traditional models. For diuron, atrazine and Mixt, the

307

traditional and phylogenetic models offered similar performances. Terbutryn is the exception

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for which traditional models performed better than did phylogenetic models.

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The ecological guilds were closely related to the phylogeny but were not clearly linked to

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species’ sensitivity to herbicides. Most of the sensitive species (centric/araphids) belonged to

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the high-profile guild, which also included some resistant species in the raphid group.

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Moreover, species belonging to the motile guild displayed various levels of sensitivity, similar

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to or lower than those of many of the high-profile species.

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The pPCA on EC50 highlighted a strong global structure and a weak local structure (Figure

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2 - Screeplot), which support the presence of a phylogenetic signal as demonstrated by

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statistical analyses. Species’ scores on the first global axis (Figure 2 – Global PC) highlight

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groups of closely related species that share the same range of herbicide sensitivities. These

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scores also reveal contrast between sensitive species, such as the Fragilariales and the centric


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species (Cyclotella meneghiniana), and resistant species, such as Nitzschia palea and

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Sellaphora minima. Species scores on the LPC1 reveal a marked contrast between Nitzschia

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palea and both Fistulifera saprophila and Gomphonema parvulum (Figure 2 – Local PC).

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These species are closely related, but their responses to the different pesticide families are

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different: Gomphonema parvulum is sensitive to triazine and resistant to phenylurea, whereas

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Fistulifera saprophila and Nitzschia palea are sensitive to phenylurea and resistant to triazine

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(Figure 2 – EC values).

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All four herbicides were negatively correlated with the first global component (Figure 3,

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Global PC), suggesting the existence of a general pattern of sensitivities within the phylogeny

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that is independent of the herbicide. However, the first local component (Figure 3, Local PC)

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seemed to oppose the two groups of herbicides, even if it involved only three species (Figure

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2, Local PC: N. palea, Fistulifera saprophila, and Gomphonema parvulum).

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Discussion

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Does phylogeny reflect herbicide sensitivity?

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Interactions between phylogeny and ecology are often studied because they provide

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complementary information that can help to explain patterns of species occurence56 or

336

extinction over time57. Nevertheless, studies relating the phylogeny of freshwater organisms

337

to their sensitivity to pesticides are less common, even if they could provide very useful data

338

for monitoring purposes. Among these few studies, Hammond et al.22 found a phylogenetic

339

signal of sensitivity for many amphibians to the insecticide endosulfan, implying that large

340

ranids were the most sensitive frogs. Similarly, Wängberg and Blanck23 found phylogeny and

341

sensitivity to be linked for a wide range of phototrophic organisms. Cyanophyta and

342

Chlorococcales exhibited different levels of sensitivity toward many chemicals. These first

343

studies gave promising results regarding the potential use of phylogeny in ecotoxicology22,23,28



Page 16 of 32

344

for these specific taxonomic groups and chemicals. To enhance the reliability of these results,

345

studies must be conducted on datasets with wide ranges of both species diversity and species

346

sensitivity28. The lack of such a sensitivity dataset in the literature makes it currently

347

impossible to carry out this type of study, especially on organisms of interest for

348

biomonitoring. Diatoms appear to be appropriate organisms due to their high degree of

349

diversity, their varying sensitivities to herbicides and their critical importance in river

350

bioassessment. These organisms are already being successfully used as indicators to assess the

351

ecological level of aquatic ecosystems1,2, which is required for regulatory monitoring, such as

352

for the European Water Framework Directory58. However, it may be difficult to identify

353

diatoms at the species-level, due to their great diversity and the fact that they require highly

354

qualified personnel and resources50,59.

355

In our study, we worked with a set of 14 taxa. These species were chosen based on their

356

presence in Lake Geneva in order to work with a wide range of taxonomic diversity in

357

diatoms. Therefore, this species set is representative of biofilm diversity in Lake Geneva; the

358

sensitivity of the species toward various pesticides has already been explored in mesocosm

359

studies in the context of this particular lake60. Moreover, the chosen taxa belong to various

360

clades that include the main freshwater orders. Diatom phylogeny has been explored on the

361

basis of various different markers, such as 18S, rbcL, psbC, cox1, ITS, and LSU, by several

362

authors61–67. The two methods (ML and BI) used to reconstruct the phylogeny produced

363

exactly the same topology, which was statistically well supported and was consistent with

364

previously published results68–70.

365

We observed a phylogenetic signal of the sensitivity of benthic diatoms to PSII herbicides

366

at both the EC10 and EC50 levels. Blomberg’s K and Pagel’s lambda statistics, which were

367

used to detect this signal, were underpowered in this study. Simulations showed that with 14

368

species, Blomberg’s K detects 80% of true-positives and Pagel’s lambda detects 75% of true-


Page 17 of 32


369

positives. This result suggests that the phylogenetic signal was most likely more pronounced

370

than is shown here. Therefore, a relationship likely exists between diatom phylogeny and

371

herbicide sensitivity. Possible explanatory hypotheses are discussed in the following section.

372 373

Sensitivity patterns in phylogeny: explanatory hypotheses

374

According to the phylogeny and the first axis of the global axis of the pPCA, two groups of

375

sensitivity toward PSII inhibitors were observed: a) one including the sole centric diatom

376

species (Thalassiosirales) and the araphid pennate diatoms (Fragilariales), which were

377

generally more sensitive than b) the pennate raphid species (Bacillariales, Naviculales,

378

Achnantales and Cymbellales). C. meneghiniana was the only centric diatom we included.

379

Moreover, this species displayed mixotrophic capabilities10, which are rare among centric

380

diatoms, indicating that no generalization could be made regarding the sensitivities of centric

381

diatoms. In our study, ancestral character estimation demonstrates that the resistant state

382

emerged very early in the tree. The main difference between these two groups is the presence

383

of the raphe, which is present only in raphid diatoms65,71,9. Raphe played an important role in

384

benthic habitat colonization, principally by enabling diatoms to move and to secrete an

385

exopolysaccharide matrix71. Nevertheless, it is hardly conceivable that the advent of the raphe

386

in diatom evolution led directly to the changes in lowered sensitivity to herbicides. Rather, we

387

hypothesize that during evolution, species that developed a raphe structure simultaneously

388

developed other characteristics (genetic, physiological or cellular traits) that enhanced their

389

resistance to PSII inhibitors. Raphid and araphid pennate diatoms are both characteristic of

390

benthic communities18,60,72. Under low light conditions, some of these diatoms can evolve

391

heterotrophic capacities, and can metabolize other organic substrates, making them less

392

dependent on photosynthesis10,73. In particular, genera such as Craticula, Encyonema,

393

Fistulifera, Gomphonema, Nitzschia and Sellaphora, most of which display motile and/or



Page 18 of 32

394

raphid characteristics, are known to be better adapted to coping with higher organic matter

395

concentrations than most centrics (e.g., Cyclotella) or araphid diatoms (e.g., Fragilariales)74.

396

The sensitivity levels of the different species are generally constant within the

397

araphid/centric diatoms clade, whereas they are variable within the raphid clade, which

398

encompasses a greater diversity of genera. Several species from this latter clade, such as

399

Mayamaea fossalis, Achnanthidium minutissimum and Encyonema silesiacum, were more

400

sensitive than were other raphid species. We observed variability within the raphid clade, but

401

more sensitivity data per genus would be required to develop a firm hypothesis regarding the

402

variation of sensitivity within the raphid clade in relation to phylogeny. However, we suggest

403

that the trophic mode may play a role in the greater sensitivity observed for these three

404

autotrophic species within the raphid clade. Our main hypothesis is that the trophic mode of

405

diatoms, defined by Van Dam et al.10, is partly related to phylogeny and indirectly influences

406

the sensitivity of species to PSII inhibitors. The results of traditional and phylogenetic

407

regressions both suggest that phylogeny provides a first general level of sensitivity to these

408

herbicides. Moreover, the trophic mode of species may help to refine the level of sensitivity

409

within clades characterized by species with a wide diversity of ecological and physiological

410

characteristics. Diatoms with higher heterotrophic capacities are less susceptible to PSII

411

inhibitors because they are able to shift to organic nutrition to sustain themselves. Moreover,

412

the mixotrophic capacities of diatoms vary greatly between species. Several studies seem to

413

confirm this hypothesis: heterotrophic diatom species are less sensitive to herbicides than are

414

autotrophic diatoms species 15–17,19,75, such as the highly sensitive araphid diatoms. Within the

415

raphid clade, species exhibit various trophic modes, and some may be heterotrophic, which

416

may explain the different levels of sensitivity found for the small number of species tested in

417

our study.


Page 19 of 32


418

Finally, we observed another level of variation among most resistant species of the raphid

419

group. As highlighted by the first local axis of the pPCA, there is greater variation in

420

sensitivity to the different herbicide families (triazine vs phenylurea) for Gomphonema

421

parvulum, Encyonema silesiacum, Fistulifera saprophila, and Nitzschia palea, especially at

422

the EC10 level. Triazine and phenylurea herbicides both inhibit photosynthesis by preventing

423

the electron flow within thylakoid membranes76. However, these two herbicide families are

424

characterized by different chemical structures77. Moreover, within each chemical family,

425

specific and different parameters influence herbicide toxicity77. The fact that herbicides

426

exhibit a range of different chemical structures may influence their uptake by algae and/or

427

their binding to the binding site and finally modulate their toxicity78.

428 429

Use of phylogeny in the context of biomonitoring

430

Our results suggest that an a priori assessment of the sensitivity of benthic diatoms to PSII

431

inhibitors can be made from both their phylogeny, which discriminates two major patterns of

432

sensitivity, and their trophic mode, which refines the sensitivity level of diatom species,

433

especially in the raphid clade, indicating that 1) phylogeny has a considerable potential for use

434

in ecotoxicological studies involving diatoms and PSII inhibitor herbicides, and 2) phylogeny

435

provides promising results to predict bioindicator sensitivity, which may be used for the

436

environmental bioassessment of herbicide impacts. Obviously, this study is a first step, and

437

more studies are needed (e.g., within clades and on other diatoms species) before an

438

operational biomonitoring tool for herbicide contamination can be proposed. In our study and

439

as a general trend, araphids and autotrophic species remain the most sensitive. Even

440

Cyclotella meneghiniana, which is defined as mixotrophic and planktonic, was characterized

441

as sensitive. This species is considered as an exception because most of the mixotrophic

442

diatoms species present in the environment are pennate10. Centric and mixotrophic species are



Page 20 of 32

443

rare. Moreover, centric diatoms are rarely found in benthic habitats, and little information is

444

available from micro-mesocosm studies. Pérès et al.17 have shown that centric (Melosira

445

varians) and araphid diatoms (Staurosira venter) tend to disappear under pressure from

446

isoproturon, whereas raphid species (Cymbella mesiana and Sellaphora minima) tend to be

447

favored. Similarly, Schmitt-Jansen and Altenburger18 observed a decrease in the relative

448

abundance of araphid species (Fragilaria sp., F. capucina var. gracilis) after exposure to

449

atrazine and isoproturon. In an agricultural watershed, the relative abundance of Fragilaria

450

capucina (araphid) tended to decrease, while that of Navicula lanceolata, Nitzschia linearis

451

and

452

IPSS (used to assess the water quality from diatom species abundance) and for ecological

453

guilds, no relevant link was observed between the ranking of species’ sensitivity and their

454

phylogeny. Indices relevant for monitoring a specific stressor cannot easily be transposed to

455

monitor another stressor. In our study, we observed that although the IPSS index is relevant in

456

the context of global pollution, it does not seem to be appropriate for herbicide risk

457

assessment, highlighting the need to develop specific biomonitoring indices for herbicide

458

contamination. In the context of risk assessment and bioassessment, integrating phylogeny

459

into ecotoxicological studies seems to offer a promising way to assess the order of species’

460

sensitivity toward PSII inhibitor herbicides. Moreover, the predicted order of species’

461

sensitivity has been supported by other experiments that integrate higher levels of complexity,

462

such as mesocosm60 and in-situ studies79. In such studies, diatoms are exposed to numerous

463

biotic and abiotic environmental factors (turbulence, light, grazing, etc.) where herbicide

464

contamination accounted only for part of the stress on the diatom assemblage. Ecotoxicology

465

is a discipline in which it is necessary to conduct studies of varying complexity, ranging from

466

molecules to ecosystems80, to provide a solid foundation for biomonitoring. Environmental

467

monitoring aims 1) at connecting pressures and impacts for a posteriori assessment, and 2) at

Nitzschia

palea

(raphids)

tended

to

increase15.

For


Page 21 of 32


468

predicting the a priori risk of contaminants. Both approaches rely on basic sensitivity data

469

that are generally too few and heterogeneous, and are obtained from model species

470

unrepresentative of the ecosystems. Using the link between phylogeny and sensitivity offers

471

the prospect of extending these data and increasing their environmental representativeness,

472

thereby improving biomonitoring tools.

473 474 475 476

ACKNOWLEDGMENTS The authors thank Sylvain Guyot and Alain Franc for their scientific assistance. This program was funded by ONEMA (French national office for water and aquatic ecosystems).

477 478

SUPPORTING INFORMATION AVAILABLE

479

Table S1: Sensitivity data (EC10 in µg.L-1) of each diatom species for each herbicide.

480

Table S2: Sensitivity data (EC50 in µg.L-1) of each diatom species for each herbicide.

481

Table S3: GenBank accession numbers for 18S and rbcL markers.

482

Figure S1: Phylogenetic trees computed with all of the combinations of methods and markers.

483

Table S5: Results of traditional and phylogenetic regressions.

484

This information is available free of charge via the Internet at http://pubs.acs.org/

485

FIGURES

486

Figure 1: Phylogenetic tree of 14 diatom species inferred from the 18S and rbcL DNA

487

sequences and their related ecological guild50, global pollution sensitivity (IPSS49), and

488

trophic mode (VDAM-NH10). The branch lengths were computed using the maximum

489

likelihood method. Statistical support (as a percentage) is provided at each node for the two

490

methods (ML bootstraps/BI posterior probabilities). The clades are replaced in their

491

bibliographic context with a superscript number corresponding to the index reference number.



Page 22 of 32

492

For the ancestral characters, the herbicide sensitivity of the diatoms was based on EC50-Mix

493

data. Black and white symbols represent sensitive and non-sensitive species, respectively.

494

Circles represent current characteristics, and diamonds represent inferred ancestral characters.

495

Ecological guilds: P= planktonic, LP= low profile, HP= high profile, M= motile; IPSS: from 1

496

(species in poor quality water) to 5 (species in good quality water); VDAM-NH: 1=

497

autotrophic, sensitive to low organic N concentrations; 2= autotrophic, tolerant to high

498

organic N concentrations; 3= facultative heterotrophic; 4= obligatory heterotrophic.

499

Figure 2: EC50, EC10 and pPCA species scores of the 14 diatom species mapped onto the

500

phylogenetic tree. The EC values are centered and scaled by their standard deviation within

501

the treatment. The scores are centered and scaled by their standard deviation for each

502

component. The larger the circle, the higher (black circle) or the lower (white circle) the

503

value. The bar plots represent the Eigenvalues for the four principal components of the pPCA.

504

The first Global and Local principal component Eigenvalues are indicated by the letters “G”

505

and “L”, respectively.

506

Figure 3: Loadings of herbicide treatments for the first Global and Local principal

507

components of the EC50 pPCA. The black triangles represent single substances and the

508

binary mixture of phenylurea compounds, black circles represent single substances and the

509

binary mixture of triazine compounds, and white diamonds represent the quaternary mixture.

510


Page 23 of 32


511

TABLE

512

Table 1: Ecological and biological metrics of diatoms considered for regression analysis.

513 Metrics Specific index49 (IPSS)

Pollution

Values/Codes

Classes

From 1 to 5

From bad toward best water quality media

sensitivity

1 2 Nitrogen uptake metabolism (VDAM-NH defined here as 3 trophic mode) 4

N-autotrophic, tolerate very low organic [N] N-autotrophic , tolerate high organic [N] Facultative N-heterotrophic, need periodically high organic [N] Obligatory N-heterotrophic, need continuously high organic [N]

10

Ecological guild50

LP (Low Profile) HP (High Profile) M (Motile) P (Planktonic)

Small species that can tolerate physical disturbance and low levels of resources Taller species, that do not resist to physical disturbance, and tolerate high nutrient levels Species that can move, and tolerate high nutrient levels Species that evolve freely in the water column

514 515

Table 2: Results of statistical analysis for phylogenetic signal of the toxicity of herbicides

516

alone and mixtures.

EC50

EC10

K

K p-value

Pagel's λ

∆AIC

0.602

0.103

1.018

0.150

Isoproturon 0.517

0.399

0.233

-1.708

Mixp

0.535

0.258

0.279

-1.561

Atrazine

0.691

0.032

1.000

3.578

Terbutryn

0.638

0.062

1.030

0.452

Mixt

0.717

0.027

1.011

4.041

Mix

0.680

0.031

0.844

1.146

Diuron

0.624

0.083

1.057

0.867

Isoproturon 0.460

0.195

0.675

-1.022

Mixp

0.627

0.054

NA

NA

Atrazine

0.860

0.007

1.062

7.448

Diuron



Terbutryn

0.401

0.178

0.290

-1.655

Mixt

0.862

0.008

1.053

5.882

Mix

0.834

0.008

1.058

5.768

Page 24 of 32

517 518

∆AIC corresponds to the difference between the AIC of the ‘star’ model and that of the

519

‘Pagel’s λ’ model. ∆AIC > 2 indicates the presence of a phylogenetic signal. NA values are

520

produced if the PGLS values are not convergent.

521

AUTHOR INFORMATION

522

Corresponding Author

523

* [email protected]

524

Tel: +33 4 50 26 78 32; Fax: + 33 4 50 26 07 60

525

Author Contributions

526

The manuscript reflects contributions from all the authors. All authors have approved the final

527

version of the manuscript. ‡ These authors contributed equally.

528

Funding Sources

529

This research was financially supported by ONEMA in the context of the 2011-2012

530

“Phylogeny and Bioassessment” program.

531 532

ABBREVIATIONS

533

CA, concentration addition

534

EC, effective concentration

535

TCC, Thonon culture collection

536

PSII, photosystem II

537

IPSS, specific polluosensitivity index

538

PPCA, phylogenetic principal component analysis 23 ACS Paragon Plus Environment

Page 25 of 32

539


GLS, generalized least squares

540 541

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Linking Diatom Sensitivity to Herbicides to Phylogeny - American

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