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Response of phytoplankton traits to environmental variables in French lakes: New perspectives for bioindication
Ecological Indicators 108 (2020) 105659
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Response of phytoplankton traits to environmental variables in French lakes: New perspectives for bioindication J. Derota,b, A. Jamoneaua, N. Teicherta,c, J. Roseberya, S. Morina, C. Laplace-Treyturea,
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a
IRSTEA, UR EABX, 50 Avenue de Verdun, F-33612 Cestas Cedex, France Estuary Research Center, Shimane University, 1060 Nishikawatsu-cho, Matsue, Shimane 690-8504, Japan c UMR 7208 BOREA, Sorbonne Université, MNHN, CNRS, UMPC, Université Caen, Univ Antilles Guadeloupe, IRD – Station Marine de Dinard – CRESCO, Dinard, France b
ARTICLE INFO
ABSTRACT
Keywords: French lakes Phytoplankton morpho-functional traits Water quality Indicator metrics Lake typology Random forest model Partial dependence plot K-means Null models
The restoration and the preservation of aquatics ecosystems is a critical issue in our contemporary society. In lake ecosystems, phytoplankton taxonomic-based indicators have been developed to evaluate water quality, but suffer of limited ecosystem ecological value. The recent development of functional approaches may allow to evaluate other aspects of ecosystem quality, and to develop new trait-based indicators responding to different environmental conditions. Here, our aim was to analyze the response of phytoplankton traits to numerous environmental variables and to identify relevant traits for the development of future indicator metrics. We used a French national database of 469 lakes, consisting in phytoplankton biovolumes and physicochemical values. The response of 84 morpho-functional traits towards environmental variables was tested with Machine Learning models, taking into account lake typology. We identified 21 traits significantly related to environmental variables. Dissolved Organic Carbon, Nitrates and Total Suspend Solids were the physiochemical parameters which had the higher influence on our traits selection. However the response of phytoplanktonic traits to environmental variables did not change according to lake typology, advocating for a consistent response at the whole national scale contrary to the classical taxonomic approach. We finally identified several candidate traits that could be used for the development of new metrics for French lakes in a context of bio-assessment programs.
1. Introduction According to Crutzen (2006), the Anthropocene is defined as a new geological period having begun at the end of the 18th century with the start of the industrial revolution. Since 1945, the Anthropocene is going into a phase of “great acceleration” (Steffen et al., 2007) where human activities cause increasingly significant changes on all ecosystems at the planet scale. One of the main consequences of environmental changes induced by this new geological time is the decline of biodiversity (Dirzo et al., 2014). In this context of widespread anthropization, aquatic ecosystems are particularly threatened (Dudgeon et al., 2006; Reid et al., 2018). This represents a strong societal issue, because of their importance for ecosystems services (fisheries, irrigation in agricultural/ domestic areas, water supplies, aquatic activities, seaside resorts …) (Reynaud and Lanzanova, 2017). Therefore, in these ecosystems, the restoration and preservation from anthropic activities is a major challenge of our modern times, but require a strong knowledge of the mechanisms controlling species assemblages.
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Corresponding author. E-mail address: [email protected] (C. Laplace-Treyture).
https://doi.org/10.1016/j.ecolind.2019.105659 Received 14 May 2019; Accepted 19 August 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
Two main mechanisms are recognized to control species assemblages: species sorting including both environmental filtering and biotic interactions (Chase and Leibold, 2003), and regional processes, related to dispersal mechanisms and the nature of the regional species pool (Ricklefs, 1987; Loreau and Mouquet, 1999; Leibold et al., 2004). The importance of these processes depends on the ability of species to use and compete for resources and/or to disperse to new localities. At the phytoplankton level, we can define a trait as physiological or morphological characteristics, which have a direct impact on vegetal cell, as well as on the whole ecosystem (Petchey and Gaston, 2006; Violle et al., 2007). Therefore, the integration of species traits and their relation to environmental variable constitute a major insight for the understanding of species assemblages (Weiher et al., 2011). From a more applied point of view, traits are consequently largely used to assess environmental conditions, and are the basis for the settlement of many biotic indices (Usseglio-Polatera et al., 2000; Kelly et al., 2009; Mondy et al., 2012). In most continental aquatic ecosystems, the primary production, mainly represented by phytoplankton taxa, plays a key role because it
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includes complex transitions between mineral and organic matters and represents the bottom of the trophic network. The study of phytoplankton traits provides essential knowledge about the response of communities to environment and ecosystem processes (Tilman et al., 1997) in supplying more details about community structure, ecosystem functioning and biotic interactions than analyses based on species identity (Krause et al., 2014; Irwin and Finkel, 2017; Kruk et al., 2017; Abonyi et al., 2018; Duarte et al., 2018). For phytoplankton communities, the most common functional classification is probably the Reynolds classification (Reynolds et al., 2002; Kruk et al., 2017) which was subsequently modified (Padisák et al., 2009), but see also Salmaso et al. (2015) for a review on other classifications. This classification was used in numerous studies to analyze the variation of functional diversity with environmental variables (Cellamare et al., 2013, 2016; De Souza et al., 2016; Kruk et al., 2017; Kruk and Segura, 2012). Usually such studies found that nutrients and physical processes both influenced functional composition of phytoplankton in temperate (Cellamare et al., 2013; Borics et al., 2016; Mutshinda et al., 2016) or tropical regions (Cardoso et al., 2017; De Souza et al., 2016; Santana et al., 2017). However only Kruk and Segura (2012) focused on the relationship between environments and traits at a large scale but used a limited number of morphology-base functional groups. Thus, to our knowledge, no study investigated the trait-environment relationships at a national scale with a large database of ecological traits. However national scale is the relevant level for the development of ecological metrics. Indeed, the polyphyletic classification of Reynolds and its derivatives are mainly based on tolerance/sensitivity of phytoplankton to environmental changes; and on few morphological characteristics (e.g. cell or colonie, surface of the cell, volume of the cell …) (Reynolds and Irish, 1997). Here we used a large species × traits database of more than 80 morphological, phylogenical and biological traits and related it to environmental factors in order to identify proper traits for lake ecological assessment. In aquatic ecosystems, interactions between environmental variables and phytoplankton traits are often nonlinear and complex (Edwards et al., 2016). To account for this problem, it is primordial to find adapted mathematical/numerical tools, such as machine learning (ML) methods, able to detect the dominant stressors in considering nonlinear effects (Kruk and Segura, 2012; Teichert et al., 2016; Kruk et al., 2017). Within a context of water quality monitoring, ecological threshold shift can be used as a basis for the classification of ecosystem health status (Large et al., 2015; Roubeix et al., 2016) and Random Forest (RF) models have, in this way, already proved their efficiency (Kruk and Segura, 2012). In this study, we aimed to use ML methods/RF models to relate the presence of large number of functional traits with environmental variables at a large spatial scale, and to determine ecological thresholds related to these environmental variables. These results should help to identify candidate traits for future implementation of new metrics for lakes bio-assessment programs.
Fig. 1. Location of the 469 lakes concerned by our study; each black dot represents a lake for which we have data used in this study.
description and basic statistical information). As each lake could be sampled several times during the study period, our final database is composed of a maximum of 2073 samplings per parameter (with a minimum of 244 for an average of 1308 samplings). The phytoplankton database encompassed a total of 1409 taxa and their associated biovolume. In order to limit the statistical artefacts induced by rare species and to remove potential determination errors, we only kept for further analyses all taxa which occurred in more than 15 samples. For each of these remaining taxa, we compiled a list of 84 morpho-functional traits according to their phylogeny, size, shape, motility, presence and number of flagella, presence of specific (endocellular) organelles, presence and nature of ornamentations, toxin production, source of carbon input and type of reproduction (all technical details about these traits, taxa, and the associated bibliography are described in Appendix 1). Moreover, commonly the phytoplankton data are expressed via their abundance (in cell/mL) at a given moment. In this study, we weighted species abundances with their corresponding biovolume to obtain species biovolume in mm3/L (see Fig. 2), as already done during the creation of the French index for Lake Assessment IPLAC (LaplaceTreyture and Feret, 2016); which used the same database, but within a shorter time period (between 2005 and 2012). This weighting offers the advantage to allow considering the relative importance of a given species (in terms of space and then biomass) within community sampled. 2.2. Determination of typological groups French lakes include a high natural typological diversity mainly influenced by their altitude and maximum depth (Laplace-Treyture and Feret, 2016). In order to analyze the effect of typology on our analyses, we created a qualitative predictor based on 4 typological groups (see Fig. 3). We have to be mindful that the RF models are able to manage at the same time quantitative and qualitative data. These 4 groups were clustered via the K-means method (Hartigan and Wong, 1979; Solidoro et al., 2007; Panigrahi et al., 2009), applied to lake maximum depth and altitude. Therefore, each study lake was assigned to a typological group.
2. Materials and methods 2.1. Study sites and environmental variables We used the French national dataset consisting of 469 lakes (see Fig. 1) surveyed for their phytoplankton communities between 2005 and 2016 for the application of the Water Framework Directive (WFD, 2000). All samplings were performed at the overhead position of the deepest part of the lake, in order to take into account the whole euphotic zone. Phytoplankton counts and physicochemical parameters were measured at the same time on the same sample by laboratories according to a standardized method (Laplace-Treyture et al., 2009), and following the European Standard NF15204 (2006). We used 15 of these chemical parameters for this study (see Table 1 below for parameter
2.3. Numerical analyses 2.3.1. Machine learning models In environmental sciences machine learning (ML) algorithms 2
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Table 1 List of physicochemical parameters and their representation in the dataset, as well as their mean and range values. Physicochemical parameter
Units
Number of available data
Percentage of available data
Mean
Range
Chlorophyll a Conductivity DOC (dissolved organic carbon) Ammonium Nitrite Nitrate Dissolved oxygen pH Phosphate Total phosphorus Silicate Temperature TSS (total suspended solids) Secchi transparency CAT (Complete alkalimetric title)
µg/L µS/cm µg/L mg/L (in mg/L (in mg/L (in mg/L pH mg/L (in mg/L (in mg/L °C mg/L m °fH
1724 728 1735 1740 1740 1738 244 924 1739 1739 1233 814 1512 1081 933
83.17% 35.1% 83.70% 83.98% 83.98% 83.84% 11.77% 44.55% 83.94% 83.84% 59.48% 39.27% 72.94% 52.15% 45.01%
13.20 230.86 5.92 0.09 0.05 4.48 96.22 7.89 0.05 0.06 4.37 16.44 11.29 9.13 6.74
0.10–380 7.40–951 0.10–56 0–3.30 0–5.40 0.03–72 10–189 4.80–12.6 0–2.53 0–1.41 0.02 – 29.6 1.17 – 29.40 0.40–410 0.10 – 21.93 0.11–26
NH4) NO2) NO3) P) P)
enhance predictive performances (i.e. the correlation coefficient R2) in comparison with traditional correlation analysis (linear regression, GLM …) (Kehoe et al., 2015; Rivero-Calle et al., 2015; Thomas et al., 2018). Numerous types of structure of ML models exist, but in this study and in order to select potential metrics and to extract physicochemical ecological thresholds, we used a Random Forest (RF) model. This type of model, which is based on a tree structure, has no prior assumptions hypotheses, is able to effectively manage gaps in datasets (missing value), has a strong predictive capability and is also adapted to nonlinear data processing (Breiman, 2001; Thomas et al., 2018). These inherent properties of this model fit to the problematic of our study. The term of “forest” in the name of the model, means that a predefined number of classifications or regression trees is built (Breiman et al., 1984). The number of tree generated is set during the parameterization phase. In a RF model the target signal is the single variable that we want to predict, in our case this signal is the considered occurrence trait weighted by its biovolume. The predictors are the explained variables used during the learning phase, corresponding to physicochemical parameters in our study and the typological groups (see above parts 2.1
Fig. 2. Conceptual diagram presenting the method for traits selection and threshold extraction. Color code: green parts are linked to targets; blue parts to predictors; yellow parts to numerical calculations; and the orange part to results.
Fig. 3. Visualization of the 4 typological groups created with the K-means method. Each color zone represents a geographical group according to altitude and maximal depth in meters. Note that both x- and y-axis are in log scale. Text arrows represent the limit of each group converted into a standardized scale.
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and 2.2 for more details). For more technical details about the functioning of RF model, please refer to the original article (Breiman, 2001). For an uninitiated audience in ML techniques, some other papers may also sum up the functioning of this RF model, in a more trivial way (Kehoe et al., 2012; Touw et al., 2012; Yajima and Derot, 2018). Except for the typological group predictors, all the others input data of RF model (traits and physicochemical parameters) were transformed via the Boxcox method (Box and Cox, 1964), in order to facilitate the visual extraction of ecological threshold. RF models were constructed with 200 trees per “forest”, which was sufficient to obtain a good stabilization of the error classification (see Fig. A2 in appendix 2 for more information). We also conserved a minimal number of 5 observations per node (as recommended by the official documentation of Matlab, https://fr.mathworks.com/help/ stats/index.html), when the model RF is used in regression mode (prediction of quantitative data). All numerical analyses were performed with Matlab software and its “statistics and machine learning” toolbox.
Cutler et al., 2007; Teichert et al., 2016), depending on our 4 typological groups (see Fig. 2). Unlike classical regression models, it is not possible to extract an equation between the input and the output with ML model based on a tree structure (Wagenhoff et al., 2017). Therefore, we used the PDP method to determine visually ecological stressor gradients (Cutler et al., 2007; Roubeix et al., 2016; Wagenhoff et al., 2017). The ecological stressor gradient is located between the two slope discontinuities: Impact initial (II) and Impact cessation (IC) (Wagenhoff et al., 2017). On the PDP plot, the x-axis corresponds to a physicochemical parameter and the y-axis is always the selected trait. Thus in our study, the II corresponds to the first slope discontinuity (the nearest to the y-axis on the left), and the IC corresponds to the last slope discontinuity, (the furthest to the y-axis on the right). This kind of gradient allows reflecting the different ecosystem health status (Holling, 1973; Odum et al., 1979; Wagenhoff et al., 2012). For each of the 4 most influential parameters, we recorded 2 thresholds (II and IC); consequently we obtained a total of 8 thresholds per selected traits. All visual determinations were based on power-scale PDP plots.
2.3.2. Method for traits selection One of our aims was to determine the morpho-functional traits of phytoplankton which could be used as potential bio-indication metrics for French lakes. Therefore, in order to make this selection among our 84 traits, we checked the goodness of fit and the statistical significance for each target signal, according to the 15 physicochemical predictors and typological groups. For the first step of this trait selection, we evaluated for each target signal the model goodness of fit with pseudoR2 (Breiman, 2001; Large et al., 2015; Teichert et al., 2016), calculated as indicated in Eq. (1).
pseudo
R2 = 1
MSE Var (y )
3. Results and discussion For the first time to our knowledge, a list of relevant phytoplankton traits related to physicochemical parameters was created, in order to further develop functional indicators of lake ecological status across the French territory. As a first step, we are going to discuss in detail the selected traits we identified via the use of ML techniques. In a second step, we will discuss the response of these traits to physicochemical parameters. Finally, we will explore the impact of the French lake natural typology on the functional diversity of phytoplankton.
(1)
3.1. Relevant traits associated with physicochemical variables
where Var(y) represents the variance of the distribution of traits occurrence weighted by their biovolume, and MSE the corresponding mean squared error. Upon completion, we got 84 pseudo-R2, once for each target signal. The second step of our selection was to test if the model was significantly different from a null model to ensure that the predictions from the fitted model are not generated by random effects (North et al., 2002; Teichert et al., 2016). For each trait, we randomly permuted the inputs predictors values (physicochemical parameters) and then compared all the pseudo-R2 from these permutations with the other nonpermutated R2, following the Eq. (2).
pvalue =
(r + 1) (n + 1)
Among the 84 phytoplankton morpho-functional traits initially defined (see Appendix 1), 21 traits appeared to be significantly influenced by physico-chemical parameters (p-value < 0.05 and pseudoR2 > 0.3). These 21 traits (see Table 2) encompassed numerous types of traits: phylogeny (presence of Chlorophyceae and presence of Euglenophyceae), cells size (nano-plankton (minimal size) and microplankton (maximal size)); life form (solitary or colonial); shape (double cone shape); motility; presence of contractile vacuoles; number of chloroplasts (absence or presence of one chloroplast); ornamentation (presence of scales, protuberances, presence of needle, big protuberance size); type of pigments (presence of chlorophyll b, presence of xanthophyll); source of carbon input (autotrophy); and type of reproduction (asexual or sexual/asexual). Among these 21 selected traits, most of them were in line with those used in former studies which aimed to identify a restricted number of functional groups (Reynolds et al., 2002; Padisák et al., 2009; Kruk and Segura, 2012). For example, Kruk and Segura (2012) proposed a classification of phytoplankton based on 7 groups, the one corresponding to “Small siliceous flagellates” (Kruk and Segura, 2012) including 4 traits also selected in our study (e.g. presence of siliceous exoskeleton, presence of flagellum, size class pico-plankton (maximal) and size class nano-plankton (maximal)). Despite this apparent similarity with former studies, we nevertheless identified two traits rarely used for the creation of functional groups: the reproduction type (Litchman and Klausmeier, 2008) and the presence of protuberances. These traits appeared to be significantly associated with environmental variables and should thus reveal selective pressure on phytoplankton species. In favorable conditions, asexual reproduction of phytoplankton allows a quick increase in population (Boero et al., 2008), inducing advantages over other phytoplankton species for a short time period (Valiela, 2013). The presence of protuberances is probably related with the development of defense mechanisms under certain environmental conditions, influencing trophic
(2)
where n is the number of permutations (n = 999) and r the number of times the pseudo-R2 from permutated model is superior to the pseudo-R2 of non-permutated model (see above Eq. (1)). By way of indication, for each trait we performed 1000 runs with RF model (999 runs with permutated predictors, and one without permutation), as we have just seen, each model is composed of 200 trees. Consequently, at the end of this analysis, 16,800,000 tree models were created. Finally, among the 84 traits tested, we only conserved those having, both a goodness of fit (pseudo-R2) higher than 0.3 (Møller and Jennions, 2002; Kruk and Segura, 2012) and a significant p-value (set at α < 0.05). 2.3.3. Thresholds extraction We performed a new RF run for each selected trait, with only the 4 quantitative predictors which had the greatest influence according to their out-of-bag errors, and the created qualitative typological variable (see also Appendix 3 for an evaluation of the number of selected physico-chemical variables). We then examined the importance of each physicochemical variable on the prediction of trait biovolume with partial dependence plots (PDP) (Friedman et al., 2001; Friedman, 2001; 4
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interactions and particularly grazing intensity (Agrawal, 1998; Litchman et al., 2010). According to our results (Table 2), the presence of big protuberances and of needles appeared to be particularly relevant as selective defense traits. Interestingly, the toxin production, which is generally the only defense mechanism which is considered in the classical classification based on morpho-functional groups, was not significantly associated with environmental variables in our study. In that context, the type and size of protuberances should probably be further explored in functional group classification studies.
coherent as waters with high DOC, Chl-a and TSS are more turbid and thus have a lower Secchi depth (see Table of correlation between physicochemical variables in Appendix 4). Chlorophyll-a concentration was always the most important explanatory variable in all trait biovolume models. This implies that this predictor was often selected by the RF model to create the first nodes (splits) in the tree structures. Therefore, we deliberately chose to use chl-a as a predictor in the models in order to test the hypothesis that some traits, which could be less representative in the whole community, could be non significantly explained by total chl-a, and that other traits, should be more intensively related to high phytoplankton biomass production. As the chl-a is a proxy of phytoplankton biomass the observed dominant influence of this parameter could be relatively logical. Indeed the correlation observed is probably due to a sampling effect as more the biomass is important more the probability to observe a trait is high, thus explaining the positive correlation between each selected trait and Chl-a concentration. This sampling effect furthermore explained some contradicting response of traits with Chl-a, such as the positive response of the presence and absence of protuberance, or the positive response of solitary and filamentous forms (Table 2). Similarly, the positive response of traits observed with DOC and TSS could also be due to an indirect effect and related to the strong correlation between all these variables (DOC, TSS and Chl-a, see Appendix 3). Indeed the positive response observed in our models could probably be the consequence rather the cause as more the biomass is important more the quantity of organic suspended compounds is expected to be high. For these two environmental parameters, as well as for Secchi depth, is thus seems to be difficult to disentangle the direct effect of
3.2. Physicochemical variables explaining phytoplanktonic traits For each of the 21 traits significantly associated with environment, we selected the four most important predictors and found that globally, seven of them (among 15) seem to show a general significant impact on our traits selection (see Table 2). Moreover, three of these parameters occurred in all trait biovolume models: Chlorophyll-a (Chl-a), Dissolved Organic Carbon (DOC), and the Total Suspended Solids (TSS). The following most important predictors were either nitrate concentration, and Secchi depth. In some rare cases, Complete Alkalimetric Title (CAT) and water temperature showed the same importance. In the light of the low occurrence of these two last parameters, it is difficult to draw general conclusions. Consequently, our discussion will more focus on the five others most influent predictors. Chlorophyll-a, Dissolved Organic Carbon, and Total Suspended Solids, always positively influenced trait biomass concentration whereas the Secchi depth appeared to be negatively related to trait biomass (see the columns direction in the Table 2). These variations are
Table 2 Overview of the 21 selected traits and their response to environmental variation. II corresponds to Impact Initiation and the IC corresponds to Impact Cessation. Arrows indicate the direction of variation from the PDP depending on the considered physicochemical parameters. The blank cells correspond to absence of significant relationship. Reminder of abbreviation: Chl-a for Chlorophyll-a; DOC for Dissolved Organic Carbon; NO3 for Nitrate; TSS for Total Suspended Solids; and CAT for Complete Alkalimetric Title.
(continued on next page) 5
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Table 2 (continued)
these variables from the indirect effect due the biological compartment itself. However, the positive response of the two phylogenical traits, i.e. presence of Chlorophyceae and Euglenophyceae, could be considered as a direct response to higher concentration of DOC and TSS as the other phylogenical groups did not evidence such response (which should be expected in case of indirect relationships). The response of Euglenophyceae to DOC is in accordance with the literature as this phylogenic group is well known to be composed of mostly heterotrophic or phagotrophic species inhabiting organic rich habitats (Reynolds, 2006). As the WFD (2000/60/EC) encourages the use of DOC to define the water health status (Koprivnjak et al., 1995; Aiken et al., 2002; Gruau et al., 2007), our results suggest that the presence of species from this phylogenic group could be a potential candidate to measure the anthropic pressure in French lakes. More interestingly is probably the response of some traits to nitrate concentrations (Table 2), such as the positive relationship observed with the presence of filamentous forms. The development of filamentous algae with nutrient inputs has already been suggested in the literature (Phillips et al., 1978) and particularly their capacity to use nitrate components (Moss, 2005). Most of the filamentous forms are also found within the Cyanophyceae group, known to be very competitive in high N:P ratio (McCarthy et al., 2009; Posch et al., 2012). But a high nitrate concentration also seems to be associated with rarer and smaller protuberances. Maybe the development of filamentous algae, which is also recognized to be a defense mechanism against predation
(Smetacek, 2001), reduced the overall grazing pressure on phytoplankton communities and thus the presence of these morphological associated traits. As current phytoplanktonic biodindicators are mainly considered to respond to phosphorous gradients (De Hoyos et al., 2014; Phillips et al., 2014; Laplace-Treyture and Feret, 2016; Wolfram et al., 2014), our results suggest that the use of specific functional traits, such as the filamentous form, might also lead to new insights for quantifying the nitrate pressure. 3.3. Impact of lake typology on phytoplankton functional diversity In order to test the response of phytoplankton traits to environmental factors according to lake typology, we created 4 main typological groups with K-means analyses (see Fig. 3). The first group (in the blue zone bottom right on Fig. 3) corresponds to shallow lakes located at mid-altitude, often located close to the Mediterranean Basin. The second group (in the purple zone at the middle top) corresponds to deep lake of mid-altitude, essentially represented by artificial dam water bodies. The third group (in the left green zone) corresponds to relatively shallow lowland lakes, represented by usually natural lakes located near the French coast. The fourth group (in the top right orange zone) corresponds to deep lakes of high altitude, mostly represented by alpine/mountainous natural lakes. The response of biovolume traits to physicochemical parameters on PDP plots are really similar whatever the typological group considered (Fig. 4 and Appendix 5), and consequently, slopes discontinuities (II 6
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Fig. 4. Example of results from PDP for the presence of protuberance depending on the four typological groups. Upper left: PDP for chlorophyll a; Upper right: PDP for DOC; Lower left: PDP for nitrate; Lower right: PDP for TSS. On each plot the left black dashed line is II and the right black dashed line is IC.
and IC see black dashed lines on Fig. 4) are located at the same thresholds. This trait-response similarity between typological groups was found for all PDP of our 21 selected-traits. Consequently, typology seems to show a limited impact on traits biovolume as the typological predictor was almost always the least influential predictor on the models (see Appendix 3), suggesting that the functional diversity of phytoplankton was more influenced by physicochemical conditions than typological features. These results are consistent with a Canadian study (Beisner et al., 2006), which demonstrated that the typology of lakes had mainly an impact on higher levels of the food web, i.e. on the zooplankton and fish communities and that phytoplankton and bacteria communities are weakly patterned by typological variables. However, in a bio-indication context, the Water Framework Directive (WFD) usually uses typological groups such as hydroecoregion to assess the ecological status of water bodies. Consequently, the national French phytoplankton indicator (IPLAC, Laplace-Treyture and Feret, 2016), took into account different lake typologies based on their altitude and mean depth to define references conditions. Compared to this taxonomical approach it appears that the use of morphofunctional traits allowed to moderate the dominant effect of typological groups, while ensuring similarity in trait-responses to environmental changes. At a large spatial scale, the trait-based approach thus seems to be more appropriate than the taxonomic one and better adapted for a global approach (e.g. metrics are more comparable at the national scale).
typological predictor seemed, for its part, more limited. As current phytoplankton taxonomic-based indicators are mainly constructed to respond to anthropogenic pressures through phosphorus inputs, our study provides a first step towards the development of new indices for refining the anthropogenic impact on lakes ecosystems. Indeed, when using a trait by trait approach, we identified several potential trait-candidates for future bio-monitoring measures, particularly for DOC and nitrate gradients. Phylogenic groups, filamentous forms as well as the presence of ornamentations represent the main traits associated to the variation of physicochemical parameters. These results further strengthen the need for considering the ornamentations in the current classification systems based on morpho-functional traits of phytoplankton, and offer new insights for the evaluation of health status in French lakes.
4. Conclusions
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.105659.
Acknowledgments This work was supported by the French Ministry of the ecological and solidarity transition (MTES, France) and funded by the “Agence Française de la Biodiversité” (AFB, France) through the convention between Irstea and AFB. Authors would like to thank the six water agencies and private labs that participated to the collection of abiotic and biotic data. They are also grateful to the lake team of Irstea Aix-enProvence for the database management. We also would like to thank our laboratory engineer S. Brouty for the map in the Fig. 1. Appendix. Supplementary data
In contrast with others studies on aquatic organisms, there are very few studies focusing on the relation between phytoplankton traits and physicochemical pressures in French lakes. The use of ML model based on a tree structure and the corresponding PDP, seemed to be well adapted to this type of studies in lacustrine ecosystems. In this study, we found that some few parameters had indirect or direct predominant influence on the trait distributions of phytoplanktonic species: Chlorophyll-a, DOC, nitrates and TSS. By contrast, the influence of the
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Response of phytoplankton traits to environmental variables in French lakes: New perspectives for bioindication J. Derota,b, A. Jamoneaua, N. Teicherta,c, J. Roseberya, S. Morina, C. Laplace-Treyturea,
T
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a
IRSTEA, UR EABX, 50 Avenue de Verdun, F-33612 Cestas Cedex, France Estuary Research Center, Shimane University, 1060 Nishikawatsu-cho, Matsue, Shimane 690-8504, Japan c UMR 7208 BOREA, Sorbonne Université, MNHN, CNRS, UMPC, Université Caen, Univ Antilles Guadeloupe, IRD – Station Marine de Dinard – CRESCO, Dinard, France b
ARTICLE INFO
ABSTRACT
Keywords: French lakes Phytoplankton morpho-functional traits Water quality Indicator metrics Lake typology Random forest model Partial dependence plot K-means Null models
The restoration and the preservation of aquatics ecosystems is a critical issue in our contemporary society. In lake ecosystems, phytoplankton taxonomic-based indicators have been developed to evaluate water quality, but suffer of limited ecosystem ecological value. The recent development of functional approaches may allow to evaluate other aspects of ecosystem quality, and to develop new trait-based indicators responding to different environmental conditions. Here, our aim was to analyze the response of phytoplankton traits to numerous environmental variables and to identify relevant traits for the development of future indicator metrics. We used a French national database of 469 lakes, consisting in phytoplankton biovolumes and physicochemical values. The response of 84 morpho-functional traits towards environmental variables was tested with Machine Learning models, taking into account lake typology. We identified 21 traits significantly related to environmental variables. Dissolved Organic Carbon, Nitrates and Total Suspend Solids were the physiochemical parameters which had the higher influence on our traits selection. However the response of phytoplanktonic traits to environmental variables did not change according to lake typology, advocating for a consistent response at the whole national scale contrary to the classical taxonomic approach. We finally identified several candidate traits that could be used for the development of new metrics for French lakes in a context of bio-assessment programs.
1. Introduction According to Crutzen (2006), the Anthropocene is defined as a new geological period having begun at the end of the 18th century with the start of the industrial revolution. Since 1945, the Anthropocene is going into a phase of “great acceleration” (Steffen et al., 2007) where human activities cause increasingly significant changes on all ecosystems at the planet scale. One of the main consequences of environmental changes induced by this new geological time is the decline of biodiversity (Dirzo et al., 2014). In this context of widespread anthropization, aquatic ecosystems are particularly threatened (Dudgeon et al., 2006; Reid et al., 2018). This represents a strong societal issue, because of their importance for ecosystems services (fisheries, irrigation in agricultural/ domestic areas, water supplies, aquatic activities, seaside resorts …) (Reynaud and Lanzanova, 2017). Therefore, in these ecosystems, the restoration and preservation from anthropic activities is a major challenge of our modern times, but require a strong knowledge of the mechanisms controlling species assemblages.
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Corresponding author. E-mail address: [email protected] (C. Laplace-Treyture).
https://doi.org/10.1016/j.ecolind.2019.105659 Received 14 May 2019; Accepted 19 August 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
Two main mechanisms are recognized to control species assemblages: species sorting including both environmental filtering and biotic interactions (Chase and Leibold, 2003), and regional processes, related to dispersal mechanisms and the nature of the regional species pool (Ricklefs, 1987; Loreau and Mouquet, 1999; Leibold et al., 2004). The importance of these processes depends on the ability of species to use and compete for resources and/or to disperse to new localities. At the phytoplankton level, we can define a trait as physiological or morphological characteristics, which have a direct impact on vegetal cell, as well as on the whole ecosystem (Petchey and Gaston, 2006; Violle et al., 2007). Therefore, the integration of species traits and their relation to environmental variable constitute a major insight for the understanding of species assemblages (Weiher et al., 2011). From a more applied point of view, traits are consequently largely used to assess environmental conditions, and are the basis for the settlement of many biotic indices (Usseglio-Polatera et al., 2000; Kelly et al., 2009; Mondy et al., 2012). In most continental aquatic ecosystems, the primary production, mainly represented by phytoplankton taxa, plays a key role because it
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includes complex transitions between mineral and organic matters and represents the bottom of the trophic network. The study of phytoplankton traits provides essential knowledge about the response of communities to environment and ecosystem processes (Tilman et al., 1997) in supplying more details about community structure, ecosystem functioning and biotic interactions than analyses based on species identity (Krause et al., 2014; Irwin and Finkel, 2017; Kruk et al., 2017; Abonyi et al., 2018; Duarte et al., 2018). For phytoplankton communities, the most common functional classification is probably the Reynolds classification (Reynolds et al., 2002; Kruk et al., 2017) which was subsequently modified (Padisák et al., 2009), but see also Salmaso et al. (2015) for a review on other classifications. This classification was used in numerous studies to analyze the variation of functional diversity with environmental variables (Cellamare et al., 2013, 2016; De Souza et al., 2016; Kruk et al., 2017; Kruk and Segura, 2012). Usually such studies found that nutrients and physical processes both influenced functional composition of phytoplankton in temperate (Cellamare et al., 2013; Borics et al., 2016; Mutshinda et al., 2016) or tropical regions (Cardoso et al., 2017; De Souza et al., 2016; Santana et al., 2017). However only Kruk and Segura (2012) focused on the relationship between environments and traits at a large scale but used a limited number of morphology-base functional groups. Thus, to our knowledge, no study investigated the trait-environment relationships at a national scale with a large database of ecological traits. However national scale is the relevant level for the development of ecological metrics. Indeed, the polyphyletic classification of Reynolds and its derivatives are mainly based on tolerance/sensitivity of phytoplankton to environmental changes; and on few morphological characteristics (e.g. cell or colonie, surface of the cell, volume of the cell …) (Reynolds and Irish, 1997). Here we used a large species × traits database of more than 80 morphological, phylogenical and biological traits and related it to environmental factors in order to identify proper traits for lake ecological assessment. In aquatic ecosystems, interactions between environmental variables and phytoplankton traits are often nonlinear and complex (Edwards et al., 2016). To account for this problem, it is primordial to find adapted mathematical/numerical tools, such as machine learning (ML) methods, able to detect the dominant stressors in considering nonlinear effects (Kruk and Segura, 2012; Teichert et al., 2016; Kruk et al., 2017). Within a context of water quality monitoring, ecological threshold shift can be used as a basis for the classification of ecosystem health status (Large et al., 2015; Roubeix et al., 2016) and Random Forest (RF) models have, in this way, already proved their efficiency (Kruk and Segura, 2012). In this study, we aimed to use ML methods/RF models to relate the presence of large number of functional traits with environmental variables at a large spatial scale, and to determine ecological thresholds related to these environmental variables. These results should help to identify candidate traits for future implementation of new metrics for lakes bio-assessment programs.
Fig. 1. Location of the 469 lakes concerned by our study; each black dot represents a lake for which we have data used in this study.
description and basic statistical information). As each lake could be sampled several times during the study period, our final database is composed of a maximum of 2073 samplings per parameter (with a minimum of 244 for an average of 1308 samplings). The phytoplankton database encompassed a total of 1409 taxa and their associated biovolume. In order to limit the statistical artefacts induced by rare species and to remove potential determination errors, we only kept for further analyses all taxa which occurred in more than 15 samples. For each of these remaining taxa, we compiled a list of 84 morpho-functional traits according to their phylogeny, size, shape, motility, presence and number of flagella, presence of specific (endocellular) organelles, presence and nature of ornamentations, toxin production, source of carbon input and type of reproduction (all technical details about these traits, taxa, and the associated bibliography are described in Appendix 1). Moreover, commonly the phytoplankton data are expressed via their abundance (in cell/mL) at a given moment. In this study, we weighted species abundances with their corresponding biovolume to obtain species biovolume in mm3/L (see Fig. 2), as already done during the creation of the French index for Lake Assessment IPLAC (LaplaceTreyture and Feret, 2016); which used the same database, but within a shorter time period (between 2005 and 2012). This weighting offers the advantage to allow considering the relative importance of a given species (in terms of space and then biomass) within community sampled. 2.2. Determination of typological groups French lakes include a high natural typological diversity mainly influenced by their altitude and maximum depth (Laplace-Treyture and Feret, 2016). In order to analyze the effect of typology on our analyses, we created a qualitative predictor based on 4 typological groups (see Fig. 3). We have to be mindful that the RF models are able to manage at the same time quantitative and qualitative data. These 4 groups were clustered via the K-means method (Hartigan and Wong, 1979; Solidoro et al., 2007; Panigrahi et al., 2009), applied to lake maximum depth and altitude. Therefore, each study lake was assigned to a typological group.
2. Materials and methods 2.1. Study sites and environmental variables We used the French national dataset consisting of 469 lakes (see Fig. 1) surveyed for their phytoplankton communities between 2005 and 2016 for the application of the Water Framework Directive (WFD, 2000). All samplings were performed at the overhead position of the deepest part of the lake, in order to take into account the whole euphotic zone. Phytoplankton counts and physicochemical parameters were measured at the same time on the same sample by laboratories according to a standardized method (Laplace-Treyture et al., 2009), and following the European Standard NF15204 (2006). We used 15 of these chemical parameters for this study (see Table 1 below for parameter
2.3. Numerical analyses 2.3.1. Machine learning models In environmental sciences machine learning (ML) algorithms 2
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Table 1 List of physicochemical parameters and their representation in the dataset, as well as their mean and range values. Physicochemical parameter
Units
Number of available data
Percentage of available data
Mean
Range
Chlorophyll a Conductivity DOC (dissolved organic carbon) Ammonium Nitrite Nitrate Dissolved oxygen pH Phosphate Total phosphorus Silicate Temperature TSS (total suspended solids) Secchi transparency CAT (Complete alkalimetric title)
µg/L µS/cm µg/L mg/L (in mg/L (in mg/L (in mg/L pH mg/L (in mg/L (in mg/L °C mg/L m °fH
1724 728 1735 1740 1740 1738 244 924 1739 1739 1233 814 1512 1081 933
83.17% 35.1% 83.70% 83.98% 83.98% 83.84% 11.77% 44.55% 83.94% 83.84% 59.48% 39.27% 72.94% 52.15% 45.01%
13.20 230.86 5.92 0.09 0.05 4.48 96.22 7.89 0.05 0.06 4.37 16.44 11.29 9.13 6.74
0.10–380 7.40–951 0.10–56 0–3.30 0–5.40 0.03–72 10–189 4.80–12.6 0–2.53 0–1.41 0.02 – 29.6 1.17 – 29.40 0.40–410 0.10 – 21.93 0.11–26
NH4) NO2) NO3) P) P)
enhance predictive performances (i.e. the correlation coefficient R2) in comparison with traditional correlation analysis (linear regression, GLM …) (Kehoe et al., 2015; Rivero-Calle et al., 2015; Thomas et al., 2018). Numerous types of structure of ML models exist, but in this study and in order to select potential metrics and to extract physicochemical ecological thresholds, we used a Random Forest (RF) model. This type of model, which is based on a tree structure, has no prior assumptions hypotheses, is able to effectively manage gaps in datasets (missing value), has a strong predictive capability and is also adapted to nonlinear data processing (Breiman, 2001; Thomas et al., 2018). These inherent properties of this model fit to the problematic of our study. The term of “forest” in the name of the model, means that a predefined number of classifications or regression trees is built (Breiman et al., 1984). The number of tree generated is set during the parameterization phase. In a RF model the target signal is the single variable that we want to predict, in our case this signal is the considered occurrence trait weighted by its biovolume. The predictors are the explained variables used during the learning phase, corresponding to physicochemical parameters in our study and the typological groups (see above parts 2.1
Fig. 2. Conceptual diagram presenting the method for traits selection and threshold extraction. Color code: green parts are linked to targets; blue parts to predictors; yellow parts to numerical calculations; and the orange part to results.
Fig. 3. Visualization of the 4 typological groups created with the K-means method. Each color zone represents a geographical group according to altitude and maximal depth in meters. Note that both x- and y-axis are in log scale. Text arrows represent the limit of each group converted into a standardized scale.
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and 2.2 for more details). For more technical details about the functioning of RF model, please refer to the original article (Breiman, 2001). For an uninitiated audience in ML techniques, some other papers may also sum up the functioning of this RF model, in a more trivial way (Kehoe et al., 2012; Touw et al., 2012; Yajima and Derot, 2018). Except for the typological group predictors, all the others input data of RF model (traits and physicochemical parameters) were transformed via the Boxcox method (Box and Cox, 1964), in order to facilitate the visual extraction of ecological threshold. RF models were constructed with 200 trees per “forest”, which was sufficient to obtain a good stabilization of the error classification (see Fig. A2 in appendix 2 for more information). We also conserved a minimal number of 5 observations per node (as recommended by the official documentation of Matlab, https://fr.mathworks.com/help/ stats/index.html), when the model RF is used in regression mode (prediction of quantitative data). All numerical analyses were performed with Matlab software and its “statistics and machine learning” toolbox.
Cutler et al., 2007; Teichert et al., 2016), depending on our 4 typological groups (see Fig. 2). Unlike classical regression models, it is not possible to extract an equation between the input and the output with ML model based on a tree structure (Wagenhoff et al., 2017). Therefore, we used the PDP method to determine visually ecological stressor gradients (Cutler et al., 2007; Roubeix et al., 2016; Wagenhoff et al., 2017). The ecological stressor gradient is located between the two slope discontinuities: Impact initial (II) and Impact cessation (IC) (Wagenhoff et al., 2017). On the PDP plot, the x-axis corresponds to a physicochemical parameter and the y-axis is always the selected trait. Thus in our study, the II corresponds to the first slope discontinuity (the nearest to the y-axis on the left), and the IC corresponds to the last slope discontinuity, (the furthest to the y-axis on the right). This kind of gradient allows reflecting the different ecosystem health status (Holling, 1973; Odum et al., 1979; Wagenhoff et al., 2012). For each of the 4 most influential parameters, we recorded 2 thresholds (II and IC); consequently we obtained a total of 8 thresholds per selected traits. All visual determinations were based on power-scale PDP plots.
2.3.2. Method for traits selection One of our aims was to determine the morpho-functional traits of phytoplankton which could be used as potential bio-indication metrics for French lakes. Therefore, in order to make this selection among our 84 traits, we checked the goodness of fit and the statistical significance for each target signal, according to the 15 physicochemical predictors and typological groups. For the first step of this trait selection, we evaluated for each target signal the model goodness of fit with pseudoR2 (Breiman, 2001; Large et al., 2015; Teichert et al., 2016), calculated as indicated in Eq. (1).
pseudo
R2 = 1
MSE Var (y )
3. Results and discussion For the first time to our knowledge, a list of relevant phytoplankton traits related to physicochemical parameters was created, in order to further develop functional indicators of lake ecological status across the French territory. As a first step, we are going to discuss in detail the selected traits we identified via the use of ML techniques. In a second step, we will discuss the response of these traits to physicochemical parameters. Finally, we will explore the impact of the French lake natural typology on the functional diversity of phytoplankton.
(1)
3.1. Relevant traits associated with physicochemical variables
where Var(y) represents the variance of the distribution of traits occurrence weighted by their biovolume, and MSE the corresponding mean squared error. Upon completion, we got 84 pseudo-R2, once for each target signal. The second step of our selection was to test if the model was significantly different from a null model to ensure that the predictions from the fitted model are not generated by random effects (North et al., 2002; Teichert et al., 2016). For each trait, we randomly permuted the inputs predictors values (physicochemical parameters) and then compared all the pseudo-R2 from these permutations with the other nonpermutated R2, following the Eq. (2).
pvalue =
(r + 1) (n + 1)
Among the 84 phytoplankton morpho-functional traits initially defined (see Appendix 1), 21 traits appeared to be significantly influenced by physico-chemical parameters (p-value < 0.05 and pseudoR2 > 0.3). These 21 traits (see Table 2) encompassed numerous types of traits: phylogeny (presence of Chlorophyceae and presence of Euglenophyceae), cells size (nano-plankton (minimal size) and microplankton (maximal size)); life form (solitary or colonial); shape (double cone shape); motility; presence of contractile vacuoles; number of chloroplasts (absence or presence of one chloroplast); ornamentation (presence of scales, protuberances, presence of needle, big protuberance size); type of pigments (presence of chlorophyll b, presence of xanthophyll); source of carbon input (autotrophy); and type of reproduction (asexual or sexual/asexual). Among these 21 selected traits, most of them were in line with those used in former studies which aimed to identify a restricted number of functional groups (Reynolds et al., 2002; Padisák et al., 2009; Kruk and Segura, 2012). For example, Kruk and Segura (2012) proposed a classification of phytoplankton based on 7 groups, the one corresponding to “Small siliceous flagellates” (Kruk and Segura, 2012) including 4 traits also selected in our study (e.g. presence of siliceous exoskeleton, presence of flagellum, size class pico-plankton (maximal) and size class nano-plankton (maximal)). Despite this apparent similarity with former studies, we nevertheless identified two traits rarely used for the creation of functional groups: the reproduction type (Litchman and Klausmeier, 2008) and the presence of protuberances. These traits appeared to be significantly associated with environmental variables and should thus reveal selective pressure on phytoplankton species. In favorable conditions, asexual reproduction of phytoplankton allows a quick increase in population (Boero et al., 2008), inducing advantages over other phytoplankton species for a short time period (Valiela, 2013). The presence of protuberances is probably related with the development of defense mechanisms under certain environmental conditions, influencing trophic
(2)
where n is the number of permutations (n = 999) and r the number of times the pseudo-R2 from permutated model is superior to the pseudo-R2 of non-permutated model (see above Eq. (1)). By way of indication, for each trait we performed 1000 runs with RF model (999 runs with permutated predictors, and one without permutation), as we have just seen, each model is composed of 200 trees. Consequently, at the end of this analysis, 16,800,000 tree models were created. Finally, among the 84 traits tested, we only conserved those having, both a goodness of fit (pseudo-R2) higher than 0.3 (Møller and Jennions, 2002; Kruk and Segura, 2012) and a significant p-value (set at α < 0.05). 2.3.3. Thresholds extraction We performed a new RF run for each selected trait, with only the 4 quantitative predictors which had the greatest influence according to their out-of-bag errors, and the created qualitative typological variable (see also Appendix 3 for an evaluation of the number of selected physico-chemical variables). We then examined the importance of each physicochemical variable on the prediction of trait biovolume with partial dependence plots (PDP) (Friedman et al., 2001; Friedman, 2001; 4
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interactions and particularly grazing intensity (Agrawal, 1998; Litchman et al., 2010). According to our results (Table 2), the presence of big protuberances and of needles appeared to be particularly relevant as selective defense traits. Interestingly, the toxin production, which is generally the only defense mechanism which is considered in the classical classification based on morpho-functional groups, was not significantly associated with environmental variables in our study. In that context, the type and size of protuberances should probably be further explored in functional group classification studies.
coherent as waters with high DOC, Chl-a and TSS are more turbid and thus have a lower Secchi depth (see Table of correlation between physicochemical variables in Appendix 4). Chlorophyll-a concentration was always the most important explanatory variable in all trait biovolume models. This implies that this predictor was often selected by the RF model to create the first nodes (splits) in the tree structures. Therefore, we deliberately chose to use chl-a as a predictor in the models in order to test the hypothesis that some traits, which could be less representative in the whole community, could be non significantly explained by total chl-a, and that other traits, should be more intensively related to high phytoplankton biomass production. As the chl-a is a proxy of phytoplankton biomass the observed dominant influence of this parameter could be relatively logical. Indeed the correlation observed is probably due to a sampling effect as more the biomass is important more the probability to observe a trait is high, thus explaining the positive correlation between each selected trait and Chl-a concentration. This sampling effect furthermore explained some contradicting response of traits with Chl-a, such as the positive response of the presence and absence of protuberance, or the positive response of solitary and filamentous forms (Table 2). Similarly, the positive response of traits observed with DOC and TSS could also be due to an indirect effect and related to the strong correlation between all these variables (DOC, TSS and Chl-a, see Appendix 3). Indeed the positive response observed in our models could probably be the consequence rather the cause as more the biomass is important more the quantity of organic suspended compounds is expected to be high. For these two environmental parameters, as well as for Secchi depth, is thus seems to be difficult to disentangle the direct effect of
3.2. Physicochemical variables explaining phytoplanktonic traits For each of the 21 traits significantly associated with environment, we selected the four most important predictors and found that globally, seven of them (among 15) seem to show a general significant impact on our traits selection (see Table 2). Moreover, three of these parameters occurred in all trait biovolume models: Chlorophyll-a (Chl-a), Dissolved Organic Carbon (DOC), and the Total Suspended Solids (TSS). The following most important predictors were either nitrate concentration, and Secchi depth. In some rare cases, Complete Alkalimetric Title (CAT) and water temperature showed the same importance. In the light of the low occurrence of these two last parameters, it is difficult to draw general conclusions. Consequently, our discussion will more focus on the five others most influent predictors. Chlorophyll-a, Dissolved Organic Carbon, and Total Suspended Solids, always positively influenced trait biomass concentration whereas the Secchi depth appeared to be negatively related to trait biomass (see the columns direction in the Table 2). These variations are
Table 2 Overview of the 21 selected traits and their response to environmental variation. II corresponds to Impact Initiation and the IC corresponds to Impact Cessation. Arrows indicate the direction of variation from the PDP depending on the considered physicochemical parameters. The blank cells correspond to absence of significant relationship. Reminder of abbreviation: Chl-a for Chlorophyll-a; DOC for Dissolved Organic Carbon; NO3 for Nitrate; TSS for Total Suspended Solids; and CAT for Complete Alkalimetric Title.
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Table 2 (continued)
these variables from the indirect effect due the biological compartment itself. However, the positive response of the two phylogenical traits, i.e. presence of Chlorophyceae and Euglenophyceae, could be considered as a direct response to higher concentration of DOC and TSS as the other phylogenical groups did not evidence such response (which should be expected in case of indirect relationships). The response of Euglenophyceae to DOC is in accordance with the literature as this phylogenic group is well known to be composed of mostly heterotrophic or phagotrophic species inhabiting organic rich habitats (Reynolds, 2006). As the WFD (2000/60/EC) encourages the use of DOC to define the water health status (Koprivnjak et al., 1995; Aiken et al., 2002; Gruau et al., 2007), our results suggest that the presence of species from this phylogenic group could be a potential candidate to measure the anthropic pressure in French lakes. More interestingly is probably the response of some traits to nitrate concentrations (Table 2), such as the positive relationship observed with the presence of filamentous forms. The development of filamentous algae with nutrient inputs has already been suggested in the literature (Phillips et al., 1978) and particularly their capacity to use nitrate components (Moss, 2005). Most of the filamentous forms are also found within the Cyanophyceae group, known to be very competitive in high N:P ratio (McCarthy et al., 2009; Posch et al., 2012). But a high nitrate concentration also seems to be associated with rarer and smaller protuberances. Maybe the development of filamentous algae, which is also recognized to be a defense mechanism against predation
(Smetacek, 2001), reduced the overall grazing pressure on phytoplankton communities and thus the presence of these morphological associated traits. As current phytoplanktonic biodindicators are mainly considered to respond to phosphorous gradients (De Hoyos et al., 2014; Phillips et al., 2014; Laplace-Treyture and Feret, 2016; Wolfram et al., 2014), our results suggest that the use of specific functional traits, such as the filamentous form, might also lead to new insights for quantifying the nitrate pressure. 3.3. Impact of lake typology on phytoplankton functional diversity In order to test the response of phytoplankton traits to environmental factors according to lake typology, we created 4 main typological groups with K-means analyses (see Fig. 3). The first group (in the blue zone bottom right on Fig. 3) corresponds to shallow lakes located at mid-altitude, often located close to the Mediterranean Basin. The second group (in the purple zone at the middle top) corresponds to deep lake of mid-altitude, essentially represented by artificial dam water bodies. The third group (in the left green zone) corresponds to relatively shallow lowland lakes, represented by usually natural lakes located near the French coast. The fourth group (in the top right orange zone) corresponds to deep lakes of high altitude, mostly represented by alpine/mountainous natural lakes. The response of biovolume traits to physicochemical parameters on PDP plots are really similar whatever the typological group considered (Fig. 4 and Appendix 5), and consequently, slopes discontinuities (II 6
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Fig. 4. Example of results from PDP for the presence of protuberance depending on the four typological groups. Upper left: PDP for chlorophyll a; Upper right: PDP for DOC; Lower left: PDP for nitrate; Lower right: PDP for TSS. On each plot the left black dashed line is II and the right black dashed line is IC.
and IC see black dashed lines on Fig. 4) are located at the same thresholds. This trait-response similarity between typological groups was found for all PDP of our 21 selected-traits. Consequently, typology seems to show a limited impact on traits biovolume as the typological predictor was almost always the least influential predictor on the models (see Appendix 3), suggesting that the functional diversity of phytoplankton was more influenced by physicochemical conditions than typological features. These results are consistent with a Canadian study (Beisner et al., 2006), which demonstrated that the typology of lakes had mainly an impact on higher levels of the food web, i.e. on the zooplankton and fish communities and that phytoplankton and bacteria communities are weakly patterned by typological variables. However, in a bio-indication context, the Water Framework Directive (WFD) usually uses typological groups such as hydroecoregion to assess the ecological status of water bodies. Consequently, the national French phytoplankton indicator (IPLAC, Laplace-Treyture and Feret, 2016), took into account different lake typologies based on their altitude and mean depth to define references conditions. Compared to this taxonomical approach it appears that the use of morphofunctional traits allowed to moderate the dominant effect of typological groups, while ensuring similarity in trait-responses to environmental changes. At a large spatial scale, the trait-based approach thus seems to be more appropriate than the taxonomic one and better adapted for a global approach (e.g. metrics are more comparable at the national scale).
typological predictor seemed, for its part, more limited. As current phytoplankton taxonomic-based indicators are mainly constructed to respond to anthropogenic pressures through phosphorus inputs, our study provides a first step towards the development of new indices for refining the anthropogenic impact on lakes ecosystems. Indeed, when using a trait by trait approach, we identified several potential trait-candidates for future bio-monitoring measures, particularly for DOC and nitrate gradients. Phylogenic groups, filamentous forms as well as the presence of ornamentations represent the main traits associated to the variation of physicochemical parameters. These results further strengthen the need for considering the ornamentations in the current classification systems based on morpho-functional traits of phytoplankton, and offer new insights for the evaluation of health status in French lakes.
4. Conclusions
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.105659.
Acknowledgments This work was supported by the French Ministry of the ecological and solidarity transition (MTES, France) and funded by the “Agence Française de la Biodiversité” (AFB, France) through the convention between Irstea and AFB. Authors would like to thank the six water agencies and private labs that participated to the collection of abiotic and biotic data. They are also grateful to the lake team of Irstea Aix-enProvence for the database management. We also would like to thank our laboratory engineer S. Brouty for the map in the Fig. 1. Appendix. Supplementary data
In contrast with others studies on aquatic organisms, there are very few studies focusing on the relation between phytoplankton traits and physicochemical pressures in French lakes. The use of ML model based on a tree structure and the corresponding PDP, seemed to be well adapted to this type of studies in lacustrine ecosystems. In this study, we found that some few parameters had indirect or direct predominant influence on the trait distributions of phytoplanktonic species: Chlorophyll-a, DOC, nitrates and TSS. By contrast, the influence of the
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