An Extended Ecosystem Model for Understanding EE2 Indirect Effects

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Article An Extended Ecosystem Model for Understanding EE2 Indirect Eﬀects on a Freshwater Food Web and Its Ecosystem Function Resilience

Ludiwine Clouzot, Charlotte Haguenauer and Peter A. Vanrolleghem * modelEAU, Université Laval, Québec, QC G1V 0A6, Canada; [email protected] (L.C.); [email protected] (C.H.) * Correspondence: [email protected]

Received: 8 May 2020; Accepted: 15 June 2020; Published: 17 June 2020

Abstract: Freshwater species are highly impacted by human activities and the consequences on ecosystem functioning are still not well understood. In the literature, a multitrophic perspective appears to be key to advance future biodiversity and ecosystem functioning (BEF) research. This paper aims at studying indirect effects of the synthetic hormone 17α-ethinylestradiol (EE2) on a freshwater food web by creating BEF links, through the interpretation of seasonal cycles and multitrophic interactions. An ecosystem model previously developed using experimental data from a unique whole-ecosystem study on EE2 was extended with the addition of Chaoborus, an omnivorous insect. During the experimental study, a collapse of fathead minnow was measured after one year of exposure. The simulation results showed that EE2 indirect effects on other fishes (horizontal diversity) and lower trophic levels (vertical diversity) were connected to multitrophic interactions with a top-down cascade effect. The results also demonstrated that adding an omnivorous, mid-trophic level group such as Chaoborus enhances resilience. Conversely, missing such a species means that the actual resilience of an ecosystem and its functioning cannot be properly simulated. Thus, the extended ecosystem model offers a tool that can help better understand what is happening after environmental perturbations, such as with EE2.

Keywords: 17(alpha)-ethinylestradiol; biodiversity; Chaoborus; ecological function; experimental lake area; freshwater ecosystem; multitrophic interactions

1. Introduction Pharmaceuticals and hormones are continuously entering the environment through release from wastewater treatment plants (WWTPs). In the last few decades, the occurrence of those emerging compounds has increasingly received attention due to their harm to the ecosystem. A review on worldwide environmental monitoring of water organic pollutants identified by European guidelines [1,2]demonstrated that pharmaceuticals represent the most studied class of Watch List compounds (42.9%), the natural estrogens E1 and E2 and the synthetic 17α-ethinylestradiol (EE2) being the second most studied (35.3%). Both the natural and synthetic hormones were previously identified as the main substances responsible for estrogenic activities in WWTPs, with concentrations as low as ng/L, sometimes exceeding their predicted no-effect concentrations (PNECs) for ecological toxicity [3]. Species responses to these environmental estrogens vary considerably, with fishes having much higher sensitivities than invertebrates [4,5]. Indirect effects on tolerant species, mediated by ecological mechanisms, may also appear in the environment but cannot be measured by single species laboratory-based toxicity tests; this is why population, community or ecosystem level studies are required [6]. EE2, one of the most potent synthetic estrogens, interferes in multiple ways with the activity of diﬀerent physiological endpoints of aquatic organisms, including the endocrine system [7–10]. In a

Water 2020, 12, 1736; doi:10.3390/w12061736 www.mdpi.com/journal/water Water 2020, 12, 1736 2 of 22 multi-year whole-ecosystem study performed at an experimental lake, [11] exposed well-defined fish and lower-trophic level populations to environmentally relevant concentrations of EE2. During this experimental study, a collapse of fathead minnow was measured after one year of exposure to EE2. A mix of reduced gamete production, increased gamete mortality and an increase of both adults and juvenile mortality was hypothesized to explain the collapse of both adult and juvenile fathead minnow due to EE2 addition in reference [12]. Since several small fishes declined in the experimental lake after EE2 additions, the authors sought evidence for a trophic cascade resulting from the associated reductions in planktivory and benthivory fish [13]. Trophic cascades (indirect effects mediated through consumer–resource interactions) are a well-studied type of indirect effect, and are generally considered in terms of ‘top–down’ (predator influence on lower trophic levels) or ‘bottom–up’ (nutrient/food/prey influence on higher trophic levels) mechanism [6]. Unlike the many studies that have shown direct effects of estrogens and their mimics on aquatic species, especially fishes, Kidd et al. [13] advance the understanding of how estrogen-induced changes in fish abundance can lead to indirect effects on freshwater food webs. This paper aims at taking further their interpretation of the direct and indirect effects of EE2 by modeling links between aquatic biodiversity and ecological functioning. In terms of extinction risk, freshwater species are among the world’s most threatened [14]. Thus, one important question to be answered is: “To what extent does species loss from freshwaters affect ecosystem functioning and their ability to provide ecosystem goods and services for people?”. “Ecosystem function” (EF) is a general term that includes stocks of materials (e.g., carbon, water and mineral nutrients) and rates of processes involving fluxes of energy and matter between trophic levels and the environment. EFs are linked to biodiversity, and can thus be broadly defined as the biological, geochemical and physical processes that take place or occur within an ecosystem. Due to such complexity, the underlying role of biodiversity in ecosystem functioning, its relevance for ecosystem service provision in general, as well as the consequences of its decline, remain poorly understood [15]. A multitrophic perspective is key to advancing future biodiversity and ecosystem functioning (BEF) research and to address some of its most important remaining challenges [16]. Research on biodiversity and ecosystem functioning is one of the most prominent topics investigated by ecologists in the last three decades, with two new domains (Aquatic Food Webs and Agricultural Landscapes) arising in the last decade [17]. Besides, the “ecosystem service” has become the most prominent science policy term used in the last 8 years (2011–2018). Ecosystem-based management (EBM) is a collaborative management approach used with the intention to restore, enhance and protect the resilience of an ecosystem so as to sustain or improve ecosystem services (ES) and protect biodiversity, while considering nature and society [18]. In their review, Eisenhauer et al. [16] concluded that understanding why and how the strength of biodiversity effects varies with environmental conditions and at which spatial scales different mechanisms operate, will be key to operationalize BEF insights for ecosystem management, society and decision making. Duffy et al. [19] pointed out that understanding how biodiversity affects functioning of complex ecosystems will benefit from integrating theory and experiments with simulations and network-based approaches. Indeed, integrated models could highlight priorities for the collection of new empirical data, identify gaps in our existing theories of how ecosystems work, help develop new concepts for how biodiversity composition and ecosystem function interact, and allow predicting BEF relations and its drivers at larger scales [20]. Besides, Queirós et al. [21] identified a need to translate observed mechanisms (short-term and single species) into conceptual models that include combinations of species and environmental gradients not yet observed (multiple stressors, long-term and interacting species). According to Barnes et al. [22], integrating trophic complexity is key to understanding how biodiversity affects whole-ecosystem functioning. Therefore, this study takes up the challenge to improve an ecosystem model that was previously developed and successfully calibrated [12], with the addition of Chaoborus, an omnivorous insect that is an important food source for higher trophic levels and preys on lower trophic levels. This extended ecosystem model will be used to confirm and take Water 2020, 12, 1736 3 of 22 further the hypotheses on EE2 indirect effects on a freshwater food web [13]. Besides, indirect EE2 effects will be compared with and without the presence of Chaoborus in the food web. Finally, how the presence of Chaoborus enhances the resilience of ecosystem functioning will be discussed. Consultation with the ecologists observing the ecosystem was made to verify that major ecosystem mechanisms were correctly represented in the model. This was necessary as a first-order assessment of model reliability when extrapolated to conditions beyond the observed state [21]. The results presented in this paper will support the transition of BEF from a description of patterns to a predictive science [16], which can help better understand the link between biodiversity and ecosystem functioning in the context of EBM.

2. Materials and Methods

2.1. Step 1: Collecting Experimental Data The experimental data used for the model were collected during a multi-year whole-lake study on Lake 260 in the Experimental Lakes Area (ELA) in northwestern Ontario, Canada (all sampling details can be found in [13]). This experimental lake is oligotrophic (high oxygen and low nutrient concentrations) and typical of boreal shield lakes. Lake 260 has an area of 34 ha and a maximum depth of 14.4 m. Six other experimental lakes in the same area were also studied as reference systems. The study started with two years of baseline data, followed by three years of data collected during the addition of EE2, and continued for seven years after the addition was stopped to assess ecosystem stability and recovery after stressor removal. EE2 was added to the epilimnion for 20–21 weeks during lake stratification. Seasonal mean concentrations for the summer were between 4.8 and 6 ng/L. Lower concentrations were measured under the ice during winter. Physico-chemical data were collected monthly during the open-water season, after lake stratification in the spring until shortly before turnover in the autumn. The epilimnion is defined as the surface layer of water with uniform temperature (ignoring any shallow temporary stratification phenomena), while the hypolimnion is defined as the bottom layer of water, also with uniform temperature. More information about the physico-chemical data used for the model, such as temperature, oxygen, light, organic matter and nutrients, can be found in [12]. A simplified food web was selected with the most relevant populations of plankton and fish naturally present in the experimental Lake 260 (Figure1). Phytoplankton (Group 1: chlorophyte, dinoflagellates and Cyanophyta; Group 2: Chrysophyta and Cryptophyta and Group 3: diatoms) and zooplankton (cladocerans, copepods and rotifers) samples were collected biweekly. Fish abundance data were based on catch-and-release methods using trap nets (spring and autumn, all species) and short (30 min) evening gill net sets on spawning shoals for lake trout (autumn). Biomass of the minnow species (fathead minnow and pearl dace) was estimated as the product of abundance and mean size from minnow trap captures, standardized by lake area. Mark-and-recapture techniques were used to estimate the abundance of lake trout (autumn data) and white sucker (spring data). Biomass was estimated as the product of abundance estimates and mean size, standardized by the lake area.

Figure 1. Conceptual model of the simplified food web used for the model (abbreviations: chloro = Figure 1. Conceptualchlorophytes; modeldino = dinoflagellates; of the simplified cyano = foodcyanophytes; web usedcryso =for chrysophytes the model and (abbreviations: chloro = chlorophytescrypto=cryptophytes).; dino = Extensiondinoflagellates; of Figure 1 in Clouzot cyano and= Vanrolleghemcyanophytes; [12]. cryso = chrysophytes and crypto=cryptophytes). Extension of Figure1 in Clouzot and Vanrolleghem [12]. Discussions with biologists and ecologists were necessary to identify the seasonal cycles of the species selected for the model (Figure 2). The objective was to highlight the potential role of seasonal cycle alterations on the food web after EE2 addition, as an aspect of the relation between biodiversity and ecosystem functions.

Figure 2. Seasonal cycles of the selected species based on expert knowledge (grey = experimental data collected during the open water season).

2.2. Step 2: Building and Calibrating an Ecosystem Model A dynamic ecosystem model was previously built and calibrated in an object-oriented framework using simplified AQUATOX equations and the software package WEST (DHI Water Environment Health, Hörsholm, Denmark) [12,23]. The model consists of a set of objects, each describing the growth of a model population in terms of its biomass concentration using differential equations including biological processes such as assimilation, photosynthesis, respiration, consumption or mortality, and additional processes such as migration, diffusion or loading. By connecting different objects and defining feeding relationships between them, a customized food web can be designed (Figure 3). In the extended ecosystem model, multitrophic interactions were complexified, with the addition of a new box for Chaoborus.

Water 2020, 12, 1736 4 of 22

In the extended version of this model presented here, Chaoborus was added to the studied ecosystem,Figure a 1. dipteran Conceptual zooplankton model of predatorthe simplified and anfood important web used food for the item model for many (abbreviations: ﬁsh species chloro (Figure = 1). Chaoboruschlorophytes;were sampled dino in= thedinoflagellates; experimental lakecyano every = cyanophytes; four weeks duringcryso the= ice-freechrysophytes season and at least 1 h aftercrypto=cryptophytes). sunset at a station Extension located over of Figure the deepest 1 in Clouzot part ofand the Vanrolleghem lake using vertical [12]. hauls from the entire water column [13]. Instars were separated using measures of head-capsule length. The emerging adult insectsDiscussions were collected with weekly biologists with and pyramidal ecologists emergence were necessary traps. to identify the seasonal cycles of the speciesDiscussions selected for with the biologists model (Figure and ecologists 2). The objectiv were necessarye was to highlight to identify the the potential seasonal role cycles of seasonal of the speciescycle alterations selected for on the the model food web (Figure after2). EE2 The addition, objective as was an aspect to highlight of the therelation potential between role ofbiodiversity seasonal cycleand ecosystem alterations functions. on the food web after EE2 addition, as an aspect of the relation between biodiversity and ecosystem functions.

FigureFigure 2. 2.Seasonal Seasonal cyclescycles ofof thethe selectedselected species species based based on on expert expert knowledge knowledge (grey (grey= =experimental experimental data data collectedcollected during during the the open open water water season). season). 2.2. Step 2: Building and Calibrating an Ecosystem Model 2.2. Step 2: Building and Calibrating an Ecosystem Model A dynamic ecosystem model was previously built and calibrated in an object-oriented framework A dynamic ecosystem model was previously built and calibrated in an object-oriented using simplified AQUATOX equations and the software package WEST (DHI Water Environment framework using simplified AQUATOX equations and the software package WEST (DHI Water Health, Hörsholm, Denmark) [12,23]. The model consists of a set of objects, each describing the growth Environment Health, Hörsholm, Denmark) [12,23]. The model consists of a set of objects, each of a model population in terms of its biomass concentration using differential equations including describing the growth of a model population in terms of its biomass concentration using differential biological processes such as assimilation, photosynthesis, respiration, consumption or mortality, equations including biological processes such as assimilation, photosynthesis, respiration, and additional processes such as migration, diffusion or loading. By connecting different objects and consumption or mortality, and additional processes such as migration, diffusion or loading. By defining feeding relationships between them, a customized food web can be designed (Figure3). In the connecting different objects and defining feeding relationships between them, a customized food web extended ecosystem model, multitrophic interactions were complexified, with the addition of a new can be designed (Figure 3). In the extended ecosystem model, multitrophic interactions were box for Chaoborus. complexified, with the addition of a new box for Chaoborus.

Figure 3. Simplified framework of the ecosystem model built in WEST with AQUATOX equations Figure 3. Simpliﬁed(full arrow: framework consumption ofterms; the da ecosystemshed arrow: loss model terms). builtExtension in of WEST Figure with2 in Clouzot AQUATOX and equations (full arrow: consumptionVanrolleghem [12]. terms; dashed arrow: loss terms). Extension of Figure2 in Clouzot and Vanrolleghem [12]. The life cycle of Chaoborus is split between aquatic ecosystems, where it spends its larvae phase (Instars 1–4), and terrestrial ecosystems, for its adult phase as an insect (Figure 4). The eggs are then released in the aquatic environment and some of them turn into instars 1–2, which is called “recruitment”. The larvae become bigger and are “promoted” to instars 3–4. After an initial phase in water, adult insects “emerge” to the terrestrial environment.

Figure 4. Conceptual model of the Chaoborus life cycle.

The mass balance used for Chaoborus was adapted from the one used for fish in reference [12], with the active respiration (when fish swim) and gamete loss (only a small fraction of gametes results in viable fish) removed. Below only the equations that were changed are presented. Instars 1–2 =−−−−−− (1) Instars 3–4

=−−−−−− (2)

where Insect1-2 (g/m3) is the biomass of Instars 1–2, Insect3-4 (g/m3) is the biomass of Instars 3–4, Cons (g/m3.d) the consumption of particulate organic matter (POM), phyto- and zooplankton, Def (g/m3.d) the defecation of unassimilated food, Resp (g/m3.d) the respiratory loss, Exc (g/m3.d) the excretion of dissolved organic matter, Mort (g/m3.d) the non-predatory mortality, Pred (g/m3.d) the consumption of Chaoborus by fish, Promo (g/m3.d) the promotion from Instars 1–2 to Instars 3–4, Recruit (g/m3.d) the recruitment from viable eggs to Instars 1–2 and Emerg (g/m3.d) the emergence from Instars 3–4 to adult insects. Initial values for these differential equations were obtained by calibration (see below).

Figure 3. Simplified framework of the ecosystem model built in WEST with AQUATOX equations Water 2020(full, 12arrow:, 1736 consumption terms; dashed arrow: loss terms). Extension of Figure 2 in Clouzot and5 of 22 Vanrolleghem [12].

TheThe lifelife cyclecycle ofof ChaoborusChaoborus isis splitsplit betweenbetween aquaticaquatic ecosystems,ecosystems, wherewhere itit spendsspends itsits larvaelarvae phasephase (Instars(Instars 1–4), and and terrestrial terrestrial ecosystems, ecosystems, for for its its adul adultt phase phase as asan aninsect insect (Figure (Figure 4). 4The). The eggs eggs are then are thenreleased released in the in theaquatic aquatic environment environment and and some some of ofthem them turn turn into into instars instars 1–2, 1–2, which which is calledcalled “recruitment”.“recruitment”. TheThe larvaelarvae becomebecome biggerbigger andand areare “promoted”“promoted” toto instarsinstars 3–4.3–4. AfterAfter anan initialinitial phasephase inin water,water, adultadult insectsinsects “emerge”“emerge” toto thethe terrestrialterrestrial environment.environment.

FigureFigure 4.4. Conceptual model of the Chaoborus lifelife cycle.cycle.

TheThe massmass balance used for Chaoborus was adapted from the one used for fish fish in reference [12], [12], withwith thethe activeactive respirationrespiration (when(when fishfish swim)swim) andand gametegamete lossloss (only(only aa small fraction of gametes results inin viableviable fish)fish) removed.removed. BelowBelow onlyonly thethe equationsequations thatthat werewere changedchanged areare presented.presented. InstarsInstars 1–21–2 dInsect=−−−−−−1–2 (1) = Cons De f Resp Exc Mort Pred Promo + Recruit (1) dt − − − − − − Instars 3–4 Instars 3–4 =−−−−−− (2) dInsect 3–4 = Cons De f Resp Exc Mort Pred + Promo Emerg (2) where Insect1-2 (g/m3dt) is the biomass− of Instars− 1–2,− Insect− 3-4 (g/m− 3) is the biomass− of Instars 3–4, Cons 3 3 (g/m .d) the consumption3 of particulate organic matter (POM), phyto-3 and zooplankton, Def (g/m .d) where Insect1–2 (g/m ) is the biomass of Instars 1–2, Insect3–4 (g/m ) is the biomass of Instars 3–4, the defecation of unassimilated food, Resp (g/m3.d) the respiratory loss, Exc (g/m3.d) the excretion of Cons ((g/m3.d) the consumption of particulate organic matter (POM), phyto- and zooplankton, dissolved organic matter, Mort (g/m3.d) the non-predatory mortality, Pred (g/m3.d) the consumption Def ((g/m3.d) the defecation of unassimilated food, Resp ((g/m3.d) the respiratory loss, Exc ((g/m3.d) of Chaoborus by fish, Promo (g/m3.d) the promotion from Instars 1–2 to Instars 3–4, Recruit (g/m3.d) the the excretion of dissolved organic matter, Mort ((g/m3.d) the non-predatory mortality, Pred ((g/m3.d) recruitment from viable eggs to Instars 1–2 and Emerg (g/m3.d) the emergence from Instars 3–4 to the consumption of Chaoborus by fish, Promo ((g/m3.d) the promotion from Instars 1–2 to Instars 3–4, adult insects. Initial values for these differential equations were obtained by calibration (see below). Recruit ((g/m3.d) the recruitment from viable eggs to Instars 1–2 and Emerg ((g/m3.d) the emergence from Instars 3–4 to adult insects. Initial values for these differential equations were obtained by 5 calibration (see below). Emergence of aquatic insects represents a loss for the system. The model assumes that emergence is determined by the net rate of growth, considered as the sum of consumption and the loss terms other than mortality.

Emerg(t) = KEm (Cons(t) De f (t) Resp(t) Exc(t)) (3) × − − − where Emerg ((g/m3.d) is the emergence from Instars 3–4 to adult insects, KEm (unitless) the fraction of growth that goes to emergence, Cons ((g/m3.d) the consumption of POM, phyto- and zooplankton, Def ((g/m3.d) the defecation of unassimilated food, Resp ((g/m3.d) the respiratory loss and Exc ((g/m3.d) the excretion of dissolved organic matter. Often, there is synchrony in insect emergence and in the model, this is assumed to be cued to temperature, with additional forcing as twice the emergence that would ordinarily be computed. Water 2020, 12, 1736 6 of 22

During recruitment, egg biomass is lost from the adult insects and is transferred to the Instars 1–2 biomass, which is, in other words, the biomass gained from successful hatching.

If T (t) > (0.8 TOpt) and T (t) < (TOpt 1) then × − Recruit(t) = PctEgg Emerg(t) 2 (4) × × Else : Recruit(t) = 0 where Recruit ((g/m3.d) is the recruitment from viable eggs to Instars 1–2, PctEgg (unitless) the fraction of adult biomass that is in the carried eggs and Emerg ((g/m3.d) the emergence from Instars 3–4 to adult insects. Finally, the model calibration was conducted following the same stepwise procedure as the one used in reference [12]. Chaoborus was added to the previously calibrated model and the AQUATOX default parameter values were selected as starting point for the model calibration [24]. The biomass concentrations measured in the experimental lake on 2 May 2000, were used as initial biomass concentrations and were not changed during calibration. Finally, a sensitivity analysis was performed, and the most inﬂuential parameters tuned.

2.3. Step 3: Simulating Multitrophic Interactions The objective of this paper was to understand the direct and indirect effects of EE2 on a freshwater ecosystem by creating links between aquatic biodiversity and ecological functioning. In order to do that, the role of seasonal cycle alterations on the food web, resulting in an increase or decrease in number of a specific population, needs to be studied. According to Duffy et al. [19], understanding how biodiversity affects functioning of ecosystems requires integrating diversity within trophic levels (horizontal diversity) and across trophic levels (vertical diversity). Distinguishing these two dimensions can help clarify how ecosystem functioning may be affected separately or simultaneously by consumptive interactions across trophic levels and competitive processes within levels. Besides, it is recommended to group species that perform similar roles in an ecosystem process into “functional groups” [24]. In the ecosystem model presented in this paper, species were divided based on their trophic status (e.g., their place in the food web as producers, decomposers and predators) and their seasonal cycles (growth bloom for plankton, spawning for fish or emergence for insects). Then, a global sensitivity analysis similar to reference [25] was used to better understand the multitrophic interactions occurring in the studied aquatic ecosystem. The sensitivity of populations was evaluated by multiplying (by five) or dividing (by five) the initial biomass of a specific functional group. The consequences on the entire food web were then interpreted from the biomass dynamics of the other groups. For example, the initial biomass of rotifers was divided by five and then the biomass dynamics over time of the other zooplankton (horizontal diversity) and other trophic levels (vertical diversity) were analyzed. The functional groups selected for the model (Figure1) were studied one by one. To allow the comparison of all simulations, increasing and decreasing the initial biomass of every single functional group, the resulting simulated biomass were normalized according to the averaged biomass obtained over time. Finally, the above simulations were performed with and without Chaoborus in the food web. Indeed, since the ecosystem model presented in this paper was extended by adding Chaoborus to the food web, it is important to assess its role in the studied freshwater ecosystem.

2.4. Step 4: Predicting EE2 Indirect Effects EE2 was added to the experimental lake at environmentally relevant concentrations; 4.8–6.1 ng/L in the epilimnion and around 2 ng/L in the hypolimnion (decrease due to sorption on sediments and bioaccumulation) [11]. In this whole-ecosystem experimental study, evidence of direct effects Water 2020, 12, 1736 7 of 22 of EE2 on the abundance of fishes was found but little evidence could be found of direct effects of this synthetic estrogen on lower-trophic-level organisms [13]. The authors also observed the most notable negative indirect effects on the food web were declines in the biomass of the top predator lake trout due to a decline in its food supply (fathead minnow, pearl dace and juveniles of white sucker). Besides, while the increases in diverse invertebrate prey species were small, interpreted together they provide a strong indication of indirect effects on the lower food web due to observed declines in predation by small-bodied fishes. Therefore, in this paper, the extended ecosystem model is used to confirm and take further the hypotheses on EE2 indirect effects on a freshwater food web [13]. First, the direct EE2 effects on fathead minnow that [12] simulated as a decrease in gamete production, increase in gamete mortality and/or increase in fish mortality, need to be confirmed as potential hypotheses with the new model including Chaoborus. Then, EE2 indirect effects on the other fishes (horizontal diversity) and the lower trophic levels (vertical diversity) will be interpreted with the objective to identify a top-down or bottom-up effect. Finally, indirect EE2 effects will be compared with and without Chaoborus in the food web.

3. Results

3.1. Extended Ecosystem Model The addition of Chaoborus to the ecosystem model built and calibrated in [12] created new multitrophic interactions. Indeed, Chaoborus feed on POM, phytoplankton C (chlorophytes, dinoflagellates and Cyanophyta) and zooplankton (rotifers, Cladocera and copepods), while fish (fathead minnow and pearl dace) feed on Chaoborus (Figure3). The most influential parameters for Chaoborus are presented in S1 (Supplementary Material: Table S1). Those values were obtained after the model was fit to the experimental data, by fine-tuning of the parameters calibrated in [12]. Then, the ecological experts validated the simulated biomass dynamics, also including the two lower data values at the end of September (Figure5).

Figure 5. Calibration results for Chaoborus (abbreviations: exp = experimental data; sim = simulation results). Figure 5. Calibration results for Chaoborus (abbreviations: exp = experimental data; sim = simulation Itresults). can be noted on the graph that, in the spring, only instars 3–4 are present in the experimental lake. Starting mid-June, the beginning of insect emergence, they are leaving the aquatic environment for theThe terrestrial most influential ecosystem, parameters as can be seen presented with a drop in S1 of (Table the instars S1) for 3–4 Chaoborus biomass. Inare July, mainly the instars related 1–2 to biomassfood consumption, starts increasing with because some the parameters adult insects arefor releasingreproduction, eggs into growth, the lake, emergence, which turn intoexcretion, larvae. Mid-July,respiration instars and mortality. 1–2 biomass The decreases addition of while Chaoborus instars also 3–4 had biomass an impact increases on calibrated because values of promotion. of some Finally,parameters instars of 3–4the biomassfunctional keep groups increasing connected due toto larvaeChaoborus growth, through up to multitrophic the end of September interactions when (for temperaturesmore details, andsee Tabl foodes availability S2–S4 in S1). start to fall and consequently, biological metabolisms slow down. After fine tuning of the calibrated parameters, the simulation graphs obtained for all trophic levels were compared with the ones in Clouzot and Vanrolleghem [12]. No significant changes can be noticed after Chaoborus was added to the model. In the example of zooplankton given in Figure 6, it clearly appears that the trend is similar to the one obtained with the previous ecosystem model. When comparing the Mean Square Residual Error (MSRE) values between the two ecosystem models, the simulation results were actually closer to the experimental data with the extended model (MSRE

values were lower).

Figure 6. Calibration results for zooplankton. MSRE values (g/m3): copepods: 77; copepods/Chaoborus: 25; rotifers: 0.99; rotifers/Chaoborus: 0.66; Cladocera: 110; Cladocera/Chaoborus: 106 (legend: zooplankton name + chao = simulation results from the extended model; zooplankton name = simulation results from [1]; exp = experimental data).

In conclusion, the ecosystem model developed in Clouzot and Vanrolleghem [12] adapted well to the addition of Chaoborus, with parameter values mostly changed based on the new trophic

Water 2020Figure, 12 ,5. 1736 Calibration results for Chaoborus (abbreviations: exp = experimental data; sim = simulation8 of 22 results).

The most inﬂuential parameters presented in S1 (Table S1) for Chaoborus are mainly related to food The most influential parameters presented in S1 (Table S1) for Chaoborus are mainly related to consumption, with some parameters for reproduction, growth, emergence, excretion, respiration and food consumption, with some parameters for reproduction, growth, emergence, excretion, mortality. The addition of Chaoborus also had an impact on calibrated values of some parameters respiration and mortality. The addition of Chaoborus also had an impact on calibrated values of some of the functional groups connected to Chaoborus through multitrophic interactions (for more details, parameters of the functional groups connected to Chaoborus through multitrophic interactions (for see Tables S2–S4 in S1). more details, see Tables S2–S4 in S1). After fine tuning of the calibrated parameters, the simulation graphs obtained for all trophic levels After fine tuning of the calibrated parameters, the simulation graphs obtained for all trophic were compared with the ones in Clouzot and Vanrolleghem [12]. No significant changes can be noticed levels were compared with the ones in Clouzot and Vanrolleghem [12]. No significant changes can after Chaoborus was added to the model. In the example of zooplankton given in Figure6, it clearly appears be noticed after Chaoborus was added to the model. In the example of zooplankton given in Figure 6, thatit clearly the trend appears is similar that the to the trend one is obtained similar withto the the one previous obtained ecosystem with the model. previous When ecosystem comparing model. the MeanWhen Squarecomparing Residual the Mean Error Square (MSRE) Residual values between Error (MSRE) the two values ecosystem between models, the two the ecosystem simulation models, results werethe simulation actually closer results to thewere experimental actually closer data to with the the experimental extended model data with (MSRE the values extended were model lower). (MSRE

values were lower). Figure 6. Calibration results for zooplankton. MSRE values (g/m3): copepods: 77; copepods/Chaoborus: 25; Figure 6. Calibration results for zooplankton. MSRE values (g/m3): copepods: 77; copepods/Chaoborus: rotifers: 0.99; rotifers/Chaoborus: 0.66; Cladocera: 110; Cladocera/Chaoborus: 106 (legend: zooplankton name 25; rotifers: 0.99; rotifers/Chaoborus: 0.66; Cladocera: 110; Cladocera/Chaoborus: 106 (legend: + chao = simulation results from the extended model; zooplankton name = simulation results from [1]; zooplankton name + chao = simulation results from the extended model; zooplankton name = exp = experimental data). simulation results from [1]; exp = experimental data). In conclusion, the ecosystem model developed in Clouzot and Vanrolleghem [12] adapted well to In conclusion, the ecosystem model developed in Clouzot and Vanrolleghem [12] adapted well the addition of Chaoborus, with parameter values mostly changed based on the new trophic interactions. to the addition of Chaoborus, with parameter values mostly changed based on the new trophic In other words, the extended ecosystem model can be used with conﬁdence to further study the role of seasonal cycle alterations on the food web, as an aspect8 of the relationship between biodiversity and ecosystem functions.

3.2. Seasonal Cycles, Multitrophic Interactions and Biodiversity For each trophic level, the simulated time-series results of the calibrated model are presented. These results correspond to the best fit of the calibrated model with the observations presented in [12]. Consultation with the scientists who gathered the experimental data confirmed that the calibrated model succeeded in simulating relatively well the main ecosystem trends observed during the open water season. In order to assess the role of seasonal cycle alterations on the food web, the individual seasonal cycles of each modeled functional group are added to the simulation graphs and the impact of multitrophic interactions discussed. The results from the global sensitivity analysis are used to help better understand how ecosystem functioning may be affected separately or simultaneously by competitive processes within trophic levels (horizontal diversity) and consumptive interactions across trophic levels (vertical diversity).

interactions. In other words, the extended ecosystem model can be used with confidence to further study the role of seasonal cycle alterations on the food web, as an aspect of the relationship between biodiversity and ecosystem functions.

3.2. Seasonal Cycles, Multitrophic Interactions and Biodiversity For each trophic level, the simulated time-series results of the calibrated model are presented. These results correspond to the best fit of the calibrated model with the observations presented in [12]. Consultation with the scientists who gathered the experimental data confirmed that the calibrated model succeeded in simulating relatively well the main ecosystem trends observed during the open water season. In order to assess the role of seasonal cycle alterations on the food web, the individual seasonal cycles of each modeled functional group are added to the simulation graphs and the impact of multitrophic interactions discussed. The results from the global sensitivity analysis are used to help better understand how ecosystem functioning may be affected separately or simultaneously by competitive processes within trophic levels (horizontal diversity) and consumptive interactions across trophic levels (vertical diversity).

Water3.2.1.2020 Primary, 12, 1736 Producers—Phytoplankton 9 of 22

Seasonal Cycles and Multitrophic Interactions 3.2.1. Primary Producers—Phytoplankton For the three modeled groups of phytoplankton, algae blooms anticipated by experts are Seasonalindicated Cycles on the and simulation Multitrophic graph Interactions (Figure 7). It can be noted that actual algae blooms did not appear on the simulation graph (no significant increase of biomass), which is consistent with the For the three modeled groups of phytoplankton, algae blooms anticipated by experts are indicated experimental data. on the simulation graph (Figure7). It can be noted that actual algae blooms did not appear on the simulation graph (no signiﬁcant increase of biomass), which is consistent with the experimental data.

FigureFigure 7. 7. SimulationSimulation resultsresults forfor phytoplanktonphytoplankton (lines)(lines) withwiththe the anticipated anticipated algae algae blooms blooms added added (blocks;(blocks; legend: legend: (A) (A) chrysophytes chrysophytes and and cryptophytes cryptophytes (B) (B) diatoms diatoms and and (C) (C) chlorophytes, chlorophytes, dinoflagellates dinoflagellates andand cyanophytes). cyanophytes). Phytoplankton are at the bottom of the food web and are eaten by numerous predators Phytoplankton are at the bottom of the food web and are eaten by numerous predators (i.e., (i.e., zooplankton, Chaoborus, fathead minnow and pearl dace). Therefore, a working theory can zooplankton, Chaoborus, fathead minnow and pearl dace). Therefore, a working theory can be that be that the increase of phytoplankton biomass during a bloom leads to more food for the predators, the increase of phytoplankton biomass during a bloom leads to more food for the predators, increasing their consumption rate. Due to those multitrophic interactions happening in freshwater increasing their consumption rate. Due to those multitrophic interactions happening in freshwater ecosystems, the effects of the phytoplankton seasonal cycles (algae blooms) are not observed in the ecosystems, the effects of the phytoplankton seasonal cycles (algae blooms) are not observed in the experimental data and also are not present in the model simulation. The physical mechanisms that experimental data and also are not present in the model simulation. The physical mechanisms that would lead to blooms in the model would be a change in temperature, light and/or nutrient availability, would lead to blooms in the model would be a change in temperature, light and/or nutrient which is overcome here by predatory pressure. Those results confirm that both individual seasonal availability, which is overcome here by predatory pressure. Those results confirm that both cycles and multitrophic interactions need to be integrated into the conversation about biodiversity and ecosystem functioning (more details in Supplementary9 Material S2). Horizontal Diversity The global sensitivity analysis of phytoplankton shows that each phytoplankton group biomass had an indirect impact upon the other phytoplankton groups (i.e., diatoms in Figure8). When their initial biomass concentration was increased, the biomass concentrations of the other phytoplankton increased as well. In that scenario, the group for which the biomass was increased became the main food source for their predators and the other phytoplankton were eaten in less quantity (which explains their increase in biomass). On the contrary, when the phytoplankton group for which the initial biomass was decreased became a limited food source, the predators eat the other phytoplankton (which explained their decrease in biomass).

individual seasonal cycles and multitrophic interactions need to be integrated into the conversation about biodiversity and ecosystem functioning (more details in Supplementary Material S2).

Horizontal Diversity The global sensitivity analysis of phytoplankton shows that each phytoplankton group biomass had an indirect impact upon the other phytoplankton groups (i.e., diatoms in Figure 8). When their initial biomass concentration was increased, the biomass concentrations of the other phytoplankton increased as well. In that scenario, the group for which the biomass was increased became the main food source for their predators and the other phytoplankton were eaten in less quantity (which explains their increase in biomass). On the contrary, when the phytoplankton group for which the initial biomass was decreased became a limited food source, the predators eat the other Waterphytoplankton2020, 12, 1736 (which explained their decrease in biomass). 10 of 22

FigureFigure 8. 8.Results Results of of the the global global sensitivity sensitivity analysis analysis when when increasing increasing (++ (++)) or or decreasing decreasing ( (−−)) thethe initial initial −− biomassbiomass concentration concentration of diatomsof diatoms (legend: (legend: phyto A:ph chrysophytes,yto A: chrysophytes, cryptophytes; cryptophytes; phyto C: chlorophytes, phyto C: dinoﬂagellateschlorophytes, dinoflagellates and cyanophytes). and cyanophytes).

Vertical Diversity Vertical diversity During the global sensitivity analysis of Chaoborus, the only phytoplankton impacted were the ones During the global sensitivity analysis of Chaoborus, the only phytoplankton impacted were the consumed by the insects (group C), with a significant decrease after the initial biomass concentration ones consumed by the insects (group C), with a significant decrease after the initial biomass of Chaoborus was increased. This result highlights consumptive interactions across trophic levels and concentration of Chaoborus was increased. This result highlights consumptive interactions across predatorytrophic levels pressure. and predatory pressure. RegardingRegarding the the results results obtained obtained for zooplankton,for zooplankton, the e fftheects effects on phytoplankton on phytoplankton were more were complex, more probablycomplex, because probably di ffbecauseerent zooplankton different zooplankton species interact species diff interacterently withdifferently phytoplankton. with phytoplankton. When the initialWhen biomass the initial concentration biomass concen of anytration zooplankton of any group zooplankton was increased group (see was phyto increased A++, (see diatoms phyto++ A++,and phytodiatoms++ C++ inand Figure phyto9), C++ all phytoplanktonin Figure 9), all increasedphytoplankton in biomass. increased Rotifers in biomass. were chosen Rotifers as anwere example chosen becauseas an example they were because not a foodthey source were not for zooplanktona food source in for the zooplankton studied system in andthe studied yet they system were impacted and yet inthey the were same wayimpacted by a changein the insame zooplankton way by a biomass change concentrations.in zooplankton When biomass looking concentrations. closer to the When other resultslooking of closer the global to the sensitivity other results analysis, of the it global can be se notednsitivity that analysis, phosphorus it can (P) be concentrations noted that phosphorus increased as well (from 0.001–0.004 to 0.002–0.007 g/m3), which could explain the increase of phytoplankton (P) concentrations increased as well (from 0.001–0.004 to 0.002–0.007 g/m3), which could explain the (for increase nitrogen, of phytoplankton see Clouzot and (for Vanrolleghem nitrogen, see [Clouzot12]). and Vanrolleghem [12]).

Figure 9. ResultsResults of of the the global global sensitivity sensitivity analysis analysis when when increasing increasing (++) (or++ decreasing) or decreasing (−−) the ( initial) the −− initialbiomass biomass concentration concentration of rotifers of rotifers (legend: (legend: (A) (A)chrysophytes, chrysophytes, cryptophytes cryptophytes (B) (B) diatoms diatoms and and (C) chlorophytes, dinoﬂagellatesdinoflagellates andand cyanophytes).cyanophytes).

Another interesting result is that when the initial biomass concentration of any zooplankton group was decreased, differences appeared between diatoms and phytoplankton A/C. Indeed, diatoms biomass concentrations decreased (see diatoms—in Figure 9) while no significant changes were observed for group A/C (not presented in Figure 9). This result could be connected to the notion of functional groups explained previously (Section 2.3.), which suggest that species performing similar roles in an ecosystem process are grouped together. When looking at the calibrated parameters used in the model for phytoplankton (see [12]), it seems that both group A and C assimilate better P than nitrogen (N; in terms of nutrient affinity), while diatoms assimilate P and N the same. This difference in assimilation could be the reason why the phytoplankton groups were not impacted in the same way after zooplankton biomass was decreased. Finally, no significant effects were observed on phytoplankton during the global sensitivity analysis of the modelled fish species (fathead minnow, pearl dace, white sucker and lake trout). The possible hypotheses are that if there is a top-down effect within the studied freshwater ecosystem, it does not go down to the bottom of the foodweb, as compensatory mechanisms are responsible for keeping overall phytoplankton biomass the same.

3.2.2. Primary Consumers—Zooplankton

Seasonal Cycles and Multitrophic Interactions For the three modeled groups of zooplankton, blooms anticipated by experts are indicated on the simulation graph (Figure 10). Similar to phytoplankton, the rotifers bloom did not appear on the simulation graph, which was consistent with the experimental data. Rotifers were only eaten by Chaoborus, which increased in biomass in August (Figure 5), during the rotifers bloom. Therefore, the food consumption of Chaoborus increased, which can explain why the peak for rotifers bloom did not appear on the simulation graph.

11 Water 2020, 12, 1736 11 of 22

Another interesting result is that when the initial biomass concentration of any zooplankton group was decreased, differences appeared between diatoms and phytoplankton A/C. Indeed, diatoms biomass concentrations decreased (see diatoms—in Figure9) while no significant changes were observed for group A/C (not presented in Figure9). This result could be connected to the notion of functional groups explained previously (Section 2.3.), which suggest that species performing similar roles in an ecosystem process are grouped together. When looking at the calibrated parameters used in the model for phytoplankton (see [12]), it seems that both group A and C assimilate better P than nitrogen (N; in terms of nutrient affinity), while diatoms assimilate P and N the same. This difference in assimilation could be the reason why the phytoplankton groups were not impacted in the same way after zooplankton biomass was decreased. Finally, no significant effects were observed on phytoplankton during the global sensitivity analysis of the modelled fish species (fathead minnow, pearl dace, white sucker and lake trout). The possible hypotheses are that if there is a top-down effect within the studied freshwater ecosystem, it does not go down to the bottom of the foodweb, as compensatory mechanisms are responsible for keeping overall phytoplankton biomass the same.

3.2.2. Primary Consumers—Zooplankton

Seasonal Cycles and Multitrophic Interactions For the three modeled groups of zooplankton, blooms anticipated by experts are indicated on the simulation graph (Figure 10). Similar to phytoplankton, the rotifers bloom did not appear on the simulation graph, which was consistent with the experimental data. Rotifers were only eaten by Chaoborus, which increased in biomass in August (Figure5), during the rotifers bloom. Therefore, the food consumption of Chaoborus increased, which can explain why the peak for rotifers bloom did not appear on the simulation graph.

Figure 10. Simulation results for zooplankton (lines) with the anticipated blooms added (blocks; legend: (A)Figure copepods 10. Simulation (B) rotifers results and (C) for Cladocera). zooplankton (lines) with the anticipated blooms added (blocks; legend: (A) copepods (B) rotifers and (C) Cladocera). With regards to copepods and Cladocera, the blooms anticipated by experts were also not present in theWith simulation regards results. to copepods Instead, biomassand Cladocera, concentrations the blooms decreased anticipated steadily. by Copepods experts andwere Cladocera also not arepresent food in sources the simulation for Chaoborus results., fathead Instead, minnow, biomas pearls concentrations dace and white decreased sucker. steadily. Besides, Copepods they feed and on phytoplankton,Cladocera are food which sources have for low Chaoborus, biomass fathead concentrations. minnow, pearl Consequently, dace and white their individualsucker. Besides seasonal, they cyclesfeed on were phytoplankton, hidden by the which multitrophic have low interactions biomass occurringconcentrations. in the freshwaterConsequently, ecosystem. their individual seasonal cycles were hidden by the multitrophic interactions occurring in the freshwater ecosystem. Horizontal Diversity Horizontal diversity The global sensitivity analysis of the zooplankton simulation results shows that the three groups had aThe similar global impact sensitivity on the analysis other of groups the zooplankto of the samen simulation trophic level results (i.e., shows Cladocera that the in three Figure groups 11). had a similar impact on the other groups of the same trophic level (i.e., Cladocera in Figure 11). For example, in the case of Cladocera, with their initial biomass concentration as the one in the experimental lake, the copepods biomass concentrations were first higher and then lower (Figure 11, see copepods). With less Cladocera, the copepods become the main food source for predators feeding on zooplankton, but they also get more food because of reduced competition with Cladocera. Until mid-July, copepods are in their bloom phase, so the increased predatory pressure did not have an impact yet, the food availability having a stronger effect. However, when their anticipated bloom is over, the predatory pressure is stronger, and their biomass concentrations decrease. Similar effects can be noted for rotifers but at a lower intensity (Figure 11, see rotifers). In the scenario of increasing the initial biomass concentration of Cladocera, the copepods concentrations increase until mid-July and then decrease (Figure 11, see copepods ++). Cladocera became the main food source for predators and thus, copepods could grow until their bloom was done (mid-July). Once again, the effects were lower for rotifers, but they lasted longer (Figure 11, see rotifers ++).

12 Water 2020, 12, 1736 12 of 22

For example, in the case of Cladocera, with their initial biomass concentration as the one in the experimental lake, the copepods biomass concentrations were first higher and then lower (Figure 11, see copepods). With less Cladocera, the copepods become the main food source for predators feeding on zooplankton, but they also get more food because of reduced competition with Cladocera. Until mid-July, copepods are in their bloom phase, so the increased predatory pressure did not have an impact yet, the food availability having a stronger effect. However, when their anticipated bloom is over, the predatory pressure is stronger, and their biomass concentrations decrease. Similar effects can be noted for rotifers but at a lower intensity (Figure 11, see rotifers).

FigureFigure 11.11. ResultsResults ofof thethe globalglobal sensitivitysensitivity analysisanalysis whenwhen increasingincreasing ((++)++) or decreasing (–)(--) thethe initialinitial biomassbiomass concentrationconcentration ofof Cladocera.Cladocera.

VerticalIn the Diversity scenario of increasing the initial biomass concentration of Cladocera, the copepods concentrations increase until mid-July and then decrease (Figure 11, see copepods ++). Cladocera became the main food During the global sensitivity analysis of Chaoborus, significant effects on zooplankton were only source for predators and thus, copepods could grow until their bloom was done (mid-July). Once again, observed when the initial biomass concentration was increased (see rotifers++, Cladocera++ and the effects were lower for rotifers, but they lasted longer (Figure 11, see rotifers ++). copepods++ inFigure Figure 11. Results 12). ofIn the that global case, sensitivity the analysis predatory when increasing pressure (++) orof decreasing Chaoborus (--) the was initial higher, which biomass concentration of Cladocera. Verticalexplains Diversity the decrease in concentration of the three zooplankton groups. Vertical Diversity During the global sensitivity analysis of Chaoborus, signiﬁcant eﬀects on zooplankton were only observed whenDuring the initialthe global biomass sensitivity concentration analysis of Chaoborus was, significant increased effects (see on rotifers zooplankton++, Cladocerawere only ++ and observed when the initial biomass concentration was increased (see rotifers++, Cladocera++ and copepods++ in Figure 12). In that case, the predatory pressure of Chaoborus was higher, which explains copepods++ in Figure 12). In that case, the predatory pressure of Chaoborus was higher, which the decreaseexplains in concentration the decrease in ofconcentrat the threeion zooplanktonof the three zooplankton groups. groups.

Figure 12. Results of the global sensitivity analysis when increasing (++) the initial biomass

concentration of Chaoborus. Figure 12.FigureResults 12. Results of the of globalthe global sensitivity sensitivity analysisanalysis when when increasing increasing (++) (the++ initial) the biomass initial biomass concentration of Chaoborus. concentrationWith regards of toChaoborus phytoplankton,. since they are a food source for zooplankton, changes in any phytoplanktonWith biomass regards toconcentrat phytoplankton,ions sincedirectly they areimpact a food sourcezooplankton for zoopla biomassnkton, changes dynamics. in any Higher concentrationsphytoplankton of zooplankton biomass concentratwere observedions directly when impact any phytoplanktonzooplankton biomass initial dynamics. biomass Higher concentration concentrations of zooplankton were observed when any phytoplankton initial biomass concentration was increased.was increased. On the On opposite, the opposite, less less abundant abundant phytoplanktonphytoplankton resulted resulted in lower in lowerconcentrations concentrations of of zooplankton.zooplankton. Finally, Finally, changes changes in any in any fish fish initial initial biomassbiomass concentration concentration did not did result not in result significant in significant changes inchanges zooplankton in zooplankton biomass biomass dynamics, dynamics, which which coulduld be be explained explained by compensatory by compensatory mechanisms mechanisms or migrationor migration of zooplankton of zooplankton to avoid to avoid potential potential increased increased predatory predatory pressure. pressure. 3.2.3. Secondary and Tertiary Consumers—Fish 3.2.3. Secondary and Tertiary Consumers—Fish Seasonal Cycles and Multitrophic Interactions Seasonal Cycles and Multitrophic Interactions 13

13 Water 2020, 12, 1736 13 of 22

With regards to phytoplankton, since they are a food source for zooplankton, changes in any phytoplankton biomass concentrations directly impact zooplankton biomass dynamics. Higher concentrations of zooplankton were observed when any phytoplankton initial biomass concentration was increased. On the opposite, less abundant phytoplankton resulted in lower concentrations of zooplankton. Finally, changes in any fish initial biomass concentration did not result in significant changes in zooplankton biomass dynamics, which could be explained by compensatory mechanisms or migration of zooplankton to avoid potential increased predatory pressure.

3.2.3. Secondary and Tertiary Consumers—Fish

Seasonal Cycles and Multitrophic Interactions ForFor thethe four four modeled modeled fish ﬁsh (fathead (fathead minnow, minnow, pearl pearldace, white dace, sucker white and sucker lake trout), and lake anticipated trout), anticipatedspawning periods spawning are periodsindicated are on indicated the simulation on the graph simulation (Figure graph 13). The (Figure same 13 biomass). The same concentration biomass concentrationpattern can be pattern noted, can with be noted,an increase with anof increaseadult biomass of adult before biomass spawning, before spawning, followed followedby a decrease by a decreaseduring spawning, during spawning, along with along an increase with an increaseof juvenile of juvenilebiomass. biomass. An increase An increaseof biomass of biomassalso appeared also appearedin the summer, in the summer,due to high due temperatures to high temperatures and food andavailability, food availability, and then a and decrease then a in decrease the fall, indue the to fall,a decrease due to ain decrease temperature in temperature and food availability. and food availability. Fish being Fish at the being top of at the topfood of web the and food feeding web and on feedinglower trophic on lower levels, trophic individual levels, individual seasonal cycles seasonal clea cyclesrly appeared clearly on appeared the simulation on the simulation graphs, along graphs, with alongeffects with of ecological eﬀects of ecologicalinteractions. interactions.

FigureFigure 13. 13.Simulation Simulation results results for ﬁshfor fish (lines) (lines) with thewith anticipated the anticipated spawning spawning added (blocks)added for(blocks)(a) fathead for (a) minnow,fathead (minnow,b) pearl dace,(b) pearl (c) white dace, sucker(c) white and sucker (d) lake and trout. (d) lake trout.

ThoseThose same same graphs graphs (Figure (Figure 13 13)) can can also also be be interpreted interpreted with with multitrophic multitrophic interactions interactions in in mind, mind, addingadding a layera layer of complexityof complexi inherentty inherent to any to freshwaterany freshwater ecosystem. ecosys First,tem.the First, initial the increase initial increase of fathead of minnowfathead biomassminnow biomass concentrations concentrations can be linked can be to linked high to concentrations high concentrations of its preys of its atpreys the sameat the timesame (Cladoceratime (Cladocera and copepods) and copepods) and the and second the increasesecond incr afterease spawning, after spawning, to high concentrationsto high concentrations of another of prey,anotherChaoborus prey, Chaoborus. The decrease. The atdecrease the end at can the then end becan explained then be explained by a decrease by a indecrease biomass in ofbiomass its prey of (Chaoborusits prey (Chaoborus) and an increase) and an of increase its predator of its (lake predator trout). (lake Similar trout). observations Similar observations can be made can for be pearl made dace, for withpearl spawning dace, with happening spawning earlier. happening Next, earlier. the increase Next, in the lake increase trout biomass in lake trout concentrations biomass concentrations after August canafter be August connected can to be the connected increase to of the all itsincrease prey (fathead of all its minnow,prey (fathead pearl minnow, dace and pearl juvenile dace white and sucker).juvenile Finally,white sucker). white sucker Finally, did white not seem sucker to did be inﬂuencednot seem to much be influenced by the rest much of the by ecosystem, the rest of withthe ecosystem, biomass concentrationswith biomass concentrations being relatively being constant relatively through constant the whole through open the water whole season. open water season.

Horizontal Diversity In the modeled ecosystem, the secondary consumers were fathead minnow, pearl dace and white sucker, while lake trout was a tertiary consumer. Therefore, horizontal diversity could only be studied at the level of secondary consumers. The global sensitivity analyses of fathead minnow and pearl dace both demonstrated similar effects on each other (i.e., fathead minnow in Figure 14). For example, a decrease of the fathead minnow initial biomass concentration resulted in lower concentrations of pearl dace (see pearl—in Figure 14), which can be explained by a change of predatory pressure. Since lake trout eats interchangeably fathead minnow and pearl dace, a reduced fathead minnow biomass leads lake trout to eat more pearl dace, resulting in a decrease of their biomass. In the case of an increase of fathead minnow concentrations, less food is available for pearl dace, which leads to a decrease of theircombination concentrations of predatory (see pearl pressure++ in Figure and food14). Inavailability conclusion, can a combination explain the ofobserved predatory impact pressure on andhorizontal food availability diversity. canSimilar explain effects the observedwere observ impacted for on the horizontal juveniles diversity. of white Similar sucker, eff whichects were are observedanother food for the source juveniles for lake of whitetrout. Conversely, sucker, which because are another lake trout food did source not forfeed lake on the trout. adults Conversely, of white becausesucker, no lake significant trout did changes not feed were on the observed adults of when white changing sucker, nothe significant initial biomass changes of fathead were observed minnow whenor pearl changing dace. the initial biomass of fathead minnow or pearl dace.

Figure 14. Results of the global sensitivity analysis when increasing (++) or decreasing ( ) the initial Figure 14. Results of the global sensitivity analysis when increasing (++) or decreasing −−(−−) the initial biomassbiomass concentrationconcentration ofof fathead fathead minnow minnow for for pearl pearl dace. dace. Vertical Diversity Vertical diversity During the global sensitivity analyses of phytoplankton and zooplankton, no significant effects During the global sensitivity analyses of phytoplankton and zooplankton, no significant effects were observed on the secondary consumers (fathead minnow, pearl dace and white sucker), except for were observed on the secondary consumers (fathead minnow, pearl dace and white sucker), except juvenile white sucker, with lower concentrations when one of its preys (copepods) decrease. Both fathead for juvenile white sucker, with lower concentrations when one of its preys (copepods) decrease. Both minnow and pearl dace had a diversified diet, which gave them a better resilience when changes fathead minnow and pearl dace had a diversified diet, which gave them a better resilience when happened in the ecosystem. On the other hand, significant effects were observed for fathead minnow, changes happened in the ecosystem. On the other hand, significant effects were observed for fathead pearl dace and juvenile white sucker when changes were applied to their only predator, lake trout. minnow, pearl dace and juvenile white sucker when changes were applied to their only predator, When the lake trout initial biomass concentration was increased, its preys’ concentrations decreased, lake trout. When the lake trout initial biomass concentration was increased, its preys’ concentrations and vice-versa. decreased, and vice-versa. Other significant changes happened when the initial biomass concentration of Chaoborus was Other significant changes happened when the initial biomass concentration of Chaoborus was increased (Figure 15), as a result of multitrophic cascade effects. First, lower concentrations of fathead increased (Figure 15), as a result of multitrophic cascade effects. First, lower concentrations of fathead minnow were observed (same effect with pearl dace; see fathead [A]/[J]++ in Figure 15). Due to higher minnow were observed (same effect with pearl dace; see fathead [A]/[J]++ in Figure 15). Due to higher concentrations of Chaoborus, the zooplankton biomass was lower due to increased predatory pressure concentrations of Chaoborus, the zooplankton biomass was lower due to increased predatory pressure (Figure 11) and they became a limited food resource for the small fishes (fathead minnow and pearl (Figure 11) and they became a limited food resource for the small fishes (fathead minnow and pearl dace). At the same time, a strong decrease in white sucker biomass can be noticed because of lower dace). At the same time, a strong decrease in white sucker biomass can be noticed because of lower food resources and higher predatory pressure from lake trout on the juveniles (see white [A]/[J]++ in food resources and higher predatory pressure from lake trout on the juveniles (see white [A]/[J]++ in Figure 15). Finally, the lake trout biomass concentrations also decreased due to its preys becoming Figure 15). Finally, the lake trout biomass concentrations also decreased due to its preys becoming limited (see trout [A]/[J]++ in Figure 15). These results demonstrate a strong connection between the secondary consumers but also across the different trophic levels. Besides, Chaoborus appeared as a key species in the studied system, where changes in its biomass concentrations impacted the whole ecosystem.

15 Water 2020, 12, 1736 15 of 22 limited (see trout [A]/[J]++ in Figure 15). These results demonstrate a strong connection between the secondary consumers but also across the diﬀerent trophic levels. Besides, Chaoborus appeared as a key species in the studied system, where changes in its biomass concentrations impacted the whole ecosystem.

FigureFigure 15.15. ResultsResults ofof thethe globalglobal sensitivitysensitivity analysisanalysis whenwhen increasingincreasing ( ++(++)) thethe initialinitial biomassbiomass Chaoborus a b c concentrationconcentration ofof Chaoborus for ( a)) fathead fathead minnow, minnow, (b ( ) )white white sucker sucker and and (c) ( lake) lake trout trout (legend: (legend: [A] [A] adults; [J]: juveniles). adults; [J]: juveniles). 3.2.4. Omnivorous, Mid-Trophic Level Group—Chaoborus 3.2.4. Omnivorous, Mid-Trophic Level Group—Chaoborus Seasonal Cycles and Multitrophic Interactions Seasonal Cycles and Multitrophic Interactions The simulation graphs for Chaoborus (Figure5), with the different phases highlighted, were previously discussedThe insimulation Section 3.1 graphs, along for with Chaoborus the multitrophic (Figure 5), interactions with the differen involvingt phasesChaoborus. highlighted,Noteworthy, were whenpreviously comparing discussed the simulation in Section graphs 3.1, along of Chaoborus with theand multitrophic fathead minnow, interactions similar trends involving were Chaoborus. observed (FiguresNoteworthy,5 and 13 whena). As withcomparing fish, individual the simulation seasonal cyclesgraphs of ofChaoborus Chaoborusappeared and fathead on the simulation minnow, graphs.similar trends were observed (Figure 5 and Figure 13a). As with fish, individual seasonal cycles of Chaoborus Verticalappeared Diversity on the simulation graphs. All zooplankton and phytoplankton C (chlorophytes, dinoflagellates and cyanophytes) are a Vertical Diversity direct food source for Chaoborus and thus, followed the same trend. For diatoms and phytoplankton A (chrysophytesAll zooplankton and cryptophytes), and phytoplankto the samen directC (chlorophytes, effect on Chaoborus dinoflagelwaslates observed and cyanophytes) because they are had a adirect direct food effect source on phytoplankton for Chaoborus Cand (Figure thus,8 followed). A reverse the esameffect tren wasd. observed For diatoms with and fathead phytoplankton minnow andA (chrysophytes pearl dace (fathead and cryptophytes), minnow in Figure the same 16b) direct because, effect in thison Chaoborus interaction, wasChaoborus observedwas because the food they source.had a direct Again, effect multitrophic on phytoplank interactionston C (Figure were directly 8). A reverse connected effect to was the ecosystemobserved with dynamics, fathead and minnow thus itsand functioning. pearl dace (fathead minnow in Figure 16b) because, in this interaction, Chaoborus was the food source. Again, multitrophic interactions were directly connected to the ecosystem dynamics, and thus its functioning.

16 Water 2020, 12, 1736 16 of 22

FigureFigureFigure 16. 16.16. Results ResultsResults of ofof the thethe global globalglobal sensitivity sensitivitysensitivity analysis analysisanalysis when whenwhen increasing increasingincreasing ( (++)++(++)) or or decreasing decreasing (–)(--)(--) the thethe initial initialinitial biomassbiomassbiomass concentration concentrationconcentration of ofof ( (a(aa))) Cladocera CladoceraCladocera or oror ( (b(bb))) fathead fatheadfathead minnow. minnow.minnow.

WhenWhenWhen comparing comparingcomparing the thethe results resultsresults of ofof the thethe global globalglobal sensitivity sensitivsensitivityity analysis analysisanalysis of the ofof thethe previous previousprevious model modelmodel [12], [12],[12], which whichwhich did notdiddid consider notnot considerconsiderChaoborus ChaoborusChaoborus, and, the, andand extended thethe extendedextended ecosystem ecosystemecosystem model model presentedmodel presentedpresented in this paper,inin thisthis it paper,paper, seemed itit thatseemedseemed the inclusionthatthat thethe inclusioninclusion of insects ofof created insectsinsects higher createdcreated resilience higherhigher resilie forresilie thencence freshwater forfor thethe freshwaterfreshwater ecosystem. ecosystem.ecosystem. For example, ForFor the example,example, sensitivity thethe analysissensitivitysensitivity of Cladoceraanalysisanalysis ofof shows CladoceraCladocera the e ﬀ showsshowsects on thethe white effectseffects sucker onon andwhitewhite lake suckersucker trout and wereand lakelake lower trouttrout in intensity werewere lowerlower when inin Chaoborusintensityintensity whenwhenwas added ChaoborusChaoborus to the waswas modeled addedadded ecosystem toto thethe modeledmodeled (Figure ecosysteecosyste 17). Similarmm (Figure(Figure results 17).17). were SimiSimi obtainedlarlar resultsresults for werewere the diobtainedobtainedﬀerent functional forfor thethe differentdifferent groups selected functionalfunctional for the groupsgroups model. selesele Therefore,ctedcted forfor whilethethe model.model. adding Therefore,Therefore, an omnivorous, whilewhile mid-trophic addingadding anan levelomnivorous,omnivorous, group such mid-trophicmid-trophic as Chaoborus levellevel, which groupgroup is suchsuch connected asas ChaoborusChaoborus to many,, whichwhich other trophicisis connectedconnected levels, toto added manymany complexity otherother trophictrophic to freshwaterlevels,levels, addedadded ecosystems, complexitycomplexity it to couldto freshwatfreshwat also leaderer ecosystems,ecosystems, to an enhanced itit couldcould resilience alsoalso leadlead of toto the anan ecosystems enhancedenhanced resilienceresilience when changes ofof thethe wereecosystemsecosystems applied. whenwhen changeschanges werewere applied.applied.

Figure 17. Results of the global sensitivity analysis when increasing (++) or decreasing (–) the initial FigureFigure 17.17. ResultsResults ofof thethe globalglobal sensitivitysensitivity analysisanalysis whenwhen increasingincreasing (++)(++) oror decreasingdecreasing (--)(--) thethe initialinitial biomass concentration of Cladocera when (a) Chaoborus is not modeled in the ecosystem [12] and when biomassbiomass concentrationconcentration ofof CladoceraCladocera whenwhen ((aa)) ChaoborusChaoborus isis notnot modeledmodeled inin thethe ecosystemecosystem [12][12] andand (b) Chaoborus is added (extended ecosystem model presented in this paper). whenwhen ((bb)) ChaoborusChaoborus isis addedadded (extended(extended ecosystemecosystem modelmodel presentedpresented inin thisthis paper).paper). 3.3. Indirect Effects of EE2 3.3.3.3. IndirectIndirect EffectsEffects ofof EE2EE2 TheThe experimentalexperimental resultsresults obtainedobtained fromfrom LakeLake 260260 showedshowed thatthat thethe strongeststrongest directdirect eeffectffect ofof EE2EE2 was onThe fathead experimental minnow, results with aobtained collapse from of the Lake fish 260 species showed in the that second the strongest year of addingdirect effect EE2 toof theEE2 waswas onon fatheadfathead minnow,minnow, withwith aa collapcollapsese ofof thethe fishfish speciesspecies inin thethe secondsecond yearyear ofof addingadding EE2EE2 toto thethe lakelake [[11].11]. DiscussionsDiscussions withwith expertsexperts inin ecotoxicologyecotoxicology andand endocrineendocrine disruptiondisruption helpedhelped identifyidentify thethe parameterslake [11]. Discussions that should with be modified experts ininthe ecotoxicol ecosystemogy model.and endocrine It was previously disruption decided helped that identify a mix the of parametersparameters thatthat shouldshould bebe modifiedmodified inin thethe ecosysteecosystemm model.model. ItIt waswas previouslypreviously decideddecided thatthat aa mixmix ofof reducedreduced gametegamete production,production, increasedincreased gametegamete mortalitymortality andand anan increaseincrease ofof bothboth adultsadults andand juvenilejuvenile mortalityreduced gamete was a potential production, hypothesis increase ford gamete explaining mortality the collapse and an increase of both adultof both and adults juvenile and fatheadjuvenile mortalitymortality waswas aa potentialpotential hypothesishypothesis forfor explainingexplaining thethe collapsecollapse ofof bothboth adultadult andand juvenilejuvenile fatheadfathead minnowminnow duedue toto EE2EE2 additionaddition [[12].12]. Therefore, thethe samesame combinationcombination ofof parametersparameters waswas appliedapplied toto thethe extendedminnow due ecosystem to EE2 model.addition [12]. Therefore, the same combination of parameters was applied to the extendedextended ecosystemecosystem model.model. InIn comparisoncomparison withwith referencereference [12],[12], thethe additionaddition ofof ChaoborusChaoborus toto thethe freshwaterfreshwater ecosystemecosystem diddid notnot impactimpact thethe EE2EE2 indirectindirect effecteffect obseobservedrved onon thethe otherother fishfish species,species, i.e.,i.e., aa decreasedecrease inin biomassbiomass ofof pearlpearl

1717 Water 2020, 12, 1736 17 of 22

In comparison with reference [12], the addition of Chaoborus to the freshwater ecosystem did not impact the EE2 indirect effect observed on the other fish species, i.e., a decrease in biomass of pearl dacedace andand white white sucker, sucker, accompanied accompanied with with a a change change in in lake lake trout trout biomass. biomass. SinceSince lakelake trouttrout could could no no longerlonger feed feed on on fathead fathead minnow, minnow, they they turned turned to to their their other other food food sources. sources. RegardingRegardingChaoborus Chaoborusitself, itself, bothboth thethe simulation simulation resultsresults andand thethe experimentalexperimental datadata forfor instarsinstars 33 andand 44 afterafter addingadding EE2 to to the the lake lake show show higher higher ChaoborusChaoborus concentrationsconcentrations than than the the results results without without the thehormone hormone (Figure (Figure 18a). 18 a).The Theincrease increase in Chaoborus in Chaoborus biomassbiomass can canbe explained be explained by the by thedecrease decrease in fish in fishpredatory predatory pressure pressure due due to tothe the collapse collapse of of the the fathead fathead minnow population.population. Similar eeffectsffects werewere observedobserved withwith CladoceraCladocera andand copepodscopepods (Figure(Figure 1818b),b), butbut atat aa lowerlower degreedegree duedue toto thethe additionaladditional predatorypredatory pressure pressure from from the theChaoborus Chaoboruspopulation. population.

FigureFigure 18. 18.Simulation Simulation results results with with and and without without EE2 EE2 in in the the lake lake for for (a) ( instarsa) instars 3 and 3 and 4 of 4Chaoborus of Chaoborusand and (b) zooplankton(b) zooplankton (Cladocera (Cladocera and copepods; and copepods; abbreviations: abbreviati expons:= experimental exp = experimental data; sim data;= simulation sim = simulation results). results). For rotifers, no significant changes in biomass concentrations were observed in the ecosystem modelFor simulations rotifers, no after significant adding EE2chan (resultsges in biomass not shown). concentrations However, it were was foundobserved in thein the experimental ecosystem lakemodel that simulations the average after rotifer adding biomass EE2 (results did increase, not shown). even However, if they do it not was constitute found in the a major experimental part of thelake diet that of the the average fish species rotifer impacted biomass did by EE2increase, [13]. even The authorsif they do raised not constitute the hypothesis a major of part complex of the multitrophicdiet of the interactionsfish species thatimpacted could explainby EE2 this[13] result,. The suchauthors as changesraised the in thehypothesis diet of zooplankton of complex predators.multitrophic Intraguild interactions competition that could and explain predation this among result, omnivorous such as changes zooplankton in the predatorsdiet of zooplankton including minnows,predators.Chaoborus Intraguildand competition cyclopoid copepods and predation may lead toamong complex omnivorous interactions. zooplankton For example, predators the effect ofincluding decreased minnows, fish predation Chaoborus may and have cyclopoid been partially copepods offset may by increases lead to incomplexChaoborus interactions.predation onFor otherexample, invertebrates. the effect Declinesof decreased in Tropocyclops fish predation, which may feed have on rotifers, been partially or changes offset in theby behaviorincreases ofin otherChaoborus invertebrate predation predators on other may invertebrates. also have affected Declines rotifers in and Tropocyclops other small, which taxa. Therefore,feed on rotifers, one of the or reasonschanges for in thethe rotifers behavior increase of other not invertebrate being captured pred byators the may simulation also have results affected may berotifers that the and model other cannotsmall taxa. simulate Therefore, any changes one of the of reasons diet or habitatfor the roti duefers to increase changes not in thebeing ecosystem, captured suchby the as simulation resource availabilityresults may or be predatory that the model pressure. cannot Besides, simulate a simplification any changes of of the diet model or habitat was to due consider to changes particulate in the detritusecosystem, as the such main as resource diet of rotifers, availability which or is predatory relatively pre closessure. to reality Besides, but a not simplification entirely accurate. of the model wasFinally, to consider the simulationparticulate resultsdetritus before as the andmain after diet adding of rotifers, EE2 which confirm is relatively there are noclose indirect to reality effects but ofnot the entirely synthetic accurate. hormone on phytoplankton. This result is consistent with the observation made duringFinally, the global the simulation sensitivity analysisresults before of fish and and after the verticaladding impactEE2 confirm on phytoplankton there are no (Sectionindirect effects3.2.1). Theof the top-down synthetic eff ecthormone confirmed on phytoplankton. for EE2 on the freshwaterThis result ecosystemis consistent thus with does the not observation go down tomade the bottomduring ofthe the global freshwater sensitivity food analysis web, showing of fish and the resiliencethe vertical of impact this trophic on phytoplankton level. (Section 3.2.1). The top-down effect confirmed for EE2 on the freshwater ecosystem thus does not go down to the 4. Discussion bottom of the freshwater food web, showing the resilience of this trophic level. 4.1. Chaoborus Contribution to Ecosystem Functioning 4. Discussion A dependence diagram of the extended ecosystem model was created to better illustrate the Chaoborus4.1. Chaoboruscontribution Contribution to the to Ecosystem freshwater Functioning ecosystem functioning (Figure 19). Each trophic level is A dependence diagram of the extended ecosystem model was created to better illustrate the Chaoborus contribution to the freshwater ecosystem functioning (Figure 19). Each trophic level is identified with a specific color, such as blue for fish. Biomass transfer through food consumption is

18 Water 2020, 12, 1736 18 of 22

representedidentified with with a specificarrows color,and percentages such as blue indicate for fish. the Biomass fraction transfer of the over throughall food food consumption consumption for is eachrepresented functional with group. arrows andFor percentagesinstance for indicate Chaoborus the, fractionits diet of was the overallcomposed food of consumption 10% rotifers, for each40% Cladocerafunctional group.and copepods, For instance 40% for Chaoborusphyto C ,(chlorophytes, its diet was composed dinoflagellates, of 10% rotifers, cyanophytes) 40% Cladocera and 10% and particulatecopepods, 40%detritus phyto (PD). C (chlorophytes, dinoflagellates, cyanophytes) and 10% particulate detritus (PD).

Figure 19. DependenceDependence diagram diagram of of the the studied freshwater food food web after adding Chaoborus toto the ecosystem model. The The percentages percentages represent the feedingfeeding preferences (abbreviations:(abbreviations: PD = particulate detritus; legend: filled filled arrows: transfer transfer from from one one gr group,oup, open arrows: transfer transfer from from multiple multiple groups). Energy and materials move between the biotic (i.e., species) and the abiotic (i.e., inorganic and Energy and materials move between the biotic (i.e., species) and the abiotic (i.e., inorganic and organic nutrients) compartments, as well as into and out of the system. Ecosystem processes are organic nutrients) compartments, as well as into and out of the system. Ecosystem processes are quantified by measuring rates of these movements (e.g., plant production), while ecosystem functioning quantified by measuring rates of these movements (e.g., plant production), while ecosystem is quantified by measuring the magnitudes and dynamics of ecosystem processes. It has been shown functioning is quantified by measuring the magnitudes and dynamics of ecosystem processes. It has that most ecosystem processes are driven by the combined biological activities of many species [24]. been shown that most ecosystem processes are driven by the combined biological activities of many Additionally, because species can vary dramatically in their contributions to ecosystem functioning, species [24]. Additionally, because species can vary dramatically in their contributions to ecosystem the specific composition or identity of species in a community is important. functioning, the specific composition or identity of species in a community is important. One way to describe the average number of times energy is transferred as it moves from basal One way to describe the average number of times energy is transferred as it moves from basal resources to top predators is the food chain length (FCL), directly related to ecosystem functioning [19]. resources to top predators is the food chain length (FCL), directly related to ecosystem functioning Since horizontal and vertical diversity interact, adding the omnivorous (i.e., feeding from more than [19]. Since horizontal and vertical diversity interact, adding the omnivorous (i.e., feeding from more one trophic level) Chaoborus to the food chain can qualitatively change diversity effects at adjacent than one trophic level) Chaoborus to the food chain can qualitatively change diversity effects at levels (Figure 19). For instance, Hanazato [26] showed that in ponds containing the zooplankton predator adjacent levels (Figure 19). For instance, Hanazato [26] showed that in ponds containing the Chaoborus, the zooplankton community structure became dominated by rotifers. In ponds without Chaoborus, zooplankton predator Chaoborus, the zooplankton community structure became dominated by zooplankton became dominated by cladocerans (a taxon that is competitively superior to rotifers). rotifers. In ponds without Chaoborus, zooplankton became dominated by cladocerans (a taxon that is Another interesting result is presented in Hanazato [27]: novel aspects of Chaoborus in driving competitively superior to rotifers). ecosystem functioning in lakes and reservoirs. Chaoborus larvae seem to account for a significant Another interesting result is presented in Hanazato [27]: novel aspects of Chaoborus in driving fraction of both the hypolimnetic and sediment oxygen demands, and effectively trap nutrients in ecosystem functioning in lakes and reservoirs. Chaoborus larvae seem to account for a significant water and sediment, and in this way they enhance internal nutrient loading. fraction of both the hypolimnetic and sediment oxygen demands, and effectively trap nutrients in Finally, the simulation results obtained with the extended ecosystem model demonstrated that water and sediment, and in this way they enhance internal nutrient loading. adding an omnivorous, mid-trophic level group such as Chaoborus, enhanced resilience (Figure 17). Finally, the simulation results obtained with the extended ecosystem model demonstrated that Conversely, missing such a species means that the actual resilience of an ecosystem and its functioning adding an omnivorous, mid-trophic level group such as Chaoborus, enhanced resilience (Figure 17). cannot be properly simulated. The stability of an ecosystem corresponds to its ability to maintain a Conversely, missing such a species means that the actual resilience of an ecosystem and its comparable functioning in the presence of perturbations that drive it away from its original state [28]. functioning cannot be properly simulated. The stability of an ecosystem corresponds to its ability to Resilience, in turn, is the ability or the time taken by a system to recover from a change due to maintain a comparable functioning in the presence of perturbations that drive it away from its perturbations and is linked to stability. original state [28]. Resilience, in turn, is the ability or the time taken by a system to recover from a change due to perturbations and is linked to stability.

19 Water 2020, 12, 1736 19 of 22

4.2. EE2 Effects Effects on Ecosystem Functioning The EE2 indirectindirect eeffectsffects onon thethe studiedstudied freshwaterfreshwater foodfood web,web, summarizedsummarized inin aa dependencedependence diagram (Figure 2020),), were connected to multitrophic interactions, such as food food resources resources availability, availability, predatorypredatory pressurepressure andand compensatorycompensatory mechanisms.mechanisms. It clearly appeared that the removal ofof fatheadfathead minnow from the the food food web web changed changed the the dynamics. dynamics. For For example, example, before before EE2 EE2 addition, addition, 80% 80% of the of lake the laketrout’s trout’s diet consisted diet consisted of fathead of fathead minnow minnow and pearl and pearl dace. dace. After AfterEE2 was EE2 added, was added, 80% of 80% the of lake the trout lake troutdiet was diet wasnow nowonly only obtained obtained from from pearl pearl dace, dace, whic whichh added added predatory predatory pressure pressure on on this this species, resultingresulting inin aa decreasedecrease ofof thethe biomass.biomass. Following a similar logic, Chaoborus benefitsbenefits from the removal of fatheadfathead minnow. minnow. While While without without EE2, EE2,Chaoborus Chaoborusrepresented represented 40% of40% the of diet the of bothdiet fatheadof both minnowfathead andminnow pearl and dace, pearl it was dace, now it onlywas consumednow only consumed by pearl dace by (40%pearl ofdace its diet),(40% whichof its diet), explains which the explains increase intheChaoborus increase inbiomass. Chaoborus biomass.

Figure 20.20. DependenceDependence diagram diagram of of the the studied studied freshwater freshwater food food web afterweb adding after adding the collapse the collapse of Figure of2. TheFigure percentages 2. The percentages represent therepresent feeding the preferences feeding pref anderences the size and of the the di sizefferent of the groups different is proportional groups is toproportional the biomass to changes the biomass due tochanges EE2 addition due to (abbreviations:EE2 addition (abbreviations: PD = particulate PD detritus;= particulate legend: detritus; filled arrows:legend: transferfilled arrows: from one transfer group, openfrom arrows: one group, transfer open from arrows: multiple transfer groups, highlightedfrom multiple in red: groups, direct andhighlighted indirect in EE2 red: eff directects). and indirect EE2 effects).

Those results alignedaligned withwith thethe wellwell understoodunderstood driversdrivers ofof thethe biodiversitybiodiversity andand ecosystemecosystem functioningfunctioning (BEF)(BEF) link: link: selection selection e ffeffects,ects, complementarity complementarity in resourcein resource use use and and species species interactions interactions [21]. Resources[21]. Resources may may be directed be directed towards towards pathways pathways that that enable enable persistence persistence in in the the newnew environmentenvironment at the expenseexpense of otherother organismal processes, li likeke growth, reproduction and foraging behavior. behavior. Changes in organismal processes consequentially impactimpact upon the ecosystem processes they mediate, likelike primaryprimary production.production. InIn thethe casecase ofof thethe syntheticsynthetic hormonehormone EE2,EE2, thethe mainmain directdirect observedobserved eeffectffect waswas thethe collapsecollapse ofof fatheadfathead minnowminnow [[13].13]. Such Such species species at at higher trophic levels nearly always reveal eeffectsffects that spanspan throughthrough thethe foodfood web.web. However, the magnitude and directiondirection of these eeffectsffects are highly variable and are didifficultfficult to to predict predict since since these these species species exhibit exhi manybit complex,many complex, indirect, non-additiveindirect, non-additive and behavioral and interactionsbehavioral interactions [20]. The simulation [20]. The simulation results obtained results from obtained the extended from the ecosystem extended modelecosystem confirmed model aconfirmed top-down a cascadingtop-down ecascadingffect of EE2 effect on theof EE2 freshwater on the freshwater food web, food as highlighted web, as highlighted in red in Figure in red 20 in. InFigure their 20. review, In their Du review,ffy et al. Duffy [19] highlighted et al. [19] highlighted growing evidence growing that evidence cascading that impactscascading of impacts predators of onpredators primary on producers primary producers often occur ofte throughn occur trait-mediated through trait-mediated indirect eff ects,indirect specifically effects, specifically by modifying by behaviormodifying rather behavior than rather via changes than via in changes herbivore in density.herbivore Besides, density. since Besides, large since predators large predators are naturally are naturally low in species diversity, a few extinctions may result in loss of the entire top predator trophic level, with disproportionately large effects on ecosystem properties and processes.

20 Water 2020, 12, 1736 20 of 22 low in species diversity, a few extinctions may result in loss of the entire top predator trophic level, with disproportionately large effects on ecosystem properties and processes. Trophic cascades refer to the phenomenon that top predators suppress the biomass of intermediate consumers, which in turn releases consumers at the next lower trophic level from predation pressure, with this alternate suppression-and-release effect propagating down to the producer level. In the case of EE2, the main direct effect was observed on fathead minnow, one of the species at higher trophic levels, with cascaded effects to the top predator (i.e., lake trout) and to lower trophic levels (down to zooplankton). Jabiol et al. [29] suggested that multitrophic interactions complicate assessments of the functional consequences of biodiversity change and that even relatively modest effects of change within single trophic levels can ramify and amplify through ecological networks to influence ecosystem functioning. Finally, increasing evidence is found that the key means by which species influence ecosystem functions (EFs) is through their functional traits (phenotypic attributes that direct niche exploitation) [30]. It has been hypothesized that beyond the lower end of a species richness gradient, the main driver of EFs is the community functional structure. Indeed, Chair et al. [24] highlighted the ability of competing species to replace or compensate for one another and thus minimize, at higher diversity, the ups and downs in functioning. Such observations are confirmed by the simulation results obtained with the extended model presented here, where a clearly higher ecosystem resilience was reached when including Chaoborus, i.e., increasing this ecosystem’s biodiversity.

4.3. In the Context of Ecosystem-Based Management (EBM) Unlike the biodiversity to ecosystem functions (BEF) relationship, Teixeira et al. [15] shows there is less established biodiversity to ecosystem services (BES) research. Whether or not biodiversity benefits from the protection of ES, and vice versa, the authors consider BES as valuable scientific knowledge to turn the concept of ecosystem services into a practical conservation tool in the formulation of day-to-day policies at national or regional scales. Indeed, the simulation results of the extended ecosystem model obtained after adding EE2 to the studied lake suggested potential species movement and behavior changes in response to variations of food resource and predatory pressure, which can, in turn, influence ecosystem functioning and the provision of ecosystem services [31]. As Queirós et al. [21] pointed out, ecosystem models are synthetic mathematical descriptions of ecosystem processes joined together, guided by a mechanistic understanding of their regulating environmental drivers and biota, which can be used to project changes in the overall ecosystem properties. In this way, ecosystem models provide a platform where empirical findings can be used to investigate large-scale questions and to offer a holistic view of ecosystems where the impacts of conservation, management and global scenarios can be assessed. The simulation results obtained with the extended ecosystem model demonstrate that adding an omnivorous, mid-trophic level group such as Chaoborus, enhances resilience. Conversely, missing such species means that the actual resilience of an ecosystem and its functioning cannot be properly simulated. In conclusion, the extended ecosystem model is a reliable and robust tool that can be used to simulate what is happening in freshwater food webs after perturbations, such as the addition of the synthetic hormone EE2. This can help better understand the link between biodiversity and ecosystem functioning in the context of EBM.

Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4441/12/6/1736/s1. S1: Extra results—extended ecosystem model, including Table S1: Calibrated values of the most influential parameters for Chaoborus; Table S2: New calibrated values for the most influential parameters of fathead minnow and pearl dace after adding Chaoborus; Table S3: New calibrated values for the most influential parameters of white sucker (juveniles [J] and adults [A]) and lake trout (adults [A]) after adding Chaoborus; Table S4: New calibrated values for the most influential parameters of the phytoplankton after adding Chaoborus. S2: Extra results—primary producers—phytoplankton. Author Contributions: Conceptualization, L.C. and P.A.V.; Data curation, L.C. and C.H.; Formal analysis, L.C. and C.H.; Investigation, L.C.; Methodology, L.C. and P.A.V.; Project administration P.A.V.; Resources, P.A.V.; Water 2020, 12, 1736 21 of 22

Software, L.C.; Supervision, P.A.V.; Validation, L.C. and P.A.V.; Visualization, L.C. and C.H.; Writing—original draft, L.C.; Writing—review and editing, L.C. and P.A.V. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded through the Canada Research Chair in Water Quality Modeling held by P.V. and a research Grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada through the Strategic Grants Program (430646-2012). Acknowledgments: The authors thank Karen Kidd, Michael Paterson, Michael Rennie, Paul Blanchfield and Alain Dupuis for sharing the unique data they collected during their whole-ecosystem study and for the numerous discussions we had around the experimental data and simulation results. Conflicts of Interest: The authors declare no conflict of interest. Besides, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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