Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy)

Journal of Great Lakes Research xxx (xxxx) xxx

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Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy) Fabio Lepori ⇑, Camilla Capelli Institute of Earth Sciences, University of Applied Sciences and Arts of Southern Switzerland, Istituto scienze della Terra, Campus Trevano, Via Trevano, CH-6952 Canobbio, Switzerland

a r t i c l e

i n f o

Article history: Received 20 May 2019 Accepted 15 December 2019 Available online xxxx Communicated by Nico Salmaso

Keywords: Food chain P control Cyanobacteria Herbivorous zooplankton

a b s t r a c t Using monitoring data from the South Basin of Lake Lugano (Switzerland and Italy), we examined seasonal responses of phytoplankton and herbivorous zooplankton biomass to nearly three decades (1989–2017) of phosphorus (P) management to ask: [1] what is the trophic structure of the lake, [2] whether trophic structure and the effects of nutrient management varied seasonally, and [3] what are the implications of the existent trophic structure for the restoration of the lake. Trophic structure varied seasonally, including a structure consistent with strong consumer control (exploitation food chain) in spring and fall, and an unexpected structure in summer, characterized by a negative correlation between phytoplankton biomass and phosphorus. This structure was explained by accumulation of inedible phytoplankton (mainly cyanobacteria) at low P concentrations. Owing to a lack of apparent resource (P) control, P-management had no detectable effects on phytoplankton biomass. The trophic structures identified in the lake provides explanations for this lack of response to P-management and for the differences between the responses of the South Basin of Lake Lugano and other perialpine lakes. Based on the results, lake restoration practice would benefit from a greater understanding and an increased ability to predict trophic structure within and across lakes. Ó 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Introduction Since the mid-1900s, cultural eutrophication, i.e., enrichment due to nutrients arising from human activities, has become a major environmental issue, causing detrimental ecological change across lakes worldwide (UNEP, 1994; Smith et al., 1999; Smith, 2003). While at the global level fresh-water eutrophication is still predicted to increase (Tilman et al., 2001), in recent decades economically-developed countries have multiplied efforts to reverse its course or mitigate its impacts (e.g., Jeppesen et al., 2005). Restoration from eutrophication is typically pursued through the management of the loadings of phosphorus (P) entering lakes from the watershed (Jeppesen et al., 2005). This ‘Pmanagement approach’ is based on the assumption that accumulation of phytoplankton biomass, a main symptom of eutrophication, is limited by the availability of this nutrient (Schindler, 1977; Smith et al., 1999). Applications of the nutrient-management approach have often, but not always, helped to attain the desired changes. To date, however, the reasons for the mixed results have not been fully resolved.

⇑ Corresponding author.

Variation in the outcome of P management is illustrated by the recent history of lakes located at the outskirts of the Alps, or perialpine lakes, which include lakes Geneva, Maggiore, Constance, Garda, Lugano, and others. In pre-industrial times, these lakes were probably oligotrophic, but most became mesotrophic or eutrophic during the second half of the 1900s (Niessen et al., 1992; Wessels et al., 1999; Salmaso et al., 2018; Tolotti et al., 2018). The effects of P management, which started around the 1970s, have varied from lake to lake. For example, in Lake Maggiore management resulted in the anticipated declines in phytoplankton biomass (Ruggiu et al., 1998) and was hailed as an example of restoration success (Smith et al., 1999). In Lake Lugano, summer phytoplankton biomass declined, but the reduction was explained by changes in food-web structure rather than variation in phosphorus concentrations (Lepori and Roberts, 2017). In Lake Geneva, phytoplankton biomass showed no signs of decline, despite a nearly four-fold decline in phosphorus concentration (Anneville and Pelletier, 2000). For this group of lakes, therefore, support for the general effectiveness of P management as a measure to reduce phytoplankton biomass remains inconclusive. The relationship between P and phytoplankton biomass depends on the processes that shape trophic structure, i.e. how biomass is partitioned into trophic levels within the biological

E-mail address: [email protected] (F. Lepori). https://doi.org/10.1016/j.jglr.2019.12.008 0380-1330/Ó 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008

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community (Leibold et al., 1997). The P-management approach, assuming a positive association between P and plant biomass, is based on a hypothesis of resource control (Smith et al., 1999), where phytoplankton biomass is regulated by the supply of nutrient (phosphorus) resources (Fig. 1a). In contrast with this singular view, ecological theory envisages two types of regulation, resource and consumer control, which create various possible patterns of biomass partitioning along gradients of productivity. For example, the exploitation ecosystem hypothesis (HEE, Oksanen et al., 1981) predicts that primary-producer biomass remains constant with increasing potential productivity where herbivores are unconstrained by predators, or conversely that primary-producer biomass increases where herbivores are constrained (Fig. 1b). Alternatively, the keystone predator hypothesis (KPH) and related ‘‘food-web models” (Leibold, 1996; Darcy-Hall, 2006) assume that primary producers vary in their edibility and predict that primaryproducer biomass increases with increasing potential productivity despite grazing, owing to increases in inedible species (Fig. 1c). Therefore, mixed responses to nutrient management among lakes may indicate differences in the interactions that shape trophic structure. Achieving a grasp of these interactions is a major goal of ecology (Leibold et al., 1997) and would be relevant for lakerestoration practice. However, to our knowledge, at present only few studies have tested these different hypotheses on lake planktonic communities (e.g., Ginzburg and Akçakaya, 1992, Steiner 2001), and none appear to have done so in the context of restoration. Another area of lake restoration that has received little attention concerns the seasonality of the effects. These effects may be important, because lake communities display dynamic seasonal successions, during which trophic structure and phytoplankton regulation vary from stage to stage. For example, according to the PEG (Plankton Ecology Group) model of seasonal succession (Sommer et al., 1986, 2012), in nutrient-rich lakes phytoplankton is controlled by herbivorous zooplankton during the clear-water phase (a period of water clarity in spring), whereas on either side of this phase nutrient-limitation has a greater relative influence. In addition, phosphorus may not be equally available across seasons. In deep, stratified lakes, for example, phosphorus depletion is particularly acute in summer and some research has suggested that the effects of nutrient management may be correspondingly greater in this season (Gaedke and Schweizer, 1993). However, existing evidence for seasonal shifts in restoration effects remains limited (Häse et al., 1998; Anneville and Pelletier, 2000), and more research is needed to examine if this response can be generalized to other lakes. This study investigates the trophic structure of the planktonic community of the South Basin of Lake Lugano (Switzerland and Italy), a perialpine lake that is recovering from past eutrophication

(Lepori and Roberts, 2017; Lepori et al., 2018). While previous research on this lake has assessed the effects of P concentration and zooplankton structure on phytoplankton biomass (Lepori, 2019), no studies have explicitly attempted to characterize the plankton trophic structure in the context of current food-chain or food-web hypotheses. We examined the seasonal responses of phytoplankton biomass and herbivorous zooplankton biomass to nearly three decades (1989–2017) of nutrient management, which have resulted in a strong reduction of P concentrations. Based on the responses observed, we asked 1) which existing theoretical hypothesis (including resource-control, HEE, and KPH) best describe the trophic structure of the lake, 2) whether trophic structure and the effects of nutrient management varied seasonally, and 3) what are the implications of the existent trophic structure for the restoration of the lake. Methods Study lake Lake Lugano (45 590 000 N, 8° 580 000 E, 271 m a.s.l.) is a natural deep lake located at the southern edge of the Alps (Switzerland and Italy), in a Continental Subarctic Climate zone (Fig. 2). A causeway built on a natural moraine splits the lake into two basins, the North Basin and the South Basin. Data for this study were collected at a station located near the deepest point of the South Basin (near the village of Figino, Fig. 2), which has a maximum depth of 95 m and a water renewal time of 1.4 years (Barbieri and Mosello, 1992). The basin is monomictic, turning over once a year during late winter (February-March). Outside the turnover period, the lake is thermally stratified, especially during the summer period. Lake Lugano became severely eutrophic (North Basin: eutrophic, South Basin: hypertrophic) during the second half of the 20th century, mainly due to increasing influx of municipal waste from the expanding conurbation within the watershed (Barbieri and Mosello, 1992). Phosphorus management by sewage collection and treatment, which started in the 1970s, has achieved a substantial reduction of phosphorus loads and in-lake concentrations, although at present the lake remains mesotrophic (North Basin) to eutrophic (South Basin; Lepori and Roberts, 2017, Lepori et al., 2018). In particular, in the South Basin, P management has resulted in a clear trend toward lower P concentrations because in this basin P concentrations are strongly coupled with variation in external P loadings (Lepori et al., 2018). This pattern contrasts with that observed in the North Basin, which is meromictic, where P concentrations in the upper layer (including the euphotic zone) depend largely on year-to-year variation in the degree of mixing between surface and deep waters (Lepori et al., 2018). The consequences of the different mixing regimes of the

Fig. 1. Classic hypotheses concerning trophic structure, i.e., partitioning of biomass into plants and herbivores along gradients of potential productivity.

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008

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Fig. 2. Geographical location of Lake Lugano and the sampling station in the South Basin (Figino).

basins for their recovery history and restoration potential are discussed in Lepori et al. (2018). The pelagic fish fauna of Lake Lugano includes the predator trout (Salmo trutta) and the planktivores bleak (Alburnus sp.), whitefish (Coregonus sp.), and Italian shad (Alosa agone). Until the 19th century, Italian shad and bleak were the dominant planktivores. However, Italian shad collapsed during the eutrophication phase, whereas bleak suffered a die-off in 1988 and became functionally extinct during the 1990s (Polli, 2004). The functional extinction has been attributed to a complex mix of causes, which included an outbreak of branchiomycosis, the degradation of spawning habitat, and the loss of lake productivity. After the decline of bleak and throughout the study period (1989–2017), densities of pelagic planktivores remained low, as indicated e.g. by the low fishing yields of whitefish and Italian shad (~<0.15 kg per day of fishing during 1996–2011; Périat et al., 2014). Although Lake Lugano has been monitored since the early 1980s, in this study we focus on the 1989–2017 period because between 1988 and 1989 the planktonic community underwent a structural and functional shift. During the shift, herbivorous zooplankton changed from being dominated by small-bodied species to being dominated by large-bodied species, apparently causing a suppression of phytoplankton biomass (Lepori and Roberts, 2017; Lepori, 2019). Because the shift occurred after the die-off of bleak, it is suspected that it represents a trophic cascade, a phenomenon where a decline in predators releases herbivores and causes a decrease in primary producers (Lepori, 2019). If so, throughout 1983–2017, the trophic structure of the lake was probably influenced by two different factors, the 1988–1989 cascade and the reduction of phosphorus (and potential productivity). The decision to restrict the analysis to 1989–2017, made a priori, aimed at separating the effects of these two factors. A separate analysis for the two periods would have been ideal, but it was impracticable due to the scarce data available for the earlier period (5–6 years of data). Patterns in nutrient concentrations and nutrient ratios indicate that phytoplankton growth in the South Basin of Lake Lugano is almost certainly P-limited, at least seasonally. Concentration of total nitrogen in the South Basin are usually in excess of 1 mg N L1, while total N:total P ratios (e.g., 52–200 by atom in 2018, unpublished data) are nearly always above the thresholds that tend to indicate P-deficiency (31:1 or 50:1, Downing and McCauley, 1992; Guildford and Hecky, 2000). For this reason, in this study we focused on P as the main potentially-limiting nutrient and excluded N from the analysis.

Data source Data for this study were obtained from the database of Lake Lugano monitoring data which is held at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). All data were collected monthly or biweekly at the Figino station (Fig. 2). Throughout the monitoring program particular attention was placed on maintaining consistent methods to ensure the comparability of the results over time (details in LSA, 1990 [water chemistry] and Simona, 2003 [plankton]). In short, total phosphorus was measured from lake-water samples collected at discrete depths (0.4 m, 5 m, 10 m, 15 m, 20 m). Its concentration was determined spectrophotometrically as reactive phosphorus after persulfate oxidation in an autoclave. Zooplankton was sampled by vertical net tows (0–50 m) using plankton nets (mesh size: 95– 100 mm) and preserved in formalin or ethanol. We expect this method to yield accurate quantification because plankton nets with similar mesh sizes (112–153 mm) have proven effective in capturing large cladocerans and copepods (which comprise most of the herbivorous zooplankton in Lake Lugano), although they slightly undersample small cladocerans (Mack et al., 2012). Moreover, in Lake Lugano, tow depths of 50 m have been shown to collect nearly all of the zooplankton present in the water column (Scascighini, 2002). In the laboratory, crustacean zooplankton was identified to species and enumerated under a dissecting microscope. For this study, which focuses on trophic levels, only the herbivorous (rather than total) crustacean zooplankton was considered. In Lake Lugano, this fraction consisted of three taxa of cladocerans (Daphnia longispina-galeata, Bosmina spp., and Diaphanosoma brachyurum) and the copepod species Eudiaptomus gracilis. On a yearly-average basis, across 1989–2017, these herbivores comprised 69% of the total biomass of crustacean zooplankton (range: 30–90%; unpublished results). Phytoplankton was sampled using a vertical integrator (Schröder bottle) across the 0–20 m layer and stored in Lugol’s solution and formalin. In the laboratory, phytoplankton samples were analyzed under an inverted microscope following Utermöhl’s method (Utermöhl, 1958). In addition, algal biovolume was calculated by multiplying cell density by species-specific biovolumes, estimated from geometric shapes and linear dimensions (Rott, 1981; Hillebrand et al., 1999). Derived variables and data analysis Total phosphorus concentrations (in mg m3) measured at discrete depths was averaged to obtain an integrated 0–20 m value (hereafter, P0-20). The biomass of phytoplankton (BIOMASS-P, in

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008

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g C m2) was calculated by converting biovolumes to carbon content using published biovolume-to-carbon factors for diatoms and non-diatomaceous phytoplankton (Reynolds, 2006). In addition, considering that size (>30–40 lm) and gelatinous coating or hard coverings are often used to define phytoplankton edibility (Sarnelle, 1992; Mehner et al., 2008; Sommer, 2012), phytoplankton biomass was separated into edible and inedible fractions by assuming that Cyanophyceae, Dinophyceae, Conjugatophyceae, and Xanthophyceae are generally inedible to herbivorous zooplankton (edibility fraction = 0.0), Bacillariophyceae are partly edible (edibility fraction = 0.5), and the small taxa Chlorophyta, Cryptophyta, and Chrysophyceae are generally edible (edibility fraction = 1.0). Although a species-by-species approach might be more accurate (Bell, 2002), in our case subdivision-level fractions were justifiable based on the edibility of the dominant genera of phytoplankton occurring in the lake throughout the study period (Table A1). For example, Bacillariophyceae were considered partly edible because they were represented in similar proportions by large inedible genera (e.g., Fragilaria) and smaller edible ones (e.g., Stephanodiscus), whereas Cyanophyceae were considered inedible because they were represented entirely by inedible taxa (e.g., Planktothrix rubsecens). The edibility fractions were used to weight the biomass of each phytoplankton subdivision and calculate the percentage of total biomass that was potentially edible to herbivorous zooplankton (EDIBILITY, in %). The biomass of herbivorous zooplankton (BIOMASS-H, in g C m2) was estimated by multiplying the abundance of each herbivore taxon by an average individual biomass and aggregating the values across taxa (details in Lepori, 2019). Because not all zooplankton species have the same impact on phytoplankton (e.g., large-bodied cladocerans are thought to have greater impact; McQueen et al., 1986; Mazumder, 1994), using total herbivorous zooplankton biomass alone could have weakened or hidden the effects of individual zooplankton subgroups. To reduce this risk, herbivorous zooplankton biomass was additionally resolved into three main functional groups (DAPHNIA, EUDIAPTOMUS, S. CLADOCERA) based on body size and feeding ecology (Sprules, 1984; Table 1). Zooplankton dry weight (DW) was converted to carbon (C) using a 2DW:1C ratio (Andersen and Hessen, 1991). The conversion of biomass to carbon was introduced to allow a meaningful comparison between the biomass of phytoplankton and that of zooplankton. For our analyses, all data were averaged within months, where necessary, and then within seasons (winter = DJF, spring = MAM, summer = JJA, fall = SON; each letter indicates a month of the year). Temporal trends in P0-20, BIOMASS-P, and BIOMASS-H were tested using the Mann-Kendall test (MK), or, if serial correlation was detected (i.e., if the lag-one autocorrelation coefficient was significantly different from zero), a modified procedure of the MannKendall test known as the ‘Trend-free pre-whitening MK test’ (TFPW-MK; Yue et al., 2002). The TFPW-MK uses a pre-whitening procedure that removes lag-one serial correlation from the time series, but does not remove any real trends, which makes it particularly suitable to test for trend in serially correlated data. The results report the Z statistic and the associated P-value, which have the same interpretation as for the non-modified MK test. In addition, the linear rates of change (Q) were estimated using Sen’s

method (Sen, 1968). Trend analyses were performed using the Excel template application MAKESENS 1.0 (Salmi et al., 2002), which was modified to implement the TFPW-MK procedure. Associations between phosphorus concentrations and plankton biomass were tested using linear regression analysis (LR; y ¼ a þ bx þ e; where y is the dependent variable, x the independent variable, and e the error), or, if serial correlation was detected (in this case, if the Durbin-Watson statistic was significant) using the Hildreth-Lu linear regression procedure (HL-LR, Hildreth and Lu, 1960). This procedure replaces the original regression model with:

yt  qyt1 ¼ a0 þ b0 ðxt  qxt1 Þ þ e where q is the autocorrelation coefficient. The value of q is estimated by running the regression model with different candidate values (here all values between 0 and +1 at increments of 0.05) and selecting the value that minimizes the sum of squared errors (SSE). After running the model with the selected q, the DurbinWatson statistic was calculated again to ensure that serial correlation was removed. In addition, the residuals from the LR and HL-LR models were visually-assessed for normal distribution. If test assumptions of lack of serial correlation and normal distribution of the residuals were met, the slope estimates b (when no serial correlation was detected) or b’ (when serial correlation required adding the HL-LR procedure) were used as a measure of the association between x and y. Regression analyses were performed using the software package Minitab (McKenzie, 2004). Results In winter, concentrations of phosphorus (P0-20) were relatively high, whereas plankton biomass (BIOMASS-P and BIOMASS-H) was at the yearly minimum. Among herbivorous zooplankton, EUDIAPTOMUS (average: 0.18 g C m2) and DAPHNIA (0.12 g C m2) were more abundant than S. CLADOCERA (0.03 g C m2). In this season, P0-20 displayed a steep negative trend from 1989 to 2017 (P < 0.001; linear rate: 1.4 mg m3 y-1), whereas BIOMASS-B, BIOMASS-H, and EDIBILITY showed no trends (Table 2, Fig. 3). Based on regression analysis, BIOMASS-P, BIOMASS-H, and EDIBILITY showed no association with P0-20 (Table 3, Fig. 4). The biomass of the herbivorous functional groups mirrored patterns of BIOMASS-H, showing no temporal trends (Table 2) and no relationships with P0-20 (Table 3). In spring, just after the annual turnover, P0-20 was still high and plankton biomass was on average considerably higher than in winter (Fig. 3). DAPHNIA was the dominant functional group of herbivorous zooplankton (average: 0.63 g C m2), followed by EUDIAPTOMUS (0.23 g C m2) and S. CLADOCERA (0.03 g C m2). As in winter, P0-20 displayed a steep negative trend (P < 0.001; linear rate: 1.8 mg m3 y1; Table 2, Fig. 3). Additionally, BIOMASSH showed a negative trend (P < 0.01, linear trend: 0.027 g C m2y1), whereas BIOMASS-P and EDIBILITY showed no trends (Table 2, Fig. 3). In this season, BIOMASS-H was positively associated with P0-20, whereas BIOMASS-P and EDIBILITY had no relationship with P0-20 (Table 3, Fig. 4). Patterns in BIOMASS-H were driven mainly by DAPHNIA, the only functional group that displayed a significant

Table 1 Herbivorous zooplankton functional groups used in the study. Functional group

description

Species in L. Lugano

Descriptive label

Small-bodied cladocerans

Small-bodied filter feeders

S. CLADOCERA

Large-bodied cladocerans Calanoid copepods

Large-bodied filter feeders Large bodied, feed by creating microcurrents which bring food particles to the mouth

Bosmina spp. Diaphanosoma brachyurum Daphnia longispina-galeata Eudiaptomus gracilis

DAPHNIA EUDIAPTOMUS

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008

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Table 2 Results of Mann-Kendall (MK) tests and Trend-free pre-whitening MK (TFPW-MK) tests (Z statistics and associated statistical significance) and estimates of Sen’s slope (Q). The symbol q represent the lag-one autocorrelation coefficient. TFPW-MK tests were added only if q was significant. Significance: *** = P < 0.001; ** = P < 0.01; * = P < 0.05, + = P < 0.1. Variable

MK test

TFPW-MK test

Z

Signif.

Q

q

Z

Signif.

Q

winter P0-20 BIOMASS-P BIOMASS-H EDIBILITY DAPHNIA EUDIAPTOMUS S. CLADOCERA

5.27 1.09 0.99 0.99 0.62 1.59 0.49

***

1.447 0.045 0.003 0.369 0.001 0.004 0.000

0.44* 0.24 0.20 0.16 0.06 0.11 0.01

4.96

***

1.398

spring P0-20 BIOMASS-P BIOMASS-H EDIBILITY DAPHNIA EUDIAPTOMUS S. CLADOCERA

5.78 1.22 2.87 0.09 2.16 0.62 1.92

***

1.831 0.049 0.027 0.000 0.024 0.002 0.000

0.42* 0.14 0.00 0.02 0.03 0.13 0.03

5.67

***

1.612

summer P0-20 summer BIOMASS-P BIOMASS-H EDIBILITY DAPHNIA EUDIAPTOMUS S. CLADOCERA

4.32 1.43 4.37 1.26 4.33 4.71 1.97

0.727 0.096 0.082 0.303 0.030 0.039 0.006

0.12 0.41* 0.12 0.20 0.30 0.01 0.11

fall P0-20 fall BIOMASS-P BIOMASS-H EDIBILITY DAPHNIA EUDIAPTOMUS S. CLADOCERA

4.71 2.16 1.86 0.64 0.36 3.28 1.11

0.598 0.082 0.024 0.333 0.003 0.016 0.005

0.07 0.01 0.38* 0.10 0.63** 0.06 0.05

** * + *** *** *** *** * *** * +

**

1.24

0.068

1.44

0.013

0.81

0.005

Fig. 3. Temporal trends in P0-20, BIOMASS-P, BIOMASS-H, and EDIBILITY, by season. The lines are fitted linear regressions (shown where a significant temporal linear trend was detected, see Table 2).

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008

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Table 3 Regression analysis of the dependent variables (BIOMASS-P, BIOMASS-H, EDIBILITY, DAPHNIA, EUDIAPTOMUS, and S. CLADOCERA) versus P0-20, by season. LR refers to the linear regression fitted to untransformed variables, HL-LR refers to the regression fitted after transforming the variables according to the Hildreth-Lu procedure. Reported are the regression coefficients (b and b’), their significance, the determination coefficient R2, the Durbin-Watson statistic (DW), and, for step 2, the autocorrelation coefficient q. Significance of b or b’: *** = P < 0.001; ** = P < 0.1; * = P < 0.05. Significance of DW: S = significant; NS = nonsignificant; IN = inconclusive.

q

Dependent variable (;)

LR

winter P-B H-B EDIBILITY DAPHNIA EUDIAPTOMUS S. CLADOCERA

b b b b b b

= = = = = =

0.00NS; R2 = 0.2%; DW = 1.48IN 0.00NS; R2 = 1.0%; DW = 1.53IN 0.15NS; R2 = 2.1%; DW = 2.14NS 0.00NS; R2 = 0.2%; DW = 1.82NS 0.00NS; R2 = 2.9%; DW = 1.60NS 0.00NS; R2 = 2.4%; DW = 1.95NS

– – – – – –

spring P-B H-B EDIBILITY DAPHNIA EUDIAPTOMUS S. CLADOCERA

b b b b b b

= = = = = =

0.03NS; R2 = 6.3%; DW = 2.25NS 0.01**; R2 = 24.8%; DW = 1.86NS 0.05NS; R2 = 1.2%; DW = 2.03NS 0.01*; R2 = 15.2%; DW = 1.72NS 0.00NS; R2 = 7.3%; DW = 1.40IN 0.00NS; R2 = 3.6%; DW = 2.01NS

– – – – – –

summer P-B H-B EDIBILITY DAPHNIA EUDIAPTOMUS S. CLADOCERA

b b b b b b

= = = = = =

0.12*; R2 = 14.4%; DW = 1.01S 0.06***;R2 = 45.5%; DW = 0.84S 0.66*; R2 = 20.3%; DW = 2.14NS 0.02**; R2 = 31.6%; DW = 1.56IN 0.04***; R2 = 54.2%; DW = 0.94S 0.00NS; R2 = 4.8%; DW = 1.65NS

fall P-B H-B EDIBILITY DAPHNIA EUDIAPTOMUS S. CLADOCERA

b b b b b b

= = = = = =

0.05NS; R2 = 4.2%; DW = 1.92NS 0.03**; R2 = 27.9%; DW = 1.37IN 0.60NS; R2 = 7.8% ;DW = 1.97NS 0.01*; R2 = 21.7%; DW = 1.06S 0.02**; R2 = 29.6%; DW = 1.79NS 0.00NS; R2 = 0.3%; DW = 1.72NS

temporal decline (Table 2) and a positive association with P0-20 (Table 3). In summer, in the middle of the stratification period, P0-20 was clearly lower compared to winter and spring (Fig. 3). In comparison, BIOMASS-P and BIOMASS-H were at or near the yearly maximum. The composition of herbivorous zooplankton was more equitable than in spring, with DAPHNIA (average: 0.64 g C m2), EUDIAPTOMUS (0.74 g C m2) and S. CLADOCERA (0.12 g C m2) all contributing substantial biomass. P0-20 displayed a negative trend (P < 0.001; linear rate: 0.7 mg m3 y1; Table 2, Fig. 3). BIOMASS-P and EDIBILITY showed no trends, whereas BIOMASSH showed a steep negative trend (P < 0.001, linear rate: 0.08 g C m2 y1; Table 2, Fig. 3). During summer BIOMASS-P was, unexpectedly, negatively associated with P0-20, whereas EDIBILITY and BIOMASS-H were positively associated with P0-20 (Table 3, Fig. 4). In other words, in summer lower phosphorus concentrations were associated with lower edibility of phytoplankton, lower biomass of herbivorous zooplankton and higher biomass of phytoplankton. Further exploration of the data (Fig. 5) revealed that the higher percentage of inedible phytoplankton toward low phosphorus concentrations was associated with higher biomass of cyanobacteria. In summer, patterns in BIOMASS-H were driven by the functional groups DAPHNIA and EUDIAPTOMUS, both of which displayed steep temporal declines (Table 2) and positive associations with P0-20 (Table 3). In fall, P0-20 was on average at the yearly minimum and like in the other seasons it displayed a negative trend (P < 0.001, linear rate: 0.6 mg m3 y1; Table 2, Fig. 3). The biomass of plankton was lower than in summer (Fig. 3). EUDIAPTOMUS (0.53 g C m2) provided the greatest share of herbivorous zooplankton biomass, but contributions by DAPHNIA (0.32 g C m2) and S. CLADOCERA (0.33 g C m2) were also substantial. BIOMASS-P displayed a negative trend (P < 0.05; linear rate: 0.08 g m2 y1; Table 2, Fig. 3), whereas BIOMASS-H and EDIBIL-

0.55 0.60 0.15 0.45

0.50 0.50

HL-LR

b’ b’ – b’ b’ –

= 0.17*; R2 = 14.4%; DW = 1.91NS = 0.05*; R2 = 21.0%; DW = 2.08NS = 0.03**; R2 = 26.7%; DW = 1.76NS = 0.02*; R2 = 20.0%; DW = 1.76NS

– b’ = 0.01NS; R2 = 2.4%; DW = 2.03NS – b’ = 0.01NS; R2 = 9.9%; DW = 2.33NS – –

ITY showed no trends (Table 2, Fig. 3). In this season, BIOMASS-H displayed a weak association with P0-20, which, however, was significant only if serial correlation was not accounted for (the DW test was inconclusive, therefore there was no basis to decide whether serial correlation should be accounted for; Table 3, Fig. 4). All the other responses were uncorrelated with P0-20. Among the zooplankton functional groups, EUDIAPTOMUS showed a negative temporal trend (Table 2) and displayed a positive association with P0-20 (Table 3). Discussion The results of this study reveal a complex and dynamic seasonal succession in the forces that shape the trophic structure of the plankton community of the South Basin of Lake Lugano. A key result is that none of the trophic structures identified is consistent with the resource (phosphorus) control model that underpins the restoration of the lake, which explains why in this study we detected no discernible effects of P management on phytoplankton biomass. The trophic structures observed provide new insights into the ecology of the lake and have implications for fundamental community ecology and restoration practice. Identification of trophic structure The trophic structures identified in this study varied depending on season. The structure observed in winter, characterized by a lack of association between phosphorus and plankton biomass, indicates that during this season plankton biomass was not limited by phosphorus. The uncoupling between phytoplankton biomass and phosphorus availability may have been expected because, in winter, phytoplankton growth is thought to be constrained primarily by low irradiance and low temperatures (e.g., Sommer et al., 1986). If so, the trophic structure observed in this season was

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008

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Fig. 4. Variation of phytoplankton biomass (BIOMASS-P) and herbivorous zooplankton biomass (BIOMASS-H) along the spectrum of phosphorus concentrations (P0-20) spanned during the study period, by season. The lines are fitted linear regressions (shown where a significant biomass- phosphorus correlation was detected, see Table 3).

shaped by resource control, even though the driving resources were light and temperature instead of nutrients. The trophic structure observed in spring and fall was also characterized by no association between phytoplankton biomass and phosphorus, but in this case the dominant functional group of herbivorous zooplankton (DAPHNIA in spring and EUDIAPTOMUS in fall) and BIOMASS-H (with a statistical caveat in the fall, see below) displayed positive associations with phosphorus. These patterns are broadly consistent with the trophic structure predicted by the exploitation ecosystem hypothesis (EEH) when herbivores are not constrained by predators (Oksanen et al., 1981; Fig. 1b). Under these circumstances, the EEH predicts that increases in potential primary production do not lead to increases in plant biomass, because the extra biomass is absorbed by an increase in herbivore biomass (we refer to this structure as an ‘exploitation food chain’). In other words, in these cases consumer control predominates over resource (nutrient) control. Compared to the EEH, however, our results indicate that the dominant functional groups of herbivorous zooplankton had stronger associations with phosphorus than the biomass of the whole trophic level (BIOMASS-H). Associations between BIOMASS-H and phosphorus were probably weaker because non-dominant groups contributed substantially to trophic-level biomass but played secondary or no roles in phytoplankton exploitation. Moreover, because the dominant groups consisted of large-bodied species (the cladoceran Daphnia longispina-galeata and the calanoid copepod Eudiaptomus gracilis, both of which reach maximum body size >2 mm), this possibility is consistent with the view that larger zoo-

plankton is more effective at regulating phytoplankton biomass (see below). These results suggest that examining food chains through the lens of trophic-level classification may fail to identify important processes, or, in other words, that food-chain processes may emerge more clearly when trophic levels are resolved into major taxa or functional groups. Prevalence of consumer control in spring and fall, two of the most productive seasons, might have been unexpected in view of the general limnological tenet that phytoplankton biomass is resource-controlled (Smith et al., 1999). However, exploitation food chains are not uncommon in lakes (Sarnelle, 1992; Mazumder, 1994). In Lake Lugano, moreover, consumer control was facilitated by the architecture of the food chain that predominated during the study period. The EEH predicts that herbivores control plant biomass where they are not constrained by predators (Oksanen et al., 1981). In planktonic food chains, in addition, consumer control is often associated with occurrence of large-bodied species of herbivorous zooplankton (Brooks and Dodson, 1965; McQueen et al., 1986). In Lake Lugano, during the study period (1989–2017), these conditions were met, because planktivore fish, the main predators of large-bodied zooplankton, were scarce (see ‘Methods’) and herbivorous zooplankton was dominated by large-bodied species (Daphnia longispina-galeata and Eudiaptomus gracilis). Therefore, the patterns observed are consistent with current understanding of the circumstances that promote consumer control in pelagic food chains. The trophic structure observed in summer, characterized by a positive association between herbivorous-zooplankton biomass

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008

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Fig. 5. Summer biomass of the main groups of phytoplankton along the spectrum of phosphorus concentrations (P0-20) spanned during the study period. Phytoplankton was classified into Inedible, Partly-edible and Edible based on criteria explained in the Methods section.

and phosphorus and a negative association between phytoplankton biomass and phosphorus, did not match any of the classic models considered in this study (Fig. 1). However, this season was additionally characterized by a positive association between phosphorus and phytoplankton edibility (driven mainly by cyanobacteria), which provides insights into the possible underlying mechanisms. Cyanobacteria are poorly edible to zooplankton owing to a combination of morphological traits (e.g., size > 30–4 0 lm) and chemical defenses (production of cyanotoxins) that prevent ingestion or obstruct filtration (Wilson et al., 2006; Tillmanns et al. 2008; however some species can mitigate the toxic effects by detoxification activity, e.g., Shams et al., 2014). As a result, cyanobacteria can constrain zooplankton biomass (Pohnert et al., 2007; Van Donk et al., 2011). Therefore, the negative association between cyanobacteria and P can explain the trophic structure observed in summer in this study: the positive association between herbivorous zooplankton and P is explained by the negative effect of cyanobacteria on zooplankton, whereas the negative association between phytoplankton biomass and P is explained by reduced consumption at low P concentrations.

Surprisingly, the positive association between P and edibility pattern is the opposite of what predicted by the keystonepredator hypothesis (KPH). This hypothesis predicts that along gradients of increasing potential productivity (i.e., phosphorus concentration in Lake Lugano, assuming that production was Plimited) keystone predators cause shifts in dominance from resource-competitors to predator-resistant species (Leibold, 1996). Therefore, if herbivorous zooplankton acted as a keystone predator of phytoplankton, inedible phytoplankton would have been expected to peak when grazing pressure was highest. Instead, the proportion of inedible phytoplankton was higher when zooplankton biomass was lower. This result suggests that herbivorous zooplankton did not determine the proportion of edible and inedible phytoplankton. Conversely, it suggests that higher inedible phytoplankton determined the lower biomass of herbivorous zooplankton. It may also seem surprising that the association between P concentrations and phytoplankton edibility in summer was apparently driven by a negative association between cyanobacteria and phosphorus, because cyanobacterial blooms are often considered a symptom of P enrichment (e.g., Fastner et al., 2016). However, certain species of cyanobacteria find optimal conditions in meso- and oligo-trophic waters and, therefore, can be favored by a decline in P concentrations. A key example is Planktothrix rubescens, which has often increased in perialpine lakes undergoing phosphorus management, where it has benefited from increased water transparency and sharp transitions between P-poor surface waters and P-rich deeper layers (Anneville et al., 2002; Ernst et al., 2009; Posch et al., 2012; Jacquet et al., 2014). Therefore, negative associations between P and cyanobacteria, while perhaps uncommon globally, are a concrete possibility in these lakes. In Lake Lugano, P. rubescens was the dominant cyanobacterial species during the study period. At our site (South Basin), P. rubescens probably contributed to the negative association between summer cyanobacteria and phosphorus. However, in this season, the association between P. rubescens biovolume and P was weak (r = 0.2, unpublished data), suggesting that the negative association between cyanobacteria and P emerged from the collective contribution of multiple species. The identification of these species would be important for restoration practice and the management of the lake (cyanobacteria, in addition to influence trophic structure, may represent a hazard for drinking water and recreational uses of the lake). Therefore, we suggest that the relationship between P and cyanobacteria warrants analysis at greater taxonomic resolution in the future. Overall, our attempt to identify the trophic structure in the lake leads to three main conclusions. First, in individual lakes, like the South Basin of Lake Lugano, trophic structure can deviate considerably from resource control, the structure that underpins restoration by nutrient management, by including strong consumer control. Second, communities that follow seasonal successions, like lake planktonic communities, may not be assigned to a single model of trophic structure, but alternate different structures along the seasonal succession. Third, classic trophic structure models (resource control, HEE, and KPH) are insufficient to describe lake communities, and should be expanded to include the possibility that edibility decreases with decreasing nutrient concentrations, at least within the range of nutrient concentrations observed in this study. Seasonal variation in restoration effects In deep stratified lakes, phosphorus tends to be most depleted in summer, which suggests that the response to phosphorus management should be greater during this season. However, the available evidence indicates a nuanced range of possibilities. Among

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008

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deep perialpine lakes, Lake Constance displayed patterns consistent with the idea of greatest effects in summer. In this lake, following nearly two decades of phosphorus control, chlorophyll concentrations declined steadily in summer, whereas spring concentrations began to decline only when phosphorus depletion expanded into this season (Häse et al., 1998). In comparison, in Lake Geneva summer phytoplankton biomass displayed a complex response, alternating an early decline to a subsequent rise attributable to mixotrophic dinoflagellates and filamentous algae (Anneville and Pelletier, 2000). In the South Basin of Lake Lugano (this study), restoration did not appear to be effective in any seasons (although in fall phytoplankton biomass showed a weak negative trend over time, this decline cannot be attributed unequivocally to nutrient management because of the lack of phosphorus control). Among deep lakes from other regions, seasonal responses appear similarly varied. In four deep Danish lakes, a reduction of P loadings was followed by a decline in lake-water P concentrations, especially in May to August, but chlorophyll did not change in any seasons, probably because P never reached limiting concentrations (Søndergaard, 2005). In ten deep lakes from Europe and North America, nutrient management resulted either in an increase (1 of 10), a decrease (7 of 10), or no change (2 of 10) of summer phytoplankton biovolume, although the effects in other seasons were not reported (Jeppesen et al., 2005). These mixed responses, although based on a small number of lakes, suggest that the seasonal effects of phosphorus management may not be generalized, probably because they depend on a combination of environmental factors (e.g., abundance of planktivore fish, plankton species composition, concentrations of other key nutrients, and hydromorphological features) that is unique to each lake .

stance and Lake Maggiore were less affected by eutrophication and P management reduced P concentrations to lower values (Lake Constance: to 22 mg m3 by 1996, Häse et al., 1998; Lake Maggiore: to 10 mg m3 by 1995, Ruggiu et al., 1998). Therefore, in these lakes, consistent with ecological theory, resource control might be associated with lower productivity. However, it is not possible to generalize from these few cases. Moreover, lakes may display alternative trophic structures at similar levels of productivity, and shifts from a structure to another also occur for stochastic factors, such as the arrival of new species and/or the functional extinctions of others (Lepori and Roberts, 2017). A challenge for the future would be to understand to what extent trophic structure is constrained by environmental factors, such as production, and to what extent it can be predicted.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements We thank all staff who contributed to the monitoring of Lake Lugano during 1989–2017 and the editors of this special issue for inviting us to present our work.

Appendix

Consequences for lake restoration practice and conclusions The identification of trophic structure is necessary to understand the response of lakes to phosphorus management. For the South Basin of Lake Lugano, the prevalence of exploitation food chains (spring and fall) and the accumulation of inedible phytoplankton at low phosphorus concentrations in summer explain why, during the study period, restoration by nutrient management had essentially no effects on phytoplankton biomass. A similar food chain was identified also in the North Basin of the lake, which shares a similar pelagic community, characterized by dominance of large-bodied zooplankton (Daphnia longispina-galeata) and low planktivory (Lepori, 2019). Greater responses of phytoplankton biomass to P management in other lakes (e.g., Lake Constance and Lake Maggiore, Häse et al., 1998; Ruggiu et al., 1998) imply that the trophic structures of these lakes were different, i.e., shaped by greater resource control (Fig. 1a or b). To gain a fuller understanding of these differences, and perhaps achieve a degree of predictive ability, it would therefore be important to identify the factors that determine differences in trophic structure among lakes. Ecological theory predicts that the relative importance of consumer control and resource control changes along gradients of productivity (Oksanen et al., 1981; Persson et al., 1988). Consumer control is predicted to be stronger at intermediate levels of productivity, whereas resource control is predicted to be prevalent either in highly productive or in unproductive ecosystems. For this reason, differences in the mode of phytoplankton control among lakes may reflect differences in productivity, which is probably determined by P supply. For example, the South Basin of Lake Lugano was severely affected by eutrophication, and so far concentrations of phosphorus have been reduced from high to moderate (P concentrations during turnovers declined from 98 mg m3 to 33 mg m3 between 1987 and 2015; Lepori et al., 2018). Lake Con-

Table A1 Dominant genera (or species, if the genus was represented by one species) of phytoplankton at the study site (Figino, Lake Lugano) throughout the study period. Subdivision

Dominant genus (species)

CYANOPHYCEAE

Aphanizomenon Oscillatoria Planktothrix rubescens Pseudoanabaena Chrysochromulina Mallomonas Asterionellaformosa Cyclotella Fragilaria Melosira Stephanodiscus Tabellaria fenestrata Ceratium hirudinella Gymnodinium helveticum Peridinium Carteria Coelastrum Eudorina elegans Oocystis Pandorina morum Phacotus lenticularis Pseudosphaerocystis Scenedesmus Sphaerocystis schroeteri Tetraëdron Kirchneriella Closterium Mougeotia Staurastrum Cryptomonas Rhodomonas Tribonema

CHRYSOPHYCEAE BACILLARIOPHYCEAE

DINOPHYCEAE

CHLOROPHYCEAE

CONJUGATOPHYCEAE

CRYPTOPHYCEAE XANTHOPHYCEAE

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008

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References Andersen, T., Hessen, D.O., 1991. Carbon, nitrogen, and phosphorus content of freshwater zooplankton. Limnol. Oceanogr. 36, 807–814. Anneville, O., Pelletier, J.P., 2000. Recovery of Lake Geneva from eutrophication: quantitative response of phytoplankton. Arch. Hydrobiol. 148, 607–624. Anneville, O., Souissi, S., Ibanez, F., Ginot, V., Angeli, N., 2002. Temporal mapping of phytoplankton assemblages in Lake Geneva: annual and interannual changes in their patterns of succession. Limnol. Oceanogr. 47, 1355–1366. Barbieri, A., Mosello, R., 1992. Chemistry and trophic evolution of Lake Lugano in relation to nutrient budget. Aquat. Sci. 54, 219–237. Bell, T., 2002. The ecological consequences of unpalatable prey: phytoplankton response to nutrient and predator additions. Oikos 99, 59–68. Brooks, J.L., Dodson, S.I., 1965. Predation, body size, and composition of plankton. Science 150, 28–35. Darcy-Hall, T.L., 2006. Relative strengths of benthic algal nutrient and grazer limitation along a lake productivity gradient. Oecologia 148, 660–671. Downing, J.A., McCauley, E., 1992. The nitrogen: phosphorus relationship in lakes. Limnol. Oceanogr. 37, 936–945. Ernst, B., Hoeger, S.J., O’Brien, E., Dietrich, D.R., 2009. Abundance and toxicity of Planktothrix rubescens in the pre-alpine Lake Ammersee, Germany. Harmful Algae 8, 329–342. Fastner, J., Abella, S., Litt, A., Morabito, G., Vörös, L., Pálffy, K., Straile, D., Kümmerlin, R., Matthews, D., Phillips, M.G., Chorus, I., 2016. Combating cyanobacterial proliferation by avoiding or treating inflows with high P load—experiences from eight case studies. Aquat. Ecol. 50, 367–383. Gaedke, U., Schweizer, A., 1993. The first decade of oligotrophication in Lake Constance. Oecologia 93, 268–275. Ginzburg, L.R., Akcakaya, H.R., 1992. Consequences of ratio-dependent predation for steady-state properties of ecosystems. Ecology 73, 1536–1543. Guildford, S.J., Hecky, R.E., 2000. Total nitrogen, total phosphorus, and nutrient limitation in lakes and oceans: Is there a common relationship?. Limnol. Oceanogr. 45, 1213–1223. Hildreth, C., Lu, J.Y., 1960. Demand Relations with Autocorrelated Disturbances, Technical Report 276, Michigan State University Agricultural Experiment Station, East Lansing, MI. Hillebrand, H., Dürselen, C.D., Kirschtel, D., Pollingher, U., Zohary, T., 1999. Biovolume calculations for pelagic and benthic microalgae. J. Phycol. 35, 403– 424. Häse, C., Gaedke, U., Beese, B., Seifried, A., Tilzer, M., 1998. Phytoplankton response to reoligotrophication in large and deep Lake Constance: photosynthetic activity and chlorophyll concentration. Adv. Limnol. 53, 159– 178. Jacquet, S., Kerimoglu, O., Rimet, F., Paolini, G., Anneville, O., 2014. Cyanobacterial bloom termination: the disappearance of Planktothrix rubescens from Lake Bourget (France) after restoration. Freshw. Biol. 59, 2472–2487. Jeppesen, E., Søndergaard, M., Jensen, J.P., Havens, K.E., Anneville, O., Carvalho, L., Coveney, M.F., Deneke, R., Dokulil, M.T., Foy, B.O.B., Gerdeaux, D., 2005. Lake responses to reduced nutrient loading–an analysis of contemporary long-term data from 35 case studies. Freshw. Biol. 50, 1747–1771. Leibold, M.A., 1996. A graphical model of keystone predators in food webs: trophic regulation of abundance, incidence, and diversity patterns in communities. Am. Nat. 147, 784–812. Leibold, M.A., Chase, J.M., Shurin, J.B., Downing, A.L., 1997. Species turnover and the regulation of trophic structure. Annu. Rev. Ecol. Syst. 28, 467–494. Lepori, F., 2019. Effects of zooplankton structure and phosphorus concentration on phytoplankton biomass in a freshwater pelagic food chain. Fundam. Appl. Limnol. 192, 305–317. Lepori, F., Roberts, J.J., 2017. Effects of internal phosphorus loadings and food-web structure on the recovery of a deep lake from eutrophication. J. Great Lakes Res. 43, 255–264. Lepori, F., Bartosiewicz, M., Simona, M., Veronesi, M., 2018. Effects of winter weather and mixing regime on the restoration of a deep perialpine lake (Lake Lugano, Switzerland and Italy). Hydrobiologia 824, 229–242. LSA, 1990. Ricerche sull’evoluzione del Lago di Lugano. Aspetti limnologici. Campagna 1988. Commissione Internazionale per la protezione delle acque italo-svizzere, Turin, Italy. Mack, H.R., Conroy, J.D., Blocksom, K.A., Stein, R.A., Ludsin, S.A., 2012. A comparative analysis of zooplankton field collection and sample enumeration methods. Limnol. Oceanogr. Methods 10, 41–53. Mazumder, A., 1994. Patterns of algal biomass in dominant odd-vs. even-link lake ecosystems. Ecology 75, 1141–1149. McKenzie, J.D., 2004. Minitab Student Release 14: Statistical Software for Education. Pearson Addison-Wesley, Boston. McQueen, D.J., Post, J.R., Mills, E.L., 1986. Trophic relationships in freshwater pelagic ecosystems. Can. J. Fish. Aquat. Sci. 43, 1571–1581. Mehner, T., Padisak, J., Kasprzak, P., Koschel, R., Krienitz, L., 2008. A test of food web hypotheses by exploring time series of fish, zooplankton and phytoplankton in an oligo-mesotrophic lake. Limnologica 38, 179–188. Niessen, F., Wick, L., Bonani, G., Chondrogianni, C., Siegenthaler, C., 1992. Aquatic system response to climatic and human changes: productivity, bottom water

oxygen status, and sapropel formation in Lake Lugano over the last 10 000 years. Aquat. Sci. 54, 257–276. Oksanen, L., Fretwell, S.D., Arruda, J., Niemela, P., 1981. Exploitation ecosystems in gradients of primary productivity. Am. Nat. 118, 240–261. Périat, G., Vonlanthen, P., Dagani, D. Studio della fauna ittica del Lago Ceresio [Study of the fish fauna of Lake Lugano]. Divisione dell’ambiente, Ufficio della caccia e della pesca. Bellinzona, Switzerland. Report available at: https://www.eawag. ch/fileadmin/Domain1/Abteilungen/fishec/projekte/Projet_Lac/ Rapport_Lago_Ceresio.pdf. Persson, L., Anderson, G., Hamrin, S.F., Johanson, L., 1988. Predator regulation and primary production along the productivity gradient of temperate lake ecosystems. In: Carpenter, S.R. (Ed.), Complex Interactions in Lake Communities. Springer-Verlag, New York, pp. 45–65. Pohnert, G., Steinke, M., Tollrian, R., 2007. Chemical cues, defence metabolites and the shaping of pelagic interspecific interactions. Trends Ecol. Evol. 22, 198–204. Polli, B. 2004. La scomparsa dal Ceresio (The disappearance of bleak from Lake Lugano). Report available at: http://www.cispp.org/pdf/alborella.pdf. Posch, T., Köster, O., Salcher, M.M., Pernthaler, J., 2012. Harmful filamentous cyanobacteria favoured by reduced water turnover with lake warming. Nat. Clim. Chang. 2, 809–813. Reynolds, C.S., 2006. The Ecology of Phytoplankton. Cambridge University Press, Cambridge. Rott, E., 1981. Some results from phytoplankton counting intercalibrations. Schweiz. Z. Hydrol. 43, 34–62. Ruggiu, D., Morabito, G., Panzani, P., Pugnetti, A. 1998 Trends and relations among basic phytoplankton characteristics in the course of the long-term oligotrophication of Lake Maggiore (Italy, in: Alvarez-Cobelas M., Reynolds C. S., Sánchez-Castillo P., Kristiansen J. (Eds), Phytoplankton and Trophic Gradients. Developments in Hydrobiology, vol 129. Springer, Dordrecht, pp. 243-257. Salmaso, N., Anneville, O., Straile, D., Viaroli, P., 2018. European large perialpine lakes under anthropogenic pressures and climate change: present status, research gaps and future challenges. Hydrobiologia 824, 1–32. Salmi, T., Maatta, A., Anttila, P., Airola, T.R. and Amnell, T. 2002. Detecting trends of annual values of atmospheric pollutants by the Mann-Kendal test and Sen’s slope estimates — the Excel template application MAKESENS. User manual, Publication on air quality, Finnish Meteorological Institute, p.35. Sarnelle, O., 1992. Nutrient enrichment and grazer effects on phytoplankton in lakes. Ecology 73, 551–560. Scascighini, V., 2002. Vertikalverteilung und Dynamik des Crustaceen – Zooplanktons im Nordbecken des Luganersees. [Vertical distribution and Dynamics of Crustacean Zooplankton in the Northern Basin of Lake Lugano] Diploma thesis. University of Basel. Schindler, D.W., 1977. Evolution of phosphorus limitation in lakes. Science 195, 260–262. Sen, P.K., 1968. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389. Shams, S., Cerasino, L., Salmaso, N., Dietrich, D.R., 2014. Experimental models of microcystin accumulation in Daphnia magna grazing on Planktothrix rubescens: Implications for water management. Aquat. Toxicol. 148, 9–15. Simona, M., 2003. Winter and Spring mixing depths affect the trophic status and composition of phytoplankton in the northern meromictic basin of Lake Lugano. J. Limnology 62, 190–206. Smith, V.H., 2003. Eutrophication of freshwater and coastal marine ecosystems a global problem. Environ. Sci. Pollut. Res. 10, 126–139. Smith, V.H., Tilman, G.D., Nekola, J.C., 1999. Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ. Pollut. 100, 179–196. Sommer, U. (Ed.), 2012. Plankton Ecology: Succession in Plankton Communities. Springer Science & Business Media, Berlin. Sommer, U., Gliwicz, Z.M., Lampert, W., Duncan, A., 1986. The PEG-model of seasonal succession of planktonic events in fresh waters. Arch. Hydrobiol. 106, 433–471. Sprules, W.G., 1984. Towards an optimal classification of zooplankton for lake ecosystem studies. Verh. Int. Ver. Theor. Angew. Limnol. 22, 320–325. Steiner, C.F., 2001. The effects of prey heterogeneity and consumer identity on the limitation of trophic-level biomass. Ecology 82, 2495–2506. Søndergaard, M., Jensen, J.P., Jeppesen, E., 2005. Seasonal response of nutrients to reduced phosphorus loading in 12 Danish lakes. Freshw. Biol. 50, 1605–1615. Tilman, D., Fargione, J., Wolff, B., D’antonio, C., Dobson, A., Howarth, R., Schindler, D., Schlesinger, W.H., Simberloff, D., Swackhamer, D., 2001. Forecasting agriculturally driven global environmental change. Science 292, 281–284. Tillmanns, A.R., Wilson, A.E., Pick, F.R., Sarnelle, O., 2008. Meta-analysis of cyanobacterial effects on zooplankton population growth rate: SPECIESspecific responses. Fundam. Appl. Limnol. 171, 285–295. Tolotti, M., Dubois, N., Milan, M., Perga, M.-E., Straile, D., Lami, A., 2018. Large and deep perialpine lakes: a paleolimnological perspective for the advance of ecosystem science. Hydrobiologia 824, 291–321. U.N.E.P. 1994. The Pollution of Lakes and Reservoirs. UNEP Environment Library No. 12, United Nations Environment Programme, Nairobi.

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008

F. Lepori, C. Capelli / Journal of Great Lakes Research xxx (xxxx) xxx Utermöhl, H., 1958. Zur vervollkommnung der quantitativen phytoplanktonmethodik: Mit 1 Tabelle und 15 abbildungen im Text und auf 1 Tafel. Verh. Int. Ver. Theor. Angew. Limnol. 9, 1–38. Van Donk, E., Ianora, A., Vos, M., 2011. Induced defences in marine and freshwater phytoplankton: a review. Hydrobiologia 668, 3–19. Wessels, M., Mohaupt, K., Kümmerlin, R., Lenhard, A., 1999. Reconstructing past eutrophication trends from diatoms and biogenic silica in the sediment and the pelagic zone of Lake Constance, Germany. J. Paleolimnol. 21, 171–192.

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Wilson, A.E., Sarnelle, O., Tillmanns, A.R., 2006. Effects of cyanobacterial toxicity and morphology on the population growth of freshwater zooplankton: metaanalyses of laboratory experiments. Limnol. Oceanogr. 51, 1915–1924. Yue, S., Pilon, P., Phinney, B., Cavadias, G., 2002. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Process. 16, 1807– 1829.

Please cite this article as: F. Lepori and C. Capelli, Seasonal variation in trophic structure and restoration effects in a deep perialpine lake (Lake Lugano, Switzerland and Italy), Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.12.008