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Is Recovery of Large-Bodied Zooplankton after Nutrient Loading Reduction Hampered by Climate Warming? A Long-Term Study of Shallow Hypertrophic Lake Søbygaard, Denmark

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Nutrient fluctuations and climate warming can synergistically affect trophic dynamics in lakes, resulting in enhanced symptoms of eutrophication, thereby potentially counteracting restoration measures. We performed a long-term study (23 years) of zooplankton in Danish Lake Søbygaard, which is in recovery after nutrient loading reduction, but now faces the effects of climate warming. We hypothesized that the recovery of large-bodied zooplankton after nutrient loading reduction would be hampered by climate warming through indirect effects on fish size structure. We found a shift in macrozooplankton from initial dominance of Daphnia spp. towards Bosmina spp. as well as a decline in the body size of copepods and an increase in the abundance of nauplii. These changes coincided with the increase in small sized fish as a result of rising water temperature. Despite a reduction in body size, the total biomass of cladocerans increased coinciding with a diminished fish catch per unit effort (CPUE), and likely then an overall reduction in the predation on zooplankton. A cascading effect to phytoplankton was evidenced by enhanced zooplankton:phytoplankton and cladoceran:phytoplankton ratios and a decrease in Chl-a:TP and Chl-a:TN ratios. Our results indicate that climate warming, through changes in the size structure of fish community, has major effects on zooplankton size structure. In Lake Søbygaard, the decline in zooplankton size did not prevent, but modulated, the positive cascading effect on phytoplankton through an expected diminished fish CPUE related to nutrient loading reduction.
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Article
Is Recovery of Large-Bodied Zooplankton after
Nutrient Loading Reduction Hampered by Climate
Warming? A Long-Term Study of Shallow
Hypertrophic Lake Søbygaard, Denmark
María Florencia Gutierrez 1, 2, *, Melina Devercelli 1, Sandra Brucet 3,4,5, Torben L. Lauridsen 5,6,
Martin Søndergaard 5,6 and Erik Jeppesen 5,6
1Instituto Nacional de Limnología (CONICET-UNL), 3000 Santa Fe, Argentina; yomimel@yahoo.com.ar
2Faculty of Biochemistry and Biological Sciences (UNL), 3000 Santa Fe, Argentina
3Department of Bioscience, Aarhus University, DK-8600 Aarhus, Denmark; sandra.brucet@gmail.com
4Aquatic Ecology Group, BETA Research Centre, University of Vic—Central University of Catalonia,
08500 Vic, Spain
5Catalan Institution for Research and Advanced Studies, ICREA, 08010 Barcelona, Spain;
tll@bios.au.dk (T.L.L); ms@bios.au.dk (M.S.); ej@bios.au.dk (E.J.)
6Sino-Danish Centre for Education and Research, 100190 Beijing, China
*Correspondence: fgutierrez@inali.unl.edu.ar; Tel.: +54-0342-4511645-104
Academic Editor: Miklas Scholz
Received: 16 June 2016; Accepted: 28 July 2016; Published: 10 August 2016
Abstract:
Nutrient fluctuations and climate warming can synergistically affect trophic dynamics
in lakes, resulting in enhanced symptoms of eutrophication, thereby potentially counteracting
restoration measures. We performed a long-term study (23 years) of zooplankton in Danish Lake
Søbygaard, which is in recovery after nutrient loading reduction, but now faces the effects of climate
warming. We hypothesized that the recovery of large-bodied zooplankton after nutrient loading
reduction would be hampered by climate warming through indirect effects on fish size structure.
We found a shift in macrozooplankton from initial dominance of Daphnia spp. towards Bosmina spp.
as well as a decline in the body size of copepods and an increase in the abundance of nauplii. These
changes coincided with the increase in small sized fish as a result of rising water temperature. Despite
a reduction in body size, the total biomass of cladocerans increased coinciding with a diminished fish
catch per unit effort (CPUE), and likely then an overall reduction in the predation on zooplankton.
A cascading effect to phytoplankton was evidenced by enhanced zooplankton:phytoplankton and
cladoceran:phytoplankton ratios and a decrease in Chl-a:TP and Chl-a:TN ratios. Our results indicate
that climate warming, through changes in the size structure of fish community, has major effects
on zooplankton size structure. In Lake Søbygaard, the decline in zooplankton size did not prevent,
but modulated, the positive cascading effect on phytoplankton through an expected diminished fish
CPUE related to nutrient loading reduction.
Keywords:
bottom-up control; cascading trophic interaction; fish; generalized linear mixed models;
phytoplankton biomass; top-down control; zooplankton
1. Introduction
Eutrophication is a key threat to lake ecosystems, causing phytoplankton blooms, loss of
macrophytes, changes in richness and composition of biological assemblages, higher turbidity and
deterioration of the whole ecosystem [
1
]. In order to mitigate and reverse eutrophication, various
restoration measures have been applied, including chemical (e.g., nutrient input reduction) [
2
,
3
]
Water 2016,8, 341; doi:10.3390/w8080341 www.mdpi.com/journal/water
Water 2016,8, 341 2 of 18
and biological interventions (i.e., biomanipulation) [
4
6
]. However, recovery is often complex and
sometimes strongly delayed [2,7,8].
Besides the internal feedback mechanisms that stabilise the eutrophic state of lakes and hamper
their recovery [
9
], climate warming constitutes an additional external problem due to its direct and
indirect effects on biological and physicochemical processes, further exacerbating the eutrophication
and potentially preventing recovery [
10
,
11
]. Moreover, the symptoms of warming and eutrophication
are similar in many aspects, making it difficult to differentiate their individual effects [10,12,13].
Among the direct effects, warming may increase the frequency of cyanobacterial blooms [
14
16
],
alter bacterioplankton and protist communities [
17
,
18
] and affect lake productivity [
19
]. For aquatic
animals, the respiration rates [
13
], size structure and life cycles [
20
22
] may be profoundly altered as
well, causing serious ecological changes through trophic cascades [23,24].
Warming may also have indirect effects on the trophic webs through shifts in fish community
composition and size structure [
25
]. Warming increases the dominance of zooplanktivorous
and omnivorous fish [
26
,
27
], change the size structure towards a higher proportion of small
individuals [
10
,
13
,
28
] and may lead to higher winter survival due to reduced duration of ice cover [
29
].
A subsequent increase in fish predation pressure on zooplankton can promote species turnover,
diminish the abundance of large-bodied cladocerans, decrease the average size of both cladocerans and
copepods [
12
,
30
] and reduce grazing on phytoplankton (i.e., weak top-down control). These factors
lead to higher algal biomass (and chlorophyll alevels), including, in some cases, potential dominance
of harmful filamentous cyanobacteria [24].
Despite the focus on individual and combined effects of global warming and eutrophication have
been in focus in several empirical and theoretical analyses [
11
,
16
,
31
,
32
], little attention has been paid
to the potential (negative) effects of warming on the recovery of lake communities after a nutrient
loading reduction [
33
,
34
]. One way to shed more light on this issue is to analyse long-term survey
data [10,35,36] on lakes where restoration measures have been implemented.
Lake Søbygaard is a shallow eutrophic lake located in Central Jutland, Denmark, which has
been in recovery from nutrient loading for more than 35 years. Since 1989, fish biomass has shown a
declining trend, coinciding, as expected, with a decrease in nutrient concentrations [
37
]. Moreover,
a major change has occurred from roach dominance to a mixed assemblage dominated by roach
and perch as seen in other North European lakes that have recovered from eutrophication [
33
,
37
,
38
].
However, despite that such changes in theory should also lead to an increase in the body size of
cyprinids and perch [
37
], a declining trend in fish body size has been observed. This decline has been
attributed to climate warming as the size change is related to increased air temperatures in April and in
the average summer air temperature (April–September) of 1.2 and 0.5
˝
C per decade, respectively [
25
].
Here, we explored the long-term changes in zooplankton composition, abundance and body size
in Lake Søbygaard in relation to fish, phytoplankton biomass and nutrients during 23 years (from 1990
to 2012) after recovery from eutrophication and in a climate warming scenario. We hypothesized that
climate warming hampered the recovery of large-bodied zooplankton after nutrient loading reduction
through indirect effects on fish size structure. We expected that decreased fish size would modify the
zooplankton assemblage by favouring the dominance of smaller organism within this community.
Such changes will counteract the effects of the nutrient loading reduction that supposedly should
lead to: (i) larger-sized cladocerans and copepods; (ii) a higher zooplankton:phytoplankton ratio; and
(iii) lower Chl-a:TP and Chl-a:TN ratios via cascading effects [33].
2. Methods
2.1. Study Area
Lake Søbygaard is located in Central Jutland, Denmark (56
˝
15
1
20
11
N, 9
˝
48
1
35
11
E). It covers
0.38 km
2
and is surrounded by coniferous forest, except the wind-exposed western shore. Submerged
vegetation is sparse or absent, and emergent and floating plants are also sparsely developed. The water
Water 2016,8, 341 3 of 18
column is well mixed in summer with no thermal stratification. The lake’s hydraulic retention time
varies from 15 to 20 days and the mean depth is 1.0 m, with a maximum of 1.9 m.
2.2. Sampling
Samplings for analyses of chemical parameters, chlorophyll-a(Chl-a) and zooplankton were
carried out monthly following the same standardised methods from 1990 to 2012. Water samples
for chemical analysis were collected with a type “Patalas” sampler at a mid-lake station at 0.5 and
1.5 m depth. Environmental parameters (dissolved oxygen: DO, conductivity, pH) were measured
in the field using portable sensors. Air temperatures were used in this work since they are a strong
predictor of water temperatures, particularly in shllow lakes [
39
,
40
]. The monitoring of this parameter
is part of the National Monitoring and Assessment Programme for the Aquatic and Terrestrial
Environments (NOVANA).
Zooplankton samples were collected at 1.5 m below the water surface with a type “Patalas”
sampler at three sampling stations (mid-lake, east and west). This sampler was built for quantitative
sampling of zooplankton forming a tube with a bottom lid which opens when lowered down the water
column. After a desired depth is reached, the bottom lid closes and traps the zooplankton animals in
the sampler. The tubular form allows sampling of the integrated water column. A 20 L water sample
(mixed from the three stations) was filtered through a 20-
µ
m mesh net. The animals retained on the
mesh were pooled and fixed with 2 mL Lugol’s solution in 100 mL tap water.
For fish samples, annual gill net surveys were conducted between August and September
every year. The lake was divided into six sections and in each section three multi-mesh size gill
nets (14 different mesh sizes ranging from 6.25 to 75 mm) were set overnight. One gill net was set
perpendicular to the shoreline, another parallel to and about 25 m from the shoreline and the third
about half the distance from the centre of the lake, for more details, see [
41
]. The most frequent
species collected were bream (Abramis brama), crucian carp (Carassius carassius), pike (Esox lucius),
perch (Perca fluviatilis), roach (Rutilus rutilus), trout (Salmo trutta), rudd (Scardinius erythropthalmus)
and zander (Stizostedion lucioperca). For the analysis, the fish were classified into three size ranges:
small (<10 cm), medium (10–25 cm) and large (>25 cm), considering that their feeding habits usually
change during their development and that small-sized fish are typically zooplanktivorous. Among
the small fish, the most abundant were Perca fluviatilis,Rutilus rutilus,Scardinius erythropthalmus and
Stizostedion lucioperca. The first one has been classified as benthi–planktivorous while the other three
were classified as exclusively planktivorous. Medium and large sized fish have different feeding
habits, including piscivorous, omnivore–benthi–piscivorous, omnivore–benthi–planktivorous and
benthi–planktivorous [26].
2.3. Sample Analysis
Nutrient concentrations were estimated according to standardised methods: Mixed total
phosphorus (TP) was determined as molybdate-reactive P [
42
] after persulphate digestion [
43
]. Total
nitrogen (TN) was determined as nitrites + nitrates after potassium persulphate digestion [
44
]. Nitrites
and nitrates were determined as nitrite on a Tecator 5012 flow-injection analyser supplied with a
copper–cadmium reductor column. For chlorophyll-aanalyses (Chl-a), 100 to 1000 mL water samples
were filtered through Whatman GF/C filters (47 mm in diameter, UK) and spectrophotometrical
determination after ethanol extraction [45].
For zooplankton, identification of organisms was made to species level, when possible, using
specific keys [
46
49
]. Zooplankton larger than 140
µ
m were counted at 40
ˆ
magnification using a
stereomicroscope (Leica MZ12, Wetzlar, Germany), while subsamples between 20 and 140
µ
m were
counted under an inverted microscope at 100
ˆ
magnification (Leitz Labovert). At least 150 individuals
of the dominant zooplankton species were counted in each sample. In periods when the suspended
particulate content was high, it was necessary to dilute the samples five to tenfold before counting.
Water 2016,8, 341 4 of 18
Daphnia spp., Bosmina spp. and copepod biomasses were determined by using length–weight
relationships [
50
52
]. When possible, 20 to 30 individuals of each taxon were measured in each sample.
2.4. Data Analysis
Zooplankton richness, abundance and diversity index [
52
] were calculated. Repeated measure
(RM) ANOVA was used for testing significant differences in each parameter among years. Previous
to this analysis, the normal distribution of data (Kolmogorov–Smirnov’s test), homoscedasticity
(Levene’s test) and sphericity (Mauchly’s test) were verified. Such statistical tests are needed to ensure
the goodness of fit of the selected RM ANOVA and inform whether data have normal distribution and
equal variances. A PERMANOVA with 9999 permutations was also performed on the Bray–Curtis
triangular matrix to determine taxonomic differences among years and between the first and second
decade under study. The analysis between the first and second decade was performed because
exploratory analyses suggested differences between the period 1990–2001 and 2002–2012 regarding
the cycles of several species.
The Daphnia:Bosmina biomass ratio was calculated to verify species replacement throughout time.
For Copepoda, nauplii:(nau+cop+adults), female:male and weight:density ratios were calculated to
detect possible changes in the size structure and stage composition. To estimate the potential grazing
rate of cladocerans and zooplankton, we used the ratio of cladocerans (Clad) and zooplankton to total
abundance of phytoplankton biomass, calculated as follows: Clad:(Chl-a
ˆ
66) and Zoo:(Chl-a
ˆ
66),
where 66 is considered a measure of algal dry mass [
8
]. To evaluate the relationships between nutrients
and phytoplankton biomass, Chl-a:TN and Chl-a:TP ratios were calculated. Fish catch per unit effort
(CPUE) was calculated as mean catch in kg/net per sampling.
The temporal trends of different biotic components and abiotic variables were tested. Significant
temporal trends in air temperature, fish CPUE, size of planktivorous fish (roach, bream and rudd),
Daphnia:Bosmina, nauplii:(nau+cop+adults), copepod female:male and weight:density ratios, grazing
rates, Chl-a:TN and Chl-a:TP were tested using the non-parametric Mann–Kendall test (Z) on average
values per year. If a significant linear trend emerged in a time series (p
Z
< 0.05), the Sen’s slope (Q)
was estimated (Excel application MAKESENS 1.0, Finnish Meteorological Institute). Positive values of
Q indicate increasing trends, while negative values of Q show decreasing trends. All the ratios were
calculated using annual or the spring–summer average of each variable.
Temporal trends in the summer densities of small (<10 cm) and large fish (>25 cm) and their
relation with temperature were analysed with generalized linear models (GLM) after checking for
absence of autocorrelation with the Durbin–Watson test. G statistic was used as a measure of the
goodness of fit of the models.
Temporal trends all through the studied period and spring–summer period in Daphnia spp.,
Bosmina spp., nauplii and Cyclopoidae densities, and in Chl-aand nutrient concentrations, were
assessed with generalized linear mixed models (GLMM) based on a Markov chain Monte Carlo
(MCMC) algorithm. These models incorporate random effects and are adjusted for temporal
autocorrelation structure. Time was considered as the fixed effect and months were included as
the random effect to account for the possible effect of autocorrelation due to seasons. The models were
run with R package MCMCglmm [
53
] under a Poisson distribution, the MCMC p-value (p
MCMC
) was
calculated and the deviance information criterion (DIC) was used as the parameter-adjusted likelihood.
Daphnia spp. and Bosmina spp. presented more zeros than the expected based on Poisson distribution,
due to the absences of such species during some periods of time, hence a zero-inflated Poisson
distribution with a log-link function was found to better adjust the GLMMs. The “zeroinfl” command
in the R package “pscl” [
54
] was used, and the p-value (p
pscl
) was calculated. The log-likelihood was
informed as goodness of fit of the model. Sinusoidal fit and GLM regression lines were used in the
graphs to show, respectively, seasonal patterns and the general tendency of variables.
Multiple GLMM analyses were performed to find the model that best explained the variations
in Daphnia spp., Bosmina spp. and Chl-ausing the same distribution and statistical packages
Water 2016,8, 341 5 of 18
mentioned before. Independent variables were all the measured abiotic variables, categories of
fish size (for the Daphnia and Bosmina models) and Bosmina,Daphnia and total zooplankton densities
(for the Chl-amodel). For Daphnia and Bosmina, the pscl models were made with summer data
according to the available information on fish density, while for Chl-athe MCMCglmm model was
based on data from all the years. All GLMM statistical analyses were performed with r version 3.1.3
(R Development Core Team).
A multivariate ordination method was applied to analyse the relationship between the abundance
of zooplankton species and environmental variables (CANOCO 5 software). This method includes a
set of techniques and analysis using several variables at once to explain or predict other dependent
variables or phenomena that occur in nature. Detrended Correspondence Analysis suggested that a
Redundancy Analysis (RDA) was appropriate since the gradient length of species did not exceed three
standard deviations [
55
]. RDA is a linear method to extract and summarise the variation in a set of
response variables that can be explained by a set of explanatory variables. Zooplankton species present
in more than 10% of the samples were included as response variables. All the abiotic variables and the
density of fish were used as explanatory variables. Response and explanatory variables (except pH)
were Hellinger-transformed and standardised by norm. A subset of the more significant variables
was selected using the forward selection option, and the significance of the first and of all axes was
analysed using unrestricted global Monte Carlo permutation tests (999 permutations).
3. Results
3.1. Changes in Temperature, Fish Assemblage and Their Influence on the Zooplankton Community
The air temperature in the period from April to July during the years 1990 to 2012 in the lake
region showed an increasing temporal trend (Q = 0.61, p
Z
= 0.150), April being the month with the
highest temperature increase (Q = 0.314, pZ= 0.009; Figure 1).
Water2016,8,3415of18
totheavailableinformationonfishdensity,whileforChlatheMCMCglmmmodelwasbasedon
datafromalltheyears.AllGLMMstatisticalanalyseswereperformedwithrversion3.1.3(R
DevelopmentCoreTeam).
Amultivariateordinationmethodwasappliedtoanalysetherelationshipbetweenthe
abundanceofzooplanktonspeciesandenvironmentalvariables(CANOCO5software).This
methodincludesasetoftechniquesandanalysisusingseveralvariablesatoncetoexplainorpredict
otherdependentvariablesorphenomenathatoccurinnature.DetrendedCorrespondenceAnalysis
suggestedthataRedundancyAnalysis(RDA)wasappropriatesincethegradientlengthofspecies
didnotexceedthreestandarddeviations[55].RDAisalinearmethodtoextractandsummarisethe
variationinasetofresponsevariablesthatcanbeexplainedbyasetofexplanatoryvariables.
Zooplanktonspeciespresentinmorethan10%ofthesampleswereincludedasresponsevariables.
Alltheabioticvariablesandthedensityoffishwereusedasexplanatoryvariables.Responseand
explanatoryvariables(exceptpH)wereHellingertransformedandstandardisedbynorm.Asubset
ofthemoresignificantvariableswasselectedusingtheforwardselectionoption,andthe
significanceofthefirstandofallaxeswasanalysedusingunrestrictedglobalMonteCarlo
permutationtests(999permutations).
3.Results
3.1.ChangesinTemperature,FishAssemblageandTheirInfluenceontheZooplanktonCommunity
TheairtemperatureintheperiodfromApriltoJulyduringtheyears1990to2012inthelake
regionshowedanincreasingtemporaltrend(Q=0.61,pZ=0.150),Aprilbeingthemonthwiththe
highesttemperatureincrease(Q=0.314,pZ=0.009;Figure1).
Figure1.Meanvaluesofairtemperature(in°C)intheperiodApril–July(a)andinApril(b)inthe
LakeSøbygaardregionduringthe23yearsofstudy(1990–2012).
ThefishcommunityinLakeSøbygaardincluded11speciesofwhichperch,roach,ruddand
zanderdominated.Themeansizeoffishwas22.7cm(annualmean:16cm;maximum:31.5cm).
Bothcaptureperuniteffortinkg/net(CPUE)andthesizesofroach,breamandrudd,whichare
consideredmainlyplanktivorous,pooledtogetherdecreasedwithtime(Q=−0.327,pZ<0.001andQ
10
11
12
13
14
15
16
17
18
19
20
Airtemperature(ºC)
a
0
2
4
6
8
10
12
14
16
18
20
1990 1995 2000 2005 2010 20
1
Airtemperature(ºC)
Time(years)
b
Figure 1.
Mean values of air temperature (in
˝
C) in the period April–July (
a
) and in April (
b
) in the
Lake Søbygaard region during the 23 years of study (1990–2012).
Water 2016,8, 341 6 of 18
The fish community in Lake Søbygaard included 11 species of which perch, roach, rudd and
zander dominated. The mean size of fish was 22.7 cm (annual mean: 16 cm; maximum: 31.5 cm). Both
capture per unit effort in kg/net (CPUE) and the sizes of roach, bream and rudd, which are considered
mainly planktivorous, pooled together decreased with time (Q =
´
0.327, p
Z
< 0.001 and Q =
´
0.334,
pZ< 0.001, for CPUE and size, respectively) (Figure 2). Similarly, the number of small fishes (<10 cm)
increased (G = 7.015, p = 0.008), while the number of large fishes (>25 cm) declined in the same period
(G = 59.672, p < 0.0001) (Figure 3). A positive relation was found between April air temperature and
small fish (G = 4.752, p = 0.029) and a negative correlation was found between April air temperature
and large fish (G = 3.91, p = 0.047) in the lake region.
A total of 71 zooplankton taxa were identified within the groups Rotifera (43 taxa), Cladocera
(21 taxa) and Copepoda (7 taxa) (Table S1, Supplementary Materials). The diversity index
(Shannon–Wiener) ranged between 1.68 (in 1993) and 0.87 (in 2003) without significant differences
across years (RM ANOVA F = 2.172, p = 0.121). The abundance of zooplankton organisms did not
differ among years (RM ANOVA F = 3.028, p = 0.077). Richness ranged between four and 27 taxa and
statistical differences were revealed among years (RM ANOVA F = 6.591, p = 0.003). The structure of
zooplankton differed significantly among years and between the first and second decade of the study
(PERMANOVA, p < 0.0001 and p = 0.0002, respectively), only 13 taxa were responsible for 70% of these
variations (Table 1).
Water2016,8,3416of18
=−0.334,p
Z
<0.001,forCPUEandsize,respectively)(Figure2).Similarly,thenumberofsmallfishes
(<10cm)increased(G=7.015,p=0.008),whilethenumberoflargefishes(>25cm)declinedinthe
sameperiod(G=59.672,p<0.0001)(Figure3).ApositiverelationwasfoundbetweenAprilair
temperatureandsmallfish(G=4.752,p=0.029)andanegativecorrelationwasfoundbetweenApril
airtemperatureandlargefish(G=3.91,p=0.047)inthelakeregion.
Atotalof71zooplanktontaxawereidentifiedwithinthegroupsRotifera(43taxa),Cladocera
(21taxa)andCopepoda(7taxa)(TableS1,SupplementaryMaterials).Thediversityindex(Shannon–
Wiener)rangedbetween1.68(in1993)and0.87(in2003)withoutsignificantdifferencesacrossyears
(RMANOVAF=2.172,p=0.121).Theabundanceofzooplanktonorganismsdidnotdifferamong
years(RMANOVAF=3.028,p=0.077).Richnessrangedbetweenfourand27taxaandstatistical
differenceswererevealedamongyears(RMANOVAF=6.591,p=0.003).Thestructureof
zooplanktondifferedsignificantlyamongyearsandbetweenthefirstandseconddecadeofthe
study(PERMANOVA,p<0.0001andp=0.0002,respectively),only13taxawereresponsiblefor70%
ofthesevariations(Table1).
Figure2.TemporaltrendofCPUEinkg/net(a)andfishsize(b)whenpoolingtogetherthe
abundanceofroach(Rutilusrutilus),bream(Abramisbrama)andrudd(Scardiniuserythropthalmus)in
theLakeSøbygaardregionduringthe23yearsofstudy(1990–2012).
Figure 2.
Temporal trend of CPUE in kg/net (
a
) and fish size (
b
) when pooling together the abundance
of roach (Rutilus rutilus), bream (Abramis brama) and rudd (Scardinius erythropthalmus) in the Lake
Søbygaard region during the 23 years of study (1990–2012).
Water 2016,8, 341 7 of 18
Water2016,8,3417of18
Figure3.Temporaltrendofsmall(<10cm)(a)andlarge(>25cm)(b)fishintheLakeSøbygaard
regionduringthe23yearsofstudy(1990–2012).
Table1.Zooplanktonspeciesaccountfor70%ofthetaxadifferenceacrossyearsandbetweenthe
first(1990–2000)andseconddecade(2001–2012)inLakeSøbygaard.ResultsofPERMANOVA
analysis(p<0.0001inbothcases).
TaxaContribution %
amongYears betweenDecades
CLADOCERA
Bosminaspp.16.1515.32
Daphniaspp.3.0833.083
COPEPODA
Cyclopoidaespp.7.277.196
Nauplii5.1535.161
ROTIFERA
Polyarthraspp.7.0656.862
Keratellacochlearis4.2164.167
Keratellaquadrata3.2513.257
Pompholyxsulcata4.1434.056
Conochilussp.3.4523.229
Brachionuscalyciflorus2.4682.478
Rotatoriasp.14.1084.623
Asplanchnaspp.2.392.325
Withinthezooplanktoncommunity,Rotiferaexhibitedcomplexandcontinuouschangesin
compositionandabundance(Figure4).Theirstructuredifferedsignificantlyamongyears,
Polyarthra,PompholyxandKeratellabeingthemaingeneraresponsibleforthedifferences(Table1).
0
500
1000
1500
2000
2500
3000
Smallfish(ind.)
50
0
50
100
150
200
250
1990 1994 1998 2002 2006 2010
Largefish(ind.)
a
b
Time(years)
Figure 3.
Temporal trend of small (<10 cm) (
a
) and large (>25 cm) (
b
) fish in the Lake Søbygaard region
during the 23 years of study (1990–2012).
Table 1.
Zooplankton species account for 70% of the taxa difference across years and between the first
(1990–2000) and second decade (2001–2012) in Lake Søbygaard. Results of PERMANOVA analysis
(p < 0.0001 in both cases).
Taxa Contribution %
among Years between Decades
CLADOCERA
Bosmina spp. 16.15 15.32
Daphnia spp. 3.083 3.083
COPEPODA
Cyclopoidae spp. 7.27 7.196
Nauplii 5.153 5.161
ROTIFERA
Polyarthra spp. 7.065 6.862
Keratella cochlearis 4.216 4.167
Keratella quadrata 3.251 3.257
Pompholyx sulcata 4.143 4.056
Conochilus sp. 3.452 3.229
Brachionus calyciflorus 2.468 2.478
Rotatoria sp. 1 4.108 4.623
Asplanchna spp. 2.39 2.325
Within the zooplankton community, Rotifera exhibited complex and continuous changes
in composition and abundance (Figure 4). Their structure differed significantly among years,
Polyarthra, Pompholyx and Keratella being the main genera responsible for the differences (Table 1).
Water 2016,8, 341 8 of 18
Water2016,8,3418of18
Figure4.Relativeabundanceofrotifersfortheperiod1990–2012inLakeSøbygaard.Averagesfor
thespring–summerperiodareusedforcalculationofproportions.Onlyspeciesaccountingformore
than3%oftotalabundanceareshown.
Cladocerawereparticularlyabundantduringtwoperiods:1994–1999and2004–2011.Daphnia
spp.andBosminaspp.werethedominantspeciescontributingmostimportantlytothezooplankton
variation(Table1).AgeneralshiftfrominitialdominanceofDaphniaspp.toBosminaspp.occurred
(Figure5a,b).Daphniaspp.decreasedsignificantlywithtimeduringthe23years(ppscl<0.001),
whereasBosminaspp.(ppscl<0.001)achievedasignificantincreasingtrends(TableS2,
SupplementaryMaterials).Accordingly,astatisticallysignificantdecreaseintheDaphnia:Bosmina
biomassratiowasobservedbothforannualdata(Q=−0.05;pZ=0.006)andsummer(Q=−0.039;pZ=
0.004)(Figure6).Thistrendconcurswithchangesobservedinthefishcommunity;thus,while
Daphniaspp.wasnegativelyrelatedtotheabundanceofsmallfishandpositivelyrelatedtotheChla
concentration(multipleGLMM,ppscl<0.001),Bosminaspp.showedanegativerelationshipwith
largesizedfish(multipleGLMM,ppscl<0.001)(Table2).DespitetheshiftfromDaphniaspp.to
Bosminaspp.dominance,thetotalbiomassofcladoceransincreasedfrom994μgL1intheperiod
1991–2004to1438μgL1intheperiod2005–2012.Thisincreasewassignificantlycorrelatedwiththe
densityofsmallfish(G=8.487,p<0.004).
Amongthechangesinothercladoceranspecies,theproportionofChydorussphaericusthrough
thestudied23yearswasparticularlynotable.ThisChydoridaewasalmostabsentatthebeginning
ofthestudyperiod,butitsdensityincreasedprogressively,reachingthehighestabundances
between2004and2009(mean:11.9;maximum:43.2ind.L1).
Copepoda(cyclopoidsonly)alsoshowedtemporalchangesinsizeandagestructure.A
significantincreaseoccurredinnaupliidensity(allperiodpMCMC<0.001;spring–summerperiod
pMCMC=0.004;Figure5)aswellasinthenauplii:(nau+cop+adults)ratiofortheentirestudiedperiod
(Q=0.145;pZ=0.026),butnotforthesummer(Q=−0.004;pZ=0.101)(Figure6).Therewasalsoan
increaseinthemale:femaleratio,significantforannualaveragesbutnotforthesummer(Q=0.332,
pZ=0.026andQ=0.285,pZ=0.056,respectively)(Figure6),andadecreasing,thoughnotsignificant,
trendintheweight:densityratio(Q=−0.217,pZ=0.089).
TheRDAanalysisexplained25.1%ofthetotalzooplanktondensityvariation(Figure7),and
CPUEoflargefishwastheonlyexplanatoryvariablecontributingsignificantlytothevariations(p=
0.007).Amongthecladocerans,Daphniaspp.waspositivelyrelatedtolargefishandChla,whereas
Bosminaspp.wasnegativelyassociatedwiththeseparameters.Mostofthesamplesfromthe1990s
werepositivelyrelatedtothisvariable.Ontheoppositesideofthegraph,samplesfromthelast
decadeofthestudywerestronglyrelatedtotheincreaseinCPUEofsmallfishandwater
temperature.Bosminaspp.waspositivelyrelatedtothesevariablesandnegativelytolargefishand
Chla.
Figure 4.
Relative abundance of rotifers for the period 1990–2012 in Lake Søbygaard. Averages for the
spring–summer period are used for calculation of proportions. Only species accounting for more than
3% of total abundance are shown.
Cladocera were particularly abundant during two periods: 1994–1999 and 2004–2011. Daphnia spp.
and Bosmina spp. were the dominant species contributing most importantly to the zooplankton
variation (Table 1). A general shift from initial dominance of Daphnia spp. to Bosmina spp. occurred
(Figure 5a,b). Daphnia spp. decreased significantly with time during the 23 years (p
pscl
< 0.001), whereas
Bosmina spp. (p
pscl
< 0.001) achieved a significant increasing trends (Table S2, Supplementary Materials).
Accordingly, a statistically significant decrease in the Daphnia:Bosmina biomass ratio was observed both
for annual data (Q =
´
0.05; p
Z
= 0.006) and summer (Q =
´
0.039; p
Z
= 0.004) (Figure 6). This trend
concurs with changes observed in the fish community; thus, while Daphnia spp. was negatively related
to the abundance of small fish and positively related to the Chl-aconcentration (multiple GLMM,
p
pscl
< 0.001), Bosmina spp. showed a negative relationship with large-sized fish (multiple GLMM,
ppscl < 0.001
) (Table 2). Despite the shift from Daphnia spp. to Bosmina spp. dominance, the total
biomass of cladocerans increased from 994
µ
g
¨
L
´1
in the period 1991–2004 to 1438
µ
g
¨
L
´1
in the
period 2005–2012. This increase was significantly correlated with the density of small fish (G = 8.487,
p < 0.004).
Among the changes in other cladoceran species, the proportion of Chydorus sphaericus through the
studied 23 years was particularly notable. This Chydoridae was almost absent at the beginning of the
study period, but its density increased progressively, reaching the highest abundances between 2004
and 2009 (mean: 11.9; maximum: 43.2 ind. L´1).
Copepoda (cyclopoids only) also showed temporal changes in size and age structure. A significant
increase occurred in nauplii density (all period p
MCMC
< 0.001; spring–summer period p
MCMC
= 0.004;
Figure 5) as well as in the nauplii:(nau+cop+adults) ratio for the entire studied period (Q = 0.145;
pZ= 0.026
), but not for the summer (Q =
´
0.004; p
Z
= 0.101) (Figure 6). There was also an increase in
the male:female ratio, significant for annual averages but not for the summer (Q = 0.332, p
Z
= 0.026
and Q = 0.285, p
Z
= 0.056, respectively) (Figure 6), and a decreasing, though not significant, trend in
the weight:density ratio (Q = ´0.217, pZ= 0.089).
The RDA analysis explained 25.1% of the total zooplankton density variation (Figure 7), and
CPUE of large fish was the only explanatory variable contributing significantly to the variations
(
p = 0.007
). Among the cladocerans, Daphnia spp. was positively related to large fish and Chl-a,
whereas Bosmina spp. was negatively associated with these parameters. Most of the samples from the
1990s were positively related to this variable. On the opposite side of the graph, samples from the last
decade of the study were strongly related to the increase in CPUE of small fish and water temperature.
Bosmina spp. was positively related to these variables and negatively to large fish and Chl-a.
Water 2016,8, 341 9 of 18
Water2016,8,3419of18
Figure5.TemporalvariationsinBosminaspp.,Daphniaspp.,naupliiandadultCyclopoidaeinLake
Søbygaardregionduringthe23yearsofstudy(1990–2012).Sinusoidalfit(grey)(a)andGLM(b)
regressionlinesareshown.
a
500
1000
2500
4000
5500
Bosmina spp.(ind.L
1
)
0
1500
3000
4500
6000
Bosmina spp.(ind.L
1
)
40
100
240
380
520
660
800
Daphniaspp.(ind.L
1
)
0
200
400
600
800
Daphnia spp.(ind.L
1
)
0
200
400
600
800
Nauplii (ind.L
1
)
50
100
250
400
550
700
Nauplii (ind.L
1
)
0
200
400
600
800
1000
1200
1990 1994 1998 2002 2006 2010
Cyclopoidae (ind.L
1
)
100
200
500
800
1100
1990 1994 1998 2002 2006 2010
Cyclopoidae (ind.L
1
)
Time(years) Time(years)
b
Figure 5.
Temporal variations in Bosmina spp., Daphnia spp., nauplii and adult Cyclopoidae in
Lake Søbygaard region during the 23 years of study (1990–2012). Sinusoidal fit (grey) (
a
) and GLM
(b) regression lines are shown.
Water 2016,8, 341 10 of 18
Water2016,8,34110of18
Figure6.Interpolatedmeanannual(a)andsummer(b)valuesofDaphnia:Bosmina,Copepod
nauplii:(nau+cop+adults),copepodfemale:male,zooplankton:Chla,cladoceran:Chla,Chla:TPand
Chla:TNratiosduring1990–2012inLakeSøbygaard.
Figure 6.
Interpolated mean annual (
a
) and summer (
b
) values of Daphnia:Bosmina, Copepod
nauplii:(nau+cop+adults), copepod female:male, zooplankton:Chl-a, cladoceran:Chl-a, Chl-a:TP and
Chl-a:TN ratios during 1990–2012 in Lake Søbygaard.
Water 2016,8, 341 11 of 18
Table 2.
Multiple GLMMs based on Markov chain Monte Carlo method showing the predictors for
the changes in the abundance (ind. L
´1
) of Bosmina spp. and Daphnia spp. for the summer period.
Estimated value, standard error (SE), p-value (ppscl), and log-likelihood.
Estimate SE ppscl log-likelihood
Bosmina spp.
intercept 73130 0.0063 <0.001 ´27,270
large fish ´0.0071 0.0000 <0.001
Daphnia spp.
intercept 44700 0.0443 <0.001 ´1291
small fish ´0.0005 <0.0001 <0.001
Chl-a0.0010 0.0002 <0.001
Water2016,8,34111of18
Figure7.FirsttwoaxesoftheRDAbasedonzooplanktonspeciesdensity.Theordinationof
samplingsisindicatedwithopencircles(a)andreferencesofmonthsandyearsaregiven(e.g.,7/05:
July2005).Speciesscoresareindicatedwithgreyarrows,andenvironmentalvariableswithblack
arrows(a,b).Temp:watertemperature;Kert.coch:Keratellacochlearis;Chyd.spha:Chydorussphaericus;
Gastr.sp:Gastropussp.;Cylp.spp:Cyclopoideaspp.;Bosmn.spp:Bosminaspp.;Nauplii:nauplii;
Pomp.sul:Pompholixsulcata;Kert.quad:Keratellaquadrata;Polya.spp:Polyarthraspp.;Brac.ang:
Brachionusangularis;Filn.term:Filiniaterminalis;Alona.sp:Alonasp.;Noth.acum:Notholcaacuminata;
Colur.sp:Colurellasp.;Brac.urce:Brachinusurceolaris;Synch.spp:Synchaetaspp.;Daphn.spp:Daphnia
spp.;Trich.spp:Trichocercaspp.;Brac.calc:Brachionuscalyciflorus;Filn.long:Filinialongiseta;Conoc.sp:
Conochilussp.
Table2.MultipleGLMMsbasedonMarkovchainMonteCarlomethodshowingthepredictorsfor
thechangesintheabundance(ind.L1)ofBosminaspp.andDaphniaspp.forthesummerperiod.
Estimatedvalue,standarderror(SE),pvalue(ppscl),andloglikelihood.
Estimate SE ppscl loglikelihood
Bosminaspp.
intercept731300.0063<0.001 27,270
largefish−0.00710.0000<0.001
Daphniaspp.
intercept447000.0443<0.001 1291
smallfish−0.0005<0.0001 <0.001
Chla0.00100.0002<0.001
Figure 7.
First two axes of the RDA based on zooplankton species density. The ordination of samplings
is indicated with open circles (
a
) and references of months and years are given (e.g., 7/05: July
2005). Species scores are indicated with grey arrows, and environmental variables with black arrows
(
a
,
b
). Temp: water temperature; Kert.coch: Keratella cochlearis; Chyd.spha: Chydorus sphaericus;
Gastr.sp: Gastropus sp.; Cylp.spp: Cyclopoidea spp.; Bosmn.spp: Bosmina spp.; Nauplii: nauplii;
Pomp.sul: Pompholix sulcata; Kert.quad: Keratella quadrata; Polya.spp: Polyarthra spp.; Brac.ang:
Brachionus angularis; Filn.term: Filinia terminalis; Alona.sp: Alona sp.; Noth.acum: Notholca acuminata;
Colur.sp: Colurella sp.; Brac.urce: Brachinus urceolaris; Synch.spp: Synchaeta spp.; Daphn.spp:
Daphnia spp.; Trich.spp: Trichocerca spp.; Brac.calc: Brachionus calyciflorus; Filn.long: Filinia longiseta;
Conoc.sp: Conochilus sp.
Water 2016,8, 341 12 of 18
3.2. Changes in the Zooplankton Assemblage and Their Influence on Phytoplankton
Besides the observed changes in the zooplankton assemblage, phytoplankton biomass (measured
as the Chl-aconcentration) decreased with time (p
MCMC
< 0.001; Table S3, Supplementary Materials).
This pattern applies both to annual data and to spring–summer seasons (Figure 8). The average
Chl-aconcentration during 1990–2000 was 192
µ
g
¨
L
´1
, followed by a decrease during 2001–2012 to
112
µ
g
¨
L
´1
. The concentrations of TN and TP had a similar decreasing temporal trend (
pMCMC < 0.001
(Table S3, Supplementary Materials) and exhibited similar seasonal and periodical fluctuations as Chl-a
(Figure 8). In the period 1990–2000, TN and TP average values were 2 and 0.5 mg
¨
L
´1
, respectively,
while for 2001–2012 they decreased to 1.4 and 0.3 mg
¨
L
´1
, respectively. As regards the ratios Chl-a:TP
and Chl-a:TN, only Chl-a:TP exhibited a significant decreasing trend for the entire study period
(
Q = ´11,986
; p
Z
= 0.001) (Figure 6). Multiple GLMM showed that TN and TP were the only significant
variables explaining the variation in Chl-a(positively related) (Table 3). The grazing pressure of total
zooplankton (zooplankton:Chl-a) and cladocerans (cladoceran:Chl-a) significantly increased for the
summer period (Q = 0.061, p
Z
= 0.003 and Q = 0.593, p
Z
< 0.001, for zooplankton and cladocerans,
respectively) as well as annually (Q = 0.010, p
Z
= 0.05 and Q = 0.005, p
Z
< 0.001, for zooplankton and
cladocerans, respectively) (Figure 6).
Water 2016, 8, 341 12 of 18
3.2. Changes in the Zooplankton Assemblage and Their Influence on Phytoplankton
Figure 8. Temporal variations in Chl-a, TN and TP concentrations in Lake Søbygaard region during
the 23 years of study (19902012): Sinusoidal fit (grey) (a) and GLM (b) regression lines are shown.
Table 3. Multiple GLMMs based on Markov chain Monte Carlo method showing the predictors for
chlorophyll-a concentrations (Chl-a) during the entire study period and the springsummer period.
The mean of the posterior distribution (post.mean), lower and upper 95% credible interval (l-95% CI
and u-95% CI), the MCMC p-value (pMCMC) and deviance information criterion (DIC) are reported.
post.mean
l-95% CI
u-95% CI
pMCMC
DIC
Chl-a (all period)
intercept
3.4927
3.0223
4.0609
<0.001
1479
Daphnia spp.
0.0015
0.0025
0.0003
0.004
TN
0.0034
0.0019
0.0049
<0.001
TP
0.0075
0.0029
0.0125
0.004
Chl-a (spring-summer)
intercept
3.6550
3.3707
3.9376
<0.001
958
Daphnia spp.
0.0014
0.0021
0.0007
<0.001
TN
0.0056
0.0039
0.0072
<0.001
TP
0.0066
0.0031
0.0094
<0.001
Figure 8.
Temporal variations in Chl-a, TN and TP concentrations in Lake Søbygaard region during the
23 years of study (1990–2012): Sinusoidal fit (grey) (a) and GLM (b) regression lines are shown.
Water 2016,8, 341 13 of 18
Table 3.
Multiple GLMMs based on Markov chain Monte Carlo method showing the predictors for
chlorophyll-aconcentrations (Chl-a) during the entire study period and the spring–summer period.
The mean of the posterior distribution (post.mean), lower and upper 95% credible interval (l-95% CI
and u-95% CI), the MCMC p-value (pMCMC) and deviance information criterion (DIC) are reported.
post.mean l-95% CI u-95% CI pMCMC DIC
Chl-a(all period)
intercept 3.4927 3.0223 4.0609 <0.001 1479
Daphnia spp. ´0.0015 ´0.0025 ´0.0003 0.004
TN 0.0034 0.0019 0.0049 <0.001
TP 0.0075 0.0029 0.0125 0.004
Chl-a(spring-summer)
intercept 3.6550 3.3707 3.9376 <0.001 958
Daphnia spp. ´0.0014 ´0.0021 ´0.0007 <0.001
TN 0.0056 0.0039 0.0072 <0.001
TP 0.0066 0.0031 0.0094 <0.001
4. Discussion
Climate warming and nutrient loading reduction showed complex effects on the trophic
chain of Lake Søbygaard from 1990 to 2012. The zooplankton assemblage underwent important
changes, some of which coincided with expectations for lakes in a recovery after a nutrient loading
reduction [
33
,
56
,
57
]. However, other changes are contrary to expectations, particularly those related to
zooplankton composition and size structure, suggesting that warming and nutrient loading reduction
have interactive rather than independent effects on zooplankton dynamics. Other studies have
also reported complex interactions of temperature and nutrient decrease on different groups of
zooplankton [35,58,59].
We found a decrease in fish biomass in terms of catch per unit effort (CPUE) along with an overall
reduction in nutrient concentrations, especially total nitrogen (TN). This is in accordance with the
expected responses of fish to nutrient loading reduction reported in other works [
33
]. Likely as a
consequence of decreased biomass of fish and total zooplankton and, particularly, increased cladoceran
biomass. This occurred despite a reduction in resources expressed as Chl-aand generally contrasts
the findings from Danish lakes in recovery from eutrophication [
33
]. In turn, the decline in fish
CPUE during recovery did not result in an increase in the proportion of large-bodied zooplankton
as expected [
37
]. In fact, both annual and summer data traced a shift from a cladoceran community
dominated by large-sized Daphnia spp. to one dominated by small-sized Bosmina spp. Moreover,
the abundance of the small-sized Chydorus sphaericus, almost absent at the beginning of the study
period, increased in the last decade. The shift from large to small species indicates enhanced fish
predation on large-bodied zooplankton [
13
,
60
], which coincides well with the increased catches of
small planktivorous fish in the lake. Less frequent surveys of other similar Danish shallow lakes have
also pointed towards reduced fish size despite nutrient loading reduction and this has been implicated
to multiple effects of warming [
25
]. Accordingly, we found that summer air temperatures, especially
in April where fish are spawning, have become warmer in the region during the study period, and the
changes were significantly correlated with the abundances of small (positive) and large fish (negative).
Concurrently, field research undertaken in numerous European lakes has shown a significant decline
in fish body size with decreasing latitude and thus higher temperatures [
20
,
21
,
25
,
61
]. The change
in fish body size might be a result of improved recruitment of fish due to higher temperatures in
spring and likely also as a result of increasing survival of young fish during winter due to a shorter
ice cover period [
29
]. Enhanced fish predation on zooplankton in Lake Søbygaard is also evident
from the strong negative correlation detected between Daphnia spp. and the abundance of small fish
(<10 cm). This agrees with experimental studies in which the increasing density and dominance of
smaller-sized fish due to warming have been observed to augment the predation pressure on large
Water 2016,8, 341 14 of 18
zooplankton species, favouring the development of small-sized species [
12
,
62
]. Therefore, a higher
temperature-mediated size of the fish assemblage in Lake Søbygaard may have overruled the effect of
nutrient loading reduction that usually results in an increase in fish size [
37
], causing a decrease instead
of the expected increase in the size of cladocerans. This corresponds with findings in subtropical and
tropical lakes in which small zooplankters dominate, even at low nutrient concentrations [
12
,
63
]. The
increase in chydorids may perhaps be related to a shift in phytoplankton from dominance by green
algae [
41
] to a higher share of cyanobacteria [
64
66
] and, potentially, also to improved conditions for
benthic algae growth. For example, it has been reported that Chydorus sphaericus is able to take the
colonies or filamentous algae between its carapace lobes and cling or clamber along them [67].
The changes in copepods followed the pattern observed for cladocerans, i.e., the abundance and
proportion of nauplii increased throughout the study period and the weight:density ratio decreased.
Field and experimental studies provide evidence that such changes also reflect alterations in the
size-selective predation pressure [
62
,
68
,
69
]. The decrease in the weight:density ratio may also be related
to a slight decrease in the dominance of C. vicinus and an increasing abundance of Mesocyclops sp.
during last decade, which was generally smaller in size. In addition, rotifers exhibited changes in
taxonomic composition during the study period, Polyarthra,Pompholyx and Keratella being the genera
mainly responsible for the among-year differences. Despite this, the total biomass and size structure of
rotifers exhibited no significant changes, which somehow contradict previous works in which the total
biomass of rotifers and small size classes were found to increase as a consequence of warming [58].
During recovery, we would expect an increase in grazing pressure on phytoplankton due to
release of fish predation and higher resource control of phytoplankton [
70
]. Accordingly, we found
a marked increase in the biomass ratios of cladoceran:Chl-aand zooplankton:Chl-adespite the size
reduction of cladocerans and copepods. The capacity of the zooplankton to control phytoplankton
seems therefore to have increased in Lake Søbygaard along with the nutrient loading reduction due to
enhanced grazing. This behaviour is in line with experimental studies demonstrating that increasing
temperatures enhance the zooplankton consumption rate [
33
,
70
], and as expected, consumers being
more sensitive than producers to a temperature increase, the top-down control on primary production
has been strengthened [71,72].
Concurrently with the enhanced grazing activity, the nutrient loading reduction has undoubtedly
contributed importantly to the decrease in Chl-a. Our results showed that Chl-afollowed the same
decreasing pattern as TN and TP, which together were the only significant variables explaining the
Chl-afluctuations during the study period. A previous study in Lake Søbygaard [
41
] revealed that TN
and TP decreased by 55 and 56%, respectively, from the period 1978–1987 to 1988–1995, whereas Chl-a
decreased from 755 (average 1978–1987) to 216
µ
g
¨
L
´1
in the latter period. In turn, the values of TN
and TP decreased by 20% and 37%, respectively, from the period 1978–1995 to the period 2000–2012
that our study covers; and Chl-adecreased by 48%, reaching an average value of 112
µ
g
¨
L
´1
. While
these values still qualify for a eutrophic lake according to OECD [
73
], they evidence a decline in the
trophic state of the lake.
In conclusion, despite the overall recovery of Lake Søbygaard following the nutrient loading
reduction, large-bodied zooplankton have not recovered similarly; rather they have exhibited a decline
in size coinciding with a shift to dominance of smaller-sized fish in lake, attributed to warming.
However, these effects have not prevented, but likely modulated, the positive cascading effects on
phytoplankton through a diminished fish CPUE related to the nutrient loading reduction.
Supplementary Materials:
The following are available online at www.mdpi.com/2073-4441/8/8/341/s1,
Table S1: Zooplankton taxa identified within the groups of Rotifera, Cladocera and Copepoda in Lake Søbygaard
from 1990 to 2012, Table S2: Statistics of the GLMMs based on Markov chain Monte Carlo method for the temporal
trend of Bosmina spp and Daphnia spp. Estimate value, standard error (SE), p-value (p
pscl
), and log-likelihood
are reported, Table S3: Statistics of the GLMMs based on Markov chain Monte Carlo method for the temporal
trend in variables shown in Figure 8. The mean of the posterior distribution (post mean), lower and upper 95%
credible interval (l-95% CI and u-95% CI), the MCMC p-value (p
MCMC
) and deviance information criterion (DIC)
are reported.
Water 2016,8, 341 15 of 18
Acknowledgments:
The study was supported by the Managing Aquatic ecosystems and water Resources
under multiple Stress funded under the 7th EU Framework Programme, Theme 6 (Environment including
Climate Change), Contract No.: 603378 (http://www.mars-project.eu), CLEAR (a Villum Kann Rasmussen
Centre of Excellence project). María Florencia Gutierrez and Melina Devercelli were supported by CONICET
(National Council of Scientific and Technical Research, Argentina). Sandra Brucet’s contribution was supported
by Marie Curie Intra European Fellowship No. 330249 (CLIMBING). We are thankful to Anne Mette Poulsen for
proofreading the manuscript, to Karina Jensen for assistance with zooplankton counting and sample processing,
and to Federico Giri and Liliana Forzani for their advice on GLMM analyses. We thank the anonymous reviewers
for their valuable comments and suggestions.
Author Contributions:
María Florencia Gutierrez has processed zooplankton samples from 2003 to 2012, analysed
the time series and wrote the manuscript. Melina Devercelli and Sandra Brucet have contributed with statistical
analyses and revisions. Torben L. Lauridsen, Martin Søndergaard and Erik Jeppesen have contributed with
historical data, corrections and revision.
Conflicts of Interest: The authors declare no conflict of interest.
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The increase in the temperature of surface waters has been studied for many decades, and various methods have been used to predict the most probable scenarios. The present study was undertaken to test the following research hypothesis: the warming of surface waters in winter (caused by the inflow of geothermal water) significantly modifies the dynamics, significance and type of relationships in zooplankton communities colonizing mine pit reservoirs. These relationships were examined with the use of network graph analysis for three thermal variants: warm winters (WW), moderate winters (MW), and cold winters (CW). The CW network was most cohesive, and it was controlled by eutrophic Rotifera (Trichocerca pusilla, Pompholyx sulcata, Keratella tecta) and Copepoda, with an equivalent number of positive and negative interspecific relationships. An increase in water temperature in winter led to a decrease in primary production, a decrease in the values of centrality attributes in MW and WW networks, and an increase in the significance of species that communicated with the highest number of species across sub-networks. Moderate winters increased the role of ecologically and functionally diverse species, which contributed to the heterogeneity of the MW network. The WW network was least cohesive, and it was controlled by small-sized psammophilous and phytophilous rotifers (Monommata maculata, Cephalodella spp.) and littoral cladocerans Alona spp. Adults Copepoda were not identified in the network, and the significance of antagonistic relationships decreased, which indicates that the WW network structure was weak and unstable. The results of the impact of warm winters and the flattening of the annual water temperature amplitude on the zooplankton network may be a projection of the expected global changes. These effects are particularly important in water reservoirs exposed to anthropogenic pressure and where changes in the thermal regime can influence future ecosystem services.
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This work deals with the consequences of climate warming on aquatic ecosystems. The study determined the effects of increased water temperatures in artificial lakes during winter on predicting changes in the biomass of zooplankton taxa and their environment. We applied an innovative approach to investigate the effects of winter warming on zooplankton and physico-chemical factors. We used a modelling scheme combining hierarchical clustering, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) algorithms. Under the influence of increased water temperatures in winter, weight- and frequency-dominant Crustacea taxa such as Daphnia cucullata, Cyclops vicinus, Cryptocyclops bicolor, copepodites and nauplii, and the Rotifera: Polyarthra longiremis, Trichocerca pusilla, Keratella quadrata, Asplanchna priodonta and Synchaeta spp. tend to decrease their biomass. Under the same conditions, Rotifera: Lecane spp., Monommata maculata, Testudinella patina, Notholca squamula, Colurella colurus, Trichocerca intermedia and the protozoan species Centropyxis acuelata and Arcella discoides with lower size and abundance responded with an increase in biomass. Decreases in chlorophyll a, suspended solids and total nitrogen were predicted due to winter warming. Machine learning ensemble models used in innovative ways can contribute to the research utility of studies on the response of ecological units to environmental change.
Technical Report
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Summary D6.1-1: Currently, practical management of water bodies focuses on the control of single stressors which are assumed to be dominant. Work by the MARS project and others using ecosystem scale and experimental observations has demonstrated that the relationships between primary stressors and ecological response indicators can be confounded through interactions with secondary pressures, giving rise to a potentially novel approach in the management of water bodies to achieve ecological recovery: the multi-stressor-response approach. Little is known about the commonality of multiple stressor effects on ecological and ecosystem service indicators and whether these vary in space and time. This information is needed to support large scale multiple-stressor management approaches, for example, to off-set the effects of climate change through abatement of nutrient stressors. This report addressed knowledge gaps in this field by developing a standard quantitative assessment of data analysis across MARS experiments, long-term monitoring, and river basin spatial monitoring case studies, providing a comprehensive comparison of responses from mesocosm to Europe in scale. The common analysis approach allowed for quantification of interaction strength and forms between three widespread stressor combinations found across Europe: (1) nutrients and high temperatures, (2) nutrients and low flow, and (3) nutrients and high flow. To synthesise multi-stressor interactions, we developed a simple and standardised analysis workflow to quantify the interacting effects of nutrient stress and one other stressor on a range of ecological responses in a consistent manner. We requested that individual analysts across the project team complete the analysis on their own datasets and report the results for syntheses. Therefore, the analysis workflow was designed to be as simple as possible and to generalise across a range of potential response variable types and study designs. Some analysts sub-setted their data set to explore the sensitivity of the relationships between multiple stressor and indicators across different spatial and - temporal scales. In total we obtained results from 47 analyses completed within 12 separate studies. Of these, 43 originated in northern and central Europe while just four analyses were from Southern Europe. In addition to nutrient stress as the most common primary stressor, the most common secondary stressors examined were high temperature and high flow. Indicators of phytoplankton responses were most commonly reported, followed by fish, with few studies of invertebrates and macrophytes. Given the large number of studies examining the abundance of cyanobacteria, and to a lesser extent fish abundance, results were also considered in relation to the ecosystem services of water supply, recreational value and fisheries. Statistically significant responses to nutrient stress (i.e. the primary stressors) were found in 85% of analyses (40 out of 47) while significant responses to the secondary stressors were apparent in only 38% of analyses (18 of 47). Responses to the secondary stressors were most commonly detected in analyses of temperature and morphology, but were rarely detected in analyses of high or low flow. Classifying overall interaction types based on both stressor effects resulted in roughly equivalent tallies among the three types of interaction considered: 18 antagonistic effects, 13 opposing effects and 16 synergistic effects across all analyses. For phytoplankton only, the results were 10 antagonistic effects, 11 opposing effects and 11 synergistic effects. There was little sign of clear differences in interaction types among the types of secondary stressor or across ecosystem type. However, this comparison was hampered by the relatively small sample size and the fact that a relatively low proportion of analyses yielded statistically significant interaction terms. Of the 47 modelled interactions, only 8 (17%) achieved statistical significance at P < 0.05 and these did not show a clear tendency towards any one of the interaction types. Our study highlights that experimental approaches often provide the clearest signal of stressor interactions. They do not, however, provide a comprehensive understanding of how stressors interact in the real-world, over varying sites and stressor gradients; for this monitoring data are more relevant. The range of responses in stressor interactions across all our case-studies highlight that it is often difficult to predict how two stressors may interact at a given site and both synergistic and antagonistic responses may be possible for the same stressor combination at sites with different characteristics or different levels of stress. Sometimes the significance of stressor effects, both acting singly or in combination, may be masked by other covariates either in different seasons or years (e.g. effects of nutrients may be masked by high flow in rivers) or at sites of differing typology (e.g. deep lakes may differ in sensitivity from shallow lakes). We offer recommendations on improving the analytical approach to detect the effects of interacting stressors in this context. An improved understanding of the impact of stressor reduction is vital to evaluate the success of potential management options and underpin practical MARS guidance on river basin management planning (RBMP). Unfortunately, the data collated here do not allow examination of stressor abatement responses. We do, however, review evidence on stressor abatement and offer some considerations with respect to developing recovery concepts within future work. Summary D6.1-2: Aim of this study was to analyse a large set of bioassessment metrics to identify and quantify stressor-specific metric responses reacting to one group of stressors but not to another. We hypothesise that stressor-specific responses occur when the individual stressors show independent ‘modes of action’ (i.e. the specific stress-induced changes of environmental factors that modify the ecological niches of the species constituting the biological community). The data used comprised three biological groups (macrophytes, benthic invertebrates, fish) covering three broad river types in Western and Central Germany. The stressor groups under investigation were physico-chemical, hydromorphological and hydrological stress. We performed linear variation partitioning to reduce the large set of metrics to a set of candidates for further non-linear analyses using a combination of boosted regression tree modelling and variation partitioning. The linear analyses revealed 16 candidate metrics that met our criteria, most of them for the medium to large lowland rivers. Macrophyte- and fish-based metrics were most relevant. In a geographically and methodologically more precise data subset, invertebrate metrics revealed more promising models than in the broader data set. Subsequent non-linear modelling resulted in two truly stressor-specific metrics, both based on invertebrate data: The Index of Biocoenotic Region (specifically indicating hydromorphological stress) and the Share of alien species (specifically indicating physico-chemical stress). We concluded that the biological community generally responds to stressors in rather an integrative than a specific way, but stressor-specific metrics can be identified. Future research on diagnostic metrics should focus on quantifying those stressor parameters that represent individual ‘modes of action’. Summary 6.1-3 This report is the Deliverable presenting the results for the 4th aim in relation to the work package task 6.4 entitled ‘Integrated River basin management: evaluation of the MARS conceptual model’. Within task 6.4 an evaluation was made on the current river basin management practises for dealing with multiple stressors and how existing river basin management plans can be improved by incorporating elements of the MARS conceptual model and the MARS Tools (WP7). We reflect on these current practises and evaluate the MARS conceptual model and MARS Tools as an aid to daily water management on a local level. In particular, we focused on two key European policy/management questions: the benefits of sustaining ecological flows and the value of green infrastructure for natural water retention measures (flood regulation and drought mitigation) in relation to other water management questions, strategies and practises. These two topics are seen only as examples, as many other aspects of RBMPs could also be assessed. Using a structured questionnaire and a workshop with WP4 case-study partners, their associated river basin managers, and a wider group of river basin managers from our applied partners and elsewhere in Europe, we were able to obtain an overview of the current practises in setting river basin management plans and selection of measures in relation to the multiple stressor challenges throughout Europe. The main aim of the questionnaire was to get a better understanding of the following questions: - How does daily water management practice deal with the selection of cost-effective measures, for water bodies exposed to multiple stressors? - Is knowledge on pressure interactions and biological response taken into account when selecting and prioritizing the measures? - How can MARS best contribute to a potential gap in knowledge and tools from the perspective of the stakeholders? We specifically challenged the workshop participants to link their current practises to the topics relevant within the MARS project and linked this to the potential need and usage of tools that could help identify the role of multi-stressor challenges within their daily management practises. With this information we defined how the conceptual model could be used in practice and what gaps in indicators or tools are currently hampering daily practise
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Complex lake ecosystem models can assist lake managers in developing management plans counteracting the eutrophication symptoms that are expected to be a result of climate change. We applied the ecological model PCLake based on 22 years of data from shallow, eutrophic Lake Søbygaard, Denmark and simulated multiple combinations of increasing temperatures (0–6 • C), reduced external nutrient loads (0%–98%) with and without internal phosphorus loading. Simulations suggest nitrogen to be the main limiting nutrient for primary production, reflecting ample phosphorus release from the sediment. The nutrient loading reduction scenarios predicted increased diatom dominance, accompanied by an increase in the zooplankton:phytoplankton biomass ratio. Simulations generally showed phytoplankton to benefit from a warmer climate and the fraction of cyanobacteria to increase. In the 6 • C warming scenario, a nutrient load reduction of as much as 60% would be required to achieve summer chlorophyll-a levels similar to those of the baseline scenario with present-day temperatures.
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We compiled and analyzed long-term (1961–2005) zooplankton community data in response to environmental variations in Lake Biwa. Environmental data indicate that Lake Biwa had experienced eutrophication (according to the total phosphorus concentration) in the late 1960s and recovered to a normal trophic status around 1985, and then has exhibited warming since 1990. Total zooplankton abundance showed a significant correlation with total phytoplankton biomass. Following a classic pattern, the cladoceran/calanoid and cyclopoid/calanoid abundance ratio was related positively to eutrophication. The zooplankton community exhibited a significant response to the boom and bust of phytoplankton biomass as a consequence of eutrophication-reoligotriphication and warming. Moreover, our analyses suggest that the Lake Biwa ecosystem exhibited a hierarchical response across trophic levels; that is, higher trophic levels may show a more delayed response or no response to eutrophication than lower ones. We tested the hypothesis that the phytoplankton community can better explain the variation of the zooplankton community than bulk environmental variables, considering that the phytoplankton community may directly affect the zooplankton succession through predator-prey interactions. Using a variance partition approach, however, we did not find strong evidence to support this hypothesis. We further aggregated zooplankton according to their feeding types (herbivorous, carnivorous, omnivorous, and parasitic) and taxonomic groups, and analyzed the aggregated data. While the pattern remains similar, the results are less clear comparing the results based on finely resolved data. Our research suggests that zooplankton can be bio-indicators of environmental changes; however, the efficacy depends on data resolution.
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