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Fisheries impacts on lake ecosystems
Fisheries impacts on lake ecosystem structure in the context of a changing climate and
Tiina NÕGES,1* Orlane ANNEVILLE,2 Jean GUILLARD,2 Juta HABERMAN,1
Ain JÄRVALT,1 Marina MANCA,3 Giuseppe MORABITO,3 Michela ROGORA,3
Stephen J. THACKERAY,4 Pietro VOLTA,3 Ian J. WINFIELD,4 Peeter NÕGES1
1Centre for Limnology, Institute of Agricultural and Environmental Research, Estonian
University of Life Sciences, Rannu 61117, Tartu County, Estonia
2CARRTEL, INRA, Université Savoie Mont Blanc, 75 Avenue de Corzent, 74200 Thonon les
3CNR Institute of Ecosystem Study, Largo Tonolli 50, 28922 Verbania Pallanza, Italy
Lake Ecosystems Group, Centre for Ecology & Hydrology, Lancaster Environment Centre,
Library Avenue, Bailrigg, Lancaster, LA1 4AP, UK
Corresponding author: Tiina.Noges@emu.ee
Key words: Fish and fishery; lake; long-term changes; ecosystem impacts; ecosystem-based
Through cascading effects within lake food webs, commercial and recreational fisheries may
indirectly affect the abundances of organisms at lower trophic levels, such as phytoplankton,
even if they are not directly consumed. So far, interactive effects of fisheries, changing trophic
state and climate upon lake ecosystems have been largely overlooked. Here we analyse case
studies from five European lake basins of differing trophic states (Lake Võrtsjärv, two basins
of Windermere, Lake Geneva and Lake Maggiore) with long-term limnological and fisheries
data. Decreasing phosphorus concentrations (re-oligotrophication) and increasing water
temperatures have been reported in all five lake basins, while phytoplankton concentration has
decreased only slightly or even increased in some cases. To examine possible ecosystem-scale
effects of fisheries, we analysed correlations between fish and fisheries data, and other food
web components and environmental factors. Re-oligotrophication over different ranges of the
trophic scale induced different fish responsesIn the deeper lakes Geneva and Maggiore, we
found a stronger link between phytoplankton and planktivorous fish and thus a more important
cascading top-down effect than in other lakes. This connection makes careful ecosystem-based
fisheries management extremely important for maintaining high water quality in such systems.
We also demonstrated that increasing water temperature might favour piscivores at low
phosphorus loading, but suppresses them at high phosphorus loading and might thus either
enhance or diminish the cascading top-down control over phytoplankton with strong
implications for water quality.
Changes in fish assemblages are commonly used to evaluate aquatic ecosystem stress because,
with their relatively long lifespan, fishes integrate the effects of short- and long-term stressors
(Dobiesz et al., 2010). Sustainable fisheries are critical for human welfare and biodiversity
conservation, with overfishing posing numerous threats to the functioning of the whole
ecosystem by triggering trophic cascades and altering food web dynamics (McIntyre et al.,
2007; Salomon et al., 2008).
The socio-ecological significance of fisheries, and major stressors that impact them, differ
between marine and freshwater ecosystems. Fishing is recognized as one of the most important
ecosystem services provided by the world’s oceans but, in addition to this, lakes provide a
diversity of other ecosystem services as well, for example the provision of drinking water. Thus,
lake management is primarily focused on maintaining high water quality which in turn
facilitates the multiple services that lakes are expected to provide (Baron and Poff, 2004). In
oceans, fish stocks suffer mainly from overfishing (Beddington et al., 2007), while in lakes a
reduction in the abundance of commercially important fish is often caused by human activities
such as eutrophication (Schindler, 2006; Alexander et al., 2017) in the lake or its catchment,
rather than by intensive fishing. Although many studies have addressed the impact of lake
fisheries management on fish stocks (Pine et al., 2009; Cowx and Portocarrero, 2011; Everson
et al., 2013; Kolding and van Zwieten, 2014; Persson et al., 2014; Suuronen and Bartley, 2014;
Fraker et al., 2015; Anneville et al., 2015; DuFour et al., 2015), the variability in lake fish
communities is still mostly related to changes in environmental pressures such as eutrophication
(Vonlanthen et al., 2012) or species introductions (Trochine et al., 2017). As a consequence,
lake management practices focus on pollution control, habitat conservation and/or manipulation
in order to enhance ecosystem health and assure the availability of all ecosystem services
including professional and recreational fishing.
Both marine and freshwater ecosystems are driven by subtle and complex combinations of
bottom-up and top-down controls which in turn are influenced by food web structure and
composition (Gallardo et al., 2016). In addition, because food web structure and composition
are sensitive to climate change and other environmental disturbances, there is also a need to
consider and understand not only the interactions among and between species but also those
with their environment. From the early 2000s, Ecosystem-Based Fisheries Management
(EBFM), which originated in the marine realm and which incorporates a holistic approach as
its basic tenet, has gained increasingly popularity around the world. As noted by Pikitch et al.
(2004), EBFM represents a new direction for fishery management and essentially reverses the
order of management priorities to start with the ecosystem rather than with the target species,
with the overall objective to sustain healthy ecosystems and the fisheries they support.
Since the 1980s (Shapiro and Wright, 1984; Carpenter et al., 1985), detailed food web studies
have recognized that cascading effects in lakes are particularly visible because aquatic
organisms are characterized by strong trophic links which can be profoundly disturbed by
changes in biodiversity. As an example, Gallardo et al. (2016) underlined the impact of invasive
fish predators on different trophic levels of aquatic ecosystems. Because of strong cascading
trophic interactions in lakes, Shapiro and Wright (1984) proposed using fish for lake restoration
by either removing planktivorous fish directly or by introducing or favouring the growth of
piscivorous fish. Both measures should favour zooplankton development and enable it to
control efficiently phytoplankton biomass. This method, “biomanipulation”, (Shapiro and
Wright, 1984) has been implemented in many lakes to improve water quality. Biomanipulation
has been extensively applied in the lakes of north-western Europe, most of all in Denmark and
the Netherlands and both successful (e.g., lakes IJzeren Man and Nannewijd) and unsuccessful
(e.g., lakes Klein Vogelenzang, Geerplas) experiences have been reported (Søndergaard et al.,
2007). As concluded by Gulati et al. (2008), the positive effects of biomanipulation have been
sustained for over a decade in less than half of all cases.
While top-down cascades from fish to phytoplankton have been a core topic in recent
limnology, they have attracted far less interest in marine ecology because lake studies have
been largely aimed at regulating eutrophication-induced algal blooms while marine studies have
been more oriented towards fish yield (Hessen and Kaartvedt, 2014). However, there are still
fewer directly EBFM-aligned studies in lakes compared to in marine systems, and in both
environments the cascading effects of fisheries on the whole ecosystem are only rarely
addressed due to their data-demanding nature. Nevertheless, because fishing can drive large-
scale ecosystem changes, fisheries management should target the recovery of entire ecosystems
to more desirable and resilient states. The partial recovery of fish stocks only is not a stable
objective, because a further change in another component of the ecosystem (e.g., in climate or
alien species) may drive the system into another catastrophic loop (Daskalov et al., 2007).
Understanding the relative importance of top-down and bottom-up mechanisms in regulating
ecosystem structure is a fundamental ecological question with implications for both fisheries
and water-quality management. Accordingly, a recent study in the Laurentian Great Lakes
underlined the importance of continued monitoring to extend time series, and of mechanistic
research to test correlative findings, with the overall goal of enhancing the ability of managers
to implement ecosystem-based management approaches (Bunnell et al., 2014).
The present study compiles and analyses long-term data from four European lakes, i.e. Lake
Võrtsjärv, Windermere (with two basins), Lake Geneva and Lake Maggiore, which differ in
trophic state and fishing pressure, to explore variation in the extent and strength of top-down
cascading effects of fish and fisheries at the ecosystem level. In particular, we attempt to
identify the potential driving factors that shape the community structure of these ecosystems by
evaluating the effects of multiple stressors (e.g., nutrient loading, fishing pressure, and
temperature) on the fisheries and food webs of these five lake basins.
We focus our study on four medium-to-large lakes for which long-term records of fisheries
activity, and associated ecosystem variables, exist. The lakes were selected to represent a wide
gradient in depth, trophic state and fisheries intensity (Fig. 1). The deepest and most
oligotrophic lake, Maggiore, has experienced the strongest fishery pressure, followed by the
shallowest and most eutrophic lake, Võrtsjärv. In both basins of Windermere, the fishing
intensity was low.
Lake Võrtsjärv is a large and very shallow lowland lake in Estonia (Tab. 1) which has suffered
from increasing nutrient loads from agriculture since the 1950s (Nõges and Nõges, 2012).
Among our case study lakes, Võrtsjärv is the second largest by surface area and the most
eutrophic (Tab. 1). Since 1961, surface water temperature has significantly increased in spring,
summer and autumn, at rates of up to 0.39°C decade-1 (for August, Nõges and Nõges, 2014).
Thirty-one fish species and one lamprey species inhabit Võrtsjärv and its tributaries
permanently. Eels (Anguilla anguilla (L.)) have been introduced to and stocked into Võrtsjärv
since 1956 and have become the most important commercial fish followed by pikeperch
(Sander lucioperca (L.)), pike (Esox lucius L.) and bream (Abramis brama (L.)). Catches of
roach (Rutilus rutilus (L.)), burbot (Lota lota (L.)) and perch (Perca fluviatilis L.) are also
considerable. Ruffe (Gymnocephalus cernuus (L.)), bleak (Alburnus alburnus (L.)) and lake
smelt (Osmerus eperlanus) have lost their commercial importance following prohibition of the
use of fine meshed trawls since the 1970s (Järvalt et al., 2004). The lake has an intensive
commercial fishery with well documented yearly catches for all commercial fish species since
1971, and at a lower resolution since 1935 (Nõges et al., 2016). In years when a severe winter
coincides with a low water level, serious fish kills may occur (Nõges and Nõges, 2012).
Windermere is a large and deep meso-eutrophic lake comprising elongated northern and
southern basins. Both phosphorus concentration and phytoplankton abundance are consistently
lower in the north than in the south basin (Tab. 1). The surface water temperature of
Windermere has shown a significant increase since the late 1980s (Winfield et al., 2008a,
2008b). The fish community includes 16 species, although only Arctic charr (Salvelinus alpinus
(L.)), perch, pike and, in recent years, introduced roach are abundant (Winfield et al., 2008a).
Commercial fisheries have not operated for many decades, but a small-scale recreational fishery
persists for Arctic charr and catch-and-release angling is practised for pike and some other
species (Le Cren, 2001).
Lake Geneva, a deep peri-alpine lake located on the border between France and Switzerland is
the largest in our study, in terms of surface area (Tab. 1). In the second half of the 20th century,
the lake experienced a rapid increase in nutrient concentrations which switched it from
oligotrophy to eutrophy, with annual mean total phosphorus (TP) concentrations reaching 89
mg/m3 (Anneville and Pelletier, 2000), but these have now been lowered as a result of a
reduction in phosphorus loadings (Tab. 1). Besides changes in trophic status, the effect of
climate variability has become evident during the last few decades: the water temperature in
the 0-20 m layer has increased markedly during winter and spring and though summer
temperatures do not show any warming trend, they are strongly influenced by subtropical
Atlantic climate variability (Molinero et al., 2007). In addition, the phytoplankton community
has undergone an important shift in species composition (Anneville et al., 2002). Within the
zooplankton community, Daphnia, one of the preferred prey items of zooplanktivorous fish,
showed an overall decrease in abundance between 1986 and 2010 (Laine and Perga, 2015). Fish
species caught by professional fishers have included Arctic charr, pike, burbot, roach, brown
trout (Salmo trutta L.), perch and whitefish (Coregonus lavaretus (L.)). The contribution of
whitefish to commercial catches decreased from 25% in 1950-1962 to 10% in 1963-1978. From
1979 to the mid-1990s, whitefish contribution remained low and catches were dominated by
percids that made up 76% of the total catches (41% for perch and 35% for roach). Since the
1990s, whitefish contributions started to increase again (up to 66% in 2010-2012) accompanied
by a decrease in roach contributions while perch remained high ranging from 32% to 70%
(Anneville et al., 2017).
Lake Maggiore is a large holo-oligomictic and naturally oligotrophic lake (Marchetto et al.,
2004); the deepest and most oligotrophic in our study (Tab. 1). In the late 1950s, TP in Maggiore
began to rise and by the late 1970s, the lake reached a trophic state close to eutrophy with
maximum TP concentrations of 30 mg/m3 during winter mixing. Since the 1980s, nutrient loads
have been significantly reduced and in-lake TP in the whole water column has decreased to 10
mg/m3 (Manca and Ruggiu, 1998; Obertegger and Manca, 2011). Phytoplankton biomass
gradually declined, with a time lag, following the reduction of nutrient loads (Fastner et al.,
2016). The abundance of Daphnia longispina galeata gr. declined with lake re-
oligotrophication, but began to increase again after 1996 (Manca et al., 2007). There are 22
native fish species in Lake Maggiore but the lake has experienced extensive intentional
introductions of fish species. In the 19th century, a fast growing coregonid form introduced
from Lake Konstanz took the local name of “lavarello” (Berg and Grimaldi, 1965). After World
War II, a slow growing coregonid form, C. macrophthalmus N. (locally called “bondella”) was
also introduced for commercial purposes from Lake Neuchatel and soon became a major target
of the commercial fishery alongside bleak (Grimaldi and Numann, 1972). Since the 1990s,
roach, ruffe, pikeperch, crucian carp (Carassius carassius (L.)), bitterling (Rhodeus amarus
Bloch), and wels catfish (Silurus glanis L.) were introduced.
In recent decades, global warming has affected lake surface temperature as well the winter
mixing, resulting in a gradual reduction of the depth reached by convective mixing (Ambrosetti
et al., 2010).
The present analysis is based on yearly statistics of commercial fish catches from the period
1971-2013. During this period, passive fishing gear (fish traps and gill nets) was used and the
intensity of fishing remained at a relatively constant level of 300-360 fyke nets and 300-360
gill nets (the number of fyke and gill net licences per year, with fyke nets set in the ice free
period, and gill nets from September up to the next spring ice-out, usually in March). In
addition, experimental trawling as a sampling method for fish stock monitoring was started in
1981. During the ice-free period (April-November), fish were caught with a bottom otter trawl
(mouth width 8 m, height 2.5 m, cod-end mesh size 12 mm). In the pelagic part of the lake 15-
20 hauls per year, lasting 15 to 30 minutes each, were made in the daytime at a trawling speed
of 4.5 km/h. Catch per unit effort (CPUE) of the trawl was calculated in kilograms per trawl-
Water chemistry, phyto- and zooplankton have been studied since 1964, 1-4 times per month.
A series of 1-litre samples was taken with a Ruttner sampler at 1-m intervals from the surface
to the bottom and mixed in a tank. For phytoplankton, a subsample of 250 ml was preserved
with acidified Lugol’s solution and analysed microscopically as described by Nõges et al.
(2010). TP was analysed according to Grasshoff et al. (1983). Zooplankton samples were taken
with a quantitative Juday net (85 µm mesh size), towed from the bottom to the surface (in 1964-
2000) or by filtering 20 L of depth-integrated water through a net of 48 µm mesh size (since
2001), preserved with acidified Lugol’s solution and counted under a stereomicroscope Nikon
(SMZ1500) in a Bogorov chamber at up to 120x magnification. For biomass calculations, the
average body length of 10 individuals from each taxon was measured. The length of adult
crustaceans was converted to weight according to Balushkina and Vinberg (1979).
Water temperature was measured daily at the outflow and data were provided by the Estonian
Meteorological and Hydrological Institute.
In the absence of commercial fisheries, the Arctic charr, perch, pike and roach populations have
been studied and annually monitored (with the exception of roach which only became numerous
in the 1990s) since the early 1940s using a range of methodologies including gill nets targeted
at Arctic charr (Winfield et al., 2008a), gill nets targeted at pike (Winfield et al., 2008b; Paxton
et al., 2009) and traps targeted at perch (Paxton et al., 2004), augmented by the collection of
Arctic charr recreational angling records since the mid-1960s (Winfield et al., 2008a) and the
use of survey gill nets at 5-year intervals since 1995 targeted at roach (Winfield et al., 2008b).
This scientific monitoring constitutes the only removal of fish from the lake, with the exception
of insignificant numbers of Arctic charr and brown trout removed by recreational anglers. The
present analysis is based primarily on basin-specific annual sampling effort, absolute catch by
numbers and weight for perch and pike, together with derived numerical CPUE and biomass
CPUE for perch and pike monitoring and annual angler numerical CPUE for Arctic charr.
These fish studies have been accompanied by more frequent, typically daily, weekly or
fortnightly, monitoring of the lake’s abiotic and biotic features including water level, water
temperature and phosphorus concentrations (Winfield et al., 2008a). The present analysis is
based primarily on annual mean inshore surface water temperature, together with basin-specific
mean concentrations of TP and Chl a during May to October of each year. TP and Chl a
concentrations were determined from integrated surface water samples collected using a
weighted plastic tube according to Mackereth et al. (1978) and Talling (1974), respectively.
Details of the methodology used to determine water temperature are given by Winfield et al.
(2008a), those used for TP concentrations and Chl a are given by Parker and Maberly (2000).
In Lake Geneva, some physical parameters started to be regularly monitored at the end of the
1950s and a standardized long-term monitoring of physical and chemical variables, as well as
plankton communities, was launched in 1974. Sampling takes place 1 or 2 times per month in
the middle of the lake at its deepest part. Sampling protocols and analytical methods for
physical, chemical and plankton variables are described in CIPEL annual reports
(http://www.cipel.org/documentation/publications-cipel/) and on the website dedicated to the
Observatory of LAkes (OLA) (http://www6.inra.fr/soere-ola). Water temperature was
measured at discrete depths with a thermometer until 1998, after which multiprobes were used
(Sharma et al., 2015). Water for nutrient measurements was collected at discrete depths and TP
concentrations were estimated according to a standardized protocol (AFNOR NF EN 1189,
Monod et al., 1984). Water for estimating phytoplankton as Chl a was sampled at discrete
depths and filtered through a Whatman GF/C filter (47mm). The pigments were extracted with
90% (v/v) acetone/water, the solution was filtered through a GF/C filter (25mm) and Chl a
concentration was measured by spectrophotometry (Strickland and Parsons, 1968).
Zooplankton was sampled from a depth of 50 m to the surface using a 200 µm mesh plankton
net. Samples were preserved in a 5% buffered formaldehyde solution. Zooplankton species
were identified and individuals were enumerated in a 0.1 mL sedimented subsample using a
dissecting microscope (Anneville et al., 2007).
Fish abundance data used in this study include commercial landing statistics compiled annually
by the cantonal fisheries agency in Switzerland and the Haute-Savoie’s Direction
Départementale des Territoires (DDT) in France. French and Swiss commercial landing data
are available since 1950, data on the number of French professional fishers are available since
1979, and fishing activity has been recorded for the last few years. The fishing activity is
thought to be fairly constant since the numbers of commercial fishing permits and nets were
kept constant at least until 1988 (Gerdeaux, 1988; Gerdeaux et al., 2006). For recent years,
CPUE values (kg/fisherman) have been computed based on French fish statistics provided by
the Haute-Savoie’s DDT. The available French data allowed CPUE computation per species
(Anneville et al., 2017) for the period 1979-2012. As these CPUEs indicated significant
correlations with the French catches (P<0.005), we assumed that French catches give a good
indication of the abundance of the different targeted fish species from the whole lake. Therefore,
French catches were used in this analysis as a proxy of fish abundance.
Data on fish and fisheries in Lake Maggiore remained scattered until the end of the 1970s when
the Italian-Swiss Commission for the Fishery (Commissione Italo-Svizzera per la Pesca -
CISPP) was established under the International Commission for the Protection of Italian-Swiss
waters (CIPAIS). Since then, the total annual catch for each species of commercial interest and
the number of active commercial fishermen have been recorded annually (Volta et al., 2011).
This enabled the calculation of CPUE both for the total catch and for the most important
commercial species as the harvest divided by the number of fishers (tonnes/individual per year).
Water temperature has been measured (Sharma et al., 2015) and samples for TP analysis have
been collected monthly since 1979 at the deepest point of the lake at 0, 5, 10, 20, 30, 50, 100,
150, 200, 250, 300 and 360 m depth. TP was analysed by spectrophotometry, after
mineralization of the samples, according to Valderrama (1981). Mean volume-weighted values
were calculated for the epilimnion (0-25 m) and for the whole water column (0-360 m). Mean
annual values were calculated as the average of 12 monthly values. Samples for Chl a and
phytoplankton analysis were collected as integrated water from 0-20 m layer. Chl a was
determined spectrophotometrically after 90% acetone extraction (Lorenzen, 1967) until 2010,
then a fluorimetric in vivo method was adopted using a bbe Fluoroprobe instrument. Between
2008 and 2010, when the two methods were compared, a strong correlation was found (r=0.9,
n=27, P<0.0001; Morabito, unpublished data). Phytoplankton samples were preserved in acidic
Lugol's solution; algal cells were counted under a Zeiss Axiovert 10 microscope, following
Lund et al. (1958). Zooplankton samples were collected monthly with two Clarke-Bumpus
plankton samplers (126 and 76 µm mesh size), towed together at a constant speed of ca 3 km/h,
along integrated sinusoidal hauls from 0 to 50 meters. The samples were preserved in ethanol
and then transferred into 5% formaldehyde before being counted in a proper sample fraction,
addressing taxa and developmental stages abundance.
Prediction of fish standing stock
To assess the impact of fishery activities on standing stocks, we first needed to estimate the fish
biomass in each lake basin (i.e., our fish community response variable). To do this, we used an
established relationship between areal fish biomass and TP concentration, derived from lake
data spanning a similar TP concentration range to our focal lakes (Yurk and Ney, 1989). Based
upon this, we used annual TP concentrations in upper/mixed layers (Fig. 1A) to predict fish
log10Fish (kg ha-1)=1.07+1.14*log10TP (mg m-3) (eq. 1)
Among our case study lakes, we had estimates of the total fish biomass only for Võrtsjärv,
calculated from pelagic trawling (method described by Nõges et al., 2016). Based on this, TP-
based fish biomass in Võrtsjärv exceeded the trawling-based fish biomass on average by a factor
of 6. Considering that pelagic trawling might underestimate the fish biomass in a large shallow
lake with an extensive littoral area, we accept that TP-based fish biomass involves a large extent
of uncertainty. However, in our study it was the only option to at least roughly estimate the fish
biomass and assess the exploitation rate of the fish stock. If the predictions are indeed biased,
the estimated exploitation rates would change but the relative differences between systems
would be unaffected.
To assess the evidence for long-term changes in the state of the case study lakes, we applied
eWater toolkit (http://www.toolkit.net.au/Tools/TREND, last accessed on 16 June 2016). We
used Mann-Kendall tests to detect trends, and cumulative deviation tests to detect step changes
in time series of our measured variables. The non-parametric Mann-Kendall test is commonly
employed to detect monotonic trends in series of environmental data, climate data or
hydrological data; and the cumulative deviation test finds temporal breakpoints based on the
rescaled cumulative sum of the deviations from the mean (see details of these methods in
Kundzewicz and Robson, 2004; Li et al., 2008).
Fish occupying different ecological niches and trophic positions are likely to respond
differently to environmental pressures. Therefore, when examining correlations between
measures of fish stocks and environmental parameters we distinguished between the main
plankti/benthivorous (MPB) and main piscivorous (MPi) fish species for each case study lake.
Pikeperch was considered the MPi and bream the MPB in Lake Võrtsjärv; pike the MPi and
perch the MPB in Windermere (as the body size of this species has generally been small in this
lake over the study period (Craig et al., 2015) and its diet consequently dominated by non-fish
prey (McCormack, 1970; Craig, 1978) even though some periods of cannibalism have been
reported (Le Cren, 1992); pike the MPi and whitefish the MPB in Lake Geneva; pikeperch the
MPi and coregonids the MPB in Lake Maggiore. To assess these correlations, we used non-
parametric Spearman rank order correlation analysis (Statistica, ver. 12, StatSoft, Inc.).
Multiple pressures related to fish communities
Mean fish standing stocks calculated from annual TP concentrations varied from 153 kg ha-1 in
Lake Maggiore up to1034 kg ha-1 in Võrtsjärv (Tab. 2). In Lake Geneva and Windermere less
than 1% of this theoretical standing stock was removed from the lake annually, while in
Võrtsjärv (1.4%) and in Maggiore (7.9%), the fishing pressure was 1-2 orders of magnitude
greater (Tab. 2, Fig. 2).
Though subject to marked inter-annual variation, TP concentrations exhibited a long-term
decrease in all case study lakes while Chl a concentrations decreased only slightly (Geneva and
Maggiore) or even increased (Võrtsjärv and Windermere) (Tab. 3). Increasing trends of water
temperature have been recorded in all lakes.
In Lake Maggiore, phytoplankton biomass was significantly positively correlated with TP and
negatively with the CPUE of main piscivore, while it was vice versa in Lake Geneva. MPB and
MPi were positively correlated in Võrtsjärv and Geneva, not correlated in Windermere, and
negatively correlated in Maggiore. In Windermere, Geneva and Maggiore, MPi was positively
correlated with water temperature (WT). In Geneva MPB was positively correlated with WT
and negatively with TP, while in Maggiore it was vice versa. Daphnia was significantly
negatively correlated with Bphyto, MPi and MPB in Lake Geneva but not in the other lakes
Some correlations showed regular changes along gradients of mean depth, TP, and Chl a (Fig.
4). For example, the correlation between phytoplankton and MPB was strong and positive in
deeper lakes with lower TP concentration (Geneva and Maggiore) but negative and weak in
shallower lakes with higher TP concentration (Võrtsjärv and Windermere). The correlation
between water temperature and MPi was positive in lakes with low and moderate TP and
chlorophyll a concentration but turned negative in Võrtsjärv characterised by high TP and Chl
Factors controlling fish communities
Among our case study lakes, Geneva and Maggiore have undergone drastic reductions in
nutrient loading and considerable changes in fish communities. During this re-
oligotrophication, the total fish CPUE and especially that of coregonids has substantially
increased in Lake Geneva (Gerdeaux et al., 2006) but decreased in Maggiore (Volta, 2000). It
must be noted, however, that the ‘starting point’ of the re-oligotrophication trend was much
higher in Lake Geneva where TP values have only now reached those from which Lake
Maggiore started to decline at the end of the 1970s. Hence, these contrasting nutrient ranges
may be the reason for the observed different responses of fish communities through food web
interactions and differences in reproductive success and survival (Massol et al., 2007).
In Lake Geneva, the decrease in TP concentration was accompanied by a decrease in roach and
perch an increase in whitefish CPUE (Gerdeaux, 2004; Anneville et al., 2017), thus the
observed switch from percid and cyprinid to coregonid dominated community could have been
induced by a change in the lake’s trophic status (Jeppesen et al., 2005). However, changes in
species contributions to commercial landings may also reflect changes in the habits of fishers.
While the decrease in the contribution of perch to landings suggests a decrease in its abundance
due to re-oligotrophication (Dubois et al., 2013), the contrasting fishery values of perch and
roach and their consequently differing target profiles may complicate interpretation of the
observed trends in catches. It seems that the trends in roach and perch follow from re-
oligotrophication, because perch should have retained its commercial value.
The increase in whitefish abundances in Lake Geneva correlated strongly with decreasing TP
concentration and increasing temperature (Fig. 3). Re-oligotrophication potentially increases
reproductive success by re-oxygenation of spawning areas, improving egg survival which is
strongly influenced by oxygen concentrations at the water-sediment interface (Müller, 1992).
Warmer spring temperatures allow whitefish larvae to grow faster, and provide a better
temporal match with the seasonal development of their prey species (Anneville et al., 2009).
In addition, a change in the age structure of the whitefish population in the 2000s also probably
contributed to the increase in the population (Anneville et al., 2017). Because of the high fishing
pressure, the whitefish cohort entering the stock used to be completely harvested (Caranhac and
Gerdeaux, 1998). Before the 2000s, catches were made up of only few cohorts, mainly age 2+
or 3+, while fishes older than 4 years were rare in the lake. In contrast, recent studies have
shown that fish caught by French fishers in recent years are older (Anneville et al., 2017). Such
a change in the age structure of the catches indicates a high level of recruitment and that the
stock is not entirely harvested anymore. The non-harvested brood stock now survive to spawn
for several years and thus can contribute to the expansion of the stock. In Lake Geneva, the
percentage of the calculated fish standing stock caught annually was one of the lowest among
our case study lakes (0.17%) and this could explain the continuously increasing fish biomass
(CPUE) in this lake (Anneville et al., 2017). However, phosphorus concentration alone is
apparently insufficient to estimate fish stock as the field data indicate that the relationship
between phosphorus and fish is not so simple. In Lake Geneva, for example, low phosphorus
concentrations are associated with high annual catches dominated by coregonid species
(whitefish) which are sensitive to trophic status and whose reproductive success is impaired by
eutrophic conditions. So, depending on fish community composition, the model may or may
not be appropriate. Furthermore, the model makes the expected and general prediction that
eutrophic lakes are more productive for fish than are oligotrophic lakes. However, in the range
of TP variations observed in our case study lakes, parameters other than phosphorus such as
pressure from fisheries and the balance between predatory and non-predatory fish may explain
a considerable part of the observed variability in fish abundance. However, as the present study
does not quantify the relative variance explained by these factors, it will be posed as a
hypothesis for a further more sophisticated analysis.
In Lake Maggiore, the pressures affecting the coregonid population were rather different. The
two deeper lakes considered in this study differ in their ‘trophic history’, with Maggiore
switching from mesotrophy to oligotrophy and Geneva from eutrophy to mesotrophy. In
contrast to Geneva, the change in coregonid harvest in Maggiore was positively correlated with
increasing TP concentration and negatively correlated with epilimnion temperature (Fig. 3),
but, according to Massol et al. (2007), data from both lakes suggest that coregonids show
highest catches at intermediate TP concentrations (15-30 µg L-1). Among our case study lakes,
the highest percentage of the calculated fish standing stock was caught annually in Maggiore
(up to 25%, exceeding other lakes by 1-2 orders of magnitude) (Tab. 2). We acknowledge that
the fish biomass values calculated from TP concentrations are only crude estimates. However,
even with this uncertainty it is still clear that the fishing pressure in Maggiore has been much
stronger than in the other lakes. Although re-oligotrophication and the introduction of several
fish species have undoubtedly had a strong impact on Lake Maggiore ecosystem, the high
fishing pressure is likely to be among the reasons explaining the strong reduction in coregonids,
trout and perch CPUE and is thus regarded as an important factor controlling the fish
community in this lake.
Strong impacts of fisheries management measures on fish community composition and the
balance between predatory and non-predatory fish species have been demonstrated in Võrtsjärv,
where the banning of small-meshed fishing gear in the 1970s caused a major change in the age
and size structure of fishes and contributed to the establishment of predatory fish control over
previously dominant ruffe and roach populations (Nõges et al., 2016). This is consistent with
the number of fyke nets and gill nets presently used, that indicate only a moderate fishing
pressure. Neither of the fish feeding groups’ abundances were correlated with Daphnia or TP
and only temperature was significantly negatively correlated with the main piscivore abundance
In Windermere, where commercial fisheries are absent, higher temperature was associated with
higher CPUE of the main piscivore (pike) as has also been observed over a longer time scale
by Edeline et al. (2016). No direct impact was detected on perch, the main planktivore in this
lake, during the present study (Fig. 3), even though over a longer time scale this environmental
parameter has been shown to have an important effect on recruitment (Paxton et al., 2004).
Top-down effects in lake ecosystems
Changes in fish abundance in Lake Geneva may have had strong implications for zooplankton.
Long-term changes in whitefish (MPB) abundance were strongly correlated with inter-annual
changes in Daphnia abundance (Fig. 3). The negative correlation between Daphnia abundance
and whitefish catches suggests whitefish control of cladoceran population which according to
Alric et al. (2013) have been under strong top-down pressure during re-oligotrophication of
Lake Geneva. Although changes in zooplankton abundance can also be caused by a bottom-up
mechanism if changes in phytoplankton species composition alter their palatability and food
value for zooplankton (Perga and Lainé, 2013), our results support rather the top-down
hypothesis that the effect of the increasing abundance of zooplanktivorous whitefish has
contributed to the long-term decrease in Daphnia. Re-oligotrophication has brought about only
a slight reduction in phytoplankton biomass in this lake (Tab. 3). As the abundance of Daphnia
was strongly negatively correlated with both whitefish and phytoplankton abundance (Fig. 3),
the substantial increase in whitefish feeding pressure on zooplankton could presumably reduce
the grazing impact of zooplankton on phytoplankton and so enable phytoplankton biomass to
increase despite the reduction in TP levels.
As phytoplankton biomass in Maggiore correlated positively with planktivorous coregonids and
negatively with the main piscivore (pikeperch), we can draw a general conclusion of strong
cascading effects of fisheries on the ecosystem of this lake. In Maggiore, a simultaneous
reduction in TP and phytoplankton took place in the 1980s-1990s, while in the 2000s occasional
high phytoplankton peaks occurred, such as that recorded in summer 2011, caused by an
exceptional bloom of Mougeotia sp. (Fig. 5B). Blooms of this taxon are known to occur in the
deep peri-alpine oligo-mesotrophic lakes, although the driving factors are still not completely
understood (Tapolczai et al., 2015). In Maggiore, the abundance of pikeperch was rather
strongly negatively correlated with both whitefish and phytoplankton (Fig. 3), which could
reflect a cascading effect of the main piscivore on phytoplankton through the food chain.
However, the cascading effect and the phytoplankton response could have been confounded in
Lake Maggiore due to the strong nutrient limitation on phytoplankton growth which developed
during the re-oligotrophication phase.
In Võrtsjärv, the present correlative analysis and a recent study by Nõges et al. (2016)
demonstrated that the main predator (pikeperch) could exert control over phytoplankton,
reflected by a significant negative correlation between phytoplankton and pikeperch biomasses
(Fig. 3) most likely caused by a cascading top-down effect through the food web. Supporting
this, Nõges et al. (2016) found negative correlations between phyto- and zooplankton
biomasses in this lake and a shift in zooplankton size structure relative to pikeperch biomass:
higher pikeperch abundances were associated with smaller rotifers and larger copepods. In
addition, the individual weight of crustacean zooplankton was smaller in years of high
abundance of small fish that stimulated ciliate domination over metazooplankton and enhanced
the domination of the microbial food web.
In Windermere, phytoplankton was likely primarily bottom-up controlled by phosphorus as no
strong correlation with any of its major fish species was detected in this study, although after
taking into account the effects of a pathogen outbreak on perch population structure in the
1970s, Edeline et al. (2016) found indications from this longer data set that a pike-dominated
intra-guild predation triggered a temperature-controlled trophic cascade passing through pike
down to dissolved nutrients.
Across all of our case study lakes, we found a stronger link between phytoplankton and
planktivorous fish, and thus a more important cascading top-down effect, in the relatively
deeper lakes Geneva and Maggiore (Fig. 4A). The strengths of these connections mean that, for
such lakes, careful ecosystem-based fishery management is of utmost importance for
maintaining high water quality and related ecosystem services such as recreational values and
suitability as drinking water supplies.
Our results also demonstrated that at certain levels of phosphorus loading, increasing water
temperature might favour piscivores (Fig. 4D) and thus enhance the potential for a cascading
top-down control over phytoplankton. Such an effect could counteract the commonly envisaged
impact of climate change supporting elevated phytoplankton development and cyanobacterial
blooms (Paerl and Huisman, 2008). Indeed, higher temperatures may be expected to reinforce
top-down control in food chains dominated by ectothermic top predators such as fish by
increasing consumption rates faster than primary production (Vasseur and McCann, 2005;
Ohlberger et al., 2011). Increasing temperature may also lead to different responses in the same
fish species depending on the latitude that determines the starting temperature (Jeppesen et al.,
2012). Trophic amplification by climate change - the intensification of trophic interactions and
pathways through the food web (Kirby and Beaugrand, 2009; Van Looy et al., 2016) - can result
in totally different effects compared to laboratory or microcosm experiments with strongly
simplified biotic structure. An increase in fish predation pressure on zooplankton and higher
importance of nutrient loading in warm southern lakes was found also by an experimental study
undertaken along a latitudinal gradient in Europe (Moss et al., 2004).
These experiments, however, showed also that at higher temperatures, higher zooplankton
biomass was required to control phytoplankton which means that the invertebrate grazers did
not benefit from the temperature increase as much as the phytoplankton. Similar conclusions
were drawn by Malve et al. (2006) in their modelling study. A higher degree of omnivorous
feeding by fish and less piscivory in subtropical and tropical lakes than in temperate lakes has
been found to limit the success of fish-based biomanipulation methods in warmer climates
(Jeppesen et al., 2005). In agreement with these findings, our results showed that at high P
loadings and Chl a concentrations the correlation between water temperature and piscivores
turned negative (Fig. 4 C,D). This finding means that in eutrophic lakes the loss of piscivores
in warmer waters might amplify the generally anticipated warming effect of increased
frequency of phytoplankton blooms. As a result, this cascading effect also has considerable
potential to cause a much greater and wider loss of ecosystem services beyond those directly
associated with commercial and recreational fisheries.
For the assessment of long-term concurrent effects of fisheries, changing trophic state and
changing climate upon lake ecosystems in five European lake basins of differing trophic states
(Lake Võrtsjärv, two basins of Windermere, Lake Geneva and Lake Maggiore), the trend
analysis, correlations and conceptual food-web analysis led to the following preliminary
assumptions which may be used as hypotheses and research questions for further more
- Decreasing phosphorus concentrations (re-oligotrophication) and increasing water
temperatures in all five lake basins have coincided with no changes or only slight decreases
in phytoplankton abundance.
- Parameters other than phosphorus, including fisheries pressure and the relative abundances
of predatory and non-predatory fish species, could explain a significant part of the observed
overall variability in fish abundance.
- Strong links between phytoplankton and planktivorous fish observed in lakes Geneva and
Maggiore, could suggest important cascading top-down effect in these relatively deep lakes
which makes their careful ecosystem-based fisheries management extremely important for
maintaining high water quality.
- Our analyses indicated that increasing water temperature might favour piscivores at low
phosphorus loadings, but suppress them at high phosphorus loadings and might thus either
strengthen or weaken the cascading top-down control over phytoplankton with strong
implications for water quality.
The present analysis was funded by MARS project (Managing Aquatic ecosystems and water
Resources under multiple Stress) funded by the European Union under the 7th Framework
Programme, Theme 6 (Environment including Climate Change) and by institutional research
funding IUT 21-02 of the Estonian Ministry of Education and Research.
The Windermere team would like to thank many past and present colleagues, too numerous to
name here, for their help in the field and laboratory. They are also indebted to the late John
Cooper and Bruce Dobson for allowing use of their records of Arctic charr fishing effort and
catches, and to Graeme McKee and colleagues of the Environment Agency for organising and
making available the current log book scheme for Arctic charr anglers. They are also grateful
to the Freshwater Biological Association for their joint stewardship of the Windermere long-
term data and to the Environment Agency for allowing use of their water level data. Underlying
components of this work were funded by the Natural Environment Research Council,
Environment Agency and United Utilities.
We also thank the Italian-Swiss Commission for the Fishery which provided fish harvest data
for Lake Maggiore and the International Commission for the Protection of Italian-Swiss Waters
(CIPAIS) for supporting long-term studies on Lake Maggiore. The Lake Geneva team would
like to thank the SOERE © OLA-IS, INRA Thonon-les-bains developed by Eco-informatics
ORE INRA Team and CIPEL (www.CIPEL.org).
Authors are deeply grateful for the editor and two anonymous reviewers for their constructive
suggestions and editing of the manuscript.
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Tab. 1. General characteristics of the case study lakes. TP and chlorophyll a (Chl a)
concentrations in the upper/mixed water columns are the mean values of the latest year of the
time series presented in Fig. 5 and Tab. 3.
TP (mg m-3)
Chl a (mg m-3)
Tab. 2. Annual theoretical fish standing stocks calculated on the basis of annual average total
phosphorus (TP) concentration in the upper/mixed water column (see Fig. 5A) from the
relationship log10Fish (kg ha-1) = 1.07+1.14*log10TP (mg m-3) published by Yurk and Ney
(1989); annual catch values based on fishery statistics and % of the annual catches from the
calculated fish standing stocks in case study lakes in 1980-2011.
Annual TP mg m-3
Fish stock, kg ha-1
Fish stock, tonnes lake-1
Annual catch tonnes lake-1
Annual catch, % of stock
Annual TP mg m-3
Fish stock, kg ha-1
Fish stock, tonnes lake-1
Annual catch tonnes lake-1
Annual catch, % of stock
Annual TP mg m-3
Fish stock, kg ha-1
Fish stock, tonnes lake-1
Annual catch tonnes lake-1
Annual catch, % of stock
Annual TP mg m-3
Fish stock, kg ha-1
Fish stock, tonnes lake-1
Annual catch tonnes lake-1
Annual catch, % of stock
Annual TP mg m-3
Fish stock, kg ha-1
Fish stock, tonnes lake-1
Annual catch tonnes lake-1
Annual catch, % of stock
NB, North Basin; SB, South Basin.
Tab. 3. Time series lengths, upward (↑) and downward (↓) trends and breakpoints, and mean
concentrations of total phosphorus (TP) and chlorophyll a (Chl a) in the upper/mixed water
layer of the case study lakes. Average values of TP and Chl a were calculated for the entire
period, as well as before and after any temporal breakpoints detected.
Lake, water layer
Windermere NB, 0-7m
Windermere SB, 0-7m
↓ 1992, P<0.01
↓ 1988, P<0.01
↓ 1989, P<0.01
↑ 1997, P<0.01
Windermere NB, 0-7m
↑ 1980, P<0.05
Windermere SB, 0-7m
↓ 1997, P<0.05
NB, North Basin; SB, South Basin; No, no detected breakpoint.
Fig. 1. Ecological gradients among the study lakes. The lake basins are plotted in a plane
defined by their mean depth and annual mean total phosphorus concentration, with summaries
of annual fish catches indicated by boxplots. The location of the median fish catch indicates the
position of each lake basin in the TP - mean depth plane. The non-outlier range covers values
that fall below the upper outlier limit (+1.5 * the height of the box) and above the lower outlier
limit (-1.5 * the height of the box). M, Maggiore; G, Geneva; WN, Windermere North Basin;
WS, Windermere South Basin; V, Võrtsjärv.
Fig. 2. Catch per unit effort (CPUE) by fish species, calculated fish biomass based on total
phosphorus (TP) concentration (see Tab. 2) and total catch data in Lake Võrtsjärv in 1979-2013
(A); CPUE by fish species, calculated fish biomass based on TP in Windermere in 1966-2012;
NB, North Basin; SB, South Basin (B); CPUE by fish species, calculated fish biomass based
on TP and total catch data in Lake Geneva in 1979-2012 (C) and CPUE by fish species
(Salmonids = trout + Arctic charr; Coregonids = lavarello + bondella), calculated fish biomass
based on TP and total catch data) in Lake Maggiore in 1979-2010 (D); t, metric tonnes.
Fig. 3. Spearman correlation coefficients of fish feeding groups (catch per unit effort of main
piscivorous (MPi) and main plankti/benthivorous (MPB) fish species; see explanation in text)
with phytoplankton biomass (Bphyto), Daphnia, total phosphorus (TP) and water temperature
(WT) in case study lakes Võrtsjärv (V), Windermere North Basin (WN), Windermere South
Basin (WS), Geneva (G), and Maggiore (M). Dashed lines denote the significance level at
Fig. 4. Changes in the strength of the correlation between phytoplankton and main
plankti/benthivorous fish (MPB) along gradients of log lake depth (A) and annual average total
phosphorus (TP) concentration of the latest year that our dataset includes, as shown in Tab. 1
(B). Changes in the strength of the correlation between water temperature (WT) and main
piscivorous fish (MPi) along gradients of log chlorophyll a (Chla) concentration (C) and TP
concentration in the latest year of the time series (D). Each point represents one lake basin.
Fig. 5. Time series of annual average total phosphorus (A) and May-October average
chlorophyll a concentration (B) in the upper/mixed water column of case study lakes.
Windermere NB, North Basin; Windermere SB, South Basin.