Introduction
How organisms take up resources, grow, reproduce, or interact with
competitors, pathogens, or consumers can be strongly affected by the
presence of other species in an ecosystem
(Stachowicz
2001; Olff et al. 2009; Kéfi et al. 2012). Interactions
among species can affect the functioning of ecosystems by regulating
fluxes of energy and matter, ecosystem productivity and metabolism, or
by mediating the response of ecosystems to perturbation
(Loreau et
al. 2001; Harmon et al. 2009; Chapin et al. 2011). Some
species interactions are more important than others for ecosystem
functioning (Angeliniet al. 2011; Falkenberg et al. 2012), such that modifying
these interactions can have disproportionate impacts on ecosystems: so
called foundation species
(Dayton 1972) can define
much of the structure of a community by creating locally stable
conditions for other species. Disturbances can influence how foundation
species can individually or interactively affect multiple ecosystem
components (Ellisonet al. 2005; Darling & Côté 2008), potentially causing
surprising effects on ecosystems
(Paine et al. 1998).
Such complexity regarding the interplay between interactions of
important species and environmental change makes forecasting ecosystem
responses to increasing anthropogenic disturbances particularly
challenging (Petcheyet al. 2015).
Eutrophication is a threat to aquatic ecosystems worldwide
(Smith et al.1999; Smith 2003), and there is growing evidence that nutrient loading
can cause both gradual and sudden shifts in ecosystem state, depending
on the nature and strength of species interactions
(Carpenter 2005). The
presence or absence of different key species, including macrophytes,
benthic and pelagic grazers, and phytoplankton, are thought to define
ecosystem responses to nutrient perturbations through positive and
negative interactions among them
(Scheffer et
al. 1993; Kéfi et al. 2016). In the network of species
interactions in shallow lakes
(Scheffer et al.1993), for example, a key interaction is the competition between
macrophytes with phytoplankton communities for dissolved nutrients and
light (Schefferet al. 1993; Ibelings et al. 2007). Assemblages of
macrophytes, that are considered to be important foundation species
(Scheffer et
al. 2003; Kéfi et al. 2016), are competitively dominant at low
nutrient loading, and can persist at intermediate nutrient loading via a
positive feedback between macrophyte growth and water transparency
(Carpenter & Lodge
1986; Jeppesen et al. 1998). Compared to macrophytes, fewer
models have investigated how benthic grazers can influence the dynamics
of ecosystem responses to nutrient pulses. However, the musselDreissena polymorpha can directly consume large amounts of
phytoplankton,
(Johengen et
al. 1995; James et al. 1997), and its occurrence has coincided
with dramatic changes in water clarity of some lake ecosystems
(Ibelings et al.2007).
Beside their expected negative effect on phytoplankton biomass,
foundation species like macrophytes and grazers can also affect other
ecosystem properties such as dissolved organic matter (DOM) and oxygen
metabolism
(Schefferet al. 1993; Olff et al. 2009; Kéfi et al. 2016).
As such, they can mediate how external disturbances reverberate through
the network of biological and abiotic interactions in aquatic ecosystems
(Narwani et al. 2019). Such effects can culminate in changes in both the
mean and variance of ecosystem parameters, which can sometimes
foreshadow a sudden shifts in ecosystem state
(Carpenteret al. 2011; Scheffer et al. 2012; Gsell et al.2016). Studies using high resolution measurements are particularly
useful for tracking the mean and variance of ecosystem metrics, for
example, phytoplankton biomass and rates of ecosystem metabolism such as
net primary productivity and respiration
(Carpenteret al. 2011; Batt et al. 2013; Nielsen et al.2013). These processes are largely driven by the autotrophic lake
community, both benthic (i.e. macrophytes) and pelagic (i.e.
phytoplankton), but can also be affected by DOM dynamics associated with
the growth and decay of biomass
(Catalán et al.2014). Photosynthesis and respiration rates can be modeled with
relatively high precision using repeated measurements of dissolved
oxygen and water temperature
(Staehr et al.2010), and have been used to assess ecosystem resistance and resilience
(Batt et al. 2013).
However, such approaches, using an array of multiple high-resolution
sensors (16 sondes with 5 parameters/sonde), have never been applied in
factorial manipulations of foundation species in aquatic ecosystems.
When facing disturbance, interactions between foundation species can
cause non-additive effects on ecosystem dynamics that are difficult to
anticipate (Narwani et al. 2019), and this may impair our ability to
quantify resistance and resilience of ecosystems with a particular
species configuration
(Schefferet al. 1993; Allgeier et al. 2011; Kéfi et al.2016; Thompson et al. 2018). However, only very few studies have
attempted to experimentally disentangle singular and synergistic effects
of key species on ecosystem dynamics in response to changing
environmental conditions
(Stachowicz
2001; Angelini et al. 2011; Falkenberg et al. 2012). In
shallow lake ecosystems, the presence of either macrophytes and mussels
have been linked to increased capacity to maintain a clear water state
with low phytoplankton abundances
(Jeppesenet al. 1998; Bierman et al. 2005; Ibelings et al.2007). Current theory suggests that both species may facilitate the
presence of each other: macrophytes can provide habitat forDreissena mussels to settle on
(Ibelings et
al. 2007; Karatayev et al. 2014b), and mussels can actively
decrease local turbidity, thus improving environmental conditions for
submerged macrophytes
(Ibelings et al.2007). Such facilitation is a common phenomenon in ecological
communities
(Stachowicz
2001; Angelini et al. 2011; Falkenberg et al. 2012),
especially for foundation species, like macrophytes and mussels.
However, there is also potential for antagonistic interactions between
macrophytes and mussels that could unfold under nutrient perturbation
scenarios: polyphenols and fatty acids produced by macrophytes to
inhibit phytoplankton growth
(Korner & Nicklisch
2002; Hilt & Gross 2008) may be harmful to filter feeding organisms
(e.g. mussels), whereas mussels can shift the composition of
phytoplankton communities to species that might be less affected by
allelochemicals
(Vanderploeg et
al. 2001; Fishman et al. 2010).
Here, we monitored freshwater ponds in high resolution to experimentally
test how a disturbance scenario characterized by multiple nutrient
pulses over two years affects pond ecosystem dynamics with and without
two important foundation species. We manipulated the presence and
absence of the macrophyte Myriophyllum spicatum and the musselDreissena polymorpha , two important foundation species that are
common in freshwater ecosystems worldwide. In a factorial pond
experiment, we perturbed all ecosystems by progressively increasing the
input of inorganic nutrients and quantified the dynamics of several
biotic and abiotic ecosystem parameters. The goal was to investigate how
the presence and absence of two important foundation species affects the
dynamics of a suite of ecosystem parameters during the process of pond
eutrophication (our disturbance regime). Specifically, we aimed at
characterizing how the nature of interactions between the two species
(additive vs. non-additive, Figure 1A) was affected by nutrient
perturbation. Contrary to our expectation of more stable clear water
conditions under the presence of both foundation species, after both
periods of nutrient perturbation (year 1, and 2), we found strong
non-additive, antagonistic effects in several ecosystem parameters (see
also Narwani et al. 2019, for the results from the first year of
manipulation). Our results demonstrate how interactions between key
species can drastically change under disturbance regimes, emphasizing
the importance of understanding how species interaction networks, and
how they change over time, can affect ecosystem responses to disturbance
(Schefferet al. 1993; Ibelings et al. 2007; Kéfi et al.2016).