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Limnol. Oceanogr., 58(1), 2013, 173–184 2013, by the Association for the Sciences of Limnology and Oceanography, Inc. doi:10.4319/lo.2013.58.1.0173 E Control of plankton seasonal succession by adaptive grazing Patrizio Mariani,a,* Ken H. Andersen,a André W. Visser,a Andrew D. Barton,b and Thomas Kiørboe a a Centre for Ocean Life, National Institute for Aquatic Resources, Technical University of Denmark, Charlottenlund Castle, Charlottenlund, Denmark b Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts Abstract The ecological succession of phytoplankton communities in temperate seas is characterized by the dominance of nonmotile diatoms during spring and motile flagellates during summer, a pattern often linked to the seasonal variation in the physical environment and nutrient availability. We focus on the effects of adaptive zooplankton grazing behavior on the seasonal succession of temperate plankton communities in an idealized community model consisting of a zooplankton grazer and two phytoplankton species, one motile and the other nonmotile. The grazer can switch between ambush feeding on motile cells or feeding-current feeding on nonmotile cells. The feeding-current behavior imposes an additional mortality risk on the grazer, whereas ambush feeding benefits from small-scale fluid turbulence. Grazer–phytoplankton feeding interactions are forced by light and turbulence and the grazer adopts the feeding behavior that optimizes its fitness. The adaptive grazing model predicts essential features of the seasonal plankton succession reported from temperate seas, including the vertical distribution and seasonal variation in the relative abundance of motile and nonmotile phytoplankton and the seasonal variation in grazer abundance. Adaptive grazing behavior, in addition to nutrient and mixing regimes, can promote characteristic changes in the seasonal structure of phytoplankton community observed in nature. While these processes certainly affect the growth rates of phytoplankton and structure the community, grazing-induced mortality rates also play an important role in shaping the seasonal succession of phytoplankton communities (Hairston et al. 1960). Zooplankton grazing is the largest source of mortality for marine phytoplankton (Calbet and Landry 2004) and has been shown to affect seasonal dynamics (Lampert et al. 1986). Moreover, the size structure of phytoplankton communities is thought to be the consequence of the interaction between nutrient availability and predator control (Armstrong 1994). Less is known, however, about how zooplankton feeding behaviors and phytoplankton community structure are related. In this predator–prey interaction, the effects go both ways, with grazers shaping the community structure and phytoplankton community structure eliciting different feeding strategies in the grazer community. In particular, grazers are able to adapt their feeding behavior to the environmental conditions (Kiørboe 2008), with repercussions throughout the plankton ecosystem (Reichwaldt et al. 2004; Kiørboe 2008; Mariani and Visser 2010). Here, we investigate the two-way interaction between the motility of the phytoplankton cells and the grazing behavior of the zooplankton. We do this by developing a simple model for a temperate plankton community with one predator, a motile prey, and a nonmotile prey, in which we analyze the effects of adaptive zooplankton grazing behavior on the abundance of the two phytoplankton species. We assume phytoplankton species are identical except for their motility. They grow in response to seasonal changes in light and compete for a common carrying capacity, whereas their losses are determined by zooplankton grazing alone. Zooplankton adopt the feeding strategy that maximizes its fitness: feeding-current feeding for nonmotile prey and ambush feeding for motile prey. Each feeding behavior has quantifiable trade-offs in terms of The structure of pelagic ecosystems is affected by physical variability and biological interactions that occur on multiple temporal and spatial scales (Mann and Lazier 1991). The strength of these interactions is highly variable and affects the function and dynamics of the food web by driving the major patterns in species composition, succession, and distribution (Reynolds 1984). Seasonal cycles of plankton communities are common features of marine environments. In many temperate regions, a change from the dominance of diatom species in spring to flagellates and dinoflagellates during summer has been observed in large lakes (Reynolds 1984), river estuaries (Turner et al. 1983), open ocean (Michaels et al. 1994), and coastal marine systems (D’Alcalà et al. 2004). Competition for light and nutrients are thought to be key factors regulating the seasonal succession of phytoplankton (Huisman et al. 1999). Differences in nutrient affinity and variation in turbulence levels have been used to explain succession in phytoplankton life forms, as depicted by Margalef’s mandala (Margalef 1978). Margalef’s paradigm suggests that large, nonmotile cells such as diatoms benefit from high-nutrient and turbulent conditions; physical mixing compensates for the sinking loss from the productive layers and brings nutrients to the surface layer, allowing for the formation of a spring bloom. In contrast, motile cells such as flagellates and dinoflagellates benefit from low-nutrient and stratified conditions in which turbulence is relatively weak because motility allows vertical migration and exploitation of nutrients from depth and light harvesting in the surface. Motility also allows phytoplankton cells in a relatively quiescent environment to utilize small-scale patchiness in the distribution of nutrients (Seymour et al. 2009). * Corresponding author: [email protected] 173 174 Mariani et al. predation risk and prey encounter rates. In this model, the single zooplankton controls the phytoplankton abundance in a manner consistent with the ‘‘killing the winner’’ hypothesis (Thingstad and Lignell 1997; Thingstad 2000), a mechanism proposed to have a strong shaping influence on the structure of aquatic microbial and autotrophic communities (Pernthaler 2005; Winter et al. 2010). The fundamental trade-offs between competition and predator defense allows for the coexistence of different phytoplankton specialists sharing a common resource. In our model, ‘‘defense specialization’’ assumes a dynamic nature as the grazer switches feeding mode, preferentially targeting the specialist that affords it the best advantage in terms of fitness. We demonstrate that adaptive zooplankton feeding behavior can lead to a seasonal succession in the phytoplankton community, from dominance of nonmotile forms during spring to motile forms during summer, which resembles the seasonal succession widely observed in temperate seas. Model formulation Zooplankton grazing behavior—We consider a model system with a zooplankton grazer switching feeding strategy between ambush and feeding-current modes when exposed to motile and nonmotile prey, respectively. Such behavior has been described for copepods (Jonsson and Tiselius 1990; Kiørboe et al. 1996), and each feeding behavior has unique costs and benefits. When exposed to motile prey the grazer adopts a sit-and-wait ambush strategy where a moving prey is perceived by the hydromechanical disturbance that it generates (Jonsson and Tiselius 1990). Alternatively, when nonmotile phytoplankton are available, the grazer generates a feeding current such that passive prey are transported toward the grazer (Kiørboe et al. 1996). The feeding current may pull the grazer through the water at a velocity that approaches the feeding current speed; hence, there is no clear distinction between a feeding-current and cruise-feeding grazer (Kiørboe 2011). Because motile flagellates are often evasive and may respond to feeding currents by escape jumps (Jakobsen 2002), they are more likely to be caught by the ambush feeders. In contrast, nonmotile prey will rarely encounter an ambush grazer and do not generate a hydromechanical signal that can be perceived by the grazer. Thus, the two feeding behaviors are specifically targeting either motile or nonmotile prey. With regard to its own mortality risk, active foraging, either by generating a feeding current or by swimming, generates a strong hydromechanical signature and increases the predator encounter speed, thus making an active grazer more susceptible to predation than an ambush feeding grazer (Kiørboe 2011). Finally, in the ambush feeding behavior, the grazer benefits much more from small-scale fluid turbulence in terms of elevated prey encounter rates than in the feeding-current behavior (Saiz and Kiørboe 1995; Kiørboe et al. 1996). The zooplankton feeding behavior is modeled by a single trait, t, interpreted as the fraction of time the grazer spends generating feeding currents. The grazer is constantly striving to adapt t to maximize its instantaneous fitness measure. Here, we use the net population growth rate as a proxy for fitness, gðtÞ~rðtÞ{dðtÞ ð1Þ where r(t) is the reproductive rate and d(t) is the death rate. An alternative formulation of the fitness measure, which might appear more realistic at the individual organism level, is g(t) 5 r(t)/d(t) (Mariani and Visser 2010). However, we show below that using this alternative formulation of our model yields the same results. Hence, Eq. 1 is used to simplify the mathematical description of the fitness gradient. Population dynamics—We consider a simple and wellstudied two-prey, one-predator system (Vance 1978) in which a grazer population feeds on two phytoplankton species, motile and nonmotile. The adaptive grazer (C) can employ either of two feeding behaviors: feeding-current feeding on the nonmotile prey (Rn) or an ambush strategy feeding on moving prey (Rm). The two prey populations have logistic growth and compete for a common resource (e.g., nutrients), which is represented by a shared carrying capacity K, 1 dRm Rm zRn zC=c ~m 1{ {ba C ð1{tÞ ð2aÞ Rm dt K 1 dRn Rm zRn zC=c ~m 1{ {bc Ct Rn dt K 1 dC ~gðtÞ~rðtÞ{dðtÞ C dt ð2bÞ ð2cÞ ~cba Rm ð1{tÞzcbc Rn t{dðtÞ where m is the growth rate of the two prey species and c is the growth yield (i.e., number of grazers produced per prey cell ingested). Motile and nonmotile phytoplankton differ only in their loss terms (Eqs. 2a,b). The linear relation between the two feeding strategies accurately describes the observed behavior of copepods feeding on a mixed diet of motile and nonmotile prey species (Kiørboe et al. 1996). We assume a linear functional response with biomass specific clearance rates ba for the ambush strategy and bc for the feeding current strategy. These nonsaturating functional responses are invoked as a simplifying assumption, but we note that this could lead to overestimations of feeding rates when phytoplankton concentrations are high (i.e., during bloom). Finally, the mortality rate d of the grazers is described as a function of the feeding behavior, dðtÞ~mzpt ð3Þ where m is a behavior-independent mortality and pt represents the additional mortality associated with the feeding current behavior. For convenience we also include in here the loss of potential reproductive rate (mathematically the same as a mortality rate) due to the extra energetic costs of generating a feeding current. Grazing behavior and plankton community We note that the assumed form of the logistic term guarantees mass conservation in the model. Notably, the limiting factor available for growth is N 5 K 2 Rm 2 Rn 2 C/c; hence, the logistic form in Eqs. (2a,b) is equivalent to first-order growth kinetics with a growth-based affinity m/K (Thingstad and Sakshaug 1990). This approximation is valid in nonsaturating nutrient conditions that likely prevail in most natural environments, but it could overestimate the uptake rate when nutrient concentrations are greatest (e.g., in upwelling systems). On the other hand, it provides a reasonably simple means of including a limiting resource with minimal model complexity. To close the system (Eqs. 2, 3), we use a dynamic model of predator behavior (Abrams and Matsuda 2004; Uchida et al. 2007) where t changes proportionally to the individual fitness gradient to maximize fitness, dt dg ~k dt dt ð4Þ where k is a constant determining the speed of behavioral change and dg/dt is the fitness gradient derived from Eq. 1. The numerical integration of t in Eq. 4 is obtained by imposing upper and lower bounds in the interval 0 # t # 1. A similar approach has been used to analyze evolutionary adaptation (Vincent et al. 1996) and community and population adaptation (Wirtz and Echkardt 1996; Merico et al. 2009). In the case of constant prey abundance, solving Eq. 4 would lead to a single, maximally fit value of t in which any further change negatively affects the fitness g(t). However, population dynamics in Eq. 2 alter the values of Rm and Rn and therefore modify the benefits of a given predator strategy, subsequently modulating the value of t. Mechanistic description of the vital rates—The growth rate m of phytoplankton is assumed to depend on light (I, mmol m22 s21) and is described by a Monod equation, m~ aI 1zaI=mMax ð5Þ where a is the initial slope of the m2I curve, and mMax is the maximum growth rate under ideal conditions. Effects of small-scale fluid turbulence and motility on the growth rates of phytoplankton are neglected. When the grazer has an ambush feeding behavior, prey are perceived by means of hydromechanical sensors, and the clearance rate is given as h i ð6Þ ba ~pa21 lðea1 Þ1=3 zu where a1 is the reaction distance to the prey, u is the velocity difference between the predator and the swimming prey (Saiz and Kiørboe 1995), l is a turbulence constant (Delichatsios and Probstein 1975), and e is the turbulence dissipation rate. Notably, prey clearance rates for ambush feeders depend on turbulence, which can vary over many orders of magnitude in the natural environment, and relative velocity. When the grazer is feeding on nonmotile prey (Rn), passive prey are captured in a capture volume determined 175 by the predator feeding current v and the reaction distance a2 (Kiørboe 2008). The clearance rate is then (Jonsson and Tiselius 1990): bc ~pa22 v ð7Þ Turbulent velocities in the pelagic ocean at the scale of zooplankton are typically much less than feeding current velocity (Yamazaki et al. 2002). Thus, under typical conditions, turbulence has little effect on prey encounter rates for the feeding-current behavior (Kiørboe and Saiz 1995). Simulation procedure—We link the ecosystem model to an idealized one-dimensional mixed layer model tuned to the North Sea (General Ocean Turbulence Model [GOTM]; Burchard et al. 2006). The model has 110 vertical levels, 1 m spatial resolution, and runs for 1 yr with time step of 1 h using realistic atmospheric forcings and a k2e turbulence closure model. We use the physical model to calculate seasonal variations in turbulence (e) and light (I) in the upper ocean layer (Fig. 1) to estimate plankton vital rates, growth (m), and loss (ba,c), in the integration of Eq. 2. Additionally, we analyze the plankton vertical transport and the seasonal cycle, directly coupling the community model to the mixed layer model to include the effects of vertical transport on the plankton. We solve the transport and reaction terms using a split method separating the numerical solution of the diffusion part and the reaction terms. We also assume that adaptive behavior has a timescale shorter than the transport timescale. Additional details on discretization and numerical integration of the coupled system can be found in Burchard et al. (2006). From arbitrary low initial concentrations, the model is integrated forward in time using physical forcing over a recursive annual cycle until it reaches a stationary seasonal cycle. The formulation of the feeding behavior in Eqs. 6 and 7 describes well the experimental results for the copepod Acartia tonsa, and parameter values for this species are used (Table 1; Saiz and Kiørboe 1995). From low (e 5 1029 m2 s23) to high (e 5 1025 m2 s23) turbulence conditions, clearance rates for ambush feeding have values between ba 5 15 and 105 mL d21, whereas filter feeding holds steady at bc 5 49 mL d21. These clearance rates compare well with typical values observed in copepod communities (Kiørboe 2011). Furthermore, we assume a constant predator : prey weight ratio of 1000 : 1 (Hansen et al. 1997) and a growth efficiency of 1 : 3 (Kiørboe et al. 1985), thus equivalent to c 5 3 3 1024. With a grazer body mass WC 5 8 mg C (a 1 mm copepod; Mauchine 1998), this implies a phytoplankton cell mass of WR 5 8 ng C and a cell size of ca. 50 mm diameter (assuming 1027 mg C mm23; Menden-Deuer and Lessard 2000), typical for large spring diatoms and later summer dinoflagellates. The typical winter concentrations of limiting nitrogen nutrients in, for example, the North Atlantic Ocean, is 10 mmol L21; hence, the phytoplankton biomass can at most reach 108 cells m23 (at C : N 5 6), which is thus our estimate of the carrying capacity of the system. To maintain a minimal model, we 176 Mariani et al. Fig. 1. Time series of (a) light, I, and (b) dissipation rate, e, as derived from the GOTM and used in the analyses of the dynamics of the population model (thick black lines). I is an interpolating function of (gray line) daily values of the available short wave radiation averaged over the mixed layer depth, and e is in the range of (gray line) daily values of dissipation rate averaged over 30 m depth. The mixed layer depth is calculated using a turbulent kinetic energy threshold of 1025 m2 s22. As references, monthly averages of (a) short-wave radiation and (b) dissipation rate as derived from GOTM at 5 m (square), 20 m (dot), and 40 m (triangle) are shown. choose the cell size to be constant and identical for both phytoplankton types. Finally, the constant describing mortality associated with feeding (p) was set to double the natural mortality rate of the grazer while employing feeding current behavior (p 5 m). Because this value of p is not well constrained by empirical evidence, we provide a sensitivity analysis on this as well as other parameter choices. The model calculates abundances of resources (cell m23) and grazers (individuals m23, henceforth ind. m23), which are then shown in units of biomass using the conversion values WR,C. We assume that zooplankton adapt their feeding behavior faster than the population dynamics of the plankton resources. This is controlled by the parameter k, which is a measure of the ratio between the population dynamics timescale and the behavioral timescale. In our simulations we use k 5 10, which leads to a clear separation between the timescales of population and adaptive dynamics while not affecting the stability of the dynamic system. All the values Table 1. Model parameters and sensitivity test expressed as percent variation of the state variables (DC*, DRm , DRn , Dt*) for a 30% increase in each parameter. Parameter Definition and units Value DC* DRm DRn Dt* a mMax a1 u a2 v c Initial slope growth curve (d21[mmol m22s21]21) Maximum growth rate (d21) Reaction distance to motile prey (cm) Prey relative velocity (cm s21) Reaction distance to nonmotile prey (cm) Feeding current velocity (cm s21) Conversion efficiency (biomass predators produced per biomass prey eaten) Behavior-independent mortality rate (d21) Predation risk (d21) Turbulence constant Carrying capacity (mmol L21) Adaptive feeding constant Biomass of individual grazer (mg C) Biomass of phytoplankton cell (ng C) 1.5 3 1022 1 0.04 0.025 0.015 0.8 3 3 1024 5 6 27 21 23 2 27.5 0 0 243 215 0 0 223 0 0 0 0 241 223 223 0 0 45 12 233 218 0 0.1 0.1 1.37 10 10 8 8 211 23 21 27.5 0 30 0 30 0 211 0 0 0 30 15 15 0 0 0 0 30 0 0 9 0 0 0 0 m p l K k WC WR Grazing behavior and plankton community and units of the parameters used in our model simulations are in Table 1. Observed data—We compare some of the model predictions with two independent sets of phytoplankton observations: a long-term time series near Stonehaven, Scotland (56u57.89N, 02u06.29W) and Continuous Plankton Recorder (CPR) data from the North Atlantic. A detailed reconstruction of the temporal variability of diatoms and dinoflagellates in a long-term station in the North Sea near Stonehaven, Scotland, has been presented in Bresnan et al. (2009). Using monthly abundance data (cells L21) for diatoms and dinoflagellates in the period 1997–2006, we computed the average seasonal cycle of abundance over all the years. Accounting for the main species composition observed in the samples, we then converted abundance to biomass concentration (mg C m23) using a constant dinoflagellate and diatom carbon contents of 0.4 (consistent for Amphidinum sp.) and 0.04 ng C cell21, respectively (Menden-Deuer and Lessard 2000); the value for diatoms is consistent with the carbon content per cell of Thalassiosira and Skeletonema species (Strathmann 1967). Using constant carbon content excludes effects of changing species composition over the seasons. Nevertheless, the approach allows for approximate estimates of the concentrations during bloom and summer conditions for both diatoms and dinoflagellates. The CPR has measured the abundances of hundreds of plankton taxa, including diatoms and dinoflagellates, in the North Atlantic for more than a half-century (Richardson et al. 2006). Although CPR data do not include the smallest cells, such as picoplankton, ecologically and biogeochemically relevant diatom and dinoflagellate taxa are wellsampled by the survey. We consider only those taxa that were monitored over the entire CPR survey duration and spatial extent and only dinoflagellate taxa that are known to be photoautotrophic or mixotrophic, thus excluding pure heterotrophs. We calculated the mean annual cycle of CPR abundance for 59 diatoms and 32 dinoflagellates, averaged over the entire North Atlantic survey area and for all years within 1958–2006. These abundances are best considered a semiquantitative measure of phytoplankton number density at roughly 10 m depth (Richardson et al. 2006). Results Fixed point and community structure—The dynamical system in Eqs. 1–4 has one stable fixed point: 8 m > Rm ~ > > cba > > > > > pzm > > > < Rn ~ cbc mðba zbc Þ½Kcba bc {ba p{mðba zbc Þ > > C~ > > > ba bc ½Kcba bc zmðba zbc Þ > > > > b > a > : t ~ ðba zbc Þ ð10Þ 177 Conditions of light, turbulence, and carrying capacity affect zooplankton feeding behavior and alter the plankton species composition at the fixed point. In particular, Rm is a decreasing function of turbulence through the dependence of ba on e, whereas Rn is constant under changing light and turbulence conditions. Hence, the ratio between the equilibrium abundances of motile to nonmotile phytoplankton (Rm : Rn ) is smaller with greater turbulence, in agreement with Margalef’s paradigm (Margalef 1978), but for quite different reasons because we only consider grazing effects in the model. Using the parameters in Table 1, we can visualize these dynamics (Fig. 2). In particular, with increasing turbulence, Rm declines while Rn remains constant (Fig. 2a). When K , Rm + Rn , the concentration of phytoplankton is too low to support zooplankton. Moreover, zooplankton abundance (C*) tracks changes in Rm + Rn and it decreases with e and increases with K (Fig. 2b). On the other hand, the adaptive trait, t*, tends to increase with e (Fig. 2c). Hence, higher turbulence favors nonmotile plankton and more feedingcurrent feeding. These relations between turbulence and plankton community are general and independent of the particular values of the parameters we use. It is noteworthy that coexistence among the phytoplankton does not occur in the absence of adaptive grazing (Vance 1978). The stability analysis of the model demonstrated that the stability of the food web is controlled solely by the two stabilizing terms: carrying capacity and predation. As demonstrated for other simple food webs (Visser et al. 2012), it can be shown that also for this system the adaptive trait (k . 0) promotes coexistence and asymptotic stability in a large parameter space. Seasonal dynamics—We examine the model ecosystem in an idealized zero-dimensional setting. Starting from the arbitrarily low initial conditions, the system represented in Eq. 2 reaches repeating annual cycles of abundance after 1 yr (Fig. 3), and we consider only subsequent years in the following. The simulated seasonal dynamics of the plankton community are consistent with the classical pattern observed in temperate oceans; a distinct maximum phytoplankton abundance in spring dominated by nonmotile phytoplankton (diatoms, or Rn ), with a subsequent increase of motile phytoplankton during summer and early autumn (flagellates, or Rm; Fig. 3a). Zooplankton abundance (C) is low during winter and increases in spring, with a maximum in summer (Fig. 3b). When compared with observed time series of diatoms and dinoflagellates (Fig. 4), we note that the model reproduces the general shape of the seasonal cycle and approximate maximum biomass for both motile and nonmotile phytoplankton, while overestimating their minimum concentrations (Fig. 4a,b). Moreover, the predicted grazer abundances (0– 14,000 ind. m23) are of a similar order of magnitude as zooplankton abundances observed in temperate oceans (Eloire et al. 2010). Hence, the model captures some aspects of real plankton communities despite its simplified structure and idealization of important processes of plankton ecology. Although the two phytoplankton populations in the model have the same growth rate, Rn alone sustains the 178 Mariani et al. that of the phytoplankton, the grazer population has a lagged response to prey abundances, maintaining the competitive advantage of nonmotile phytoplankton over motile plankton until e reduces, yielding ba , bc. The phytoplankton uptake rate contributes to maintaining the decoupling between phytoplankton and grazers. In our model, we assume first-order kinetics for both uptake and predation dynamics, thus neglecting saturating effects, which can potentially alter the delayed grazer response to phytoplankton biomass increase. Notably, species abundances tend to follow the fixed point throughout the year, but less so in winter and when the spring bloom occurs. As previously suggested (Evans and Parslow 1985), the bloom formation in this system is a deviation from the general quasi-equilibrium behavior of the model. The results above are quite similar when the model is applied in the vertical column setup (Fig. 5). In early spring, the phytoplankton community is dominated by nonmotile species (Rn, Fig. 5a) and abundances start increasing when the column is still well mixed. Rn reaches its maximum at the beginning of the stratification (day 90) in the surface layers (Rn < 300 mg C m23 at depth , 35 m). Later, stratification of the water column reduces e at depth (. 25 m) favoring the increase of motile species (Rm, Fig. 5b), but also the increase of grazer biomass (C, Fig. 5c). Feeding strategies are highly variable close to the surface in summer, whereas ambush feeding is favored at 20–50 m (t, Fig. 5d). Feeding-current feeding (t 5 1) is dominant in winter and early spring when the concentration of grazers is low (Fig. 5c,d). The deep phytoplankton maximum dominated by motile cells is established after the spring bloom at ca. 30 m depth and deepens during summer until autumn. In autumn, mixing and limitation of light decrease Rm to minimum values; in winter an equilibrium is reached between phytoplankton species with Rn . Rm. The abundance of zooplankton starts increasing in spring (day 70), is at a maximum in midsummer, but reduces to lower values in late autumn. Zooplankton abundance is uniform in the upper 20 m but declines at depth. Fig. 2. Concentrations of (a) phytoplankton, (b) grazers, and (c) magnitude of the behavioral trait at the fixed point. Rm , Rn , and t* are functions of turbulence, whereas C* is function of turbulence and carrying capacity. increase in biomass around day 110. Thus, rather than reflecting changes in K and I (Sverdrup 1953), this effect in our model is driven by predation. The increase of turbulence at the end of summer enhances the feeding rate on motile species, yielding Rn . Rm (around day 250), a condition that holds until the next spring. During spring, the zooplankton feed mainly on nonmotile phytoplankton because this is the most abundant prey (Fig. 3c). However, because the growth rate of the zooplankton is slower than Sensitivity to the parameters—The sensitivity of model predictions to the choice of parameter values was tested using summer conditions of I and e and calculating the concentrations at the fixed point for changing parameter values. The model outcomes are generally robust to changes in parameter values but are particularly sensitive to changes affecting the encounter rates (Table 1). In particular, the effects of predation (p), conversion efficiency (c), and encounter radius on nonmotile (a2) and motile (a1) phytoplankton are shown in Fig. 6. When no behavior-dependent mortality is prescribed in the model (p 5 0), the motile species dominates over nonmotile species because bc . ba in summer (Fig. 6a). However with p 5 0 the model will produce a larger spring increase of Rm over Rn (data not shown). Increasing p reduces C* and increases Rn , whereas Rm is not affected (Fig. 6a). On the other hand, increasing c affects all three species: C* increases and Rm and Rn decrease proportionally. It is worth noting that changes in c, which describes the simulated cell size, are not affecting the results for the phytoplankton community structure. Grazing behavior and plankton community 179 Fig. 3. Seasonal dynamics of (a) nonmotile (Rn, gray) and motile phytoplankton (Rm, black), (b) grazers (C), and (c) the behavioral trait t. Also shown are the values at the fixed point of the four state variables Rm , Rn , C*, and t* (dashed lines). In panel (c), the gray line shows the value of the behavioral trait (tratio) obtained with a different formulation of fitness (i.e., g[t] 5 r[t]/d[t]). The parameters with the largest effects are the encounter radii (a1,2), which determine phytoplankton community structure and grazer feeding behavior (Fig. 6c,d). Phytoplankton biomass is particularly sensitive to changes in the encounter radius; that is, a change of 30% in this value produces . 40% change in the relative biomass of one phytoplankton species. This is expected from the quadratic form, in which encounter radius appears in the feeding interactions with motile and nonmotile species. Variation among grazer species in hydrodynamic sensitivity and, hence, perception distances to phytoplankton prey, are likely to be of this magnitude, but phytoplankton species composition, biomasses, and phenology show similarly high variability. However, the predicted seasonal change in the relative significance of motile and nonmotile is robust to changes of parameters because the summer concentrations are controlled by the relative values of the fixed point (Fig. 2a). 180 Mariani et al. growth, mortality, predation, and adaptive grazing. Community dynamics are set not only by what is physically possible but also what is best in terms of fitness trade-offs for the myriad of individual organisms that make up the ecosystem (Margalef 1998). The ubiquitous actions of nonlinear and local interactions among a large number of biological and physical components result in the emergence (Levin 2005) of a marine food web within which trophic interactions take place in an ever changing setting. In pelagic ecosystems, light, nutrient availability, and turbulence are normally considered the major factors affecting the structure and function of the plankton community (Margalef 1978; Huisman et al. 1999, 2004), whereas the role of predation by zooplankton has received much less attention. However, we have demonstrated using a simple community model that adaptive zooplankton grazing behavior can promote coexistence among motile and nonmotile phytoplankton groups and drive a seasonal succession from nonmotile to motile forms. Additionally, adaptive grazing behavior enhances the stability of the model food web. Fig. 4. Average seasonal cycle of (a) modeled biomass of Rn (white) and Rm (black) shown as monthly averages (bars) and daily values (dark line, motile; gray line, nonmotile species); (b, c) diatoms (white bars) and dinoflagellates (dark bars) from (b) biomass data from a station near Stonehaven, Scotland (56u57.89N, 02u06.29W), averaged over 1997–2006 and (c) abundance data from the Continuous Plankton Recorder (CPR) survey in the entire North Atlantic survey area, averaged over 1958–2006. In panel (a), the maximum peak of the daily plot of nonmotile species occurring at , 600 mg C m23 is not shown. In panel (c), the dinoflagellates (dinofl.) exclude heterotrophic taxa but include photoautotrophs and mixotrophs. Finally, it is interesting to note that when an alternative formulation of fitness is used in the model (i.e., g[t] 5 r[t]/ d[t]), the results for the seasonal dynamic of the three species are the same, and the only difference that can be distinguished is for t, which has oscillations with slightly smaller amplitudes during summer and relative higher values during winter (Fig. 3c). Discussion General—In the pelagic ocean, plankton community dynamics are governed by a complex interaction between physical and biological processes. Physical variables such as light, temperature, turbulence, and upwelling have a direct effect on the marine ecosystem. Superimposed on the physical environment are biological processes, such as Seasonal cycle—The model reproduces characteristic features of the seasonal phytoplankton and zooplankton succession that are well documented in temperate oceans: (1) a distinct, short spring phytoplankton increase, (2) the seasonal variation in the relative abundance of motile and nonmotile phytoplankton (Margalef 1978; Cullen et al. 2002), (3) a deep phytoplankton maximum dominated by motile forms during summer, and (4) the seasonal variation in abundance of grazers with highest biomass during summer (Kiørboe 2008). We in particular emphasize the predicted seasonal successions of the phytoplankton from nonmotile to motile forms: this is a consistent feature of phytoplankton succession in temperate oceans and transcends variations in species composition (Margalef 1978). In our model, it is solely the result of the adaptive grazing behavior and is thus generated by processes entirely different from those that are normally considered to govern phytoplankton succession (turbulence, nutrients). Phytoplankton development is sustained by an increase in light availability during spring, and it is mainly composed of nonmotile phytoplankton. Dominance of nonmotile species in spring is a result of their higher abundance in winter. This is governed by grazing pressure that in autumn tends to be higher on motile species. In winter, grazer abundance is too low to control the nonmotile biomass. This pattern is supported by a recent analysis of bloom formation in the North Atlantic Ocean (Behrenfeld 2010) that found an important role for grazing in controlling the phytoplankton seasonal cycle. It is the seasonally varying balance between phytoplankton growth and grazing that controls bloom development (Evans and Parslow 1985; Irigoien et al. 2005; Behrenfeld 2010) and possibly the community structure of phytoplankton. However, in our model, the nonsaturating formulation of the feeding rates can likely overestimate the grazing pressure during the bloom phase, thus affecting the extent of the bloom period. Grazing process—One model prediction in particular is counterintuitive and in apparent conflict with experimental Grazing behavior and plankton community 181 Fig. 5. Seasonal dynamic of (a) Rn, (b) Rm, (c) C, and (d) behavioral trait t from the simulations with the coupled one-dimensional model. The results are for the stationary limit cycle reached by the model at year 7 after starting from uniformly low initial concentrations. (d) As reference, the black line indicates the edge of grazer concentration C 5 15 mg C m23. observations: that feeding-current behavior is favored under more turbulent conditions and ambush behavior under less turbulent conditions. Theory and experiments both suggest that prey encounter rates are significantly enhanced by moderate levels of turbulence in ambushfeeding grazers but not in feeding-current feeding ones (Kiørboe and Saiz 1995; Saiz and Kiørboe 1995). Moreover, copepods can switch between the two feeding behaviors, adopting the most rewarding feeding behavior (Kiørboe et al. 1996). This apparent discrepancy between model and observations is due to grazing interactions and population dynamics: in situations where ambush feeding would be most favorable, the abundance of motile cells is reduced to the extent that feeding-current feeding becomes more rewarding and vice versa. This dynamic aspect of grazer interactions is not easily reproduced in laboratory experiments and, therefore, often not considered. Our results and a growing appreciation for the importance of grazing in shaping plankton communities (Armstrong 1994; Behrenfeld 2010) provide a complementary view to the classic bottom-up paradigm, which considers competing phytoplankton species with different growth, swimming, and sinking rates but all with an equal grazing term. We suggest that trophic interactions, in particular adaptive feeding behavior of zooplankton grazers, might be important for structuring plankton communities. Additionally, the model predicts that there should be vertical gradients in the relative significance of the two feeding modes. Limitations of the model—The model we have analyzed is highly idealized and disregards a number of processes thought to affect phytoplankton community structure. In particular, the model does not include some processes often considered key in explaining the seasonal variation in phytoplankton community structure, viz. trade-offs in phytoplankton nutrient competition (Huisman et al. 1999; Litchman et al. 2007), and phytoplankton mortality due to sinking (Huisman et al. 2002). This simplification is deliberate because it focuses attention on the effects of trophic interactions, their modulation by environmental factors (turbulence and light) and adaptive behavior, in shaping phytoplankton communities. However, the differential grazing effect in our model might be overestimated with respect to motility as a trait. Other properties of the prey, such as cell size or ‘‘quality’’ (i.e., stoichiometry), are known to influence grazing preferences of copepods (Kiørboe 2008). These properties correlate both positively 182 Mariani et al. Fig. 6. Summer values of Rn (light gray) Rm (dark gray), and C* (black) as function of (a) predation risk (p), (b) conversion efficiency (c), and (c) reaction distance to nonmotile prey (a2) and (d) motile prey (a1). Dot symbols indicate the parameter values used in the model. as well as negatively with motility, depending on the current species composition. Although such additional processes can be included in the model, increasing model complexity comes at the cost of increasing the number of parameters. Additional, detailed modeling analyses exploring different plankton traits and their trade-offs, along with adaptive grazing behaviors, are interesting directions that can be addressed in future. We have considered a generalist grazer that can switch between feeding in either of two modes, and we have been able to make a quantitative analysis because the trade-offs between the two feeding modes are known. In nature, however, there are specialist grazers that are obligate ambush or feeding current feeders, and their feeding behavior is not adaptive. To supplement the model with specialist feeders requires knowledge also of the trade-offs between being a generalist and a specialist for the two feeding modes. We can hypothesize that the specialists are better at their métier than the generalists in terms of higher encounter rates and lower mortality rates. However, we are presently unable to quantify these trade-offs. Quantification could be achieved from a reanalysis of, for example, the encounter rates of the two feeding modes (Kiørboe 2011), wherein the organisms are divided between specialists and generalists. Modeling implications—Laboratory and direct observations suggest that copepod feeding behaviors can adapt to changing local environmental conditions. Plasticity in behavior implies changes in copepod vital rates, such as feeding and mortality rates (Mariani and Visser 2010). We have shown that when behaviorally driven changes in vital rates are introduced in a simple population model, several features of the plankton community can be reproduced. We do not contend that this model is a fully realistic description of plankton communities because the model does not resolve many processes that are important in plankton dynamic. For example, we do not explicitly resolve the sinking of cells, which could be responsible for the overestimate of winter concentrations of diatoms (Fig. 5). Similarly, a more detailed description of the nutrient uptake dynamics could promote a competitive advantage for dinoflagellates in summer. On the other hand, the simplicity of the model makes the interpretation of the results easier than more complex models of plankton dynamics, which typically include many parameters and variables. Using this simple model, we are able to demonstrate that fitnessoptimizing adaptive responses of grazers’ foraging behavior can promote seasonal changes in plankton community. We suggest that more comprehensive, global models of marine ecosystems should account for behavioral changes and adaptation of zooplankton (Follows et al. 2007). It has been suggested that behavioral control of zooplankton feeding on multiple resources can be heuristically described using several classes of switching functional Grazing behavior and plankton community responses (Gentleman et al. 2003) and that subtle changes in the functional response can produce remarkable differences in species distribution and abundances simulated by complex ecosystem models (Anderson et al. 2010). Although a systematic comparison between our approach and switching functional responses is outside the scope of our paper, we note that our approach is consistent with an active switching functional response (Gentleman et al. 2003). Indeed, the fixed point t* is equivalent to the standard parameterization of constant predator preference on multiple resources (Gentleman et al. 2003). However, using t* in the integration of Eq. 2, the two resources have a neutral dynamic, and the model would produce no changes in phytoplankton community under seasonal forcing. Our model description of varying predator preference (Eq. 4) differs from a simple switching function because it depends not only on relative abundances but also the other factors influencing grazer fitness (turbulence, light, and predation). The emergent community structure of the modeled system arises as much through fitness-optimizing behavior of grazers as it does through population dynamics. This property of the model is based upon a mechanistic description of the encounter process (with both predator and prey) and on observed adaptive feeding behavior in zooplankton. Acknowledgments We are grateful to Jens Rasmussen and Eileen Bresnan as well as the Sir Alister Hardy Foundation for Ocean Science for the data in Fig. 4b. Thanks also to Chris Klausmeier for discussions during the revision of the paper. The authors also thank the reviewers and the editor R.W. Sterner for their valuable comments during the manuscript review process. This work was supported by a Danish Research Council grant (272-07-0485). P.M. was supported by the projects: ‘‘Sustainable fisheries, climate change and the North Sea ecosystem’’ (SUNFISH, Danish agency for Science, Technology and Innovation) and ‘‘Towards coast to coast networks of marine protected areas’’ (COCONET, EU FP7 Grant Agreement 287844). A.D.B. was supported by the Gordon and Betty Moore Foundation’s Marine Microbiology Initiative. 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