Download Selective feeding by protozoa: model and

Journal of Plankton Research Vol.20 no.S pp.831-846, 1998
Selective feeding by protozoa: model and experimental behaviors
and their consequences for population stability
Suzanne L.Strom and Harilaos Loukos1
Shannon Point Marine Center, Western Washington University, 1900 Shannon
Point Road, Anacortes, WA 98221 and 'Joint Institute for the Study of the
Atmosphere and Ocean (JISAO), Box 354235, University of Washington, Seattle,
WA 98195, USA
Abstract. Selective feeding by zooplankton can have profound consequences for the stability of grazer
and prey populations, as demonstrated by the behavior of plankton dynamics models. We present an
analytical approach—calculation of prey ratio trajectories—that reveals unambiguously whether
selective feeding behavior is stabilizing (i.e. provides a refuge for preferred prey species at low prey
concentrations) or destabilizing (i.e. results in elimination of prey populations). Prey ratio trajectories
were calculated for three modeled selective feeding behaviors. Constant selection was consistently
destabilizing, while selection behaviors that changed in response to either prey ratio or prey
abundance could be stabilizing. Batch culture experiments with four protozoan grazer species (three
ciliates, one heterotrophic dinofiagellate) demonstrated that protozoa fed selectively in every case,
weakly preferring the larger of the two algal species offered. Stabilizing selection was observed only
in the experiment with Favella sp., however, meaning that only this species altered its selection
behavior in response to changing experimental conditions. Because protozoa are the major grazers
of phytoplankton in many planktonic systems, our findings indicate that the use of selective feeding
behaviors to stabilize plankton dynamics models needs careful evaluation. The modeling and
graphical techniques presented here are a tool for linking further exploration of selective feeding
behaviors with the development of plankton dynamics models.
Introduction
'The value of information is generally finite, and partial reductions in ambiguity often
may be good enough.' (Stephens and Krebs, 1986, p. 79)
'God is in the details.' (L. Mies van der Rohe)
Selective feeding has long fascinated ecologists, from the details of individual
feeding behavior to the implications for predator-prey dynamics and coevolution. Selective feeding is denned as an imbalance between the proportion
of prey types in a predator's diet and the proportion in the environment. The
phenomenon is a compelling one because it suggests that predator feeding
behavior may regulate not only the biomass, but also the composition of the prey
community. In marine planktonic ecosystems, selective feeding by protozoa
should be of particular importance given that these grazers are thought to constitute the major source of bacterial and-phytoplankton mortality (Pomeroy, 1974;
Pace, 1988; Sherr and Sherr, 1993). Thus, selective feeding by planktonic protozoa could strongly influence the size and composition of marine bacterial and
phytoplankton communities.
The phenomenon of selective feeding is also important in the context of prey
population stability. This can be seen in an examination of ecosystem dynamics
models. Such models attempt to replicate and predict natural temporal patterns
in generalized biomass compartments (e.g. primary producers, herbivores) and
© Oxford University Press
831
S.LStrom and H.Loukos
ecosystem function (e.g. nutrient flux, material export). The basis for the models
often constitutes a balance between experimentally determined organism
responses and capabilities, on the one hand, and parameter adjustments needed
to replicate field observations, on the other. A long-standing conundrum in such
models (e.g. Steele, 1974) is the mechanism by which predators avoid eliminating their prey populations. Certain types of selective feeding can provide a solution to the problem. For example, Fasham et al. (1990) chose a prey ratio-based
selection function (see below) to describe zooplankton grazing on bacteria,
detritus and phytoplankton in their North Atlantic model. Because prey ratiobased selection relieves grazing pressure on the rarest prey types, the behavior
creates a refuge for prey populations, and stabilizes both predator and prey
abundances.
There is a good deal of evidence suggesting that planktonic protozoa are
capable of selective feeding (see reviews by Stoecker, 1988; Verity, 1991b). On a
mechanical level, some protozoans feed most efficiently (i.e. exhibit highest clearance rates) over a narrow prey size range, indicating that many potential prey
particles may be excluded from the diet simply by virtue of size (summarized by
Hansen et al., 1994). Other morphological attributes (e.g. spines) can similarly
restrict availability. On a behavioral level, ciliates and flagellates clearly have
chemosensory abilities, and can locate and remain near preferred prey types (e.g.
Levandowsky and Kaneta, 1987; Sibbald etai, 1987; Verity, 1991a). Furthermore,
high-speed videos show that ciliates can actively reject less preferred prey
particles even after they are captured (Taniguchi and Takeda, 1988; Stoecker et
al, 1995). Studies comparing diet with the composition of available prey provide
additional evidence for selective feeding: protozoans have been shown variously
to prefer live over dead cells, 'high-quality' over 'low-quality' foods, algae over
glass beads, etc. (summarized in Verity, 1991b).
This paper presents a critical look at both model formulation of selective
feeding as a stabilizing predation behavior, and at experimental data supporting
this hypothesis. By extending previous modeling efforts, we show that stabilizing
behavior can be obtained through abundance-based selection. A new method for
the analysis of data from simple batch culture experiments is described; this
method reveals whether protozoan grazers exhibited stabilizing or destabilizing
predation behavior during experiments, although it does not unambiguously
reveal the behavioral basis of that selection. We conclude that, on the one hand,
the repertoire of grazer behaviors that tend to stabilize plankton dynamics
models should be extended to include abundance-based selection. On the other
hand, the limited available evidence suggests that stabilizing selection behavior
among protozoan grazers is uncommon, pointing to the need for further experimental and theoretical work on this important ecological phenomenon.
Selective feeding mechanisms: behavioral basis
Constant selection
With constant selection, grazer preference does not vary with changes in prey
availability; rather, grazing is continuously more effective on one prey type than
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Protozoan selective feeding
on another. Selection is an invariant function of grazer feeding capability and
prey morphological or physiological state (hence 'constant'); the terms 'mechanical selection' and 'passive selection' have also been used (e.g. Fenchel, 1986;
Monger and Landry, 1991; Verity, 1991b). Differences in ability to feed could
arise at the level of prey capture, handling, ingestion, or some combination of all
three. The net result is that the availability of prey to the grazer is not the same
as it would appear from a count of prey abundance in the environment. Size and
shape are probably the most obvious cause of differences in prey availability, and
many studies have reported size selectivity by protozoa (e.g. Andersson et ai,
1986; Chrzanowski and Simek, 1990; Kivi and Setala, 1995). There may also be
differences in the predator's ability to detect various prey types: some may be
sensed at a greater distance from the grazer, increasing the encounter rate
between grazer and prey (Gerritsen and Strickler, 1977; Price and Paffenhofer,
1983). Constant selection does not involve behavioral changes on the part of the
grazer, although longer term morphological or physiological adaptation could, of
course, alter the range of available prey. In the short term, however, constant
selection does not vary with variations in the abundance or composition of the
prey community.
Prey ratio-based selection
This and the abundance-based selection described below take as their premise
the idea that protozoa can feed selectively, in response to differences in prey size,
nutrient or toxin content, or any of a host of other variables. They go on to
describe how selection might vary as prey availability changes—hence their
importance for plankton dynamics models.
In prey ratio-based selection, selective feeding is dependent on the proportion
(ratio) of prey types in the environment. The most commonly invoked form of
ratio-based selection has been termed 'switching', defined by Murdoch (1969, p.
336) as follows: 'Given that both prey (types) are eaten when presented alone to
a predator, does the predator switch from the one prey when it becomes rare to
the other which is more abundant?' By providing a refuge from predation when
a prey type becomes rare, this behavior can prevent the elimination of any prey
type and can stabilize prey abundance (but see May, 1977). At equal prey abundances, a grazer may or may not exhibit a preference for one prey type over
another. The phenomenon is more robust when selection is strong (i.e. preference for the more abundant prey type is marked), and weak selection can, under
certain sets of conditions, have counter-intuitive destabilizing effects (Hutson,
1984).
Ratio-based selection requires behavioral changes on the part of the grazer.
Somehow, the grazer must sense the composition of the prey field and alter
feeding behavior in response; in consequence, this and the abundance-based
selection described below are sometimes termed 'active selection'. For protozoa,
the underlying biological mechanisms are not clear, although it seems possible
that information on prey composition obtained during digestion, for example,
might feed back to other cellular components, triggering alterations in cell surface
833
S.I~Strom and H.Loukos
properties that affect encounter, capture or handling capabilities. In support of
this, Tarran (1991) found that the heterotrophic dinoflagellate Oxyrrhis marina
apparently 'learned' to avoid latex beads when offered in a mixture with algae,
showing no selectivity at the beginning of the experiment, but feeding only on
algal cells after several hours of exposure to the mixture.
Other data suggestive of prey ratio-based selection exist for protozoan grazers.
Oxyrrhis marina has been shown to switch from a diet of Isochrysis galbana to
cannibalism as I.galbana concentrations decreased (Flynn etal, 1996). Grazing a
preferred alga to low concentration, followed by a switch to disproportionate
grazing on a more abundant algal species, was found in studies of the
chrysomonad flagellate Paraphysomonas imperforata (Goldman and Dennett,
1990) and the dinoflagellate Gymnodinium sp. (Strom, 1991).
Abundance-based selection
This mechanism postulates that selective feeding is a function of prey abundance.
Abundance-based selection has been formalized in the basic prey model of foraging theory (summarized by Stephens and Krebs, 1986). The behavioral argument
postulates that a grazer will feed preferentially on the highest energy-yielding
prey when it is abundant, because in this case ingestion of alternate prey will
lower net energy assimilation by 'getting in the way'. When the highest energyyielding prey is sparse, however, ingestion of alternate prey is predicted because
this will increase net energy assimilation (Lehman, 1976; Stephens and Krebs,
1986).
Under the most rigorous set of assumptions (i.e. encounter with individual prey
items is sequential and random; energetic costs of prey rejection are zero; searching and handling are mutually exclusive), the prey model predicts that a less
preferred prey type (Q) should either always be ingested or always be ignored
when encountered ('all-or-nothing' selection). However, either individual variance in the abundance of preferred prey (P) giving rise to ingestion of Q, or nutrient and toxin constraints (e.g. Q is an important source of a limiting nutrient, P
and/or Q contain a toxin that can only be tolerated in limited amounts) will lead
to partial selection (Stephens and Krebs, 1986). Similarly, significant energetic
costs associated with rejection can lead to selection that is dependent on the abundance of both prey types, rather than just the abundance of preferred prey
(Lehman, 1976; DeMott, 1990). Our model of abundance-based selection (below)
includes these variants on 'all-or-nothing' selection.
As for prey ratio-based selection, abundance-based selection requires behavioral changes on the part of the grazer. There is evidence for abundance-based
selection in a recent study by Jiirgens and DeMott (1995), which examined
feeding on a mix of prey types by the flagellates Bodo saltans and Spumella sp.
Selection strength was directly proportional to the concentration of bacterial
prey. Similarly, Heinbokel (1978; his Figure 10) found that the ciliate Eutintinnus
pectinus fed preferentially on l.galbana when the concentration of that alga was
high, but exhibited no selectivity at low l.galbana concentrations.
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Protozoan selective feeding
Method
Grazing experiments
Experimental procedures are described in detail in Strom and Morello (1998).
Briefly, four species of protozoan grazers (three ciliates, one heterotrophic dinoflagellate) isolated from coastal Washington and Oregon (USA) waters were
tested in simple growth assays to determine which paired combination of phytoplankton species supported the highest protozoan growth rates. These phytoplankton species were grown to late exponential phase in stock cultures; each
experiment was initiated by adding the two phytoplankton species to sterilized
0.2-um-filtered sea water in each of six or eight replicate 23 1 polycarbonate
carboys (Table I). Protozoan grazers were then added to half (three or four) of
these carboys at initial concentrations of 0.1-19.7 cells ml"1. The initial volume in
carboys was 11-23 1, while initial total algal concentration was 10 400-29 600 cells
ml"1 (194-271 ug C I"1) (Table I). Carboys were then incubated at 13°C in dim
light (<1 umol photons nr 2 s"1) with a 12:12 h light:dark cycle. After gentle
mixing, samples were withdrawn at 12-24 h intervals for 6-10 days for determination of algal abundance (by epifluorescence microscopy) and protozoan grazer
abundance (by inverted microscopy). Algal biomass was estimated from algal
abundances and cell volumes using the equations of Montagnes et al. (1994; see
also Strom and Morello, 1998).
Protozoan grazer abundance during each sampling interval was calculated from
a logarithmic average (e.g. Heinbokel, 1978); clearance and ingestion rates were
calculated using the equations of Frost (1972). These equations account for
changes in control algal abundance in determining clearance and ingestion rates.
Selective feeding models
The approach used here stems from the work of Chesson (1978,1983), and has
been applied in various forms to plankton dynamics and food web models by Pace
et al. (1984), Evans (1988), Fasham et al. (1990), Armstrong (1994), and possibly
Table I. Grazer and algal species, and initial concentrations (IC) for selective grazing experiments.
Initial concentrations are in cells ml' 1 for grazers and in pg C I'1 for algae
Exp.
Grazer
IC
1
Favella sp.
0.1
2
0.4
3
Strombidinopsis
acuminatum
Amphidinium sp.
14.9
4
Uronema sp.
19.7
AlgaQ
AlgaP
Species
IC
Species
IC
Gymnodinium
simplex
Prorocentrum
minimum
Pyrenomonas
salina
Pyrenomonas
salina
154
Pyrenomonas
salina
Pyrenomonas
salina
Isochrysis
galbana
Isochrysis
galbana
117
49
156
204
145
82
49
Alga P, preferred algal species; alga Q, less preferred algal species.
835
SJLStrom and H.Loukos
others. In particular, model development follows that of Fasham et al. (1990; their
Appendix A), although the arguments here are developed more extensively. In
this approach, the probability of ingesting any particular prey type is proportional
to the combined probabilities of encountering and capturing that prey type. This
combined probability (Chesson's a, here p for preferred prey type P, q for less
preferred prey type Q) can be used to model feeding success by multiplying it by
the prey abundance NP or NQ. In this case, 'One can think of the predator behaving as if there were pNP individuals of prey type P instead of NP and the probability of ingestion is the probability of selecting, at random, a prey of type P from
this distorted population' (Chesson, 1978, p. 212, our notation).
In the models described below, preference values p and q were considered to
describe the relationship between the proportion of a prey type in the environment (NyJNtola\, where Ntotai = total P re y abundance) and the proportion in the
diet (£>p/£\otai. where £>,oui = total prey in the diet) when NP = NQ, such that:
£Wotal
and
P
Thus, the total (p + q) sums to the total number of prey types in the system (here
2) when NP = NQ, and the preference values can be used directly, as shown below,
to scale grazing (e.g. in the no preference case, p = q = 1, and pNf = NP). Note
that p and q are prescribed values that are constants for a given model run, and
could be measured readily in a simple experimental system.
The specific grazing rate (G) was modeled using the Michaelis-Menten equation:
where g is the maximum specific grazing rate, kg is the half-saturation constant
for grazing and A = pNP + qNQ = the total phytoplankton concentration as
perceived by the grazers (see Table II for a summary of model notation). Grazing
on a particular prey type (e.g. Q) is then scaled to the perceived abundance of Q
by substitution of preference-scaled prey concentrations into equation (2):
Table II. Model variables and initial conditions
Variable
Symbol
Value
Units
Maximum specific grazing rate
Grazing half-saturation constant
Gross growth efficiency
Grazing saturation level (= 2 &g)
Constant selection coefficients
Prey ratio-based selection coefficients
Abundance-based selection coefficients
Initial conditions
Concentration of preferred algal species
Concentration of less preferred algal species
Grazer concentration
8
4
100
0.25
200
day 1
ugCI-1
250
250
2
ugCI- 1
MgCM
ugCI-'
836
k
J
i
•"sal
ugCl- 1
P. 9
P*,<7*
N?
NQ
H
Protozoan selective feeding
~u
kg + (pNP
+
(3)
qNQ.g
y
The simplest case, constant selection, is represented by equation (3). In the case
of prey ratio-based selection, preferences are denoted by p* and q*, which
depend on prey proportion in the environment:
p* =
pNP
and
pNP + qNQ
(4)
pNP + qNQ
Substitutingp* and q* for p and q in equation (3), grazing on prey type Q is then
described by:
GQ =
(5)
g
Abundance-based selection was modeled in two different ways, corresponding
to the predictions of foraging theory under different energy- or nutrientlimitation scenarios (see above). First, grazing on the less preferred species (Q)
was modeled as 'all or nothing' (e.g. full selection or no selection), with the transitional prey abundance a function of NP. The rigorous (and biologically unrealistic) 'all-or-nothing' assumption was relaxed by assuming some degree of
variance around the transitional prey abundance; below, we illustrate selection
patterns corresponding to high and low levels of variance. This variance represents partial selection, and could correspond to differing perceptions of the transitional prey abundance, among individuals in a grazer population. Second,
selection was modeled as directly proportional to prey abundance. In this case,
selection becomes progressively weaker as prey abundance decreases, rather than
alternating between 'all' and 'nothing' states. Throughout, /?** and q** are used
O.Oq100
200
300
400
500
1
Abundance of P (^gC liter )
Fig. L Logistic curves used to model variance around 'all-or-nothing' transitional prey abundance
(see the text for a description of the abundance-based selection model). Slope values of 30 and 6 used
to represent transition point variances of ±10% i4 sat and ±50% ^sat, respectively.
837
S.L*Stroin and H.Loukos
to denote abundance-based preferences, and were substituted for p and q [equation (3)] in the various model runs.
Grazer selectivity in the case of 'all or nothing' abundance-based selection was
simulated using a logistic curve with asymptotic minimum = 0 and asymptotic
maximum = q (Figure 1):
(6)
where b = slope of logistic curve and the transitional prey abundance = Aax = the
lowest prey concentration at which grazing is saturated. This formulation assumes
that selection will occur only when prey concentrations are high enough to saturate grazing, an assumption that could easily be varied. Slope (b) values of 6 and
30 were used to simulate grazer populations in which 90% of the individuals transitioned between 'all' and 'nothing' behavior within ±50% Asat (a 'messy' population) and ±10% Aat (a 'tidy' population), respectively (Figure 1).
Selection proportional to the entire range of prey abundance was simulated in
two ways.
1. For selection proportional to preferred prey abundance Q?** and q** <* NP):
lfNP<Asat,p**=l-^-(l-p)
and q** = l-^L(l-q)
•"sat
,
(7)
-"sat
If NP>Asat,p**=p,q**
=q
2. For selection proportional to the abundance of each individual prey type {p**
oc Nj>, q** « NQ), P** is defined as in equation (7), while q** is defined analogously:
lfNQ<Asat,q** = l-^(l-q)
(8)
•"sat
IiNQ>:Asat,q**
=q
Models were run as a series of 0.1 day time steps. Ingestion (/, ug C'h1) was
calculated separately for P and Q during each time interval At according to:
(9)
(and analogously for Q), where the grazer concentration (H) was given by:
// ( 0 = £(/„,_„ + /Q ( ,- D) + //(,. i)
(10)
1
For model runs, values of the maximum specific grazing rate g (4 day ) and gross
growth efficiency E (0.25) were based on average values from these grazing
experiments (Strom and Morello, 1998; see Table II). The half-saturation
constant kg (100 ug C I"1) was estimated based on literature values (Heinbokel,
1978; Verity, 1985). Note that the only effect of changing g or E in this model is
to change the time required for the grazer population to remove all phytoplankton. Since model output is normalized to time (see below), values of g and
838
Protozoan selective feeding
E ultimately have no effect on prey ratio trajectories and assessments of stability.
Model runs were initialized with conditions (phytoplankton and grazer abundances) resembling those at the start of actual grazing experiments (Tables I and
II), and allowed to run until all phytoplankton were consumed. For simplicity,
phytoplankton growth during model runs was 0 (as in grazing experiments); the
effect of varying phytoplankton growth rate could be investigated readily with
this model formulation.
Results
Selective feeding was observed during all four of the grazing experiments, as indicated by continuous decreases in the relative abundance of the preferred prey
over time (Figure 2). Changes in A/pWQ in control carboys were
4
6
Time (d)
Fig. 2. Prey composition, grazer and prey abundance during grazing experiments. Upper panels:
Np:NQ = preferred prey abundance:less preferred prey abundance (ug C I"'). Lower panels: • , grazer
abundance; A, Pyrenomonos salina abundance; O, abundance of Gymnodinium simplex (A), Prorocentrum minimum (B) or Isochrysis galbana (C, D). An asterisk indicates the preferred algal species
in each experiment.
839
S.L-Strom and H.Loukos
Table III. Dimensions of algal species used in experiments. Measurements are averages of 40
observations; all cells preserved in 5% acid Lugol's. Length (L) and width (W) in um, ER is the
effective radius = (L + W)I2. p = logC/yFjJ/logCf/fig/f/?,™) (Monger and Landry, 1991), where Fis
the clearance rate. F\JFsm was calculated as the average of clearance rate ratios from all carboys and
all time intervals except the first two of each experiment. As the larger alga was always preferred,
ERflERQ = ERJER
Exp. Alga
1
L
11.2
Gymnodinium simplex
11.2
Pyrenomonas salina
8.0
Prorocentrum minimum' 1 11.1
Prorocentrum minimum* 16.1
9.0
Pyrenomonas salina
Pyrenomonas salina
9.4
Isochrysis galbana
4.0
Pyrenomonas salina
9.2
3.8
Isochrysis galbana
W
ER
6.7
6.7
4.7
10.0
14.6
4.9
5.0
3.7
5.0
3.6
9.0
9.0
6.4
na
15.4
7.0
7.2
3.9
7.1
3.7
ERp/ER0
0
1.4
1.5
2.2
1.5
1.8
0.4
1.9
1.0
••Dimensions of cell contents.
^Dimensions of thecae.
non-directional and slight by comparison (data not shown). Protozoan grazers
showed a consistent preference for the larger of the two prey species offered.
Note that Pyrenomonas salina was offered in every experiment; it was the
preferred prey, however, only during experiments 3 and 4 when it was the larger
of the two algal species (Table III). Furthermore, the larger species was preferred
regardless of whether it was initially more or less abundant than the smaller
species (Table I).
No major differences among the four grazing experiments were seen when
data were viewed in a conventional manner (i.e. as changes in prey abundance
or prey ratio over time; Figure 2). We explored several alternative methods of
evaluating changes in prey ratio [Np/(NP + NQ)] over the course of each experiment. It seemed likely that the trajectory of the change could be related to both
preference strength and the selection mechanism. As the independent variable
(Figure 3), we used the cumulative amount of algae ingested, rather than time,
to normalize among experiments with different time courses. Cumulative algae
ingested was expressed as a fraction of total algae ingested to normalize among
replicate carboys within an experiment. This approach (Figure 3) shows how the
prey ratio changes as increasing amounts of algae are ingested by grazers. Plotting against cumulative Q ingested (as opposed to cumulative P, or cumulative
P + Q) provided the best separation between the three grazing models, as well
as among the varying degrees of preference (e.g. magnitudes of p and q) for a
given model.
The three selective feeding models are distinguished by fundamental differences in the behavior of the prey ratio when plotted as a function of cumulative
Q ingested. The strongly concave-downward trajectories of the constant selection
model (Figure 4A), with values of the prey ratio going to zero (i.e. all preferred
prey removed by grazers), show that, as modeled here, this feeding mechanism is
fundamentally destabilizing. In contrast, the concave-upward trajectories of the
ratio-based model (Figure 4B), in which the prey ratio always remains above
840
Protozoan selective feeding
0.3 -
o
0.6- o o
\ ^
o
oo
f
a
_
0.4 -
<p
On
2
»
o
0.2 -
o
0.2 A) Favetla
0.00.0
0.2
0.4
0.6
0.8
Cum P. salina ingested
1.0
0.2
0.4
0.6
0.8
Cum P salina ingested
0.8 -
o
o
a
§
0.6-
1.0
o
a
o
^^^^
°
^ \
\
" o
2 0.4 %
Si
a. 0.2 -
\
o
o
\°
o
oo
0) Uronema
0.00.2
0.4
0.6
0.8
Cum I. galbana ingested
1.0
0.0
V
" 1 ""
1
I"
0.2
0.4
0.6
0.8
Cum I galbana ingested
,—
1 0
Fig. 3. Data from four grazing experiments (observations from three or four replicate carboys pooled)
showing prey ratio trajectories (see the text). Cumulative ingestion normalized to total ingestion for
comparison among carboys and experiments (curves drawn by visual inspection). Note that experiments were sometimes terminated before all algae were grazed, whereas models (Figure 4) were
always run until all prey were grazed.
zero, show how this behavior stabilizes population dynamics by providing a
refuge for the preferred prey.
Abundance-based selection can lead to either stabilizing or destabilizing prey
trajectories, depending on the model version used. All-or-nothing grazing with
preference based on the log curve (Figure 4C and D) mimics constant selection
because, with this experimental design, most of the grazing takes place at prey
concentrations below A^, where q** varies from q by only a few percent [equation (6)]. The draw-down of P before any Q are ingested distinguishes abundancebased from constant selection models, but would be hard to detect given realistic
microscope-based cell count variances. Selection proportional to prey abundance
(Figure 4E and F) mimics ratio-based selection in these experiments because prey
proportion and prey abundance varied together over the course of the incubation.
Taken as a whole, the curves indicate that abundance-based selection can lead to
stabilizing predator-prey interactions only when selective feeding behavior
persists down to low prey concentrations.
Experiment data, plotted as for the models, show that protozoa in three out of
four experiments had destabilizing selective feeding behavior (Figure 3). For
Strombidinopsis, Amphidinium and Uronema, the curves are strongly concave
841
SJLStrom and H.Loukos
oo
02
04
os
OmQngnM
oe
00
0.2
04
OS
OS
CumOmgraad
Fig. 4. Prey ratio trajectories as predicted by different selective feeding models. Trajectories represent prey ratios [Np/(Np + NQ)] as a function of the cumulative ingestion of the less preferred prey
species (Q). Cumulative ingestion normalized to total ingestion for comparison with experiment
results. Lines show trajectories for values of q (preference for algal species Q) ranging from 0.1 to 1.0
(see the text). (A) Constant selection. (B) Prey ratio-based selection. (C + D) 'All-or-nothing' abundance-based selection with logistic curve slope values equal to (C) 30 and (D) 6. (E + F) Abundancebased selection with (E) selection proportional to abundance of the preferred prey type P; (F)
selection for P proportional to abundance of P, selection for less preferred prey type Q proportional
to abundance of Q.
downward. Not all experiments were run until prey were completely removed, so
not all curves go to prey ratios of zero. All three do, however, decrease to low
prey ratios relative to initial ratios. Further, the shapes of the curves indicate that
selection against the less-preferred prey type was weak (i.e. by comparison with
model curves, values of q were only slightly <1).
The remaining experiment, with the tintinnid Favella sp. as grazer, resulted in a
fundamentally different type of selection behavior. The weakly concave-upward
842
Protozoan selective feeding
curve and the slight decrease in prey ratio (Figure 3A) demonstrate a stabilizing
selection mechanism. Further, Favella showed a complete reversal in preference
during the last time interval of the experiment when the abundance of the
preferred Gymnodinium simplex became very low (average 15 ug C I"1). A transition to preferential grazing on Rsalina during this last time interval resulted in a
sharp increase in the prey ratio. Removal of preferred prey to this level or lower
in other experiments did not result in similar preference reversals.
Discussion
All protozoan grazers tested showed selective feeding, with consistent preference
for the larger of the two prey species offered. Though common, preference for
larger prey is by no means universal (Hansen et al, 1994), and may simply indicate that prey characteristics likely to override size (e.g. ease of capture, nutrient
or toxin content) were eliminated by the use of optimized diets in our experiments.
More importantly, selection against smaller prey was weak in all four experiments. That is, prey ratio trajectories most closely resembled model curves with
q values close to one. This finding agrees with the prediction of the Force-Balance
model (Monger and Landry, 1990,1991), which predicts that clearance rates (F)
should be roughly proportional to the 0.7-1.0 power of prey radius (/?). The
coefficient p, which indicates the strength of clearance rate size dependence, is
given by:
(£) &Y
<»>
(Monger and Landry, 1991). Values of P calculated for our experiments ranged
from 0.4 to 1.5 (Table III), substantiating the idea that size-based preferences are
roughly proportional to prey radius and lead to weak selection.
Selective feeding was destabilizing in three out of the four experiments, as
shown unequivocally by prey ratio trajectories (Figure 3). This means that three
of the protozoan species, while capable of feeding selectively, did not alter their
selection behavior in response to changing conditions in the batch cultures.
Alteration of selection behavior is required to produce stabilizing prey trajectories, i.e. to create a behavioral refuge for the preferred prey when its concentration is low. It is important to note that weak selection can still be destabilizing,
as is clear from both actual and modeled prey ratio trajectories (Figures 3
and 4).
Uniquely among these protozoa, the tintinnid Favella sp. did exhibit stabilizing selection behavior. Selective feeding behavior by this ciliate must have
changed during the course of the incubation, in response to changing prey ratios,
prey abundances, or some combination of both. Behavioral shifts by Favella are
also indicated by the complete reversal in algal preference during the last time
interval of this experiment, a phenomenon not seen when other grazers experienced similar variations in prey availability.
Two high-speed video studies of Favella (Taniguchi and Takeda, 1988; Stoecker
843
S.LStrom and H.Loukos
et al., 1995) indicate that this ciliate can actively reject particles after capture.
Stoecker et al. (1995) also showed that Favella usually ingests larger particles at
higher rates, due in part to enhanced capture efficiency in the outer and middle
ciliary zones. In other words, Favella is better able to capture large particles with
the entire ciliary apparatus, and has the ability to reject less desirable particles
after capture. It is also suggestive that rejection behavior was more frequent when
overall food densities were higher, in agreement with an abundance-based selection mechanism. Thus, one possible explanation for Favella's unique selection
behavior is that this ciliate has an unusual ability to modify its feeding behavior.
An alternative hypothesis is that phenotypic changes over the incubation period
caused changes in feeding behavior in this, but not the other, protozoan species
studied.
The modeling technique presented here shows that abundance-based selective
feeding can be stabilizing under certain conditions. Specifically, grazer behavior
must change in response to prey abundance even at very low prey concentrations.
The 'all-or-nothing' abundance response predicted under the most basic assumptions of foraging theory is not stabilizing because the abundance response 'turns
off' at relatively high food levels. On the other hand, selection behavior that
responds to the entire range of prey abundance provides a behavioral refuge for
the preferred prey that is essential for stability. We propose that abundance-based
selection is an alternative hypothesis for the real-world temporal stability of many
planktonic ecosystems, and should be explored further in ecosystem models and
grazing experiments.
Stabilizing selective feeding behavior was not common among the protozoa
that we studied. This calls into question the legitimacy of using such behaviors as
stabilizing mechanisms in plankton dynamics models. Because the nature of
herbivore-phytoplankton interaction is so critical to model stability, we urge
further exploration of both the behavioral repertoire of protozoan herbivores and
the range of approaches that can provide model stability. An additional unresolved question is whether stabilizing selection behaviors can be used in models
as proxies for whole community responses (e.g. Fasham et al., 1990). In other
words, to what extent can the grazer species assemblage—the 'meta-population'—alter in response to changes in prey availability, and can such alterations
give rise to stabilizing selection behaviors that are not achievable by single
species? Our modeling and graphical technique is a tool for closer articulation of
model and experiment development. Use of this theoretical framework to design
and optimize experiments, and translation of results directly into ecosystem-level
plankton dynamics models, should speed progress in this research area.
Acknowledgements
The Uronema sp. culture was graciously provided by B. and E.Sherr. We thank
T.A.Morello, W.Arthurs, J.Holmes and K.Thompson for assistance with culture
maintenance, cell counts and grazing experiments. Helpful comments were
provided by G.Wolfe, B.Frost, the SPMC Plankton Group and two anonymous
reviewers. This research was supported by NSF grant OCE 9301698 (to S.S.). H.L.
844
Protozoan selective feeding
was supported by a JISAO fellowship under a cooperative agreement with
NOAA (NA67RJ0155). This publication is JISAO contribution no. 478.
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Received on December 4,1996; accepted on December 9, 1997
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