Oecologia (2007) 153:281–290
DOI 10.1007/s00442-007-0728-9
POPULATION ECOLOGY
Predator–prey encounter rates in freshwater piscivores:
effects of prey density and water transparency
Håkan Turesson Æ Christer Brönmark
Received: 18 October 2006 / Accepted: 15 March 2007 / Published online: 24 April 2007
Springer-Verlag 2007
Abstract One of the most fundamental components of
predator–prey models is encounter rate, modelled as the
product of prey density and search efficiency. Encounter
rates have, however, rarely been measured in empirical
studies. In this study, we used a video system approach to
estimate how encounter rates between piscivorous fish that
use a sit-and-wait foraging strategy and their prey depend
on prey density and environmental factors such as turbidity. We first manipulated prey density in a controlled pool
and field enclosure experiments where environmental factors were held constant. In a correlative study of 15
freshwater lakes we then estimated encounter rates in
natural habitats and related the results to both prey fish
density and environmental factors. We found the expected
positive dependence of individual encounter rates on prey
density in our pool and enclosure experiments, whereas the
relation between school encounter rate and prey density
was less clear. In the field survey, encounter rates did not
correlate with prey density but instead correlated positively
with water transparency. Water transparency decreases
with increasing prey density along the productivity gradient and will reduce prey detection distance and thus
predator search efficiency. Therefore, visual predator–prey
encounter rates do not increase, and may even decrease,
with increasing productivity despite increasing prey
densities.
Communicated by Roland Brandl.
H. Turesson C. Brönmark (&)
Department of Ecology, Limnology, Lund University,
Ecology Building, 223 62 Lund, Sweden
e-mail: Christer.Bronmark@limnol.lu.se
Keywords Encounter rate Piscivore Ambush forager Turbidity Water colour
Introduction
Predation by piscivorous fish is an important structuring
force in freshwater systems and a number of studies have
shown that piscivores may have strong effects down both
pelagic and benthic food chains (e.g. Carpenter and Kitchell 1993; Brönmark et al. 1997). However, individual
properties of predators and prey, such as foraging strategies
and prey defence adaptations, may cause decoupling of
consumer-resource dynamics and create patterns in food
chains that are not embraced by present theory (e.g.
Hambright et al. 1991; Brönmark and Weisner 1996).
Several researchers have emphasised that to be able to
understand processes at the population and community
level it is a necessity to understand the behaviour of individual predators and prey and the constraints that are imposed on their behavioural strategies (Persson et al. 1997;
Fryxell and Lundberg 1997; Lima 1998). Variation in
potential foraging strategies in predators and defence
strategies in prey is enormous and the exact nature of the
behavioural interaction between predator and prey is crucial for understanding mechanisms behind changes in
predator consumption rate and its implication for predator
and prey population dynamics.
Most of the current theory on interactions in populations
and communities has been derived from Lotka-Volterratype consumer-resource models. A fundamental component of the functional response in these model is encounter
rate, i.e. the number of prey detected by each predator per
unit time. The rates at which predators encounter different
prey types have rarely been measured despite their
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importance in understanding predator–prey dynamics. This
is in part due to the inherent difficulty in determining when
an encounter has occurred. In experimental studies,
encounter rate is usually controlled by the experimenter or
assumed to relate directly to the estimated consumption
rate (Anderson 1984; Gregory and Levings 1996).
In spite of encounter rate being a fundamental component of foraging models there are no good estimates of this
parameter for piscivores. However, there are quite detailed
theoretical predictions for how encounter rates should vary
with different characteristics of the predator and the prey,
as well as the ambient conditions of the environment. In
aquatic consumers that use vision to detect their prey visual
foraging models have been successfully used to study
different constraints affecting consumption rates (e.g.
Beauchamp et al. 1999; Utne-Palm 2002; Mazur and
Beauchamp 2003). Encounter rate (ER) is formally defined
as ER = aN where a is search efficiency and N is prey
density. In the simplest case, encounter rate increases linearly in relation to prey density. However, different
mechanisms may affect search efficiency and produce
other than linear and proportional relationships between
prey density and encounter rate. Search efficiency can be
viewed as a volume (SV) that the predator can search per
unit of time, modelled as a cylinder:
Oecologia (2007) 153:281–290
and Katsuwonus pelamis, (Bonabeau and Dagorn 1995)],
would have the result that school encounter rates do not
increase proportionally to prey density, resulting in nonlinear relationships between encounter rate (schools) and
prey density.
In this study, we used a video recording system to
estimate how encounter rate of piscivorous fish relates to
prey density in controlled artificial pool and field enclosure
experiments where we manipulated prey densities (N),
whereas factors that could affect search efficiency were
held constant. The video was kept stationary, hence mimicking the view of a ambush predator, for example northern
pike (Esox lucius). Pike is a common piscivore in northern
temperate lakes and is a typical ambush predator, attacking
its prey from a hide-out in littoral vegetation (Savino and
Stein 1989). To get an estimate of what factors determine
encounter rate in natural habitats where encounter rate is a
function of both search efficiency and prey density, we
used underwater cameras placed in the littoral zone in 15
freshwater lakes. The lakes were situated along a productivity gradient, which allowed us to relate encounter rate to
fish density and environmental characteristics.
Materials and methods
SV = p RD SS T
Greenhouse pool experiment
where RD is reaction distance, SS is swimming speed of
the piscivore, and T time (Beauchamp et al. 1999). For sitand-wait predators search efficiency is not a function of
swimming speed as they are stationary when searching for
prey and encounter rate will thus be positively related to
prey density and to the maximum distance that a predator
can detect its prey (reaction distance). Reaction distance, in
turn, is dependent on light intensities, contrasts, prey size,
water transparency, etc. (Howick and O’Brien 1983; Breck
1993; Beauchamp et al. 1999; Vogel and Beauchamp 1999;
Mazur and Beauchamp 2003).
Prey fish behaviours, such as schooling, may also affect
encounter rate; prey fish are often schooling (Krause et al.
2000; Krause and Ruxton 2002) and piscivores will thus
often encounter schools of prey rather than single fish. A
hunting piscivore will, most often, be able to eat only one
prey, regardless of school size, because prey will respond
behaviourally upon attack, and after capturing and handling
one prey the others will be gone (Turesson and Brönmark
2004). Thus, measuring the rate at which a predator
encounters schools of prey may make more sense than
measuring encounter rate with individual prey. Further, a
positive relation between prey density and school size, as
found in some fish species [guppies, Poecilia reticulata
(Seghers 1981); tuna, Thunnus albacares, Thunnus obesus,
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To study the effect of prey density on encounter rate under
controlled conditions we performed an experiment in a
greenhouse. Four circular test pools (2.44 m diameter,
water depth of 250 mm) were each equipped with video
cameras mounted 3.4 m above the centre of the tank. The
pools were kept bare to keep all fish visible on the video
recordings. On the light blue pool bottom we drew a black
200-mm grid. Pools were filled with oxygenised tap water
and 50% of the water was changed 3 times per week.
Between pools and along greenhouse walls we mounted a
dark green tarpaulin to a height of 2.8 m to shield experimental fish from disturbances. We used roach (Rutilus
rutilus) as prey at densities of 1, 2, 4, 8, 12, 16, 24, and 32
fish per pool. The experiments were run in full daylight.
Two sets of trials per day allowed us to run each fish
density once per day and replicate each density 4 times in
consecutive days. The different densities were randomised
to different pools with the limitation that we were running
each density once per pool, thus controlling for both pool
and day effects. Experimental fish were from the 1-year-old
(1+) cohort and caught with a handnet in nearby Lake
Krankesjön. They were used 4–7 days after capture and
individual fish were used in one replicate only. About 1 h
before the trials fish were lightly anaesthetised with
Oecologia (2007) 153:281–290
283
MS-222 and measured to the nearest millimetre (total
length). All fish were in the 56–66 mm range. We started
the video recordings when the fish had been acclimated for
1 h in the pool and recorded for 61 min. To manage the
time-consuming video analysis we were forced to restrict
our analysis to three 10-min periods from each trial, at 10–
20, 30–40, and 50–60 min into the recordings.
For the analyses we defined four of the grid squares
(400 mm · 400 mm) along the side of a pool as the ‘‘area
of encounter’’ for a hypothetical predator and recorded
number of fish and duration of all encounters in this area.
An encounter event was defined as when one or more fish
were in the encounter area, irrespective of how many they
were. When the first fish of a school entered the area the
encounter started and when the last fish left the encounter
ended. The traditional encounter rate where each encountered prey is counted is here termed ‘‘individual encounter
rate’’. Individual encounters were calculated as the sum of
all individual fish in a school. However, if predators do not
benefit by increasing capture rates when encountering a
larger school of prey due to prey behaviour, a more useful
definition of prey encounter would be ‘‘each occasion
where the predator detects prey, regardless of the number
of prey per encounter’’ and we term this ‘‘school
encounter’’ and measure it as ‘‘school encounter rate’’.
Results were analysed with linear regression.
tied, fish counted, and replaced with new fish. In some
replicates all fish were not recovered, for unknown reasons,
but a minimum of 90% of the fish remained in all replicates, with the exception of the five-fish density, which we
therefore excluded from the analyses. The order of treatments was randomised with the limitation that no more
than one replicate of each fish density was filmed on the
same day. After the experiments a sub-sample of 100 roach
was measured (62.8 ± 4.7 mm, mean ± SD total length).
Underwater video cameras (Dyfo Systems AB, Sweden)
were used mounted 0.32 m from the net and 0.40 m under
the water surface in the middle of one side of an enclosure,
filming towards the centre of the enclosure at an angle of 8
downwards from the horizontal plane. A Secchi disc facing
the camera was visible at a maximum distance of 1.2 m
from the camera front lens and fish were detected at a
distance of up to about 0.8 m from the camera. The last
15 min of each of the 4 filmed hours was analysed for
encounters, resulting in 1 h total per replicate. School
encounters were defined as when one or more fish entered
the camera’s visual field, irrespective of how many there
were. When the first fish entered the encounter started and
when the last fish in the school left the visual field, the
encounter ended. For individual encounters we counted
each fish in a school as a separate encounter. Results from
the experiments were analysed with linear regression.
Field enclosure experiment
Lake survey
In order to investigate the effect of increasing prey density
on encounter rate in a natural environment and at a larger
scale we performed a large-scale enclosure experiment. We
built five enclosures (10 m · 10 m) in a eutrophic pond
(80 m · 25 m, mean depth 0.90 m) situated close to Lund.
The enclosures were constructed of a wooden frame where
we attached a net (mesh size: 6 mm). The net extended
0.45 m over the water surface and went down to the sediment. It also covered the whole bottom area of the
enclosures. By lifting the net an enclosure was therefore
easily emptied to control for number of remaining fish and
to replace fish between replicates. Four enclosures were
used for the experiments while the fifth was used for
holding fish between trials. Fish were 1+ roach caught in
Lake Vombsjön 2–4 weeks before the experiments, and
due to the large number of fish used we chose to re-use fish.
From a pool of 600 roach the experimental fish were
haphazardly assigned to replicates.
Enclosures were stocked with densities of five, ten, 15,
20, 40, 60, 80, and 120 roach, respectively, and each
density was replicated 3 times. We released the roach in the
enclosures no later than 1600 hours the day before experiments and filmed for 4 h starting at 0900–0930 hours in
the morning. After each replicate the enclosure was emp-
Fifteen lakes in southern Sweden were filmed from 12
August to 28 August 2002 to investigate how encounter
rate relates to prey density and environmental characteristics in natural lake systems. The lakes were chosen
along a wide gradient of total P content (10–210 lg total
P l–1 of the water and, to represent lakes with a range of
prey fish densities, turbidity, and water colour. The main
criteria when choosing the lakes were that they should be
situated in southern Sweden and had been fished with
standardised survey multi-mesh gillnet fishing according
to Swedish standards (Appelberg 2000) at least twice in
the summer (25 June–8 August) from 1997 to 2001.
Twelve lakes in the data base of the Swedish National
Board of Fisheries (www.fiskeriverket.se) fit these criteria
(lakes Bodarpasjön, Bäen, Humlesjön, Krageholmssjön,
Lerjesjön, Rammsjön, Store Damm, Södra Kroksjön,
Vesljungasjön, Värsjön, Vässlarpsjön, and Fiolen). To
extend the productivity range we added three eutrophic
lakes: lakes Krankesjön, Fjällfotasjön, and Sjöbergasjön
that had been fished only once, in 2001 (M. Svensson,
unpublished data). Fish abundance was estimated as the
number of fish captured per unit effort (CPUE) where one
net in 1 night is the unit of effort. The mean of the two
latest fishing surveys in the lakes that were fished more
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than once from 1997 to 2001 was used. The total P data
are from the database of the Department of Environmental Assessment, Swedish University of Agricultural
Sciences, Uppsala (www.ma.slu.se).
Each lake was filmed once with three underwater video
cameras that were placed in the littoral zone at the edge of
the emergent macrophyte zone, i.e. the habitat where it is
most likely that a northern pike would encounter a prey.
The minimum distance between cameras was 9 m and they
were connected by cables to standard VHS-video recorders
on land. The average water depth at the camera sites was
0.87 ± 0.07 m (mean ± SD, n = 15). Each camera was
mounted on a pole so that the lens centre was 0.48 m from
the lake bottom, filming at an angle of 8 downwards from
the horizontal plane, and thus, the bottom was visible in the
clearer lakes. Two iron poles (12 mm diameter) were
placed 0.80 m and 1.30 m in front of the cameras to give
references for estimation of fish size and distance from the
camera. The poles were painted black and white in vertical
100-mm sections. Video recordings were taken between
1600 and 2000 hours, but only the last 2.5 h of each tape
was analysed. Encounter rate (encounters per hour) from an
individual lake was calculated as a mean from all three
cameras (7.5 h in total from each lake). Directly after
filming, surface water temperature (23.2 ± 0.9C) was
measured at the filming site. Camera detection distance
(CDD) was measured as the maximum distance where a
black and white contrast (the iron poles) could be detected
by the camera. Water samples were taken for later analyses
of water colour (mg Pt l–1) and turbidity (Jackson turbidity). Data from the lake survey were analysed with linear
regressions.
In all of these lakes perch (Perca fluviatilis) is an
important species and perch encounters were registered
separately from cyprinid encounters. Several cyprinid
species are present in the lakes but as cyprinids were
sometimes hard to identify to species from the video
recordings, the cyprinids were pooled when analysing
data. Roach (R. rutilus) is the most widely distributed of
the cyprinids and is present in 14 of the 15 lakes. Roach
encounters constitute a majority of the cyprinid encounters in these lakes. Less common cyprinids in the lakes
are rudd Scardinius erytrophthalmus, bream Abramis
brama, white bream Blicca bjoerkna, bleak Alburnus
alburnus, tench Tinca tinca and crucian carp Carassius
carassius. Besides perch, pike E. lucius is an important
piscivore, present in all lakes. Other piscivores present in
some lakes include eel Anguilla anguilla and pikeperch
Stizostedion lucioperca. From the recordings we quantified both school encounter rate and individual encounter
rate with fish smaller than 150 mm, as these fish are
under the highest risk of predation (Brabrand and Faafeng
1993; Margenau et al. 1998).
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Results
Greenhouse pool and field enclosure experiments
Encounter rate for individual fish in the pool experiment
increased in relation to fish density (linear regression
R2 = 0.94, P < 0.001; Fig. 1a), whereas there was no significant relation between school encounter rate and fish
density (R2 = 0.11, P = 0.063; Fig. 1b). This was a consequence of fish usually forming a single school, irrespective of fish density. Duration time for school
encounters increased with fish density (R2 = 0.63,
P < 0.001) as a result of increasing numbers of fish per
school (Fig. 1c).
In the field enclosure experiment the variation in
encounter rate among replicates was very high (Fig. 1d–e),
and in two replicates (densities ten and 40 fish per enclosure) no fish were encountered at all. However, both
individual and school encounter rates increased with fish
density (R2 = 0.33, P = 0.007, and R2 = 0.46, P = 0.001,
respectively; Fig. 1d–e). The size of the encountered
schools also increased with fish density (R2 = 0.25,
P = 0.028; Fig. 1f). The school size recorded is the number
of fish visible with the camera, which likely underestimated
actual school sizes as some school members may have
passed outside the camera’s visual field.
Lake survey
Fish were recorded on video in 14 of the 15 lakes and fish
smaller than 150 mm made up 72–100% (mean 92%) of
the fish encountered in these lakes. This size group consisted of cyprinids and perch and, in addition, two pike
(excluded from encounter rate analyses). Larger fish
(>150 mm; not included in the analyses) were represented
by cyprinids, perch and a single large pike.
Encounters with only one fish were the most common
encounter; 77 ± 22% (mean ± SD, n = 14) of all encounters in the lakes consisted of a solitary fish. There was no
correlation between fish density (CPUE) and proportion of
encounters with single fish (individual encounter rate) for
cyprinids (r = –0.06, P = 0.86), for perch (r = 0.24,
P = 0.42) or for all fish combined (r = –0.05, P = 0.86).
Thus, there was no indication that fish density influenced
fish schooling and for the lake survey, only rate of school
encounters is presented in further results. There was no
significant relation between fish density (CPUE from gill
net survey) and school encounter rate, for cyprinids, perch,
or cyprinids + perch pooled together (linear regression,
cyprinids, R2 = 0.11, P = 0.23; perch, R2 = 0.08, P = 0.29;
cyprinids + perch, R2 = 0.38, P = 0.15, n = 15; Fig. 2a–c).
School encounter rates for cyprinids, perch, and cyprinids + perch were positively related to CDD (linear
Oecologia (2007) 153:281–290
Fig. 1 Encounter rates,
encounter duration times, and
mean school size, dependent on
prey fish density in the pool
(a–c) and enclosure (enc.) (d–f)
experiments. ERi Individual
encounter rate, ERs school
encounter rate
regression, cyprinids, R2 = 0.35, P = 0.028; perch,
R2 = 0.0.40, P = 0.01; cyprinids + perch, R2 = 0.52,
P = 0.002; Fig. 2d–f).
Encounter duration time varied between the lakes. To
test for a relationship between CDD and encounter duration, we compared median times of those encounters
involving only a single fish, as encounter duration time
correlates positively to school size (within individual
lakes). In cyprinids, single prey encounter duration was
0.9–3.5 s and correlated positively with CDD (Spearman
rank correlation, R = 0.840, P = 0.001, n = 11). In perch,
median encounter duration was in the range 1.1–3.8 s except for one outlier at 16.5 s. This was a foraging perch that
repeatedly explored a food patch within the camera’s field
of vision. Single perch encounter duration correlated positively with CDD (Spearman rank correlation, R = 0.714,
P = 0.006, n = 13, outlier included).
The density of fish (CPUEcyprinid + perch) was positively
related to total P (linear regression, F = 5.8, R2 = 0.49,
P = 0.004). Separating fish densities into densities of
cyprinids (CPUEcyprinid) and perch (CPUEperch) showed
that both cyprinids and perch increased along the P gradient (F = 5.8, R2 = 0.31, P = 0.03 and F = 4.7,
R2 = 0.27, P = 0.05, respectively). There were also strong
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a
d
b
e
c
f
correlations between total P levels and CDD (r = –0.76,
P = 0.001) and total P and turbidity (r = 0.93, P < 0.001)
in the 15 lakes.
Encounter rates is a function of both prey density (N)
and search efficiency, which in turn is related to reaction
distance (camera detection distance, CDD). To determine
the relative importance of these factors for encounter rate
we did a multiple stepwise regression (P = 0.05 for a
variable to enter the regression). However, the resulting
regression only included CDD; density was not entered into
the equation. This was true for all three fish categories
(cyprinids, perch, cyprinids + perch). Reaction distance for
piscivores depends on the optical properties of the water,
which is affected by, e.g. turbidity and water colour. CDD
decreased with increasing turbidity (r = –0.73, P = 0.002;
Fig. 3a) whereas there was no significant correlation
between CDD and water colour (r = –0.18, P = 0.54;
Fig. 3b). However, a multiple regression with CDD as
the dependent variable and turbidity and water colour
as independent variables included both variables in
the equation (CDD = –0.54 · colour – 12.1 · turbidity +
208.4, R2 = 0.68). Turbidity explained most of the
variation (b = –0.84, P < 0.001) in comparison to colour
(b = –0.39, P = 0.036).
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Fig. 2a–c ERs related to fish
density (fish captured per unit
effort; CPUE) and camera
detection distance in 15 lakes
along a productivity gradient.
Fish density is presented for
cyprinid (cyp, cypr) fishes only
(a), perch only (b), or total
number of fish (c). For other
abbreviations, see Fig. 1
Discussion
Encounter rate is a fundamental component when modelling predator–prey dynamics but despite its importance
empirical estimates are rare. Encounter rate is the product
of the predator’s search efficiency and density of prey.
However, under constant environmental conditions search
efficiency is assumed to be constant and encounter rate
should then increase linearly with prey density. In the
pool and field enclosure experiments where environmental
conditions were controlled for and only prey density was
varied, we found an increase in individual encounter rate
with increasing prey density. However, one of the
assumptions of the functional response is that prey have a
random spatial distribution and, thus, each and every prey
constitute a separate encounter. This may be misleading
when prey organisms show defence behaviours that could
affect encounter rates. For example, if prey fish are
schooling, an increase in prey fish density will not result
in a higher encounter rate of prey if larger schools are
formed. Further, a piscivore will on most occasions only
be able to capture one prey from a school of prey fish,
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a
d
b
e
c
f
independent of school size, and may even have decreasing
capture success with school size, due to the confusion
effect (Landeau and Terborgh 1986; Krause and Godin
1995; Krause et al. 1998; Turesson and Brönmark 2004).
Thus, from a piscivore’s point of view, encounter rate of
schools should be a more appropriate measure than
individual encounter rate. In the pool experiment there
was no increase in school encounter rate with prey density. When prey fish density increased, larger schools
were formed. Other studies have also found that fish
school sizes increased with increasing fish densities
(Seghers 1981; Bonabeau and Dagorn 1995). Thus,
schooling behaviour resulted in a decoupling of the
relationship between encounter rate and prey density in
the pool experiment. School size was also a positive
function of prey density in the field enclosure experiment,
but here increasing prey density resulted in an increasing
school encounter rate albeit the slope was low and variation large. Thus, our results suggest that if school
encounter rate is used in foraging models instead of
individual encounter rate, encounter rates will vary with
prey schooling behaviour at constant prey density. This
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287
a
b
Fig. 3 The relationship between camera detection distance and
turbidity (a) and water colour (b). JTU Jackson turbidity
complicates models, but may also facilitate understanding
the importance of schooling-related processes. For
example, schools of prey gradually disperse at lower light
intensities (Dobler 1977; Ryer and Olla 1998; Miyazaki
et al. 2000). This means that prey encounter rates for a
predator less dependent on light may increase in low
illumination when prey schools break up, and this could
be one explanation for why piscivores often choose to
feed at dawn or dusk (Pitcher and Turner 1986; Dobler
1977).
In the field survey of lakes we found no increase in
encounter rate with prey density. Instead, the slopes of the
regressions were negative in all cases, although the relations were non-significant. No or negative relationships
between prey fish density and different measures of foraging efficiency (consumption rate, growth) have also
been found for piscivorous lake trout (Eby et al. 1995).
The lack of a positive relation between fish density and
school encounter rate in the field study cannot be explained by prey schooling behaviour, as the incidence of
schooling did not change along the prey density gradient,
as it did in the pool and enclosure experiments. In the
field study, there may even be reasons to expect an
opposite relation, at least for cyprinids. In clear water,
where cyprinid density is often lower than in turbid waters, there may be a larger need for schooling than in
turbid water, and school cohesion may also be facilitated
in clear water (Ryer and Olla 1998; Miyazaki et al. 2000).
This could lead to weak or no relationships between
cyprinid density and school sizes in turbid lakes. The lack
of a relation between prey fish density and encounter rate
may instead be due to environmental factors affecting
search efficiency.
Prey density was the only factor that was varied in the
laboratory and in the field enclosure experiment, whereas
search efficiency, the other component of the encounter
rate function, was constant. However, search efficiency
may vary with changes in characteristics of predators and
prey, such as size, speed of movement, vision, and changes
in environmental conditions. Beauchamp et al. (1999)
modelled search efficiency (or search volume) of pelagic
piscivores as a function of piscivore swimming and reaction distance to prey and, further, Gerritsen and Strickler
(1977) suggested that changes in reaction distance have the
largest relative effect on encounter rate, compared to prey
density and predator and prey movement speeds. Reaction
distance is affected by environmental factors such as
ambient light levels and turbidity; it declines with
decreasing light levels (Howick and O’Brien 1983; Vogel
and Beauchamp 1999; Mazur and Beauchamp 2003) and
increasing turbidity (Miner and Stein 1996; Vogel and
Beauchamp 1999; Mazur and Beauchamp 2003). Thus,
changes in environmental conditions such as light conditions, turbidity, and water colour between systems, or
temporally or spatially within systems, should affect
encounter rate of piscivores by changing their reaction
distance and thus their search efficiency. The 15 study
lakes could be arranged along a gradient of increasing
productivity, estimated as total P concentration. Along this
gradient fish density and turbidity increased while water
clarity, measured as CDD, decreased. This pattern is in
accordance with earlier studies of North European lakes,
where cyprinid and total fish density have been shown to
increase and water transparency decrease along a P gradient (Svärdson 1976; Persson et al. 1991; Jeppesen et al.
2000; Olin et al. 2002). Water clarity (CDD) correlated
positively with both encounters of cyprinids and perch and
this effect was strong enough to overcome the decreasing
cyprinid density in the clearer, more oligotrophic lakes. For
a visual predator this means that while encounter rate is
expected to increase as a consequence of increasing prey
densities in more eutrophic systems, turbidity influences
prey detection distance and thereby search efficiency,
which leads to a lower encounter rate with prey and lower
realised prey availability (Beauchamp et al. 1999; Vogel
and Beauchamp 1999) and, thus, encounter rate should
peak in lakes of intermediate productivities (Beauchamp
et al. 1999).
The lower water transparency in nutrient-rich lakes has
been suggested to be one important explanation for the
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peak of piscivore biomass to total fish biomass in lakes of
intermediate productivity (Jeppesen et al. 2000; Olin et al.
2002). In particular, the proportion of piscivorous perch has
been shown to peak in mesotrophic lakes and be lower in
eutrophic lakes (Persson et al. 1991) and northern pike also
disappear in the most productive lakes. Seehausen et al.
(2003) argued, based on optimal foraging modelling, that
the collapse of piscivore diversity in eutrophying lakes is a
result of reduced encounter rate with increasing turbidity.
The change in fish community structure with eutrophication may also be affected by the loss of submerged macrophytes that is known to be common, especially in shallow
eutrophic lakes (Jeppesen et al.1998). On a lower level,
however, encounter rate should affect consumption rates,
and thus condition, of individual piscivores. The model of
Beauchamp et al. (1999) suggested that the consumption
rate for pelagic piscivores should be highest in intermediately productive lakes. In a correlative study of 37 Canadian lakes, with Secchi depth comparable to the lakes of
this study, Craig and Babaluk (1989) found that the condition factor of pike decreased with Secchi depth. Skov
et al. (2002) found no difference in pike condition or
consumption rate in an experiment where 0+ pike were
foraging on perch in reduced water transparency vs. a
clear-water control. However, water transparency was here
varied by changes in water colour, not turbidity. Water
colour changes light conditions but increasing turbidity, i.e.
a higher particle concentration, results in increasing backscattering of light and reduced contrast of prey against the
background.
In this study, environmental factors affected encounter
rate when compared among lakes. It is reasonable to assume that the same pattern could be found in a single lake
if measures were taken over time. It is known that small
fish will influence zooplankton negatively and phytoplankton positively through trophic cascade effects, which
will in turn affect turbidity and detection distances (Carpenter and Kitchell 1993). The large fluctuations in turbidity over a yearly cycle, especially in lakes subject to
summer blooms of green algae or cyanobacteria, may
give very different conditions for piscivores over the year
and between years in the same lake. Craig and Babaluk
(1989) indeed found that growth rate of northern pike was
lowest during the summer when water was turbid and
increased when water became clear during late autumn.
Further, turbidity may also vary spatially within lakes
(Scheffer et al. 1994). Hence, models assuming that
encounter rate is dependent on constant predator search
efficiency, taking into account only changes in prey
density, should give poor estimates of piscivore consumption rates.
What we measured is naturally not an exact estimate of
predator–prey encounter rate as our results rest on the
123
Oecologia (2007) 153:281–290
critical assumption that the registration of prey fish by the
camera system correlates with predator–prey encounter
rate of a sit-and-wait piscivore, such as northern pike. As
our cameras only registered visual encounters the results
may be regarded as rough estimates of encounters for a
visual predator. Piscivores may also detect prey by other
senses, such as by olfaction or the lateral line system
(Pohlmann et al. 2001). However, New et al. (2001) found
that muskellunge (Esox masquinongy) vision was of primary importance for location of and orientation towards
prey and that the lateral line system was only used during
the final stage of the attack when the prey was very close to
the piscivore (within centimetres). Another difference between the camera’s perception and a real predator is that
the camera’s visual field was restricted to a narrow field in
a forward direction. As real predators have a much wider
visual field, our encounter rate estimates are likely underestimates of predator–prey encounter rate. However, it is
important to note that even if we do not know exactly how
our measures of CDD scale to reaction distances in an
ambush piscivore, the slope, and sign of the relationships
between encounter rate and prey density and search efficiency should still be the same and, thus, our main conclusions should not be affected by differences in camera
and piscivore detection distances.
In conclusion, in the pool and enclosure experiments
we found overall the expected positive dependence of
encounter rates on prey density considering individual
encounter rates. The relationship between prey density
and school encounter rate was less strong. In the field
survey, however, we found no relationship between prey
density and encounter rate and, if anything, the relationship was negative. Our interpretation of the field results is
that although encounter rate mechanistically relates positively to prey density, other factors along a productivity
gradient that correlate to prey density will reduce prey
detection distance and thus predator search efficiency.
Predator search efficiency, as a function of turbidity may
have a stronger negative influence on predator–prey
encounter rates along this gradient, than the positive effects from increased prey densities. For a visual piscivore
this may mean that prey availability does not increase,
and maybe even decreases with increasing productivity
despite increasing prey densities (cf. Beauchamp et al.
1999).
Acknowledgements We are grateful to Thomas Lakowitz for good
help during the many hours of video analysis. Thanks to Mikael
Svensson for providing fish abundance data. Peter Eklöv, Anders
Persson, Christian Skov, and Patrik Stenroth commented on an earlier
version of the manuscript. This study was supported by a grant from
the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (to C. B.). The study complies with the
current laws in Sweden; ethical concerns on care and use of experi-
Oecologia (2007) 153:281–290
mental animals were followed under permission from the Malmö/
Lund Ethical Committee.
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