FEMS Microbiology Ecology 44 (2003) 79^87
www.fems-microbiology.org
Bacterial tracking of motile algae
Greg M. Barbara , James G. Mitchell
School of Biological Sciences, Flinders University of South Australia, G.P.O. Box 2100, Adelaide 5001, Australia
Received 24 May 2002 ; received in revised form 29 October 2002; accepted 8 November 2002
First published online 3 December 2002
Abstract
We investigated whether bacterial motility and chemotaxis in the ocean enables bacteria to approach and follow microscopic, moving,
point sources of nutrients. The turbulent nature of the ocean combined with the imprecision of run and tumble chemotaxis has led to the
assumption that marine bacteria cannot cluster around microscopic point sources. Recent work, however, shows that marine bacteria use
a run and reverse strategy. We examine the ability of marine bacteria that use run and reverse chemotaxis to respond to and follow a
moving point source. The addition of the 6 Wm in diameter motile algae Pavlova lutheri to cultures of the marine bacteria
Pseudoalteromonas haloplanktis and Shewanella putrefaciens revealed bacterial tracking individual free-swimming algae. The marine
bacteria travelled at up to 445 Wm s31 when tracking, up to 237 Wm s31 when not tracking and up to 126 Wm s31 in cultures without the
algae. Tracking maintained bacteria within one run length of an alga. The bacteria appeared able to steer, consecutively turning up to 12
times toward the motile algae. They recovered from the occasional incorrect turn to continue moving around the swimming alga,
indicating that marine bacteria can track moving point sources and form transient phyto-bacterial associations. Our analysis suggests
tracking increases nutrient uptake by bringing cells into regions of high nutrient concentrations and by increased advection from high
speeds. This result describes what is, apparently, one of the tightest spatial and temporal links between free-living primary and secondary
producers in planktonic ecosystems.
1 2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.
Keywords : Motile algae; Bacterial tracking; Point source ; Chemotaxis
1. Introduction
Bacterial chemotaxis is e¡ective for cell migrations of
centimetres [1]. The presence of a concentration gradient
causes a chemosensory response that alters the stopping,
and thus the turning, frequency of the bacteria to either
increase or decrease the run-lengths of the cells. The e¡ect
of these behavioural changes is a general slow migration
of the bacteria either away from or towards areas of high
chemical concentration [2]. The generalised random walk
model for bacterial chemotaxis, involves bacteria swimming in a relatively straight line ‘run’ and stopping to
complete a tumble before swimming o¡ in a new random
direction, therefore migration of a few centimetres may
require many hours. However, the limits of e⁄ciency for
* Corresponding author. Tel. : +61 (8) 201 5346;
Fax : +61 (8) 201 3015.
E-mail address : greg.barbara@£inders.edu.au (G.M. Barbara).
these migrations are based on statistical arguments and
observation of selected species, such as Escherichia coli
[2,3]. Recent work shows marine bacterial chemotaxis differs from that of terrestrial and enteric bacteria, with migration producing bands or clusters tens of micrometres in
their smallest dimension in a few seconds rather than
hours [4^6]. Common to work on both forms of chemotaxis are the use of ¢xed nutrient sources and gradients
that are long lived compared to the typical 1-s swimming
paths between turns. In contrast to centimetre-long nutrient gradients, some nutrient sources such as microalgae
and detritus are tens of micrometres across and move either by swimming, sinking or advection.
Marine bacteria inhabit a largely oligotrophic environment with ephemeral, nutrient-enriched particles or pointsource microenvironments [7,8]. Blackburn et al., [5] used
a numerical model to support the hypothesis [9] that pelagic marine bacteria could take advantage of point sources of phytoplankton exudate release. The ability of bacteria to associate with discrete sources of nutrients would
be advantageous, enabling bacteria to bene¢t from transient microenvironments and move on once the nutrient
0168-6496 / 02 / $22.00 1 2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.
PII : S 0 1 6 8 - 6 4 9 6 ( 0 2 ) 0 0 4 5 2 - X
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source was depleted. However, the turbulent nature of the
ocean means that what is a few centimetres away from a
bacterium one minute might be tens of centimetres or a
metre away the next minute. Scale is an important consideration in marine bacterial chemotaxis, if it is going to be
useful in the ocean then it would need to be better adapted
to respond rapidly over short distances. The oligotrophic
high £ow regime environment experienced by marine bacteria may cause strong selection for rapid signal detection,
unlike E. coli taxis which is far from the theoretical limits
proposed by Segall et al. [10].
Motile marine bacteria have a modi¢ed swimming behaviour from that of Brown and Berg’s E. coli random
walk model [2]. Typically, marine bacteria swim at much
higher speeds, up to 400 Wm s31 [11,12], than E. coli with
maximum speeds of only 40 Wm s31 [13]. Marine bacteria
also exhibit a run and reverse strategy, which enables them
to form tight ( 6 20 Wm wide) swarm bands around air
bubbles or chemical attractants [4,6,14,15]. Based on this
run and reverse behavior Luchsinger et al. [16] developed a
computer model, which showed that bacteria would be
able to orient themselves around ¢xed point sources of
nutrients in a shear ¢eld. As such, both £ow and motility
would be required for marine bacteria to cluster around a
stationary nutrient source. However, this assumed that
marine bacteria relied on £ow alone to bring them toward
a nutrient patch with motility having purely a control
function. Luchsinger et al. [16] did not consider the possibility that bacteria could actively seek out point sources
of nutrients.
In previous studies the nutrient source was assumed to
be either a stationary point source or a gradient [1,2,5,6].
Here, we examine the ability of bacteria to respond to a
moving point source and explore the limits of manoeuvring by investigating the extent to which bacteria can
follow a moving point source. We used a marine system,
because marine bacteria are highly motile, ocean turbulence places a premium on the ability to remain close to
a nutrient source, and background nutrients are low,
which ensures a high signal to noise ratio. Using dark-¢eld
microscopy, we observed and measured bacterial isolates
capable of tracking free-swimming algal cells. Our results
show that previous studies based on models which use
standard chemotactic parameter values and assumptions
[17^19], need to be revised or new models developed to
take into account the new motility response described
here.
Ryther [20] and Guillard [21]. P. lutheri cells have dual
£agella and a cell diameter of 4^6 Wm [22].
The bacterial isolates’ medium was based on that of
Malmcrona-Friberg et al. [23] with the modi¢cation, that
vibrio arti¢cial seawater (VAS) [12] replaced the salts solution. Cultures of the motile marine bacteria Shewanella
putrefaciens and Pseudoalteromonas haloplanktis were taken from VAS slopes and transferred to VAS plates for
3 days, until colonies formed. Individual colonies were
selected for those displaying the highest motility. These
were inoculated into sterile 1% Tryptic Soy Broth (TSB;
Difco Laboratories) in an overnight shaker at room temperature (23‡C) for 12 h. TSB was chosen as a nutrient
source to increase cell number and stimulate motility [12].
The cultures were then checked for motility and an aliquot
of each of the motile cultures was transferred to conical
£asks, to a ¢nal concentration of 0.01% TSB, sealed and
left at room temperature for 36 h to stimulate motility
[12].
2.2. Bacterial tracking of phytoplankton cells
Four trials were set up with combinations of the single
bacterial and algal species. After the 36 h, 30-Wl aliquots
from either of the two bacterial cultures, S. putrefaciens or
P. haloplanktis were placed into a 1-mm-deep microscope
slide chamber [14] with a 10-Wl aliquot of the alga P. lutheri
(108 Q 23 cells). This gave a ratio of 1000:1 bacterial to
algal cells, which is similar to concentration ratios found
in the open ocean [24]. The mixed samples of bacteria and
alga were then covered with a glass coverslip but not
sealed.
Bacterial and algal cell trajectories were recorded for
cells tracking and for bacteria greater than 50 Wm away
from an alga. To determine if there was a general cue for
tracking, bacteria were recorded in samples with and without algae. Additionally, in a control to test for bacterial
entrainment by algal cells, 0.8-Wm-diameter latex beads
(Sigma, Australia) were used as a substitute for bacteria
with samples of P. lutheri.
Each trial was run at room temperature, 23‡C, for 40
min and experiments for each treatment were repeated
four times. Bacterial and algal cultures were only mixed
during the experiments in the microscope slide chambers,
at no other time during their culturing process were they
exposed to each other.
2.3. Microscopy
2. Materials and methods
2.1. Phytoplankton and bacterial strains
The free-swimming golden brown alga, Pavlova lutheri,
was grown in axenic batch cultures at 20‡C in F2 medium
without a silicon source, modi¢ed from Guillard and
Observation of bacterial and algal swimming was under
dark-¢eld, 200U magni¢cation videomicroscopy (JVC
TK-1280E video camera and Olympus BHT, trinocular
microscope) with the ¢eld of view at mid-depth in the
microscope slide chamber. The video image was recorded
continuously by a video cassette recorder with a frame
speed of 24 frames s31 (Panasonic NV-HS1000). A heat
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81
dissipation ¢lter consisting of water in a container 3 cm
thick was used to minimise the e¡ect of any temperature
changes caused by the microscope lamp [6].
2.4. Swimming trajectory measurement
Cell paths of the algae and bacteria were measured
frame by frame, traced o¡ of the video screen and converted into swimming trajectories with x,y coordinates and
time in seconds [4]. By plotting the swimming trajectories
of the cells with x,y coordinates it was possible to calculate
run length and speed, as well as the turn angle and distance of bacterial cells in relation to tracked algae, enabling correct number of turns by bacteria toward alga
to be recorded. The concentration of motile bacteria and
algal cells within each sample was measured from video
images and calculated using the focal depth and direct
counts of those cells in the ¢eld of view [4,6].
3. Results
The algae, swimming with an average speed of 37 Wm
s31 ( Q 26 standard deviation), were tracked by either individual cells or groups of up to ¢ve bacteria at a time.
Less than 10% of the algal cells were tracked at any one
time and not all bacteria in the suspension tracked algal
cells at the same time. We measured no di¡erence in the
tracked or non-tracked swimming behaviour of the algae.
Bacterial abundance, obtained from dark-¢eld microscopy counts, ranged between 1.6U106 and 2.4U106 cells
ml31 , while algal cell concentrations were 2.1U103 cells
ml31 to 3.3U103 cells ml31 . Treatments were repeated
on four separate occasions with each of the two bacterial
strains, P. haloplanktis and S. putrefaciens, and tracking
was observed in each of four replicate slide preparations.
The results of 200 frames from controls, using 0.8-Wm
latex beads as a passive substitute for bacteria, showed
no ‘tracking’ or entrainment of beads by any algae (data
not shown).
Fig. 1. These plots show three of the best bacterial tracking paths of
the algae P. lutheri, each track represents the path of individual cells.
a,b: Plots of a P. lutheri algal cell tracked by P. haloplanktis bacteria.
c: Plot of a P. lutheri algal cell tracked by a S. putrefaciens bacterium.
There are no error bars for these plots of algae tracked by bacteria because they are direct tracks of algal and bacterial cell paths. The x,y coordinates are in micrometres. Tracks of the algal cells always start in
the bottom left hand corner of each ¢gure, bacterial tracks generally
start to the left of the plot but not at the same x,y coordinates as the
algal track.
Table 1
Maximum and mean speeds, mean angle of turn, distance from alga, run length and turning frequency for all the tracking and non-tracking bacteria
and algae
P.
P.
S.
S.
S.
P.
S.
lutheri algal cell
haloplanktis tracking alga
putrefaciens tracking alga
haloplanktis not tracking alga
putrefaciens not tracking alga
haloplanktis without alga
putrefaciens without alga
N
Mean time
tracked
(s)
Distance from
algal cell
(Wm)
Mean run
length
(Wm)
Maximum*, and
average speed
(Wm s31 )
Mean turn
angle
(‡)
Turns s31
Maximum*, and
average correct
turns
246
569
156
100
100
100
100
N/A
1.2 (. Q 0.4)
0.7 ( Q 0.2)
N/A
N/A
N/A
N/A
N/A
4 ( Q 3)
6 ( Q 4)
52 ( Q 9)
57 ( Q 11)
N/A
N/A
24
5 ( Q 3)
5 ( Q 2)
19 ( Q 16)
18 ( Q 9)
25 ( Q 13)
27 ( Q 11)
176*,
401*,
445*,
237*,
204*,
126*,
103*,
86
47
59
90
97
93
89
3 ( Q 4)
12 ( Q 5)
16 ( Q 6)
6 ( Q 2)
6 ( Q 3)
4 ( Q 3)
3 ( Q 2)
N/A
12, 7( Q 3)
8, 5 ( Q 3)
N/A
N/A/
N/A
N/A
37 ( Q 26)
197 ( Q 54)
186 ( Q 69)
146 ( Q 44)
103 ( Q 48)
87 ( Q 24)
72 ( Q 24)
( Q 44)
( Q 9)
( Q 6)
( Q 12)
( Q 19)
( Q 43)
( Q 40)
N = the total number of run lengths measured. To help de¢ne bacterial tracking the maximum and average correct consecutive turns has been given
for tracking bacteria, where ‘correct’ refers to turns toward the algal cell path. Values in parentheses are standard deviation calculated from the N of
each condition.
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the ghost tracks. The speeds of the tracing bacteria were
comparable to tracking bacteria, ranging from 29 to 396
Wm s31 , with an average speed of 179 Q 84 Wm s31 . The
cells tracing the ghost paths achieved a maximum of eight
consecutive correct turns in a row with an average of 5 Q 2
correct consecutive turns toward the freshest end of the
ghost paths. The algal cells that left the ghost tracks had
average swimming speeds of 64 Q 30 Wm s31 .
Bacteria in cell suspensions with algae were also recorded intersecting motile algal swimming paths and not
tracking algae (Fig. 3a). Bacteria that were not tracking
algae generally had lower average speeds (Table 1), which
ranged from 19 to 237 Wm s31 . Bacteria that were in sus-
Fig. 2. The comparison of speeds of the tracked algal cells and their
tracking bacteria for the tracks depicted in Fig. 1a,b for P. haloplanktis
and Fig. 1c for S. putrefaciens.
The bacterial isolates, P. haloplanktis and S. putrefaciens, tracked algae at mean distances of 4 Q 3 Wm and
6 Q 4 Wm, respectively (Table 1 and Fig. 1). The bacteria
tracked the algae, on average 1.8 Q 0.4 s for P. haloplanktis
and 0.7 Q 0.2 s for S. putrefaciens. Individual bacterial
speeds were not constant while tracking the alga but
ranged from 5 to 401 Wm s31 for P. haloplanktis and
from 9 to 445 Wm s31 for S. putrefaciens (Table 1 and
Fig. 2). Bacteria increased their speed and shortened their
run length nearly reversing direction at the end of each run
with a high number of correct turns toward the tracked
algal cell (Table 1 and Fig. 1). The mean turn angle was
less and the turns per second were greater for a tracking
bacterium than for non-tracking bacteria (Table 1).
Four bacteria (three P. haloplanktis and one S. putrefaciens) were recorded tracing over old algal swimming
paths (ghost paths) (Fig. 3b,c). The bacterial tracings
were on average 0.5 Q 0.2 s behind the algal cells, with
mean distances of 15 Q 3 Wm from algal cells that made
Fig. 3. a: A non-tracking bacterium, S. putrefaciens, swimming path
that intersects an algal cell path. b,c: P. haloplanktis swimming paths
that intersect an old algal cell path (ghost path) after the algal cell has
passed.
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pensions free of algae had even lower speed ranges, 12^126
Wm s31 , than bacteria with algae. Two-tailed, unpaired
t-tests were performed on the di¡erence in speeds for bacteria with and without algae. The higher speeds of bacteria
with algae were found to be signi¢cantly di¡erent from the
speeds of bacteria without algae (P 6 0.001 for P. haloplanktis and S. putrefaciens). Bacteria not associated
with algae also had longer run lengths and lower turning
frequencies (Table 1).
4. Discussion
The marine bacterial isolates followed the algae in a
non-random manner. The bacterial speeds far exceeded
the algal cells’ speeds, with the bacteria over-taking the
alga and then reversing to swim back over the algal cell
path (Figs. 1 and 2). The motile bacteria are able to track
free-swimming individual algal cells (Fig. 1) using high
speed, increased turning frequency and high numbers of
consecutive turns towards the algae (Table 1 and Fig. 2).
The bacteria actively track the algae, rather than simple
entrainment, since tracks of bacteria that crossed algal cell
paths were not reoriented toward the swimming algae
(Fig. 3a). Additionally, the results from the substitution
of 0.8-Wm latex beads for bacteria indicated no entrainment of beads by any algae. The presence of the algae
caused a signi¢cant change in the behaviour of the bacteria, with signi¢cantly higher speeds for bacteria regardless
of whether they were tracking algae. This suggests that the
algae are secreting exudates that act as a general cue to
stimulate bacterial motility. These exudates and cues from
the algae enabled bacteria, using chemotaxis, to orient and
track the algae. In this discussion, we explain how the high
speeds and turning frequency allow a bacterium to remain
in a speci¢c moving zone in relation to an alga and how
the high speed permits a greater sensitivity to chemical
gradients. These two features combine to enable tracking
of a moving attractant, in this case a motile algal cell,
giving the bacteria increased access to algal exudates and
thus a possible competitive advantage.
4.1. Tracking control mechanisms. How do they work ?
It is generally accepted that bacteria measure chemical
changes while moving through spatial gradients of attractants in a biased random walk [25]. Alternating between
smooth runs and tumbles that randomly reorient them, the
bacteria alter the time between tumbles to extend runs and
carry them in a favourable direction [2]. However, traversing gradients of chemoe¡ectors using a biased random
walk, requires multiple runs which would not enable bacteria to precisely position themselves around a point
source [3,15]. The standard random walk model does not
seem to explain how the marine bacteria tracked the algae
at distances approximately 1 run length wide (Table 1 and
83
Fig. 1). The bacteria tracked the motile algae, using the
run-reverse strategy which enables them to reverse direction after each stop instead of randomly tumbling, allowing them to react faster to chemical gradients.
Despite the standard chemotactic model appearing to
come up short, bacterial tracking can still be explained
using some of the same principles described by Purcell
[25] since the same two-step measuring mechanism can
apply. To show this we estimate the length that a bacterium must travel to make one concentration measurement.
The length is L = D/v, where L is the length (or runlength), v is the bacterial velocity and D is molecular diffusivity of water (1035 cm32 s31 ) [25]. Assuming v = 350
Wm s31 , within the range measured for the tracking bacteria (see Table 1 and Fig. 2), the length is 3 Wm. Turn
decisions are assumed to be made from two concentration
measurements [15], therefore the calculated tracking distance is 6 Wm. This is approximately the same as the average distance maintained between a tracking bacterium and
an alga. However, the bacteria do not always travel at the
same velocity (Fig. 2), so the required length will change
while tracking the algae. The length, however, is short
enough to allow the bacteria to make two measurements
while tracking to rapidly detect an exudate gradient plume
from the algae. Rapid detection, run-reverse strategy and
a high turning frequency would enable the bacteria to stay
within a small area, however it still does not explain how
the bacteria remain with the motile algae. The tracking of
the algae was not a standard chemotactic response reliant
on the turn frequency of the bacteria, but a more complex
response reliant on correctly turning toward the algae.
If bacterial swimming directions were chosen entirely at
random one would expect the mean turn angle to be 90‡
with a standard deviation of 39‡ allowing for Brownian
reorientation [26]. This was the case for the bacteria in the
controls and bacteria not tracking algae, suggesting they
are capable of tumbling as well as reversing while the
bacteria that tracked algae used reversals, producing
much smaller angles (Table 1), which helped them to
track. However tighter turning angle alone would not result in tracking, bacteria also need to turn toward the
algae consecutively in the correct direction.
Turning in the correct direction toward an attractant
indicates the bacteria are steering, a behaviour already
noted for the sulfur bacterium Thiovulum majus, but in
that case steering occurred over distances of millimetres
and a turn in either direction was suitable [27]. The bacteria in our study are controlling their direction at the
micrometre scale with only one direction being suitable.
Since the bacteria are using run and reverse reorientation
to target the small (6-Wm diameter) moving algae, the directional choice is limited to either toward or away from
the algae. Therefore, the probability of a bacterium turning toward an alga consecutively 12 times (Table 1) on
chance alone is about 1 in 20 000, showing that, the directional turns of the bacteria are not randomly chosen. Fur-
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Fig. 4. Di¡usivity of the bacterial cells toward the tracked algal cells
versus distance of the bacteria from the algal cells for a: P. haloplanktis
(y = 65.6U1:55 , R2 = 0.77, n = 569) and b: S. putrefaciens (y = 109.6U1:16 ,
R2 = 0.69, n = 156). Note, n is the number of runs recorded and points
with the same symbols representing all of the runs of an individual bacterium. This shows that the individual bacteria were altering their Di¡usivity during their tracking (di¡usivity was calculated by multiplying the
speed and distance of a bacterium from a tracked alga).
the measurable limits of our experimental set up. Therefore it is likely that some of the positions of the bacterial
turns were not recorded accurately due to a phenomena
known as aliasing; where the measuring frequency, in this
case the frame speed, is similar to the maximum measured
frequency, in this case the turning frequency [28]. Therefore the distance travelled by bacteria between frames is
underestimated; this impacts on the recorded turn angle
and the bacterial speed. Since the x,y coordinates taken of
the bacterial paths would not have captured the entire
bacterial path, the real speeds must be greater than we
measured (see Fig. 5). The real turning angle will have
been overestimated since the measured paths were shorter
(Fig. 5). Since bacterial speed is greater than measured,
this implies that the minimum length a bacterium must
travel to make a comparative concentration measure is
shorter than our calculated 6 Wm. Without higher speed
cameras we cannot say precisely how fast the bacteria are
tracking the algae.
Additional sources of error come from our inability to
observe the bacterial swimming three-dimensionally and
only recorded the x and y coordinates of the bacterial
and algal swim paths. We do not discount the z-axis since
cells were moving through all three dimensions, however
the run-reverse behaviour of the bacteria involves the bacteria controlling their movement predominantly within a
two-dimensional plane, albeit horizontally x^y or vertically z^y or z^x. Therefore we believe that the recorded
trajectories of bacteria moving through the x^y plane were
relatively accurate. The limited focal depth of the x^y introduced another error source since we measured predominantly at the mid-depth of the chamber yet the microscope chamber we used was V100 Wm. Therefore it is
likely that we were only observing a fraction of the track-
thermore turning correctly is probably more important
than the frequency of turns in enabling bacteria to track
the algae. For example the turns s31 for S. putrefaciens
were higher than that of P. haloplanktis, yet S. putrefaciens
tracked for less time because it had a lower number of
correct consecutive turns toward the algae (Table 1).
4.2. Error sources
While the bacterial tracks were measured directly and
plotted as x,y coordinates, the turning frequencies of the
bacteria were inferred from the directional change of the
bacterial tracks. The variation in turning frequency for the
tracking bacteria indicates that some of the bacteria made
over 20 turns s31 (Table 1) which was close to the 24
frames s31 speed of our VCR. This means that the turning
frequency of the tracking bacteria is at least approaching
Fig. 5. An idealised tracked where the time interval between points is
equal, showing how aliasing would overestimate the turning angle and
underestimate the speed of a tracking bacterium. The solid line is the
measured cell path of the bacteria while the dashed line represents the
real bacterial cell path missed due to the measuring frequency (e.g.
frames s31 ) being too large.
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ing behaviour and recorded tracks would be an underestimate of the real number of tracking bacteria.
4.3. Reasons to track
There are two ways for bacteria to increase nutrient
uptake rate, move into a region of high nutrients or by
swimming fast enough to increase di¡usive £ux, here the
tracking bacteria do both. Berg and Purcell [3] proposed
that increased speed could provide an increase in molecular uptake for the bacterial cell, superior to that of diffusion alone, provided the bacterium could sink or swim
fast enough through the water. The nutrient-depleted £uid
close to the surface of the swimming bacteria would be
replaced faster than if they remained motionless. This
quicker renewal of molecules increases the nutrient gradients near the cell, which in turn increases the rate of
nutrient di¡usion £ux toward the cell [29]. The idea that
tracking bacteria may be increasing their di¡usive £ux and
thus enhancing molecular uptake by swimming around the
motile algal cell is tempting. However this has been
thought to be unlikely based on calculations from the
low speeds recorded for bacteria such as E. coli [17].
Here we calculated the increase in di¡usive £ux for the
tracking bacteria from the non-dimensional variable av/D
where a is the radius of the bacterium, v is velocity and D
is molecular di¡usivity [3]. From our data of a bacterium
tracking an alga, a = 0.5 Wm, again assuming v = 350 Wm
s31 , (see Table 1 and Fig. 2), and using a standard value of
1035 cm32 s31 for D, av/D = 0.2. Taking into account that
our speed measurements are underestimates of the bacterial speeds, the percentage increase in di¡usive £ux would
be at least 30%, (¢g. 4 in [3]) thus signi¢cantly increasing
the nutrient or material acquisition of the tracking bacteria. However this estimate of the increase in di¡usive £ux
does not necessarily relate directly to the molecular uptake
rate of the bacteria, since Berg and Purcell [3] were assuming a swimming cell moving through an environment with
a homogenous nutrient concentration. The bacterial tracking of motile algae is an environment of steep nutrient
gradients, where chemotaxis is used to detect changes in
concentration at di¡erent places or times along the algal
trail.
The 30% increase in di¡usive £ux is only a conservative
estimate of the nutrient gain received by tracking bacteria.
Studies of bacterial uptake rates have shown that estimations of nutrient transfer from models are often underestimates of real uptake rates [30,31]. This is likely due to
physical models oversimplifying the combined e¡ects of
physical and biochemical processes involved in nutrient
uptake by marine bacteria.
The tracking bacteria show evidence for physical and
biochemical coupling to increase their nutrient uptake.
By slowing down as they approach the algae and speeding
up as they get further away (Fig. 2) the bacteria control
their di¡usivity toward the algae (Fig. 4). The control in
85
di¡usivity allows the bacteria to take advantage of the
high nutrient levels close to algae and increase uptake
rates at the edges of the gradient plume using the higher
speeds to increase di¡usive £ux. The di¡usivity of tracking
bacteria ¢t within a narrow envelope where the di¡usivity
range (speeds) a bacterium can have, narrows with greater
distance from the tracked alga. As the bacteria increase
their tracking distance they must use increasing control of
their speed and turning or risk losing the motile algae. A
small group of bacteria in Fig. 4a show a distinct drop o¡
in their di¡usivity, as they exceed 14 Wm from the algae,
these values directly relate to points in tracks when bacteria stopped tracking algae, indicating that there is a limit
to the distance that bacteria can reach before they are no
longer able to successfully track motile algae. No such
drop o¡ was observed for S. putrefaciens, but this may
be due to the small number of tracks recorded for this
species.
The tight control of the bacterial di¡usivity toward the
motile algae could provide tracking bacteria with a competitive advantage over non-tracking bacteria. Since motility is one of the largest genetic loads most bacteria possess [32] there would be strong selection for any energetic
gain provided by a competitive motility strategy. Bacteria
with superior chemotaxis and motility properties have
been shown to out-compete cells with higher growth rates
[33,34] with generation growth rate increases as low as
0.04% giving a signi¢cant advantage to bacteria in mixed
cultures [35]. Therefore the V30% di¡usive £ux gained
from the speed increase for bacteria that track algal signals could provide a signi¢cant competitive advantage
over that of bacteria not associated with algae. This may
explain why the bacteria in samples with algae had a general speed increase due to the nutrient gain from the algae.
4.4. How long can bacteria track the signal?
The ability of bacteria to trace an algal signal approximately half a second after an algal cell had past through a
region, was recorded for bacterial tracks of ghost paths
(Fig. 3b,c), suggesting that the bacteria are responding to
a chemical trail or discrete exudate left behind by the algal
cells. This phenomena of bacterial tracing was similar to
that of the tracking bacteria with comparable speeds, turn
angles and correct consecutive turns (Table 1) indicating
that the bacteria were responding to the same type of
signal. This implies that the algae were leaving discrete
exudate signals, stimulating the general speed increase
seen for bacteria in samples with algae.
4.5. Tracking in the pelagic environment
It is likely that the bacterial tracking of algae in the
pelagic environment is an intermittent phenomenon that
is a response to discrete algal cues. Recent ¢ndings showed
that over 60% of marine bacteria have the potential to be
FEMSEC 1476 3-4-03
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G.M. Barbara, J.G. Mitchell / FEMS Microbiology Ecology 44 (2003) 79^87
motile but for only a few seconds at a time [36]. It may be
that most marine bacteria can track algae under the right
conditions, such as lysing phytoplankton blooms or discrete point-source releases of nutrients. Tracking by bacteria of single algae may also help to explain how marine
bacteria associate with other free-£oating or sinking particles in the open ocean, such as marine snow [37,38].
Increased nutrient uptake rates of bacteria attached to
aggregates and marine snow over uptake rates of freely
dispersed bacteria has been well documented [37^39].
However there are discrepancies in the current knowledge
of known bacterial uptake rates and measured carbon
turnover for total pelagic bacteria [38,39]. The nutrient
uptake rate of bacteria tracking phytoplankton could be
greater than freely dispersed bacteria not associated with
nutrient sources. Therefore nutrient turnover by bacteria
that track may play a pivotal role in adding to the rapid
transfer of consumed dissolved nutrients into the food
web. This suggests that while chemotaxis has been important for elucidating complex protein signalling pathways,
chemotaxis may also be important in mediating nutrient
exchange between phytoplankton and bacteria.
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
Acknowledgements
This work was supported by ARC (Australian Research
Council) and the Flinders University of South Australia.
We also thank V. Mirabelli, Dr. Bob Belas and an anonymous reviewer for their constructive criticism on an earlier version of this manuscript.
[22]
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