Download Application of a predator–prey overlap metric to determine the

ICES Journal of
Marine Science
ICES Journal of Marine Science (2016), 73(4), 1051– 1061. doi:10.1093/icesjms/fsw001
Original Article
Application of a predator – prey overlap metric to determine
the impact of sub-grid scale feeding dynamics on ecosystem
productivity
Adam T. Greer‡* and C. Brock Woodson
University of Georgia, College of Engineering, 200 D.W. Brooks Drive, Boyd Graduate Studies 708, Athens, GA 30602, USA
*Corresponding author. tel: +1 228 688 2325; fax: +1 228 688 1121; e-mail: [email protected]
Present address: Department of Marine Science, University of Southern Mississippi, 1020 Balch Blvd, Stennis Space Center, MS 39529, USA.
‡
Greer, A. T., and Woodson, C. B. Application of a predator–prey overlap metric to determine
the impact of sub-grid scale feeding dynamics on ecosystem productivity. – ICES Journal of Marine Science, 73: 1051– 1061.
Received 4 May 2015; revised 21 December 2015; accepted 4 January 2016; advance access publication 2 February 2016.
Marine ecosystem models assume spatially homogeneous population dynamics at sub-grid scale resolution, despite evidence that marine systems
are highly structured on fine scales. This structuring can influence the predator– prey interactions driving trophic transfer and thereby overall ecosystem production. Here we apply a statistic, the AB ratio (zAB), to quantify increased predator production due to fine-scale overlap with its prey. We
calculated zAB from available literature sources (spatial observations of predator and prey) and from data obtained with a towed plankton imaging
system, demonstrating that organisms from a range of trophic levels and oceanographic regions tended to overlap with their prey both in the horizontal and vertical dimensions. The values of zAB indicate that spatially homogeneous calculations underestimate productivity. This pattern was
accentuated when accounting for swimming over a diel cycle and by increasing sampling resolution, especially when prey were highly aggregated.
We recommend that ecosystem models incorporate more fine-scale information both to more accurately capture trophic transfer processes and to
capitalize on the increasing sampling resolution, data volume, and data sharing platforms from empirical studies.
Keywords: ecosystem models, fine-scale distribution, marine ecosystem productivity, predator– prey, spatial overlap, trophic transfer.
Introduction
At the heart of full or “end-to-end” ecosystem models are the trophic
interactions that drive ecosystem state (Rose et al., 2010). While
predator–prey interactions occur on the scale of individuals, many
ecosystem processes, especially predation, cannot be accurately
described with organism mass balance and energy flows among
food web components because fine-scale processes, including prey
concentrations and behavioural interactions, are either neglected or
estimated crudely (Denman and Powell, 1984; Verity and Smetacek,
1996). The relatively large grid-cell size of most marine ecosystem
models leads to misrepresentation of the spatial component of
trophic interactions (i.e. predator–prey overlap), and the selection
of model grid size can also have a large impact on which ecosystem
dynamics are captured (Fulton et al., 2004). While single-species
and spatially aggregated ecosystem models (e.g. Ecosim) have been
moderately successful due to specific ‘tuning’ facilities, ecosystem
# International
modelling is moving increasingly toward more mechanistic
approaches such as agent-based models (ABMs) where behaviour
is explicitly defined and must include details about predator–prey
distributions at scales relevant to these interactions.
Recent research demonstrating that marine ecosystems are highly
structured on fine scales could have a profound impact on our understanding of trophic interactions and how these interactions are represented in ecosystem models (Woodson and Litvin, 2015). Sub-grid
scale aggregations are known as “zones of enrichment” that can
occur in the horizontal (fronts) or the vertical dimension (thin
layers; e.g. McManus et al., 2003). Fronts, small-scale areas where
two differing water masses meet, form via several different mechanisms (Mann and Lazier, 2006) but all share the common theme of
having some sort of convergent or confluent flow resulting in the aggregation of plankton (Marra et al., 1990; Houghton, 1997; Bakun,
2006). Previous research has shown that fronts can have large
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A. T. Greer and C. B. Woodson
impacts on phytoplankton productivity (Levy et al., 2001), secondary
production (Barange, 1994; Zhang et al., 2013), and the biomass production of higher trophic levels (Cotte et al., 2007). Thin layers are
aggregations of plankton (.2× background concentration) that
span ,5 m in the vertical direction and sometimes several kilometres
in the horizontal (Dekshenieks et al., 2001; McManus et al., 2003).
Thin layers are known as sites of increased biological activity
(Alldredge et al., 2002) and can have impacts on higher trophic
levels by increasing the probability of prey encounter for predators,
such gelatinous zooplankton, and larval/adult fish aggregating near
or inside the layer (Clay et al., 2004; Greer et al., 2013, 2014;
Benoit-Bird and McManus, 2014). Such zones of enrichment on
fine scales exist in a variety of marine environments, but the key component affecting ecosystem productivity is the rate at which predators
can exploit their prey, which should cascade up the foodweb to affect
higher trophic level production.
Empirical studies have made it apparent that grid cell averaging
of predator–prey processes, which occur on the scale of individuals
(Fuchs and Franks, 2010), is difficult and sometimes misleading if
there is strong predator–prey overlap on fine scales. However, for
the purposes of developing an ecosystem model, researchers must
choose an effective grid-cell size to represent physical and biological
processes. For the models to deal with biological heterogeneity, we
can apply Reynolds decomposition, which takes a temporally or spatially fluctuating value and breaks it down into a mean term and a
perturbation term. The addition of the perturbation term results
in a second production term that accounts for sub-grid scale predator–prey covariance and the ratio of this term to the mean covariance is formalized as the AB ratio (Woodson and Litvin, 2015):
zAB =
A′ B′
,
AB
where the numerator represents the average product of the differences between the concentrations of predators and prey and their respective mean concentration, and the denominator is the product of
the average concentrations of predators (A) and prey (B). The variable zAB is a good indicator of predator–prey spatial overlap and has
a simple interpretation of being related to increased production
created by spatial overlap (assuming a linear or “type I” functional
response). For example, a ratio near zero would indicate that the
mean is properly estimating the predator–prey encounters (i.e.
fine-scale spatial overlap is minimal), but a ratio of 1 would mean
there is the potential for 100% higher production of the predator
due to its spatial overlap with prey, compared with the expected production using average concentrations of predators and prey. The
variable zAB can also be negative if predators and prey are concentrated in differing portions of the water column resulting in
reduced production.
To document the degree of predator–prey overlap and its trophic
impact across different trophic levels, we conducted an extensive literature review of studies that contained comparable vertical and
horizontal distributions of predators and prey to determine how
often and to what extent the mean concentration misrepresents
the actual predator– prey trophic interaction potential. We used
data from many types of sampling equipment (e.g. plankton nets,
acoustics, pumps, and in situ imaging), characterizing predator
and prey distributions across a range of trophic levels in a variety
of environments, predominately in coastal areas. The calculation
of zAB provides a metric to determine the degree to which fine-scale
spatial overlap of predators and prey, which is currently not
incorporated into ecosystem models, influences the productivity
of different trophic levels and the ecosystem as a whole.
Methods
Literature review
We reviewed the available scientific literature for studies (see
Supplementary data for publication list), displaying the vertical or
horizontal distributions of multiple trophic levels with directly comparable plots (i.e. similar measurement resolution). Many studies
show high-resolution distributions of phytoplankton and nutrients,
but these were not included in the analysis because they are not
exactly “trophic interactions,” and the vertical zonation of nutrients
is well described (nutrient concentrations typically increase with
depth). Due to the difficulty of extracting quantitative information
from graphs utilizing image analysis, two-dimensional plots with
no colour information (i.e. labelled contours) were not examined
in the analysis. Avast majority of studies had a minimum vertical resolution of ,30 m and were conducted in coastal areas. Studies were
categorized based on the region they were conducted (tropical, temperate, estuary, and polar). Figures were cut from portable document
format (pdf) versions of the papers using the Adobe Acrobat snapshot tool and saved as images in ImageJ (v1.47v, Rasband, 1997–
2012) for further analysis.
We examined 85 publications to quantify the spatial overlap of
predators and calculate zAB. Within these publications, 141 figures
were analysed for a total of 1139 measurements from the literature.
86.7% of the studies examined used vertical distributions, while 6.5
and 1.2% of the measurements were taken from vertical twodimensional and horizontal two-dimensional interpolations, respectively. The discrepancy between the types of figures analysed
is due to the rarity of fine-scale studies conducted across multiple
trophic levels with comparable two-dimensional distribution
plots. A majority of the literature data came from studies in temperate and tropical ecosystems (55.1 and 19.4%, respectively), while estuarine and polar studies were less common (12.6 and 12.8%).
In situ plankton imaging data collection
In addition to predator–prey distributions from the literature, zAB
was calculated using data obtained from several research cruises
using the In Situ Ichthyoplankton Imaging System (ISIIS; Cowen
and Guigand, 2008). ISIIS data were used from three different locations collected during summer of 2010: northern Monterey Bay, CA
(R/V Shana Rae), USA, Stellwagen Bank, MA, USA (NOAA ship
Delaware II), and the shelf edge of Georges Bank, MA, USA
(NOAA ship Delaware II).
ISIIS uses a collimated light source to produce shadowgraph
images of zooplankton in the size range of 500 mm to 13 cm.
This shadowgraph lighting technique creates a large depth of field,
producing a large sample volume (8 L per image, 16 images per
second). The ISIIS instrument package also contains a CTD
(Seabird) to synoptically measure physical properties of the water
column. The physical data (salinity, depth, and temperature measured at 2 Hz) are merged with the image data using the common
nearest physical data time stamp. This processing was performed
in R (v3.0.2, R Core Team, 2013). The abundances of fish larvae, ctenophores, hydromedusae, salps, siphonophores, chaetognaths,
copepods, and appendicularians were calculated at various sites
and placed into 5 m bins, similar to the binning procedure used
for the literature data. The average concentration across several profiles calculated on each sampling day by taking the number of each
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The impact of sub-grid scale feeding dynamics on ecosystem productivity
taxa within a certain depth bin divided by the volume sampled in
that bin (calculated from the seconds spent in the depth bin,
speed of the ship, and ISIIS image volume). This method was used
because some of the taxa were rare, so several profiles were needed
to obtain an accurate measure of organism concentration with
depth. The variable zAB was then calculated using this average
vertical distribution.
zAB calculation
The variable zAB was obtained using two different methods, depending on the plot type. For vertical distribution plots, analysis was conducted in ImageJ (v1.47v, Rasband, 1997–2012). The water column
mean was obtained by tracing the outline of the polygon created by
the line graph and using ImageJ to calculate the resulting area. This
area was then divided by the measured height of the plot in pixels to
get the pixel value of the mean concentration. The variables A′ and B′
were obtained by measuring the value of each measurement on the
plot in pixels within a minimum of a 2 m bin. The variable A′ was
calculated by taking the difference of the prey value and the prey
mean concentration. The same procedure was done for the predators to obtain B′ . Both sets of values were then multiplied, and
these values were averaged to obtain A′ B′ . The 2 m minimum bin
size was chosen because it approximated the shortest distance that
a zooplankter could move within an hourly time frame to exploit
a nearby prey resource. Because pixels units were used for all measurements, there was no need to convert to units for calculating
zAB. For plots with two-dimensional spatial distribution maps of
filled contours, an image analysis programme written in MATLAB
(Mathworks, version R2014a) was used to calculate average values
and zAB in a similar manner to the vertical distributions.
Trophic levels and statistical analyses
Trophic level was assigned to each predator–prey interaction, and
only three trophic levels were used. The first level included interactions between phytoplankton and their grazers, including ciliates,
copepods, appendicularians, salps, and other heterotrophic grazers.
This was the most difficult group to assign because there are many
taxa in trophic level 1 that feed on each other and demonstrate
“prey switching” behaviour under certain conditions (Kiørboe
et al., 1996). Zooplankton groups known to feed on the predators
(grazers) in the trophic level 1 group were considered trophic level
2. These taxa included chaetognaths, fish larvae, hydromedusae,
ctenophores, siphonophores, and any other zooplankton known to
primarily consume phytoplankton grazers (copepods and other
predators from trophic level 1). Trophic level 3 was composed of all
nektonic animals, including adult fish, sharks, and whales. Higher
trophic-level assignments, although they certainly existed, were not
considered due to the rarity of high-resolution observations of top
predators with synoptic prey field measurements.
Values of zAB were calculated and plotted in relation to the location of the sample, the trophic level, or the predator/prey type.
Mean zAB per group was calculated in R (v3.0.2, R Core Team,
2013) using the package “plyr” (Wickham, 2011). The standard
error was estimated using the standard deviation divided by the
square root of the number of samples in each category of the zAB
measurement. Differences in variation between regions were determined using F-tests.
To examine the effect of swimming on zAB, we recalculated the
statistic using known approximate swimming distances for each organism over a 1 d period. The assumption was that the organisms use
some cue to locate prey patches (they initiate swimming in the
correct direction), and the distance that they search in 1 d can be calculated using their average swimming speed. Horizontal and vertical
two-dimensional data were excluded from this part of the analysis
because of the difficulty of assuming a swimming direction in a twodimensional environment. Four different swimming speeds were
used: 0.0001 m s21 for microzooplankton, 0.0025 m s21 for copepods, jellies, and small crustaceans, 0.01 ms21 for larval fish and
shrimp, and 0.15 m s21 for adult fish. These were then converted
to distances that the organism could travel within a day. We then
designed a program in R that would find the maximum prey concentration within the swimming range assigned to that predator. The
values of B′ (predator) would find the maximum A′ for the A′ B′
calculation. These values were averaged for the depth bins where
predators had higher abundances than the water column mean
(i.e. predator concentrations . water column mean, B′ positive),
giving the swimming adjusted zAB.
The swimming adjusted zAB is also likely affected by the vertical
resolution of the sample. To test this hypothesis, we examined variability in the resolution of the measurements both within the literature and ISIIS data. For the literature data, we only evaluated vertical
measurements and compared the zAB under different resolutions
and trophic levels. For the ISIIS data, since images are taken continuously, we could effectively choose the vertical resolution of the measurements. We recalculated zAB for the Stellwagen Bank dataset,
which contained distributions of copepods, gelatinous zooplankton, and larval fish, for 10 different vertical resolutions ranging
from 0.5 to 30 m in a 60 m water column (see Greer et al., 2014)
(Figure 1).
Results
The variable zAB calculated on the ISIIS data revealed strong patterns
in relation to taxon and the oceanographic region sampled.
Monterey Bay had the shallowest water column (20 m), most variable stratification of the sites sampled, and also relatively low zAB
across all taxa. These sites were sampled over several days, but predator and prey taxa generally did not strongly overlap (with a few
exceptions) and most were present in detectable amounts throughout the water column (Figure 2). Stellwagen Bank and Georges Bank
had much deeper water columns (60 and 80 –120 m, respectively), and distributions were more aggregated. Many taxa occupied
distinct and relatively small portions of the water column, which
explains why most of zAB were much greater than zero. If these
small vertical habitats happened to overlap between predators and
prey, as they did with fish larvae, hydromedusae, and copepods aggregating near the surface near Stellwagen Bank, then zAB was quite
high. The most consistently high values of zAB occurred at Georges
Bank, where most zooplankton taxa aggregated near surface near the
shelf-slope front, a semi-permanent feature associated with elevated
productivity. The Stellwagen Bank and Monterey Bay sampling, on
the other hand, did not target a particular physical feature driving
plankton aggregations.
The variable zAB changed among the environments sampled
(Figure 3). The values of zAB were skewed normally distributed
with a mean close to, but slightly greater than zero (zAB of zero
means spatial overlap is no different from what would be expected
with random distributions), with the positive skew resulting in
74.3% of zAB calculations being larger than zero. The variation
in zAB was significantly higher in temperate systems (F test,
P , 2.2e216). Overall, 64.2% of the zAB values were at least
+0.2 from zero in temperate environments, indicating that the
mean was often not a good estimate of the actual trophic potential
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A. T. Greer and C. B. Woodson
Figure 1. (a) Schematic diagram showing a distribution where the mean is a good estimator of predator– prey interactions (random distribution,
the (A′ B′ ) perturbation term is much less than the mean term (AB)) compared with a patchy distribution where the perturbation term far exceeds
the mean term. The variable zAB will be extremely high in the right panel. (b) Hypothetical vertical distribution of predators and prey demonstrating
how zAB is calculated. The vertical distributions of predators and prey are overlaid (i.e. bars are not stacked on top of one another).
in these systems. Means were slightly better estimates of trophic
productivity for tropical systems, where 53.4% of the zAB measurements were at least +0.2 from zero. The most striking difference of
zAB among regions was the much lower variation for the estuarine
environments (F test, P , 2.2e216). The average concentrations
in the estuarine environments most often reflected the true
trophic potential based on spatial overlap (74.7% of zAB between
20.2 and 0.2), indicating predator–prey distributions close to
The impact of sub-grid scale feeding dynamics on ecosystem productivity
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Figure 2. Average zAB calculated from in situ plankton imaging (ISIIS) data. Georges Bank sampling occurred near a shelf-slope front. Monterey Bay
samples were in shallow waters (20 m) deep and Stellwagen Bank samples occurred in waters 60 m deep. Error bars represent +1.96 SE of the
AB ratio measurement.
Figure 3. Histogram of zAB values for all taxa and trophic levels. The variation in zAB is significantly different between regions, and higher zAB tend to
be associated with the lower trophic levels.
random. Extreme zAB values tended to be dominated by lower
trophic levels in all environments, whose predator–prey
interactions are generally easier to measure on the same spatial
scale than for higher trophic levels.
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A. T. Greer and C. B. Woodson
Figure 4. The variable zAB plots for different trophic groups with all potential prey items pooled. Negative values are included in the calculations of zAB for
(a) phytoplankton grazers, (b) larger mixotrophic grazers, (c) gelatinous zooplankton, (d) larval fish with and without swimming, (e) adult fish, and (f)
whales. Error bars represent +1.96 SE of the zAB measurement. All zAB measurements except for (d) are calculated without predator swimming.
Many taxa displayed consistent aggregation with potential prey
items. Small grazers generally displayed positive spatial overlap
with potential prey (phytoplankton), with ciliates, euphausiids,
and heterotrophic dinoflagellates significantly above zero. Larger
grazers had higher zAB, with salps and doliolids having the highest
mean values, but the measurements for these taxa were variable
(Figure 4a,b). Gelatinous zooplankton were also a mixed group
with some counterintuitive results showing lobate and cydippid
ctenophores differing remarkably from Mnemiopsis spp. and
Pleurobrachia spp. (Figure 4c). Fish larvae average zAB across different taxa also tended be slightly .0, but cod larvae, scombrid larvae,
pollock larvae, and general fish larvae were the only ones significantly .0. The swimming adjusted zAB demonstrated that fish larvae can
experience much higher production if they can locate prey patches
within the water column (Figure 4d). Adult fish displayed consistently positive zAB values, and myctophid fish were extremely overlapped with copepod prey in a few instances. However, likely due
to small sample sizes, none of these relationships were significant
(Figure 4e). Whales also aggregated near potential prey, with
minke and right whales having zAB significantly .0 (Figure 4f).
zAB changes with respect to prey items
The values of zAB were not consistent across different potential prey
items. Copepods and their prey were the most well represented
predator–prey relationship in the literature. They tended to overlap
most with the all-encompassing “phytoplankton” category (no
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The impact of sub-grid scale feeding dynamics on ecosystem productivity
Figure 5. The variable zAB for different prey times among best-represented taxa. (a) Prey items for copepods, (b) chaetognaths, (c) fish larvae, and
(d) adult fish. Error bars represent +1.96 SE of the zAB measurement.
specific taxon mentioned in the literature, most often measured as
chlorophyll a fluorescence) and least with nanoplankton, ciliates,
and marine snow; however, there was considerable variability in the
measurements (Figure 5a). Larval fish zAB values (swimming not considered) with prey items were much more elevated when compared
with copepods, with the highest ones associated with copepods,
appendicularians, and general zooplankton. The lowest zAB for
larval fish was with copepod nauplii, which are thought to be the preferred prey item for most larval fishes (Figure 5b). Chaetognaths were
positively associated with a variety of calanoid copepods, crustaceans,
and appendicularians, while fish were also strongly overlapped with
copepods (Figure 5c,d). The variable zAB for fish tended to be slightly
positive or near zero for all prey items, with extremely high values
occurring for myctophid fish preying on copepods.
Sampling resolution effects
The effect of vertical sampling resolution on the swimming adjusted
zAB was considered for both literature and ISIIS data. For the first
trophic level in the literature data, zAB tended to steadily rise with
coarser resolution, reaching a peak at 15 m. Higher trophic level
zAB peaked at 5– 10 m; however, only two different resolutions
were represented in the top trophic level due to low sample size.
These measurements were littered with positive outliers, highlighting the extreme variability in zAB across the different systems examined (Figure 6). For the ISIIS data, samples from Stellwagen Bank
encompassing different trophic levels revealed a strong pattern
with higher zAB associated with higher resolution. This was
especially prominent for species preying on copepods, which aggregated in a subsurface thin layer during the study (Figure 7).
Discussion
Both the literature review and in situ plankton image data produced
strong evidence of consistently enhanced predator–prey spatial
overlap in a variety of environments. With our simplified procedure
of assigning trophic levels, we still detected average increased ecosystem production due to spatial overlap of over 200% (Table 1). This is
likely a conservative estimate because we grouped some lower
trophic level species into the same category that are known to feed
on each other and graze phytoplankton (e.g. copepods and ciliates;
Calbet and Saiz, 2005), which shortened the food chain from typical
estimates of seven levels in many marine systems (Pope et al., 1994).
With longer food chains and consistent overlap across each trophic
interaction, we would expect much higher values of ecosystem productivity. In reality, foodwebs are not so simple, and there is the possibility of negative feedbacks or indirect effects that could significantly
impact the productivity of different organisms (Polis and Holt, 1992;
Montoya et al., 2009; Sato et al., 2015). Nevertheless, our analysis indicates that spatial distribution of organisms, in addition to their overall
abundance, must be given high consideration in ecosystem models,
and zAB provides a method of incorporating this kind of information.
The predator–prey spatial overlap does not have the same degree
of influence across environments. Tropical environments, in particular, appear highly influenced by mechanisms of aggregation,
as predators and prey tended to overlap often. Feeding incidence
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A. T. Greer and C. B. Woodson
Figure 6. Boxplots showing the swimming adjusted zAB with respect to sampling resolution in the literature (many datasets used). Outliers are
shown as points, and 22 extremely high outliers were removed for display purposes.
Table 1. Mean AB ratios (zAB) +1 for each trophic level in each
region of literature measurements.
Region
Estuary
Polar coastal
Temperate coastal
Tropical coastal
Trophic
level 1
1.0981
1.4689
1.5054
3.0627
Trophic
level 2
1.2476
1.1998
1.4007
1.8781
Trophic
level 3
0.7303
1.1295
1.3742
1.3685
Total increased
production for a
top predator
1.0005
1.9906
2.8977
7.8717
The total increased ecosystem production from spatial overlap was
determined from the product of the mean zAB +1 for each trophic level.
Therefore, 1.0000 corresponds to no increase in production due to spatial
overlap (i.e. what would be expected if predators and prey were distributed
randomly).
Figure 7. Calculation of zAB (with swimming) with varying resolution
using the same dataset from Stellwagen Bank. For copepods, zAB was
calculated as predators of phytoplankton, which was measured as
chlorophyll a fluorescence. All other taxa had zAB calculated with
copepods as prey.
of larval fish is elevated in tropical environments compared with
larvae at higher latitudes (Llopiz, 2013), despite extreme differences
in primary productivity. Although not as well studied as their temperate counterparts, tropical environments, with their consistent
stratification, have strong potential for thin layer and patch formation, allowing for predator exploitation. At the other extreme,
estuarine or river-influenced environments did not have much
predator–prey spatial overlap. This is likely because these
environments are shallow and relatively uniform in physical properties throughout the water column, except a freshwater lens near the
surface. However, plankton thin layers have been detected in estuaries (Donaghay et al., 1992), and major differences in organism
composition have been detected on vertical scales of 10 m in riverinfluenced environments despite relatively uniform physical parameters (Greer et al., in revision). Therefore, the potential for vertical
spatial overlap between predators and prey is still apparent in estuarine environments, but it may not occur as frequently or with the
same intensity as in other oceanographic environments with stratified conditions. Alternatively, it is also possible that the most
common sampling equipment used in estuaries is simply not
capable of resolving the fine-scale spatial overlap in these shallow
environments.
The impact of sub-grid scale feeding dynamics on ecosystem productivity
The variability in zAB highlights some potential caveats with this
analysis, primarily due to a lack of understanding in both the
broader spatiotemporal context of organism distributions and the
detailed empirical descriptions of marine foodwebs. The distributions used in this study only represent a temporal snapshot of predators and prey, which likely influences the degree of variation in
the zAB estimate if organism patches in the ocean are ephemeral. It
is often difficult to tell what events lead up to the snapshot distribution without a broader spatiotemporal context, which is only
occasionally provided in biological oceanographic studies. The distributions at any moment in time could have resulted from movement, differential survival, or a combination of several factors.
This shortcoming highlights the need for process-oriented studies
to elucidate the drivers of distributional changes in organisms, including the possibility of top-down interactions and indirect
effects; the results of which can be counterintuitive (Montoya
et al., 2009). The circumstances surrounding top-down vs. bottomup effects in marine systems are poorly understood in comparison to
those in freshwater systems, where trophic cascades are thought to
be much more common (Verity and Smetacek, 1996). Factoring
in top-down and indirect effects is crucial to accurate interpretation
of zAB. The literature generally does not provide information on the
nutritional quality of the prey items near the predators, which is
critical for predicting the ecological value of predator–prey spatial
overlap and other characteristics of population dynamics and nutrient cycling (Bullard and Hay, 2002; Sailley et al., 2015). For example,
diatoms, when in extremely high concentrations, can be toxic to
copepods (Miralto et al., 1999; Tosti et al., 2003), and the response
to toxic diatoms can be region dependent, possibly due to local
adaptations (Lauritano et al., 2012). Ecosystem models also suffer
from an inability to resolve indirect effects (e.g. behavioural
changes, non-lethal interactions, etc.); therefore, this should be an
avenue of empirical investigation that could make major contributions to the accuracy of these models.
Another weakness in the available empirical data related to foodwebs is the lack of information on prey vulnerability (e.g. the degree
to which prey is available to predation). This is an important theoretical consideration that has major implications for understanding
the trophic impact of described predator–prey distributions. Prey
moving from protected to vulnerable states, and the frequency
with which this occurs, forms the basis of a theoretical predator–
prey framework known as Foraging Arena Theory, on which some
ecosystem models are based (Ahrens et al., 2012). According to
this theory, specific temporal or spatial windows constitute “foraging arenas” in which a certain proportion of prey is vulnerable
to exploitation. These foraging arenas can form through a variety
of mechanisms: behavioural, physical, or a combination of factors.
The ubiquity of plankton thin layers detected in the literature, and
also with ISIIS, could indicate that one aspect of Foraging Arena
Theory, restricted prey distribution or activity, is quite common
in coastal systems. Under the Foraging Arena Theory, this could indicate that prey resources are abundant in these zones, causing a reduction in activity level, assuming higher amounts of activity results
in increased predation risk (Werner and Anholt, 1993). This creates
an interesting scenario whereby prey are all vulnerable, in the sense
that they are exposed in the plankton with no place to hide, but they
can reduce their probability of predator detection by staying close
together. If, however, predators can detect the small patch, and
other predators are attracted to the feeding, then there can be extremely efficient trophic transfer (i.e. total carnage from the prey’s
perspective). Finally, our analysis focuses on fine-scale variations
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in predator–prey distributions. At these scales, prey handling
time, satiation, and light levels can become important and may
limit production. The use of ABMs or controlled laboratory experiments that deal directly with these interactions can mitigate these
issues.
With current knowledge of marine foodwebs, especially the
complex interactions among zooplankton, it becomes apparent
that many high-resolution studies could have missed important components of trophic interactions. If, for example, the predominant prey
item is not sampled adequately, this would lead to a calculated zAB that
does not reflect the true trophic interaction potential that the predator
is experiencing. Most studies used some form of plankton net system,
which do not reliably capture gelatinous zooplankton or microzooplankton (including small stages of copepods) (Hamner et al.,
1975; Remsen et al., 2004; Turner, 2004), and many studies reviewed
here did not make a distinction between different types of phytoplankton, zooplankton, or fish larvae. Microzooplankton are
thought to be the most influential phytoplankton grazers (Pierce
and Turner, 1992; Lenz et al., 1993; Verity et al., 1993) and are difficult
to sample with nets that simultaneously target macrozooplankton.
There is also a high degree of mixotrophy within the zooplankton
community (Flynn et al., 2013; Mitra et al., 2014) and higher
trophic levels (Chikaraishi et al., 2014). These facts highlight the difficulty in resolving marine foodwebs in the field when the organism of
interest may be consuming prey that is not well sampled with the
available equipment.
Incorporation of zAB into ecosystem models was demonstrated
in Woodson and Litvin (2015) where zAB was added as a scaling
factor to each predator growth term (zABgAB). In this formulation,
g is the growth rate of B, and B and A are the mean concentrations of
predator and prey, respectively. This model used the convergence
rate across a grid cell and swimming capabilities of predators and
prey to estimate sub-grid scale overlap of predator and prey. This
formulation could be used in the vertical domain as well, or the parameterization could be based on other hydrographic properties
such as stratification or light levels to define the sub-grid scale distributions of prey in an agent-based model. Woodson and Litvin
(2015) only provided a few examples of zAB measured in the field.
This study expands on this effort, providing field estimation of
zAB across a range of coastal environments, showing a significant
effect of predator–prey overlap in marine systems that can be
used in ecosystem models.
Field estimates of zAB suggest that many ecosystem models
are underestimating productivity, especially in the plankton. The
extent of the errors in productivity are discussed briefly in Woodson
and Litvin (2015). Since productivity at the base of the food chain
will likely cascade to higher trophic levels, errors in high trophic level
production could be immense, as the impact will be multiplicative.
For example, consider a simple phytoplankton-zooplankton-fish-top
predator food chain. If zAB +1 between producers and zooplankton
is 3, and zooplankton-fish is 2, and fish-top predator is 4, the total
increased productivity for the top predator will be a factor of 24. The
overall effect of sub-grid scale predator–prey interactions on overall
productivity is the subject of ongoing research.
Our results suggest that predator–prey spatial overlap is significant in marine ecosystems worldwide, and inclusion of sub-grid
scale distributions will be critical for improving existing ecosystem
models. Characterizing the vertical distributions of various organisms can also make for an interesting “stratified sampling” strategy,
allowing researchers to target certain areas of interest (i.e. thin layers
or other transition zones) with other sampling equipment to
1060
quantify biological rates. Quantifying these rates on relevant spatial
scales is critical information for ecosystem models. With wider availability and spatiotemporal extent of high-resolution observations,
gradual teasing apart of various food web components, and a
consideration for their spatial relationships, ecosystem processes
should become increasingly predictable. Close, synergistic collaboration is needed between empirical researchers (oceanographers and
fisheries scientists) and modellers to make strides in improving
ecosystem predictability.
Supplementary data
Supplementary material is available at the ICESJMS online version
of the manuscript.
Acknowledgements
We thank the many ship crews and scientists who have assisted with
the collection of plankton imaging data over the past several years.
Three anonymous reviewers helped to improve earlier versions of
the manuscript.
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