Ecology Letters, (2008) 11: 867–881
REVIEW AND
SYNTHESIS
Neil Rooney,1*† Kevin S.
McCann1† and John C. Moore2
1
Department of Integrative
Biology, University of Guelph,
Guelph, ON, Canada N1G 2W1
2
Natural Resource Ecology
Laboratory, Colorado State
University, Fort Collins, CO
80523, USA
*Correspondence: E-mail:
[email protected]
†
These authors contributed
equally to this work.
doi: 10.1111/j.1461-0248.2008.01193.x
A landscape theory for food web architecture
Abstract
Ecologists have long searched for structures and processes that impart stability in nature.
In particular, food web ecology has held promise in tackling this issue. Empirical
patterns in food webs have consistently shown that the distributions of species and
interactions in nature are more likely to be stable than randomly constructed systems
with the same number of species and interactions. Food web ecology still faces two
fundamental challenges, however. First, the quantity and quality of food web data
required to document both the species richness and the interaction strengths among all
species within food webs is largely prohibitive. Second, where food webs have been well
documented, spatial and temporal variation in food web structure has been ignored.
Conversely, research that has addressed spatial and temporal variation in ecosystems has
generally ignored the full complexity of food web architecture. Here, we incorporate
empirical patterns, largely from macroecology and behavioural ecology, into a spatially
implicit food web structure to construct a simple landscape theory of food web
architecture. Such an approach both captures important architectural features of food
webs and allows for an exploration of food web structure across a range of spatial scales.
Finally, we demonstrated that food webs are hierarchically organized along the spatial
and temporal niche axes of species and their utilization of food resources in ways that
stabilize ecosystems.
Keywords
Asynchrony, energy channels, food webs, interaction strength, metabolism, resource
compartments, size, space.
Ecology Letters (2008) 11: 867–881
INTRODUCTION
Food webs have been a central organizing concept in
ecology for the better part of the last century (Elton 1927).
Not only do they provide an appealing visualization of the
feeding linkages among species within a community, but
they also provide a natural structure within which various
subdisciplines of ecology can operate. Areas as disparate as
foraging behaviour and biogeochemical cycling have been
incorporated into the food web framework, and this is
perhaps its greatest strength. Yet as intuitively appealing
(and promising) as food webs may be, there exist challenges
in quantitatively documenting food webs in nature and
harnessing the food web framework to generate meaningful
predictions regarding the structures and processes that
impart stability in nature.
A primary challenge lies in the inherent topological
complexity of food webs, which are often comprised of
scores of species and an overwhelming number of interactions. As enumerating all present species and quantifying all
interactions within an ecosystem is an unfeasible task, the
data for most food webs are incomplete. This paucity of
data has the potential to result in misleading or spurious
relationships (Moore et al. 1989; Winemiller 1989; Martinez
1991; Polis 1991). Even with the vast complexity and
variability of the data, however, researchers have documented informative and repeatable patterns in nature. The
search for such food web statistics and the quest to seek out
empirical patterns and their consequences for stability have
been active and turbulent areas of research for over 30 years
(Cohen 1978; Yodzis 1981; Cohen & Briand 1984; Sugihara
et al. 1989). Food web ecologists continue to catalogue an
impressive number of food web patterns and a range of
models capable of reproducing these patterns (Cohen et al.
1985; Williams & Martinez 2000; Dunne 2006; Montoya
et al. 2006). Beyond the arrangement of species and linkages,
recent work has incorporated body size and species
abundance into food web patterns, highlighting informative
patterns of energy flow and connectedness in nature (Cohen
et al. 2003; Woodward et al. 2005a). Further, recent research
2008 Blackwell Publishing Ltd/CNRS
868 N. Rooney, K. S. McCann and J. C. Moore
on the diet breadth of organisms has also been used to
explain how network properties of food webs can emerge
from foraging behaviour (Beckerman et al. 2006).
As encouraging as these results have been, there are
concerns that such empirical approaches lack explicit spatial
and temporal components (Polis & Winemiller 1996). In
fact, ecologists are beginning to find that the variability of
food webs in space and time are critical to the stability and
function of ecosystems (Tilman et al. 1998; de Ruiter et al.
2005; Neutel et al. 2007). While spatial patterns in ecological
systems have been extensively scrutinized (Levin 1992), this
focus has largely been on the distribution of populations or
spatial patterns in competition and consumer-resource
dynamics (Hastings 1980, 1990; de Roos et al. 1991; Fryxell
et al. 2005). Integrating spatial structure into the larger food
web framework has proved more challenging. As a first step,
McCann et al. (2005) linked foraging behaviour, movement
and trophic position within a food web framework to show
that higher order consumers have the potential to stabilize
large food webs by coupling lower level webs in space.
Extending this approach to incorporate biomass flux in
eight documented food webs, Rooney et al. (2006) implicitly
incorporated a spatial context into their analysis. They
showed that food webs are comprised of energy channels
based on discrete basal resources which are coupled by top
predators, a result that interestingly recapitulates an observation made by Lindeman (1942) some 64 years earlier. The
resulting architecture, which also exhibits consistent differences in biomass turnover rates between the coupled energy
channels, was shown to impart both equilibrium and nonequilibrium stability to food webs. Whereas, this work has
identified key stabilizing structures and processes in welldocumented food webs, it still leaves ecologists with the task
of quantifying interactions within their webs of interest.
Further, the work does not explain how the observed
overarching architecture emerges within the food webs.
While there has been a longstanding tradition of looking
at the role of size and metabolism on important ecological
traits (Peters 1983; Brown et al. 2004), ecologists have also
argued that body and metabolism ought to allow us an
alternative viewpoint of food web structure (Warren &
Lawton 1987; Cohen et al. 2003). More recently, Woodward et al. (2005b) began to outline how body size could
be more formally incorporated into our view of food web
structure and function. They argue that measuring body
size promises a way to collapse sets of covarying traits into
one variable, without necessarily having to observe the
traits directly. Specifically, they examine relationships
between body size, home range size, ingestion and
production rates, numerical abundance and nutrient turnover. The authors end their article by asking whether
metabolic theory could be applied to predict the structure
and dynamics of complex ecological networks.
2008 Blackwell Publishing Ltd/CNRS
Review and Synthesis
Here, we provide a framework for identifying key food
web structures and processes at the landscape scale. It
should be noted that the goal of this paper is not to present
a comprehensive review of factors that influence food web
stability. Rather, we endeavour to review and synthesize
several previously disparate disciplines (food web ecology,
macroecology and behavioural ecology) and present a
framework that integrates them into a conceptual landscape
theory for food web architecture. The relationships upon
which this framework is based are general relationships,
often spanning orders of magnitude of variation. Although
there are interesting and informative exceptions to some of
these relationships (some of which are discussed later in the
paper), we will not focus on the exceptions in the hopes of
presenting a general model. Thus, in what follows, we
review well-developed concepts in food web ecology,
macroecology and behavioural ecology and project whole
food web consequences of some identified relationships. We
then show how both the horizontal and vertical structure of
food webs can be explained by these empirical relationships,
and speculate about the utility of this framework across a
variety of spatial scales. We find that there is an excellent
match between this collective framework and patterns in
real food webs at the landscape scale. Finally, we review
some recent food web concepts that explore the implications of landscape scale food web structure for the stability
of whole food webs in space and time.
A BRIEF REVIEW OF EMPIRICAL PATTERNS IN
FOOD WEB STRUCTURE
We will refer to two food webs used in the analysis of
Rooney et al. (2006) to illustrate our points – the Cantabrian
Sea Shelf marine food web (original data from Sanchez &
Olaso 2004) and the Central Plains Experimental Range
(CPER) shortgrass prairie soil food web. Trophic interactions within these food webs were measured as flux rates of
g C m)2 year)1 between trophic groups. Primary producers
and detritus were assigned trophic positions of 1 and, where
not directly reported, higher order consumer trophic
positions (TPC) were calculated as:
TPC ¼ 1 þ
n
X
PC TPR
ð1Þ
1
where n is the number of resources consumed by the
consumer, PC is the proportion of the consumers diet
accounted for by a resource and TPR is the trophic position of the resource. In similar manner, the proportion of
carbon derived from basal resources (% BRC) was calculated as:
n
X
% BRC ¼
PC % BRR
ð2Þ
1
Review and Synthesis
A landscape theory for food web architecture 869
where n is the number of resources consumed by the
consumer, PC is the proportion of the consumers diet
accounted for by a resource and % BRR is the proportion of
carbon derived from the basal resource in the resource
being consumed. A full description of the methodologies
for deriving food web characteristics can be found in
Rooney et al. (2006).
The results of this analysis showed the food webs to be
structured such that lower order consumers derive the
bulk of their energy from individual basal resources
(phytoplankton or detritus in the marine system and
bacteria or fungus in the soil system), whereas higher order
consumers tend to integrate across the energy channels
(Fig. 1).
Further analysis of the process rates within these
defined energy channels revealed consistencies in biomass
turnover rates between coupled energy channels. Here,
biomass turnover rates are represented by the production : biomass (P : B) ratio, defined as the summed
annual biomass production (g C m)2 year)1) divided by
the mean annual biomass (g C m)2) for a given trophic
guild within an energy channel. Coupled energy channels
were shown to have consistently asymmetric biomass
turnover rates within food webs. Within marine food
webs, phytoplankton energy channels were shown to have
consistently higher biomass turnover rates compared
detrital energy channels, and within soil food webs,
bacterial based energy channels displayed consistently
higher biomass turnover rates compared with fungal
based energy channels. Thus the resulting architecture that
emerged across all studied food webs was that of paired
energy channels with asymmetric energy flux (Rooney
et al. 2006). We will now go on to explore some possible
mechanisms that may result in this emergent architecture
using concepts borrowed from macroecology, behavioural
ecology and food web theory.
Trophic position
(a)
Cantabrian sea shelf
RELATIONSHIPS AND HYPOTHESES FROM
MACROECOLOGY
Body size and metabolism have shown promise as major
organizing axes for ecology (Peters 1983; Brown et al.
2004). Body size is tightly correlated with a number of
fundamental ecological characteristics (see Box. 1). These
patterns appear to be general, spanning enormously
different evolutionary histories and body sizes. Metabolism
forms one of these broad patterns with body-size. As the
metabolic rate governs resource uptake and expenditure,
metabolism can be used as a general framework for
understanding patterns in biomass flux. Brown et al. (2004)
have referred to this framework as the metabolic theory of
ecology (MTE), and used this theory to explore numerous
patterns in life history (e.g. mortality, development rate)
and ecosystem processes (e.g. biomass production, biogeochemical cycles). This body of research provides
compelling evidence for a slow-fast metabolic continuum
across body-sizes. Specifically, we outline the following
three size-based relationships that are directly related to
biomass flux:
R.i(a) as body size increases, the metabolic rate per unit
of biomass tends to decrease (Peters 1983; Brown
et al. 2004).
R.i(b) as body size increases, the consumption rate per
unit of biomass (consumption ⁄ weight) tends to
decrease (Peters 1983; Brown et al. 2004).
R.i(c) as body size increases, the biomass turnover rate
(biomass ⁄ weight) tends to decrease (Peters 1983;
Brown et al. 2004).
Thus, the given differences in mean body size between
distinct organisms in a food web then, these relationships
immediately identify differences in biomass flux. Small
organisms tend to have higher loss rates, higher growth
(b)
5
CPER steppe grass
4
3
2
1
0
20
40
60
80
100
Phytoplankton derived carbon (%)
0
20
40
60
80
100
Bacteria derived carbon (%)
Figure 1 Lower order consumers tend to specialize on specific basal resources, whereas higher order predators ultimately integrate energy
from the distinct energy channels. As these energy channels show different biomass turnover rates (P : B ratios), the food webs are structured
such that top predators couple energy channels (adapted from Rooney et al. 2006) that exhibit asymmetric energy fluxes. Figures show data
from (a) The Cantabrian sea shelf where the two major basal resources are phytoplankton and detritus, and (b) the CPER shortgrass steppe
ecosystem where bacteria and fungi serve as basal resources.
2008 Blackwell Publishing Ltd/CNRS
870 N. Rooney, K. S. McCann and J. C. Moore
rates and greater biomass turnover than larger organisms.
These relationships are readily identifiable in both the
Cantabrian Sea Shelf food web and the CPER soil food
web, where production : biomass ratios decline with
increasing body size (Fig. 2). A further empirical relationship not directly linked to biomass flux allows additional
insight into how the body size influences other aspects of
food web structure:
2008 Blackwell Publishing Ltd/CNRS
2
Log (Production: Biomass ratio)
Allometry: the empirical study of power formulas involving
body mass which can be expressed mathematically as:
ln(Y) = a + b*ln(body Size); where Y is a specific attribute of
interest, a is the intercept, and b is the slope of the empirical
relationship
Metabolism-body size: Both historical and recent temperature-corrected estimates of slope suggest the slope is 0.75.
Thus, the per unit weight metabolic cost scales with body size
to the )0.25. Larger organisms, therefore, tend to have lower
metabolic costs (Brown et al. 2004)
Consumption rate-body size: Both historical and recent
temperature-corrected estimates of slope suggest the slope is
0.75. Thus, the per unit weight consumption rate scales with
body size to the )0.25. Larger organisms, therefore, tend to
have a lower consumption rates per unit weight (Brown et al.
2004)
Longevity-body size: Higher metabolism per unit weight
implies higher biomass loss rates and mortality rates. Both
historical and recent temperature-corrected estimates of the
mortality body size slope suggest the slope is 0.75. Thus, larger
organisms with lower metabolic costs tend to live longer and
have lower mortality rates (Brown et al. 2004)
Brain size-body size: Brain size scales with body size such
that larger organisms tend to have bigger brains, with the slope
of the relationship equal to 0.75 (Harvey & Pagel 1988)
Turnover Rate: [production (P): biomass (B) ratio] If we
assume that on long time scales the rate of biomass loss (flux
out) must be equal to the rate of biomass gain (must be true for
species that do not go extinct) then a slow metabolism implies
slow biomass gain. This implies that the turnover (P : B) ought
to have slope of approximately )0.25. The empirical relationship is consistent with this except within trophic levels appears
to be slightly more negative (Kerr & Dickie 2001). Nonetheless,
larger organisms tend to have lower turnover rates (Brown et al.
2004)
Movement and metabolism: lower cost of transportation
means movement ought to be more fully adapted in larger
organisms. Estimates of the movement body size slope suggest
the slope is near 0.75 (Peters 1983)
Brain size and foraging behaviour: Larger more mobile
organisms experience a wider variety of habitats. Further,
increased brain size has been found to correlate with ability to
make foraging decisions within a complex habitat (Eisenberg &
Wilson 1978; Harvey et al. 1980; Martin 1981; Budeau & Verts
1986)
(a) 2.5
1.5
1
0.5
0
–0.5
–1
–6
–4
–2
0
2
4
6
Log (Body size)
(b)
1.2
Log (Production: Biomass ratio)
Box 1 Allometry and metabolic theory
Review and Synthesis
1
0.8
0.6
0.4
0.2
0
–12
–11
–10
–9
–8
–7
–6
–5
–4
Log (Body size)
Figure 2 Biomass
turnover rates decrease with increasing
body size. This pattern is consistent in both (a) The Cantabrian
sea shelf (slope = )0.26, r2 = 0.78, P < 0.001), and (b) the
CPER shortgrass steppe ecosystem (slope = )0.11, r2 = 0.67,
P = 0.002).
R.ii as body size increases, trophic position tends to
increase (e.g. Warren & Lawton 1987).
Although there are exceptions to this pattern to be found in
nature (e.g. Layman et al. 2005), this pattern has been
observed in many food webs (e.g. McCann et al. 2005;
Jennings et al. 2007) and the pattern is consistent for the two
food webs examined in this paper, although to a lesser
extent within the soil food web (Fig. 3). This pattern will be
explored more fully later in the paper.
Review and Synthesis
(a)
A landscape theory for food web architecture 871
in trophic position, mobility and foraging ability. By piecing
together these six broad relationships we can generate some
elementary, but very useful, hypotheses about the architectural framework of food webs on the landscape. Below, we
present hypothesized food web properties that emerge from
the synthesis of the above empirical relationships (i.e. R.i–iv).
On the basis of relationships i and ii above, we can
generate hypotheses as to the distribution of energy fluxes
within the food web. Specifically:
5
4.5
4
Trophic position
3.5
3
2.5
2
H.i Biomass turnover rates (P : B ratios) of species
should decrease with increased trophic position
within food webs.
1.5
1
0.5
0
–6
–4
–2
0
2
4
6
Log (Body size)
(b)
5
H.ii(a) Higher trophic level organisms in the food web
ought to increasingly couple the more spatially
isolated lower trophic levels through consumption.
H.ii(b) Lower trophic levels should be more spatially
isolated or compartmentalized compared with
higher trophic levels.
4
Trophic position
On the basis of the notion that the larger organisms are
more mobile on the landscape (R.iii), and will move across a
broader range of habitats than organisms at lower trophic
levels, we can generate two separate, but linked hypotheses
regarding the gross architecture of food webs.
3
2
1
0
–13
–12
–11
–10
–9
–8
–7
–6
–5
–4
Log (Body size)
Figure 3 Larger organisms tend to have higher trophic positions in
(a) The Cantabrian sea shelf (slope = )0.31, r2 = 0.59, P < 0.001),
and to a lesser extent (b) the CPER shortgrass steppe ecosystem
(slope = 0.13, r2 = 0.21, P = 0.11).
Two additional macroecological relationships have been
documented that inform us to how food webs might
operate in space and time.
R.iii as body size increases, the metabolic cost of transport
per unit biomass tends to decrease and so larger
organisms tend to be more mobile (Peters 1983).
R.iv as body size increases, total brain complexity
increases (Harvey & Pagel 1988).
Thus, given the significant changes in mean body size
between distinct organisms in a food web then, these three
relationships (R.ii–iv) immediately identify gross differences,
Finally, there exists a body of research on the allometric
relationship between brain and body size (Box 1). Before
delving into this relationship too far, we make a distinction
between the evolution of increased brain complexity and
size observed across phyla within the animal kingdom, and
the increase in the relative size of brains relative to body
mass within and among vertebrates (e.g. birds or mammals). For both frames of comparative reference, empirical
evidence points to the ability for taxa with larger and
complex brains to process, integrate and store more
information. Specifically, brain size relative to body mass
has been shown to correlate to innovation (Lefebvre et al.
1997, 1998), cognition (Sol et al. 2005), learning (Bouchard
et al. 2007), and the ability to store complex information
from a heterogeneous environment (Budeau & Verts
1986). Further, increased relative brain size also maps to
increased sensory perception (Hutcheona et al. 2002), no
doubt essential for increased foraging efficiency. While
much of this research has focused on relative brain size, a
recent meta-analysis found that even within taxa, absolute
brain size is the best predictor of cognitive ability (Deaner
et al. 2007). This relationship is likely to be even stronger
across taxa, within food webs, for example, from
zooplankton to tuna, where the complexity of the brain
(i.e. development) and brain function increases. Taken
together, these findings suggest that organisms with larger
and more complex brains (i.e. more highly developed) have
2008 Blackwell Publishing Ltd/CNRS
872 N. Rooney, K. S. McCann and J. C. Moore
H.iii Higher trophic level organisms ought to have more
developed foraging abilities and therefore be more
likely to exhibit type III functional responses on
average than those that populate lower trophic
positions.
TESTING HYPOTHESES AT THE LANDSCAPE SCALE
To test hypothesis H.i, we examined the relationship
between trophic position and P : B ratios in our representative food webs. We note here that our analysis of the food
webs is not meant to be exhaustive, and the various
correlations presented are not necessarily statistically independent since they share various variables in various
permutations. The analyses, however, do provide a valuable
tool for exploring the hypotheses presented in the preceding
section. Figure 4 shows that for both the Cantabrian Sea
food web and, to a lesser extent, the CPER soil food web,
P : B ratios decrease with increased trophic position. In fact,
for the eight food webs summarized in Rooney et al. (2006),
in 20 of 24 cases increases in trophic level also are followed
by decreases in P : B ratios or biomass turnover. Thus,
biomass turnover does appear to decrease with increasing
size and trophic level (P < 0.001). Interestingly, as biomass
flux based estimates of interaction strength are subsumed by
the definition of biomass turnover rate (Rooney et al. 2006),
this relationship implies that interaction strengths should
decrease as trophic level increases. As such, macroecological
relationships within food webs unfold to result in ordered
distributions of interaction strengths which may have
profound implications for the stability of food webs. There
will, of course, be exceptions to this general pattern. For
example, Bascompte et al. (2005) found that a few shark
species were involved in the bulk of strongly interacting
tritrophic (predator–consumer–resource) food chains in a
2008 Blackwell Publishing Ltd/CNRS
Log (Production: Biomass ratio)
(a) 2.5
2
1.5
1
0.5
0
–0.5
–1
0
1
2
3
4
5
6
4.5
5
Trophic position
(b)
1
0.9
Log (Production: Biomass ratio)
the capacity for increased sensory perception and the
ability to not only innovate but learn and store acquired
information. These characteristics, combined with the
increased scale of movement of larger bodied organisms,
most certainly ought to translate into increased foraging
abilities.
One can therefore generate a prediction more formally in
terms of how this enhanced foraging ability ought to change
attributes of the functional response. Given that the world is
a heterogeneous collection of resources (i.e. there are high
and low densities of resources on the landscape), then
higher order organisms with greater brain complexity ought
to more readily store information that enables them to leave
low density patches for more profitable high density
patches. From the perspective of any local patch then, such
a large mobile consumer ought to display a type III
functional response. Thus, our final hypothesis is:
Review and Synthesis
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
2
2.5
3
3.5
4
Trophic position
Figure 4 Consistent with the earlier observed patterns, biomass
turnover rates tend to be slower in higher order predators in (a)
The Cantabrian sea shelf (slope = )0.59, r2 = 0.62, P < 0.001). (b)
The relationship is not, however, significant in the CPER
shortgrass steppe ecosystem (P = 0.23).
large Caribbean marine food web. Such strongly interacting
top predators are key in the occurrence of trophic cascades.
Interestingly, even when these instances occur, the general
pattern remains. For each of their top 10 predatory species
identified in Bascompte et al. (2005), mean predator–
consumer interactions were always less than the mean
consumer–resource interactions. On average, interaction
strengths were six-fold higher between consumers and
resources compared with predator–consumer interactions.
Review and Synthesis
Scale of movement (km)
(a) 103
A landscape theory for food web architecture 873
North sea shelf
102
Trophic
position
1
10
1
5
100
10–5
10–4
10–3
10–2
10–1
100
101
102
100
101
102
Body size (m)
(b) 102
CPER shortgrass steppe
Scale of movement (m)
101
100
10–1
10–2
10–3
10–4
10–5
10–4
10–3
10–2
10–1
Body size (mm)
Figure 5 The relationship between body size and movement is
also linked to trophic position. Higher order predators tend to be
larger and more mobile than their prey in both a) the North Sea
Shelf (adapted from McCann et al. 2005) and b) the CPER
shortgrass steppe.
With respect to hypotheses H.ii(a) and H.ii(b), Fig. 1
shows that based on feeding linkages, higher trophic level
organisms derive energy from different energy channels,
suggesting increased movement among habitats compared
with lower trophic level organisms. Further evidence for this
hypothesis comes from the empirical relationship between
body size, trophic position and movement (Fig. 5). Higher
order predators tend to be larger and have greater scales of
movement than their prey. While acknowledging that data
are only available for two ecosystems, this relationship
appears invariant to scale as we observe this across the
expansive Atlantic Ocean food web (used here as an
example of a marine food web, in place of the Cantabrian
sea shelf where the data was unavailable) at the kilometre
scale and within the CPER soil food web at the kilometre
subcentimetre scale (Fig. 5a, b respectively). This and other
similar hierarchical examples suggest that food webs should
generally be compartmentalized at lower resource levels but
increasingly coupled by higher order mobile predators
(Moore & Hunt 1988). These results taken together point to
an organizational and a functional compartmentation within
food webs, which has historically been a source of
controversy in food web ecology (Pimm & Lawton 1980).
Earlier attempts to detect compartments were, however,
hampered by the fact that the data often did not include
measures of biomass flux or interaction strength. Recently,
using a novel technique from the sociological literature,
Krause et al. (2003) found evidence of compartmentation.
Krause et al. (2003) noted that the ability to detect
compartments appeared to dependent upon the detail of
the food web data (both resolution and interaction strength)
whereby more complete webs showed more evidence of
compartmentation.
It is difficult to assess foraging ability (i.e. hypothesis H.iii)
using the food webs presented in this paper. Nonetheless,
the literature on phylogeny and brain size and development
have used novel studies to show that organisms with greater
brain size and development also appear to display enhanced
abilities to respond to complex foraging cues (Eisenberg &
Wilson 1978; Harvey et al. 1980). It would be a mistake to
interpret H.iii as saying that these organisms only populate
upper trophic positions as they populate food webs as
consumers along with those with lesser develop nervous
systems and less complex brains throughout the food web.
However, concerns of study-bias notwithstanding (Hassell
et al. 1977), the upper trophic positions where higher order
predators reside tend to be populated by taxa from the
apexes of either the deuterostome or protostome lines within
the animal kingdom (e.g. chordates, cephalopods, and
arthropods); groups which have highly developed nervous
systems and brains, and which also tend to have more
sigmoidal functional responses than lower trophic level
organisms (Holling 1965).
PATTERNS WITHIN FOOD WEBS
While the established empirical relationships have identified
some gross properties of food web architecture, it would be
interesting to see whether these relationships can help
explain some more specific food web characteristics when
taken in the context of coupled energy channels. We now
explore the connection between our identified food web
relationships and specific attributes of the energy channels
identified within food webs. Since we have shown that food
webs couple many very different habitats from above, it
behoves us to question whether different habitats have
different food web traits.
If we take three relationships together (R.i–iii) that body
size should increase with trophic position, that biomass
turnover should decrease with increasing body size, and that
fast and slow energy channels are ultimately coupled by
2008 Blackwell Publishing Ltd/CNRS
874 N. Rooney, K. S. McCann and J. C. Moore
higher order consumers, a number of predictions ensue with
respect to the internal architecture of food webs. First, for
given trophic levels, fast channels should, on average,
contain smaller organisms compared with slower channels.
The result should be of different slopes and intercepts for
the relationships between trophic position and body size for
fast and slow energy channels. Further, given that higher
order predators couple the energy channels, the differences
should diminish as one ascends through the food web, and
this is in fact observed in Fig. 6a,b. This pattern suggests
that taking into account energy channels within food webs
may help explain the absence of this relationship in food
webs that are based on numerous basal resources (Layman
et al. 2005). Examining the partitioned food webs also
provides further insights into another established allometric
relationship, that between trophic position and P : B ratio.
When we partition the relationship between trophic position
and P : B ratio between energy channels, a pattern emerges
that helps to explain some of the earlier observed variation
in the relationship at the whole food web scale. That is to
say that organisms in slow energy channels have, for a given
trophic position, lower P : B ratios (Figs 6c,d). These
relationships also converge at higher trophic levels, reflecting once again the spatial coupling of energy channels by
higher order predators.
Since biomass flux based estimates of interaction
strength are subsumed by the definition of biomass
turnover rate (see Rooney et al. 2006), relationship (R.iii)
can be re-interpreted in terms of interaction strength (per
unit of predator biomass). Thus, interaction strength
ought to decrease with increasing body size, suggesting
differential energy fluxes and interaction strengths between
energy channels. This exact pattern of asynchronous
biomass turnover was pointed out by the analysis of
Rooney et al. (2006). In 11 of 11 possible cases, pelagic
biomass turnover at a given trophic level was greater than
benthic biomass turnover (P < 0.0001). For soil food
webs the analyses yield a similar trend with the smallersized bacterial channel organisms tending to biomass
turnover at a greater rate than the fungal channel
organisms, but the result is only moderately significant
(P = 0.07). More precise measures of interaction strength
will clearly depend on the ratio of predator : prey biomass
ratios and other factors (de Ruiter et al. 1995; Emmerson
& Raffaelli 2004). However, these results may also yield
insights in light of the results of Bascompte et al. (2005),
who found a non-random distribution of interaction
strengths in a marine food web. Specifically, they found
that the occurrence of strong interactions on two
consecutive levels of food chains occurs less frequently
than expected by chance. Further, when they do occur,
they are accompanied by strong omnivorous links more
often than expected by chance. Given this relationship,
2008 Blackwell Publishing Ltd/CNRS
Review and Synthesis
one might predict strong interactions within fast channels
to be more associated with ominvory, and the presence of
larger bodied, lower trophic level species in adjacent slow
channels might provide energetically feasible omnivorous
alternatives.
FOOD WEB ARCHITECTURE ACROSS SPATIAL
SCALES
It would be interesting to know if these energy channels also
occur at smaller and larger spatial scales as suggested by
both the Cantabrian Sea Shelf marine food web and the
CPER shortgrass prairie soil food web. There are some
reasons to expect that they may. At smaller spatial scales
within aquatic ecosystems, for example, the pelagic or open
water zone has zooplankton and pelagic planktivores that
couple the classic planktonic grazing chain (based on
inorganic nutrients) to the microbial loop (based on organic
carbon). Interestingly, the classic pelagic grazing chain also
tends to have larger organisms (e.g. crustaceans) than the
microbial loop (e.g. protists, Fig. 7a). The summed production and trophic interactions within the pelagic zone are
often collapsed into one energy channel by limnologists who
study benthic–pelagic coupling in lakes. The resulting food
web then incorporates both benthic and pelagic players, and
this architecture also appears to exhibit similar body size
relationships between channels (Fig. 7b). Further, at very
large scales, birds and mammals couple aquatic and
terrestrial ecosystems (Fig. 7c), which again display differential body size spectra for given trophic levels. Interestingly, this pattern has also been suggested to account for
differences in trophic cascades between terrestrial and
aquatic habitats (Shurin & Seabloom 2005; Shurin et al.
2006), which could be thought of as a signature of energy
channel strength. The underlying suggestion here is that the
pattern of distinct channels, differential size within channels,
and consumer coupling may exist across spatial scales. All
appear to show some degree of resource compartmentation
(as evidenced by different basal resources) as well as
coupling by higher order predators in the food webs.
Further work is required but the adaptation of organisms to
different aspects of the ecosystem (e.g. pelagic vs. benthic,
dry vs. wet soil, grazing vs. detrital, above-ground vs. belowground, terrestrial vs. aquatic) may tend to produce resource
compartments defined by asymmetric body size relationships. If so, then we would also expect a tendency for
asymmetric rates of biomass turnover in these different
compartments.
Recent research has proposed a Ôgrowth hypothesisÕ that
potentially ties into the above landscape theory (Elser et al.
1996). Specifically, this research argues that body size
ought to correlate with N : P ratios such that larger
organisms have lower P levels and higher N : P ratios.
Review and Synthesis
(a)
6
A landscape theory for food web architecture 875
Detrital channel
Phytoplankton channel
(b) –4
Fungal channel
Bacterial channel
–5
4
2
Log (Body size)
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–6
0
–2
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–8
–9
–10
–11
–4
–12
–6
–13
0
1
2
3
4
5
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3
4
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6
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Detrital channel
Phytoplankton channel
2
(d)
1.5
1
0.5
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–0.5
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Bacterial channel
0.9
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Log (Production: Biomass ratio)
(c) 2.5
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0.8
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0
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5
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Trophic position
Figure 6 Basic metabolic relationships differ between groups of organisms that occur in fast and slow energy channels. The relationship
between body size and trophic position differs such that for a given trophic position, organisms within the slower energy channels are
larger. This trend holds true for (a) Cantabrian Sea Shelf, where the slope of the relationship between body size and trophic position
within the phytoplankton channel (slope = 2.77, r2 = 0.79, P < 0.001) is significantly higher (ANCOVA F = 12.31, d.f. = 23, P = 0.002 for
interaction effect) than the slope for the same relationship within the detrital channel (slope = 0.74, r2 = 0.28, P = 0.03). A similar pattern
holds for (b) the CPER shortgrass steppe ecosystem where the relationship between body size and trophic position within the bacterial
channel (slope = 3.1, r2 = 0.75, P = 0.005) is significantly higher (ANCOVA F = 10.07, d.f. = 9, P = 0.02 for interaction effect) than the
slope for the same relationship within the fungal channel, where the slope is not significantly different from 0 (P = 0.55). The energy
channels also exhibit consistent and highly suggestive differences in the relationship between trophic position and biomass turnover in (c)
Cantabrian Sea Shelf, where the slope of the relationship between body size and log (P : B ratio) within the phytoplankton channel
(slope = )0.7, r2 = 0.61, P = 0.003) appears to be more negative (ANCOVA, F = 2.76, d.f. = 23, P = 0.11 for interaction effect) than the
slope for the same relationship within the detrital channel (slope = )0.38, r2 = 0.71, P = 0.001). Note that this relationship does not
include detritus (P : B ratio of 0) as a trophic level of 1, which would make the slope of the relationship shallower in the detrital channel.
The difference in slopes does hold for the CPER shortgrass steppe ecosystem where the slope between body size and log(P : B ratio)
within the bacterial channel (slope = )0.45 r2 = 0.86, P = 0.003) is significantly more negative (ANCOVA F = 6.43, d.f. = 9, P = 0.03 for
interaction effect) than the slope for the same relationship within the fungal channel, where the slope is not significantly different from 0
(P = 0.73).
2008 Blackwell Publishing Ltd/CNRS
876 N. Rooney, K. S. McCann and J. C. Moore
Review and Synthesis
(a)
(b)
(c)
Figure 7 Spatial scaling of the coupled energy channel architecture. The horizontal lines represent the range in body size observed within
energy channels and trophic guilds. Green and red boxes around the lines correspond to fast (small) and slow (large) energy channels
respectively. Based on body size, it appears that the coupling of energy channels by higher order consumers operates at spatial scales varying
from (a) within habitat (classic chain–microbial loop coupling), through (b) between habitats (benthic-pelagic coupling) to (c) between
ecosystems (aquatic-terrestrial coupling).
Further, low N : P ratios tend to imply more rapid growth
rates since organisms laden with phosphorous heavy RNA
will tend to produce organisms capable of rapidly
synthesizing proteins (Sterner 1995; Elser et al. 1996).
Thus, low relative N : P ratios tend to produce high
relative growth rates. If this pattern holds across body size
then their arguments are entirely consistent with the food
web predictions above. Additionally, their lower level
mechanism offers insight into how these different resource
compartments ought to recycle nutrients. Benthic pathways, for example, ought to have lower phosphorous
demands than the faster growing pelagic pathway. Similarly, fungal pathways ought to have lower phosphorous
demands than bacterial pathways. We leave this interesting
intersection of ideas for future research.
2008 Blackwell Publishing Ltd/CNRS
EXCEPTIONS AND LIMITATIONS
As with all such general patterns, there exist exceptions and
limitations to the general model. For example, within some
food webs, consumers can be more mobile compared with
their predators despite their similar size (Hebblewhite &
Merrill 2007). Further, some of the relationships outlined
above do not apply to arthropods, where smaller organisms
often have higher trophic positions compared with their
larger prey (e.g. parasitoids and biting insects). Such smaller
bodied organisms may in fact have increased dispersal rates,
and thus may functionally couple otherwise ecologically
isolated habitats. It is interesting to note, however, that these
two very attributes can lead to a similar pattern of coupled
habitats outlined in this paper. Eveleigh et al. (2007)
Review and Synthesis
document that spruce budworm outbreaks in a balsam fir
and mixed deciduous forest stands result in a higher
diversity of primary parasitoids and higher-order generalist
parasitoids (hyperparasitoids) and a greater percentage of
species at higher trophic levels. In this case, even though the
higher order predators are smaller, they respond to
ecological cues at the landscape scale to suppress the
outbreak of a resource within a defined habitat.
SOME EMERGING THEORETICAL IMPLICATIONS
Although it has been long known that the population
dynamics of species within ecological systems and the
dynamics of systems as a whole are variable, ecologists are
only now really beginning to fully embrace this fact
(DeAngelis & Waterhouse 1987; Chesson & Huntley
1997; Tilman et al. 1998). Recent theory has argued that
variability in resources in both space and time create a
complex biological canvas that consumers can react to by
decoupling from some consumer–resource interactions or
re-initiating other consumer–resource interactions (Holt
2002; McCann et al. 2005). If this decoupling occurs when
densities in the resources are low, on average, then such
biological structure can drive persistent food webs by
allowing the subsystem a reprieve from consumptive
pressures exactly when it needs it – when the subsystem is
experiencing low average densities. Similarly, if coupling
occurs when resource densities are high, then top-down
pressure is engaged just when a resource is growing
excessively.
The food web architecture outlined in the preceding
sections lends itself to these theoretical arguments (Box 2).
In a sense, the food web is organized such that the dynamics
are driven by a combination of both bottom–up and top–
down forces that inspire and mute variability. Landscape
scale food webs imply that the basal resources are ultimately
separated by large ecological distances in space. As such,
there is a tremendous amount of variation in space in the
productivity and density of these lower level organisms. The
mobile higher order predators, on the other hand, respond
to this lower level variance. Given their ability to store
complex information, then they can rapidly respond to this
variation. This rapid behavioural response is critical in that it
allows large mobile organisms the ability to respond at time
scales much faster than population dynamics. Finally, the
intermediate consumers in this architecture are defined by
series of trophic levels with very different rates of
production. As such, they beget energy pathways or
channels that supply energy to the higher levels at different
rates. These differential pathways, or asymmetrical production, tend to drive the asynchronous responses of the
different pathways discussed above (Rooney et al. 2006).
Thus, the identified landscape scale architecture has the key
A landscape theory for food web architecture 877
Box 2 Bottom–up and top–down synergy: a potent stabilizing
mechanism
The food web architecture discussed above provides large scale
mechanisms that enable a system to respond to a variable world
(see Fig. B.2.i below) in a manner that promotes the persistence
of complex webs. Specifically, we highlight three integral aspects of the structure that recent theory has used to argue
promotes persistent complex webs
Lower trophic level organisms (the variation): the basal
resources spread out across the landscape to provide a heterogeneous landscape. Due to the different life histories, different
habitat characteristics, etc. resources can be expected to readily
show some degree of asynchronous production in space and
time. In other words, these lower level organisms produce
variation across the landscape
Higher level organisms (the couplers): The higher order
consumers are mobile and capable of responding to complex
environmental information. Given resources of high and low
density on the landscape such a coupler will tend to move in
space to reduce the high density resource and therefore
simultaneously release the low density resource from
predation pressure. Note, that if space is limited or homogeneous then the stabilizing influence of higher order predators will be compromised. In such a case, they can drive
strong top-down suppression. Thus, in spatially constrained
ecosystems, mobile predators can be destabilizing (McCann
et al. 2005)
Intermediate pathways or energy channels (the asymmetry): Food webs show pathways with different abilities to
propagate energy. This serves two aspects of regulating the
dynamics of food webs. One, such an architecture allows the
ability for a food web to respond rapidly to a large perturbation
(i.e. the fast pathway or channel). Two, as the fast channel
responds the combination of slow and fast channels acts to
drive compensatory dynamics of the different pathways (Rooney et al. 2006). In other words, the internal asymmetry of
energy flow readily promotes asynchronous dynamics. This
asynchrony thus helps maintain the precise conditions required
for the couplers to mute lower level variability (see Couplers
above)
2008 Blackwell Publishing Ltd/CNRS
878 N. Rooney, K. S. McCann and J. C. Moore
ingredients for maintaining persistent non-equilibrium ecosystems. Unfortunately, anthropogenic modifications may
be threatening these key ingredients (Box 3).
Along these lines, recent spatially motivated food web
theory has found that ecosystem size, or the homogenization
of habitats, ought to seriously threaten natureÕs balance
(McCann et al. 2005). In small ecosystems, the mobile
predatorsÕ stabilizing influence can be reversed as heightened
mobility in a spatially limited ecosystem can effectively
synchronize lower level resources. This synchrony reduces
habitat variability and unites runaway dynamics. The
homogenization of habitats has similar outcomes. In both
cases, the loss of lower level variability in space compromises
the ability for the mobile generalist foragers to mute runaway
dynamics (McCann et al. 2005). This theory depends on
asynchronous dynamics in space. There exists a burgeoning
literature on the synchronizing influence of large scale abiotic
drivers on population dynamics (the Moran effect). This
literature is largely focused on population level phenomena
and theoretically and empirically tends to show diminished
synchrony with increasing spatial scale (Lande et al. 1999). In
a very simplified sense then this research suggests that nearby
organisms ought to be more correlated, and so the stabilizing
role of predators coupling in space may therefore rely on
coupling of food webs at large spatial scales. Given that
different species have different traits, then the synchronizing
effect of large scale environmental drivers on different
species may be weaker than that found within a species
(Engen & Saether 2005). These ideas beg for an empirical
analysis of the covariance dynamics between different
species and subsystems across spatial scales. Such empirical
findings may aid our understanding of what inspires unified
subsystem dynamics (i.e. resources increase or decrease
together) as well as what inspires asynchrony. It may also
help us identify critical levels of aggregation for food web
dynamics. In other words, asynchronous trophic aggregates
may identify important functional groupings for a given
spatial and temporal scale.
CONCLUSION
Over 30 years ago, Schoener (1974) reviewed the available
literature and concluded that food, habitat, and time were
the three principal resources or niche axes of the niche
hypervolume for individual species. Taken within this
context, our work places the relationships among species
along these very axes. We show that the trophic interactions
among species form the foundation of a hierarchical
organization of communities that lends itself to dynamically
stable architectures. Here, we have used body size and
metabolism to reveal some general properties of food webs
that are matched by available data. Specifically, food webs
are large comprehensive entities whereby localized resources
2008 Blackwell Publishing Ltd/CNRS
Review and Synthesis
Box 3 Human influences on the landscape
Research suggests that human influences have homogenized
space and production on the landscape. Further, human harvesting and habitat fragmentation has eliminated numerous
mobile higher order predators (Fig. B.3.i below). Thus, the
human influence appears to be targeted directly at eliminating
the ÔvariabilityÕ and the ÔcouplersÕ that underlie landscape level
food webs and their stability
Resource homogenization (loss of resource variation):
Elevated inputs of phosphorous and nitrogen runoff from
surrounding landforms, often disproportionately affects a single
compartment (e.g. pelagic or bacterial); thus converting a spatially structured and compartmented ecosystem to one that is
much more homogeneous in its basal production. This is
common to both aquatic and agricultural ecosystems (Hendrix
et al. 1986; Scheffer et al. 1997). Invasive species have also
dramatically influenced the structure of ecosystems. For
example, the zebra mussel, has completely shunted the pelagic
production in lakes to the nearshore (Hecky et al. 2004). Hecky
et al. (2004) referred to this as the Great lakes benthic energy
shunt, and again human impact has strongly skewed or
homogenized basal production
Large predator removal (loss of couplers): Human modification has ubiquitously harmed large mobile predators. Fisheries researchers have now shown that culling has led to the
fishing down of the food web, a progressive decline in top
predators (Pauly et al. 1998; Jackson et al. 2001). Terrestrial
ecosystems also have lost large mobile organisms due to habitat
fragmentation and culling (Terborgh et al. 2001). There has
been documented losses in diversity, stability and function
accompanying such changes (Jackson et al. 2001; Terborgh et al.
2001; Worm et al. 2006). In a very recent survey of 48 quantitative parasitoid-pollinator food webs across a gradient in human modification, researchers found that highly modified webs
were dominated by one or a few pathways (Tylianakis et al.
2007). Here, parasitoid : host ratios were inflated and parasitism rates elevated which influences critical ecosystem services
like pollination (Tylianakis et al. 2007)
Review and Synthesis
(food) and habitats are progressively coupled by more
mobile higher order predators or consumers. We then
review theory and relate it to the landscape architecture of
food webs. We argue that the large organisms promote the
balance and maintenance of a diverse and variable assemblage of organisms while the diverse lower level organisms,
in turn, form a complex system of species capable of
differentially responding to an ever changing world.
Interestingly, variation in body size also maps to horizontal
variation in food web characteristics. Specifically, differences in body size explain variation in energy flux rates
within coupled energy channels, which has been shown to
be an important stabilizing architectural component of food
webs (Rooney et al. 2006). In this sense, the landscape
architecture discussed here implies that nature is an
intriguing balance of bottom–up (habitat heterogeneity)
and top–down (predator) forces. Current human influences
tend to homogenize resources and remove higher order
couplers thus seriously threatening the non-equilibrium
balancing act of complex food webs.
ACKNOWLEDGEMENTS
We thank J. Newman, and A. de Bruyn for helpful
comments. This work was supported by grants from
NSERC to K.S.M. and the US National Science Foundation
to J.C.M. Support was also provided by a National Center
for Ecological Analysis and Synthesis (NCEAS) grant to the
ÔDetritus Working GroupÕ.
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A landscape theory for food web architecture 881
Editor, Jordi Bascompte
Manuscript received 18 January 2008
First decision made 19 February 2008
Second decision made 13 March 2008
Manuscript accepted 24 March 2008
2008 Blackwell Publishing Ltd/CNRS