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Ecology, 88(3), 2007, pp. 647–657
Ó 2007 by the Ecological Society of America
LOCAL DISTURBANCE CYCLES AND THE MAINTENANCE OF
HETEROGENEITY ACROSS SCALES IN MARINE METAPOPULATIONS
TARIK C. GOUHIER
AND
FRÉDÉRIC GUICHARD1
Department of Biology, McGill University, 1205 Doctor Penfield, Montre´al, Québec H3A 1B1 Canada
Abstract. In marine systems, the occurrence and implications of disturbance–recovery
cycles have been revealed at the landscape level, but only in demographically open or closed
systems where landscape-level dynamics are assumed to have no feedback effect on regional
dynamics. We present a mussel metapopulation model to elucidate the role of landscape-level
disturbance cycles for regional response of mussel populations to onshore productivity and
larval transport. Landscape dynamics are generated through spatially explicit rules, and each
landscape is connected to its neighbor through unidirectional larval dispersal. The role of
landscape disturbance cycles in the regional system behavior is elucidated (1) in
demographically open vs. demographically coupled systems, in relation to (2) onshore
reproductive output and (3) the temporal scale of landscape disturbance dynamics. By
controlling for spatial structure at the landscape and metapopulation levels, we first
demonstrate the interaction between landscape and oceanographic connectivity. The temporal
scale of disturbance cycles, as controlled by mussel colonization rate, plays a critical role in the
regional behavior of the system. Indeed, fast disturbance cycles are responsible for regional
synchrony in relation to onshore reproductive output. Slow disturbance cycles, however, lead
to increased robustness to changes in productivity and to demographic coupling. These
testable predictions indicate that the occurrence and temporal scale of local disturbance–
recovery dynamics can drive large-scale variability in demographically open systems, and the
response of metapopulations to changes in nearshore productivity.
Key words: demographic coupling; demographically open system; dispersal limitation; disturbance
cycles; metapopulation; mussels; rocky intertidal communities; synchrony.
INTRODUCTION
Understanding the maintenance of large-scale variability in the abundance of subdivided populations and
communities still remains a great challenge for ecologists. An increasing number of studies contribute to
elucidating the role of dispersal limitation and species
interactions for species coexistence, stability, and
spatiotemporal dynamics (Hassell et al. 1991, 1994,
Mouquet and Loreau 2003). However, the role of the
interaction between fundamental properties of ecological communities and dispersal limitation for the
generation of large-scale dynamics remains largely
unknown. One such property is the process of population disturbance and recovery. Disturbance dynamics
has long been shown to be a major driver of community
dynamics and species diversity in marine intertidal
landscapes (Paine 1966, Levin and Paine 1974, Paine
and Levin 1981, Sousa 1984). It has more recently been
suggested to be a cause of hierarchical and cyclic
ecosystem dynamics (Gunderson and Holling 2002).
Here we present a hierarchical metapopulation model to
Manuscript received 24 April 2006; revised 18 August 2006;
accepted 1 September 2006. Corresponding Editor: P. T.
Raimondi.
1 Corresponding author.
E-mail: [email protected]
elucidate the relative contributions of local landscape
disturbance dynamics and limited dispersal to the
maintenance of large-scale variability and synchrony in
marine populations.
The interplay between demographic processes and
limited dispersal in spatially explicit systems can
produce large-scale patterns in homogeneous environments including phase-locked oscillations (Blasius et al.
1999), stationary patterns, spiral waves (Hassell et al.
1991, 1994, Solé et al. 1992), and scale invariance
(Guichard 2005). However, we still ignore the degree to
which hierarchies of such spatial processes (linking local
interactions at the individual level to large-scale
dispersal limitation in metapopulations) can interact to
control regional dynamics and stability. Such hierarchies
have been suggested to control cycles of ecosystem
connectivity and resilience (sensu Gunderson and
Holling 2002), and to allow quantification of ecosystem
complexity (Kolasa 2005). Here we adopt hierarchies of
spatial processes to integrate current theories of marine
coastal ecosystems across spatial scales.
In rocky intertidal landscapes, wave disturbances play
an important role in maintaining biodiversity by
displacing competitively dominant species and allowing
subordinate species to colonize vacant substrates (Levin
and Paine 1974, Paine and Levin 1981, Paine 1984). The
spatial interplay between abiotic disturbance and biotic
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TARIK C. GOUHIER AND FRÉDÉRIC GUICHARD
recolonization has been shown to explain species
composition and distribution (Wootton 2001a), and
the effects of habitat fragmentation on the structure of
competitive communities (Roy et al. 2004). It has further
been shown to generate landscape-level patchiness and
disturbance cycles in model systems that are suggested
to explain strong temporal fluctuations in some natural
mussel populations (Guichard et al. 2003). However,
long-term data are still required to establish the causal
relationship between local disturbance dynamics and
temporal fluctuations. Also, because rocky intertidal
habitats are naturally subdivided into fragmented
landscapes, the role of these disturbance cycles at spatial
scales larger than intertidal landscapes (;100 m) has yet
to be elucidated.
Many coastal species have a larval dispersal stage, and
local disturbances can influence regional dynamics of
subdivided populations provided they can feedback into
large-scale processes through current-mediated larval
dispersal. Because of the potential large-scale larval
dispersal, initial theoretical frameworks of coastal
ecosystems started from the assumption of demographically open populations (Johnson 2005) in which local
reproductive output has no direct effect on local larval
supply (Levin and Paine 1974, Roughgarden et al. 1985).
Demographically open populations, therefore, constitute a specific case of open systems that are more
generally defined by the input of larvae from nonlocal
sources (Johnson 2005). Here we contrast demographically coupled with demographically open populations
to elucidate the implications of open system theories for
the maintenance of variability in coastal ecosystems.
Coastal systems were also studied as well-mixed
metapopulations (Iwasa and Roughgarden 1986,
Roughgarden and Iwasa 1986) and closed communities
(Connolly and Roughgarden 1998, 1999). However,
because of their equilibrium behavior and their treatment of space and dispersal limitation, these theoretical
frameworks provide limited opportunity for local
disturbance dynamics to feedback into large-scale
dynamics, relying instead on nearshore oceanographic
heterogeneity to explain the large-scale distribution of
abundance. Recent empirical studies showing strong
larval dispersal limitation in many benthic species
(Kinlan and Gaines 2003, Menge et al. 2003, Palumbi
2003) suggest the existence of hierarchies of spatial
processes (from landscapes to the scale of larval
dispersal) and directs us toward the study of their
synergistic interaction.
Here we incorporate disturbance cycles at the
landscape level into a metapopulation framework, and
investigate their ability to scale up and explain regional
dynamics in relation to changes in onshore productivity,
within both demographically coupled and demographically open systems (Roughgarden and Iwasa 1986,
Alexander and Roughgarden 1996, Menge et al. 2003;
but see Johnson 2000). Here local disturbance cycles are
shown to play a critical role in explaining the
Ecology, Vol. 88, No. 3
maintenance of variability and the onset of regional
synchrony in relation to onshore productivity. We
further elucidate the role of temporal scales for
predicting how disturbance cycles can maintain spatial
heterogeneity across gradients of oceanographic productivity, and we predict onshore and nearshore
conditions allowing the applicability of open system
theory at multiple scales.
THE MODEL
Landscape dynamics
The model incorporates the spatially explicit landscape mussel disturbance model developed by Guichard
et al. (2003) within a metapopulation framework
consisting of a series of spatially structured landscapes
coupled through larval dispersal. Each landscape is
represented by a square grid composed of 256 3 256
cells. The spatial extent of each landscape corresponds
to the fragmentation scale of rocky intertidal systems,
and cells correspond to spatially homogeneous 1-m2
areas that can be in one of three states: empty, occupied
by mussels, or wave disturbed (Fig. 1A).
Each cell interacts with its eight nearest neighbors
with periodic boundary conditions applied within each
landscape. More precisely, wave disturbances spread
through cells in each landscape according to a disturbance parameter a0. This parameter, which is always set
to one, dictates the probability that a disturbance will
spread from a wave-disturbed cell to a neighboring
occupied cell. This local spread of disturbances emulates
the natural propagation of waves through weakened gap
edges in mussel beds (Paine and Levin 1981, Denny
1987). Local positive density-dependent mechanisms
during recruitment and growth allow mussels to colonize
empty substrata (Bayne 1964, Paine and Levin 1981).
Specifically, if a cell is empty, colonization probability is
a function of the proportion of occupied neighbors and
of a colonization parameter, a2, that corresponds to the
probability that the colonization of an empty cell will be
successful provided mussel larvae are available (Fig.
1A).
Metapopulation dynamics
We implemented the metapopulation as a onedimensional array of 100 landscapes where each
landscape is coupled to a single neighboring landscape
through unidirectional larval dispersal (Fig. 1). Periodic
boundary conditions are implemented at the metapopulation level in order to avoid edge effects observed with
absorbing boundary conditions.
Larval supply in each landscape i is a saturating
function of larval production. Within each landscape,
larval supply is assumed to be well-mixed and its
probability Pi within each cell of landscape i is
calculated as a Poisson process with a rate proportional
to the larval contribution Li1 of the neighboring
landscape i 1 (Caswell and Etter 1999):
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FIG. 1. Model dynamics diagrams. (A) Each landscape in the model is composed of a lattice of 256 3 256 cells. Each cell can be
either empty (bare substratum), disturbed by waves, or occupied by mussels. Wave-disturbed cells transition to the empty state.
Empty cells either remain empty, if there are no larvae available to colonize the empty space, or become colonized by larvae
originating from a neighboring landscape through a positive density-dependent process proportional to a2. Occupied cells become
wave disturbed with a probability of a0 if any neighboring cell is disturbed or if a new disturbance occurs. (B) Spatial coupling
between each landscape defined as unidirectional and nearest-neighbor larval dispersal. The larval production (Li) of each
landscape is a function of mussel cover (qi) and fecundity ( f ). (C) Larval supply is a saturating function of fecundity, which thus
controls both demographic coupling between landscapes and onshore productivity: increases in fecundity below the saturation
point generate a response to increased onshore productivity (demographically coupled regime), while increases above the saturation
point result in a demographically open system.
Pi ¼ 1 eLi1 :
ð1Þ
Each landscape i is connected to its neighboring
landscape i þ 1 through larval dispersal
Li ¼ qi f
ð2Þ
where Li represents the number of larvae dispersed from
landscape i to landscape i þ 1, and qi and f are the mussel
cover and fecundity in landscape i, respectively (Fig. 1B).
These dispersed larvae are incorporated into the dynamics of landscape i þ 1 during the following iteration,
where one iteration represents the average time needed to
update all cells. Cells are randomly selected for update
among 256 3 256 cells in each landscape. This
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TARIK C. GOUHIER AND FRÉDÉRIC GUICHARD
Ecology, Vol. 88, No. 3
asynchronous update at the landscape level introduces
stochasticity and has been shown to approximate a
continuous time process (Durrett and Levin 1994).
All simulations were run for 3000 iterations, the first
1000 iterations having been discarded in order to
exclude transient behavior from our analysis. Unidirectional dispersal was implemented as a simplified version
of nearshore currents observed along the coast of
Oregon or California on the west coast of the United
States (Gaylord and Gaines 2000), but we also used a
Gaussian dispersal kernel that allowed larvae in each
landscape to be exported to its 10 nearest neighbors
(larval dispersal over 10% of the domain). Since this
more computationally intensive implementation resulted
in no qualitative (and very little quantitative) change in
the overall results, the previously mentioned nearestneighbor discrete dispersal was used to generate all the
results presented here.
ulation. Indeed, variability in fecundity has been
observed in coastal invertebrate species and has been
associated with nearshore properties such as primary
productivity and upwelling events (Leslie et al. 2005).
Varying fecundity below the larval saturation point in
Eq. 1 thus allows us to explore the response of the
metapopulation to increased onshore productivity.
However, increases in fecundity beyond the larval
saturation point result in demographically open systems
as landscapes are saturated with larvae, and larval
supply becomes completely independent of productivity
and constant in a homogeneous environment. Thus, we
vary fecundity across the saturation point f ¼ 5 defined
by the saturating functional response of larval supply to
fecundity in order to study metapopulation dynamics in
demographically coupled (fecundity below saturation)
and open (fecundity above saturation) systems (Fig. 1C).
Elucidating the importance of landscape dynamics
Regional time series analysis.—At the metapopulation
level, we analyzed the time series of total mussel cover
obtained from all 100 landscapes by extracting all local
minima and maxima found over 2000 post-transient
iterations and then plotted these extremes in relation to
fecundity. This analysis was conducted for both a single
closed population and all metapopulations.
Landscape time series analysis.—For all metapopulation simulations, the minimum and maximum landscape
cover along the metapopulation transect was recorded
and subsequently averaged over 100 iterations. These
mean maxima and minima were then plotted against the
fecundity value in order to reveal the spatial variability
in cover across the metapopulation.
Synchrony.—The Kendall coefficient of concordance
was used as an index of metapopulation synchrony.
Kendall’s coefficient of concordance is a nonparametric
statistic that measures the association between multiple
ranked variables (Zar 1999). It is commonly used to
measure the level of agreement among several rankings
and, as such, has been suggested as the most effective
method for determining synchrony between multiple
data series (Buonaccorsi et al. 2001). Furthermore, it has
already been applied in intertidal systems to characterize
gap dynamics in mussel beds (Paine and Levin 1981).
Here the coefficient of synchronization is calculated by
first ranking (Ri) the mussel cover time series (2000
iterations) for each landscape i. Kendall’s coefficient of
concordance (Wc) is then calculated as follows (Zar
1999):
X 2
Ri
X
R2i T
X
Wc ¼ 2 3
ð3Þ
L ðT TÞ L
s
In order to elucidate the relative contribution of
landscape spatial processes to regional dynamics, wellmixed models were created at both the landscape and
the metapopulation scales and their results were
compared to those of the main spatially explicit
metapopulation and to closed population dynamics.
Because the deterministic well-mixed model was not
analytically tractable (Guichard et al. 2003), we used
stochastic simulations in order compare well-mixed
landscapes and metapopulations to the stochastic
spatially explicit model described in the previous section.
Specifically, the well-mixed landscape dynamics was
created by randomizing the neighbors of each cell, which
resulted in a lack of disturbance cycles, lower mussel
cover, and smaller temporal fluctuations. Because the
effects of the mean and variance of landscape mussel
cover can be confounded with the specific effect of
disturbance cycles, we obtained an adjusted well-mixed
model (hereafter referred to as ‘‘stochastic mean-field’’
model) by adding stochastic fluctuations to the mussel
cover of each landscape at the end of each iteration in
order for the coefficient of variation of the stochastic
mean-field model to match that of the spatially explicit
model. The metapopulation mean-field model was
obtained by randomizing each landscape’s neighbor
(i.e., larval input) at each iteration.
The scale of landscape disturbance cycles was further
controlled by varying the recolonization rate a2. This
rate determines the temporal scale of colonization as
well as the landscape connectivity and thus controls the
temporal signature of disturbance dynamics without
having any strong effect on mussel cover and spatial
patterns in demographically open landscapes (Pascual et
al. 2002, Guichard et al. 2003).
Onshore productivity and demographic regimes
We use fecundity as a parameter driving both onshore
productivity and demographic coupling in the metapop-
Analysis of results
12
where L represents the total number of landscapes (100),
T represents the total number of iterations (2000), and
Rs is an adjustment for tied ranks within each landscape
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651
(Fig. 2A). In order to atone for this naturally occurring
discrepancy, whose effects could potentially be confounded with those of space, an adjusted mean-field
model (referred to as stochastic mean-field model) was
devised by adding stochasticity in mussel cover so as to
match the mussel cover and variance of the spatially
explicit model (Fig. 2A).
Temporal scale of disturbance cycles.—The temporal
scale of disturbance cycles decreases with a2 (Fig. 2B)
and can be characterized as slow (a2 ¼ 0.2) or fast (a2 ¼
1). This temporal signature is important for landscapelevel dynamics as fast disturbance cycles become quickly
subdued in response to larval saturation (Fig. 3A),
whereas slow disturbance cycles maintain landscape
mussel cover variability as the system becomes demographically open (Fig. 3B).
Metapopulation dynamics
Response to onshore productivity.—Fast disturbance
cycles produce highly synchronized oscillations in
metapopulation mussel cover in response to increased
but nonsaturating onshore productivity, whereas satu-
FIG. 2. Mussel cover time series for single closed landscape
simulations with nonsaturated fecundity ( f ¼ 3). (A) Time series
of mussel cover with fast disturbance cycles (a2 ¼ 1.0) featuring
spatially explicit (dotted line), mean-field (heavy solid line), and
stochastic mean-field landscape dynamics (thin solid line; see
The Model: Elucidating the importance of landscape dynamics
for details). (B) Time series of mussel cover with spatially
explicit dynamics exhibiting either fast (a2 ¼ 1.0, dotted line) or
slow (a2 ¼ 0.2, solid line) disturbance cycles.
such that for ti number of ties in the ith group and j
groups of tied ranks
X
s¼
j
X
ðti3 ti Þ:
i¼1
RESULTS
Disturbance cycles within closed landscapes
Occurrence of disturbance cycles.—Disturbance cycles
are revealed by temporal oscillations in mussel cover
(Fig. 2A), and are driven by the spatially explicit
interplay between local colonization and disturbance.
Disturbance cycles depend on the local (spatially
explicit) nature of disturbance dynamics, and never
appear in mean-field landscapes (Fig. 2A). A side effect
of controlling for spatial structure at the landscape level
is the resulting difference in mussel cover and variance
between spatially explicit and mean-field simulations
FIG. 3. Landscape time series of the mussel cover in relation
to fecundity for a single closed landscape featuring spatially
explicit landscape dynamics and either (A) fast (a2 ¼ 1.0) or (B)
slow (a2 ¼ 0.2) disturbance cycles.
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rating productivity results in regional stability (Figs. 4A,
6). The importance of these landscape disturbance cycles
for regional dynamics is revealed by the robustness of
regional synchrony to the scale of larval dispersal
(nearest neighbor vs. mean field; Figs. 4B, 6), as well
as by the lack of such synchrony in the absence of
spatially explicit disturbance cycles (Figs. 4C, D, 6). This
is further corroborated by the absence of synchronized
oscillations in the stochastic mean-field model results,
which indicates that the difference in regional behavior
produced by these models is attributable to landscape
disturbance cycles and is not simply caused by the
inherent differences in mussel cover and variance
between the spatially explicit and mean-field models
(Figs. 4E, F, 6).
The temporal scale of these landscape disturbance
cycles also plays an important role in regional pattern
formation, as slow disturbance cycles maintain spatial
heterogeneity (Fig. 5A), and thus never display synchronized oscillations (Fig. 6). Interestingly, spatial
heterogeneity can also be observed with mean-field
landscape dynamics, indicating that slow disturbance
cycles are not necessary for the maintenance of spatial
variability in response to increased onshore productivity
(Fig. 5C).
Maintenance of variability in demographically open
systems.—Slow disturbance cycles are robust to increases in nearshore productivity as both spatial heterogeneity and regional temporal stability are maintained (Fig.
5A), even in the face of saturating fecundity levels that
drive both fast disturbance (Fig. 4C) and mean-field
models (Figs. 4E, 5C, E) to spatial homogeneity. These
results and their invariance to alternate metapopulation
configurations reveal the robustness of local and slow
disturbance cycles to regional processes (Fig. 5B).
DISCUSSION
A number of recent studies have revealed the role of
nearshore productivity in predicting the response of
local benthic communities (Connolly and Roughgarden
1998, 1999). However, observations still fail to broadly
support large-scale nearshore heterogeneity as a general
predictor of community structure (Menge et al. 2003,
2004). We showed that in a spatially subdivided
population, the temporal scale of landscape disturbances
determines their potential to (1) scale up and drive
metapopulation dynamics as well as its response to
changes in nearshore productivity, and (2) to render the
system robust to regional processes. Here we start from
well-documented local disturbance dynamics in rocky
intertidal communities (Wootton 2001b, Guichard et al.
2003) to address assumptions of demographically open
system theories, and to provide a set of testable
predictions to explain variability and coherence in
coastal ecosystems (Table 1). Our results further
highlight the importance of hierarchies of spatial
processes in predicting the persistence of ecological
systems under changing environments.
Ecology, Vol. 88, No. 3
Disturbance cycles and regional dynamics
Since the original formulation of metapopulation
theory (Levins 1969), important progress originated
from the realization that subdivided populations and
communities can be characterized by nonequilibrium
dynamics, which interact with dispersal limitation to
predict the persistence of populations and long transients (Hassell et al. 1991, 1994). More recently, theories
of ecosystem resilience (Gunderson 2000, Scheffer et al.
2001, Scheffer and Carpenter 2003), of panarchies
(Gunderson and Holling 2002), and of criticality
(Pascual and Guichard 2005) have relied on the
dynamics of disturbance and connectivity to explain
how cycles of disturbance and recovery can occur across
scales and cause sudden and drastic shifts in homogeneous landscapes.
In rocky intertidal communities naturally occurring
wave disturbances dislodge the competitively dominant
mussel species (Paine and Levin 1981, Paine 1984, Sousa
1984). This disturbance and the subsequent recolonization by mussels results in disturbance cycles that have
been used to predict species distribution patterns at the
landscape level (Wootton 2001a, b, Guichard et al.
2003). At larger scales, rocky intertidal communities
are assumed to be naturally fragmented into landscapes
and the prediction of large-scale variability has mostly
relied on oceanographic and dispersal processes, which
largely assumes linear averaging of spatial interactions
within landscapes. Our results generalize previous
applications of disturbance dynamics in intertidal
landscapes and predict conditions allowing local dynamics to scale up: when local recovery rates and
disturbance cycles are fast, changes in onshore reproductive potential (i.e., fecundity) can scale up and
produce regional (coherent) fluctuations. In contrast,
constraints on the recovery of populations after
disturbance prevent such synchrony and rather lead to
landscape disturbance cycles that are robust to regional
processes (Table 1).
Dispersal between regulated populations and communities and correlation in environmental fluctuations
can cause spatial correlation in ecological systems
(Bjornstad et al. 1999, Liebhold et al. 2004). The
interplay between these factors can control large-scale
patterns in homogeneous environments including synchronized oscillations (Earn et al. 2000) and phaselocked oscillations (Blasius et al. 1999). Here we
contribute to theories of synchrony as they apply to
ecology by showing how spatial ecological interactions
at the individual level can synchronize dynamics at
higher (metapopulations) organizational levels. We
further demonstrate that this effect is caused by the
temporal signature of ecological disturbance cycles
resulting from spatially explicit disturbance dynamics.
Our study is the first to predict the importance of
hierarchies of spatially explicit processes involving
individual and dispersal scales in coastal ecosystems. It
more specifically elucidates conditions under which
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653
FIG. 4. Landscape and regional time series maxima and minima of the mussel cover in relation to fecundity for
metapopulations of 100 landscapes with fast disturbance cycles (a2 ¼ 1). Mean metapopulation mussel cover (solid line) was
calculated by obtaining the average mussel cover along the transect for each iteration and subsequently averaging these values over
2000 iterations. The regional time series graph (dots) was generated by plotting the local minima and maxima of the
metapopulation mussel cover in relation to fecundity. The landscape time series graph (open circles) was obtained by determining
the minimum and maximum landscape mussel cover across the entire metapopulation at each iteration, and independently
averaging these minima and maxima over 100 iterations. Mean-field landscape models (C and D) and stochastic mean-field
landscape models (E and F) were used in order to elucidate the specific contribution of disturbance cycles. Stochastic mean-field
landscapes (E and F) had their mussel cover coefficient of variation adjusted to match that of the spatially explicit landscapes (A
and B) by adding random fluctuations with mean b and variance r. A, C, and E are based on spatially explicit metapopulation
models, while B, D, and F represent mean-field metapopulation models.
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TARIK C. GOUHIER AND FRÉDÉRIC GUICHARD
Ecology, Vol. 88, No. 3
FIG. 5. Landscape and regional time series maxima and minima of the mussel cover in relation to fecundity for
metapopulations of 100 landscapes with slow disturbance cycles (a2 ¼ 0.2). Symbols and other parameter values are the same as in
Fig. 4.
localized processes at the landscape (i.e., disturbance/
recovery) rather than at the metapopulation (i.e.,
dispersal) level could be essential for the prediction of
large-scale dynamics. In contrast with previous theoretical frameworks (Connolly and Roughgarden 1999), our
results suggest that emergent phenomena scaling up
from local processes could be the determinant for testing
the assumption of demographic decoupling between
populations and for predicting their regional response to
oceanographic fluctuations.
Disturbance cycles and open system theory
Open system theory was initially applied to predict the
local dynamics of marine populations and communities.
Because recruitment was assumed to be limited by local
supply of homogeneously distributed larvae rather than
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655
FIG. 6. Coefficient of synchrony in mussel cover as a function of fecundity for spatially explicit metapopulations with fast and
slow disturbance cycles, and consisting of 100 spatially explicit, mean-field landscapes or stochastic mean-field landscapes. Only the
metapopulation with spatially explicit landscapes and fast disturbance cycles showed a response of synchrony to fecundity.
by their production, frameworks originally decoupled
onshore community dynamics from larval production,
which lead to the inherent stability of metapopulation
models (Roughgarden and Iwasa 1986). Large-scale
oceanographic processes such as supply-side theory were
introduced to explain observed variability in recruitment
(Roughgarden et al. 1987) and community structure
(Menge and Sutherland 1987). Nearshore oceanography
was similarly integrated into theories of bottom-up and
top-down controls.
While theoretical and recent empirical advances have
promoted limited dispersal and a degree of demographic
coupling between neighboring landscapes, the assumption of demographically open systems still proves to be a
useful simplification (Hixon et al. 2002, Menge et al.
2003). In an effort to provide a more general framework
integrating these two views and allowing their empirical
validation, we formulated specific local and regional
predictions related to demographic coupling between
nearshore and onshore processes. For instance, we
predict that populations experiencing slow onshore
recovery rates relative to disturbance intensity can
explain the maintenance of regional variability in open
systems (Table 1). This prediction defines conditions
allowing results that are based on local demographically
open populations to be directly scaled up to regional
dynamics. It more generally provides an alternative
hypothesis to extrinsic environmental forcing (Lewin
1986) for the maintenance of regional heterogeneity in
the distribution of onshore abundance and larval supply
in coastal communities. Given the increasing availability
of long-term data, predictions from our model will help
testing for the occurrence of locally driven disturbance
cycles and of demographic coupling, two phenomena
that still lack strong empirical support in natural coastal
systems.
Regional response to changes in onshore productivity
Predicting the causes and consequences of large
fluctuations in nearshore and onshore physical condi-
TABLE 1. Response of local and regional population dynamics to local disturbance, to increase in regional larval production, and
to demographic coupling.
Onshore productivity
Demographically open systems
Scale of
biological response
Fast local disturbance
Slow local disturbance
Fast local disturbance
Slow local disturbance
Regional
Local
synchrony (homogeneous)
high-frequency cycles
robust (heterogeneous)
low-frequency cycles
quasi-equilibrium
equilibrium
quasi-equilibrium
low-frequency cycles (robust)
Notes: Imposed variations include changes in regional productivity (increase in fecundity) in demographically coupled or open
systems, and in the pace of local disturbance (fast or slow). Heterogeneity of regional (metapopulations) and local (landscape)
populations are affected in terms of regional synchrony and of local fluctuations. In a demographically open system, local larval
production and larval supply are decoupled. Productivity refers to changes in the per capita reproductive output (fecundity) across
entire metapopulations (see The Model: Onshore productivity and demographic regimes for details).
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tions is crucial for the development of a predictive
framework applied to coastal ecosystems (Menge et al.
2003, Hughes et al. 2005). For example, fluctuations in
nearshore phytoplankton concentration have been
associated with large-scale climatic drivers (Barber and
Chavez 1986, Chavez et al. 1999), and have been shown
to affect recruitment, growth, and fecundity of benthic
invertebrates. Resulting patterns of recruitment and
reproductive output can show large and uncoupled
heterogeneity (Menge et al. 2003, Leslie et al. 2005), but
we have yet to elucidate the regulation of recruitment by
onshore reproductive output, and its response to
environmental fluctuations. Our results are the first to
suggest how changes in fecundity can affect regional
dynamics and persistence of coastal ecosystems through
the onset of synchrony, and should be used to guide the
analysis and interpretation of large-scale coastal data
sets. More precisely, when the recovery rate of disturbed
populations is fast relative to disturbance and independent of reproductive output, local disturbance dynamics
can control population response to environmental
change at the regional level and most specifically predict
the onset of regional synchrony in relation to onshore
reproductive potential.
Interestingly, the same increase in onshore reproductive potential fails to induce synchrony for metapopulations exhibiting slow recovery and high amplitude
disturbance cycles. This result is compatible with
existing metapopulation theories predicting increased
probability of metapopulation synchrony with increasing demographic rates and fluctuations of abundance
(Earn et al. 2000, Keeling and Rohani 2002, Guichard
2005). More importantly, our results predict the
sensitivity of these predictions to the spatial nature
and temporal signature of local dynamics (Table 1).
CONCLUSION
We have shown how local cycles of disturbance and
recovery can scale up and govern regional dynamics in
both demographically open and coupled marine metapopulations. Our study predicts how properties of
onshore productivity and disturbance dynamics affect
both local and regional behavior of coastal ecosystems.
This hierarchical framework complements existing
theories of marine ecosystems by demonstrating the
importance of combining nonequilibrium dynamics and
dispersal limitation as an endogenous cause of spatiotemporal dynamics and regulation. One important
consequence of this work is the unsuspected importance
of local processes for regional dynamics. This stresses
the importance of incorporating knowledge of both
local and regional processes when conducting empirical
studies of large-scale coastal populations. More precisely, variability in local recovery and disturbance rates, as
well as regional nearshore oceanographic processes
strongly interact to predict the onset of synchrony and
the maintenance of heterogeneity across scales. The
model presented here should be extended to metacom-
Ecology, Vol. 88, No. 3
munity dynamics in order to address problems of
competitive and trophic interactions (Connolly and
Roughgarden 1999), and constitutes an important step
toward the full integration of local community ecology
into a nonequilibrium theory of large-scale coastal
ecosystems.
ACKNOWLEDGMENTS
We acknowledge support from the Natural Science and
Engineering Research Council of Canada and from the James
S. McDonnell Foundation. We thank M. Anand and two
anonymous reviewers for useful comments on a previous
version of the manuscript.
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