vol. 171, no. 5
the american naturalist
may 2008

Complex Positive Connections between Functional Groups Are
Revealed by Neural Network Analysis of Ecological Time Series
Simon F. Thrush,* Giovanni Coco,† and Judi E. Hewitt‡
National Institute of Water and Atmospheric Research, P.O. Box
11-115, Hamilton, New Zealand
Submitted September 18, 2007; Accepted December 27, 2007;
Electronically published March 14, 2008
Online enhancements: appendixes.
abstract: The relationships between functional linkages within
communities and community dynamics are fundamental to biodiversity-stability relationships. By teasing apart the hidden layers
within artificial neural networks (ANNs), we developed webs defining
how functional groups influence each other’s temporal dynamics.
ANNs were based on 15 years of bimonthly monitoring of macrobenthic communities on three intertidal sandflats in Manukau
Harbor (New Zealand). Sites differed in web topology and diversity,
with the site dominated by one functional group exhibiting only a
few strong links, the lowest a-, b-, and g-diversity, and the highest
temporal stability in a-diversity. However, positive interactions between functional groups, nonconcordant with harborwide or sitespecific environmental variables, always dominated the interaction
webs. The increased number of links we observed with increased
temporal variation of species richness within functional groups and
overall diversity supports the insurance hypothesis. While our findings suggest that there may be no consistent model characterizing
the topology of temporal interactions between functional groups,
decreasing diversity is likely to decouple interactions between functional groups and decrease ecosystem functionality.
Keywords: temporal dynamics, diversity-stability, functional groups,
artificial neural networks, time-series analyses, macrofauna.
Despite their theoretical, empirical, and applied applications to ecology, the relationships between biodiversity and
the stability, resilience, and functionality of communities
* Corresponding author; e-mail: [email protected].
†
E-mail: [email protected].
‡
E-mail: [email protected].
Am. Nat. 2008. Vol. 171, pp. 669–677. 䉷 2008 by The University of Chicago.
0003-0147/2008/17105-42737$15.00. All rights reserved.
DOI: 10.1086/587069
have been hard to define (Goodman 1975; Huston 1994;
Carpenter et al. 2001; Loreau et al. 2001; Ives and Carpenter 2007). Debate on the strength and functional form
of such relationships has had a long history (Elton 1958;
Whittaker 1975; Raffaelli 2006; Houlahan et al. 2007). Recent syntheses haves indicated that communities tend to
be numerically dominated by weak interactions, with stability dependent on species or functional groups capable
of providing differential responses to changes in species
composition or environmental factors and delivering different elements of ecosystem function (McCann 2000;
Wootton and Emmerson 2005; Hector and Bagchi 2007).
Empirical support for these relationships between biodiversity and stability/function across different ecological
systems is often limited, in part because of the practical
limitations of empirical studies in encompassing long-term
community dynamics. This leads to disparities between
empirical and theoretical investigations.
Over temporal scales relevant to community stability, resource management, and conservation, scaling issues often
cloud the debate on biodiversity-stability and biodiversityfunction relationships. One problem is that rigorous experimental tests are rarely conducted over sufficient space
and timescale to reflect broadscale emergent patterns. Moreover, few studies have addressed how diversity relates to
functional linkages within communities (Cottingham et al.
2001). Time-series data are needed both to describe the
temporal dynamics of populations and communities and to
provide insight into the interactions between species and
environmental factors that influence community stability
and resilience (Gaston and McArdle 1994; Ives et al. 2003).
Temporal information on the dynamics of populations,
functional groups, and communities is also important in
defining the magnitude of ecological response of anthropogenic stressors. Thus, time-series data may be especially
useful in elucidating relationships between diversity and
functional interactions within communities, even though
the increased temporal replication typically limits spatial
replication in such data sets.
Interaction webs describing the nature, pathway, and
strength of links between species have been developed
670 The American Naturalist
from experimental manipulations (Paine 1980; Wootton
1994; Menge 1995). The description of links between community members has provided important insights into
community function and spatial heterogeneity (Menge et
al. 2004). However, the direction and strength of effects
and the importance of direct and indirect interactions are
often sensitive to the duration of experiments (Menge
1997; Tegner et al. 1997; Dayton et al. 1999). Thus, techniques that enable us to define the links between community members over time would provide a new perspective. With sufficient temporal data, relationships
within sites could be used to contrast sites with different
community dynamics, diversity, or levels of environmental
forcing. But to do this we need to develop techniques to
characterize the strength of interactions between populations, functional groups, or other aggregate community
variables over time.
Disentangling the effects of diversity, community composition, and species interactions on temporal dynamics
requires robust statistical techniques (Ives et al. 2003). To
examine relationships between community members, we
took the novel approach of teasing apart the hidden layers,
or “neurons,” within artificial neural networks (ANNs).
ANNs enable models to be built that link explanatory and
dependent variables (Bishop 1995; Bose and Liang 1996),
and they have become popular in species-environment
applications because of their ability to encompass nonlinearities and complex interactions (Olden et al. 2006;
Ozesmi et al. 2006). But their internal complexity has
meant that in most ecological applications, ANNs are
treated as a black box, where input variables are simply
used to make a prediction irrespective of the nature of the
relationships between input variables and hidden layers.
However, techniques have been developed to disentangle
the ANN and estimate the role of each input variable
(Vaughn 1996; Benı́tez et al. 1997; Dimopoulos et al. 1999;
Musick et al. 2000; Olden and Jackson 2002). Here, we
couple two of these techniques to define the nature and
strength of relationships between community members.
We built ANNs from data sets derived from 15 years of
bimonthly monitoring of macrobenthic communities on
three intertidal sandflats in Manukau Harbor, New Zealand. The different communities at each site reflect community dynamics, with a range of starting conditions but
from similar habitats, and integrate across trophic levels
(Pridmore et al. 1990; Turner et al. 1995). We were interested in identifying general patterns that link diversity
to function, and we consequently focused on functional
groups. Functional groups are commonly used in ecology;
in benthic ecology the shared traits are generally related
to feeding, mobility, and interactions with the sediment
or sediment-water interface (see, e.g., Bremner et al. 2006a,
2006b). The use of functional groups also allowed us to
compare across the three sites, despite species composition
varying between sites, by averaging over species-specific
effects (Doak et al. 1998). In biodiversity-ecosystem function relations, the manipulation of functional groups has
been recommended (Balvanera et al. 2006), although their
role has not been thoroughly investigated in theoretical
studies (Wootton and Emmerson 2005). In our analyses,
we provide new insight into the temporal dynamics of
functional groups in relation to diversity both within and
across groups. Thus our study provides a novel long-term
assessment of the functional links within three different
diverse, multitrophic communities.
In this article, we examine the topology of the interaction web between functional groups, derived from the
ANNs, to determine the relationship between diversity and
the degree of connectivity between functional groups. Both
negative (Rhoads and Young 1970; Black and Peterson
1988; Wilson 1991; Olafsson et al. 1994) and positive (Gallagher et al. 1983; Thrush et al. 1992, 1996; Norkko et al.
2006) interactions have been described in soft-sediment
communities. However, Houlahan et al. (2007) demonstrated that compensatory dynamics were rare in ecological
time series. Moreover, positive effects tend to become more
apparent with increasing scale of observation (Bruno and
Bertness 2001). Thus we predicted that positive interactions would dominate. Defining the potential for positive
interactions in community dynamics is important, especially in broadscale data sets, because many community
theories are driven by negative interactions (predations
and competition; e.g., McCann 2000). We also predicted
that increasing evenness in the distribution of individuals
among species within functional groups would lead to
many connections between groups and that community
dominance by one functional group would produce few,
but strong, connections. Furthermore, we expected that
the topology of the functional group interaction web, derived from ANNs, would provide a new way to assess longterm functional links within multitrophic communities.
Methods
Time-series data were collected from three sites (Auckland
Airport [AA], Cape Horn [CH], and Clarks Beach [CB])
located near the three main channels draining Manukau
Harbor, North Island, New Zealand (Pridmore et al. 1990).
This harbor is a large (340 km2), shallow inlet with intertidal sandflats making up about 40% of the area. The
three sites (each 9,000 m2) were all located in the midlow tide level and were chosen for their similarity in overall
physical appearance and predominance of fine sand sediments. Data on macrofaunal communities were collected
every 2 months (October 1987–February 2005), with the
exception of October and December 1988 at all three sites
Complex Positive Connections between Functional Groups 671
and February 1997–February 2000 at CH. Each site was
divided into 12 equal sectors (each 25 m # 30 m), and
on each sampling occasion, one core sample (13 cm in
diameter, 15 cm deep) was taken from each sector (Hewitt
et al. 1993; Thrush et al. 1994a). After collection, samples
were sieved (500 mm) and preserved in either 5% formalin
or 70% isopropyl alcohol. All the macrofauna were sorted,
identified to the lowest practical level, and counted.
For each site, we calculated the total (across all times)
species richness (g-diversity), the temporal average species
richness (a-diversity), and species turnover (b-diversity;
b p g ⫺ a). We calculated the temporal consistency of
species richness over the time series for each site and the
temporal consistency of each functional group using both
log standard deviation and the coefficient of variation
(Cottingham et al. 2001). Because both variables revealed
the same patterns between sites and groups, we present
only the coefficient of variation. We also calculated average
species richness and Pielou’s evenness both across species
within each functional group and between functional
groups at each site (Pielou 1975).
We then took all of the macrofaunal species and allocated them to six functional groups: five were feeding
groups, and the other we called RARE (defined as sporadically occurring taxa with a total density of !25 individuals over the entire time series). The five feeding groups
were surface deposit feeders (SRDEP), subsurface deposit
feeders (SBDEP), suspension feeders (SUSP), predator/
scavengers (PRED), and intermediate taxa capable of
switching feeding mode between surface deposit and suspension feeding (INTER). Taxa were allocated to functional groups based on natural-history information (see
Pridmore et al. 1990). We found the RARE group to be
important in influencing the stability of ANN modeling.
In our preliminary ANN development, we found that excluding RARE produced models with less explanatory
power. Also, inclusion of RARE consistently produced
ANNs with higher explanatory power than the ANNs obtained by substituting RARE with a time series of random
numbers. In both terrestrial and marine systems, most
species are rare, that is, represented by a small number of
individuals and/or present at only a few sites or times.
These rare species are theoretically important in maintaining the stability of ecosystem functioning, via the insurance hypothesis (Yachi and Loreau 1999; Pfisterer and
Schmid 2002), especially in changing environments (Loreau et al. 2001). This is because, theoretically, the more
diverse a community, the higher its potential for connections between species. Therefore, when species within the
community decrease in fitness or abundance, other species
are able to maintain the interaction web and ensure stability and resilience in function. Previous research in marine systems suggests that rare species may play a role in
community resilience (Ellingsen et al. 2007) and also in
community and ecosystem functioning (see Thrush and
Dayton 2002 and references therein).
Full details of ANN development and the determination
of the strength of temporal connections between functional groups are presented in appendix A in the online
edition of the American Naturalist. All input variables to
the ANNs (functional types or environmental variables)
were standardized to facilitate postprocessing and analysis
of variable importance, irrespective of differences in the
abundance of functional groups across sites. We developed
ANNs with the simplest structure; two hidden nodes in
one hidden layer and a single-node output layer predicted
a specific functional type (INTER, SBDEP, RARE, PRED,
SUSP, or SRDEP) using the same functional type at the
previous time step (t⫺1), other functional types, or environmental variables. We included the previous time step
in the ANN because our initial model development identified only first-order autocorrelation as important. Inclusion of previous time steps (t⫺2, t⫺3, t⫺4) did not increase
the ANN explanatory power.
We also investigated the role of both global and local
environmental variables in affecting the time series of abundance within each functional group. Global environmental
variables were the same across all sites; the variables were
an index of atmospheric pressure variation across New Zealand that encapsulates the strength of the westerly wind band
over New Zealand (Salenger and Mullen 1999), the Southern Oscillation index (SIO–Troup index; see http://www
.bom.gov.au/climate/glossary/soi.shtml; McBride and Nicholls 1983), and annual maximum and minimum air temperatures recorded at Auckland Airport, on the shore of
Manukau Harbor. Local environmental variables were derived from a water-column sampling program conducted
adjacent to the benthic monitoring sites and included oxygen concentration, salinity, turbidity, and temperature
(Wilcock and Martin 2003). These were the only available
local-scale data collected over the entire ecological time series. Sediment grain-size data had been collected from the
sites initially (Pridmore et al. 1990) and on other occasions
later in the time series. While insufficient data were available
for their inclusion in the ANN analysis, there were no apparent temporal trends in grain size (S. F. Thrush, G. Coco,
and J. E. Hewitt, unpublished data).
Results
Over the 15 years encompassed by our data, the macrobenthic communities at the three sites remained different,
despite some within-site variation. Appendix B in the online edition of the American Naturalist provides background information on the temporal dynamics of the functional groups at the three sites and the overall contribution
672 The American Naturalist
of the functional groups to macrofaunal abundance. At
site AA, SRDEP consistently dominated, while other functional groups varied in importance with no clear seasonality in abundance. The bivalve Macomona liliana was the
most dominant species on all but five sampling occasions,
with 18 other species ranking among the five most dominant species (with the dominance of individual species
varying both seasonally and between years). The dominance of functional groups varied over time at site CH,
with SBDEP or INTER dominating the initial part of the
time series and SRDEP dominating toward the end of the
time series. This reflected a shift in communities dominated by the polychaete Heteromastus filiformis (SBDEP)
or Boccardia syrtis (INTER) to domination by Magelona
?dakini (SRDEP). Strong seasonality was apparent in the
other dominant species, with 31 species represented in the
five most dominant species recorded over the time series.
Site CB showed the most variability in functional dominance, with switches mainly between the SRDEP and
SBDEP groups. This reflected the variability in species that
were numerically dominant (H. filiformis on 25 occasions,
the bivalve Nucula hartvigiana [SRDEP] on 26 occasions,
B. syrtis on 16 occasions, the polychaete Macroclymenella
stewartensis [SBDEP] on 12 occasions, M. liliana on 10
occasions, and occasional dominance by the amphipods
Torridoharpinia hurleyi [SRDEP] and Paracalliope novaezelandiae [SRDEP], the crab Halicarcincus whitei [PRED],
and the polychaetes Euchone sp. [SUSP] and Prionospio
aucklandica [SRDEP]). Thus, despite the overall similarities in habitat type, community dynamics were distinctly
different at each site.
Total species richness (a-diversity) and species turnover
(b-diversity) were lowest at AA and similar at CH and CB
(table 1). Temporal stability in species richness (CV) indicates that the most stable site was the one with the lowest
diversity. Community evenness and between–functional
group evenness followed the same trend, with lowest values
at AA. The low between–functional group evenness at AA
was driven by low evenness of the SRDEP and SUSP functional groups.
The interaction webs derived from the ANNs for each
site demonstrate differences in connections among functional groups; as predicted, most of the connections were
positive (fig. 1). Moreover, while the strongest interactions
were due to temporal autocorrelation (t⫺1) within functional groups, it was also apparent that negative connections tend to be weak.
Some consistent patterns of connection across the sites
emerged. When PRED interacted with rare species (RARE)
or SRDEP interacted with INTER, there was always a negative connection. Conversely, when PRED interacted with
SBDEP, there was always a two-way positive connection.
PRED also positively affected INTER at two of the three
sites. Positive and generally bidirectional connections between INTER and RARE occurred at both CH and CB.
Overall, INTER tended to be more affected by other functional groups rather than being a driver of change. Both
SBDEP and SUSP tended to have an equal balance in
influencing, and being influenced by, other functional
groups, and often their connections ran in both directions.
Generally there were few strong bidirectional interactions,
indicating that connectivity in the temporal dynamics of
functional groups implied by the ANNs was mediated
through multiple group connections.
The site with the lowest diversity, species turnover, and
evenness between functional groups, but with high temporal consistency, exhibited the least connectivity among
functional groups. Comparisons of diversity and evenness
with the number of links between functional groups at the
three sites indicated that there may be a relationship with
increasing diversity and evenness leading to more but
weaker connections (cf. table 1 and fig. 1). This may have
been simply driven by the higher dominance (low evenness) of SRDEP at AA, which exhibited a few strong connections.
There was no apparent relationship between the evenness within a functional group and the number of links
between functional groups (Pearson’s R p ⫺0.31, P p
.200). This relationship does not support the general importance of species-specific niche differentiation within the
functional group; we expected a positive relationship between the within-group evenness and links between groups
if species-specific niches were generally important. Only a
weak relationship between the temporal stability in the
number of individuals within a functional group and number of links from that group was apparent (Spearman’s
r p 0.41, P p .0907). However, the more the species richness within functional groups varied over time, the more
connections existed between functional groups (Spearman’s r p 0.41, P p .0201).
Our analysis of links between functional groups was
based on time-series data, and thus, apparent relationships
may also be driven by environmental drivers with links
emerging as a result of concordant or discordant dynamics.
The high frequency of positive relationships between functional groups could indicate an important role of environmental forcing on the relationships between functional
groups or that net positive relationships between groups
dominate interactions over time. However, none of the
environmental variables available at either the local or
global scale were identified by the ANNs as significant
influences on the temporal variation in the density of functional groups.
Complex Positive Connections between Functional Groups 673
Table 1: Summary community and functional group diversity statistics for the three
sites in Manukau Harbor
Parameter
Community:
g-diversity (all times)
b-diversity (between times)
a-diversity (site mean ⫹ SD)
Coefficient of variation in a (all times)
Overall species evenness (all times)
Functional group:
Within-group evenness:
SBEDP
INTER
PRED
RARE
SUSP
SRDEP
Between-group evenness
AA
CH
CB
95
68.7
26.3 ⫹ 3.8
14.3
.61
136
103
33 ⫹ 9.8
29.6
.71
137
98.3
38.7 ⫹ 8.4
21.6
.82
.67
.67
.77
.64
.45
.45
.61
.55
.38
.70
.49
.62
.61
.71
.54
.51
.77
.77
.73
.66
.77
Note: See “Methods” for definitions of functional group and site abbreviations.
Discussion
Overall, positive connections between functional groups
dominated the webs derived for each site. This is an intriguing result that adds further weight to other empirical
studies that emphasize the role of positive connections
between community members. The dominance of positive
interactions agrees with a recent analysis of ecological time
series (Houlahan et al. 2007) that demonstrated that positive covariance is the norm in nature. However, based on
the lack of evidence for competition, Houlahan et al.
(2007) inferred that it was environmental drivers that
caused positive relationships. In contrast, we explicitly
tested the role of both local and regional environmental
drivers and found no significant relations with temporal
trends in the abundance of functional groups. While we
were limited in the range of environmental variables with
comparable time series we could include in our analysis,
the variables used either directly or as surrogates reflect
major environmental forces acting on sandflat communities. For example, the broadscale variables related to
atmospheric pressure gradients drive variation in wind,
waves, and rainfall, while the local-scale variables such as
turbidity reflect sediment input to the harbor and resuspension; both are expected to drive variability in sediment
characteristics.
While there will always be the potential of missing a key
environmental driver, we would expect that if environmental forcing were driving temporal dynamics in functional
groups, we would have seen at least a weak effect in our
ANN analysis of environmental variables; yet this was not
apparent. The lack of importance of environmental drivers
in influencing abundance within functional groups over
time may indicate that individual species respond to en-
vironmental drivers in specific ways, allowing for species
substitution within the functional group as the environment
changes, an important component of the insurance hypothesis. Similarly, long-term experiments on the response
of grassland communities to climate change have highlighted the potential for complex interactions to ameliorate
direct climate effects. This informative experiment revealed
that when environmental change is sustained over years,
feedbacks and species interactions dominated responses, despite short-term changes in individual species response (Suttle et al. 2007).
While the positive connections identified through our
ANN analysis of the temporal dynamics of functional
groups do not necessarily imply causal relationships, manipulative experiments on the timescale of our time series
would be difficult to maintain. Moreover, there is a growing recognition of the importance of positive interactions
between community members, especially in marine benthic communities where individual species or functional
groups can modify habitat structure and nutrient dynamics
(Bertness and Leonard 1997; Thrush and Dayton 2002;
Lohrer et al. 2004; Solan et al. 2004; Hewitt et al. 2005;
Coco et al. 2006). Often, positive interactions between
species are more commonly detected at broader spatial
scales because they are emergent properties of processes
interacting across scales (Bruno and Bertness 2001; Bruno
et al. 2003). Our analyses of the 15-year time series of the
macrofauna from intertidal sandflats indicated that temporally, a similar pattern may emerge that is not strongly
driven by environmental drivers. Positive interactions are
especially important in community dynamics because the
breaking of these links can lead to hysteresis and regimeshift effects.
674 The American Naturalist
Figure 1: Interaction webs derived from artificial neural network models
of temporal changes in abundance within the six functional groups.
Dashed arrows indicate negative correlations, and full arrows indicate
positive correlations. Arrow direction indicates the influence of one group
on another. Arrow thickness indicates the strength of correlation. Values
next to each arrow are the product of Spearman’s rank correlation coefficient and the relative importance (%) of the weight associated with
the variable. When no line is present, connections were extremely weak
and nonsignificant.
The importance of positive interactions was further
highlighted by the absence of negative links between suspension feeders and deposit feeders, generally known to
soft-sediment ecologists as the trophic group amensalism
hypothesis (Rhoads and Young 1970; Aller and Dodge
1974; Myers 1977). This hypothesis was based on obser-
vations of sediment destabilization by deposit feeders that
increased sediment resuspension and negatively influenced
adjacent suspension feeders. Overall, the small number
and low strength of negative interactions between functional groups did not indicate compensatory dynamics.
Intertidal flats are often disturbance-dominated habitats
(Woodin 1981; Levin 1984; Thrush et al. 1996; Thrush
1999), where recovery dynamics and restrictions to communities reaching theoretical carrying capacity are important mechanisms. Similarly, our results emphasized the
importance of multiway and positive connections between
functional groups over time and the need to weight positive interactions over negative ones in theoretical models
of community interaction and assembly.
The level of connectivity in the interaction webs emphasized the difficulty of divining cause and effect relationships. Nevertheless, the patterns common across sites
can be supported mechanistically. The mainly weak interactions of PRED with RARE, INTER, and SBDEP were
common to CB and CH (app. B) and may be related to
the higher diversity of infaunal predators at these sites
(Pridmore et al. 1990). Here, the underpinning mechanism
may increase diversity, skewing consumer-resource interactions toward dominance by weak interactions (McCann
2000; Wootton and Emmerson 2005). The weak negative
interactions between SRDEP and INTER may be accounted for by the abundance of bivalve Macomona liliana
at AA and CB; the feeding of adult Macomona on the
sediment surface can have a strong density-dependent influence on community structure (Thrush et al. 1997, 2000,
1994b). The positive, and generally two-way, interactions
between INTER and RARE at CH and CB may be a consequence of the high densities that occurred over the time
series of the polychaete Boccardia syrtis, which can form
extensive tube mats that stabilize sediment from windwave disturbance (Thrush et al. 1996). Finally, the balanced interactions of SBDEP and SUSP with other functional groups may be linked, via the differential effects of
these species on nutrient flux and the productivity of microphytobenthos (Thrush et al. 2006). The mechanistic
and theoretical support for interpretation of the ANNproduced webs suggests that this technique is capable of
assessing functional links over time. This technique provides not only a long-term perspective against which to
view theories but also the opportunity to investigate the
implications of different temporal dynamics on functional
changes and community stability.
The differences in web topology observed at the three
sites were not due to density variation between sites because of the data scaling used in the ANNs. Instead, they
seemed to be related to differences in temporal dynamics,
at the species and functional group levels, and a-, b-, and
g-diversity. The site that exhibited the lowest a-, b-, and
Complex Positive Connections between Functional Groups 675
g-diversity (AA) had the highest temporal stability in species richness and displayed few, but strong, connections
between functional groups. Overall, high temporal variability in species richness within the functional groups
tended to lead to more connections. Because of statistical
averaging (as a consequence of the formation of functional
groups), we should expect a negative correlation between
community variability and increasing species richness
when species fluctuations are not perfectly synchronous
(Doak et al. 1998). This was apparent in our analysis. As
evenness decreased, statistical averaging in aggregate variables such as functional groups increased, leading to a
weakening of the negative relationship between richness
and community variability. Despite strong seasonality and
variance in dominance structure at both population and
functional group levels at two of the sites, all three sites
exhibited persistence in community structure. This indicates that the greater number of connections existing in
diverse sites could play an important role in community
persistence in the face of temporal variability of populations.
The stability of ecological communities has been related
to interactions between three components of community
structure: species composition, diversity, and species interactions (Ives et al. 2003). Stability in ecological systems
is also a multifaceted concept that can be defined in terms
of return to equilibrium, change in response to disturbance, or change in temporal variability (Ives and Carpenter 2007). Differences in the topology of the interaction
webs we detected are important because the degrees of
direct and indirect interaction within the web can profoundly affect the way communities respond to change
(van Veen and Murrell 2005). This has certainly been demonstrated for food webs (Polis and Strong 1996), but the
links we identified between feeding functional groups reflect more than just trophic relationships and may incorporate many types of both direct and indirect species interactions. Our analyses of the temporal dynamics of
functional group interactions indicate that more connections can occur with high temporal variability in species
richness, supporting the role of species substitutions in
functional groups in dynamic ecosystems. Thus, declines
in overall diversity will lead to a loss of intrinsic stabilizing
mechanisms and accelerated simplification of multitrophic
communities. To effectively manage ecosystems, we need
to be able to understand the implications of different temporal dynamics and to tie temporal trends to functional
changes and resilience. Long-term data can contribute to
debates over fundamental ecological relationships. Resolving such issues is not purely of academic interest because relations between diversity, resilience, and ecological
function can profoundly influence the direction of policy
and the nature of management actions in response to environmental issues.
Acknowledgments
The Auckland Regional Council maintains the monitoring
program of Manukau Harbor; we thank the council for
access to the data and the wisdom of collecting it. We also
thank M. Gevrey for proving insights on the evaluation
of two-way interactions, MMC for providing the DST time
necessary to run the final set of calculations, and the many
people who helped collect, sieve, sort, and identify the
samples. This analysis was supported by New Zealand
Foundation for Research Science and Technology grant
C01X0501.
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Editor: Donald L. DeAngelis