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Journal of Experimental Marine Biology and Ecology 366 (2008) 116–122
Contents lists available at ScienceDirect
Journal of Experimental Marine Biology and Ecology
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j e m b e
Habitat variation, species diversity and ecological functioning in a marine system
J.E. Hewitt a,⁎, S.F. Thrush a, P.D. Dayton b
a
b
National Institute Water and Atmospheric Research, PO Box 11-115, Hamilton, New Zealand
Scripps Institute of Oceanography, La Jolla, California 92093-0227
a r t i c l e
i n f o
Keywords:
Biological traits
Evenness
Functional diversity
Functional redundancy
Habitat heterogeneity
Richness
a b s t r a c t
The expectation that long-term, broad-scale changes in the relative abundance of species, homogenisation of
habitats and decreases in diversity will affect ecosystem function has led to an increasing number of studies
on functional diversity and composition. Such studies frequently consider the effect of environmental
gradients and anthropogenic impacts, but rarely the effect of biogenic habitat variation. In marine softsediment systems, habitat variability is likely to be of particular importance because of the strong link
between habitat and species diversity. In this study we examine the link between functional trait diversity
(as richness and evenness) and composition, and habitat variation in two locations with different regional
species pools. We found similar functional traits occurring in the two locations, but differences between
habitats within the locations. High evenness within traits was apparent (across both locations and habitats)
reflecting the potential for the maintenance of function with the loss of individual species. Between-habitat
differences in functional traits were driven by differences in organism densities rather than the presence/
absence of individual traits, emphasising the importance of density shifts in driving function. Furthermore,
our demonstration of habitat variation as a driver of functional composition and diversity suggests that
habitat heterogeneity should be explicitly included within studies trying to predict the effect of species loss
on ecosystem function.
© 2008 Elsevier B.V. All rights reserved.
1. Introduction
Globally, marine environments are under stress from a variety of
human activities. Dramatic and chronic changes in the relative
abundance of species, homogenisation of habitats and decreases in
diversity have been documented in many places. Increasingly, ecologists
expect that these changes will have concomitant effects on the way
ecosystems function (e.g., Gray, 1997; Petchey, 2000; Loreau et al., 2001;
Hughes et al., 2003). The need to prove to managers that decreases in
biodiversity have ramifications beyond the simple loss of species, and to
find generalities across regions with different species pools, has led to
the concept of considering functional diversity and composition as a
complement to species diversity. Assessments of functional diversity
and composition are generally undertaken to address 4 main themes:
comparisons across regions and along environmental gradients, the
detection of anthropogenic impacts, relationships between species and
functional diversity; and relationships with stability and resilience of
ecosystem function.
The concepts of functional diversity and composition are complex and
often used without an accompanying definition. Those definitions that
exist are based on the concept that the ability of an ecosystem to function
⁎ Corresponding author. NIWA, PO Box 11-115, Hamilton, New Zealand. Tel.: +64 7
8567026; fax: +64 7 856 0151.
E-mail addresses: [email protected] (J.E. Hewitt), [email protected]
(S.F. Thrush), [email protected] (P.D. Dayton).
0022-0981/$ – see front matter © 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.jembe.2008.07.016
may be more related to species-specific traits than species richness
(Walker, 1992; Loreau et al., 2001; Hooper et al., 2005). For example,
functional diversity is “the value and range of the functional traits of
organisms in a given ecosystem” (Tilman, 2000; Dıaz and Cabido, 2001).
In simple terms, functional diversity leads to an understanding of
communities and ecosystems based on what organisms do rather than on
evolutionary history as reflected in taxonomic relationships (Petchey and
Gaston, 2006). Although there are a burgeoning variety of methods for
measuring functional diversity and composition (see Petchey and Gaston,
2006), this focus on functional or biological traits has highlighted the
importance of integrating natural history into ecology (Dayton, 2003).
Theoretical studies on the relationship between species and
functional diversity suggest that the relationship will differ between
systems, dependent on the number of species and organisms
occurring within specific functional groups (Micheli and Halpern,
2005). The degree of this overlap (assumed to represent the potential
for functional redundancy) is predicted to lead to differences in how
loss of species will affect ecological functioning, with high functional
redundancy expected to increase functional stability and resilience,
mainly through niche partitioning. Higher niche complementarity
should allow more complete use of the resource potentially increasing
productivity and invasion resistance (Dukes, 2001; Petchey, 2003). If
higher niche complementarity within a community reflects a greater
variety of potential responses to environmental conditions, it may be
expected to provide a buffer to change and increase ecosystem
stability and resilience (Tilman, 1996; Doak et al., 1998).
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J.E. Hewitt et al. / Journal of Experimental Marine Biology and Ecology 366 (2008) 116–122
The nature of the relationship between species and functional
diversity is unknown for most ecosystems, posing important
challenges for ecologists (Naeem, 2002; Micheli and Halpern, 2005;
Norling et al., 2007). This is particularly true in marine benthic
communities where the research effort is relatively recent, spear
headed by the translation of biological trait analysis to the marine
environment by Bremner et al. (2003). Furthermore, many studies of
functional biodiversity focus on the number of functional traits,
without considering within-trait diversity, and infer overall functional
redundancy from differences between taxonomic and functional
diversity. Studies on within-region differences are focussed on
understanding impacts and the link between habitat (particularly
biogenic-structured habitats) and functional diversity remains largely
unexplored. This is despite work demonstrating the strong effect of
habitat diversity on estimates of biodiversity (Conner and McCoy,
1979; Tokeshi, 1999; Ellingsen, 2002).
In this study we attempt to merge work on habitat-biodiversity
relationships with functional trait diversity and composition, and ask
whether there are functional differences between soft-sediment
habitats. We investigate within-functional-trait diversity explicitly,
by considering within-trait species richness and evenness, with the
aim of determining whether the potential for functional redundancy is
related to specific functions and habitats. The study is carried out on
soft-sediment macrofauna in two near-coast areas of New Zealand,
oceanographically distinct with different species pools, both to create
some generality and to allow us to determine whether differences in
functionality occur as a result of different species pools constraining
the pool of traits available to local areas. We expected that differences
in functional composition would be more closely linked to habitats
than regional species pools.
2. Methods
2.1. Data collection
The data analysed in this study were collected from two locations in
New Zealand likely to have different species pools, the Northern Hauraki
Gulf (NHG, 36.4°S; 174.8°E), and Tonga Island Marine Reserve (TIMR,
40.9°S; 173.8°E). These locations are separated by approximately
500kms. NHG is on the northeast coast of the North Island, opening to
the Pacific Ocean and within the North Auckland current system. TIMR is
on the northwest coast of the South Island, opening to the Tasman Sea.
Neither locations are exposed to commercial bottom fishing or industrial
contamination, and both contain a variety of soft-sediment habitats.
The collection of data analysed in this study is described by Thrush
et al. (2006a) and (1998). Briefly, surveys were conducted in Kawau
Bay and nearby exposed areas of the Hauraki Gulf (February 1994), in
Kawau Bay (February 1999) and in TIMR (September 2003). For all
three studies, detailed quantitative video sampling of 20m transects
were used to describe habitats based on both physical characteristics
and the density of habitat-forming organisms (Table 1). Macrofaunal
core samples (10cm diam, 12cm deep), were located along the
Table 1
Habitat descriptions derived from video imaging of the seafloor
NHG
Homogeneous bare sand (HSB)
Atrina and sponges (BIO)
Mud with worm tubes (MW)
Coarse sand dense algal turf (ALG)
TIMR
Homogeneous sand, sparse
epifauna (HSS)
Sandy-mud, encrusted rubble
patches (RP)
Mud with hermit crabs (MC)
Sand, bivalves and echinoderms (SBE)
Shelly sand, mobile epifauna, tubeworms (SHM)
Mud with dense Atrina zelandica beds (MA)
Coarse sand, heavily bioturbated (CS)
Sand with bivalves and gastropods (SBG)
Sand, mobile epifauna (SME)
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Table 2
Traits based on species behaviour, size and shape, selected as influencing, either directly
or indirectly, important ecosystem functions
General category
Trait
Motility
Sedentary or only moving within fixed tube structure
Limited free movement, e.g., withdrawal into sediment
Freely motile in or on sediment
Semi-pelagic
Suspension,
Deposit
Predator
Scavenger
Grazer
Permanent burrow
Simple hole or pit
Tube
Mound
Trough- producing troughs in sediment
Trampling across sediment surface
Surface (top 2 cm)-to-deep (N 2 cm deep)
Deep-to-surface
Surface mixing
Deep mixing
Small (0.5 - 5 mm longest dimension, exclusive)
Medium (5-20 mm longest dimension, exclusive)
Large (N = 20 mm longest dimension)
Veniform length NNN width
Globulose, length N or = width
Contains calcium carbonate
Protruding above sediment surface
Attached to other animals or small hard surfaces
Top 2 cm
Deeper than 2 cm
Feeding
Habitat structure
Sediment moving
Size
Form
Living position in sediment
Traits are grouped into general categories likely to be functional important.
transects and collected by diving. In 1999 and 2003, 5 - 10 cores
were collected per site such that within-site variability was maximised. In 1994, 15 core samples were collected at a number of sites
throughout the Hauraki Gulf. For this paper, sites from exposed areas
to the north of Kawau Bay were selected for use and 6 - 10 samples
from each site chosen that maximised the within-site species richness
(as per Ellingsen et al., 2007). Note that Ellingsen et al. (2007)
investigated and dismissed the potential for differences between the
1994 and 1999 Kawau Bay data. In TIMR sites encompassed a
maximum linear extent of about 10km with a depth range of 8-24m,
while at NHG sites encompassed a maximum linear extent of 30km
and ranged from 6-25m depth. Both locations had sites varying from
sheltered to wave-exposed.
Core samples were sieved on a 0.5mm mesh sieve and preserved in
70% Isopropyl alcohol. Later organisms were counted and identified to
as low a taxonomic resolution as practical (generally to species level).
The upper size of taxa represented in the data is considered to be ~
75mm as larger, epibenthic, organisms were not considered to be well
sampled.
Given the dispersal potential of soft-sediment macrofauna and the
spatial scale of our sampling, most species would have the potential to
move from one habitat to another within each location.
2.2. Defining functional traits
The use of functional traits to define functional diversity has been
pioneered in terrestrial and freshwater systems (e.g., Keddy, 1992;
Townsend and Hildrew,1994), where links between functional traits and
ecosystem processes have been established (e.g., Usseglio-Polatera et al.,
2000; Dıaz and Cabido, 2001). In marine systems, ecosystem functionality has for many years focussed on feeding mechanisms, but recently
biological trait analysis has been developed (BTA, Bremner et al., 2003).
The biological traits used generally reflect life history, morphology and
behaviour that influence ecosystem processes (e.g., size, feeding,
influence on sediment and hydrodynamics). Processes of importance
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in marine ecosystems are nutrient fluxes across the sediment –water
interface, bioturbation and irrigation, habitat creation, secondary
production, sediment stability/transport and carbon sequestration. The
biological traits we have selected to represent functionality (Table 2) are
either directly linked to these processes or are indirect indicators (as per
Lavorel and Garnier, 2002). They contain traits found to be important by
Bremner et al. (2003) and Norling et al. (2007), but do not include
measures of fitness (Schratzberger et al., 2007) or estimates of the spatial
extent or temporal frequency of dispersal. Disregarding traits that are
not functionally significant at the scale of the study is critical for
calculating a value that predicts ecosystem functioning well (Dıaz and
Cabido, 2001).
Information for assigning taxa to functional traits was derived from
taxonomic and natural history texts, body characteristics (e.g., jaw
structures) and personal observations from video and core processing.
While for some general categories (e.g., size and shape, see Table 2),
species were only allocated to one trait (e.g., small or large), for others
species could either exhibit multiple traits (e.g. feeding as a predator
Table 3
R- and p-values of ANOSIM comparisons between the functional composition of the
different habitats at the two locations (NHG, TIMR)
a- NHG
SHM
SBE
ALG
BIO
CS
HSB
MA
MW
0.72
0.001
0.69
0.001
0.54
0.001
0.03
0.246
0.32
0.001
0.10
0.088
0.52
0.001
0.60
0.001
0.31
0.001
-0.03
0.608
0.29
0.001
0.37
0.001
0.11
0.034
0.48
0.001
0.06
0.095
0.78
0.001
0.15
0.008
0.37
0.001
0.18
0.011
0.36
0.001
0.23
0.001
0.81
0.001
0.25
0.029
0.29
0.009
0.12
0.128
0.58
0.001
0.20
0.041
0.08
0.197
SBE
ALG
BIO
CS
HSB
MA
b- TIMR
HSS
SME
0.08
0.091
MC
0.27
0.009
0.04
0.307
SME
RP
0.35
0.003
0.10
0.082
- 0.07
0.731
MC
RP
SBG
0.05
0.045
- 0.01
0.505
0.11
0.018
0.17
0.038
and a scavenger) or not exhibit a trait at all (e.g., sediment
structuring). Where species exhibited multiple traits within a general
category, fuzzy coding (Chevenet et al., 1994) was used to assign
species to traits, with allocation across the general category summing
to 1. Care was also taken that the traits used did not overlap in terms of
functionality. Thus, at first glance some key ecosystem engineers may
seem to be missing, (e.g., epibenthic bivalve beds), however, these are
covered by combinations of traits (e.g., sedentary + surface living +
contains calcium carbonate). Furthermore, we believe that by using
traits that cover important functions we have avoided the pitfall of
being limited by the number of guilds of functional groups present in a
given assemblage (Mouillot et al., 2007).
Frequently (although not in a majority of cases), information for
individual genera or species was not available and information from
other species in the same family were used. Where different species
within a family had been recorded as exhibiting different traits, the
taxon was assigned equal probabilities of a particular trait occurring.
We do not feel that this will have compromised our analyses as
changing a randomly selected 10% of these species to probabilities
Table 4
Numbers of functional traits within each habitat for which the minimum number of
taxa representing the trait was 0 or 1 taxon only at the within-core or within-site scale
Location
NHG
TIMR
Fig. 1. Non-metric multidimensional scaling ordination plots based on Bray-Curtis
similarities calculated from raw data for (a) taxonomic and (b) functional trait
information. Black-filled shapes represent site data from TIMR; other shapes represent
data from NHG.
Habitat
SHM
SBE
ALG
BIO
CS
HSB
MA
MW
HSS
SME
MC
RP
SBG
Within-core
Within-site
0 Taxon
1 Taxon
0 Taxon
1 Taxon
11
17
10
19
11
7
12
8
12
12
7
12
14
5
3
4
7
3
7
1
6
8
8
7
9
4
4
3
1
3
1
2
1
0
1
3
0
1
1
2
0
2
2
3
2
0
4
3
3
4
2
1
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reflecting a dominant behaviour (randomly chosen) did not change
our overall results.
Two datasets were constructed. The first comprised the abundance
of each species multiplied by its fuzzy coding for each trait. The second
dataset was constructed from the first by summing across species to
give a single value for each trait in each sample. The first dataset was
used to calculate taxa richness (as number of taxa) and Pielou's
evenness within each functional trait at a variety of scales (core, site,
habitat and location). The second dataset was the basis for the
comparisons of functional composition between regions and habitats
119
and for the calculations of functional-trait richness and evenness for
each location/habitat combination.
2.3. Statistical analyses
Statistical analyses were based around answering 3 main questions.
1 Are differences in function driven by regional species pools? To
answer this question we tested whether there were differences
in both species composition and functional traits between the
Fig. 2. Taxa evenness and richness calculated across the two locations for each functional trait.
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two locations using one-way ANOSIMs (Clarke and Gorley, 2006)
on Bray-Curtis similarities calculated from raw data. As there
were differences in habitat types occurring at the two locations,
we also tested for differences in species composition and
functional traits between habitat types at each location
separately (again using one-way ANOSIMs). One-way general
linear models were used to test whether there were differences
in functional trait richness or evenness between the two
locations and then between habitat types in the two locations
separately.
2 How does habitat affect function? This utilised the results of the
one-way ANOSIMs conducted across habitat types at each
location on functional traits.
3 Within-function diversity. To look at differences in taxa richness
and evenness within functional traits between locations we
used a t-test that paired the different functional traits. We also
considered whether taxa richness within a functional trait was
affected by total number of individuals in that trait and whether
taxa evenness was affected by either taxa richness or total
number of individuals, by calculating Spearman's rho correlations between these variables at each location separately.
3. Results
3.1. Are differences in function driven by regional species pools?
Out of the 374 taxa observed across locations, only 112 were held in
common. 79 taxa were only found in TIMR and 183 were only found in
NHG. Clear differences in species composition were observed between
the two locations (R = 0.65, p = 0.001) and between most of the habitat
types within the locations (R = 0.725, p b 0.001 and R = 0.391, p = 0.001
for NHG and TIMR respectively) (Fig. 1). However, when the same
analyses were conducted on functional trait composition, differences
between habitats within a location were greater (R = 0.314, p = 0.001 and
R = 0.113, p = 0.008 for NHG and TIMR respectively) than the differences
between the two locations (R = 0.044, p = 0.06) (Fig. 1). What little
difference was observed between locations may be a result of habitat
differences between the two locations (Table 1). No differences in
functional trait richness or evenness were observed between the two
locations (dfmodel = 1, dferror = 234, p N 0.15 for both richness and
evenness), although there were differences between habitats in
functional trait richness at both locations (dfmodel = 4, dferror = 66, p =
0.0405 and dfmodel = 7, dferror = 158, p = 0.0605 at TIMR and NHG
respectively) and in functional trait evenness at NHG (dfmodel = 4, dferror =
66, p = 0.2709 and dfmodel = 7, dferror = 158, p b 0.0001 at TIMR and NHG
respectively).
3.2. How does habitat affect function?
Differences in functional trait composition between habitats were
observed at both locations, although there were more differences at
NHG than at TIMR (Table 3). At NHG, the habitat that was most highly
structured by epifauna (BIO) was less likely to be different from other
habitats, probably due to its higher within-site variability of microhabitats and biodiversity. This also occurred at TIMR for the rubble
habitat (RP), previously demonstrated to have high within-site
variability (Hewitt et al., 2005). Although taxa had previously been
demonstrated to show a high degree of habitat specificity (Thrush
et al., 2006a), all functional traits occurred in most habitats. Thus,
differences between habitats were most usually driven by higher
numbers of taxa and individuals in functional traits, occurring in
certain habitats, rather than absence of functional traits.
However, at the within-core scale, functional traits were frequently
missing, although the number of those missing varied between habitats
(Table 4). This generally reflected within-site heterogeneity as, at the
within-site scale, numbers of missing traits were generally low.
3.3. Within-function diversity
Differences in taxa richness and evenness within functional traits
were observed between locations (Fig. 2). Richness was higher at NHG
overall traits (paired t-test, t = 6.43, p b 0.0001) and evenness was
lower (t = 6.451 p b 0.0001). At TIMR very high evenness (N 0.95) was
observed for attached organisms, predators and scavengers, organisms producing troughs and mounds in the sediment surface and
organisms moving sediment from depth to the surface. At NHG, low
evenness (b 0.65) was observed for organisms moving sediment from
the surface to depth, those producing permanent burrows and semipelagic organisms.
There were also differences between taxa richness and evenness
within the functional traits in different habitats at the two locations. In
both locations, the more mobile, exposed habitats with coarse sands,
mobile epifauna and high species richness (SHM, SBE) were least
likely to show within-trait evenness. Also, traits providing habitat
structure on the sediment surface were most likely to exhibit high
evenness (Table 5). At NHG, shape traits, followed by size, feeding and
mobility traits, were next most likely to exhibit high evenness, while
at TIMR feeding and size traits were next most likely to exhibit high
evenness. In both locations traits related to position in the sediment
and type of bioturbation were least likely to be highly even.
There was no positive relationship (p b 0.05) between evenness
and either richness or total number of individuals found in the
Table 5
Numbers of each functional trait category exhibiting (a) high evenness (N0.95) and (b)
low evenness (b 0.5) for each habitat type in both locations
Types of functions
total
(a) NHG
Sediment moving
Feeding
Motility
Living position
Form
Size
Habitat structure
Sum
5
6
6
0
7
6
15
(a) TIMR
Sediment moving
Feeding
Motility
Living position
Form
Size
Habitat structure
Sum
2
7
5
2
4
6
11
(b) NHG
Sediment moving
Feeding
Motility
Living position
Form
Size
Habitat structure
Sum
1
0
2
1
1
0
3
(b) TIMR
Sediment moving
Feeding
Motility
Living position
Form
Size
Habitat structure
Sum
0
0
0
0
0
0
1
SHM
HSB
BIO
ALG
SBE
CS
MA
MW
1
3
1
2
1
1
1
2
2
2
1
1
2
7
1
1
3
1
1
1
1
4
7
2
2
3
13
1
2
3
11
HSE
1
3
1
1
1
2
4
13
RP
MC
1
2
1
1
2
5
1
2
3
9
SBG
1
2
2
1
1
1
HSB
BIO
ALG
SBG
CS
MA
MW
1
1
0
0
1
1
0
SME
1
2
4
SHM
1
1
3
1
1
1
8
1
1
5
0
0
1
2
0
SME
HSE
RP
MC
SBG
1
1
The values designated as high and low were defined by the distribution of values
observed Total is the sum across the habitats for a category and sum is the sum across all
traits for each habitat.
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J.E. Hewitt et al. / Journal of Experimental Marine Biology and Ecology 366 (2008) 116–122
functional traits, at NHG, and only a weak one between evenness and
total number of individuals (Spearmans rho = 0.31) at TIMR. Positive
relationships between richness and total number of individuals were
found in both locations (Spearmans rho = 0.65 and 0.85 at NHG and
TIMR respectively). The lack of strong increasing relationships
between evenness and either richness or total number of individuals
found in the functional traits, suggests that these differences may be
functionally important.
4. Discussion
Our results demonstrate similar representation of functional traits
across the two locations, suggesting that the regional species pool is
not a major constraint. Differences were, however, observed between
habitats within each location, confirming the importance of habitat
filtering. High evenness within functional traits was apparent (across
both locations and habitats). We also observed variation in ecological
function both at the scale of habitats and within habitats. At the small
scale (cores), not all functional traits were represented and many traits
were represented by a single species, similar to Bremner et al. (2003)'s
observation that the use of biological traits highlighted small-scale
heterogeneity. However, between-habitat differences in functional
traits were driven by differences in organism densities rather than the
presence/absence of individual traits, emphasising the importance of
density shifts in driving the functional attributes of habitats. This
variation across scales in the distribution of functions underscores the
need to scale-up and integrate across seafloor landscapes in order to
accurately assess ecosystem functioning.
The presence of high (or low) evenness is of fundamental
importance to community dynamics. Evenness is often used to
argue for or against the competing theories of species composition
being driven by environmental filters rather than competition (Zobel,
1997), with high evenness suggesting that species have been
environmental filtered and thus share many traits. High evenness
within functions at the relatively coarse-scale description of ecosystem functions used in this and many other studies (Bremner et al.,
2003; Bell, 2007; Mouillot et al., 2007; Schratzberger et al., 2007), also
reflects the potential for the maintenance of function with the loss of
individual species.
Coastal seafloor areas that are not seriously degraded are
recognised for their habitat diversity, which is considered to
contribute to their often high species richness (Gray et al., 1997;
Gray, 2002; Thrush and Dayton, 2002; Thrush et al., 2006b). Our
results, showing differences in the densities of specific functional
traits between habitats, emphasises the potential importance of
habitat variation to ecosystem functioning in coastal soft-sediments.
In particular, we observed higher numbers of organisms that transfer
sediment from the surface to depth in shelly-sand habitats than
muddy habitats. More suspension-feeders were found in complex
biogenic habitats; while simple mud habitats were least likely to have
large organisms but most likely to have organisms producing mounds
on the sediment surface. In soft-sediment systems there is the
potential for a strong feedback loop between certain functions and
habitat, with species influencing sediment topography (e.g., Van
Blaricom, 1982; Graf, 1999) and interacting with hydrodynamics (e.g.,
Green et al., 1998; Ciutat et al., 2007), to influence grain size (Rhoads,
1974), and creating small patches of hard surfaces for other species to
colonise (Gutiérrez et al., 2003). This feedback is likely to be strongly
influenced by three factors that also influence the role of organisms in
contributing to ecosystem function: density, size and spatial arrangement (Thrush and Dayton, 2002 and references therein). In particular,
the existence of this feedback increases the potential for densitydependent effects, and the spatial arrangement of functional traits and
habitats, at a variety of scales to be important.
The existence of different habitats with varying magnitude of
specific functional traits increases functional diversity and suggests
121
that ecosystems have evolved as an interconnected landscape of
habitats (Margalef, 1968). Thus, the functions supported by the traits
we calculated may not only accumulate in importance with increasing
area or habitat diversity but interactions between them may lead to
more effective recycling and processing of energy and matter. Such
phenomena have been proposed as system-scale indicators of
ecosystem health (Rapport et al., 1998). Moreover decreasing diversity
is likely to decouple interactions between functional groups (Thrush
et al., 2008) with fewer species available to contribute to a number of
functions. This is likely to decrease the potential for interactions
between functions (e.g. facilitation between grazers and bioturbators)
thereby decreasing ecosystem functionality. While integrated acrosshabitat field measures of fluxes and other ecosystems functions are
needed to truly test the validity of such indicators, our analysis
suggests that habitat variability and variation in the functional traits of
macrofauna may well be a useful surrogate for more complicated
measures of ecosystem health.
Our findings also suggest that habitat heterogeneity will contribute in a non-random way to the implications of species loss for
ecosystem functioning, especially in soft-sediments where much of
the habitat structure is biogenic (Gray, 2002; Thrush and Dayton,
2002). The non-random nature of this contribution will be increased
by the non-random nature of many impacts, which often focus on
specific habitats or functional groups (e.g., trawling removing all
habitat-structuring epifauna, or fishing focussing on large predators).
Although relatively few studies have evaluated the functional
consequences of realistic non-random changes in biodiversity,
Bracken et al. (2008) suggest this can markedly alter conclusions
and size of effects predicted. As the consistent conclusion of many
studies of species loss due to anthropogenic activities is that coherent
changes in species composition are observed as key species are
removed or habitats altered (e.g., Thrush and Dayton, 2002; Altieri and
Witman, 2006), the focus by theoretical studies on random removal of
species becomes problematic. Moreover, our demonstration that
habitat variation is a driver of functional composition and diversity
also argues for a need to explicitly include habitat heterogeneity
within studies trying to predict the effect of species loss on ecosystem
function.
Acknowledgements
The datasets were collected under contract to the New Zealand
Foundation for Research Science and Technology. Early work identifying potential habitats in the Tonga Island Marine Reserve was funded
by the New Zealand Department of Conservation. Collaboration with
John Gray fostered our interest in the role of habitat variation in
affecting diversity. Julie Bremner and an unknown reviewer helped
clarify and focus the manuscript. [SS]
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