ICES Journal of
Marine Science
ICES Journal of Marine Science (2015), 72(8), 2385– 2397. doi:10.1093/icesjms/fsv094
Original Article
Effects of reef attributes on fish assemblage similarity
between artificial and natural reefs
Jennifer E. Granneman‡* and Mark A. Steele
Department of Biology, California State University, 18111 Nordhoff St, Northridge, CA 91330-8303, USA
*Corresponding author: tel: + 1 727 452 0127; fax: + 1 727 553 1189; e-mail: [email protected]
Present address: College of Marine Science, University of South Florida, 140 7th Ave. South, St. Petersburg, FL 33701, USA.
‡
Granneman, J. E., and Steele, M. A. Effects of reef attributes on fish assemblage similarity between artificial and natural reefs. – ICES
Journal of Marine Science, 72: 2385 – 2397.
Received 23 November 2014; revised 14 April 2015; accepted 28 April 2015; advance access publication 23 May 2015.
Artificial reefs are used to enhance populations of marine organisms, but relatively few studies have quantitatively evaluated which attributes of reef
structure are most critical in determining whether assemblages of organisms on artificial reefs are similar to those on natural reefs. Using five pairs of
artificial and natural reefs that spanned 225 km in the Southern California Bight, we evaluated how well fish assemblages on artificial reefs mimicked
those on natural reefs and which attributes of reefs best predicted assemblage structure. Along underwater visual transects, we quantified fish
species richness, density, and size structure, as well as substrate structure (rugosity and cover of substrate types), giant kelp density, and invertebrate
density. Artificial reefs that were more similar in physical structure to natural reefs (low relief, low rugosity, and composed of small- to medium -sized
boulders) supported fish assemblages that were similar to those on natural reefs. Fish species richness was not significantly different between artificial and natural reefs, but density and biomass tended to be higher on average on artificial reefs, body size was slightly smaller, and assemblage
structure differed between the two reef types. Generally, artificial reefs extended higher off the seabed, were made of larger boulders, had higher
rugosity, harboured more invertebrates, and supported less giant kelp. At both the within-reef (transect) and whole-reef scales, fish density and
biomass were positively correlated with complex substrate structure, positively correlated with invertebrate density, and negatively correlated
with giant kelp abundance, which was sparse or absent on most artificial reefs. Our results indicate that artificial reefs can support fish assemblages
that are similar to those found on natural reefs if they are constructed to match the physical characteristics of natural reefs, or they can be made to
exceed natural reefs in some regards at the expense of other biological attributes.
Keywords: artificial reefs, assemblage, habitat, physical structure, reef fish.
Introduction
Artificial reefs are increasingly used to supplement existing natural
reefs and mitigate damage to natural reefs (Hueckel et al., 1989;
Seaman et al., 1989; Ambrose, 1994). For these purposes, it would
be valuable to know whether artificial reefs can be made to mimic
natural reefs in terms of the assemblages of marine organisms they
support. To this end, it is essential to know which habitat features
of natural reefs must be replicated on artificial reefs. While many
studies have evaluated whether populations or assemblages of
marine organisms on artificial reefs are similar to those on natural
reefs (e.g. Ambrose and Swarbrick, 1989; Carr and Hixon, 1997;
Clark and Edwards, 1999; Burt et al., 2009; Walker and Schlacher,
2014), few have quantitatively explored which habitat features of
# International
reefs cause similarities or differences in the assemblages of marine
organisms on artificial vs. natural reefs.
It is commonly noted that populations of fish, the focus
of our study, tend to be more dense on artificial reefs than on
natural reefs (Chandler et al., 1985; Jessee et al., 1985; Ambrose
and Swarbrick, 1989; Bohnsack, 1989; Caley and St John, 1996;
Sherman et al., 2002), but why this occurs is not clear because
there are several co-varying factors that might cause this difference.
Potentially important habitat differences between artificial and
natural reefs that could account for this difference in fish density
include the materials used to build the reef (e.g. rocks, concrete,
sunken boats, etc.), substrate complexity, vertical relief, size, and
spatial configuration. For example, Brock and Norris (1989)
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2386
found that reefs built of concrete blocks supported greater species
richness and biomass of fish than did natural reefs or artificial
reefs built of surplus concrete pipes or used tires. The higher densities of fish on artificial reefs are often attributed to greater habitat
complexity than on natural habitats (e.g. Jessee et al., 1985), and
there is a sound basis for this supposition given that habitat
complexity has been shown to affect densities of fish on tiny artificial
reefs (e.g. Shulman, 1984). However, artificial reefs differ from
natural reefs in additional ways. Given that they are usually
smaller, artificial reefs generally have a higher ratio of reef perimeter
to reef surface area compared with natural reefs. This difference
has also been suggested to contribute to the higher density of fish
generally found on artificial reefs (Jessee et al., 1985; Belmaker
et al., 2005) by attracting fish from a proportionately larger area
than would large natural reefs which have low edge to area ratios
(DeMartini et al., 1989), and by providing preferred habitat for
ecotone specialists (Topping et al., 2005; Bellquist et al., 2008).
Moreover, artificial reefs often have higher vertical relief, which
may enhance larval settlement of some species (Rilov and
Benayahu, 2000). Species that establish themselves on artificial
reefs (or fail to) may also impact the rest of the community that
develops. For example, studies off the coast of California suggest
that higher abundances of giant kelp on natural reefs relative to artificial reefs may be one of the primary causes of differences in fish
assemblages between artificial and natural reefs (Ebeling et al.,
1980; Larson and DeMartini, 1984; Ebeling and Laur, 1985;
Danner et al., 1994).
Our ability to evaluate which reef attributes are key to establishing communities on artificial reefs that mimic those on natural
reefs is hampered by the lack of spatially replicated comparisons
of large artificial reefs with large natural reefs. Here we use that
approach with five large artificial reefs (900 –700 000 m2) that
were each paired with a nearby, large, natural reef for comparison.
We evaluated which artificial reefs harboured fish assemblages
that were most similar to those on natural reefs, and we quantitatively explored which habitat features were the best predictors of the fish
assemblage and how these differed between artificial and natural
reefs. On these large reefs in the Southern California Bight, we quantified fish density, size structure, and species diversity, as well as
physical and biological attributes of the habitat: reef height, rugosity,
substrate composition, area, and invertebrate and giant kelp density.
Using these data, we answered two questions: (i) How do fish assemblages differ between artificial and natural reefs? (ii) Which habitat
attributes correlate with or best predict fish assemblage structure?
Methods
Study sites and survey methods
We studied 10 reefs that spanned 225 km of coastline within the
Southern California Bight (Figure 1). Five artificial reefs and the
natural reefs closest to them were sampled between June and
August 2009. The artificial reefs studied were Topanga, Wheeler
J. North, Pendleton, Torrey Pines 2, and Pacific Beach. These artificial reefs were chosen because they were the only reefs in the
Southern California Bight that were relatively large and resembled
natural reefs in physical structure (e.g. not emergent breakwaters).
Additionally, these artificial reefs were relatively close to natural
reefs (,7.5 km apart; median ¼ 4.35 km), and thus were likely to
experience similar oceanic conditions, such as temperature, surge,
and turbidity. The five artificial reefs studied were constructed
to mitigate impacts to natural reef habitat or provide new fishing
J. E. Granneman and M. A. Steele
opportunities. Paired reefs were at similar depths, with an average
difference of only 0.5 m depth (maximum difference ¼ 2.3 m;
Table 1); the average depth of reef pairs ranged from 8 to 16 m.
The artificial reefs spanned a broad range of sizes (three orders of
magnitude difference), ages, and configurations (Table 1), but
they were all composed of similar materials, primarily of quarry
rock. They were all built as separate piles of quarry rock or concrete
(concrete only in certain areas on the Wheeler J. North reef in the
San Clemente region), hereafter referred to as reef modules,
whereas the natural reefs surveyed were single, contiguous reefs
without distinct, isolated patches of reef habitat.
At each reef, the fish assemblage, invertebrates, giant kelp, and
substrate composition were surveyed along band transects by two
scuba divers using standard methods (Stephens et al., 2006). On
both reef types, only rocky reef habitat was sampled, i.e. large
sandy patches between portions of rocky reef were not sampled.
Each natural reef was divided into 10 blocks of approximately
equal size, and 5– 6 of these blocks were randomly chosen to be
sampled. Divers sampled these blocks by haphazardly placing two
30-m long transects within the block. The two transects within
each block were separated by at least 5 m.
Because the artificial reefs sampled were made up of 1 –74
spatially discrete modules of various sizes rather than contiguous
reef habitat, these reefs were surveyed in a slightly different
manner. One to 12 modules were sampled on each artificial reef,
with a total of 4 –12 transects per reef. The Torrey Pines artificial
reef consisted of a single relatively large reef module and it was
sampled with five transects. The Pacific Beach artificial reef
consisted of 24 small modules arranged in 12 pairs; six of these
pairs of closely spaced modules were randomly selected and a
single transect was done on each module in the pair. On the
other three artificial reefs, 2 –5 modules were each sampled with
two transects per module. All transects were at least 5 m from the
next nearest transect. Transect length was 30 m when module
dimensions allowed, but on some smaller modules transect length
was reduced so that the entire transect was on the reef. The
average transect length on artificial reefs was 24.4 m and the
minimum was 12.5 m.
Along the transects, reef fish were counted within two 2 × 2 m
windows, one along the benthos (0–2 m above the bottom) and
the other above it in water column 3 –5 m above the bottom.
Thus, we sampled fish in approximately the bottom one-third to
half of the water column, depending on the depth of the site. We
did not sample in the upper portion of the water column for
reasons of logistics and safety (we wished to minimize any possibility
of a boat colliding with a diver). Thus, our study best estimates the
fish assemblage found within 5 m of the bottom.
The size of each fish encountered on transects was estimated by
eye, a procedure shown to produce reasonably accurate estimates
of fish size (e.g. within 1 –2 cm and 5% of actual length: Bellwood
and Alcala, 1988; Wormald and Steele, 2008). The same two
divers completed all transects in the study. One diver counted and
estimated the sizes of all bottom-dwelling (benthic) fish species
and the other diver surveyed only fish species that reside in the
water column. While counting and sizing fish, transects were transited only once to avoid double counting individuals. Divers were
trained to estimate fish size by estimating the size of falling pieces
of PVC pipe of known length in a pool, and such training has
been shown to be effective (Bell et al., 1985). The size of fish
≤10 cm in total length was estimated to the nearest cm, whereas
the size of fish .10 cm was estimated to the nearest 5 cm. Reefs
2387
Fish assemblage similarity between artificial and natural reefs
Figure 1. Locations of the five artificial and natural reef pairs in the Southern California Bight.
Table 1. Characteristics of reefs sampled in this study, including date established (Est.), mean reef depth (seabed), height above seabed, number
of modules (Mod #), module (Mod) length (L), width (W), spacing (S), total reef area, and distance between paired artificial and natural reefs.
Region
Artificial reefs
Topanga
San Clemente
Pendleton
Torrey Pines
Pacific Beach
Natural reefs
Topanga
San Clemente
Pendleton
Torrey Pines
Pacific Beach
Est.a
Depth (m)
Height (m)
Mod #
Mod L (m)
Mod W (m)
Mod Sb (m)
1987
1999
1980
1975
1987
9.0
14.7
13.9
14.1
16.2
1.6
,1.0
4.9
2.6
3.3
3
74
8
1
24
120.0
46.1
24.4
65.0
19.9
51.4
40.4
21.6
18.1
15.0
152
1– 100
14
NA
168
Depth (m)
Height (m)
6.7
14.8
14.0
14.2
15.5
,1.0
,1.0
,1.0
,1.0
,1.0
Distance to artificial reef (km)
4.35
0.32
4.35
7.40
1.61
Areac (m2)
9682
704 153
3260
924
5591
Areac (m2)
87 676
3 512 187
3 710 674
1 421 749
11 333 326
a
Reef establishment dates and module numbers are from Lewis and McKee (1989) and Elwany et al. (2011).
Spacing between modules for all reefs except Wheeler J. North reef (San Clemente artificial reef) was estimated from images with scales in Lewis and McKee
(1989). Module spacing for Wheeler J. North reef was from S. Schroeter and D. Reed (pers. comm.).
c
Artificial reef area is the sum of all modules on a reef; module area was calculated using an equation for an ellipse and substituting the measured values of
length and width; for unmeasured modules, means of measured modules were used. Natural reef area was calculated by determining the perimeter of the reef
using a Garmin GPSmap 60CS. The scaled map of the reef generated with the Garmin Blue Chart Cartography Software was analysed using ImageJ version 1.43
(Wayne Rasband, [email protected], NIH, Bethesda, MD, USA) to determine total reef area.
b
were sampled only when underwater visibility was .3 m. Divers did
not turn over rocks or use ichthyocides or anaesthetics to discover
fish that were hiding within the reef, so our estimates of density
and biomass are likely biased against small species that reside
within the reef framework, which can contribute substantially to
species richness and density, but little to biomass (Allen et al., 1992).
2388
Physical and biological measurements of reef characteristics were
made along the same benthic transects laid out for the fish surveys,
after the fish were counted. Rugosity was measured every 10 m along
each transect by draping a 4-m-long chain directly on the substrate,
parallel with the transect line (Risk, 1972; Luckhurst and Luckhurst,
1978). Rugosity was calculated as one minus the ratio of the
draped chain to the stretched chain length and separate measurements on each transect were averaged. Substrate type (sand;
cobble [,10 cm]; small [10 –30 cm], medium [30– 75 cm], and
large boulder [.75 cm]) was recorded every meter. Giant kelp
(Macrocystis pyrifera) density was measured as the number of
stipes within a 2-m-wide band along each transect. Stipe density is
relatively easy to quantify and is a good predictor of kelp canopy
cover (Carr, 1994) and kelp biomass (Reed et al., 2009). Reef
depth (seabed) and reef height were measured using dive computers. Reef height above the seabed was measured at the apex of the
reef. To estimate the density of reef-associated invertebrates that
could be prey for fish, invertebrates in major taxonomic categories
(barnacles, sea cucumbers, gorgonians, mussels, sea snails, sea
stars, urchins, worms, crabs, nudibranchs, limpets, lobsters, and
sea hares) were identified and counted in situ within 0.25-m2 quadrats placed every 2 m along each benthic transect.
Statistical analysis
Fish densities and biomass were calculated by summing the benthic
and water column portions of each transect and expressing these
values per m3. Biomass was calculated by summing the predicted
weights of all fish on the transect, calculated from equations relating
total length to weight, which were taken either from the literature or
from data on fish collected in a related project (Granneman and
Steele, 2014). Although this procedure for estimating biomass
undoubtedly introduces some error, it is unlikely to systematically
bias the comparison between artificial and natural reefs, and it is
commonly used (e.g. Ambrose and Swarbrick, 1989; Bohnsack
et al., 1994; Sandin et al., 2008; Santos et al., 2011). Densities of
giant kelp and invertebrates were expressed as the number per m2.
For fish density, fish biomass (g m23), reef depth, rugosity,
giant kelp density, and invertebrate density, we used mixed-model,
nested analysis of variance (ANOVA) to test for differences between
artificial and natural reef types (hereafter referred to as “reef types”;
a fixed factor) and among the five regions (Pacific Beach, Pendleton,
San Clemente, Topanga, and Torrey Pines; treated as a random
factor). We were most interested in the reef type × region interaction because we wished to know if certain reef pairs contained artificial reefs with fish assemblages that were more similar to those on
natural reefs, and if so, evaluate why, based on the characteristics of
the reefs. Because two or more transects were sampled within each
block (or module), we included block (as a random factor) that
was nested within the reef type × region interaction to account
for the lack of independence between transects in proximity. Data
were transformed as necessary to satisfy the assumptions of normality and homoscedasticity. SYSTAT 13 was used for all parametric
statistical tests.
The multivariate fish assemblage structure was compared
between artificial and natural reefs with PERMANOVA using the
same statistical model just described for ANOVA (i.e. including
the factors reef type, region, and block, and all possible interactions).
We visually summarized the multivariate fish assemblage structure
using non-metric multidimensional scaling (nMDS) on the same
resemblance matrix to help interpret the results of PERMANOVA.
After a significant reef type × region interaction was detected,
J. E. Granneman and M. A. Steele
pairwise comparisons were used to determine in which reef pairs assemblage structure differed. As in the preceding ANOVAs, replicates
in this analysis were transects. Densities of each species of fish were
log(x + 1) transformed. Bray –Curtis dissimilarity was used for the
resemblance matrix. After significant reef type × region interactions were found, univariate PERMANOVA on each species of
fish, using the same statistical model, was used to elucidate which
species in the assemblage were driving these effects. PRIMER v6
with the PERMANOVA+ add on was used for these tests and all
other multivariate tests (described below).
We used a principal components analysis (PCA) to provide a
multivariate summary of reef substrate. Data were the per cent
cover of the five substrate types (sand; cobble; small, medium, and
large boulder), which were arcsine-square-root transformed and
normalized (mean subtracted and divided by the standard deviation). The principal components (PCs) derived from this analysis
were then used in ANOVA (the same model as previously described,
with factors reef type, regions, and blocks; and benthic transects as
replicates), to test whether the substrate composition differed
between artificial and natural reefs. The derived PCs were also
used in predictive models of fish assemblage structure described
below.
Fish species richness on the two reef types was compared by constructing rarefaction curves of species richness for a given number of
sampled individuals on each reef type, pooling across reefs.
Rarefying the number of species by the number of individuals
sampled avoided potential problems with different areas (transect
lengths) sampled on some artificial reefs. Because large numbers
of individuals were sampled on both reef types, both rarefaction
curves reached asymptotes. Rarefaction curves were constructed
using EstimateS (Colwell, 2006). Additionally, the size structure of
all fish species observed was compared between the artificial and
natural reefs, pooling across replicate reefs, using a two-sample
Kolmogorov–Smirnov test.
To evaluate which reef characteristics were the best predictors of
fish density and biomass at the scale of transects, we used ordinary
least-squares best subsets regression. Data for the artificial and
natural reefs were combined for these analyses. The regression
analyses used four physical and two biological variables as predictors: transect depth, rugosity, substrate PC1, substrate PC2, giant
kelp density, and invertebrate density. Fish density, fish biomass
(g m23), giant kelp density, and invertebrate density were all logtransformed. Multicollinearity among the predictor variables was
within an acceptable range (variance inflation factor ,10 always;
Quinn and Keough, 2002). We used AICc selection criteria and r 2
to determine which predictor variables best explained fish density
and biomass.
To quantify how the multivariate fish assemblage was related
to the physical and biological attributes of reefs at the scale of transects, we used the multivariate analogue of the linear regressions just
described, distance-based linear models using the DistLM procedure (McArdle and Anderson, 2001). This procedure evaluates
which predictor variables significantly explain variation in a multivariate data cloud; in this case, the densities of each fish species
encountered. With these models, we also used best subsets procedures, AICc selection criteria, and r 2 to determine which predictor
variables were most important. Bray–Curtis dissimilarity was used
for the resemblance matrix after log(x + 1) transforming fish
density.
At the scale of entire reefs (rather than transects), we used
Pearson correlation to evaluate which physical and biological
2389
Fish assemblage similarity between artificial and natural reefs
attributes were correlated with fish density and biomass. Multiple
regression was inappropriate for these analyses because of the
small number of replicates (n ¼ 10 reefs) relative to the large
number of highly multicollinear predictor variables (maximum
variance inflation factor ¼ 177). Seven predictor variables were
evaluated: the six used in multiple regression at the transect scale,
as well as reef size (area).
Results
Fish assemblage
Pooled across reefs, fish species richness was similar on artificial and
natural reefs. A total of 36 species of fish was observed on all the reefs
surveyed, with 33 species observed on natural reefs and 32 on artificial reefs (Table 2). Rarefaction curves showed that there was no
meaningful difference in fish species richness between reef types
(Figure 2). The size structure of fish, which was also pooled across
reefs, however did differ between artificial and natural reefs
(Kolmogorov–Smirnov test, p , 0.001; Figure 3). More small fish
(≤30 cm) were present on artificial reefs, and more large fish were
present on natural reefs (.30 cm).
Fish density differed between some artificial reef-natural reef pairs,
but was similar on others (reef type × region: F4,43.2 ¼ 7.4, p ,
0.001; Figure 4a). Density on artificial reefs was similar to that on
natural reefs in the San Clemente and Topanga regions, whereas it
was much higher on artificial reefs than on natural reefs in the
Pacific Beach, Pendleton, and Torrey Pines regions (Figure 4a). Fish
biomass (g m23) showed the same general pattern, with the smallest
differences between the two reef types in the San Clemente and
Topanga regions (reef type × region: F4,43.1 ¼ 8.9, p , 0.001,
Figure 4b). These same general patterns were evident when the analyses were restricted either to only species typically found within
5 m of the bottom (the zone we sampled; reef type × region:
density: F4,42.6 ¼ 6.0, p , 0.001; biomass: F4,42.9 ¼ 9.3, p , 0.001;
Table 3), or when the analyses included only species that inhabit
the entire or upper water column (reef type × region: density:
F4,43.0 ¼ 5.9, p , 0.001; biomass: F4,42.9 ¼ 2.6, p ¼ 0.05). Across
reef types, water column species accounted for 73% of the density
but only 47% of the biomass, because two small-bodied, water
column species (Chromis punctipinnis and Oxyjulis californica) were
the most abundant in our transects (Table 2). The percentage contribution of the two species groups (water column and demersal/
benthic) to density and biomass was fairly similar on artificial and
natural reefs, with slightly smaller contributions of water column
species on artificial reefs (water column : benthic/demersal: 70 : 30
and 77 : 23 for density on artificial and natural reefs, respectively;
43 : 57 and 56 : 44 for biomass, respectively; Table 3).
Like density and biomass, the multivariate fish assemblage structure differed between some artificial–natural reef pairs, but not all
of them (reef type × region: pseudo-F4,43.1 ¼ 3.0, p ¼ 0.001; multivariate differences are summarized graphically with nMDS in
Figure 5). Fish assemblages did not differ significantly between reef
types in the San Clemente region (t8 ¼ 1.10, p ¼ 0.27), but did in
the other four pairs (Pacific Beach: t10 ¼ 2.31, p ¼ 0.005; Pendleton:
t9 ¼ 3.14, p ¼ 0.003; Topanga: t6 ¼ 1.96, p ¼ 0.043; Torrey Pines:
t9.5 ¼ 2.87, p ¼ 0.004). Averaged across reefs, the multivariate fish assemblage structure differed significantly between artificial and natural
reefs (PERMANOVA: reef type: pseudo-F1,4.16 ¼ 3.3, p ¼ 0.006).
Species that differed in density between artificial and natural reefs,
or showed reef type × region interactions, were predominantly
benthic and demersal species, as well as a few water column species.
Two species were consistently and significantly more abundant on
artificial reefs than on natural reefs (Rhinogobiops nicholsii and
Semicossyphus pulcher; Table 2). A third species, Scorpaena guttata,
shared this statistically significant pattern of abundance when averaged across reefs, but the difference was inconsistent across reef
pairs (i.e. there was a significant reef type × region interaction).
Several other species (e.g. C. punctipinnis, Embiotoca jacksoni,
Hypsypops rubicundus, Oxylebius pictus, and Sebastes auriculatus)
tended to be more abundant on artificial reefs than natural reefs,
but this pattern was not consistent among all five reef pairs (as indicated by significant reef type × region interactions; Table 2). No
species was significantly more abundant on natural reefs than on artificial reefs.
Reef attributes
Measurements of key physical and biological attributes of the reefs
revealed patterns of differences between specific reef pairs similar
to those noted for fish (Figure 6). Although reef rugosity was on
average 3× greater on artificial reefs than on natural reefs and
higher on all artificial reefs than their paired natural reefs (F1,4.1 ¼
42.5, p ¼ 0.003), the magnitude of this difference was smallest for
the pairs in the San Clemente and Topanga regions (reef type ×
region: F4,43.0 ¼ 3.3, p ¼ 0.02). Natural reefs were significantly
larger than artificial reefs (1.92 vs. 0.15 km2, respectively; paired
t-test: t ¼2 3.68, d.f. ¼ 4, P ¼ 0.02), but again, this difference
was smallest in the San Clemente and Topanga regions. Artificial
reefs were taller than natural reefs, averaging 3.5 m vertical relief,
whereas natural reefs appeared to be generally ,0.5 m high
(Table 1), but their exact height was not measured because this
relief was too small to measure with a dive computer. The artificial
reefs in the San Clemente and Topanga regions were more similar in
height to natural reefs than the other three artificial reefs. Reef depth
was similar between the two reef types (F1,4.3 ¼ 1.0, p ¼ 0.38), a
pattern consistent among regions (reef type × region: F4,42.1 ¼
0.6, p ¼ 0.68).
Giant kelp was on average 3× more dense on natural reefs than
on artificial reefs, but this difference was not quite statistically different (F1,4.03 ¼ 6.8, p ¼ 0.059) because there was a notable exception
to this pattern: the artificial reef in the San Clemente region had
slightly greater kelp density than the nearby natural reef (reef
type × region: F4,42.7 ¼ 9.0, p , , 0.001). On average, artificial
reefs had higher densities of invertebrates than did natural reefs
(F1,4.04 ¼ 18.9, p ¼ 0.01), but again, the artificial reef in the San
Clemente region was very similar to the paired natural reef (reef
type × region: F4,42.2 ¼ 6.7, p , 0.001). Anemones, worms, and
gorgonians were the three most abundant taxa of invertebrates on
the reefs. On average, anemones and gorgonians were much more
abundant on artificial reefs than on natural reefs (11- and
9-fold more dense, respectively: mean + SE: 61.8 + 11.1 vs. 5.4 +
0.9 and 18.8 + 2.0 vs. 2.0 + 0.5 m22), but the magnitude of this
difference was inconsistent among regions (reef type × region:
F4,42.7 ¼ 23.9, p , 0.001; F4,41.3 ¼ 3.3, p ¼ 0.02). There were large
differences in anemone density in the Torrey Pines and Pacific
Beach regions, but not in the other three regions. Gorgonians
were more dense on artificial reefs in all five regions, but less so in
the San Clemente and Pacific Beach regions. Densities of worms
were generally similar between the two reef types (13.3 + 1.9 vs.
16.6 + 2.1 m22 on artificial and natural reefs, respectively; F1,4.01 ¼
0.4, p ¼ 0.56), but this apparent similarity belied differences among
reef pairs (reef type × region: F4,41.9 ¼ 2.7, p ¼ 0.04). Worm densities were approximately twice as high on artificial vs. natural reefs
2390
J. E. Granneman and M. A. Steele
Table 2. Fish species encountered on artificial and natural reefs, their habitat association, their density (fish/100 m3; mean + 1 SE), and results
of univariate PERMANOVA testing for differences in density for each species between reef types and testing the reef type × region interaction.
Fish species
Alloclinus holderi
Anisotremus davidsoni
Brachyistius frenatus
Chromis punctipinnis
Cheilotrema saturnum
Embiotoca jacksoni
Girella nigricans
Gymnothorax mordax
Halichoeres semicinctus
Hermosilla azurea
Heterostichus rostratus
Hypsurus caryi
Hypsypops rubicundus
Lythrypnus dalli
Medialuna californiensis
Ophiodon elongatus
Oxyjulis californica
Oxylebius pictus
Paralichthys californicus
Paralabrax clathratus
Paralabrax nebulifer
Phanerodon furcatus
Rathbunella hypoplecta
Rhinogobiops nicholsii
Rhacochilus toxotes
Rhacochilus vacca
Scorpaena guttata
Sebastes atrovirens
Sebastes auriculatus
Sebastes carnatus
Sebastes nebulosus
Sebastes serriceps
Semicossyphus pulcher
Sphyraena argentea
Stereolepis gigas
Triakis semifasciata
Common name
Island kelpfish
Sargo
Kelp perch
Blacksmith
Black croaker
Black perch
Opaleye
California moray
Rock wrasse
Zebra perch
Giant kelpfish
Rainbow seaperch
Garibaldi
Bluebanded goby
Halfmoon
Lingcod
Senorita
Painted greenling
California halibut
Kelp bass
Barred sand bass
White seaperch
Stripedfin ronquil
Blackeye goby
Rubberlip seaperch
Pile perch
California scorpionfish
Kelp rockfish
Brown rockfish
Gopher rockfish
China rockfish
Treefish
California sheephead
Pacific barracuda
Giant sea bass
Leopard shark
Habitat
Benthic
WC
WC, can.
WC
Demersal
Demersal
WC
Benthic
Demersal
WC
WC
Demersal
Demersal
Benthic
WC
Benthic
WC
Benthic
Benthic
WC
Demersal
WC
Benthic
Benthic
Demersal
Demersal
Benthic
Demersal
Benthic
Benthic
Benthic
Benthic
Demersal
WC
WC
Demersal
Artificial reef density
0.03 + 0.02
0.07 + 0.05
0.10 + 0.10
32.11 + 14.92
0.59 + 0.59
2.89 + 0.70
0.19 + 0.07
0.06 + 0.06
0.49 + 0.22
0.02 + 0.02
0+0
0.07 + 0.03
2.36 + 1.11
0.12 + 0.07
0.29 + 0.18
0.06 + 0.06
7.40 + 2.80
1.06 + 0.42
0.01 + 0.01
4.19 + 1.91
1.65 + 0.61
0.43 + 0.38
0+0
1.74 + 0.64
0.11 + 0.05
0.09 + 0.04
1.15 + 0.49
0.37 + 0.35
1.74 + 0.95
0.18 + 0.17
0.08 + 0.04
0.31 + 0.14
2.48 + 0.86
0+0
0+0
0+0
Natural reef density
0.06 + 0.04
0.09 + 0.07
0.53 + 0.36
3.85 + 1.72
0+0
1.18 + 0.18
0.18 + 0.11
0.01 + 0.01
0.26 + 0.12
0+0
0.48 + 0.43
0.17 + 0.10
0.20 + 0.07
0.06 + 0.06
0.01 + 0.01
0+0
10.89 + 4.20
0.15 + 0.07
0+0
3.82 + 1.25
1.42 + 0.44
0.63 + 0.32
0.01 + 0.01
0.63 + 0.27
0.02 + 0.02
0.23 + 0.07
0.22 + 0.13
0.37 + 0.16
0.17 + 0.06
0.08 + 0.05
0.05 + 0.03
0.04 + 0.02
1.04 + 0.38
0.16 + 0.16
0.02 + 0.01
0.01 + 0.01
P: reef type
0.721
0.684
0.220
0.067
0.454
0.066
0.561
0.333
0.264
0.534
0.091
0.439
0.085
0.630
0.181
0.528
0.253
0.059
0.538
0.930
0.836
0.774
0.549
0.045
0.187
0.276
0.050
0.809
0.060
0.607
0.917
0.129
0.010
0.360
0.199
0.360
P: reef type 3 region
0.550
0.899
0.586
0.003
0.044
0.038
0.174
0.068
0.128
0.529
0.621
0.153
0.001
0.223
0.042
0.597
0.685
0.076
0.572
0.001
0.001
0.024
0.601
0.826
0.248
0.050
0.012
0.048
0.005
0.036
0.337
0.220
0.244
0.390
0.713
0.618
Habitat refers to typical position in water column and is based on Larson and DeMartini (1984), Stephens et al. (2006), and personal observations.
WC, throughout the water column; can, kelp canopy (in the upper water column); Demersal, typically in the water column but near the bottom (typically within
5 m of the bottom); Benthic, typically resting on the bottom. Statistically significant values in bold.
Figure 2. Rarefaction curves (+95% CI) comparing species diversity
of fish observed on artificial and natural reefs.
Figure 3. Size distributions of fish observed on artificial and natural
reefs. Samples were pooled across all reefs within a type (artificial or
natural).
2391
Fish assemblage similarity between artificial and natural reefs
Predictors of fish density, biomass, and assemblage
structure
Figure 4. Density and biomass of fish on artificial and natural reefs in
five regions in the Southern California Bight. Values shown are
means + 1 SE. Sample sizes: n ¼ 12, 12, 12, 4, and 5 transects per reef for
the five artificial reefs in the Pacific Beach, Pendleton, San Clemente,
Topanga, and Torrey Pines regions, respectively; and n ¼ 12, 10, 10, 12,
and 10 for the five natural reefs in the corresponding regions.
in the San Clemente and Topanga regions, but half as high on artificial
reefs in the Pacific Beach, Pendleton, and Torrey Pines regions.
On average, artificial reefs were composed of larger rocks than
were natural reefs, but the San Clemente and Topanga artificial
reefs were more similar in substrate composition to natural reefs
than the other three artificial reefs (Figure 6). Two principal components summarized 84% (59 and 25%, respectively) of the total variation in substrate type among sites, and so these two PCs were used
to compare substrate composition between the reefs. The PC 1 axis
was strongly positively correlated with large boulders and negatively
correlated with sand, cobble, and small boulders (Table 4). PC 2 was
strongly negatively correlated with medium boulders and positively
correlated with sand. PC 1 was greater on average on artificial reefs
than on natural reefs (F1,4.5 ¼ 39.9, p ¼ 0.003), but the magnitude
of this difference varied among reef pairs (reef type × region:
F4,43.5 ¼ 5.9, P , 0.001; Figure 3). The PC 1 scores for the San
Clemente and Topanga artificial reefs were much lower than those
of the other three artificial reefs and, thus, they were more similar
to those on the natural reefs. Four of five reef pairs were fairly
similar in PC 2 scores, though the fifth pair, Torrey Pines, was
notably different (reef type × region: F4,43.6 ¼ 5.2, p ¼ 0.002;
Figure 6). Averaged across reefs, PC 2 did not differ systematically
between artificial and natural reefs (F1,4.1 ¼ 1.2, p ¼ 0.34).
Substrate composition, as summarized by PC1, was the best predictor of fish density, biomass, and multivariate assemblage structure at
the scale of transects (n ¼ 97). The best multiple regression model
for predicting fish density included only two variables, PC1 and
giant kelp density (AICc ¼ 219.1; r 2 ¼ 0.42). Fish density was positively related to PC1 (standardized slope ¼ 0.50) and negatively
related to giant kelp density (standardized slope ¼ 2 0.20). This
model, however, was only slightly better than a one-predictor
model with only PC1 (AICc ¼ 221.7; r 2 ¼ 0.39). The best model
for predicting fish biomass included PC1, PC2, and invertebrate
density (AICc ¼ 217.9; r 2 ¼ 0.49), but that model was only trivially
better than a model that only included PC1 and PC2 (AICc ¼ 217.9;
r 2 ¼ 0.48). Both of these models were improvements over the model
with only PC1, but PC1 accounted for most of the explained variation (AICc ¼ 223.4; r 2 ¼ 0.43).
Although PC1 was the best predictor of fish density and biomass
at the transect scale, other variables explained statistically significant amounts of the variation in fish density in single-predictor
models. That they were less important in the multiple regression
models just discussed reveals that these predictor variables were
largely redundant. In simple linear regression models, fish
density was positively associated with rugosity and invertebrate
density (r 2 ¼ 0.25, p , , 0.001; r 2 ¼ 0.27, p , , 0.001, respectively), negatively associated with giant kelp density (r 2 ¼ 0.25,
p , , 0.001), and not significantly associated with depth or PC2
(r 2 ¼ 0.003, p ¼ 0.57; r 2 ¼ 0.003, p ¼ 0.59, respectively). Results
were similar for fish biomass, except that depth explained a small
but statistically significant amount of the variation in biomass
(rugosity: r 2 ¼ 0.18, p ,, 0.001; invertebrate density: r 2 ¼ 0.35,
p , , 0.001; giant kelp density: r 2 ¼ 0.16, p , , 0.001; depth:
r 2 ¼ 0.06, p ¼ 0.02; PC2: r 2 ¼ 0.03, p ¼ 0.08).
The multivariate structure of the fish assemblage measured at the
scale of transects was also most strongly tied to the substrate composition. Two models were about equally supported: one with
three variables, substrate PC1, PC2, and depth (AICc ¼ 698.1,
r 2 ¼ 0.323), and the other with those variables plus rugosity
(AICc ¼ 698.5, r 2 ¼ 0.336). The best model with five predictors
was not as well supported based on AICc (AICc ¼ 699.8, r 2 ¼
0.343) and included giant kelp density. The six variable model had
even less support (AICc ¼ 701.8, r 2 ¼ 0.345). In single-predictor
models, all six variables significantly predicted assemblage structure
(all p ¼ 0.001). The model with only PC1 as the predictor explained
the most variation in assemblage structure (r 2 ¼ 0.190). The
remaining five single-predictor models ranked as follows: invertebrate density (r 2 ¼ 0.136), rugosity (r 2 ¼ 0.125), depth (r 2 ¼
0.120), PC2 (r 2 ¼ 0.060), and giant kelp density (r 2 ¼ 0.052).
At the scale of entire reefs, many of the “predictor” variables were
strongly intercorrelated (Table 5), complicating biological interpretation of their correlations with fish density and biomass. But, as at the
scale of transects, fish density and fish biomass (g m23) were significantly positively correlated with substrate PC1 (r ¼ 0.80 and 0.73, respectively, both p , 0.05). Other variables, however, had similar or
stronger correlations (Table 5). Fish density and biomass were positively correlated with invertebrate density (r ¼ 0.81 and 0.78, respectively); and negatively correlated with kelp density (r ¼ 20.73 and
20.69, respectively). Additionally, both fish density and biomass
were negatively correlated with reef area (r ¼ 20.74 and 20.61,
respectively).
2392
J. E. Granneman and M. A. Steele
Table 3. Density and biomass m23 (means + 1 SE) of fish on artificial and natural reefs in five regions with fish species divided into those
typically found within 5 m of the bottom (benthic and demersal species, see Table 2) and those typically found throughout the water column
or in the upper water column.
Reef type
Pacific Beach
Density, benthic, and demersal fish
Artificial
0.27 + 0.03
Natural
0.07 + 0.02
Density, water column fish
Artificial
0.80 + 0.16
Natural
0.25 + 0.07
Biomass, benthic, and demersal fish
Artificial
49.5 + 3.6
Natural
12.4 + 4.1
Biomass, water column fish
Artificial
43.6 + 13.4
Natural
16.6 + 5.5
Pendleton
San Clemente
Topanga
Torrey Pines
0.23 + 0.03
0.09 + 0.01
0.07 + 0.01
0.08 + 0.01
0.08 + 0.02
0.02 + 0.01
0.23 + 0.02
0.06 + 0.02
0.39 + 0.04
0.19 + 0.03
0.07 + 0.03
0.12 + 0.05
0.17 + 0.04
0.32 + 0.07
0.81 + 0.20
0.15 + 0.01
25.3 + 2.7
9.2 + 1.6
5.1 + 1.0
12.3 + 3.1
22.0 + 4.1
3.9 + 1.3
35.5 + 8.1
11.5 + 3.6
18.7 + 3.4
7.0 + 1.1
1.9 + 0.8
8.8 + 4.6
10.1 + 1.5
10.0 + 2.8
22.4 + 7.4
17.5 + 3.2
Figure 5. nMDS plot of fish assemblage structure on five artificial and
five paired, nearby natural reefs. Each point represents a single transect
(n ¼ 99 total). Legend refers to five regions (PB: Pacific Beach; PEN:
Pendleton; SC: San Clemente; TOP: Topanga; TOR: Torrey Pines) and
reef type (AR: artificial reef; NR: natural reef).
Discussion
The results of our study are consistent with the intuitive prediction that
fish assemblages on artificial reefs will be similar to those on natural reefs
if the reef types are physically similar. Averaged across the five artificial
reefs studied, fish assemblages differed from those found on five
nearby natural reefs, but two artificial reefs (Wheeler North in the San
Clemente region and Topanga) harboured assemblages quite similar
to those found on nearby natural reefs. Those artificial reefs were built
of smaller boulders than the other artificial reefs and they had relatively
low vertical relief and rugosity, thus making them more structurally
similar to natural reefs in the region. Aside from their physical structure,
these two artificial reefs were quite different from each other, differing
greatly in size, age, kelp densities, invertebrate densities, as well as
being .100 km apart. Taken together, these results strongly suggest
that the physical structure of these two reefs is what made their fish
assemblages most similar to those on natural reefs.
All the artificial reefs we studied, including San Clemente and
Topanga, were more structurally complex than nearby natural
reefs, which were generally composed of small boulders, cobble,
and sand. The artificial reefs studied had greater rugosity and
relief because they were built of larger boulders that were piled
higher than occurs on natural reefs. This difference is typical of
artificial and natural reefs in the region (Ambrose and Swarbrick,
1989). The most structurally complex artificial reefs (Pacific
Beach, Pendleton, and Torrey Pines) supported fish assemblages
that were approximately two- to fivefold more dense and with
two- to threefold more biomass than those on nearby natural
reefs. These differences are probably attributable to greater availability and heterogeneity of refuge spaces for fish (Shulman, 1984;
Chandler et al., 1985; Jessee et al., 1985; Ambrose and Swarbrick,
1989; Caley and St John, 1996; Sherman et al., 2002). Most studies
that have compared fish density between artificial and natural
reefs have found higher densities on artificial reefs (e.g. Bohnsack
and Sutherland, 1985; Ambrose and Swarbrick, 1989; Carr and
Hixon, 1997). The difference in biomass (g m23) was somewhat
less exaggerated than numerical density due to the abundance
of small fish on artificial reefs, as has been noted previously
(Ambrose and Swarbrick, 1989; Anderson et al., 1989; Bohnsack
et al., 1994). This difference in the size of fish might be due to
enhanced recruitment of small, young fish to the higher relief and
structurally more complex artificial reefs (e.g. Jessee et al., 1985;
Ambrose and Swarbrick, 1989; DeMartini et al., 1989), coupled
with the presence of older, bigger fish on natural reefs (Bohnsack
et al., 1994). Our data are consistent with this idea because we
found more small fish on artificial reefs and more large fish on
natural reefs.
Because we did not sample in the upper half of the water column,
our estimates of fish density and biomass per unit area of reef are
certainly biased low. Moreover, this bias should be greatest on
reefs with abundant giant kelp, which provides structure in the
upper water column to which many species orient [e.g. kelp
perch, kelp bass, giant kelpfish, and señorita; reviewed in Stephens
et al. (2006)]. Because kelp was more dense on natural reefs than
artificial reefs, we may have overestimated the difference in fish
density and biomass between the two reef types. However, restricting our comparisons to fish species that occur almost exclusively
within the zone we sampled (within 5 m of the bottom) does not
change our general findings. These benthic and demersal species
were more dense and had higher biomass on artificial reefs on
average, but artificial reefs that were more similar in structure to
natural reefs supported densities and biomass of these fish more
similar to those on natural reefs. When the entire fish assemblage
is considered, however, a substantial fraction of the total density
and biomass of reef fish can be found in the mid- and upper water
Fish assemblage similarity between artificial and natural reefs
2393
Figure 6. Characteristics of artificial and natural reefs in five regions (mean + 1 SE): (a) rugosity, (b) reef depth, (c) principal component 1
summarizing substrate composition, (d) principal component 2 summarizing substrate composition, (e) giant kelp density, and (f) invertebrate
density. Sample sizes as in Figure 2, except for (f) invertebrate density, n ¼ 8 for the San Clemente region natural reef.
Table 4. Results of PCA on substrate composition of reefs showing
the per cent of variation explained by each principal component and
the coefficients.
PC
1
2
3
4
5
%
Variation
58.90
25.30
11.40
3.90
0.50
Small
Sand Cobble boulder
20.37 20.50 20.49
0.49
0.18 20.38
0.70 20.59 20.12
20.03
0.44 20.75
0.35
0.44
0.19
Medium
boulder
20.24
20.74
0.38
0.49
0.07
Large
boulder
0.57
20.16
0.01
20.08
0.80
column on low-relief reefs that support giant kelp (Larson and
DeMartini, 1984). Comprehensive assessment of similarity of fish
assemblages between artificial reefs and natural reefs that support
kelp should include sampling throughout the water column (e.g.
Reed et al., 2006).
Despite the rarity of giant kelp on most of the artificial reefs, our
estimates of fish species richness were very similar between reef
types. This is surprising given that kelp removal experiments demonstrate a reduction in the diversity of foodwebs, especially in higher
trophic levels (Byrnes et al., 2011). Yet, most of the artificial reefs
in this study never supported a persistent kelp bed, despite several
2394
J. E. Granneman and M. A. Steele
Table 5. Correlations among biological and physical attributes of reefs, tested using reefs as replicates, including both artificial and natural
reefs (n ¼ 10).
Fish density
Fish biomass
Substrate PC1
Substrate PC2
Rugosity
Depth
Reef area
Invert. density
Kelp density
Fish dens.
1
0.83
0.80
0.24
0.66
0.13
20.74
0.81
20.73
Fish biom.
PC1
PC2
Rug.
Depth
Reef area
Invert. dens.
Kelp dens.
1
0.73
0.38
0.50
0.27
20.61
0.78
20.69
1
20.03
0.93
0.25
20.86
0.93
20.72
1
20.20
20.06
20.09
0.08
20.06
1
0.14
20.81
0.78
20.64
1
0.24
0.32
0.35
1
20.79
0.87
1
20.61
1
Pearson correlation coefficients (r) are given. Values in bold represent statistically significant correlations at p , 0.05.
transplant experiments with kelp (Ambrose and Anderson, 1989).
Thus, these artificial reefs may represent an alternate stable state
for reefs capable of supporting diverse foodwebs comparable to
natural reef systems. Furthermore, the highly dynamic nature of
giant kelp abundance may not promote the evolution of kelp specialists (Stephens et al., 1984; Patton et al., 1985; Holbrook et al., 1990),
at least to the extent found on coral reefs, where coral specialists
are common. Nevertheless, some macrophyte specialists have
evolved, e.g. Brachyistius frenatus and Heterostichus rostratus, and
species like these feature prominently in studies that show a strong
influence of giant kelp on fish assemblages (Ebeling et al., 1980;
Larson and DeMartini, 1984; Ebeling and Laur, 1985; Danner
et al., 1994). But even these two species, the only two true macrophyte specialists encountered along our transects, are not strictly
giant kelp specialists and will associate with other macrophytes,
such as understory algae and smaller kelps.
Surprisingly, we found that fish density was negatively related
to giant kelp density at the scale of transects and at the scale of
entire reefs. We interpret this result not as a negative effect of kelp,
but instead mostly as a statistical by-product of the positive effect
of high substrate complexity on fish density and the negative
effect of that substrate configuration on kelp density (Deysher
et al., 2002). Also, by not sampling in the upper water column
(the kelp canopy zone), we had limited ability to detect fish-kelp
associations in species known to associate with kelp and that
occupy this zone (e.g. kelp perch, kelp bass, and señorita).
Density and biomass of fish were both positively related to the
density of invertebrates on the reefs. This finding may indicate
that reef-associated invertebrates contribute to the ability of a reef
to support fish, or that many invertebrates and fish respond to the
physical characteristics of reefs in a similar manner. Invertebrates
were 3× more dense on artificial reefs than on natural reefs.
Ambrose and Swarbrick (1989) noted a similar, though less exaggerated pattern on reefs in the Southern California Bight. The higher
cover and average size of rocks, coupled with higher vertical relief
and structural complexity, may benefit invertebrates by providing
more attachment spots and crevices for shelter and foraging; and
the high relief may accelerate currents over the reef, thus benefiting
filter-feeding invertebrates (Jessee et al., 1985). Deysher et al. (2002)
suggested that moderate levels of sand abrasion or burial are necessary to clear rocks of invertebrates (specifically gorgonians) that
would otherwise out compete giant kelp, which is a foundation
species and ecosystem engineer. Giant kelp facilitates the spatial
coexistence of groups of species, such as sessile invertebrates
and understory algae (Arkema et al., 2009), but high-relief artificial reefs in California, which experience low levels of disturbance
by sand during storm events, end up being dominated by invertebrates at the expense of kelp and other potentially important
species not measured in this study (e.g. understory algae).
Consistent with this idea, we observed low kelp density and high
densities of invertebrates, including anemones and gorgonians, on
high-relief reefs.
The artificial reefs we studied ranged in age from 10 to 34 years.
All five reefs were old enough for a key successional change to have
already played out: the shift from giant kelp presence to near
absence when densities of long-lived invertebrates (gorgonians)
climb. Deysher et al. (2002) noted that this shift occurred within
5 years on the six artificial reefs they studied in southern
California, which all supported kelp populations within 1 – 2
years of construction. Four of the five artificial reefs we studied
were over 22+ years old. The fifth reef (Wheeler North in the
San Clemente region) was younger. It was built in two phases:
9 ha of it were 10 years old at the time of this study, and the
remaining 61 ha of it were only 1 year old. We sampled only
the 10-year-old portion of the reef. The addition of 61 ha of reef
probably reduced the density of fish on the 9 ha of 10-year-old
reef by attracting some of fish to the new habitat (D. Reed,
S. Schroeter, and H. Page, unpublished data; MAS, personal observation). Nevertheless, over the 5 ensuing years after our sampling for
this study, fish density and biomass on the San Clemente artificial
reef has remained similar to the nearby, paired natural reef, with
increases in both density and biomass on both reefs (D. Reed,
S. Schroeter, and H. Page, unpublished data).
We found that fish density declined with reef area, indicating
that smaller reefs supported greater densities of fish. Reef size in
our study, however, was tightly correlated with other influential
variables such as substrate composition, invertebrate density, and
kelp density, thus making the actual effect of reef size difficult to
resolve. Nevertheless, there are good reasons to believe that reef
size may affect fish assemblages. For example, the greater perimeter
to surface area relationships of small reefs may attract fish from a
proportionately greater area, and this would result in greater densities on small reefs (DeMartini et al., 1989). Moreover, small reefs
may provide better habitat for ecotone specialists such as blackeye
gobies and California sheephead (Topping et al., 2005; Bellquist
et al., 2008), which were both significantly more abundant on
artificial reefs than on natural reefs. The isolation of small units of
high relief rock in an expanse of sand substrate may further serve
to concentrate fish on small reefs.
Although our sampling was conducted only during a single
period in 1 year, we believe that the patterns we detected are generally consistent over time given that we focused on resident reef
2395
Fish assemblage similarity between artificial and natural reefs
species, which are not migratory. In support of this supposition,
Ambrose and Swarbrick (1989) found that their one-time sample
of one of the reefs we studied was very similar to the mean of temporally replicated sampling done over 3 months on that same reef.
Additionally, our “snapshot” approach detected differences in fish
density and biomass between reef types that were similar to those
noted by other researchers in the study region (e.g. Ambrose and
Swarbrick, 1989, DeMartini et al., 1989). For instance, fish density
on the Torrey Pines artificial reef, Pacific Beach natural reef,
Pendleton natural reef, and San Mateo natural reef was similar to
results from previous studies (Ambrose and Swarbrick, 1989;
Wilson and Lewis, 1990; Johnson et al., 1994) and benthic fish
density on the Pendleton natural reef was similar to estimates
obtained by Reed et al. (2006). This similarity across decades suggests that our one-time sampling likely detected patterns that are
relatively stable over time. Moreover, 3 of the 10 reefs in this study
have been sampled every year since we sampled them for the
present work, and the general patterns of differences or similarity
among them have remained relatively consistent (D. Reed,
S. Schroeter, and H. Page, unpublished data).
The artificial reefs we studied spanned a very large range of sizes
(approximately three orders of magnitude), but there was very
limited replication (five reefs of each type). That, combined with
the correlative nature of our study, limits the extent to which we
can be certain about the causes of differences we detected among
reefs. For example, in this study, reef size, relief, rugosity, substrate
composition, kelp density, invertebrate density, and reef age are all
somewhat confounded, given that the San Clemente artificial reef
was the largest artificial reef, the youngest, and structurally the simplest (low relief and low rugosity by virtue of being built of smaller
boulders in a single layer). Concern about this confounding is mitigated by finding similar results for the Topanga artificial reef, which
also had low relief and low complexity, but was much smaller, much
older (22 years at the time of this study), and lacked giant kelp.
Moreover, our results, which suggest that matching the structural
complexity and vertical relief of natural reefs is a key to artificial
reefs functioning like natural reefs, are congruent with results of
well-replicated, experimental studies on very small (1 m2), artificial patches of habitat, which demonstrate the importance of structural complexity (e.g. Shulman, 1984, Steele, 1999; Sherman et al.,
2002). In the present study, even the smallest of these reefs was relatively large by the standards of artificial reefs (900 m2) and thus
capable of sustaining resident populations of large-bodied reef
fish (DeMartini et al., 1994), which cannot be said of experimental
studies on small patches of reef. Future studies that include larger
numbers of large artificial reefs and that carefully quantify both
the attributes of the marine communities on them as well as their
physical characteristics are needed to disentangle the effects of reef
age, reef size, and configuration (e.g. many smaller patches or a
single, contiguous, large reef). Large artificial reefs, such as those
studied here, however, are not abundant, so carrying out such
studies will be challenging.
Our findings suggest that artificial reefs can replicate natural reefs
if they are constructed to physically mimic natural reef habitat. Our
study also indicates that relatively unnatural physical features of artificial reefs (e.g. high relief and high complexity) can support higher
densities of certain taxa than are found on natural reefs, at least in the
temperate, rocky reef system we studied. This might not be the case
in other systems (e.g. Burt et al., 2009; Campbell et al., 2011). Higher
densities of some species on artificial reefs may come at the cost of
lower densities of other species, including potentially important
ones, such as giant kelp and associated species in our study region.
Designing artificial reefs, so they incorporate a variety of physical
features (e.g. high relief and low relief areas), may enable them
to support unusually high densities of desirable species (e.g. some
ecologically and economically important reef fish) while ensuring
that other key species (e.g. giant kelp and associated fauna) are
supported.
Acknowledgements
Without the extensive help of A. Bagdasaryan in the field and laboratory, this work would not have been possible. We thank L. Allen,
S. Dudgeon, P. Edmunds, J. Grabowski, and several anonymous
reviewers for providing helpful comments, which greatly improved
this manuscript. This research was supported by funding from the
following sources: COAST Graduate Student Award for Marine
Science Research, Graduate Fellowship for Outstanding Research
Promise in Science and Mathematics, California State University
Northridge’s Graduate Studies, and the Association of Retired
Faculty Memorial Award.
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