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Effects of marine cage aquaculture on macrofauna assemblages associated
with Posidonia oceanica meadows
A. Terlizzia; G. De Falcob; S. Fellinea; D. Fiorentinoa; M. C. Gambic; G. Cancemid
a
Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali, Università del Salento, CoNISMa,
Lecce, Italy b Istituto per l'Ambiente Marino Costiero - IAMC-CNR, Torregrande-Oristano, Italy c
Stazione Zoologica Anton Dohrn, Laboratorio di Ecologia Funzionale ed Evolutiva, Napoli, Italy d
EVEMar, Etude et Valorisation de l'Environnement Marin - Porto-Vecchio, Corse, France
First published on: 15 April 2010
To cite this Article Terlizzi, A. , De Falco, G. , Felline, S. , Fiorentino, D. , Gambi, M. C. and Cancemi, G.(2010) 'Effects of
marine cage aquaculture on macrofauna assemblages associated with Posidonia oceanica meadows', Italian Journal of
Zoology, 77: 3, 362 — 371, First published on: 15 April 2010 (iFirst)
To link to this Article: DOI: 10.1080/11250000903464075
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Italian Journal of Zoology, September 2010; 77(3): 362–371
Effects of marine cage aquaculture on macrofauna assemblages
associated with Posidonia oceanica meadows
TIZO
A. TERLIZZI1*, G. DE FALCO2, S. FELLINE1, D. FIORENTINO1, M. C. GAMBI3, &
G. CANCEMI4
Aquaculture impact on macrofauna of Posidonia oceanica
Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali, Università del Salento, CoNISMa, Lecce, Italy, 2Istituto
per l’Ambiente Marino Costiero - IAMC-CNR, Torregrande-Oristano, Italy, 3Stazione Zoologica Anton Dohrn,
Laboratorio di Ecologia Funzionale ed Evolutiva, Napoli, Italy, and 4EVEMar, Etude et Valorisation de l’Environnement
Marin - Porto-Vecchio, Corse, France
Downloaded By: [Felline, Serena] At: 13:09 7 September 2010
1
(Received 21 July 2009; accepted 27 October 2009)
Abstract
Marine cage aquaculture has the potential to severely impact Posidonia oceanica seagrass meadows and its associated fauna.
In order to assess the impact of fish farming in a littoral bay of Corsica, France, physico-chemical (mud and organic matter)
and biological (density and compactness of P. oceanica beds and associated macrofauna) variables have been examined in
two stations putatively impacted to different degrees (St1 and St2) and in two unimpacted stations (Controls: C1 and C2).
Principal Component Analysis of meadow structural variables (shoot density and compactness of matte) and abiotic variables
(mud and organic matter percentage into sediment) highlighted differences among stations and, particularly, differences
between impacted stations and controls. Results from C1 and C2 totally overlapped. St1 was the station which differed the
most from controls. Compared with control stations, St1 and St2 were characterized by higher values of organic matter and
mud and by lower values of shoot density and matte compactness. Permutational multivariate analysis of variance
(PERMANOVA) did not show significant differences between C1 and C2 in the structure of macrofauna assemblages,
whereas significant differences between the two impacted sites and between these and the control conditions emerged. Differences in assemblages were well correlated with the measured abiotic variables. The analysis showed that the species most
responsible for difference among stations were typical of muddy sediment with high organic matter content. These species had
higher mean abundance values at impacted stations than at controls. This study suggests fish farming determines an increase
of organic matter and sedimentation, which resolve in changes in structural properties of the seagrass meadows and, consequently, changes in the associated macrofauna assemblages.
Keywords: Aquaculture, seagrasses, organic enrichment, environmental impact assessment, biodiversity
Introduction
Boat anchoring, dredging or other destructive fishing methods, coastal engineering and spoil disposal
are all examples of human impacts that affect the
structure and the functioning of marine habitats
(Boström et al. 2006).
Aquaculture (fish and shellfish farming) is a further activity, which is growing rapidly to meet high
demand of resources and to face the parallel collapse
of natural fish populations. It can determine major
environmental problems and impacts which could
be much more severe when habitat-forming species,
such as seagrass meadows, are involved (Karakassis
et al. 1999; Pergent et al. 1999; Holmer et al. 2003;
Pergent-Martini et al. 2006; Pusceddu et al. 2007).
The impact of fish farming ranges through a variety of disturbances, often acting in a synergistic way,
such as deterioration of water column and sediments,
shading and physical damage (Cancemi et al. 2003;
Sarà 2007).
The presence of fish cages increases by 2–3 times
the input of organic matter at the bottom and adjacent areas by accumulation of uneaten food and
faecal pellets (Karakassis et al. 2000; Holmer et al.
2003; Pergent-Martini et al. 2006). Organic matter
is mineralized and then starts to release nutrients
*Correspondence: A. Terlizzi, Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali, Università del Salento, CoNISMa, 73100 Lecce, Italy.
Email: [email protected]
ISSN 1125-0003 print/ISSN 1748-5851 online © 2010 Unione Zoologica Italiana
DOI: 10.1080/11250000903464075
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Aquaculture impact on macrofauna of Posidonia oceanica
(e.g. nitrogen and phosphorus) to the surrounding
waters (Delgado et al. 1999). Such organic matter
input affects primarily oxygen availability in the
benthos living beneath fish cages, leading eventually
to anoxic conditions (Porrello et al. 2005) and proliferation of anaerobic sulphate-reducing bacteria
(Holmer & Kristensen 1992; Heijs et al. 2000;
Kristensen 2000) with a toxic effect for seagrasses
and consequent below-ground organ mortality
which are fundamental for plant survival (Delgado
et al. 1999). Nutrients in the water column boost
phytoplankton, epiphyte and macroalgal growth,
thus causing a reduction in water transparency.
This reduction, in combination with the mere presence
of the fish cage (Holmer et al. 2003), affects the
photosynthetic capacity of plants, resulting in a
decrease of shoot density and biomass (Delgado et al.
1999; Holmer et al. 2003). After farming cessation,
the decrease of rhizome growth reduces the expansion
capability of P. oceanica meadows, thus affecting
the recolonization in the previously impacted area
(Holmer et al. 2003).
Food supply attracts macrobenthic predators,
fishes and sea urchins (D’Amours et al. 2008) and
the increase in herbivore pressure leads to a reduction
in shoot size, resulting in a further decrease in
photosynthetic capability (Holmer et al. 2003).
Such conditions imply low diversity of benthic
macro- and meiofauna (Clarke 1993; Borja et al. 2000;
Terlizzi et al. 2005b) and increase of abundance and
dominance of opportunistic species (Karakassis et al.
2000; La Rosa et al. 2001; Mirto et al. 2002;
D’Amours et al. 2008). The extent of the impact is
determined by the size of fish farming area, by the
adopted techniques, by the site-specific features
(e.g. bottom topography, water exchange) and by
the distance between cages and seabed (Håkanson
et al. 1988; Sarà 2007). Actually, a tall water column
between cages and the seabed and high current
speeds increase waste dispersion and determine a
lesser degree of sedimentation. Moreover, highspeed currents increase oxygenation, thus accelerating aerobic decomposition of organic matter and
preventing sulfides formation (Black & McDougall
2002; Porrello et al. 2005).
Posidonia oceanica meadows are facing a widespread
regression in the Mediterranean with a consequent
degradation of the services they provide to marine
ecosystems and, indirectly, humans. The aim of this
study is to investigate the response of macrofauna
assemblages associated with Posidonia oceanica
meadows to the disturbance caused by cage aquaculture activities. These may have, in fact, far-reaching
cascade effects on the associated fauna composition
and abundance via reduction of shoot density and
363
increasing local patchiness of the bed, and the load
of organic matter and fine fractions on the sediment.
Materials and methods
Study area and sampling design
The study area is situated within the Bay of Figari
(41°28′13″N, 9°44′09″E) along the southwestern
coast of Corsica (France).
The bay is a typical ria, a narrow fluvial valley submerged by sea level rise. A river (Carcerone) carries
water and sediments in the inner sector of the bay,
forming a delta system. The cross-profile of the bay
is typically V-shaped with a maximum depth not
exceeding 15 m. Sediments of Figari Bay are mainly
fine-grained with high values of mud content. Sediments are sandier in the inner sector of the bay, close
to the mouth of the river. The transition from sandy
to muddy sediments is associated with an abrupt
increase of water depth corresponding to the delta
front. In the central sector of the Bay, in the proximity
of the fish farm cages, sediments are fine-grained,
with mud content exceeding 90%. Toward the outer
sector of the Bay, sediments show a mixed sandy–
muddy grain size composition (Figure 1).
The Bay is part of the Natural Reserve of the
Bouches of Bonifacio, with the exception of the
Figure 1. Study area and positioning of investigated stations. C1, C2:
control stations, St1, St2: impacted stations by marine cages. The
mud percentage (<63 μm) in the sediments of the bay is also reported.
The legend of mud percentage values is reported on the right.
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364
A. Terlizzi et al.
inner part, which hosts a marina and the fish farm ‘A
Dorada’.
Fish farming is placed at about 200 m off the
shore of the bay and it is made up of 30 floating
cages measuring 25 m2, with an individual volume
of 60 m3, grouped into six groups of five cages. Each
group is separated by about 20 m and they float over
a depth of 10 m (Cancemi et al. 2003). Annual production is estimated at approximately 20–30 tons of
Dicentrarchus labrax (sea bass) and Sparus aurata
(sea bream) and the amount of food distributed each
year is about 38 tons (Pergent et al. 1999). Since fish
farming was established in 1985, Posidonia oceanica
beds, which represent the most widespread habitat
in this area, have suffered a progressive regression in
the bay (Cancemi et al. 2003).
Four sampling stations were selected at the same
depth (–10 m) and along the gradient of increasing
distance from the fish farming facilities (Figure 1).
St1 and St2 are located about 100 and 500 m
from the cages, respectively. Station St1 is outside
the marine protected area, whereas the others are
inside.
Control stations (C1 and C2) were placed where the
P. oceanica meadow is in a pristine state of conservation and were randomly chosen among a set of possible
locations representative of unimpacted conditions.
Multiple controls were adopted to unconfound natural variability in assemblage pattern in undisturbed
condition from the variability induced by disturbance
under investigation (e.g. Underwood 1994; Terlizzi
et al. 2005a). Sampling operations were carried out
by SCUBA diving on May 2007.
Sampling procedure
The abiotic variables measured to represent P. oceanica
meadows were: organic matter, expressed as LOI
(Loss On Ignition), mud (fractions <63 μm), matte
compactness and shoot density.
For the estimation of LOI and mud content three
replicate PVC core samples were taken at each station
and immediately frozen until subsequent processing.
Cores were 3 cm in diameter and 10 cm in length.
The compactness of the matte was assessed
through a penetrometer, following the method
described by Francour et al. (1999). This parameter
is a good indicator of the matte status and it can
reveal a weakening of this structure and trend of
erosion (Cancemi et al. 2003). At each of the four
sampling stations the measurement was repeated
five times at randomly selected sites. Compactness
was defined as strong (penetration < 50 cm),
medium (50 cm < penetration < 100 cm) and weak
(penetration > 100 cm).
Ten measures of density (shoots for m2; Giraud
1977) were performed at each site using a quadrate
of 40 cm × 40 cm (1600 m3 of surface that is optimal
for an assessment of P. oceanica density; Panayotidis et
al. 1981; Buia et al. 2004). At each station, macrobenthic fauna was sampled by SCUBA divers by means of
an air lift sampler (Terlizzi & Russo 1996; Buia et al.
2004) with a 0.4-mm mesh bag held by a 40 × 40
cm of metal frame and in three replicates. After collection samples were fixed in buffered 7% formalin.
Abiotic data analysis
In the laboratory, the sediment cores, after defrosting,
were placed in an oven at 50°C for 24–48 h
(depending on granulometric size) and cut into
three fractions, then pulverized and homogenized.
To evaluate sedimentary organic matter, about
1.5 g of sediment were sieved to obtain a finer fraction
(<500 μm), oven-dried for a further hour and then
weighed before and after a period of 3 h at 500°C;
the loss of dry weight was expressed as a percentage
of organic matter (Carver 1971; De Falco et al.
2004).
In order to determine the mud fraction, about 100
g of dry sediment were dry-sieved by a set of sieves
with a mesh from 4000 to 500 μm to get sediment
fraction inferior to 500 μm. This fraction was oxidized
in 10% H2O2 to remove organic matter and wet-sieved
at 63 μm to obtain the mud fraction (Lorenti & De
Falco 2004).
Principal Component Analysis (PCA) was
employed using mean values for each variable at
each station, highlighting the differences among
stations compared with structural features of
meadow (matte compactness and shoot density) or
characteristics of sediment (organic matter and mud
content percentage).
Taxonomic identification and macrofaunal analyses
In the laboratory, samples were sorted under magnification and macrofauna separated into main taxonomic
groups and stocked in 70% ethanol for further identification by expert taxonomists. Specimens of
Mollusca, Isopoda, Decapoda, Tanaidacea and
Amphipoda were all identified at species level, whereas
Polychaeta were identified mainly at higher taxonomic levels (families, genera). Data were organized in a species vs. abundance matrix and
analysed by multivariate and univariate techniques
to quantify potential impact of source of disturbance
on structure of assemblages associated with P. oceanica
meadows. Given the presence of non-replicated
stations for each of the two distances from the
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Aquaculture impact on macrofauna of Posidonia oceanica
source of disturbance (i.e. St1 and St2) and the use
of multiple controls, this is an asymmetrical design
(sensu Glasby 1997). Distance-based permutational
multivariate analysis of variance (PERMANOVA;
Anderson 2001; McArdle & Anderson 2001) was
employed to test the null hypothesis of no significant
differences among stations. The analysis was based
on Bray–Curtis dissimilarities (Bray & Curtis 1957)
calculated on untransformed data (12 samples × 158
taxa) and each term of the analysis was tested using
999 random permutations of appropriate units
(Anderson & ter Braak 2003). The analysis was
done in two steps. First, the four stations were considered as separate units, investigating with pair-wise
comparisons the difference between C1 and C2 with
the PERMANOVA statistic and 999 permutations.
The lack of differences between two controls indicates
that assemblages belong to the same statistical population and they represent comparable conditions of
faunal assemblages in the P. oceanica meadows. A
more powerful statistical test was therefore done
using a unique control condition employing a further
PERMANOVA to analyse the difference between
St1 and St2, and between these and C (Terlizzi et al.
2005a). Multivariate patterns of variation among
stations were visualized by non-metric multidimensional scaling (nMDS) (Kruskal & Wish 1978) of
sites’ centroids. The SIMPER procedure (Clarke
1993) was employed to analyse the percentage
contribution of each variable to the Bray–Curtis
dissimilarities among samples, allowing the identification of taxa most affected by the impact. A cut-off
criterion was applied to allow identifying a subset of
species whose cumulative percentage contribution to
the observed value of dissimilarity reached 80%.
The relationships between multivariate patterns of
assemblage structure and patterns of environmental
variables were examined by superimposing bubble
plots, representing the values of this variable as a circle of different size, on the biotic ordination of the
corresponding station.
Diversity profiles at each station were visualized
by plotting k-dominance curves displaying the
cumulative proportion abundance against the log
species rank (Lambshead et al. 1983).
Multivariate analyses were repeated for the amphipod and mollusc data set separately to test whether a
single group can be used as a surrogate in fish farming impact studies on diversity of macrofauna
assemblages associated to P. oceanica meadows.
Given that, for polychaetes, the taxonomic resolution was, for most taxa, at levels higher than species,
no attempt was made to reanalyse the performances
of the polychaetes data set. The use of polychaetes
only in the assessment of environmental impacts can
365
be in some cases problematic if the analyses are conducted at rough levels of taxonomic resolution
(Musco et al. 2009). On the other hand, lower taxonomic resolution (genus level) has been proven to
still be useful to highlight differences in polychaete
composition along a depth gradient on a Posidonia
oceanica bed (Gambi et al. 1995).
PERMANOVA was also employed to test, in a univariate context, the same hypothesis tested using multivariate analyses. The analysis was conducted on the
mean number of taxa (S), mean number of individuals
(N), Simpson’s diversity (1 – l′) and evenness (J′).
All statistical analyses were performed using the
computer program PRIMER v6 (Clarke & Gorley
2006), including the add-on package PERMANOVA+ (Anderson et al. 2008).
Results
Meadow features
Matte compactness was 110 cm (± 0.0 SE) at St1, 97
cm (± 8.0) at St2, 71.7 cm (± 5.0) at C1 and 75.8 cm
(± 7.7) at C2. According to Francour et al. (1999), St1
was classified as ‘weak’, St2, C1 and C2 as ‘medium’.
Following Pergent et al. (1995), shoot density at
St1 (132 ± 28.9 shoot/m2) and St2 (216.4 ± 33.1)
was classified as ‘anomalous’, sub-normal values for
that specific depth. Control stations C1 and C2
were classified as ‘normal’ with 361.5 ± 23.9 shoot/
m2 and 390.6 ± 38.8 shoot/m2, respectively.
Univariate statistical analyses
A total of 3041 specimens of benthic invertebrates
were collected and characterized as 158 taxa of
Mollusca (951 individuals, 81 species), Amphipoda
(1052 individuals, 26 species), Decapoda (127 individuals, 14 species), Isopoda and Tanaidacea (85
individuals, 9 species) and Polychaeta (826 specimens
belonging to 28 taxa, of which 16 families and the
others recognized at genus and species level).
Permutational analysis of variance on mean number
of individuals revealed an overall significant difference
among stations (Table I). Pair comparisons of these
differences revealed that differences occurred only
between C vs. St1. No differences were detected
among other pairs of stations (Table I; Figure 2).
There were no significant differences among stations
in the mean number of species (Table I). The mean
value of the evenness index (J′), instead, significantly
differed (Table I), with the lowest value occurring at
St1 (0.70 ± 0.007 SE; Figure 2). Diversity,
expressed as Simpson’s diversity index (1 – l′), also
varied significantly across stations, with the lowest
366
A. Terlizzi et al.
Table I. Permutational analysis of variance testing differences among the three conditions (St1, St2 and C) in terms of number of
individuals (N), number of species (S), evenness index (J´) and Simpson’s index (1 – l). Results of pair-wise tests are reported for
significant differences among stations.
N
Source
df
Impact
2
Residual
9
Pair-wise comparisons
St1 vs. C
St2 vs. C
St1 vs. St2
S
MS
59480
16315
F
3.65
t
2.3875
1.8367
0.9329
p
*
ns
ns
p
*
MS
281.42
195.80
J´
F
1.44
p
ns
MS
0.03
0.00
F
50.07
t
9.61
2.80
8.66
p
***
**
***
1-l´
p
***
MS
0.0045
0.00016
t
6.74
0.57
7.59
F
28.27
p
**
p
*
ns
ns
Downloaded By: [Felline, Serena] At: 13:09 7 September 2010
*p < 0.05; **p < 0.01; ***p < 0.001; ns: not significant (p > 0.05).
Figure 2. Mean (± SE, n = 3) values of variables analysed at each of four stations studied.
value occurring at St1 (0.89 ± 0.006). Pair-wise
comparisons of St1 and St2 vs. C revealed the only
significant test to occur between St1 and the average
of controls (Table I; Figure 2).
Patterns of evenness index were reflected by plotting
k-dominance curves for species abundance at four
sampling stations (Figure 3), which showed that St1
was characterized by nine species accounting for 80%
of total abundance value. Although in a weaker way,
St2 was also characterized by few dominant species.
Curves referring to control stations had a different
shape with a greater number of species for the same
abundance values than St1 and St2. The curves at
C1 and C2 were overlapping, indicating a comparable pattern of species equitability between controls.
Multivariate analysis
Principal Component Analysis of meadow structural
variables (shoot density and compactness of matte)
Figure 3. K-dominance curves for species abundances at four
study stations (x-axis logged). St1 = black squares; St2 = black
triangles; C1 = white squares; C2 = white circles.
and abiotic variables (mud and organic matter percentage into sediment) highlighted differences
among stations and, particularly, differences
between impacted stations and controls (Figure 4).
Aquaculture impact on macrofauna of Posidonia oceanica
367
Figure 4. Biplot of principal component analysis ordination displaying differences among four stations in relation with abiotic variables.
PC1 explained 98% of total variability.
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Table II. PERMANOVA testing differences between St1, St2 and C based on Bray–Curtis dissimilarities data for
the whole data set (158 taxa × 12 samples) and for amphipods and molluscs data analysed separately. Results of
pair-wise tests were also reported.
All data set
Source
Impact
Residuals
Pair wise tests
St1 vs. St2
St1 vs. C
St2 vs. C
Molluscs
d.f.
2
9
MS
5348.9
1477.5
F
3.6203
t
1.8129
2.0881
1.764
p
*
**
*
p
***
Amphipods
MS
7448.3
2215.8
F
3.3614
t
2.0491
1.6831
1.6684
p
ns
**
*
p
**
MS
4348.1
1181.1
F
3.6815
t
2.2935
1.2206
2.2032
p
*
**
ns
p
**
*p < 0.05; **p < 0.01; ***p < 0.001; ns: not significant (p > 0.05).
The first axis explained 98% of total system variability. Considering environmental variables, C1 and
C2 were virtually overlapping and St1 was the most
different station. The PCA plot confirmed the controls as characterized by higher values of shoot density and matte compactness and by lower values of
LOI and mud than St1 and St2. Like St1 but in a
weaker way, St2 was characterized by lower compactness of matte and by high values of LOI and
mud content (Figure 4).
Permutational multivariate analysis of variance performed on the whole data set (158 variables × 12
samples) did not show significant differences between
C1 and C2. Based on this result, a further PERMANOVA was carried out, considering a single control condition (C), in order to test for differences in
total assemblage structure between St1 and St2 and
between these and C. The analysis showed significant
differences between the two impacted sites, and
between these and the control condition (Table II).
These differences were well portrayed by nMDS
plot of the 12 samples, which showed samples at
St1 grouped and separated from other samples
(Figure 5A). St1 showed a lesser scattering among
Figure 5. A, Non-metric multidimensional scaling ordinations
(nMDS) on the basis of the Bray–Curtis dissimilarities measure
of individual replicates comparing macrofauna assemblages from
impacted (St1 and St2) and control stations (C1 and C2). 䊐 =
C2; 䉫 = C1; ▲ = St1; = St2. B, macrofauna MDS plot of stations’ centroids with values of LOI at four stations superimposed
as circles of differing sizes.
replicate units than control sites. The same space
arrangement was found at St2 group. This indicates
that impact determined changes in the spatial heterogeneity among replicate units both at St1 and St2.
Control sites showed similar condition of spatial heterogeneity among replicates.
The plot of mean assemblage for each station (the
centroids of three replicates) with superimposed
abiotic variable mean value, showed clearly that the
368
A. Terlizzi et al.
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differences between control and impacted stations
were heavily influenced by the values of LOI. Similar
patterns (data not reported) were obtained plotting
the differences in assemblage structure of macrofauna related to other variables such as mud and
shoot density (Figure 5B).
As suggested by the test for differences in the J
index, SIMPER showed that St1 and St2 were characterized by few abundant species. These species
were also found at the control condition, but with
lower abundances. Some species (e.g. the amphipod
Pereionotus testudo) were found only at St1, other
species characterized St1 and St2 (e.g. molluscs
Retusa truncatula and Loripes lucinalis and the tanaid
Leptochelia dubia).
The mollusc Rissoina (Rissoina) bruguieri and the
amphipod Metaphoxus simplex were absent at St1
and present at St2 and C (Table III).
SIMPER also showed a gradient for species most
responsible for difference among stations. For
example, mean abundance values of gastropod Rissoella inflata (living in mud with high organic matter
content) were 91.6 at St1, 33.3 at St2 and 2.3 at C.
The same pattern was showed by the amphipod
Caprella acanthifera. The opposite pattern was
observed for other species that characterize P. oceanica
leaf stratum, as Syllinae spp. which had mean
abundance values of 4.3 at St1, 6.6 at St2 and 9.0 at
C (Table III).
PERMANOVA performed separately on mollusc
and amphipod assemblages showed similarly that C1
and C2 did not differ and thus they were analysed as a
single control condition (Table II).
PERMANOVA on mollusc assemblages did not
show significant difference between St1 and St2, but
revealed St1 and St2 as differing significantly from
Table III. SIMPER analysis of species contributing the most (%) to dissimilarity between pairs of stations. The average dissimilarity between each pair of stations was reported in parentheses. For each species the average abundance (Avg. ab.) at each of the
three conditions (St1, St2 and C) are provided. For each comparison only species up to 80% cumulative percentage are reported.
Taxa
St1
St2
C
Avg. ab. Avg. ab. Avg. ab.
Rissoella inflata
Caprella acanthifera
Apherusa veratri
Aora spinicornis
Sunamphithoe pelagica
Prionospio spp.
Exogoninae (Exogone spp./Sphaerosyllis spp.)
Gouldia minima
Protodorvillea kefersteini
Paraonidae
Scissurella costata
Caecum trachea
Pholoe minuta
Pereionotus testudo
Venericardia antiquata
Retusa truncatula
Phtisica marina
Loripes lucinalis
Dexamine spinosa
Perioculoides aequimanus
Lumbrineridae
Lucinella divaricata
Rissoina bruguieri
Mysella bidentata
Synchelidium haplocheles
Alvania geryonia
Syllinae
Cymodoce hanseni
Leptochelia dubia
Parvicardium exiguum
Capitellidae
Metaphoxus simplex
91.67
46.33
50.67
36
22.33
5
36
9.67
13.33
8.67
0.67
3
1
5
1.67
3.67
0.67
1
3
3.67
0.67
1.33
0
0.67
1
0
4.33
5
2.67
2.67
0.67
0
33.33
5.33
20
11.33
1.67
18.67
39.33
15.33
15
2.33
6
4.67
5.33
0
5
2
3.67
3.33
2
5
3.33
3
2.67
3.33
3.33
2.33
6.67
3.33
1
3
3
2
2.33
1.83
24
8.83
0.67
2.67
9.5
0.67
1.17
1.67
3.67
0
5.17
0
1.33
0
0.83
0
5.33
1
0.5
0.17
1.67
0.33
0.33
0.5
9
5.33
0
0.17
0.5
3.33
St1 vs. St2 53.36 St1 vs. C 76.52 St2 vs. C 68.13
Contrib %
Contrib %
Contrib %
15.2
10.85
8.98
7.21
4.68
3.9
3.65
3.16
2.08
1.78
1.51
1.31
1.25
1.18
1.06
0.9
0.9
0.85
0.82
0.78
0.78
0.73
0.72
0.71
0.68
0.68
0.66
0.64
0.63
0.63
0.62
0.59
21.4
10.56
8.11
7.43
3.97
1.05
6.4
1.98
2.69
1.88
0.79
0.5
0.91
1.01
0.46
0.72
0.22
0.21
1.13
0.69
0.18
0.39
0.35
0.21
0.34
0.1
1.45
1.19
0.73
0.59
0.2
0.77
7.21
1.4
5.2
2.29
0.48
5.52
10.33
4.88
4.82
0.62
1.93
1.48
1.07
0
1.31
0.62
0.97
1.01
1.25
1.47
0.95
1
0.92
1
1.03
0.81
2.08
1.43
0.37
0.98
0.89
0.83
Aquaculture impact on macrofauna of Posidonia oceanica
the control condition C (Table II). PERMANOVA
on amphipod assemblage showed significant differences between St1 and other stations, but St2 did
not differ from C.
Downloaded By: [Felline, Serena] At: 13:09 7 September 2010
Discussion
This study highlighted a significant effect of the
aquaculture cages in modifying the assemblage
structure of mobile invertebrates associated with P.
oceanica meadows as a likely consequence of changes
in sediment features and meadow structure (matte
compactness and shoot density).
The impact was revealed in terms of: an increase
in mean abundance values at the station closest to
the fish farming (St1); a selection of few abundant
species in the same station; changes in the mean values
of univariate diversity indexes at the St1 station
compared with controls; a drastic change in
assemblage structure in St1 and St2, which differed
from those characterizing the controls; a higher
small-scale variability among replicate units in the
controls respect to the two impacted stations.
One of the more striking aspects of the obtained
results is that studies related to diversity changes
have to be carried out defining biodiversity itself
under its many aspects. Wrong inferences about
effects of an environmental impact on biodiversity
can be done when it is expressed simply as a number
of species. Analyses carried out on the other variables
(e.g. Simpson and J′ indexes) showed significant
differences between St1 and C. Therefore, impact
was revealed by a change in biodiversity (species
identity and evenness) and not just by species
number. The analysis of evenness (analysis of variance
on J′ index and graphic inspection of quantitative
dominance curves) related to impact showed controls
stations (C1 and C2) as overlapping. The significant
differences among St1, St2 and controls are consistent
with a gradient of the perturbation caused by fish
farming activities.
This outcome is also supported by multivariate
analysis, which showed significant differences in the
whole assemblage structure between St1 and St2
and between these two and the control conditions.
When analysing mollusc data only, significant differences resulted between St1 vs. C and between St2 vs.
C, but there were no differences between St1 vs. St2.
This result widens the range of habitats in which the
use of molluscs only has been demonstrated to act as a
valid surrogate in the detection of environmental
impacts (Bevilacqua et al. 2009) and suggests that, at
least in Posidonia beds, molluscs could be used in routine procedures of environmental monitoring for the
assessment of the impact of fish farms.
369
By detecting only difference between St1 and C
and not between St2 and C the use of amphipods
only demonstrates a less affordability in the detection of impact by fish farming.
There are likely multiple causal mechanisms, possibly acting interactively, underlying the observed
differences in assemblages between the impacted
and control stations.
The identification and quantification of these
mechanisms would require more detailed manipulative
experiments than the correlative approach we used.
In addition, the lack of temporal replication in the
study and the consideration of seasonality that characterizes vagile fauna associated to P. oceanica meadows
(e.g. Gambi et al. 1992; Scipione et al. 1996) limit
the basis for any general inferences concerning the
impact. Nonetheless, on the basis of the outcome of
this study, some hypotheses regarding specific ecological mechanisms operating through the considered stations can be formulated and some general
tentative ideas offered.
Figari Bay is not characterized by significant
urbanization and the presence of the Carcerone river
cannot justify the organic enrichment of the bay
given that percentages of organic matter recorded in
the pro-delta (3–13%) are lower than those near the
cages and exhibit a marked decline (from 25.0 to
5.6%) with distance from fish farm facilities (Cancemi
et al. 2007). The massive presence of species indicating
organic enrichment can be therefore reasonably
assumed as an effect of fish farming.
For example, the polychaetes Protodorvillea kefersteini and Prionospio spp. were among the most
abundant species at St1 and St2, and emerged by
the SIMPER analysis as responsible for differences
between those stations and the control condition. Also,
the abundance of the amphipod Caprella acanthifera at
St1 in comparison with St2 and C can reasonably be
interpreted as an effect of organic increase. This species
is known to tolerate stressed habitats characterized
by high rates of sedimentation and strong inputs of
organic matter (Guerra-García & García-Gómez
2001).
The increased sedimentation and organic load of
the impacted stations could also explain the different
pattern of small-scale variability among replicate
units observed among stations, with control sites
showing a higher scattering in the nMDs ordination
model. Similar results have been highlighted by
Borg et al. (2006) with invertebrate fauna associated
with dead and alive P. oceanica matte off the Malta
coast, suggesting that sediment properties and other
features of the dead as more homogeneous than
those occurring in the living meadow. Sediment
features and shoot density values in the impacted
Downloaded By: [Felline, Serena] At: 13:09 7 September 2010
370
A. Terlizzi et al.
stations of our study may suggest some interesting
parallelisms and analogies with those occurring in a
dead Posidonia matte.
Analysis of species mainly responsible for changes
in assemblages across stations supports the hypothesis
that changes of sedimentation rates induce major
changes in benthic assemblages. For example, species
living in muddy substrates as Retusa truncatula,
Loripes lucinalis and Leptochelia dubia were exclusively recorded at St1 and St2.
Sedimentation can influence mean shoot density
(Delgado et al. 1999; Pergent et al. 1999; Ruiz et al.
2001; Cancemi et al. 2003) and indirectly increase
the size of patches of dead matte. Instead, species
typical of P. oceanica leaf stratum like, namely the
polychaetes Syllinae spp., Chrysopetalum debile,
Pholoe minuta, the gastropods Rissoina (Rissoina)
bruguieri and Alvania geryonia and the amphipods
Dexamine spinosa and Metaphoxus simplex characterized
the control stations and contributed the most to
characterize difference in assemblage between C and
the impacted stations. Particularly, Alvania geryonia,
R. bruguieri and M. simplex were not found at St1.
Therefore, aquaculture facilities modify biodiversity
patterns of fauna associated to P. oceanica meadows
and also determine changes in the meadows living
into the Marine Protected Area boundaries as
suggested by data about St2. Marine reserves are not
isolated from all critical impacts (Allison et al.
1998). Our results confirm that marine reserves
offer no protection from important threats such as
the spread of contaminants. That is, protected communities can be strongly impacted by human activities acting outside the reserve boundaries. Conserving
P. oceanica is a target to be pursued not only for the
ecosystem functions performed by the seagrass, but
also for protecting a highly diversified biotope and is
recognized as a priority habitat in the frame of the
European Habitat Directive. Changes of seagrassassociated assemblage biodiversity as well as being
indicative of the effects of localized sources of disturbance represent a kind of impact that could cause concerns to the trophic nets acting on larger scale than
those regarding a single meadow. For conservation
purposes, these are all issues to be taken in careful
consideration, calling for wide-scale environmental
management of Mediterranean seagrass ecosystems
(Boudouresque et al. 2006).
Acknowledgements
Financial support was provided by the Office de
l’Environnement de la Corse (Réserve naturelle des
Bouches de Bonifacio). Louis Paoli provided assistance
during fieldwork. D. Scuderi (University of Catania), assisted with the identification of Mollusca. M.
Lorenti and V. Zupo (Stazione Zoologica Anton
Dohrn di Napoli) provided the necessary expertise
for the identification of Tanaidacea and Isopoda,
and Decapoda, respectively.
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