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Macrofauna community patterns in a Chiloe Island channel used intensely for aquaculture: the ecological status of its benthic environment

Abstract

Background

It is known that aquaculture may produce negative environmental effects on marine ecosystems. Southern Chile is one of the most important salmon and mussel-producing areas in the world. Here we assess the ecological status of benthic communities near farming centers in Caucahue Channel, Chiloe, which has been used intensely for salmon and mussel production for 30 years.

Methods

The macrofauna, sediments and water column were characterized at distances of 5 to 100 m from three salmon and three mussel-producing centers. Information was also obtained from reference sites 500 to 3000 m from these aquaculture farms. The macrofauna and environmental conditions during winter were analyzed using uni- and multivariate analysis and the AZTI Marine Biotic Index (AMBI) as an indicator of benthic community condition.

Results

(i) There is a high degree of spatial dissimilarity in macrofauna and environmental variables among sampling sites and types of environments (far from or near farming centers) and between the northern and southern areas of the channel; (ii) sediment structure (mean grain size and percentage of total organic matter) correlated with the observed dissimilarities in macrofauna communities; and (iii) the level of perturbation according to AMBI was heterogeneous, with sites in the undisturbed/normal range to moderately disturbed/polluted.

Conclusions

We found a high spatial dissimilarity in benthic macrofauna and environmental variables among sampling sites, environmental types and between the northern and southern areas of the channel. AMBI and multivariate community-environment analysis are useful tools to define the level of perturbation of a geographic area at different spatial scales, using all the ecological information from each sample and replicates.

Background

Benthic habitats sustain diverse communities composed of species with distinct responses to natural and anthropogenic stress [1]. Benthic macrofauna have many features that make them useful for assessing environmental quality [2,3,4,5,6,7]. Among these are sedentary species with low mobility, many of them highly sensitive to perturbation. These species have life cycles that range from months to years, which facilitates identifying environmental effects for long periods of time and in specific locations. Consequently, the study of changes in the community structure of benthic macrofauna has become a useful tool in assessing anthropogenic effects on aquatic ecosystems [8,9,10,11]. For a proper interpretation of the observed community patterns, it is important to know other aspects of the species such as ecology, life history and sensitivity to pollution, because some of these features could mask the effects of perturbation [12]. For instance, indirect development (i.e. pelagic larval stages) could result in impacts being masked by recruitment coming from outside the impacted area, and long life-cycles of some species could result in slow responses to some specific perturbations.

The effects of increased organic matter content in marine sediments on benthic communities have been widely documented and generally include quantitative and qualitative structural changes [2, 13,14,15]. Pearson & Rosenberg [2] developed a model for changes in species richness, abundance and biomass of benthic macrofauna according to the level of organic enrichment of sediments. They argued that continued increases in organic matter over time modify community structure (loss of diversity), with a final persistence of tolerant and opportunistic species until the environment becomes anoxic and eventually produces the complete loss of species (azoic environment).

Different environmental effects have been reported in relation to salmon and mussel farms, the most important being the increased flow of organic matter to the seafloor [16,17,18,19,20,21], which accumulates in sediments and later oxidizes [22,23,24], resulting in a decrease in dissolved oxygen in the water column and also in the oxidation-reduction potential of the sediments [25, 26]. If the flow of organic matter is sufficiently high, it can increase the levels of hydrogen sulfide and ammonium in the sediments due to increased activity of specific groups of bacteria [26], resulting in completely anoxic and azoic environments.

There have been increasing efforts in recent years to monitor and assess the effects of aquaculture farming centers on marine ecosystems (e.g. [27]). Biological and ecological indicators have been used to assess the status of benthic communities living below or near farming centers, mainly for fish and filtering bivalves [21, 27,28,29]. One of the biological indicators that has provided better results is the AZTI Marine Biotic Index (AMBI), as described by [30]. AMBI aids in assessing the quality of marine sediments [30, 31] based on variations in benthic macrofauna communities due to environmental changes [30, 32]. AMBI is based on the relative abundance of macrofauna species, which are assigned to five ecological groups according to their sensitivity to pollution stress or perturbation [33]. In Chile there is no official list of species included in AMBI, however, many macrofauna species living in the South Pacific are cosmopolitan or have been already identified in other ecosystems, and therefore they are classified in the AMBI taxa listing, which allows the assignment to the Ecological Groups (See also [11, 34]).

The objective of this study was to assess changes in macrofauna community structure in relation to distance from the aquaculture centers in Caucahue Channel (inner sea of Chiloe), an area used intensely for salmon culture and bivalve farming in the past 30 years. This study does not seek to quantify the ecological conditions directly beneath fish cages or mussel long-lines, but rather the general ecological condition of the Caucahue Channel. Community dissimilarity was assessed at different spatial scales, using a high spatial resolution sampling design and comparing benthic macrofauna between sampling sites near (few meters) and far (dozen of meters) from six farming centers (3 for bivalves and 3 for salmon) using uni- and multivariate ecological indicators of diversity and the biotic index AMBI as an indicator of the ecological status. We searched for the environmental variables that best explain the multivariate structure of benthic macrofauna. The general working hypothesis is that the benthic macrofauna community from sites closer to farming centers (few meters) are in poorer ecological condition (i.e. less diversity of sensitive ecological groups based on the AMBI indicator) than those far (dozens of meters) from farming centers.

Methods

Study area

The study area was Caucahue Channel (42.12° S, 73.44° W), which separates Caucahue Island from Chiloé Island in southern Chile. Caucahue Channel is described as a coastal environment with two openings to the inner sea, one facing north and the other south [35, 36] (Fig. 1). There is intensive aquaculture in the channel, producing mainly mussels and salmonids. The channel receives fresh water mainly from precipitation and some discharges from freshwater runoff and domestic waste [37]. Aquaculture has been permitted in the Caucahue Channel since 1985 (www.subpesca.cl), and at present there are 55 aquaculture concessions (salmonids, mollusks and seaweeds) occupying an area of approximately 843.81 ha. Of these, 37 produce mollusks (570.14 ha), mainly mussels, 13 produce salmonids (258.47 ha) and 5 produce algae (15.2 ha). The total area of the channel is approximately 42.32 km2, of which 20% or about 8.44 km2 are allocated for aquaculture concessions. This study incorporates biological and environmental information near six active aquaculture farms, three for mussels (MYT sites) Q1 (43.11 ha), Q9 (15.54 ha), Q11 (14.64 ha) and three for salmonids (SAL sites) Q2 (29.76 ha), Q6 (12.57 ha) and Q12 (50.0 ha) (Fig. 1).

Fig. 1
figure1

Caucahue Channel showing sampling sites. Q sites are near active aquaculture centers (green area with black dots = mussel farm, red area with black dots = salmon farm). Blue dots (C sites) represent reference sites far from active aquaculture centers. Grey zones correspond to northern, southern and outer areas of the channel

Benthic and water column sampling

Sampling was conducted between June 21 and 24, 2014 (beginning of the austral winter) on board the L/M Dr. Jürgen Winter (Austral University of Chile). The samples were collected near six farming centers (Q1, Q2, Q6, Q9, Q11 and Q12) (Fig. 1). The distances between sampling sites and mussel centers were between 5 and 50 m, while the distances from salmonid farms were between 25 and 100 m (Fig. 2). There were also 14 reference sampling sites (REF sites) far from active farming centers (between 500 and 3000 m) in Caucahue Channel and beyond the channel in the inner sea (Figs. 1, 2). The REF sites were selected after conducting a complete assessment of the channel. The following criteria were used for selecting the REF sites: (i) generate spatial gradients in relation to each of the six selected aquaculture farms, (ii) the gradient should move away from other active aquaculture farms, and (iii) to characterize the whole Caucahue Channel (See Figs. 1, 2). There are other sea uses of Caucahue Channel in addition to aquaculture activity, including an artisan fishing port, artisanal fisheries, wastewater discharge from a small town and small craft navigation [37].

Fig. 2
figure2

Photographs of Caucahue Channel showing gradients of distances from active aquaculture farms and surrounding areas of references sampling sites. a-d Q sites near and far from active mussel farms, e-h)Q sites near and far from active salmon farms, i-l C references sites far from active aquaculture farms

Sediment samples were gathered with a van Veen grab sampler with a bite of 0.051 m2. Four replicates were taken at each site, three to analyze macrofauna and one to determine total organic matter content (%TOM) and sediment grain size. The macrofauna samples were preserved in the field with 4% formalin buffered with borax. The samples for grain size and %TOM were kept in cold storage until analysis. Due to a failure in the Redox potential sensor for sediments, these data were discarded. Seawater samples were collected with a Niskin bottle from the surface (0 m) and near the seafloor (1–2 m above the bottom) at all sites. Temperature (°C), salinity, dissolved oxygen concentration (mg L− 1), redox potential (mV), pH, conductivity (mS cm− 1) and total dissolved solids (TDS; g L− 1) were recorded using a YSI-556 MPS multiparameter probe.

The sediment samples obtained for macrofauna analysis were sieved in the laboratory with a 500-μm sieve [38, 39]. The sieved material was placed in plastic flasks and preserved in 96% ethanol. All organisms found in the samples were separated with the aid of a stereomicroscope and identified to the lowest taxonomic level possible. Total biomass per species/taxon was estimated as wet weight with an electronic analytical scale with a precision of 0.0001 g. Sediment grain size was determined by automatic sifting through a series of geological sieves (from 2 mm to 63 μm mesh size) [40]. Mean grain size and sediment sorting were calculated by the moment method with the GRADISTAT v8.0 program [41]. Total organic matter content in sediments (%TOM) was estimated as the percentage lost by sediment after calcination at 550 °C for 4 h [42].

Data analysis

To determine macrofauna community dissimilarity in Caucahue Channel at different spatial scales, the factors channel area (AREA), environment (ENV), farming center (CENTER) and sampling site (SITE) were assessed by multivariate analyses. The AREA factor was used to compare sampling sites in the northern, southern and outer areas of the channel (See Fig. 1, Table 1). The ENV factor was assessed by analyzing the role of proximity to farming centers, with three assigned levels: salmonids (SAL), mussels (MYT), and reference (REF). The CENTER factor was assessed by assigning the sites to respective farming centers (Q1, Q2, Q6, Q9, Q11, Q12) and reference sites (REF). The SITE factor was used to compare sampling sites independent of their proximity to farming centers or the area to which they belong (See Table 1 for details). Similarity measures for benthic macrofauna (using all identified species) were determined using abundance data (ind. 0.051 m− 2) and biomass (g 0.051 m− 2), based on the Bray-Curtis index with fourth root transformed data. The fourth root transformation was applied to remove the greater weight of dominant species from the analysis and to give more importance to less common species [43]. A PERMDISP (“Permutational Multivariate Dispersion Analysis”) was applied to assess the homogeneity of multivariate dispersion. The statistical differences in benthic macrofauna for the spatial factors of the analysis were assessed by a PERMANOVA (“Permutational Multivariate Analysis of Variance”). A posteriori pair-wise analysis was applied when the PERMANOVA found significant differences (p < 0.05) for any factor. The statistical differences in environmental conditions in Caucahue Channel for the aforementioned spatial factors were estimated based on the set of variables obtained in the sediments and the water column. Distance measurements of the environmental variables were obtained based on Euclidian distances with normalized data. An unrestricted model was used for all the analyses, with 9999 permutations. The multivariate analyses were performed using the statistical software PRIMER v7 and PERMANOVA+ [44,45,46,47].

Table 1 Classification of sampling sites according to factor analysis performed with PERMANOVA. The factors were channel area (AREA), environment (ENV), farming center (CENTER) and sampling sites (SITE). Qi corresponds to aquaculture sites, REF corresponds to reference sites. See also Fig. 1 and Methods for details

Spatial dissimilarity/distances for biological and environmental variables were represented graphically with non-metric multidimensional scaling (nMDS), which was obtained with resemblance measures/transformations similar to those described for the PERMANOVA analysis. The nMDS were represented by the bubble plot method that incorporates the most representative species of the community (obtained by SIMPER analysis) and the most important environmental variables (obtained by DISTLM analysis). The SIMPER (“Similarity Percentages Analysis”) identifies the percentage contribution of different taxa. The DISTLM (“Distance-based Linear Model”) determines which environmental variable or sets of variables are significantly related to multivariate community ordination (e.g. abundance and biomass), using distance-based linear models. The Best procedure, with 9999 iterations, and the Bayesian information criterion for model selection (BIC [43, 48]) were used to select the environmental variables that best explained the multivariate distribution of the community. The univariate indices of species richness (S), Shannon-Wiener diversity (H′), Simpson dominance (D), and Pielou evenness (J’) were calculated based on all replicates for the ENV factor. Total abundance and biomass ind/g 0.153 m− 2 for ENV factor were standardized per sampling site (which incorporates all replicates per site). The univariate indices were estimated with PRIMER v7 software [46, 49].

The degree of environmental condition (i.e. Ecological Status) was analyzed with the AMBI indicator [30] with software v5.0 (http://ambi.azti.es) [31, 50,51,52]. Some modifications were incorporated in the estimations because some taxa were not initially assigned to any ecological group. The modifications consisted of assigning these taxa to an ecological group according to their taxonomic relationship (genus or family) using two designation criteria (i) the best ecological group (AMBI-1) and (ii) the poorest ecological group (AMBI-2) to which it could belong (See [38] for details). The AMBI coefficient/index (BC/BI) has a score from 0 (Normal/Undisturbed/High Status) to 7 (Azoic/Extremely disturbed/Bad Status) [53]. Finally, the values estimated for the AMBI were incorporated with color range in the nMDS analysis of macrofauna, to visualize whether spatial community groupings (AREA, ENV, CENTER and SITE) have similar ecological status for this bio-indicator.

Results

Macrofauna community diversity

A total of 170 taxa/species were identified in Caucahue Channel, representing 86 families, 51 orders, 24 classes and 12 phyla. Mean total abundance was 671.8 (± 438.3) and 187.8 (± 535.5) ind/0.153 m2 (with and without mytilids) and average total biomass was 12.3 (± 12.9) and 11.1 (± 64.6) g/0.153 m2 (with and without mytilids). Excluding mytilids, the most abundant groups were annelids (60%), nematodes (13%) and other mollusks (12%), while the most important in terms of biomass were annelids (35%), echinoderms (33%) and other mollusks (17%). Figure 3 shows the mean values of total abundance, total biomass, species richness, diversity, evenness, dominance and AMBI, according to the ENV factor. Total abundance and total biomass of benthic macrofauna were lowest at SAL sites (Fig. 3a, b). Species richness was slightly higher at REF sites (Fig. 3c). Shannon-Wiener diversity and Pielou evenness were slightly lower at MYT sites, and Simpson dominance was highest at MYT sites (Fig. 3d-f).

Fig. 3
figure3

Average values (standard error) for the ENV factor of total abundance (a), total biomass (b), species richness (c), Shannon-Wiener diversity (d), Pielou evenness (e), Simpson dominance (f), AMBI-1 (g), AMBI-2 (h). The solid line in g and h represents the cutoff values for levels of disturbance according to AMBI: Undisturbed (blue, 1.2), Slightly disturbed (green, 3.3) and Moderately disturbed (yellow, 5.0)

The PERMDISP analysis had a value of P(perm) < 0.05 for macrofauna density and biomass (fourth root transformation) only among sampling sites. A posteriori pair-wise analysis found a significant difference between 28.6% (abundance) and 42.8% (biomass) comparisons. These percentages, mainly associated with site Q6 (71.4% of the total pair-wise test), indicate that there is no multivariate overdispersion in the abundance and biomass data, which can thus be assessed by PERMANOVA.

Figure 4 shows the nMDS analysis of macrofauna abundance and biomass with the Bray-Curtis resemblance measure and fourth root-transformed data. The REF sites (green) are grouped in the center of the nMDS for abundance and biomass, with the centroids in SAL and MYT sites shifted to the right (Fig. 4a, b). The groupings tend to separate into specific nMDS zones according to their proximity to aquaculture farms (e.g. Q12 to the right and Q2 to the left; Fig. 4c, d), as was observed for all the sampling sites individually (e.g. replicates from a site with the same symbols; Fig. 4e, f). The PERMANOVA analysis for abundance and biomass fourth root-transformed yielded P(perm) values of < 0.05 for the factors AREA, ENV, CENTER and SITE (Table 2). Almost all the pair-wise analyses yielded P(perm) values < 0.05.

Fig. 4
figure4

nMDS for benthic macrofauna in Caucahue Channel fourth-root transformation of Bray-Curtis index. a, b factor ENV; c, d factor CENTER; e, f factor SITE. a, c, e correspond to abundance, and b, d, f) to biomass. In A and B, crosses indicate centroids for the three groupings: MYT with blue circles, SAL with red and REF with green. In c and d each color represents a farm (Q1: blue, Q9: red, Q11: grey, Q2: orange, Q12: light blue, Q6: pink, REF: green). In e and f each similar symbol/color represents a sampling site

Table 2 Output of PERMANOVA main tests for (A) abundance fourth-root transformed with the Bray-Curtis index, (B) biomass fourth-root transformed with the Bray-Curtis index. Factors of analysis were channel area (AREA), environment (ENV), farming center (CENTER) and sampling site (SITE). P(perm) < 0.05 values are in bold

Multivariate diversity and environmental variables

The PERMDISP analysis for the set of environmental variables for sediments and the water column at the sampling sites had a P(perm) value of 0.709, F: 2.27 df: 6,45, because of which the PERMANOVA analysis was applied. The PERMANOVA analysis yielded P(perm) values < 0.05 for the factors AREA, ENV and CENTER (Table 3). As with the abundance and biomass data, the pair-wise tests yielded P(perm) values of < 0.05 for most of the analyses. The nMDS analysis yielded a similar pattern to those of macrofauna for AREA, ENV and CENTER (Fig. 5). The mean values for farming centers (CENTER) and reference sites (REF) for the environmental variables are shown in Table 4. The DISTLM analysis for macrofauna abundance and biomass as a function of the environmental variables found a series of significant variables (Table 5a, b), although according to the BIC the best fit in both cases was with mean grain size (Table 6a, b). The second-best model for abundance included surface pH and Sorting, and the second-best model for biomass also included %TOM (Table 6a, b).

Table 3 Output of the main PERMANOVA tests for water column variables. Factors of analysis were channel area (AREA), environment (ENV) and farming center (CENTER). P(perm) < 0.05 values are in bold
Fig. 5
figure5

Output of nMDS for environmental variables in Caucahue Channel. a Different colors represent ENV factor (MYT, SAL, REF sites). b Different colors represent CENTER factor (mussels: Q1, Q9, Q11; salmonids: Q2, Q6, Q12; and reference sites). c Different colors represent the AREA factor (Northern area, Southern area and Outer area)

Table 4 Average values (±DE) of environmental variables recorded at surface and deeper strata of each sampling site in Caucahue Channel. REF = references sites; Q1, Q9, Q11 = mussel farms; Q2, Q6, Q12 = salmon farms
Table 5 Results of the marginal test for DISTLM analysis, using the BEST procedure for the Bray-Curtis resemblance matrix based on abundance (a) and biomass (b). Data were standardized and then fourth-root transformed. P(perm) < 0.05 values are in bold. BIC = Bayesian Information Criterion. SS = Sum of Squares. For the DISTLM analysis we used all the data described in Table 3. In what follows we only present the first 10 best environmental variables according to BIC criteria. Res. d.f. = 45 (Abundance and Biomass analysis)
Table 6 Best group of variables selected by DISTLM analysis, utilizing the BEST procedure and Bayesian Information Criterion (BIC) for resemblance matrixes based on (a) abundance and (b) biomass. BIC = Bayesian Information Criterion and RSS = Residual Sum of Squares

By overlapping species bubble abundance-plots with nMDSs, Fig. 6 shows the taxa that contribute most to the dissimilarity between environmental types (ENV) according to the SIMPER analysis (See also Tables 7 and 8). The most representative taxa close to the mussel farming centers (MYT) were Ophiuroidea, Nematoda, Ampharetidae, Chaetozone sp., Cirrophorus sp. and Prionospio sp., while the families Spionidae and Cirratulidae were dominant near salmonid farming centers (SAL). The families Cirratulidae, Ampharetidae and Paraonidae were predominant in the reference sites (REF). Overlaying the variables selected by the DISTLM analysis (Tables 5 and 6) with the nMDS plot obtained from environmental variables showed that MYT sites had higher mean grain size and sorting values, and lower pH values (Fig. 7).

Fig. 6
figure6

nMDS of benthic macrofauna abundance obtained by untransformed Bray-Curtis index. The species that contributed most to the differences among environments are superimposed by bubble plots of density obtained from SIMPER analysis. Mussels (blue), Salmon (red) and Reference (green). The size of the bubble indicates the magnitude of species abundance. Stress: 0.26

Table 7 Output of SIMPER analyses obtained from abundance species data. In bold the most representative taxa for similarity within each group (MYT, SAL, REF sites)
Table 8 Output of SIMPER analyses obtained from abundance species data. In bold the most representative taxa for dissimilarity among groups (MYT, SAL, REF sites)
Fig. 7
figure7

nMDS of the set of environmental variables (surface and sea floor of the water column and sediments) that contributed most to the differentiation found by the DISTLM analysis. TOM % (a, b), Mean grain size (c, d), Sorting (e, f), pH superficial (g, h). ENV factor (left), MYT (light blue), SAL (orange) and REF (green); and CENTER factor (right); (Q1: orange, Q9: yellow, Q11: grey, Q2: light blue, Q6: purple, Q12: blue, REF: green). Stress: 0.17

Ecological status

Figure 8a and b show the two AMBI scenarios obtained for the best (AMBI-1) and poorest (AMBI-2) ecological group assignment based on the macrofauna abundance replicates using a color scale (i.e. finer spatial scale observation data). No defined spatial pattern was observed for the AMBI index as a function of environment (ENV) or center type (CENTER) or within the channel areas (AREA), although the reference sites (triangles) showed the best level of ecological status classification (blue and green symbols) with respect to MYT and SAL sites (squares and circles). Some MYT and SAL sites also showed high scores for this classification in some sampling sites. Values for the replicates based on AMBI-1 and 2 were between 0.9 and 3.6 for REF sites, 0.6 and 3.9 for MYT sites, and 0.9 and 4.9 for SAL sites. Following Muxica et al. [53], REF and MYT sites can be categorized in the range from the upper limit of undisturbed to the lower limit of moderately disturbed classification (from impoverished to transitional to polluted benthic community health), while SAL sites can be categorized from the upper limit of undisturbed to the upper limit of moderately disturbed classification (from impoverished to polluted benthic community health). Figure 3g and h show the average AMBI-1 and 2 values for the three types of environment (REF, MYT and SAL), considering the two scenarios of species assignment, “best” (Fig. 3g) and “poorest” (Fig. 3h). The results are broadly similar in the two cases, and the three types of environments are classified globally as slightly disturbed (i.e., AMBI range < 3 but > 2 on average in both scenarios), SAL sites having the poorest condition on average (Fig. 3g, h).

Fig. 8
figure8

Best (a) and poorest (b) ecological group assignment of species to obtain AMBI-1 and AMBI-2. AMBI values are superimposed in colors on each macrofauna replicate obtained in Caucahue Channel as represented by nMDS. The nMDS for abundance was obtained through the Bray-Curtis index and fourth root transformed data. For the environment factor (ENV), circles represent mussel farms (MYT), squares represent salmon farms (SAL) and triangles represent reference sites (REF). The color indicates the level of disturbance according to the AMBI: undisturbed (blue), slightly disturbed (green) and moderately disturbed (yellow)

Discussion

Macrofauna community and environmental variables

There is increasing interest in and need for defining adequate environmental indicators to determine the status of marine ecosystems (e.g. [8, 9, 11, 54]). It has been shown that aquaculture can have negative effects on receiving environments, especially in relation to changes in the benthic substrate [55,56,57,58,59,60]. Several authors have reported that the environmental condition that best describes the ecological impact of aquaculture is organic enrichment under culture centers, and the consequent physical-chemical changes in sediments and the fauna that inhabit sediments (e.g. [26, 28, 56, 57]). It has been reported that the effects of organic enrichment on benthic ecosystems decrease with distance from the source of the perturbation [2]. It was found that near shellfish aquaculture farms abundance increased and organic matter decreased with distance from culture centers [61]. A study of benthic ecosystem affected by a large salmon farm in a Norwegian fjord [20] found that in a 3 km transect the highest abundance, biomass and species richness was found 250 m from the center, and decreased at greater distances; these results follow the pattern proposed by the Pearson & Rosenberg model [2]. Hargrave [55] reviewing published data established a series of empirical relationships including an inverse relationship between benthic macrofauna diversity and redox potential, as well as free dissolved sulfides. Borja et al. [27] studied seven fish farms, two bivalves farms and one mixed farm and found that the effect of organic enrichment around the centers generally extended for some 50 m, and that the response of benthic biomass, abundance and species richness was highly variable and depended mainly on local hydrodynamics, sediment characteristics and the age and level of production of the farming center. This concurs with the results reported by Chamberlain [13], who found that the effects of organic matter are local, approximately 40 m from the bivalve farming centers and that biodeposits (i.e. true feces and uningested pseudofeces) accumulate up to 30 m from these centers in shallow (10–15 m) sites with lower hydrodynamic flow, while with higher hydrodynamic flow the deposits are dispersed up to 90 m. Wilding & Nickell [28] found similar results for bivalve farms, but in this case the distance at which the impact of biodeposits (shell-hash) was evident did not exceed 5 m. The scope of the environmental effect of increased organic matter from farming centers depends on the dispersive capacity of the water column near the centers [13], which is influenced by advective transport of biodeposits and their redistribution on the seafloor [62], and by the location of the farming centers; there is less dispersion in shallow and low velocity sites [61, 63]. Similar effects related to substrate change and biodiversity loss have been described in other parts of the world for mytilids and other mollusk farms such as oysters (e.g. [27, 29, 64]).

Various authors have reported that the main environmental impacts of salmon farming in Chile are related to physical-chemical changes to sediments and loss of benthic biodiversity [19, 59, 65, 66]. Our study conducted in Caucahue Channel in winter conditions found 170 taxa belonging to different taxonomic groups with polychaetes, nematodes, mollusks and echinoderms the most important. At the spatial scale of the entire channel our findings indicate the presence of a high number of species. However, species/taxon richness was low on average, in the range of 15 to 18 taxa in each sampling site, with slightly higher numbers in reference sites (REF). Few differences between sampling sites near and far from culture centers were recorded in the univariate diversity index at the ENV spatial scale of analysis (i.e. REF, SAL and MYT sites; See Fig. 3). However, the multivariate analysis indicated that benthic macrofauna are highly heterogeneous in both abundance and biomass at all the studied spatial scales (see Fig. 4). Although species richness per site was generally low and diversity was relatively homogeneous from the univariate perspective, the presence of species and their relative abundance were dissimilar at different spatial scales in Caucahue Channel (among sampling sites, between sites near and far from farming centers, among farming centers and between geographic areas in the channel, see Fig. 9). The high degree of spatial heterogeneity (high degree of beta diversity or spatial species turnover) determines the high number of total taxa identified. The global differences observed between sites near and far from farming centers (SAL and MYT versus REF sites), such as the displacement of centroids with respect to REF sites (See Fig. 4) suggest that the three types of environments present dissimilar ecological features. Two aspects should be considered in interpreting these results. First, this study does not seek to quantify the ecological conditions directly beneath fish cages or bivalve long-lines, but rather at sites near centers (between 5 and 100 m away), and at sites far from farming centers (between 500 and 3000 m away), that is, in the global context of the Caucahue Channel. Sites near and far from bivalve and salmonid farms can thus be considered distinct in terms of the relative abundance/biomass of macrofauna species. These differences were also found among the six farming centers and between these and the REF sites (See Fig. 4c, d and Fig. 9). A second aspect is related to how long this area has been used for aquaculture (approximately 30 years), which is sufficient time to structure resident populations that are stable under these conditions. The two aspects (i.e. distance from the centers of the sampling sites and the time that Caucahue Channel has been used for aquaculture) suggest that the macrofauna in the Caucahue Channel presents diverse very local populations, with significant spatial heterogeneity in abundance and biomass, which could result in some species acting as sources of organisms at this spatial scale, subsidizing areas in poorer environmental condition and linking macrofauna biodiversity from small spatial scales to the entire channel (see Fig. 9). Spatial abundance differentiation can be observed in Fig. 6, which highlights nine taxa and/or main groups of organisms that indicate the high degree of spatial heterogeneity in species distribution and abundance. The overall community structure of Caucahue Channel should be compared to that immediately below the cages and long-lines to identify taxa and populations that benefit from or suffer disadvantages from aquaculture.

Fig. 9
figure9

Conceptual model for community dissimilarity of benthic macrofauna and for the ecological status of Caucahue Channel at different spatial scales of observation (i.e. factor of analysis). From top to bottom the factors are: AREA (northern, southern and outer), ENV (salmon, mussel and references), CENTER (Qi to Ci) and SITE (each sampling site, represented with a black circle). From the PERMANOVA analysis significant differences were observed for each of these 4 factors between their different levels of comparison, which are indicated by areas with segmented lines. The ascending and descending black arrows represent hierarchy links present from the entire study AREA to the sampling sites (SITE) and vice versa. The black arrows pointing in two ways represent the relationship between the sampling sites and each of the replicates used for the estimations of ecological status using AMBI. The color circles represent a spatially explicit environmental traffic light to the smallest spatial scale possible (i.e., each replicate). For each of the factors studied in the Caucahue Channel (AREA, ENV and CENTER) it is possible to identify replicates (i.e. sampling SITES) ranging from undisturbed (blue circles) to moderately disturbed (yellow circles). For further details see the Discussion section

As described above, the distribution and abundance of benthic macrofauna has classically been associated with sediment characteristics [67, 68]. Abundance, biomass and species richness may reach very low values and defaunation when organic matter levels are extremely high [2, 69]. Larger mean grain size of sediment is associated with lower organic matter content, given that larger grains allow more interstitial water, which in turn results in more oxygenated environments [70, 71]. Based on the DISTLM analysis (see Tables 5 and 6 for macrofauna abundance and biomass), our results indicate that mean grain size and %TOM are the most important sediment variables in explaining the spatial dissimilarity in the benthic macrofauna community at different spatial scales (e.g. between REF sites and SAL and MYT sites), suggesting a benthic environment with heterogeneous input levels of organic matter coming from the surface. For example, SAL environments had smaller average grain size, while the other two environments (REF and MYT) had sediments in the entire size range (fine to coarse, but with higher average; See Table 4). While %TOM values explained spatial differences among sites, the average percentages were generally not very high (< 4%; See Table 4). A heterogeneous input of organic matter, probably related to local circulation, could determine the mean grain size of sediments [16,17,18, 20, 24, 72] and probably the spatial heterogeneity in abundance and distribution of macrofauna species in Caucahue Channel. During autumn-winter conditions (17 April to 21 July 2011), [36] measured currents in Caucahue Channel using an acoustic Doppler current profiler (ADCP). They found (near Q11, See Fig. 1) that the mean flow of the total current located near the center of the channel was stratified, showing 2 layers (eastward at the surface and westward at the bottom) with speeds lower than 30 cm s− 1. Along the channel tidal currents behaved approximately as a standing wave, explaining around 80% of the total variance. The cross-channel tidal currents explained between 40 and 60% of the total variance. In a time scale of days, residual currents from north to south have a mean flow near 6 cm s− 1, favoring net transport in this direction depending on the fortnightly cycle (neap or spring time period). Sobarzo et al. [36] showed also that Caucahue Channel is dominated by tidal oscillatory flows most of the time, with a secondary transport flow north-south on a time scale of days. Therefore, it is expected that local circulation conditions strongly influence sedimentation rates at each sampling site, which are also influenced by the particulate organic matter in suspension derived from nearby aquaculture farms.

Ecological status of Caucahue Channel

The environmental quality of a marine area is generally estimated by the application of ecological criteria like abundance, biomass and species richness [2]. Benthic macrofauna is usually used in environmental quality studies because of their sensitivity and low mobility, among other life cycle characteristics; they integrate environmental conditions and changes that have occurred through time very effectively [10, 73, 74]. As noted above, the univariate indicators of diversity did not show major differences in Caucahue Channel, but the multivariate analysis did. Differences were found between the REF sites and the MYT and SAL sites in relation to abundance and total community biomass, with lower values for both variables at SAL sites (See Fig. 3). This can also be observed in Fig. 6 with the presence of dominant taxa in some SAL sites, mainly polychaetes of the families Spionidae and Cirratulidae. This suggests that the relative species abundance analysis at different spatial scales may be more useful than univariate indicators for detecting effects on the environment (i.e. assigning the relative abundance of each taxon to an ecological group). Borja et al. [27] concluded that a diverse set of indicators is needed to assess the environmental impact of aquaculture, including the AMBI as well as the characteristics of the culture center and the hydrodynamics of the area. The AMBI coefficient of the average values per site type (REF, MYT, and SAL; see Fig. 3) in Caucahue Channel globally indicated a slightly disturbed classification (Good Ecological Status according to [53]; AMBI < 3.3; sensu [30]). However, analyzing the biotic index at a finer spatial scale (sampling or replicate sites), AMBI reached nearly 5 (with AMBI ranging from 0.9 to 3.6 for REF, 0.6 to 3.9 for MYT and 0.9 to 4.9 for SAL), that is, polluted/moderately disturbed (Moderate to Poor Ecological Status, according to [53]) (see also Fig. 8). It is interesting to note that the highest values of AMBI are at sites near salmon farms. Three studies have previously reported AMBI values in Chilean Patagonia. In Aysén Fjord (an area used intensively for salmon aquaculture) and in Baker Fjord (an area not impacted by aquaculture) Quiroga et al. [75] found mean AMBI values from 2.7 to 4.0 and 1.5 to 2.4, respectively. Working in the southwest area of Seno Reloncaví, Pino et al. [39] found mean AMBI values between 2.3 and 2.5 (near mussel long lines) and mean AMBI values between 1.9 and 3.4 in sampling reference sites. And Urbina [66], analyzing exposed and protected areas in Reloncaví estuary and in Chiloé Island, finding mean AMBI values between 2.3 and 3.0 (near a salmonid farm) and between 0.1 and 2.0 in control sites. Our results indicated AMBI values ranging from 0.6 (near MYT ENV) to 4.9 (near SAL ENV), which is within the range of previously reported values, although with some scores above the maximum AMBI values reported previously for areas near salmon farms.

Evaluating environmental quality by assigning relative species abundance to ecological groups [30, 53], the impact of farming centers on the benthic communities located near the centers in Caucahue Channel (mainly SAL sites) is intermediate, characterized by the presence of Ecological Groups (EG) II to V. Some species/taxa are indicative of greater levels of disturbance, such as an abundance of opportunistic species like Capitella capitata (EG V; SAL sites) [2], and they may be dominant in areas directly beneath aquaculture centers [14]. In this study there was a lower proportion of Capitella sp. particularly at SAL sites, which were dominated by organisms of the families Cirratulidae [e.g. Chaetozone sp. (EG IV) and Spionidae (EG III)]. In MYT sites the dominant species were Ophiuroidea (EG II), nematodes (EG III), polychaetes of the family Cirratulidae like Chaetozone sp. (EG IV) and of the Spionidae family (EG III) like Prionospio sp. (EG II and IV). The polychaete families Cirratulidae (EG IV), Paraonidae (EG II), Ampharetidae (EG I and III) and Prionospio sp. (EG II and IV) were dominant at REF sites. The spatial dissimilarity of each of these species/taxa and their relative abundance/biomass is explained in the community in the winter, according to the DISTLM analysis, by the heterogeneity of the environmental variables mean grain size and %TOM. Integrating the information obtained from the EG (e.g. AMBI) with the nMDS analysis obtained from the relative abundance of the species (See Figs. 4, 6, 8), we characterized the benthic macrofauna community spatially at particular sites in Caucahue Channel. Integration of this type of ecological information (presence/abundance of taxon, ecological group and environmental variables) provides a robust quantitative tool to evaluate the degree of disturbance/pollution of benthic environments from small to large spatial scales (see Fig. 9).

Most research on the effects of aquaculture on benthic communities has been aimed at salmonid or mytilid production (see references above). In this analysis of Caucahue Channel we included both aquaculture activities. We determined under winter conditions that the macrofauna is highly dissimilar and associated with sediment structure. The level of perturbation is highly heterogeneous according to AMBI criteria, with sites ranging from undisturbed (Normal or High Ecological Status) to moderately disturbed (Moderate/Poor Ecological Status) according to benthic community health and site disturbance classification (sensu [30, 53]). It would be useful to assess the classifications made in other seasons besides winter. For example, [76] found lower production of bivalve biodeposits in winter than in summer and autumn. Sampling in different seasons should provide greater certainty about the ecological impact of aquaculture farms in Caucahue Channel and other areas in the inner sea of Chiloe.

Conclusions

The main conclusions are (i) there was a high degree of spatial dissimilarity in benthic macrofauna and environmental variables in sediments and the water column among sampling sites, environmental types (near or far from farming centers) and between the northern and southern areas of the channel; (ii) sediment structure (mean grain size and %TOM) correlated with community dissimilarities; and (iii) the AMBI index and the multivariate community-environment approximation allow defining the level of perturbation of a particular geographic area at different spatial scales, using the most detailed ecological information for the replicates or samples.

Availability of data and materials

The datasets used in the current study are available from the corresponding author on reasonable request.

Abbreviations

ha:

Hectares

ENV:

Environment

CENTER:

Farming center

SITE:

Sampling site

AREA:

Channel area

SAL:

Salmonids

MYT:

Mussels

REF:

Reference

EG:

Ecological Groups

References

  1. 1.

    Forde J, Shin PK, Somerfield PJ, Kennedy RM. M-AMBI derived from taxonomic levels higher than species allows ecological status assessments of benthic habitats in new geographical areas. Ecol Indic. 2013;34:411–9.

    Article  Google Scholar 

  2. 2.

    Pearson T, Rosenberg R. Macrobenthic succession in relation to organic enrichment and pollution of the marine environments. Oceanogr Mar Biol Annu Rev. 1978;16:229–311.

    Google Scholar 

  3. 3.

    Gray JS, Carr MR, Clarke KR, Green RH, Pearson TH, Rosenberg R, Warwick RM. Analysis of community attributes of the benthic macrofauna of Frierfjord/Langesundfjord and in a mesocosm experiment. Mar Ecol Prog Ser. 1988;46:151–65.

    Article  Google Scholar 

  4. 4.

    Carrasco FD, Gallardo VA. La contaminación marina y el valor de la macroinfauna en su evaluación y vigilancia: casos de estudio en el Litoral de Concepción, Chile. Biología Pesquera. 1989;18:15–27.

    Google Scholar 

  5. 5.

    Levin LA, Boesch DF, Covish A, Dahm C, Erseus C, Ewel KC, Kneib RT, Moldenke A, Palmer MA, Snelgrove P, Strayer D, Weslawski JM. The function of marine critical transition zones and the importance of sediment biodiversity. Ecosystems. 2001;4:430–51.

    CAS  Article  Google Scholar 

  6. 6.

    Paul JF, Scott KJ, Campbell DE, Gentile JH, Strobel CS, Valente RM, Weisberg SB, Holland AF, Ranasinghe JA. Developing and applying a benthic index of estuarine condition for the Virginian Biogeographic Province. Ecol Indic. 2001;1:83–99.

    Article  Google Scholar 

  7. 7.

    Dauvin JC. Paradox of estuarine quality: benthic indicators and indices, consensus or debate for the future. Mar Pollut Bull. 2007;55:271–81.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. 8.

    Díaz RJ, Solan M, Valente RM. A review of approaches for classifying benthic habitats and evaluating habitat quality. J Environ Manag. 2004;73:165–81.

    Article  Google Scholar 

  9. 9.

    Pinto R, Patricio J, Baeta A, Fath BD, Neto JM, Marques JC. Review and evaluation of estuarine biotic indices to assess benthic condition. Ecol Indic. 2009;9:1–25.

    Article  Google Scholar 

  10. 10.

    Marques JC, Costa MJ, Elliott M. Introducing “assessing ecological quality in estuarine and coastal systems – management perspective”. Estuar Coast Shelf Sci. 2012;112:1–3.

    Article  Google Scholar 

  11. 11.

    Borja A, Marín SL, Muxika I, Pino L, Rodríguez JG. Is there a possibility of ranking benthic quality assessment indices to select the most responsive to different human pressures? Mar Pollut Bull. 2015;97:85–94.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12.

    Beauchard O, Veríssimo H, Queirós AM, Herman PMJ. The use of multiple biological traits in marine community ecology and its potential in ecological indicator development. Ecol Indic. 2017;76:81–96.

    Article  Google Scholar 

  13. 13.

    Chamberlain J, Fernandes TF, Read P, Nickell TD, Davies IM. Impacts of biodeposits from suspended mussel (Mytilus edulis L.) culture on the surrounding surficial sediments. ICES J Mar Sci. 2001;58:411–6.

    Article  Google Scholar 

  14. 14.

    Callier M, McKindsey C, Desrosiers G. Multi-scale spatial variations in benthic sediment geochemistry and macrofaunal communities under a suspended mussel culture. Mar Ecol Prog Ser. 2007;348:103–15.

    Article  Google Scholar 

  15. 15.

    Ysebaert T, Hart M, Herman PMJ. Impacts of bottom and suspended cultures of mussels Mytilus spp. on the surrounding sedimentary environment and macrobenthic biodiversity. Helgol Mar Res. 2009;63:59–74.

    Article  Google Scholar 

  16. 16.

    Hall POJ, Anderson LG, Holby O, Kollberg S, Samuelsson M. Chemical fluxes and mass balances in a marine fish cage farm. I Carbon Marine Ecol Prog Ser. 1990;61:61–73.

    CAS  Article  Google Scholar 

  17. 17.

    Holmer M, Kristensen E. Impact of marine fish cage farming on metabolism and sulfate reduction of underlying sediments. Mar Ecol Prog Ser. 1992;80:191–201.

    CAS  Article  Google Scholar 

  18. 18.

    Karakassis I, Tsapakis M, Hatziyanni E, Papadopoulou KN, Plaiti W. Impact of cage farming of fish on the seabed in three Mediterranean coastal areas. ICES J Mar Sci. 2000;57:1462–71.

    Article  Google Scholar 

  19. 19.

    Soto D, Norambuena F. Evaluation of salmon farming effects on marine systems in the inner seas of southern Chile: a large-scale mensurative experiment. J Appl Ichthyol. 2004;20:493–501.

    Article  Google Scholar 

  20. 20.

    Kutti T, Ervik A, Hansen PK. Effects of organic effluents from a salmon farm on a fjord system. I. Vertical export and dispersal processes. Aquaculture. 2007;262:367–81.

    CAS  Article  Google Scholar 

  21. 21.

    Keeley NB, Forrest BM, Crawford C, Macleod CK. Exploiting salmon farm benthic enrichment gradients to evaluate the regional performance of biotic indices and environmental indicators. Ecol Indic. 2012;23:453–66.

    CAS  Article  Google Scholar 

  22. 22.

    Ye LX, Ritz DA, Fenton GE, Lewis ME. Tracing the influence on sediments of organic waste from a salmonid farm using stable isotope analysis. J Exp Mar Biol Ecol. 1991;145:161–74.

    Article  Google Scholar 

  23. 23.

    Karakassis I, Tsapakis M, Hatziyanni E. Seasonal variability in sediment profiles beneath fish farm cages in the Mediterranean. Mar Ecol Prog Ser. 1998;162:243–52.

    Article  Google Scholar 

  24. 24.

    Kutti T, Ervik A, Høisæter T. Effects of organic effluents from a salmon farm on a fjord system. III. Linking deposition rates of organic matter and benthic productivity. Aquaculture. 2008;282:47–57.

    CAS  Article  Google Scholar 

  25. 25.

    Hargrave BT, Duplisea DE, Pfeiffer E, Wildish DJ. Seasonal changes in benthic fluxes of dissolved oxygen and ammonium associated with marine cultured Atlantic salmon. Mar Ecol Prog Ser. 1993;96:249–57.

    Article  Google Scholar 

  26. 26.

    Sanz-Lázaro C, Marín A. Assessment of Finfish Aquaculture Impact on the Benthic Communities in the Mediterranean Sea. Dyn Biochem Process Biotechnol Mol Biol. 2008;2(Special Issue 1):21–32.

  27. 27.

    Borja A, Rodríguez JG, Black K, Bodoy A, Emblow C, Fernandes TF, Forte J, Karakassis I, Muxika I, Nickell TD, Papageorgiou N, Pranovi F, Sevastou K, Tomassetti P, Angel D. Assessing the suitability of a range of benthic indices in the evaluation of environmental impact of fin and shellfish aquaculture located in sites across Europe. Aquaculture. 2009;293:231–40.

    Article  Google Scholar 

  28. 28.

    Wilding TA, Nickell TD. Changes in Benthos Associated with Mussel (Mytilus edulis L). Farms on the West-Coast of Scotland. PLoS One. 2013;8(7):e68313. https://doi.org/10.1371/journal.pone.0068313.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Gallardi D. Effects of bivalve aquaculture on the environment and their possible mitigation: a review. Fish Aquac J. 2014;5:105. https://doi.org/10.4172/2150-3508.1000105.

    Article  Google Scholar 

  30. 30.

    Borja A, Franco J, Pérez V. A marine biotic index to establish the ecological quality of soft-bottom benthos within European estuarine and coastal environments. Mar Pollut Bull. 2000;40:1100–14.

    CAS  Article  Google Scholar 

  31. 31.

    Borja A, Muxica I. Guidelines for the use of AMBI (AZTIʼs marine biotic index) in the assessment of the benthic ecological quality. Mar Pollut Bull. 2005;50:787–9.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. 32.

    Borja A, Muxika I, Franco J. The application of a marine biotic index to different impact sources affecting soft-bottom benthic communities along European coasts. Mar Pollut Bull. 2003;46:835–45.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Grall J, Glémarec M. Using biotic indices to estimate macrobenthic community perturbations in the Bay of Brest. Estuar Coast Shelf Sci. 1997;44(suppl. A):43–53.

    Article  Google Scholar 

  34. 34.

    Borja A, Marín S, Nuñez R, Muxika I. Is there a significant relationship between the benthic status of an area, determined by two broadly-used indices, and best professional judgment? Ecol Indic. 2014;45:308–12.

    Article  Google Scholar 

  35. 35.

    Riquelme R, Campos P, Troncoso A, Salamanca M, Sobarzo M, Agurto C, Bravo L. Evaluación de las agrupaciones de concesiones de acuicultura implementadas en la industria del cultivo del salmón en Chile en base a zonas estimadas por modelos de trayectoria de partículas. Mecánica Computacional. 2013;32:2795–810.

    Google Scholar 

  36. 36.

    Sobarzo M, Bravo L, Iturra C, Troncoso A, Riquelme R, Campos P, Agurto C. Hydrodynamics of a channel occupied by the aquaculture industry in southern Chile: implications for connectivity between farms. Aquaculture Environ Interact. 2018;10:291–307.

    Article  Google Scholar 

  37. 37.

    Informe Ambiental Quemchi. Estudios previos plan regulador comunal de Quemchi, Región de Los Lagos; 2014. p. 81.

    Google Scholar 

  38. 38.

    Hernández-Miranda E, Veas R, Krautz MC, Hidalgo N, San Martín F, Quiñones RA. Bio-Indicadores de contaminación marina costera y filtros de exclusión de organismos en sistemas de captación de agua de mar. En "Programas de monitoreo del medio marino costero: diseños experimentales, muestreos, métodos de análisis y estadística asociada". Juan C. Castilla, José M. Fariña y Andrés Camaño (Eds). Ediciones Universidad Católica. Santiago, Chile; 2020. p. 269–88.

  39. 39.

    Pino L, Marín SL, Núñez R. Indicadores bióticos y fracción de tamaños en la definición de la macrofauna. Lat Am J Aquat Res. 2015;43:329–36.

    Article  Google Scholar 

  40. 40.

    Buchanan JB. Measurements of the physical and chemical environment. In: Holme NA, McIntyre AD, editors. Methods for the study of marine benthos, IBP Handbook N° 16. Oxford: Blackwell; 1971. p. 30–52.

    Google Scholar 

  41. 41.

    Blott SJ, Pye K. GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf Process Landf. 2001;26:1237–48.

    Article  Google Scholar 

  42. 42.

    Byers S, Mills E, Stewart P. A comparison of methods of determining organic carbon in marine sediments, with suggestion for a standard method. Hydrobiologia. 1978;58:43–7.

    CAS  Article  Google Scholar 

  43. 43.

    Anderson M, Ellingsen K, McArdle B. Multivariate dispersion as a measure of beta diversity. Ecol Lett. 2006;9:683–93.

    PubMed  Article  PubMed Central  Google Scholar 

  44. 44.

    Anderson M. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.

    Google Scholar 

  45. 45.

    Anderson M. PERMANOVA: a FORTRAN computer program for permutational multivariate analysis of variance. New Zealand: Department of Statistics, University of Auckland; 2005. p. 24.

    Google Scholar 

  46. 46.

    Clarke KR, Gorley RN. PRIMER v6: user manual/tutorial. Plymouth: PRIMER-E; 2006.

    Google Scholar 

  47. 47.

    Anderson M, Gorley RN, Clarke KR. PERMANOVA+ for PRIMER: guide to software and statistical methods. Plymouth: PRIMER-E; 2008.

    Google Scholar 

  48. 48.

    McArdle BH, Anderson MJ. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology. 2001;82:290–7.

    Article  Google Scholar 

  49. 49.

    Clarke KR, Warwick RM. Change in marine communities: an approach to statistical analysis and interpretation. 2nd ed. Plymouth: PRIMER-E; 2001.

    Google Scholar 

  50. 50.

    Muxika I, Borja A, Bald J. Using historical data, expert judgment and multivariate analysis in assessing reference conditions and benthic ecological status, according to the European water framework directive. Mar Pollut Bull. 2007;55:16–29.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Borja A, Mader J, Muxika I. Instructions for the use of the AMBI index software (version 5.0). Rev Invest Marina AZTI-Tecnalia. 2012;19(3):71–82.

    Google Scholar 

  52. 52.

    Teixeira H, Weisberg SB, Borja A, Ranasinghe JA, Cadien DB, Velarde RG, Lovell LL, Pasko D, Phillips CA, Montagne DE, Ritter KJ, Salas F, Marques JC. Calibration and validation of the AZTI’s marine biotic index (AMBI) for Southern California marine bays. Ecol Indic. 2012;12:84–95.

    Article  Google Scholar 

  53. 53.

    Muxika I, Borja A, Bonne W. The suitability of the marine biotic index (AMBI) to new impact sources along European coasts. Ecol Indic. 2005;5:19–31.

    Article  Google Scholar 

  54. 54.

    Maes J, Liquete C, Teller A, Erhard M, Paracchini ML, Barredo JI, Grizzetti B, Cardoso A, Somma F, Petersen JE, Meiner A, Gelabert ER, Zal N, Kristensen P, Bastrup-Birk A, Biala K, Piroddi C, Egoh B, Degeorges P, Fiorina C, Santos-Martín F, Naruševičius V, Verboven J, Pereira HM, Bengtsson J, Gocheva K, Marta-Pedroso C, Snäll T, Estreguil C, San-Miguel-Ayanz J, Pérez-Soba M, Grêt-Regamey A, Lillebø AI, Malak DA, Condé S, Moen J, Czúcz B, Drakou EG, Zulian G, Lavalle C. An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosystem Serv. 2016;17:14–23.

    Article  Google Scholar 

  55. 55.

    Hargrave BT. Empirical relationships describing benthic impacts of salmon aquaculture. Aquaculture Environ Interact. 2010;1:33–46.

    Article  Google Scholar 

  56. 56.

    Holmer M, Wildish D, Hargrave B. Organic Enrichment from Marine Finfish Aquaculture and Effects on Sediment Biogeochemical Processes. In: Hargrave B, editor. Environmental Effects of Marine Finfish Aquaculture. Berlin, Heidelberg: Springer Berlin Heidelberg; 2005. p. 181–206.

    Google Scholar 

  57. 57.

    Keeley NB, Macleod CK, Hopkins GA, Forrest BM. Spatial and temporal dynamics in macrobenthos during recovery from salmon farm induced organic enrichment: when is recovery complete? Mar Pollut Bull. 2014;80:250–62.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  58. 58.

    Barrett LT, Swearer SE, Dempster T. Impacts of marine and freshwater aquaculture on wildlife: a global meta-analysis. Rev Aquac. 2019;11:1022–44.

    Article  Google Scholar 

  59. 59.

    Quiñones RA, Fuentes ME, Montes RM, Soto D, León-Muñoz J. Environmental issues in Chilean salmon farming: a review. Rev Aquac. 2019;11(2):375–402.

    Article  Google Scholar 

  60. 60.

    Weitzman J, Steeves L, Bradford J Filgueira R. Far-Field and Near-Field Effects of Marine Aquaculture. In World Seas: an Environmental Evaluation (2nd Edition) Volume III: Ecological Issues and Environmental Impacts, Ed. 2 (pp. 197–220). Amsterdam: Elsevier Ltd.

  61. 61.

    Weise AM, Cromey CJ, Callier MD, Archambault P, Chamberlain J, McKindsey CW. Shellfish-DEPOMOD: Modelling the biodeposition from suspended shellfish aquaculture and assessing benthic effects. Aquaculture. 2009;288:239–53.

    Article  Google Scholar 

  62. 62.

    Giles H, Broekhuizen N, Bryan KR, Pilditch CA. Modelling the dispersal of biodeposits from mussel farms: the importance of simulating biodeposit erosion and decay. Aquaculture. 2009;291:168–78.

    Article  Google Scholar 

  63. 63.

    Crawford CM, Macleod CKA, Mitchell IM. Effects of shellfish farming on the benthic environment. Aquaculture. 2003;224:117–40.

    Article  Google Scholar 

  64. 64.

    Mckindsey CW, Archambault P, Callier MD, Olivier F. Influence of suspended and off-bottom mussel culture on the sea bottom and benthic habitats: a review. Can J Zool. 2011;89:622–46.

    Article  Google Scholar 

  65. 65.

    Buschmann AH, Riquelme VA, Hernández-González MC, Varela D, Jiménez JE, Henríquez LA, Vergara PA, Guíñez R, Filún L. A review of the impacts of salmonid farming on marine coastal ecosystems in the Southeast Pacific. ICES J Mar Sci. 2006;63:1338–45.

    Article  Google Scholar 

  66. 66.

    Urbina M. Temporal variation on environmental variables and pollution indicators in marine sediments under sea Salmon farming cages in protected and exposed zones in the Chilean inland Southern Sea. Sci Total Environ. 2016;573:841–53.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  67. 67.

    Etter RJ, Grassle JF. Patterns of species diversity in the deep sea as a function of sediment particle size diversity. Nature. 1992;360:576–8.

    Article  Google Scholar 

  68. 68.

    Martins R, Sampaio L, Rodrigues AM, Quintino V. Soft-bottom Portuguese continental shelf polychaetes: Diversity and distribution. J Mar Syst. 2013;123–124:41–54.

    Article  Google Scholar 

  69. 69.

    McNally WH, Mehta AJ. Sediment Transport and Deposition in Estuaries (Sample Chapter). In: Encyclopedia of Life Support Systems (EOLSS): Coastal Zones and Estuaries; 2004. Retrieved from http://www.eolss.net/sample-chapters/c09/E2-06-01-04.pdf.

    Google Scholar 

  70. 70.

    Ziebis W, Huettel M, Forster S. Impact of biogenic sediment topography on oxygen fluxes in permeable seabeds. Mar Ecol Prog Ser. 1996;140:227–37.

    CAS  Article  Google Scholar 

  71. 71.

    Janssen F, Huettel M, Witte U. Pore-water advection and solute fluxes in permeable marine sediments (II): benthic respiration at three sandy sites with different permeabilities (German bight, North Sea). Limnol Oceanogr. 2005;50(3):779–92.

    CAS  Article  Google Scholar 

  72. 72.

    Findlay RH, Watling L, Mayer LM. Environmental impact of salmon net-pen culture on marine benthic communities in Maine: a case study. Estuaries. 1995;18:145–79.

    CAS  Article  Google Scholar 

  73. 73.

    Dauer D. Biological criteria, environmental health and estuarine macrobenthic community structure. Mar Pollut Bull. 1993;26:249–57.

    Article  Google Scholar 

  74. 74.

    Salas F, Marcos C, Neto JM, Patrıcio J, Pérez-Ruzafa A, Marques JC. User-friendly guide for using benthic ecological indicators in coastal and marine quality assessment. Ocean Coast Manag. 2006;49:308–31.

    Article  Google Scholar 

  75. 75.

    Quiroga E, Ortiz P, Reid B, Gerdes D. Classification of the ecological quality of the Aysen and baker fjords (Patagonia, Chile) using biotic indices. Mar Pollut Bull. 2013;68:117–26.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  76. 76.

    Hatcher A, Grant J, Schofield B. Effects of suspended mussel culture (Mytilus spp.) on sedimentation, benthic respiration and sediment nutrient dynamics in a coastal bay. Mar Ecol Prog Ser. 1994;115:219–35.

    Article  Google Scholar 

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Acknowledgements

We wish to thank A. Araneda, S. Miranda, S. Pérez, P. Constanzo and F. San Martín for their support in the fieldwork and sample analysis. We would also like to thank the captain and crew of the R/V Jürgen Winter of the Universidad Austral de Chile.

Funding

This research was funded by the Interdisciplinary Center for Aquaculture Research, INCAR (FONDAP-ANID 15110027). The funders had no role in the study design, data collection, analysis, decision to publish or preparation of the manuscript.

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EHM and RQ conceived and designed the field sampling. RE and PS analyzed macrofauna samples. EHM, RE, RV and MCK analyzed the data. EHM and RQ wrote the paper. All authors read and approved the final manuscript.

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Correspondence to Eduardo Hernández-Miranda.

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Hernández-Miranda, E., Estrada, R., Strange, P. et al. Macrofauna community patterns in a Chiloe Island channel used intensely for aquaculture: the ecological status of its benthic environment. Rev. Chil. de Hist. Nat. 94, 1 (2021). https://doi.org/10.1186/s40693-021-00098-z

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Keywords

  • Salmon farming
  • Mussel farming
  • Aquaculture environmental impact
  • Benthic community
  • AMBI
  • Multivariate analysis