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INTRODUCTIONTop The Mediterranean is considered one of the Large Marine Ecosystems of the world, owing to its bathymetry, hydrography, productivity and trophic webs (). It is a semi-enclosed sea connected to the Atlantic Ocean through the Gibraltar Strait, to the Black Sea through the Dardanelles Strait and to the Red Sea through the artificial Suez Channel (Fig. 1). It acts as a concentration basin, and evaporation is higher in its eastern basin, causing the water level to decrease and salinity to increase from west to east (). While temperature also increases eastwards (), surface productivity, organic matter availability at the seafloor and the biomass of megabenthic fauna of deep ecosystems decrease eastwards (, , ). The Mediterranean has narrow continental shelves and a large area of open sea. In fact, the continental shelf covers about 20% of the Mediterranean bottoms, whereas the slope covers about 60% (). Therefore, a large part of this basin can be classified as deep sea (). The high species richness, coupled with a high proportion of endemism, makes the Mediterranean one of the world’s ‘biodiversity hotspots’ (, , ). Environmental variables such as temperature, productivity and distance from the Strait of Gibraltar have been shown to be causes of fish species richness distribution (, ). However, this high biodiversity is presently threatened by the combined action of anthropogenic impacts, introduction of alien species and climate change (). Among human activities, fisheries are one of the most important factors affecting marine resources and ecosystems. It is well known that fisheries have profoundly modified the structure of marine ecosystems (, , ). Effects of fishing on marine ecosystems include shifts in the food-web structure due to changes in predator-prey relationships (); changes in size structure due to vulnerability and selection of fishing for large individuals (, , ); genetic selection of species with particular life-history traits, such as a higher growth rate and earlier age-at-maturity (, ); changes in the spatial distribution of target species (e.g. ); effects on the population of non-target species (, ); and decrease of habitat complexity and changes on the benthic community structure (e.g. ). The natural resources of the Mediterranean have been subject to human exploitation since ancient times, when coastal communities started to use different fishing gears, some of which are still in use (). Dramatic long-term changes in marine communities took place before the industrialization of fisheries that occurred in the 1950s, and have already been documented in some areas, such as the Adriatic Sea (). Until 1950, the exploitation of Mediterranean resources was limited to fishing areas shallower than 200 m depth. In the last few decades, with the decline of stocks on the continental shelf, increasing market demand and the introduction of new technologies, trawl fisheries have expanded offshore and towards the deeper waters of the continental slope (, ) to target valuable resources such as red shrimps (e.g. , , ). In this area, fisheries are assessed within the framework of the General Fisheries Commission for the Mediterranean (GFCM), the regional fisheries management organization of the Mediterranean. Of the 27 Mediterranean stocks of fishing target species assessed by the GFCM in its last report, about 80% were considered overexploited (). The presence of a high diversity of species and the absence of large monospecific stocks comparable to those inhabiting some wide areas of the open oceans are characteristic of the Mediterranean demersal fisheries (). Assessment at a community level is therefore crucial, particularly due to the multispecies nature of the bottom trawl fishery and also because a decline in the diversity of demersal assemblages has been reported due to fishing exploitation (e.g. , , ). Assessment at a community level is also a requirement for the implementation of an ecosystem-based management of fisheries (). The aim of this work is to study the demersal fish diversity pattern in the Mediterranean at a large spatial and temporal scale and to assess whether this pattern is being affected by the general fishing overexploitation of demersal resources in the area. To do so, we used fishery-independent data collected under the framework of the MEDITS trawl surveys carried out during the last 20 years. The detected trends were compared with the spatio-temporal variation in bottom trawl fishing effort in the Mediterranean Sea. MATERIALS AND METHODSTop Data Demersal fish were collected during MEDITS bottom trawl surveys conducted from 1994 to 2015 in 14 geographical sub-areas (GSAs) along the European coasts of the Mediterranean Sea. Some GSAs have gaps in their sampling years: i) GSA 5 started sampling in 2001; ii) there are no data in 2002 for GSA 8 (technical problem of the boat); and iii) there are no data for 2002, 2007, 2009-2013 and 2015 for GSAs 20, 22 and 23. Sampling was performed during spring-summer in daylight hours using the GOC73 experimental gear, whose efficiency for catching demersal species has been tested by and . For more details about the sampling strategy and protocol see and the MEDITS handbook, instruction manual version 9 (). A total of 17540 hauls performed between 46 and 866 m depth were analysed (Table 1, Fig. 1). Hauls shallower than 46 m depth were excluded from the analysis because they could not be found for all GSAs. The catch of each sample was sorted, identified to species level, counted, weighed and standardized to square km by using the horizontal opening of the net and the distance covered in each haul. Species with a pelagic or mesopelagic behaviour, like most species of the families Myctophydae (e.g. Ceratoscopelus maderensis) and Cupleidae (e.g. Engraulis encrasicolus), were excluded from the analysis. A species accumulation curve for each GSA was performed and we confirmed that differences in number of species were not due to differences in the number of hauls considered for each GSA (Table 1, Fig. S1).

Table 1. – Name of the areas and number of samples analysed for each GSA.

GSA	Area	Samples
GSA 1	Northern Alboran Sea	743
GSA 5	Balearic Islands	650
GSA 6	Northern Spain	1459
GSA 7	Gulf of Lions	1143
GSA 8	Corsica	451
GSA 9	Ligurian, northern and central Tyrrhenian Sea	2468
GSA 10	Central and southern Tyrrhenian Sea	1333
GSA 11	Sardinia	1811
GSA 16	Strait of Sicily	1492
GSA 17	Northern Adriatic Sea	2296
GSA 18	Southern Adriatic Sea	1684
GSA 20	Eastern Ionian Sea	308
GSA 22	Aegean Sea	1427
GSA 23	Crete	175

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Fig. 1. – Map of the study area showing the 17540 hauls sampled between 1994 and 2015 in 15 geographical sub-areas (GSAs). Each colour corresponds to one of the GSAs defined by the GFCM (http://www.gfcm.org). The smaller map shows the location of the Mediterranean and its connections to the Atlantic Ocean through the Gibraltar Strait (A), the Black Sea through the Dardanelles Strait (B) and the Red Sea through the artificial Suez Channel (C).

Fish assemblages and diversity Cluster analysis was used to analyse the structure of demersal fish assemblages and to identify different assemblages according to depth strata in each GSA. Relationships among samples were detected by hierarchical agglomerative clustering with group-average linkage after a forth root transformation of the data. The distance used to make groups was the Bray-Curtis similarity. These analyses were performed using PRIMER 7 (). The calculus of diversity indices explained below was made taking into account the groups of samples obtained from the cluster analysis. The N₉₀ diversity index was calculated following the procedure described by . It is the mean number of species contributing up to 90% of within-group similarity calculated from abundance data expressed as N km^–2 and assigned a priori to groups. The calculation of N₉₀ starts with the calculation of the contribution of each species to the within-group similarity using the Bray-Curtis similarity index (), as proposed by :

S_{j k} (i) = 100 \times \frac{2 \times \min (y_{i j}, y_{i k})}{Σ_{i = 1}^{p} (y_{i j} + y_{i k})}

where y_ijis the abundance of the species i at the sampling site j, y_ik is the abundance of the species i at the sampling site k, p is the total number of species in j and k, and min (y_ij, y_ik) is the minimum value of the abundance of species i between the sampling sites j and k, also considering zeros.

The contribution of each species i to the total similarity of the group S_i is the mean value of S_jk(i) for the assigned group, and the total similarity in a group (Sim) is the addition of S_i for all the species in the group:

$S i m = Σ_{i = 1}^{p} S_{i}$ .

Then, the contribution of S_i is calculated as a percentage of Sim. Species contributions are calculated for each re-sampling in a jack-knife routine, which removes a number of samples each time, producing lists of contribution to similarity by species in each re-sampling. Because the groups of samples for each GSA, strata and year were large, we removed 10% of the samples in each re-sampling with a 50% replacement. That is, 50% of samples removed in a re-sampling must be different from previous ones. In this way, we obtain values of deviation for N₉₀ other than 0 for groups with a large number of observations. The N₉₀ diversity index is the mean number of species which accumulate up to 90% of within-group similarity in all the re-samplings. N₉₀ was calculated using R scripts, version 3.1.1 (R Core Team 2014R Core Team. 2014. R: A Language and Environment for Statistical Computing. http://www.r-project.org/). Similarity percentage analysis (SIMPER) for each group of samples was also undertaken to see their species composition.

Diversity indices, such as species richness (S) and Pielou evenness (J’), which have shown some kind of response to fishing impact for demersal fish assemblages in the Mediterranean (Farriols et al. 2017Farriols M.T., Ordines F., Somerfield P.J., et al. 2017. Bottom trawl impacts on Mediterranean demersal fish diversity: Not so obvious or are we too late? Cont. Shelf Res. 137: 84-102.), were also included in this work. These traditional indices are also helpful for comparison with previous works. S is the raw number of species in each haul and J’ was calculated as follows:

$J^{'} = \frac{Σ_{i = 1}^{S} p_{i} \ln p_{i}}{\ln S}$ ;

where p_i is the proportion of all individuals belonging to species i and S is the total number of species in the sample.

Fishing effort

Information on annual fishing effort was collected from the working group reports of the GFCM (http://www.fao.org/gfcm/data/safs/en/) and the Scientific, Technical and Economic Committee for Fisheries (STECF, https://stecf.jrc.ec.europa.eu/reports/medbs). Fishing effort data were compiled by trawl fleet targeting different species. The units vary between the different reports, being mainly provided in terms of number of vessels, kilowatts per days at sea and gross tonnage per days at sea (see Table S1).

To estimate fishing effort in each depth stratum obtained from cluster analysis, the strata were associated with the main target species of the fleets. Because target species varied between GSAs, we considered i) Mullus barbatus or Mullus surmuletus for the continental shelf; ii) Nephrops norvegicus or Parapenaeus longirostris for the shelf break/upper slope; and iii) Aristeus antennatus or Aristaeomorpha foliacea for the lower slope.

To compare temporal trends in fishing effort and demersal fish diversity, the longest series of fishing effort available for each GSA and depth stratum regardless of the kind of units were selected. When we found no values of fishing effort for a certain GSA, experts were contacted to obtain a trend in number of vessels in that area.

Temporal and spatial analysis

In order to analyse temporal trends in diversity, linear regressions were fitted to the mean values of S, J’ and N₉₀ for each year, GSA and depth stratum. Linear regression analyses with the annual values of fishing effort in each GSA (see Fishing effort section in Materials and Methods) and depth stratum were also performed. The exploration of the scatter plots of the time series together with the comparison of Pearson (assuming linear pattern) and Spearman (suitable also for other monotonic patterns than the linear) correlation coefficients were done. The values of both correlation coefficients were similar, indicating that the detected trends could be fitted using a simple linear model. Thus, the linear regression and the Pearson coefficient of correlation were presented along with the coefficient of determination (i.e. variance explained). These analyses were carried out with R, version 3.1.1 (R Core Team 2014R Core Team. 2014. R: A Language and Environment for Statistical Computing. http://www.r-project.org/).

In order to observe spatial differences in diversity by GSA, time series of mean values and standard deviation of each diversity index (see Data section for years included in each GSA) were plotted. For those series with a significant temporal trend, the diversity values at the beginning and the end of the time series were plotted instead of mean values and standard deviation.

SIMPER analysis for each group of samples from N₉₀ was also performed to see differences in species composition in each GSA. The percentage of contribution of each species to within-group similarity was calculated as the mean value of species contributions to similarity, taking all groups of observations by year and stratum for each GSA into account.

RESULTSTop

Community structure

Results from cluster analysis detecting main fish assemblages for each GSA are shown in Figure 2. Three groups of samples were selected from most GSAs, corresponding to a level of similarity of between 30% and 40%. Maximum, minimum and mean depths of each cluster group per GSA were obtained. According to these depth values, samples were grouped in three different depth strata: shelf, shelf break/upper slope and lower slope (Table 2, Fig. 2). For GSAs 17 and 23 only two groups were selected. GSA 17 has no samples below 350 m, and GSA 23 has a negligible sample number over 496 m (Table 2). For GSAs 7 and 20 samples in the lower slope group were not enough to calculate the N₉₀ throughout the time series, so both lower slope groups were omitted from the temporal and spatial analysis.

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Fig. 2. – Cluster of samples obtained from MEDITS surveys. The data used for the cluster analysis were the double root transformation of abundances of demersal fish species for each sample during the sampling period of each GSA. The dashed line shows the similarity level used to classify the depth strata: shelf, shelf break/upper slope (SB/US) and lower slope. The number in brackets represents the number of samples in each depth stratum.

Table 2. – Minimum, maximum and mean depth of samples grouped in each depth stratum (shelf, shelf break/upper slope and lower slope) from cluster analysis for each GSA.

GSA	Strata	Minimum depth	Maximum depth	Mean depth
GSA 1	Shelf	50	168	76
	Shelf break/upper slope	118	373	203
	Lower slope	219	807	519
GSA 5	Shelf	46	258	108
	Shelf break/upper slope	316	698	402
	Lower slope	581	756	678
GSA 6	Shelf	50	147	84
	Shelf break/upper slope	82	392	183
	Lower slope	257	798	505
GSA 7	Shelf	55	155	93
	Shelf break/upper slope	214	705	421
	Lower slope	414	866	699
GSA 8	Shelf	56	158	94
	Shelf break/upper slope	261	510	350
	Lower slope	405	583	510
GSA 9	Shelf	50	399	118
	Shelf break/upper slope	141	640	340
	Lower slope	364	757	559
GSA 10	Shelf	50	350	125
	Shelf break/upper slope	170	616	365
	Lower slope	339	693	594
GSA 11	Shelf	50	292	97
	Shelf break/upper slope	109	357	198
	Lower slope	219	725	496
GSA 16	Shelf	51	220	94
	Shelf break/upper slope	108	654	333
	Lower slope	436	794	630
GSA 17	Shelf	50	235	91
GSA 17	Shelf break/upper slope	62	332	180
GSA 18	Shelf	50	349	104
	Shelf break/upper slope	111	397	270
	Lower slope	247	732	501
GSA 20	Shelf	55	189	94
	Shelf break/upper slope	149	664	379
	Lower slope	483	800	654
GSA 22	Shelf	50	340	109
	Shelf break/upper slope	107	708	336
	Lower slope	337	791	579
GSA 23	Shelf	57	155	91
GSA 23	Shelf break/upper slope	115	496	245

In 9 out of the 12 GSAs showing lower slope samples, samples from the shelf break/upper slope clustered with samples from the lower slope. The exceptions were GSAs 6, 11 and 22, where samples from the shelf break/upper slope clustered with those from the continental shelf. Minimum, maximum and mean depths for each group of samples from cluster analysis are shown in Table 2. Mean depth of continental shelf samples ranged from 76 m in GSA 1 to 125 m in GSA 10, while for the shelf break/upper slope they ranged from 180 m in GSA 17 to 421 m in GSA 7, and for the lower slope between 496 m in GSA 11 and 699 m in GSA 7.

Temporal trends

Although the analysis of temporal evolution for N₉₀, S and J’ showed no significance in most GSAs and depth strata (Table 3, Fig. 3 and Figs S2-S4), some trends were detected. N₉₀ increased on the continental shelf of GSAs 1, 8 and 20, the shelf break/upper slope of GSAs 7, 11 and 18 and the lower slope of GSA 11 and only decreased on the shelf break/upper slope of GSA 5 (Table 3, Fig. 3 and Fig. S2). S increased on the continental shelf of GSAs 8 and 10, the shelf break/upper slope of GSAs 7, 8, 10 and 22 and the lower slope of GSAs 8, 10, 11, 16 and 18 and decreased on the shelf break/upper slope of GSA 17 and the lower slope of GSA 9 (Table 3, Fig. 3 and Fig. S3). J’ increased on the continental shelf of GSA 7, the shelf break/upper slope of GSAs 7 and 8 and the lower slope of GSA 11 and decreased on the continental shelf of GSAs 10 and 16, the shelf break/upper slope of GSAs 5 and 22 and the lower slope of GSAs 1 and 8 (Table 3, Fig. 3 and Fig. S4). These trends were confirmed when the last year of the time series (2014) for GSAs 20, 22 and 23 was excluded (Table 3).

Table 3. – Results of linear regression analysis of the time series for N₉₀, species richness (S) and Pielou evenness (J’) for each GSA and depth stratum (shelf, shelf break/upper slope and lower slope). Slope values of the adjusted linear regressions (b), adjusted R-squared values and p-values (p) are presented. *, p <0.05; **, p <0.01; ***, p <0.001. For GSAs 20, 22 and 23: ^a results for time series 1994-2006; and ^b results for time series 1994-2014.

Index	GSA	Shelf			Shelf break/upper slope			Lower slope
Index	GSA	b	Adjusted R²	p	b	Adjusted R²	p	b	Adjusted R²	p
N₉₀	GSA 1	0.209	0.051	*	0.047	–0.050	0.383	0.001	0.430	0.955
	GSA 5	–0.090	–0.010	0.287	–0.286	0.473	**	0.037	–0.052	0.565
	GSA 6	–0.027	–0.029	0.683	0.063	–0.034	0.321	–0.112	0.031	0.059
	GSA 7	0.041	0.200	0.462	0.079	0.391	*
	GSA 8	0.195	–0.052	**	0.089	0.294	0.070	0.041	0.376	0.175
	GSA 9	–0.005	–0.044	0.923	–0.027	0.105	0.483	–0.013	–0.049	0.493
	GSA 10	–0.101	0.463	0.128	0.068	0.005	0.121	–0.014	0.025	0.523
	GSA 11	0.084	–0.031	0.136	0.090	0.119	*	0.083	0.338	*
	GSA 16	–0.066	0.455	0.398	0.019	0.025	0.593	0.012	–0.044	0.540
	GSA 17	0.012	0.088	0.540	0.013	0.083	0.798
	GSA 18	–0.036	0.004	0.647	0.141	–0.046	*	–0.036	0.031	0.111
	GSA 20^a	0.487	0.503	**	0.115	–0.066	0.553
	GSA 20^b	0.315	–0.066	**	0.225	0.007	0.069
	GSA 22^a	0.054	–0.065	0.577	–0.110	0.023	0.289	–0.053	–0.003	0.351
	GSA 22^b	0.064	–0.070	0.279	0.044	0.344	0.579	0.015	0.045	0.706
	GSA 23^a	0.075	0.061	0.231	–0.156	–0.007	0.360
	GSA 23^b	0.099	–0.090	0.098	0.038	0.152	0.740
S	GSA 1	–0.044	–0.040	0.658	0.010	–0.049	0.898	0.036	0.005	0.305
	GSA 5	0.013	–0.082	0.914	–0.136	0.092	0.154	–0.089	–0.005	0.351
	GSA 6	–0.030	–0.044	0.728	0.090	0.018	0.254	–0.017	–0.041	0.674
	GSA 7	–0.055	–0.017	0.427	0.142	0.331	**
	GSA 8	0.161	0.169	*	0.149	0.180	*	0.134	0.203	*
	GSA 9	–0.027	–0.026	0.504	–0.023	–0.026	0.505	–0.061	0.151	*
	GSA 10	0.122	0.138	*	0.197	0.411	***	0.165	0.336	**
	GSA 11	0.064	0.026	0.226	0.041	–0.017	0.433	0.080	0.227	*
	GSA 16	0.026	–0.035	0.600	0.112	0.101	0.082	0.105	0.145	*
	GSA 17	0.010	–0.048	0.861	–0.130	0.196	*
	GSA 18	0.041	–0.031	0.550	0.109	0.055	0.152	0.142	0.278	**
	GSA 20^a	0.297	0.149	0.118	0.582	0.492	**
	GSA 20^b	0.166	0.072	0.182	0.230	0.117	0.136
	GSA 22^a	0.099	–0.042	0.471	0.359	0.410	*	–0.251	0.465	*
	GSA 22^b	0.089	0.019	0.286	0.304	0.546	**	–0.031	–0.077	0.712
	GSA 23^a	–0.021	–0.111	0.957	–0.095	–0.088	0.742
	GSA 23^b	0.274	0.035	0.257	0.134	–0.034	0.466
J’	GSA 1	0.003	0.051	0.160	0.000	–0.050	0.988	–0.004	0.430	***
	GSA 5	–0.003	–0.010	0.370	–0.020	0.473	**	0.002	–0.052	0.562
	GSA 6	0.001	–0.029	0.533	0.001	–0.034	0.589	–0.002	0.031	0.210
	GSA 7	0.004	0.200	*	0.007	0.391	**
	GSA 8	0.000	–0.052	0.937	0.004	0.294	**	–0.004	0.376	**
	GSA 9	0.001	–0.044	0.745	0.002	0.105	0.077	0.000	–0.049	0.874
	GSA 10	–0.008	0.463	***	0.002	0.005	0.304	–0.002	0.025	0.229
	GSA 11	–0.001	–0.031	0.554	0.004	0.119	0.064	0.004	0.338	**
	GSA 16	–0.005	0.455	***	0.002	0.025	0.230	0.000	–0.044	0.729
	GSA 17	–0.002	0.088	0.098	–0.004	0.083	0.104
	GSA 18	0.002	0.004	0.312	–0.001	–0.046	0.790	–0.001	0.031	0.211
	GSA 20^a	–0.003	–0.023	0.406	–0.003	–0.090	0.688
	GSA 20^b	–0.001	–0.066	0.663	0.004	0.007	0.320
	GSA 22^a	–0.002	–0.059	0.549	–0.013	0.673	***	–0.003	–0.042	0.461
	GSA 22^b	0.001	–0.070	0.710	–0.007	0.344	*	–0.004	0.045	0.237
	GSA 23^a	–0.004	–0.065	0.548	–0.018	0.329	*
	GSA 23^b	0.000	–0.090	0.937	–0.009	0.152	0.093

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Fig. 3. – Trends obtained from linear regression of N₉₀, species richness (S), Pielou evenness (J’) and fishing effort (FE) per GSA and depth stratum (shelf, shelf break/upper slope and lower slope). Trends obtained from the analysis are in continuous lines and trends from expert knowledge in discontinuous line and grey background. n.s., non-significant trends; -, no data available. For GSAs 20, 22 and 23 trends of time series 1994-2006 and 1994-2014 are presented. When results of trends differ, trends from 1994-2006 are presented on the left and trends from 1994 to 2014 on the right of the cell.

When quantitative analysis in temporal evolution of fishing effort could be made, the detected significance mainly showed a decreasing trend (Table 4, Fig. 3). This is the case of the continental shelf in GSAs 1, 5, 6 and 7, the shelf break/upper slope in GSAs 1, 11, 17 and 18 and the lower slope in GSAs 5 and 11. It increased only on the continental shelf and the lower slope of GSA 18. Expert knowledge suggested increasing trends in fishing effort for the lower slope in GSAs 20, 22 and 23 and decreasing trends on the continental shelf of GSAs 8, 9, 16, 20, 22 and 23 and on the lower slope of GSAs 1, 7 and 8 (Table 4, Fig. 3).

Table 4. – Results of linear regression analysis of the time series of fishing effort of the longest series available for each GSA and depth stratum (shelf, shelf break/upper slope and lower slope). Slope values of the adjusted linear regressions (b), adjusted R-squared values and p-values (p) are presented. Qualitative values of slopes are obtained from expert knowledge. *, p <0.05; **, p <0.01; ***, p <0.001; ns, non-significant.

GSA	Shelf			Shelf break/upper slope			Lower slope
GSA	b	Adjusted R²	p	b	Adjusted R²	p	b	Adjusted R²	p
GSA 1	–8.800	0.717	*	–10.890	0.865	***	Decreasing
GSA 5	–5.118	0.898	***	7.500	0.163	0.175	–7.530	0.526	**
GSA 6	–11.659	0.808	***	3.248	0.071	0.204	0.000	0.510	0.117
GSA 7	–13.821	0.765	**				Decreasing
GSA 8	Decreasing						Decreasing
GSA 9	Decreasing			1.820	–0.081	0.874	8.109	–0.088	0.669
GSA 10	Increasing		ns	–7.385	0.197	0.063	Decreasing		ns
GSA 11	63.572	0.390	0.058	–13.226	0.372	*	–25.227	0.867	***
GSA 16	Decreasing						–4.700	0.167	0.272
GSA 17	9.617	–0.054	0.467	–6.822	0.360	*	Decreasing		ns
GSA 18	75.448	0.644	**	–14.548	0.581	*	29.516	0.301	*
GSA 20	Decreasing						Increasing
GSA 22	Decreasing						Increasing
GSA 23	Decreasing						Increasing

In 6 of the 7 cases in which an increment of N₉₀ was detected, it coincided with a decrease in fishing effort (Tables 3-4, Fig. 3). Of the 11 cases showing increases in species richness, S, only in 3 cases was the increase in S coupled with a decrease in fishing effort. In 5 cases there was no trend in fishing effort, while in only one case the increase in S was coupled with an increase in fishing effort. In 2 cases, no information on the temporal evolution of fishing effort was available (Tables 3-4, Fig. 3). 10 GSAs showed significant trends in Pielou eveness, J’; in 2 of them J’ increased and fishing effort decreased, while 3 GSAs showed a decrease in both J’ and fishing effort. Two GSAs showed a decrease in J’ coupled with no trend in fishing effort, while no information on fishing effort trend was available in 3 cases (Tables 3-4, Fig. 3).

Spatial patterns

Mean values of N₉₀, S and J’ showed differences between GSAs and depth strata (Fig. 4). Regarding N₉₀, the continental shelf of GSAs 5, 11, 16 and 22 showed higher values than the rest of the GSAs in this depth stratum and even than the shelf break/upper slope and lower slope values. Within the shelf break/upper slope, the highest values of N₉₀ were estimated in GSAs 7, 16, 17, 18, 20 and 22, while on the lower slope the highest values were for GSAs 6, 8 and 18. S showed similar values on the continental shelf of all GSAs, with the exception of GSA 10, which showed a lower value. A similar situation was observed on the shelf break/upper slope, with similar values of S in all GSAs, except for GSA 17 with a lower value, and GSAs 7 and 8, which showed the highest values. On the lower slope, the values of S were similar in all GSAs, with the exception of GSAs 9, 10 and 16, which showed lower values. J’ showed similar values on the continental shelf in all GSAs, except GSAs 9 and 10, which showed lower values. The same scenario was observed on the shelf break/upper slope, with similar values of J’ in all GSAs with the exception of GSAs 1, 5, 6 and 11, which showed lower values. On the lower slope, GSA 11 showed the lowest value of J’, while similar values were obtained in the rest of the GSAs.

Full size image

Fig. 4. – Mean values and standard deviations of N₉₀, species richness (S) and Pielou evenness (J’) during the whole time series considered for each GSA and depth strata (shelf, shelf break/upper slope and lower slope). In series with a significant temporal trend, values at the beginning and end of the time series are presented in red. Red arrows point to the last value of the time series. For GSAs 20, 22 and 23 the whole time series were taken into account (1994-2014). Note that in some cases the trend of time series does not match the arrow’s direction (see Table 2).

The similarity percentage (SIMPER) analysis also showed differences in the species contribution between GSAs and depth strata (Tables 5 and 6). The species with the highest percentage contribution to within-group similarity on the continental shelf, shelf break/upper slope and lower slope, respectively, were the following: Serranus hepatus, Gadiculus argenteus and Galeus melastomus in GSA 1; Scyliorhinus canicula, G. argenteus and Phycis blennoides in GSA 5; Merluccius merluccius, Micromesistius poutassou and P. blennoides in GSA 6; Mullus barbatus, G. argenteus and G. melastomus in GSA 8; M. merluccius, G. argenteus and P. blennoides in GSA 9; Glossanodon leioglossus, Chlorophthalmus agassizi and Hymenocephalus italicus in GSA 10; S. hepatus, G. leioglossus and P. blennoides in GSA 11; M. merluccius, G. argenteus and Nezumia sclerorhynchus in GSA 16; M. merluccius, Helicolenus dactylopterus and P. blennoides in GSA 18; and S. hepatus, Argentina sphyraena and N. sclerorhynchus in GSA 22 (Table 5). The species with the highest percentage contribution to within-group similarity were T. minutus on the continental shelf and G. argenteus on the shelf break/upper slope in GSA 7; Lepidotrigla cavillone on the continental shelf and C. agassizi on the shelf break/upper slope in GSA 20; and L. cavillone on the continental shelf and A. sphyraena on the shelf break/upper slope in GSA 23. In GSA 17, M. merluccius was the species with the highest contribution on both the continental shelf and the shelf break/upper slope (Table 5).

Table 5. – Similarity Percentage analysis (SIMPER) summary table of species appearing in the 90% cut-off of within-group similarity. A is the mean abundance (individuals km^–2) of each species, and %C is the mean value of the percentage contribution of each species to within-group similarity, taking into account each SIMPER made by group of GSA, depth strata and year. Depth strata are shelf, shelf break/upper slope (SB/US) and lower slope.

GSA 1 Shelf	A	%C	GSA 1 SB/US	A	%C	GSA 1 Lower slope	A	%C
Serranus hepatus	1629	31	Gadiculus argenteus	7829	37	Galeus melastomus	2533	26
Pagellus acarne	4684	24	Helicolenus dactylopterus	2509	16	Nezumia aequalis	2046	22
Merluccius merluccius	418	8	Micromesistius poutassou	7519	12	Hoplostethus mediterraneus	1460	15
Cepola macrophthalma	687	6	Lepidopus caudatus	6280	9	Coelorinchus caelorhincus	1315	13
Mullus barbatus	1237	6	Merluccius merluccius	1217	8	Phycis blennoides	364	8
Callionymus maculatus	514	3	Phycis blennoides	706	5	Etmopterus spinax	245	3
Arnoglossus laterna	237	3	Scyliorhinus canicula	491	3	Trachyrincus scabrus	744	2
Serranus cabrilla	338	2	Coelorinchus caelorhincus	873	2	Micromesistius poutassou	299	2
Arnoglossus thori	544	2
Scyliorhinus canicula	158	2
Lesueurigobius sanzi	255	1
Trachinus draco	103	1
GSA 5 Shelf	A	%C	GSA 5 SB/US	A	%C	GSA 5 Lower slope	A	%C
Scyliorhinus canicula	1348	25	Gadiculus argenteus	24060	68	Phycis blennoides	425	30
Serranus hepatus	1464	12	Galeus melastomus	2222	9	Galeus melastomus	440	24
Serranus cabrilla	743	11	Coelorinchus caelorhincus	1592	5	Nezumia aequalis	226	19
Trachinus draco	548	9	Chlorophthalmus agassizi	2906	5	Hymenocephalus italicus	174	7
Mullus surmuletus	1163	6	Phycis blennoides	461	3	Notacanthus bonaparte	55	4
Lepidotrigla cavillone	741	5	Helicolenus dactylopterus	506	3	Lepidion lepidion	93	4
Merluccius merluccius	1007	5				Symphurus ligulatus	45	3
Glossanodon leioglossus	28236	5
Chelidonichthys cuculus	893	5
Trigloporus lastoviza	448	4
Scorpaena notata	163	1
Pagellus erythrinus	179	1
Mullus barbatus	312	1
GSA 6 Shelf	A	%C	GSA 6 SB/US	A	%C	GSA 6 Lower slope	A	%C
Merluccius merluccius	2955	45	Micromesistius poutassou	57532	40	Phycis blennoides	698	43
Trisopterus minutus	2248	19	Gadiculus argenteus	9766	20	Galeus melastomus	381	22
Cepola macrophthalma	514	7	Merluccius merluccius	4285	19	Micromesistius poutassou	300	4
Mullus barbatus	487	7	Trisopterus minutus	1754	6	Nezumia aequalis	79	4
Serranus hepatus	408	7	Helicolenus dactylopterus	914	3	Trachyrincus scabrus	166	4
Lepidotrigla cavillone	250	3	Scyliorhinus canicula	811	3	Hymenocephalus italicus	55	3
Pagellus erythrinus	98	1				Gadiculus argenteus	422	3
Lophius budegassa	56	1				Symphurus nigrescens	67	2
						Scyliorhinus canicula	130	2
						Coelorinchus caelorhincus	128	2
						Gaidropsarus biscayensis	58	2
GSA 7 Shelf	A	%C	GSA 7 SB/US	A	%C	GSA 7 Lower slope	A	%C
Trisopterus minutus	6435	47	Gadiculus argenteus	6804	39
Merluccius merluccius	3239	19	Galeus melastomus	1470	11
Eutrigla gurnardus	1651	12	Micromesistius poutassou	7815	10
Serranus hepatus	896	5	Phycis blennoides	561	8
Lepidotrigla cavillone	1297	4	Coelorinchus caelorhincus	1068	7
Cepola macrophthalma	561	2	Helicolenus dactylopterus	719	7
Lesueurigobius friesii	513	2	Lepidorhombus boscii	417	6
			Trigla lyra	565	5
GSA 8 Shelf	A	%C	GSA 8 SB/US	A	%C	GSA 8 Lower slope	A	%C
Mullus barbatus	4127	20	Gadiculus argenteus	17009	47	Galeus melastomus	1246	29
Lepidotrigla cavillone	1287	19	Galeus melastomus	2979	11	Hymenocephalus italicus	535	19
Scyliorhinus canicula	941	15	Chlorophthalmus agassizi	4386	9	Coelorinchus caelorhincus	405	12
Serranus hepatus	1157	14	Micromesistius poutassou	2373	8	Phycis blennoides	248	8
Pagellus erythrinus	538	10	Scyliorhinus canicula	1119	7	Helicolenus dactylopterus	169	6
Chelidonichthys cuculus	352	5	Lepidotrigla dieuzeidei	1147	4	Nezumia sclerorhynchus	223	6
Serranus cabrilla	283	3	Argentina sphyraena	842	3	Hoplostethus mediterraneus	254	6
Mullus surmuletus	373	2				Etmopterus spinax	196	4
Trigloporus lastoviza	162	1				Chlorophthalmus agassizi	280	4
Lepidotrigla dieuzeidei	432	1
GSA 9 Shelf	A	%C	GSA 9 SB/US	A	%C	GSA 9 Lower slope	A	%C
Merluccius merluccius	4334	55	Gadiculus argenteus	5253	52	Phycis blennoides	490	27
Trisopterus minutus	1076	16	Merluccius merluccius	4894	13	Hymenocephalus italicus	527	27
Mullus barbatus	551	8	Phycis blennoides	564	12	Galeus melastomus	439	22
Serranus hepatus	274	4	Chlorophthalmus agassizi	896	6	Nezumia sclerorhynchus	239	9
Lepidotrigla cavillone	271	4	Galeus melastomus	289	4	Etmopterus spinax	105	7
Arnoglossus laterna	110	3	Micromesistius poutassou	1322	4
Glossanodon leioglossus	4801	2
GSA 10 Shelf	A	%C	GSA 10 SB/US	A	%C	GSA 10 Lower slope	A	%C
Glossanodon leioglossus	16087	12	Chlorophthalmus agassizi	11376	61	Hymenocephalus italicus	1152	49
Merluccius merluccius	2725	57	Phycis blennoides	479	8	Nezumia sclerorhynchus	351	19
Lepidotrigla cavillone	598	3	Hymenocephalus italicus	1128	7	Phycis blennoides	199	11
Mullus barbatus	502	7	Gadiculus argenteus	1290	7	Galeus melastomus	255	10
Serranus hepatus	490	4	Merluccius merluccius	1039	6	Etmopterus spinax	70	4
Lepidopus caudatus	463	2	Helicolenus dactylopterus	226	2
Trisopterus minutus	236	2
Cepola macrophthalma	91	3
Arnoglossus laterna	80	2
GSA 11 Shelf	A	%C	GSA 11 SB/US	A	%C	GSA 11 Lower slope	A	%C
Serranus hepatus	1751	26	Glossanodon leioglossus	77021	51	Phycis blennoides	879	31
Merluccius merluccius	2903	22	Merluccius merluccius	6301	13	Hymenocephalus italicus	818	20
Lepidotrigla cavillone	1131	9	Trisopterus minutus	5084	11	Gadiculus argenteus	8361	18
Trisopterus minutus	2474	9	Argentina sphyraena	4673	9	Galeus melastomus	987	12
Mullus barbatus	840	7	Lepidotrigla dieuzeidei	1590	5	Chlorophthalmus agassizi	3734	5
Serranus cabrilla	344	4	Scyliorhinus canicula	1175	4	Etmopterus spinax	122	3
Chelidonichthys cuculus	368	3				Merluccius merluccius	848	2
Scyliorhinus canicula	335	3
Mullus surmuletus	200	2
Trigloporus lastoviza	191	2
Trachinus draco	105	2
Argentina sphyraena	2800	2
Citharus linguatula	227	1
GSA 16 Shelf	A	%C	GSA 16 SB/US	A	%C	GSA 16 Lower slope	A	%C
Merluccius merluccius	970	25	Gadiculus argenteus	5100	26	Nezumia sclerorhynchus	982	40
Serranus hepatus	785	11	Merluccius merluccius	1622	20	Hymenocephalus italicus	573	19
Lepidotrigla cavillone	1327	10	Chlorophthalmus agassizi	3368	14	Galeus melastomus	318	17
Chelidonichthys cuculus	548	7	Coelorinchus caelorhincus	1231	10	Nezumia aequalis	383	7
Mullus barbatus	580	7	Hymenocephalus italicus	768	8	Hoplostethus mediterraneus	134	5
Mullus surmuletus	261	5	Phycis blennoides	314	7	Phycis blennoides	79	3
Raja miraletus	302	5	Lepidopus caudatus	2220	6
Serranus cabrilla	181	4
Citharus linguatula	234	3
Argentina sphyraena	912	2
Trisopterus minutus	280	2
Trigloporus lastoviza	131	2
Arnoglossus laterna	116	2
Lepidotrigla dieuzeidei	588	2
Scyliorhinus canicula	102	2
Zeus faber	52	2
Trachinus draco	77	1
GSA 17 Shelf	A	%C	GSA 17 SB/US	A	%C	GSA 17 Lower slope	A	%C
Merluccius merluccius	931	28	Merluccius merluccius	2155	43
Mullus barbatus	1344	20	Micromesistius poutassou	3434	33
Trisopterus minutus	861	18	Trisopterus minutus	312	5
Serranus hepatus	1150	13	Lepidopus caudatus	489	5
Cepola macrophthalma	327	6	Gadiculus argenteus	379	3
Lepidotrigla cavillone	359	2	Lesueurigobius friesii	201	2
Merlangius merlangus	161	2
Eutrigla gurnardus	108	2
GSA 18 Shelf	A	%C	GSA 18 SB/US	A	%C	GSA 18 Lower slope	A	%C
Merluccius merluccius	939	43	Helicolenus dactylopterus	466	11	Phycis blennoides	405	20
Trisopterus minutus	424	20	Chlorophthalmus agassizi	663	10	Nezumia sclerorhynchus	305	14
Mullus barbatus	317	7	Micromesistius poutassou	823	8	Galeus melastomus	282	13
Serranus hepatus	175	6	Glossanodon leioglossus	2993	7	Hoplostethus mediterraneus	319	11
Lepidotrigla cavillone	376	4	Argentina sphyraena	1378	6	Coelorinchus caelorhincus	318	10
Chelidonichthys cuculus	232	4	Lepidopus caudatus	338	4	Hymenocephalus italicus	254	10
Arnoglossus laterna	106	3	Gadiculus argenteus	587	4	Etmopterus spinax	167	9
Cepola macrophthalma	81	2	Phycis blennoides	109	4	Helicolenus dactylopterus	117	4
Lesueurigobius friesii	74	2	Chelidonichthys cuculus	470	4
			Scyliorhinus canicula	146	3
			Lepidorhombus boscii	65	2
			Arnoglossus rueppelii	153	2
			Lepidotrigla cavillone	238	2
			Merluccius merluccius	895	25
GSA 20 Shelf	A	%C	GSA 20 SB/US	A	%C	GSA 20 Lower slope	A	%C
Lepidotrigla cavillone	1299	26	Chlorophthalmus agassizi	6565	28
Serranus hepatus	1416	24	Gadiculus argenteus	9073	24
Mullus barbatus	1145	15	Argentina sphyraena	3257	14
Merluccius merluccius	527	11	Merluccius merluccius	860	6
Arnoglossus laterna	329	5	Lepidopus caudatus	444	4
Trisopterus minutus	359	3	Helicolenus dactylopterus	361	4
Pagellus erythrinus	272	2	Scyliorhinus canicula	187	3
Argentina sphyraena	2318	2	Peristedion cataphractum	714	2
Citharus linguatula	142	2	Coelorinchus caelorhincus	588	2
			Hymenocephalus italicus	677	2
			Phycis blennoides	129	2
GSA 22 Shelf	A	%C	GSA 22 SB/US	A	%C	GSA 22 Lower slope	A	%C
Serranus hepatus	1774	19	Argentina sphyraena	12691	27	Nezumia sclerorhynchus	626	43
Trisopterus minutus	1666	13	Gadiculus argenteus	5823	18	Hymenocephalus italicus	575	14
Merluccius merluccius	1890	11	Merluccius merluccius	505	10	Trachyrincus scabrus	251	9
Citharus linguatula	569	11	Micromesistius poutassou	2037	7	Phycis blennoides	119	8
Lepidotrigla cavillone	887	10	Chlorophthalmus agassizi	5423	6	Coelorinchus caelorhincus	317	8
Mullus barbatus	785	5	Phycis blennoides	169	5	Hoplostethus mediterraneus	105	6
Chelidonichthys cuculus	968	4	Scyliorhinus canicula	372	5	Etmopterus spinax	66	4
Lophius budegassa	178	4	Lepidorhombus boscii	151	4
Argentina sphyraena	3244	4	Coelorinchus caelorhincus	1395	4
Serranus cabrilla	221	3	Hymenocephalus italicus	801	3
Scyliorhinus canicula	195	3	Helicolenus dactylopterus	148	2
Arnoglossus laterna	164	2	Lepidopus caudatus	431	2
Dentex maroccanus	458	2
Mullus surmuletus	246	1
GSA 23 Shelf	A	%C	GSA 23 SB/US	A	%C	GSA 23 Lower slope	A	%C
Lepidotrigla cavillone	2773	38	Argentina sphyraena	16100	49
Mullus barbatus	3330	21	Chlorophthalmus agassizi	9753	10
Serranus hepatus	2784	12	Merluccius merluccius	1305	9
Citharus linguatula	262	7	Chelidonichthys cuculus	758	6
Pagellus erythrinus	627	6	Coelorinchus caelorhincus	657	6
Arnoglossus laterna	855	4	Helicolenus dactylopterus	330	5
Serranus cabrilla	263	3	Lepidotrigla cavillone	400	2
			Phycis blennoides	109	2
			Gadiculus argenteus	3003	2

Table 6. – Number of years that each species contributed to the 90% cut-off of within-group similarity, taking into account each similarity percentage analysis (SIMPER) by GSA, depth stratum and year during the time series. Depth strata are shelf, shelf break/upper slope (SB/US) and lower slope.

Stratum	Species	GSA
Stratum	Species	1	5	6	7	8	9	10	11	16	17	18	20	22	23
Shelf	Argentina sphyraena	-	-	-	-	3	4	1	9	13	-	2	9	6	-
	Arnoglossus imperialis	-	-	-	-	-	-	-	-	4	-	-	-	-	-
	Arnoglossus laterna	8	-	3	6	-	12	10	-	10	4	12	11	8	4
	Arnoglossus rueppelii	-	2	-	-	-	-	-	-	-	-	-	-	-	-
	Arnoglossus thori	7	3	-	-	5	-	-	6	4	-	-	-	-	-
	Callionymus maculatus	12	-	1	2	-	-	-	-	-	2	3	-	-	-
	Cepola macrophthalma	16	-	18	11	-	4	13	4	1	18	11	-	3	-
	Chelidonichthys cuculus	-	14	-	-	17	-	1	15	19	3	13	7	12	-
	Chelidonichthys obscurus	-	-	-	-	-	-	-	-	7	-	-	-	-	-
	Citharus linguatula	-	-	2	1	-	1	-	6	18	1	-	4	14	12
	Deltentosteus collonianus	-	-	-	-	-	-	-	1	-	-	-	-	-	-
	Deltentosteus quadrimaculatus	1	3	1	-	4	-	1	5	-	-	-	-	-	2
	Dentex maroccanus	-	-	-	-	-	-	-	-	-	-	-	1	6	-
	Diplodus annularis	-	-	-	-	-	-	-	1	-	-	-	2	1	-
	Eutrigla gurnardus	-	-	1	22	-	-	-	-	-	5	3	1	3	-
	Glossanodon leioglossus	-	12	-	-	-	8	17	-	-	-	-	-	-	-
	Gymnammodytes cicerelus	-	-	-	-	-	-	-	1	-	-	-	-	-	-
	Helicolenus dactylopterus	-	-	-	-	-	-	2	-	-	-	-	-	-	-
	Lepidopus caudatus	1	-	-	-	-	1	8	-	1	-	2	-	-	-
	Lepidotrigla cavillone	4	14	12	18	20	15	16	22	22	9	15	14	14	13
	Lepidotrigla dieuzeidei	1	-	-	-	4	-	-	1	6	-	-	3	2	-
	Lesueurigobius friesii	-	-	1	7	-	4	1	-	-	2	6	-	3	-
	Lesueurigobius sanzi	6	-	1	-	-	-	-	-	-	-	-	-	-	-
	Lesueurigobius suerii	-	-	-	-	-	-	4	-	-	-	-	-	-	-
	Lophius budegassa	-	-	6	2	-	-	-	-	-	2	6	-	14	1
	Merlangius merlangus	-	-	-	-	-	-	-	-	-	10	-	-	-	-
	Merluccius merluccius	21	14	22	22	2	22	22	22	22	22	22	13	14	1
	Microchirus boscanion	1	-	-	-	-	-	-	-	-	-	-	-	-	-
	Micromesistius poutassou	-	-	-	1	-	-	-	-	-	-	3	-	-	-
	Mullus barbatus	17	4	20	2	20	21	20	22	19	22	17	14	14	13
	Mullus surmuletus	-	14	-	-	7	-	-	10	17	-	-	-	4	1
	Ophichthus rufus	2	-	2	-	-	-	-	-	-	-	-	-	-	-
	Pagellus acarne	21	-	2	-	2	-	-	-	1	-	-	-	-	4
	Pagellus bogaraveo	5	-	-	-	-	-	-	-	-	-	-	-	-	-
	Pagellus erythrinus	3	5	3	-	21	-	5	3	4	2	-	8	-	12
	Phycis blennoides	-	-	-	-	-	-	3	-	-	-	1	-	-	-
	Pomatoschistus marmoratus	1	-	-	-	-	-	-	-	-	-	-	-	-	-
	Pomatoschistus microps	1	-	-	-	-	-	-	-	-	-	-	-	-	-
	Raja clavata	-	2	-	-	-	-	-	-	-	-	-	-	2	-
	Raja miraletus	-	-	-	-	4	-	-	2	22	-	-	-	-	-
	Scorpaena notata	1	4	-	-	-	-	-	2	-	-	-	-	-	-
	Scorpaena scrofa	-	3	-	-	-	-	-	-	-	-	-	-	-	-
	Scyliorhinus canicula	5	14	-	-	21	-	-	15	12	-	-	-	12	-
	Serranus cabrilla	10	14	-	-	13	-	-	20	21	-	-	2	14	4
	Serranus hepatus	22	14	20	20	20	22	18	22	21	22	18	13	14	5
	Symphurus nigrescens	2	-	1	-	-	-	-	-	-	-	1	-	2	-
	Trachinus draco	6	14	-	-	-	-	-	12	8	-	-	-	-	-
	Trigloporus lastoviza	-	14	-	-	2	-	-	11	11	-	-	-	-	-
	Trisopterus luscus	-	-	1	-	-	-	-	-	-	-	-	-	-	-
	Trisopterus minutus	-	2	20	22	-	22	6	22	5	22	22	7	14	-
	Uranoscopus scaber	-	-	-	-	-	-	-	-	3	-	-	-	-	-
	Zeus faber	-	3	-	-	1	-	-	1	10	-	-	1	-	-
SB/US	Argentina sphyraena	-	-	6	3	10	3	-	19	5	-	6	12	14	14
	Arnoglossus rueppelii	1	-	2	-	-	-	-	-	-	-	10	-	-	1
	Callionymus maculatus	2	-	-	-	-	-	-	-	-	-	-	-	-	-
	Cepola macrophthalma	-	-	1	-	-	-	-	-	-	3	-	-	-	-
	Chelidonichthys cuculus	-	-	-	-	1	-	-	1	-	1	11	-	1	10
	Chlorophthalmus agassizi	-	12	-	3	18	18	22	-	20	-	15	11	8	6
	Coelorinchus caelorhincus	5	8	-	20	4	3	5	-	20	-	-	3	12	2
	Epigonus denticulatus	1	-	-	-	-	-	-	-	-	-	-	-	-	-
	Epigonus telescopus	-	-	-	-	-	-	-	-	1	-	-	-	-	-
	Gadiculus argenteus	21	14	20	22	21	22	19	1	21	8	6	12	13	5
	Galeorhinus galeus	-	-	-	-	-	-	-	-	-	-	-	-	-	1
	Galeus melastomus	-	10	-	22	20	15	2	-	-	-	-	1	1	-
	Glossanodon leioglossus	-	-	-	-	2	-	-	22	-	-	7	-	-	-
	Helicolenus dactylopterus	17	4	11	21	1	7	5	-	7	1	22	8	9	5
	Hoplostethus mediterraneus	-	-	-	-	-	-	-	-	-	-	-	1	-	1
	Hymenocephalus italicus	-	2	-	-	-	1	19	-	20	-	-	5	11	2
	Lepidopus caudatus	10	-	1	-	-	1	5	-	15	10	5	5	2	-
	Lepidorhombus boscii	-	2	-	19	5	1	-	-	-	-	7	3	9	2
	Lepidorhombus whiffiagonis	-	-	-	-	-	-	-	-	-	1	1	-	-	-
	Lepidotrigla cavillone	-	-	-	-	-	-	-	-	-	-	2	-	-	3
	Lepidotrigla dieuzeidei	-	-	-	-	9	-	-	9	-	-	3	3	1	1
	Lesueurigobius friesii	-	-	-	-	-	-	-	-	-	7	2	-	-	1
	Lophius budegassa	-	-	1	-	-	-	-	-	-	2	-	-	2	1
	Merluccius merluccius	15	-	22	2	4	22	18	21	22	22	22	10	14	10
	Micromesistius poutassou	10	4	22	21	18	11	3	2	-	22	16	-	10	2
	Mullus barbatus	-	-	3	-	-	-	-	-	-	2	7	-	-	-
	Mullus surmuletus	-	-	-	-	-	-	-	-	-	-	-	-	-	1
	Nezumia sclerorhynchus	-	-	-	-	-	-	-	-	-	-	-	2	1	-
	Pagellus bogaraveo	5	-	-	1	-	-	-	-	-	-	1	-	2	-
	Peristedion cataphractum	-	-	-	-	-	-	-	-	-	-	4	8	-	1
	Phycis blennoides	9	7	4	22	1	21	22	-	21	5	13	4	9	4
	Scorpaena elongata	1	-	-	-	-	-	-	-	-	-	-	-	-	-
	Scyliorhinus canicula	10	-	8	1	20	-	-	8	-	-	10	4	13	-
	Serranus hepatus	3	-	-	-	-	-	-	-	-	-	-	-	-	-
	Symphurus nigrescens	1	-	-	-	-	-	1	-	-	-	1	-	-	-
	Synchiropus phaeton	-	1	-	1	-	-	-	-	-	-	-	-	-	-
	Trachyrincus scabrus	-	-	-	1	-	-	-	-	-	-	-	-	-	-
	Trigla lyra	-	-	-	14	1	-	-	-	-	-	2	-	-	-
	Trisopterus minutus	-	-	12	-	-	1	-	19	-	14	-	3	4	-
	Zeus faber	-	-	-	-	-	-	-	-	-	-	1	-	-	1
Lower slope	Alepocephalus rostratus	1	-	-	-	-	-	-	-	-	-	-	-	-	-
	Argentina sphyraena	-	-	-	-	-	-	-	1	-	-	-	-	-	-
	Chimaera monstrosa	-	-	-	-	-	-	-	-	1	-	3	-	2	-
	Chlorophthalmus agassizi	-	-	-	-	10	-	-	16	-	-	6	-	-	-
	Coelorinchus caelorhincus	22	-	9	-	21	-	1	-	7	-	21	-	10	-
	Epigonus denticulatus	5	-	5	-	-	-	-	-	-	-	-	-	-	-
	Etmopterus spinax	13	7	2	-	13	21	7	16	1	-	20	-	5	-
	Gadiculus argenteus	2	-	15	-	4	-	-	22	-	-	-	-	-	-
	Gaidropsarus biscayensis	-	-	6	-	-	-	-	-	-	-	-	-	-	-
	Galeus melastomus	22	14	22	-	21	22	21	22	22	-	22	-	2	-
	Glossanodon leioglossus	-	-	-	-	-	-	-	1	-	-	-	-	-	-
	Helicolenus dactylopterus	5	-	9	-	19	-	-	7	1	-	16	-	-	-
	Hoplostethus mediterraneus	22	-	1	-	15	2	4	-	17	-	21	-	7	-
	Hymenocephalus italicus	-	11	13	-	21	22	21	22	21	-	20	-	13	-
	Lepidion lepidion	-	6	3	-	-	-	-	-	-	-	-	-	-	-
	Lepidopus caudatus	1	-	1	-	-	-	-	-	-	-	-	-	-	-
	Lepidorhombus boscii	-	-	3	-	1	-	-	1	-	-	-	-	1	-
	Lophius budegassa	-	-	-	-	-	-	-	-	-	-	-	-	1	-
	Merluccius merluccius	-	-	-	-	-	-	-	8	-	-	3	-	1	-
	Micromesistius poutassou	6	-	20	-	-	-	-	-	-	-	1	-	-	-
	Mora moro	-	3	-	-	-	-	-	1	-	-	-	-	-	-
	Nettastoma melanurum	-	-	-	-	-	-	1	-	-	-	-	-	-	-
	Nezumia aequalis	22	14	18	-	-	1	-	-	3	-	-	-	-	-
	Nezumia sclerorhynchus	-	-	-	-	16	21	21	4	19	-	22	-	13	-
	Notacanthus bonaparte	-	5	5	-	-	-	-	-	-	-	-	-	-	-
	Pagellus bogaraveo	-	-	1	-	1	-	-	-	-	-	-	-	-	-
	Phycis blennoides	21	14	22	-	19	22	22	22	13	-	21	-	13	-
	Polyacanthonotus rissoanus	-	1	-	-	-	-	-	-	-	-	-	-	-	-
	Scyliorhinus canicula	-	-	12	-	-	-	-	-	-	-	-	-	-	-
	Symphurus ligulatus	-	3	2	-	-	-	1	-	-	-	-	-	-	-
	Symphurus nigrescens	-	-	8	-	-	-	-	2	-	-	-	-	1	-
	Trachyrincus scabrus	11	-	13	-	2	3	-	-	-	-	1	-	9	-

Some species showed a high percentage contribution to within-group similarity in most years of the time series and for most of the GSAs (Table 6). On the continental shelf, these species were S. hepatus, L. cavillone, M. barbatus and M. merluccius. On the shelf break/upper slope, only G. argenteus was present in all GSAs, while on the lower slope those species were P. blennoides, G. melastomus and Etmopterus spinax.

DISCUSSIONTop

The results have confirmed that demersal fish assemblages are highly structured in the Mediterranean. In fact, we were able to identify three common assemblages in most GSAs corresponding to the continental shelf, shelf break/upper slope and lower slope strata of each area. There were only two GSAs, the northern Adriatic Sea and Crete, that did not present lower slope assemblages, due to the shallower depth surveyed in these areas compared with the rest of GSAs. Although the number of samples was not enough to follow their temporal series, the Gulf of Lions and the eastern Ionian Sea also followed this depth structure. The results confirm the findings of previous works on the structure of demersal assemblages in the Mediterranean, showing that for fishes (Ungaro et al. 1999Ungaro N., Marano G., Marsan R., et al. 1999. Analysis of demersal assemblages from trawl surveys in the South Adriatic Sea. Aquat. Living. Resour. 12: 177-185., Labropoulou and Papaconstantinou 2004Labropoulou M., Papaconstantinou C. 2004. Community structure and diversity of demersal fish communities: the role of fishery. Sci. Mar. 68: 215-226., García-Ruiz et al. 2015García-Ruiz C., Lloris D., Rueda J.L., et al. 2015. Spatial distribution of ichthyofauna in the northern Alboran Sea (western Mediterranean). J. Nat. Hist. 49: 1191-1224.) and other taxonomic groups (Tserpes et al. 1999Tserpes G., Peristeraki P., Potamias G., et al. 1999. Species distribution in the southern Aegean sea based on bottom-trawl surveys, Aquat. Living Resour. 12: 167-175., Colloca et al. 2003Colloca F., Cardinale M., Belluscio A., et al. 2003. Pattern of distribution and diversity of demersal assemblages in the central Mediterranean Sea. Est. Coast. Shelf Sci. 56: 469-480., Massutí and Reñones 2005Massutí E., Reñones O. 2005. Demersal resource assemblages in the trawl fishing grounds off the Balearic Islands (western Mediterranean). Sci. Mar. 69: 167-181.) they are strongly organized along a depth gradient.

Despite the similar bathymetric gradient along the Mediterranean, the results showed differences in the bathymetric limitations and composition of demersal fish assemblages between GSAs. This is not surprising considering that oceanographic conditions vary between GSAs, and bathymetric distributions of communities respond according to these variations. In fact, we incorporated cluster analysis to escape from the assumption that communities are structured according to MEDITS strata, and we made an analysis based on real assemblages for each GSA. Therefore, the analysis of demersal fish diversity based on cluster analysis for each particular area of the Mediterranean is more accurate than the assignation of a depth stratum to the samples analysed for the whole Mediterranean, the method that has been generally used up to now.

Our results show a stability and even recovery of demersal fish diversity in the Mediterranean. Of the 114 temporal series analysed, only 27% showed a significant trend, with an increasing pattern in 71% of the cases showing significant trends. N₉₀ and species richness (S) showed increasing trends in most cases (87.5% and 84.6%, respectively), while Pielou evenness (J’) was the indicator that showed the highest proportion of decreasing trends (60%). This stability was also shown in the only study analysing long temporal series from bottom trawl survey data (1994-2012) for the whole Mediterranean (Granger et al. 2015Granger V., Fromentin J.-M., Bez N., et al. 2015. Large-scale spatio-temporal monitoring highlights hotspots of demersal fish diversity in the Mediterranean Sea. Prog. Oceanogr. 130: 65-74.). These authors took into account three scales of analysis corresponding to 18 GSAs, 7 biogeographical zones and 2 basins at a depth ranging from 10 to 800 m. The assemblages by depth were not considered, which could explain why they did not detect any recovery.

The continuous increase in fisheries in the last few decades has led to the overexploitation of the main commercial stocks in most Mediterranean areas (Colloca et al. 2013Colloca F., Cardinale M., Maynou F., et al. 2013. Rebuilding Mediterranean fisheries: toward a new paradigm for ecological sustainability in single species population models. Fish Fish. 14: 89-109., Sartor et al. 2014Sartor P., Colloca F., Maravelias C., et al. 2014. Critical assessment of the current understanding/knowledge of the framework of the Ecosystem Approach to Fisheries in the Mediterranean and Black Seas. Sci. Mar. 78: 19-27.). However, bottom trawl fisheries in the Mediterranean have decreased recently, due to the economic losses of this activity (Quetglas et al. 2017Quetglas A., Merino G., González J., et al. 2017. Harvest Strategies for an Ecosystem Approach to Fisheries Management in Western Mediterranean Demersal Fisheries. Front. Mar. Sci. 4: 106.; Table 4) and the implementation of additional management measures, such as the prohibition of bottom trawling within 1.5 nautical miles of the coast (EC Regulation 1967/2006). However, this recent measure has possibly displaced part of the trawl fishing effort from the shelf to deeper bathymetric zones (Tserpes et al. 2011Tserpes G., Tzanatos E., Peristeraki P. 2011. Spatial management of the Mediterranean bottom-trawl fisheries: the case of the southern Aegean Sea. Hydrobiologia 670: 267-274.). Our results show that increasing trends in N₉₀ and S and decreasing trends in J’ coincide in some cases with decreasing trends in bottom trawl fishing effort. There could therefore be a cause and effect relation, because it is in accordance with the expected effect of fishing on biodiversity. The increasing trend in N₉₀ with decreasing fishing effort reinforces the previous results that confirm the usefulness of this index for detecting the effects of fishing on demersal fish diversity. The increase of evenness with increasing fishing effort has been suggested by some authors (Murawski 2000Murawski S.A. 2000. Definitions of overfishing from an ecosystem perspective. ICES J. Mar. Sci. 57: 649-658., Zhou et al. 2010Zhou S., Smith A.D.M., Punt A.E., et al. 2010. Ecosystem-based fisheries management requires a change to the selective fishing philosophy. PNAS 107: 9485-9489.) due to the reduction of dominant species by fishing (Cury et al. 2000Cury P., Bakun A., Crawford R.J.M., et al. 2000. Small pelagics in upwelling systems: patterns of interaction and structural changes in ‘’wasp-waist’’ ecosystems. ICES J. Mar. Sci. 57: 603-618., Rice 2000Rice J.C. 2000. Evaluating fishery impacts using metrics of community structure. ICES J. Mar. Sci. 57: 682-688.) and has been confirmed by the study of the effects of fishing on evenness indices (D’Onghia et al. 2003D’Onghia G., Mastrototaro F., Matarrese A., et al. 2003. Biodiversity of the upper slope demersal community in the eastern Mediterranean: preliminary comparison between two areas with and without trawl fishing. J. Northwest Atlantic Fish. Sci. 31: 263-273., Farriols et al. 2017Farriols M.T., Ordines F., Somerfield P.J., et al. 2017. Bottom trawl impacts on Mediterranean demersal fish diversity: Not so obvious or are we too late? Cont. Shelf Res. 137: 84-102.). However, the expected increase in S and decrease in J’ with decreasing fishing effort are not always observed in our results. There are some differences in the aspects of diversity that each of these indices capture. Increasing N₉₀ values with decreasing fishing effort indicate an increase in the frequency of occurrence and the evenness of the distribution of species abundances due to expansion to areas with the most favourable environmental conditions. On the other hand, an increase in S and a decrease in J’ with decreasing fishing effort implies an increase in the number of species and an increase in the dominance of some species, respectively. Although both number of species and evenness are also affecting N₉₀, the calculation of each of these indices is extremely different. N₉₀ takes into account the homogeneity or heterogeneity of all the samples of a stratum and year for each GSA in its calculation and involves the most frequent and abundant species in the group without losing species identity through the comparison among all the samples in the group. In contrast, S and J’ in a group are calculated from their mean values and consequently species identity is lost. This may explain why extreme values of fishing effort were needed to detect the effects of fishing in S and J’ in previous works (Farriols et al. 2017Farriols M.T., Ordines F., Somerfield P.J., et al. 2017. Bottom trawl impacts on Mediterranean demersal fish diversity: Not so obvious or are we too late? Cont. Shelf Res. 137: 84-102.). In some cases, N₉₀ showed no trend when there was a trend in fishing effort and viceversa. This finding could be due to several causes. It is either too early to detect the effects of decreasing fishing effort on demersal fish diversity or the decrease is not sufficiently important to change the diversity trend. Similarly, increasing trends in fishing effort could not result in a decrease in fish diversity due to the adaptation of demersal fish communities to fishing exploitation.

It must also be considered that there is a high complexity in the evaluation of fishing effort in the whole area. Available temporal series used to analyse fishing effort do not cover the whole time series of demersal fish diversity in all cases (see Table S1), and the inclusion of more years of the temporal series to the analysis could lead to different trends of fishing effort. In addition, as the nominal spatio-temporal pattern of fishing effort on a Mediterranean-wide level is not available, the use of different effort estimates in the areas may increase the uncertainty of the model. Moreover, number of fishing vessels is a poor proxy for effort, because does not account for other capacity changes (e.g. length overall, or kilowatts), because it does not account for technological creep and or temporal and spatial changes of fishing operations (Anticamara et al. 2011Anticamara J.A., Watson R., Gelchu A., et al. 2011. Global fishing effort (1950-2010): Trends, gaps, and implications. Fish. Res. 107: 131-136.). For instance, though the regulation decreasing fishing capacity has been in place since 1991, the gross tonnage of the fleets may be increasing because boats are decreasing in number (decommissioning) but increasing in size over time (e.g. Fortibuoni et al. 2017Fortibuoni T., Giovanardi O., Pranovi F., et al. 2017. Analysis of Long-Term Changes in a Mediterranean Marine Ecosystem Based on Fishery Landings. Front. Mar. Sci. 4: 33.) or because vessels have increased trawling time. This issue is enormously relevant for the Mediterranean Sea, where fisheries are managed by effort control and technical measures in contrast to quotas (northern EU seas; see Cardinale and Scarcella 2017Cardinale M., Scarcella G. 2017. Mediterranean Sea: A Failure of the European Fisheries Management System. Front. Mar. Sci. 4: 72.) and should be considered when the results are interpreted. However, in the Spanish and French Mediterranean, restrictions on hours of trawling would not permit an unlimited increase in fishing effort with a decreasing number of vessels (REAL DECRETO 1440/1999, de 10 de septiembre; Arrêté n° 99-162 du 10 juin 1999). In any case, a more appropriate indicator than number of vessels should be used for fishing effort whenever possible.

Regarding spatial patterns, we did not find the expected longitudinal decreasing west-east pattern in species richness observed in previous works on fish communities (Quignard and Tomasini 2000Quignard J.P., Tomasini J.A. 2000. Mediterranean fish diversity. Biol. Mar. Mediterr. 7: 1-66., Coll et al. 2010Coll M., Piroddi C., Steenbeek J., et al. 2010. The biodiversity of the Mediterranean Sea: estimates, patterns, and threats. PloS ONE 5: e11842.). Nor was this trend observed for N₉₀ and J’ in any depth stratum. The absence of a western/eastern decreasing trend further suggests that primary production or temperature regime are possibly not the major factor explaining large-scale patterns of diversity in demersal fish assemblages, as suggested by Gaertner et al. (2007)Gaertner J.C., Bertrand J.A., Relini G., et al. 2007. Spatial pattern in species richness of demersal fish assemblages on the continental shelf of the northern Mediterranean Sea: A multiscale analysis. Mar. Ecol. Prog. Ser. 341: 191-203.. However, it is difficult to compare our results with diversity values obtained with non-standardized data mainly collected from fish inventories from other works. Moreover, due to the limited sampling approach (i.e. data concerning only one guild of fishes or limited to specific depths, gear or habitat), some of regional inventories are useless for comparative studies (Psomadakis et al. 2012Psomadakis P.N., Giustino S., Vacchi M. 2012. Mediterranean fish biodiversity: An updated inventory with focus on the Ligurian and Tyrrhenian seas. Zootaxa 3263: 1-46.). Recent studies based mainly on standardized time series data also question the previously considered west-east decreasing diversity trend in the Mediterranean (Gaertner et al. 2013Gaertner J.C., Maiorano P., Mérigot B., et al. 2013. Large-scale diversity of slope fishes: pattern inconsistency between multiple diversity indices. PloS ONE 8: e66753., Granger et al. 2015Granger V., Fromentin J.-M., Bez N., et al. 2015. Large-scale spatio-temporal monitoring highlights hotspots of demersal fish diversity in the Mediterranean Sea. Prog. Oceanogr. 130: 65-74., Peristeraki et al. 2017Peristeraki P., Tserpes G., Lampadariou N., et al. 2017. Comparing demersal megafaunal species diversity along the depth gradient within the South Aegean and Cretan Seas (Eastern Mediterranean). PLoS ONE 12: e0184241.).

The highest diversity values were found on the continental shelf of insular areas, such as the Balearic Islands, Sardinia, Sicily and the Aegean Sea. This higher diversity can be explained by taking into account the peculiarities of the distinct biogeographic sectors within the Mediterranean (Lejeusne et al. 2010Lejeusne C., Chevaldonné P., Pergent-Martini C., et al. 2010. Climate change effects on a miniature ocean: the highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25: 250-260.), which can be characterized by the shallow water biota (Bianchi et al. 2012Bianchi C.N., Morri C., Chiantore M., et al. 2012. Mediterranean Sea biodiversity between the legacy from the past and a future of change. In: Stambler N. (ed), Life in the Mediterranean Sea: a look at habitat changes. Nova Science Publishers, New York, pp. 1-55.). In the Strait of Sicily, for example, the meeting of western and eastern Mediterranean species produces a peak in fish species richness in the central Mediterranean (Ben Rais Lasram et al. 2009Ben Rais Lasram F., Guilhaumon F., Mouillot D. 2009. Fish diversity patterns in the Mediterranean Sea: deviations from a mid-domain model. Mar. Ecol. Prog. Ser. 376: 253-267., Garofalo et al. 2007Garofalo G., Fiorentino F., Gristina M., et al. 2007. Stability of spatial pattern of fish species diversity in the Strait of Sicily (central Mediterranean). Hydrobiologia 580: 117-124.). The greater sampling effort of the present work compared with previous ones (Morri et al. 1999Morri C., Bianchi C.N., Cocito S., et al. 1999. Biodiversity of marine sessile epifauna at an Aegean island subject to hydrothermal activity: Milos, Eastern Mediterranean Sea. Mar. Biol. 135: 729-739., Koukouras et al. 2001Koukouras A., Voultsiadou E., Kitsos M.S., et al. 2001. Macrobenthic fauna diversity in the Aegean Sea, affinities with other Mediterranean regions and the Black Sea. Bios 6: 61-76.) could affect the unexpectedly high diversity values found in the Aegean Sea. The presence of algae facies deeper than 50 m around the Balearic Islands is likely to enhance demersal fish diversity in this area. Coralligenous and maerl communities are very characteristic of the Mallorca-Menorca continental shelf up to 85-90 m depth (Canals and Ballesteros 1997Canals M., Ballesteros E. 1997. Production of carbonate particles by phytobenthic communities on the Mallorca-Menorca shelf, northwestern Mediterranean Sea. Deep-Sea Res. Part II 44: 611-629., Ordines and Massutí 2009Ordines F., Massutí E. 2009. Relationships between macro-epibenthic communities and fish on the shelf grounds of the western Mediterranean. Aquat. Conserv.: Mar. Freshw. Ecosyst. 19: 370-383.), and this has been pointed out as a plausible reason for the differences observed between the coastal demersal resources of the Balearic Islands and the adjacent Iberian Peninsula (Massutí and Reñones 2005Massutí E., Reñones O. 2005. Demersal resource assemblages in the trawl fishing grounds off the Balearic Islands (western Mediterranean). Sci. Mar. 69: 167-181.). In fact, habitat type has been shown to affect the distribution of demersal species, most of them being more abundant and showing a better condition in maerl and Peyssonnelia beds (Ordines and Massutí 2009Ordines F., Massutí E. 2009. Relationships between macro-epibenthic communities and fish on the shelf grounds of the western Mediterranean. Aquat. Conserv.: Mar. Freshw. Ecosyst. 19: 370-383., Ordines et al. 2009Ordines F., Quetglas A., Massutí E., et al. 2009. Habitat preferences and life history of the red scorpion fish, Scorpaena notata, in the Mediterranean. Est. Coast. Shelf Sci. 85: 537-546., 2015Ordines F., Bauzá M., Sbert M., et al. 2015. Red algal beds increase the condition of nekto-benthic fish. J. Sea Res. 95: 115-123.), which have also shown high diversity of fish.

The results of SIMPER analyses reinforce the idea of maerl and Peyssonnelia beds causing high diversity values also on the continental shelves of Sicily, Sardinia and the Aegean Sea. The species Serranus cabrilla, Scyliorhinus canicula and Mullus surmuletus, which in the Balearic Islands have shown to be more abundant in these habitats (Ordines and Massutí 2009Ordines F., Massutí E. 2009. Relationships between macro-epibenthic communities and fish on the shelf grounds of the western Mediterranean. Aquat. Conserv.: Mar. Freshw. Ecosyst. 19: 370-383.) contribute to N₉₀ mainly in this archipelago, Sardinia, Sicily and the Aegean Sea. Similar habitats to those found on the Balearic shelf have been reported in some of these areas, like the Aegean Sea (Georgiadis et al. 2009Georgiadis M., Papatheodoroy G., Tzanatos E., et al. 2009. Coralligene formations in the eastern Mediterranean SeaQ Morphology, distribution mapping and relations to fisheries in the South Aegean Sea (Greece) based on high-resolution acoustics. J. Exper. Biol. Ecol. 368: 44-58.). The presence of a higher number of vulnerable species like demersal chondrichthyans in the Balearic Islands, Sardinia, Sicily and the Aegean Sea (Bertrand et al. 2000Bertrand J.A., Gil de Sola L., Papakonstantinou C., et al. 2000. Contribution on the distribution of elasmobranchs in the Mediterranean (from the MEDITS surveys). Biol. Mar. Mediterr. 7: 385-399., Damalas and Vassilopoulou 2011Damalas D., Vassilopoulou V. 2011. Chondrichthyan by-catch and discards in the demersal trawl fishery of the central Aegean Sea (Eastern Mediterranean). Fish. Res. 108: 142-152., Ramírez-Amaro et al. 2015Ramírez-Amaro S., Ordines F., Terrasa B., et al. 2015. Demersal chondrichthyans in the western Mediterranean: Assemblages and biological parameters of their main species. Mar. Freshw. Res. 67: 636-652.) compared with adjacent areas could also contribute to the higher fish diversity values found there.

The spatial distribution of the bottom trawl fishing effort by GSA shows that the number of vessels per km² is low on the continental shelf of the Balearic Islands, Sardinia and the Aegean Sea (Colloca et al. 2017Colloca F., Scarcella G., Libralato S. 2017. Recent Trends and Impacts of Fisheries Exploitation on Mediterranean Stocks and Ecosystems. Front. Mar. Sci. 4: 244.). The coincidence of areas with a low fishing effort with areas with a high diversity is in accordance with previous works, in which higher values of N₉₀ and S and lower values of J’ were associated with areas with a low fishing effort (Farriols et al. 2017Farriols M.T., Ordines F., Somerfield P.J., et al. 2017. Bottom trawl impacts on Mediterranean demersal fish diversity: Not so obvious or are we too late? Cont. Shelf Res. 137: 84-102.). The lower fishing effort exerted by the relatively smaller bottom trawl fleets in these areas could have preserved, at least to some extent, their fish diversity along with a better conservation of their sensitive and essential habitats, such as maerl and Peyssonnelia beds. These habitats are precisely those most affected by the low selectivity and damaging collateral effects of bottom trawling on seabed communities, which decrease the presence of biogenic habitats, leading to a reduction in the biodiversity on exploited bottoms (e.g. Norse and Watling 1999Norse E.A., Watling L. 1999. Impacts of mobile fishing gear: the biodiversity perspective. Am. Fish. Soc. Symp. 22: 31-40., Smith et al. 2000Smith C., Papadopoulou K.N., Diliberto S. 2000. Impact of otter trawling on an eastern Mediterranean commercial trawl fishing ground. ICES J. Mar. Sci. 57: 1340-1351., Hiddink et al. 2006Hiddink J.G., Jennings S., Kaiser M.J., et al. 2006. Cumulative impacts of seabed trawl disturbance on benthic biomass, production, and species richness in different habitats. Can. J. Fish. Aquat. Sci. 63: 721-736.).

Spatial patterns of demersal fish diversity on the shelf-break/upper slope and lower slope of the Mediterranean are different to those detected on the continental shelf. Areas with the highest diversity values on the continental shelf do not coincide with areas with the highest diversity values on the shelf break/upper slope and lower slope. Although the assignment of depth strata was different in previous works and the comparison is not straightforward, a different pattern on shelf and slope areas was also observed for species richness (Gaertner et al. 2007Gaertner J.C., Bertrand J.A., Relini G., et al. 2007. Spatial pattern in species richness of demersal fish assemblages on the continental shelf of the northern Mediterranean Sea: A multiscale analysis. Mar. Ecol. Prog. Ser. 341: 191-203., 2013Gaertner J.C., Maiorano P., Mérigot B., et al. 2013. Large-scale diversity of slope fishes: pattern inconsistency between multiple diversity indices. PloS ONE 8: e66753.). This is likely due to differences in the distribution of cumulative threats to marine biodiversity, which are mainly concentrated in coastal areas and on the continental shelf of the Mediterranean (Coll et al. 2012Coll M., Piroddi C., Albouy C., et al. 2012. The Mediterranean Sea under siege: Spatial overlap between marine biodiversity, cumulative threats and marine reserves. Global Ecol. Biogeogr. 21: 465-480.), and to the presence of particular habitats on the shelf break and slope bottoms, which may represent potential hot spots of biodiversity (Danovaro et al. 2010Danovaro R., Company J.B., Corinaldesi C., et al. 2010. Deep-sea biodiversity in the Mediterranean Sea: The known, the unknown, and the unknowable. PloS ONE 5: e11832.). Although the distribution of deep-sea diversity is different to that on the continental shelf, it is affected by similar factors: changes in spatial distribution of fishing effort together with habitat type. For example, higher N₉₀ values on the slope of northern Spain could be related to the presence of submarine canyons in the area where high values of biodiversity have been reported (see Fernández-Arcaya et al. 2017Fernández-Arcaya U., Ramirez-Llodra E., Aguzzi J., et al. 2017. Ecological Role of Submarine Canyons and Need for Canyon Conservation: A Review. Front. Mar. Sci. 4: 5. for a review). However, the description of deep-sea habitats has just been implemented for some particular areas of the Mediterranean, and this information is not exhaustive at all (Danovaro et al. 2010Danovaro R., Company J.B., Corinaldesi C., et al. 2010. Deep-sea biodiversity in the Mediterranean Sea: The known, the unknown, and the unknowable. PloS ONE 5: e11832.). Moreover, an intensive habitat mapping based on MEDITS samples would be useful to relate demersal fish assemblages to their corresponding habitats, as has been done in some continental shelf areas (e.g. Ordines and Massutí 2009Ordines F., Massutí E. 2009. Relationships between macro-epibenthic communities and fish on the shelf grounds of the western Mediterranean. Aquat. Conserv.: Mar. Freshw. Ecosyst. 19: 370-383.).

The outcomes of the present study show that at large temporal and spatial scales bottom trawl fisheries have reduced the diversity of demersal assemblages in the Mediterranean. However, in recent decades a generally stable scenario or even a slight recovery trend have been highlighted. This result would have not been expected if the alarming overexploitation status of Mediterranean stocks were taken into account, which underlines the importance of using diversity indices to study the effects of fishing on demersal assemblages. Therefore, a change from the assessment of demersal resources based on exploited monospecfic stocks to one based on the study of whole demersal fish assemblages is needed due to the high multispecificity of the bottom trawl fishery in the Mediterranean (Caddy 1993Caddy J.F. 1993. Some future perspectives for assessment and management of Mediterranean fisheries. Sci. Mar. 57: 121-130., Lleonart and Maynou 2003Lleonart J., Maynou F. 2003. Fish stock assessments in the Mediterranean: state of the art. Sci. Mar. 67: 37-49.). The inclusion of species other than target ones made in this work through diversity indices is therefore important for the implementation of an ecosystem-based fisheries management (Browman and Stergiou 2004Browman H.I., Stergiou K.I. 2004. Perspectives on ecosystem-based approaches to the management of marine resources. Mar. Ecol. Prog. Ser. 274: 269-303.).

ACKNOWLEDGEMENTSTop

The present study could not have been done without the work of all participants and crew in the MEDITS scientific surveys, funded by the European Union Data Collection Framework for the Common Fisheries Policy, the funding projects supporting this research (ECLIPSAME Project CTM2012-37701 and CLIFISH project CTM2015-66400-C3-1-R MINECO/FEDER) and the FPI Fellowship (BES-2013-065112) from the Spanish Ministry of Economy and Competitiveness granted to MTF.

REFERENCESTop

Anticamara J.A., Watson R., Gelchu A., et al. 2011. Global fishing effort (1950-2010): Trends, gaps, and implications. Fish. Res. 107: 131-136.
https://doi.org/10.1016/j.fishres.2010.10.016

Ben Rais Lasram F., Guilhaumon F., Mouillot D. 2009. Fish diversity patterns in the Mediterranean Sea: deviations from a mid-domain model. Mar. Ecol. Prog. Ser. 376: 253-267.
https://doi.org/10.3354/meps07786

Bertrand J.A., Gil de Sola L., Papakonstantinou C., et al. 2000. Contribution on the distribution of elasmobranchs in the Mediterranean (from the MEDITS surveys). Biol. Mar. Mediterr. 7: 385-399.

Bertrand J.A., Gil de Sola L., Papaconstantinou C., et al. 2002. The general specifications of the MEDITS surveys. Sci. Mar. 66: 9-17.
https://doi.org/10.3989/scimar.2002.66s29

Bianchi C.N., Morri C., Chiantore M., et al. 2012. Mediterranean Sea biodiversity between the legacy from the past and a future of change. In: Stambler N. (ed), Life in the Mediterranean Sea: a look at habitat changes. Nova Science Publishers, New York, pp. 1-55.

Bosc E., Bricaud A., Antoine D. 2004. Seasonal and interannual variability in algal biomass and primary production in the Mediterranean Sea, as derived from 4 years of SeaWiFS observations. Global Biogeochem. Cycles 18: GB1005.
https://doi.org/10.1029/2003GB002034

Bray J.R., Curtis J.T. 1957. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. 27: 325-349.
https://doi.org/10.2307/1942268

Browman H.I., Stergiou K.I. 2004. Perspectives on ecosystem-based approaches to the management of marine resources. Mar. Ecol. Prog. Ser. 274: 269-303.
https://doi.org/10.3354/meps274269

Caddy J.F. 1993. Some future perspectives for assessment and management of Mediterranean fisheries. Sci. Mar. 57: 121-130.

Callaway R., Alsvâg J., de Boois I., et al. 2002. Diversity and community structure of epibenthic invertebrates and fish in the North Sea. ICES J. Mar. Sci. 59: 1199-1214.

Canals M., Ballesteros E. 1997. Production of carbonate particles by phytobenthic communities on the Mallorca-Menorca shelf, northwestern Mediterranean Sea. Deep-Sea Res. Part II 44: 611-629.
https://doi.org/10.1016/S0967-0645(96)00095-1

Cardinale M., Scarcella G. 2017. Mediterranean Sea: A Failure of the European Fisheries Management System. Front. Mar. Sci. 4: 72.
https://doi.org/10.3389/fmars.2017.00072

Ciannelli L., Fisher J.A.D., Skern-Mauritzen M., et al. 2013. Theory, consequences and evidence of eroding population spatial structure in harvested marine fishes: a review. Mar. Ecol. Prog. Ser. 480: 227-243.
https://doi.org/10.3354/meps10067

Clarke K.R. 1993. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18: 117-143.
https://doi.org/10.1111/j.1442-9993.1993.tb00438.x

Clarke K.R., Gorley R.N., Somerfield P.J., et al. 2014. Change in marine communities: an approach to statistical analysis and interpretation. PRIMER-E, Plymouth, 260 pp.

Coll M., Piroddi C., Steenbeek J., et al. 2010. The biodiversity of the Mediterranean Sea: estimates, patterns, and threats. PloS ONE 5: e11842.
https://doi.org/10.1371/journal.pone.0011842

Coll M., Piroddi C., Albouy C., et al. 2012. The Mediterranean Sea under siege: Spatial overlap between marine biodiversity, cumulative threats and marine reserves. Global Ecol. Biogeogr. 21: 465-480.
https://doi.org/10.1111/j.1466-8238.2011.00697.x

Colloca F., Cardinale M., Belluscio A., et al. 2003. Pattern of distribution and diversity of demersal assemblages in the central Mediterranean Sea. Est. Coast. Shelf Sci. 56: 469-480.
https://doi.org/10.1016/S0272-7714(02)00196-8

Colloca F., Cardinale M., Maynou F., et al. 2013. Rebuilding Mediterranean fisheries: toward a new paradigm for ecological sustainability in single species population models. Fish Fish. 14: 89-109.
https://doi.org/10.1111/j.1467-2979.2011.00453.x

Colloca F., Scarcella G., Libralato S. 2017. Recent Trends and Impacts of Fisheries Exploitation on Mediterranean Stocks and Ecosystems. Front. Mar. Sci. 4: 244.
https://doi.org/10.3389/fmars.2017.00244

Cury P., Bakun A., Crawford R.J.M., et al. 2000. Small pelagics in upwelling systems: patterns of interaction and structural changes in ‘’wasp-waist’’ ecosystems. ICES J. Mar. Sci. 57: 603-618.
https://doi.org/10.1006/jmsc.2000.0712

Daan N., Gislason H., Pope J.G., et al. 2005. Changes in the North Sea fish community: evidence of indirect effects of fishing? ICES J. Mar. Sci. 62: 177-188.
https://doi.org/10.1016/j.icesjms.2004.08.020

Damalas D., Vassilopoulou V. 2011. Chondrichthyan by-catch and discards in the demersal trawl fishery of the central Aegean Sea (Eastern Mediterranean). Fish. Res. 108: 142-152.
https://doi.org/10.1016/j.fishres.2010.12.012

Danovaro R., Dinet A., Duineveld G., et al. 1999. Benthic response to particulate fluxes in different trophic environments: a comparison between the Gulf of Lions-Catalan Sea (western-Mediterranean) and the Cretan Sea (eastern-Mediterranean). Progr. Oceanogr. 44: 287-312.
https://doi.org/10.1016/S0079-6611(99)00030-0

Danovaro R., Company J.B., Corinaldesi C., et al. 2010. Deep-sea biodiversity in the Mediterranean Sea: The known, the unknown, and the unknowable. PloS ONE 5: e11832.
https://doi.org/10.1371/journal.pone.0011832

Dayton P.K., Thrush S.F., Agardy M.T., et al. 1995. Environmental effects of marine fishing. Aquat. Conserv. 5: 205-232.
https://doi.org/10.1002/aqc.3270050305

Demestre M., Martín P. 1993. Optimum exploitation of a demersal resource in the western Mediterranean: the fishery of the deep-water shrimp Aristeus antennatus (Risso, 1816). Sci. Mar. 57: 175-182.

D’Onghia G., Mastrototaro F., Matarrese A., et al. 2003. Biodiversity of the upper slope demersal community in the eastern Mediterranean: preliminary comparison between two areas with and without trawl fishing. J. Northwest Atlantic Fish. Sci. 31: 263-273.
https://doi.org/10.2960/J.v31.a20

Dremière P.Y., Fiorentini L., Cosimi G., et al. 1999. Escapement from the main body of the bottom trawl used for the Mediterranean international trawl survey (MEDITS). Aquat. Living Resour. 12: 207-217.
https://doi.org/10.1016/S0990-7440(00)88471-5

Duda A.M., Sherman K. 2002. A new imperative for improving management of large marine ecosystems. Ocean Coast. Manage. 45: 797-833.
https://doi.org/10.1016/S0964-5691(02)00107-2

Farriols M.T., Ordines F., Hidalgo M., et al. 2015. N90 index: a new approach to biodiversity based on similarity and sensitive to direct and indirect fishing impact. Ecol. Indic. 52: 245-255.
https://doi.org/10.1016/j.ecolind.2014.12.009

Farriols M.T., Ordines F., Somerfield P.J., et al. 2017. Bottom trawl impacts on Mediterranean demersal fish diversity: Not so obvious or are we too late? Cont. Shelf Res. 137: 84-102.
https://doi.org/10.1016/j.csr.2016.11.011

Farrugio H., Oliver P., Biagi F. 1993. An overview of the history, knowledge, recent and future research trends in Mediterranean fisheries. Sci. Mar. 57: 105-119.

Fernández-Arcaya U., Ramirez-Llodra E., Aguzzi J., et al. 2017. Ecological Role of Submarine Canyons and Need for Canyon Conservation: A Review. Front. Mar. Sci. 4: 5.
https://doi.org/10.3389/fmars.2017.00005

Fiorentini L., Dremière P.Y., Leonori I., et al. 1999. Efficiency of the bottom trawl used for the Mediterranean international trawl survey (MEDITS): Efficacite du chalut de fond utilise pour le programme international d’evaluation des ressources halieutiques de Mediterranee (MEDITS). Aquat. Living. Resour. 12: 187-205.
https://doi.org/10.1016/S0990-7440(00)88470-3

Fortibuoni T., Libralato S., Raicevich S., et al. 2010. Coding Early Naturalists’ Accounts into Long-Term Fish Community Changes in the Adriatic Sea (1800-2000). PLoS ONE 5: e15502.
https://doi.org/10.1371/journal.pone.0015502

Fortibuoni T., Giovanardi O., Pranovi F., et al. 2017. Analysis of Long-Term Changes in a Mediterranean Marine Ecosystem Based on Fishery Landings. Front. Mar. Sci. 4: 33.
https://doi.org/10.3389/fmars.2017.00204

Fromentin J.M., Fonteneau A. 2001. Fishing effects and life history traits: A case study comparing tropical versus temperate tunas. Fish. Res. 53: 133-150.
https://doi.org/10.1016/S0165-7836(00)00299-X

Gaertner J.C., Bertrand J.A., Relini G., et al. 2007. Spatial pattern in species richness of demersal fish assemblages on the continental shelf of the northern Mediterranean Sea: A multiscale analysis. Mar. Ecol. Prog. Ser. 341: 191-203.
https://doi.org/10.3354/meps341191

Gaertner J.C., Maiorano P., Mérigot B., et al. 2013. Large-scale diversity of slope fishes: pattern inconsistency between multiple diversity indices. PloS ONE 8: e66753.
https://doi.org/10.1371/journal.pone.0066753

García-Ruiz C., Lloris D., Rueda J.L., et al. 2015. Spatial distribution of ichthyofauna in the northern Alboran Sea (western Mediterranean). J. Nat. Hist. 49: 1191-1224.
https://doi.org/10.1080/00222933.2014.1001457

Garofalo G., Fiorentino F., Gristina M., et al. 2007. Stability of spatial pattern of fish species diversity in the Strait of Sicily (central Mediterranean). Hydrobiologia 580: 117-124.
https://doi.org/10.1007/s10750-006-0460-1

Georgiadis M., Papatheodoroy G., Tzanatos E., et al. 2009. Coralligene formations in the eastern Mediterranean SeaQ Morphology, distribution mapping and relations to fisheries in the South Aegean Sea (Greece) based on high-resolution acoustics. J. Exper. Biol. Ecol. 368: 44-58.
https://doi.org/10.1016/j.jembe.2008.10.001

General Fisheries Commission for the Mediterranean (GFCM). 2016. Report of the Working Group on Stock Assessment of Demersal Species (WGSAD). Rome.
http://www.fao.org/gfcm/reports/technical-meetings/detail/en/c/471253/

Gislason H. 2002. The effects of fishing on non-target species and ecosystem structure and function. In: Sinclair M., Valdimarsson G. (eds), Responsible Fisheries in the Marine Ecosystem. CAB International, Wallingford, pp. 255-274.
https://doi.org/10.1079/9780851996332.0255

Granger V., Fromentin J.-M., Bez N., et al. 2015. Large-scale spatio-temporal monitoring highlights hotspots of demersal fish diversity in the Mediterranean Sea. Prog. Oceanogr. 130: 65-74.
https://doi.org/10.1016/j.pocean.2015.08.002

Guijarro B., Massutí E., Moranta J., et al. 2008. Population dynamics of the red shrimp Aristeus antennatus in the Balearic Islands (western Mediterranean): Short spatio-temporal differences and influence of environmental factors. J. Mar. Syst. 71: 385-402.
https://doi.org/10.1016/j.jmarsys.2007.04.003

Hall S.J. 1999. The Effects of Fishing on Marine Ecosystems and Communities. Blackwell Science, Oxford. 274 pp.

Hiddink J.G., Jennings S., Kaiser M.J., et al. 2006. Cumulative impacts of seabed trawl disturbance on benthic biomass, production, and species richness in different habitats. Can. J. Fish. Aquat. Sci. 63: 721-736.
https://doi.org/10.1139/f05-266

Jennings S., Dulvy N.K. 2005. Reference points and reference directions for size-based indicators of community structure. ICES J. Mar. Sci. 62: 397-404.
https://doi.org/10.1016/j.icesjms.2004.07.030

Jørgensen C., Enberg K., Dunlop E.S., et al. 2007. Managing Evolving Fish Stocks. Science 318: 1247-1248.
https://doi.org/10.1126/science.1148089

Kaiser M.J., de Groot S.J. 2000. The effects of fishing on non-target species and habitats. Biological, conservation and socio-economics issues. Blackwell Science, Oxford, 399 pp.

Kaiser M.J., Collie J.S., Hall S.J., et al. 2002. Modification of marine habitats by trawling activities: prognosis and solutions. Fish Fish. 3: 114-136.
https://doi.org/10.1046/j.1467-2979.2002.00079.x

Koukouras A., Voultsiadou E., Kitsos M.S., et al. 2001. Macrobenthic fauna diversity in the Aegean Sea, affinities with other Mediterranean regions and the Black Sea. Bios 6: 61-76.

Labropoulou M., Papaconstantinou C. 2004. Community structure and diversity of demersal fish communities: the role of fishery. Sci. Mar. 68: 215-226.
https://doi.org/10.3989/scimar.2004.68s1215

Lejeusne C., Chevaldonné P., Pergent-Martini C., et al. 2010. Climate change effects on a miniature ocean: the highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25: 250-260.
https://doi.org/10.1016/j.tree.2009.10.009

Lleonart J., Maynou F. 2003. Fish stock assessments in the Mediterranean: state of the art. Sci. Mar. 67: 37-49.
https://doi.org/10.3989/scimar.2003.67s137

Masnadi F., Criscoli A., Lanteri L., et al. 2018. Effects of environmental and anthropogenic drivers on the spatial distribution of deep-sea shrimps in the Ligurian and Tyrrhenian Seas (NW Mediterranean). Hydrobiologia 816: 165-178.
https://doi.org/10.1007/s10750-018-3581-4

Massutí E., Reñones O. 2005. Demersal resource assemblages in the trawl fishing grounds off the Balearic Islands (western Mediterranean). Sci. Mar. 69: 167-181.
https://doi.org/10.3989/scimar.2005.69n1167

MEDITS Working Group. 2017. MEDITS-Handbook, Version n. 9. MEDITS Working Group, 106 pp.
http://www.sibm.it/MEDITS%202011/principaledownload.htm

Meléndez M.J., Báez J.C., Serna-Quintero J.M., et al. 2017. Historical and ecological drivers of the spatial pattern of Chondrichthyes species richness in the Mediterranean Sea. PLoS ONE 12: e1075699.
https://doi.org/10.1371/journal.pone.0175699

Moranta J., Quetglas A., Massutí E., et al. 2008. Research trends on demersal fisheries oceanography in the Mediterranean. In: Mertens L.P. (ed), Biological Oceanograpgy Research Trends. Nova Science Publishers, New York, pp. 9-65.

Morato T., Watson R., Pitcher T.J., et al. 2006. Fishing down the deep. Fish Fish. 7: 24-34.
https://doi.org/10.1111/j.1467-2979.2006.00205.x

Morri C., Bianchi C.N., Cocito S., et al. 1999. Biodiversity of marine sessile epifauna at an Aegean island subject to hydrothermal activity: Milos, Eastern Mediterranean Sea. Mar. Biol. 135: 729-739.
https://doi.org/10.1007/s002270050674

Murawski S.A. 2000. Definitions of overfishing from an ecosystem perspective. ICES J. Mar. Sci. 57: 649-658.
https://doi.org/10.1006/jmsc.2000.0738

Norse E.A., Watling L. 1999. Impacts of mobile fishing gear: the biodiversity perspective. Am. Fish. Soc. Symp. 22: 31-40.

Ordines F., Massutí E. 2009. Relationships between macro-epibenthic communities and fish on the shelf grounds of the western Mediterranean. Aquat. Conserv.: Mar. Freshw. Ecosyst. 19: 370-383.
https://doi.org/10.1002/aqc.969

Ordines F., Quetglas A., Massutí E., et al. 2009. Habitat preferences and life history of the red scorpion fish, Scorpaena notata, in the Mediterranean. Est. Coast. Shelf Sci. 85: 537-546.
https://doi.org/10.1016/j.ecss.2009.09.020

Ordines F., Farriols M.T., Lleonart J., et al. 2014. Biology and population dynamics of by-catch fish species of the bottom trawl fishery in the western Mediterranean. Mediterr. Mar. Sci. 15: 613-625.
https://doi.org/10.12681/mms.812

Ordines F., Bauzá M., Sbert M., et al. 2015. Red algal beds increase the condition of nekto-benthic fish. J. Sea Res. 95: 115-123.
https://doi.org/10.1016/j.seares.2014.08.002

Peristeraki P., Tserpes G., Lampadariou N., et al. 2017. Comparing demersal megafaunal species diversity along the depth gradient within the South Aegean and Cretan Seas (Eastern Mediterranean). PLoS ONE 12: e0184241.
https://doi.org/10.1371/journal.pone.0184241

Pranovi F., Raicevich S., Franceschini G., et al. 2001. Discard analysis and damage to non-target species in the ‘rapido’ trawl fishery. Mar. Biol. 139: 863-875.
https://doi.org/10.1007/s002270100646

Psomadakis P.N., Giustino S., Vacchi M. 2012. Mediterranean fish biodiversity: An updated inventory with focus on the Ligurian and Tyrrhenian seas. Zootaxa 3263: 1-46.
https://doi.org/10.11646/zootaxa.3263.1.1

Quetglas A., Merino G., González J., et al. 2017. Harvest Strategies for an Ecosystem Approach to Fisheries Management in Western Mediterranean Demersal Fisheries. Front. Mar. Sci. 4: 106.
https://doi.org/10.3389/fmars.2017.00106

Quignard J.P., Tomasini J.A. 2000. Mediterranean fish diversity. Biol. Mar. Mediterr. 7: 1-66.

R Core Team. 2014. R: A Language and Environment for Statistical Computing. http://www.r-project.org/

Ramírez-Amaro S., Ordines F., Terrasa B., et al. 2015. Demersal chondrichthyans in the western Mediterranean: Assemblages and biological parameters of their main species. Mar. Freshw. Res. 67: 636-652.
https://doi.org/10.1071/MF15093

Rice J.C. 2000. Evaluating fishery impacts using metrics of community structure. ICES J. Mar. Sci. 57: 682-688.
https://doi.org/10.1006/jmsc.2000.0735

Roberts C.M. 2002. Deep impact: the rising toll of fishing in the deep sea. Trends Ecol. Evol. 17: 242-245.
https://doi.org/10.1016/S0169-5347(02)02492-8

Sabatini A., Locci I., Deiana A.M., et al. 2013. Temporal trends in biodiversity of the middle-slope assemblages in Sardinian seas (Central-Western Mediterranean). J. Mar. Biol. Ass. U.K. 93: 1739-1752.
https://doi.org/10.1017/S0025315413000258

Sardà F., Calafat A., Flexas M.M., et al. 2004. An introduction to Mediterranean deep-sea biology. Sci. Mar. 68: 7-38.
https://doi.org/10.3989/scimar.2004.68s37

Sartor P., Colloca F., Maravelias C., et al. 2014. Critical assessment of the current understanding/knowledge of the framework of the Ecosystem Approach to Fisheries in the Mediterranean and Black Seas. Sci. Mar. 78: 19-27.
https://doi.org/10.3989/scimar.04021.17B

Smith C., Papadopoulou K.N., Diliberto S. 2000. Impact of otter trawling on an eastern Mediterranean commercial trawl fishing ground. ICES J. Mar. Sci. 57: 1340-1351.
https://doi.org/10.1006/jmsc.2000.0927

Tecchio S., Ramírez-Llodra E., Sardà F., et al. 2011. Drivers of deep Mediterranean megabenthos communities along longitudinal and bathymetric gradients. Mar. Ecol. Prog. Ser. 439: 181-192.
https://doi.org/10.3354/meps09333

Tserpes G., Peristeraki P., Potamias G., et al. 1999. Species distribution in the southern Aegean sea based on bottom-trawl surveys, Aquat. Living Resour. 12: 167-175.
https://doi.org/10.1016/S0990-7440(00)88468-5

Tserpes G., Tzanatos E., Peristeraki P. 2011. Spatial management of the Mediterranean bottom-trawl fisheries: the case of the southern Aegean Sea. Hydrobiologia 670: 267-274.
https://doi.org/10.1007/s10750-011-0667-7

Ungaro N., Marano G., Marsan R., et al. 1998. Demersal fish assemblage biodiversity as an index of fishery resources exploitation. Ital. J. Zool. 65: 511-516.
https://doi.org/10.1080/11250009809386876

Ungaro N., Marano G., Marsan R., et al. 1999. Analysis of demersal assemblages from trawl surveys in the South Adriatic Sea. Aquat. Living. Resour. 12: 177-185.
https://doi.org/10.1016/S0990-7440(00)88469-7

Zhou S., Smith A.D.M., Punt A.E., et al. 2010. Ecosystem-based fisheries management requires a change to the selective fishing philosophy. PNAS 107: 9485-9489.
https://doi.org/10.1073/0pnas.0912771107

SUPPLEMENTARY MATERIAL

The following supplementary material is available through the online version of this article and at the following link:
http://scimar.icm.csic.es/scimar/supplm/sm04977esm.pdf

Table S1. – Temporal series of fishing effort measures in number of vessels, kilowatt per day at sea (kW*days at sea) and gross tonnage per days at sea (GT*days at sea) for each GSA and species. Species considered in each depth stratum are i) Mullus barbatus or Mullus surmuletus for the continental shelf; ii) Nephrops norvegicus or Parapenaeus longirostris for the shelf break/upper slope; and iii) Aristeus antennatus or Aristaeomorpha foliacea for the lower slope. Effort measures used to calculate trends in fishing effort for each GSA and stratum are marked with (*). References are listed below the table.

Fig. S1. – Species accumulation curves for each GSA. Note that for all GSAs asymptotic values of species counts are reached.

Fig. S2. – Mean values of N₉₀ diversity index during the period 1994–2015 for each GSA and depth strata. Black dots, shelf; blue dots, shelf break/upper slope; red dots, lower slope.

Fig. S3. – Mean values of species richness (S) during the period 1994–2015 for each GSA and depth strata. Black dots, shelf; blue dots, shelf break/upper slope; red dots, lower slope.

Fig. S4. – Mean values of Pielou evennes (J’) during the period 1994–2015 for each GSA and depth strata. Black dots, shelf; blue dots, shelf break/upper slope; red dots, lower slope.