D6.2 Report on Biodiversity Indicators, Trends

Home , Alloteuthis media, Argyropelecus, Centracanthus cirrus, Dromia personata, Ethusa mascarone, Eurynome aspera

DEEPFISHMAN Management And Monitoring Of Deep-sea Fisheries And Stocks

Project number: 227390

Small or medium scale focused research action Topic: FP7-KBBE-2008-1-4-02 (Deepsea fisheries management)

DELIVERABLE D 6.2

Title: Report on biodiversity indicators, trends monitoring and evaluation of information pertinence for deep-water fish and invertebrates

Due date of deliverable: M 24 (April 2011)

Actual submission date: M 27 (July 2011

st Start date of the project: April 1 , 2009 Duration : 36 months

Organization Name of lead coordinator: Ifremer

Dissemination Level: PP (Restricted to programme participants)

Date: 27 July 2011

CHAPTER 1

Data Review on the Distribution and Extent of Deep-Sea Macrobenthic Communities: Trends in Biomass and Abundance from the North East

Atlantic

Deep-Sea Benthic Data Review

Data Review on the Distribution and Extent of Deep- Sea Macrobenthic Communities: Trends in Biomass and Abundance from the North East Atlantic.

Prepared by A. Kenny and C. Barrio

CEFAS

1 Deep-Sea Benthic Data Review

March, 2011

Table of Contents

Introduction...... 3 Materials and Methods ...... 3 Results & Discussion ...... 7 References ...... 11 Appendix 1. (to be provided) ...... 12

2 Deep-Sea Benthic Data Review

Data Review on the Distribution and Extent of Deep- Sea Macrobenthic Communities: Trends in Biomass and Abundance from the North East Atlantic.

Introduction In a brief review of deep-sea ecology undertaken as part of the Deepfishman project (Kenny and Barrio, 2010) the importance of major physiographic habitat features was highlighted in defining broad scale trends in deep sea benthic community structure. In particular, it was noted that large mega-habitats defined as; i. continental slope, ii. seamounts, iii. hydrothermal vents (and other chemosynthetic primary producing habitats), iv. canyons, and v. trenches, were amongst the most important types of physiographic features in the deep-sea. It was also noted that within each of these features significant variations in benthic invertebrate diversity, biomass and abundance occur, largely attributed to variations in substrate type, local hydrodynamic processes and the supply of energy in the form of nutrients and organic carbon. Whilst a significant body of literature and information has been acquired on local deep sea diversity, trophic conditions and topographic characteristics from specific projects such as HERMES and the Atlantic Frontier Environmental Network (AFEN), there is still a need to synthesise the collective published knowledge to better understand and to define the general processes determining benthic diversity and function in the deep sea. In particular, there is a need to understand how these processes relate to populations of deep-sea commercial fish species. Only through a fundamental understanding of such processes can we ensure that the most appropriate management measures are identified and applied. In essence, a redefinition of the criteria for the identification and definition of “eco-regions” of the deep-sea environment is required. The aim of this study was to collate quantitative data on the status (diversity, biomass, abundance) of the main taxonomic groups of macrobenthic invertebrates in areas confined to the North East Atlantic deep-sea region (in depths >400m). Materials and Methods We used published literature as the primary source of data to compile a meta-data table of deep sea ecosystem observations. In total 56 scientific papers were reviewed (see Appendix 1), but of these only 6 of these contained data which could be directly compared between studies (or cases). However, of these 6 papers, several different studies were documented and therefore in total 17 separate mega-habitat cases were directly comparable, namely; Hughes and Gage (2004; 1 spur/ridge, 1 basin and 1 abyssal plain), Gage (2001; 1 abyssal plain), Henry and Roberts (2007; 1 carbonate mound), Bett (2001; 2 continental slope), Heip et al. (2001; 1 shelf break, 1 slope and 1 abyssal plain), Duineveld et al. (2000; 3 canyon and 3 spur) and finally the OECD (1989, 1 abyssal plain). All cases were

3 Deep-Sea Benthic Data Review

included in a meta-data table for analysis, (Table1), with each case representing a large mega-habitat feature such as a canyon or continental slope etc. The approximate geographic locations of all these studies (used in this analysis) is presented in Figure 1. One of the problems we observed, which limited the amount of data we could include in this analysis, was the diverse way in which deep sea benthic samples are processed. Some researchers would describe only the mega-epifauna, whereas others would use different sieve sizes to sort different fractions of fauna from sediment samples. In addition, in all cases the identification of species was incomplete and hence comparisons between the studies in terms of species richness and diversity was not possible. The consistent identification and recording of species in the deep sea is clearly a problem and guidance (including taxonomic keys) on deep sea taxonomy suitable for routine comparative assessment studies, is urgently needed.

Figure 1. Approximate location of reported studies in the literature used in this analysis The meta-data presented in Table 1 was further reduced to provide two matrices of cases vs. variables each being slightly different in their dimensions (Table 2 and Table 3). Each data matrix was then analysed using Principal Components Analysis to examine similarities between cases (i.e. mega-habitat types) in terms of their variable attributes (e.g. total biomass, abundance, latitude, depth etc.). The data in both matrices were first transformed using; log (X + 1) and then standardised by applying:

Where is the mean of the variable values is the variable value and is the standard deviation of variable values. All cases were assigned to their appropriate large mega-habitat type.

4 Deep-Sea Benthic Data Review

5 Deep-Sea Benthic Data Review

Table 1. Meta-data values extracted from a review of 56 published papers from which 6 papers provided sufficiently directly comparable data covering a total of 17 cases of large mega-habitats. Column header code: C = Case

Table 2. Matrix used for PCA comprising 13 cases representing 6 different mega-habitat features and 4 Table 3. Matrix used for PCA comprising 8 cases representing 5 different mega-habitat ecosystem level variables. Column header codes: S = continental slope, AP = abyssal plain, C = canyon. features and 7 ecosystem level variables.

6 Deep-Sea Benthic Data Review

Results & Discussion A total of 6 different large mega-habitat features were included in this analysis, although not all types were represented in equal numbers, namely; i. deep sea basin (1), ii. shelf break (1), iii. continental slope (3), iv. deep sea spurs (3), v. canyons (3) and vi. abyssal plain (3). The univariate plot of total macrobenthic biomass against depth using the reduced case matrix (which excludes canyons – see Table 3) reveals a reasonably significant trend with depth (see Figure 2). In general, biomass declines with increasing depth (which has been well documented), with some of the lowest values of biomass observed in the abyssal plain which occur at depths between 3,500 m and 4,000 m. The highest values of biomass were observed in 2 of the continental slope cases and one case of a deep sea basin, all occurring between 750 m and 1,500 m in depth. There is also an indication that biomass initially rises from the shelf break (at approximately 500 m) down to depths of about 1,500 m, but this may simply be an artefact of the small data set. There is also some indication that biomass varies according to mega-habitat type, in addition to depth. For example, by examining the relationship between biomass and depth for the complete matrix (Table 2) it is apparent that it is a combination of depth and mega-habitat feature which determine levels of biomass.

Figure 2. Biomass plotted against depth with cases labelled according to their mega-habitat type. (note this data corresponds to Table 2 and excludes a number of cases of spur and all cases of canyon. Figure 3, at first reveals no obvious trend between total biomass and depth, but if mega-habitat type is also considered a pattern of variation is revealed. For example, all cases of canyon have relatively high macrobenthic biomass, despite being among the deepest habitats sampled. This is also true for the cases of spur studied, e.g. of the 4 cases studied, 2 have high biomass values, again despite the spur cases being among the deepest habitats studied. It appears that beyond a depth of about 2000 m (accepting that this is

7 Deep-Sea Benthic Data Review

only a small data set) the best predictor of total biomass appears to be the type of mega- habitat feature despite the depth varying between 2,500m and 4,500 m for all the canyon, spur and abyssal plain cases. This is perhaps not so surprising since the pathway which directly couples the relatively rich supply of organic carbon in the photic zone with the benthos is comparatively weak at these great depths, so the relatively high levels of biomass associated with the canyon and spur cases is probably to do with these features somehow increasing the available amount of organic matter and other nutrients. By contrast, above about 2000 m, depth appears to become more important as a predictor of biomass, as also exemplified in Figure 2.

Figure 3. Biomass plotted against depth with cases labelled according to their mega-habitat type. (note the magnitude of variation in biomass at depths below about 2000 m appears to be more related to mega-habitat type than depth, whereas at depths below about 2000 m depth appears to be more important in determining biomass and not mega-habitat type).

The relationship between biomass, depth and habitat type was further explored using Principal Components Analysis. Considering the PCA ordination associated with the 15 sample cases included in Table 2, it is apparent that the mega-habitat types have all formed contiguous clusters and these can be defined in terms of their biomass, abundance and depth (Figure 4). For example, the continental slope cases are characterised by relatively low biomass, high abundance and small depth, whereas the canyon cases are characterised by relatively high biomass, moderate abundance and large depths. Intermediate between these two types are the spur cases which have moderately high biomass, moderate abundance and large depths. The abyssal plain cases are characterised by comparatively low biomass, low abundance and large depths.

8 Deep-Sea Benthic Data Review

Figure 4. PCA ordinations of the data presented in Tables 2 and 3. Depth and biomass explain most of the variation between sample cases (mega-habitat types) when most of the deep sea spur and all canyon cases are excluded from the analysis (ordination on the left), whereas when canyons and all spur cases are included depth becomes orthogonal to biomass, in other words biomass is more related to mega-habitat type and not depth. This is in agreement with the univariate plots of biomass and depth presented in figures 2 and 3. S = continental slope, AP = abyssal plain, C = canyon.

9 Deep-Sea Benthic Data Review

It is unfortunate that diversity and productivity data were not included in the cases used directly in this analysis, as the resilience of these systems (e.g. the mega-habitat types) to disturbance, particularly from fishing activity, remains a critical issue to be addressed for fisheries management.

It has long been known that in open shelf sea systems a general relationship exists between marine ecosystem productivity and biodiversity, such that biodiversity is often associated with enhanced productivity and biomass when measured at the regional scale (Worm et al. 2006; Loreau, 2008, Bolam et al, 2010). In a comprehensive study of the UK shelf seabed macrobenthic communities (comprising 155 samples), wet weight biomass values ranged from 5 – 800 g.m-2, this compares to a range of less than 0.1 – 5 g.m-2 for deep-sea samples in the present study. By contrast, α diversity in the deep sea for the relatively high biomass sample cases associated with canyons and spur/ridges is generally much higher than for shelf samples with comparable biomass. Given the comparatively low levels of biomass, but high levels of diversity, it is likely that environmental stability has contributed to the development of high functional species diversity to maximise the limited energy resources in the deep sea.

Furthermore, an analysis of deep sea nematode communities from a recent global- scale study based on 270 datasets from 116 deep-sea sites, showed that functioning of these ecosystems is not only positively, but is exponentially related to biodiversity in all the deep-sea regions investigated (Danovaro et al. 2008). Results suggest that higher biodiversity supports higher rates of ecosystem processes and an increased efficiency with which these processes are performed (Danovaro et al. 2008). These exponential relationships support the hypothesis that mutually positive functional interactions (ecological facilitation) are much more prevalent in deep-sea ecosystems compared to coastal and shelf ecosystems, and it seems likely this is associated (in part) with increased environmental stability. Although there is still no full understanding of all the processes regulating deep-sea food webs and the ecological role of each species, it is hypothesized that the increase in bioturbation of the seafloor may increase benthic fluxes and the redistribution of food within the sediment, leading to an increase in ecosystem functioning. These results suggest that biodiversity loss in deep-sea ecosystems might be associated with significant reductions in functioning.

This assertion clearly has implications for deep sea fishing methods which directly disturb deep sea (non chemosynthetic) habitats, as it is very likely that such habitats would take many years (if not decades) to re-establish the levels of functional diversity required to sustain the relatively low levels of biomass and production observed at these depths.

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References Kenny, A. J. and Barrio, C. F, (2010). The ecology of deep-sea ecosystems: a brief review. Feeder Report for WP2 Deepfishman FP7 project, Cefas, pp50. Chase, J.M., Leibold, M.A., (2003). Spatial scale dictates the productivity-biodiversity relationship. Nature 416, 427-430. Danovaro, R., Gambi, C., Dell., Anno, A., Corinaldesi, C., Fraschetti, S., Vanreusel, A., Vincx, M. and Gooday, A.J. (2008). Exponential decline of deep-sea ecosystem functioning linked to benthic biodiversity loss. Current Biology 18(1): 1–8. Loreau, M. (2008). Biodiversity and Ecosystem Functioning: The Mystery of the Deep Sea. Current Biology 18(3): R126–R128. Martinez, N.D., (1996). Defining and measuring functional aspects of biodiversity. In: K.J. Gaston (Ed.), Biodiversity - A biology of numbers and differences, pp. 114-118. Blackwell Science, Oxford. Bolam, S. G., Barrio-Frojan, C. R. S., Eggleton, J. D. (2010). Macrofaunal production along the UK continental shelf. Journal of Sea Research 64 (2010) 166–179 Worm, B., Barbier, E.B., Beaumont, N., Duffy, J.E., Folke, C., Halpern, B.S., Jackson, J.B.C., Lotze, H.K., Micheli, F., Palumbi, S.R., et al. (2006). Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790. Duineveld, G., Lavaleye, M., Berghuis, E., de Wilde, P.,(2001). Activity and composition of the benthic fauna in the Whittard Canyon and the adjacent continental slope (NE Atlantic). Oceanologica Acta, ⋅ Vol. 24 – No. 1, 69 – 83. Flach, E., Heip, C., (1996). Vertical distribution of macrozoobenthos within the sediment on the continental slope of the Goban Spur area (NE Atlantic). Mar Ecol Prog Ser 141: 55-66, Heip, C., Duineveld, G. Flach, E., Graf, G., Helder, W., Herman, P., Lavaleye, M., Middelburg, J., J., Pfannkuchec, O., Soetaert, K., Soltwedel, T., de Stigter, H. Thomsen, L., Vanaverbeke, J., de Wilde, P., (2001). The role of the benthic biota in sedimentarymetabolism and sediment- water exchange processes in the Goban Spur area (NE Atlantic). Deep-Sea Research II 48, 3223–3243. Bett, B.J., 2001. UK Atlantic Margin Environmental Survey: Introduction and overview of bathyal benthic ecology. Continental Shelf Research, 21, 917-956. Henry, L-A., Roberts, J. M., (2007). Biodiversity and ecological composition of macrobenthos on cold-water coral mounds and adjacent off-mound habitat in the bathyal Porcupine Seabight, NE Atlantic. Deep-Sea Research I 54, 654–672. Gage, J. D., (2001). Deep-sea benthic community and environmental impact assessment at the Atlantic Frontier. Continental Shelf Research 21, 957–986. Hughes, D. J., Gage, J. D., (2004). Benthic metazoan biomass, community structure and bioturbation at three contrasting deep-water sites on the northwest European continental margin. Progress in Oceanography 63, 29–55.

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Appendix 1. (to be provided) List of references reviewed from which sources of data were compiled to undertake this comparative data analysis.

12 Chapter 2

Diversity indices in the Icelandic autumn deep-water survey (case study 4)

Contents

1. Introduction ...... 2 2. Material and method ...... 2 2.1. Description of the survey ...... 2 2.2. Sampling per area ...... 4 2.2.1. Area A ...... 4 2.2.2. Area B ...... 5 2.2.3. Area C ...... 6 2.2.4. Area D ...... 7 2.3. Diversity indices ...... 8 2.4. Depth effect and temporal trends in fish diversity ...... 8 2.5. Depth and temporal effect on raw data ...... 8 2.6. Modelling ...... 9 3. Results ...... 9 3.1. Temporal trends ...... 9 3.1.1. Area A, depth strata 350-750 m ...... 9 3.1.2. Area A, depth strata 750-1000 m ...... 10 3.1.3. Area A, depth strata >1000 m ...... 11 3.1.4. Area B ...... 12 3.1.5. Area C ...... 13 3.1.6. Area D ...... 14 3.2. Depth effect ...... 15 3.3. Model of depth, temporal and spatial effects ...... 16 3.3.1. Choice of the best model ...... 16 3.3.2. Estimation of the depth and year effects ...... 17 3.3.3. Estimation of the spatial effect ...... 18 4. Discussion ...... 22 5. References ...... 24

1 Diversity indices in the Icelandic autumn deep-water survey (case study 4)

1. Introduction

Species composition of deep-water fish is know to vary much with depth at local scale and which latitude and longitude at larger scale (Gordon and Bergstad 1992; Koslow 1993; Lorance 1998).Diversity of deep-water fish also varies with depth and spatially (Lorance et al. 2002; Campbell et al. in press). Taxonomic distinctness indices were seldom used for deep water fish but a recent study used them to reveal spatial and depth trend (Campbell et al. in press).

In this report species diversity and taxonomic distinctness indices are used to assess the depth and spatial distribution of the deep-water demersal fish community based upon data from the Icelandic autumn bottom trawl survey. Diversity indices are calculate for every trawl haul, the of indices by depth and by year is investigated using graphical representation of raw data and general additive models (GAM) to identify the environmental factors for diversity and the temporal trends.

2. Material and method 2.1. Description of the survey

The data analysed covers years 1996 to 2009. The survey covers the slope around Iceland and Northern part of the Reykjanes ridge. In 2000, substantial additions were made to the sampling plan in order to accommodate for redfish distribution (Figure 1). To account for this, analysed were carried out for a number of smaller areas where different range of years and depths were sampled (Figure 2).

Figure 1 Stations in the Icelandic Autumn survey black=deep haul 1996-2005, blue= additions after 2000.

679 678 677 676 675 674 673 672 671 670 669 668 667 666 665 664 663 662 661 660 66°30' 629 628 627 626 625 624 623 622 621 620 619 618 617 616 615 614 613 612 611 610 66° 579 578 577 576 575 574 573 572 571 570 569 568 567 566 565 564 563 562 561 560 65°30' 529 528 527 526 525 524 523 522 521 520 519 518 517 516 515 514 513 512 511 510 65° 479 478 477 476 475 474 473 472 471 470 469 468 467 466 465 464 463 462 461 460

64°30' 429 428 427 426 425 424 423 422 421 420 419 418 417 416 415 414 413 412 411 410 64° 379 378 377 376 375 374 373 372 371 370 369 368 367 366 365 364 363 362 361 360 63°30' 329 328 327 326 325 324 323 322 321 320 319 318 317 316 315 314 313 312 311 310 63° 279 278 277 276 275 274 273 272 271 270 269 268 267 266 265 264 263 262 261 260 62°30' 229 228 227 226 225 224 223 222 221 220 219 218 217 216 215 214 213 212 211 210 62° 179 178 177 176 175 174 173 172 171 170 169 168 167 166 165 164 163 162 161 160 29° 28° 27° 26° 25° 24° 23° 22° 21° 20° 19° 18° 17° 16° 15° 14° 13° 12° 11° 10°

3 Area D is the southeastern Iceland-Ferry island ridge. Traditionally redfish but also Greenland halibut were exploited in this area.

This reports aims at assessing abundance and fish diversity trends in these four areas. Results are presented in numbers only in this first exploration of the survey data. Further analysis including indices and indicators in biomass are required. Nevertheless, data in weight were not available and biomass will have to be estimated based upon length distribution and length weight relationships.

2.2. Sampling per area

The sampling per area is described by the number of haul per year per 200 meter depth band and by the distribution of tows (all years combined by 50 depth band). For further analysis grouping of tows per strata was carried out (post stratification) using the following strata: - 0 to 200 m (shelf) - 200 to 350 m (shelf break) - 350 to 750 m (upper slope) - 750 to 1000 m (mid slope) - deeper than 1000 m (mid slope) In this report "depth band" will be used to denoted the 200 m depth band and depth strata to denote the above strata.

2.2.1. Area A In this area the sampling covered depth form 250 to 1290 m. Although the shallowest part was not covered in the first four years, the sampling was well balanced with similar number of hauls per 200 m depth bands over time (Table 1). The 200 m depth bands were chosen arbitrary to represent the distribution of the sampling over time. The sampling was slightly more intense in the depth bands 400-600 m and 1000-1200 m.

Table 1. Number of haul per depth strata in the area A from 1996 to 2009 (n=583).

Depth band (meters) Year 200-400 400-600 600-800 800-1000 1000-1200 >1200 1996 - 9 7 9 13 2 1997 - 7 8 6 15 2 1998 - 8 8 6 15 2 1999 - 9 6 7 15 2 2000 2 11 7 7 15 2 2001 2 10 7 8 14 2 2002 2 10 7 8 15 2 2003 2 10 8 8 13 2 2004 2 10 8 8 13 2 2005 2 11 7 8 13 1 2006 2 10 9 8 13 - 2007 2 10 8 8 13 1 2008 2 10 9 8 12 1 2009 2 10 8 8 13 1

In order to get a sufficient number of haul per strata a further grouping was done based upon the distribution of hauls by depth for all years combined (Figure 3). Combining all years, there was a small number of hauls shallower that 350 m and there are slightly less haul from 750 to 850 m. Then larger depth strata were define from 350 to 750 m, this strata correspond to what is usually considered the upper slope. The distribution of the rest of the hauls displays a peak in number of haul between 1050m and 1100 m, hauls are in relatively similar number by 50 m depth band from 750 to 1000 m. Then two strata were defined from 750 to 1000 m and deeper that 1000 m.

40 Number of hauls of Number

250 750 1000 1300 Depth

Figure 3. Distribution of haul per depth in the area A of the Iceland trawl survey all haul combined 1996-2009 (n=583). The red lines depict the depth strata limits at 350, 750 and 1000 m.

2.2.2. Area B In this area the sampling covered depth form 180 to 1370 m. The number of tows bellow 800 meters was small, then only two large depth strata could be considered (Table 2). For consistency with area A, a350-750 m and a deeper than 750 m strata were used. Because only the deeper depths were sampled with only 3 hauls per year before 2000, the analysis was restricted to 2000-2009. The sampling was slightly more intense in the depth bands 400-600 m and 1000-1200 m.

Table 2. Number of haul per 200 m depth bands in the area B from 1996 to 2009 (n=231).

Depth band (meters) Year 0-200 200-400 400-600 600-800 800-1000 1000-1200 >1200 1996 - - - - - 1 2 1997 - - - - - 1 2 1998 - - - - - 1 2 1999 - - - - - 1 2 2000 1 2 7 6 2 1 2

5 2001 1 2 8 7 1 1 2 2002 1 2 7 7 2 1 2 2003 1 2 7 7 2 1 2 2004 1 2 7 7 2 1 2 2005 1 3 6 8 1 1 2 2006 1 4 6 7 1 1 2 2007 1 3 6 8 1 1 2 2008 1 2 7 8 1 1 2 2009 1 3 6 7 2 1 2

In terms of larger depth strata, only the strata 350 – 750 m was sampled with a sufficient number of hauls for investigating temporal trends (Figure 4).

20 Number of hauls of Number

150 350 750 1000 1400 Depth

Figure 4. Distribution of haul per depth in the area B of the Iceland trawl survey all haul combined 1996-2009 (n=231). The red lines depict the depth strata limits at 350, 750 and 1000 m.

2.2.3. Area C In this area, the sampling was mainly concentrated in the depth band 400 - 600 m (Table 3) the sampling covered depth from 150 to 850 m with some gaps (Figure 5). Only one or two hauls per year were sampled deeper than 750 m so that only the large strata 350-750 m could be considered.

Table 3. Number of haul per 200 m depth bands in the area C from 2000 to 2009 (n=125).

Depth band (meters) Year 0 – 200 200 – 400 400 – 600 600 - 800 800 - 1000 2000 NA NA 8 NA 1 2001 2 1 8 1 NA 2002 1 2 8 1 1

6 2003 1 2 8 1 1 2004 1 2 8 1 1 2005 1 2 9 NA 1 2006 1 2 9 NA 1 2007 1 2 9 NA 1 2008 1 2 9 NA 1 2009 1 2 9 NA 1

Number of hauls of Number 15

150 350 750 850

Depth

Figure 5. Distribution of haul per depth in the area C of the Iceland trawl survey all haul combined 2000-2009 (n=125). The red lines depict the depth strata limits at 350, 750 and 1000 m.

2.2.4. Area D

This area was sampled since 1996 with an increase number of haul since 2000, a total of 403 haul were sampled for the whole time series. There were hauls in all 200 m depth bands from 1996 to 2009. Nevertheless, the distribution of the total number of hauls per depth shows a small number of tows shallower than 250 m and deeper then 750 m (Figure 6). As a consequence, there was enough data (n=276 hauls) for investigating temporal trends only for the upper slope (350-750 m).

Table 4. Number of haul per 200 m depth bands in the area D from 1996 to 2009 (n=403).

Depth band (meters) Year 0 – 200 200 – 400 400 – 600 600 - 800 800 - 1000 1996 1 3 4 5 3 1997 1 3 5 5 2

7 1998 1 3 4 6 2 1999 1 3 4 6 2 2000 2 7 15 6 3 2001 2 8 15 6 3 2002 2 8 14 7 3 2003 2 8 13 8 3 2004 2 8 13 8 3 2005 2 8 14 7 3 2006 2 8 14 8 2 2007 2 8 14 7 3 2008 2 8 13 9 2 2009 2 8 14 8 2

30 Number of hauls of Number

100 350 750 950

Depth

Figure 6. Distribution of hauls per depth in the area D of the Iceland trawl survey all hauls combined 1996-2009 (n=403). The red lines depict the depth strata limits at 350, 750 and 1000 m.

2.3. Diversity indices

See section diversity indices in the chapter on the survey from the eastern Ionian Sea (chapter 3).

2.4. Depth effect and temporal trends in fish diversity

2.5. Depth and temporal effect on raw data

Visual investigation of temporal trends in species diversity indices were made where sufficient data were available (i.e. a sufficient number of tow for several year in an area and depth strata). Temporal trends were not investigate d by 200 m depth bands because there was

8 not enough tows in any area but by depth strata in area A, where there most more data and globally in area B, C and D. As the number of hauls per depth band was properly balanced over time in every area (Tables 1-4) the observed trends should not be biased but estimates are means across all factors, in particular depth. The depth effect was estimated from the distribution of diversity indices per depth band and depth strata (i.e. shelf, shelf break, upper slope, mid slope).

2.6. Modelling

GAM modelling was carried out to evaluate the interaction of the year and depth effect and investigate the existence of a spatial effect within area. To ways to represent a spatial effect were tried (i) adding a factor for rectangles depicted in figure 1 (and corresponding to ICES rectangles), (ii) adding a smooth for latitude and longitude. Then, 13 models with increasing complexity between explanatory variables were tried for every area: model 1: index= s(depth) model 2: index= s(year) model 3: index= s(depth)+s(year) model 4: index= te(year,depth) model 5: index=s(depth)+s(year)+as.factor(rectangle) model 6: index=te(depth,year)+as.factor(rectangle) model 7: index=s(depth)+s(lat,lon) model 8: index=s(year)+s(lat,lon) model 9: index=s(depth)+s(year)+s(lat,lon) model 10: index=te(depth,year)+s(lat,lon) model 11: index=te(year,lat,lon) model 12: index=s(depth)+te(year,lat,lon) model 13: index=te(depth,year)+te(year,lat,lon) where te(,) denotes a tensor product smooth term. The models were fitted with REML (residual maximum likehood) of the GAM function in R (R Development Core Team 2008). The models were fitted for 5 diversity indices (denoted index in the formulas above): N0 and the taxonomic distinctness indices Δ, Δ*, Δ+ and Λ+.Models 1 to 3 were fitted for all data combined and by areas, models 4-13 were fitted by area only. As there was less data in area B and C and these areas are not very far away from each other, the models were also fitted on the data for these two areas combined. The quality of the fit was assessed from residual plots. The best model was identified using the Akaike's information criterion (AIC).

3. Results 3.1. Temporal trends

3.1.1. Area A, depth strata 350-750 m The abundance and diversity indices per trawl station show no clear trend over time (Figure 7). The species richness (N0) may suggest some increase with lower values observed before the year 2000. Hill's N1 and N2 indices (corresponding to the Shannon and Simpson indices) also suggest some increase, with some high value occurring in 2003, 2008 and 2009. Among taxonomic distinctness indices, Δ may show a slight increase with higher value in the second half of the time series. This is

9 consistent with the increase in N1. Δ* shows a rather dome shaped patterns. The average taxonomic distinctness (Δ+) and the variation in taxonomic distinctness Λ+ show no variation suggesting no change in the relatedness of the species list per station. Quality of fit were inspected for inspection of residual plots, model were selected using the AIC criteria.

10 higher values in the second half of the time series. Hill's N1 and N2 vary without pattern, low values being observed in the early 2000s. Taxonomic distinctness indices show no trend.

11 observed, this may be as well a year effect than an actual trend. There may be a slight increase in species richness at the end of the time series. N1 and N2, show no variations. Taxonomic distinctness indices show variations with a slight increase in Δ* and Δ+. over the five last years. Nevertheless, there is no overall trend from 1996 to 2009.

12 None of the diversity indices showed pattern for the two depth strata (350-750 m and deeper than 750 m) considered for this area (results not shown).

3.1.5. Area C In the depth strata 350-750 m the species richness was high (about 15 species per tow) and rather stable from 2000 to 2009 (Figure 10). There is an apparent increasing pattern in N1 and Δ but for both a lower value was observed in 2009 so that the time series might rather be considered without trend. There is possibly a slight decrease in Λ+ so that there may be some changes in the community in this area at the sampled depths. For example, the increase in N1 and Δ could derive from a decline in the abundance of the dominant species but at the same time some rarer species would have declined too inducing a decline in Λ+. However, as changes are small with overlaps of the distribution of the indices between years this is mainly speculative.

13 Figure 10. Abundance (top left), diversity indices from the Hill's series (N0, N1 and N2) and taxonomic distinctness indices (right column) of the fish community sampled by the Icelandic autumn survey in area D, depth stratum 350-750 m from 2000 to 2009 (n=88 hauls). A few extreme values are not represented for legibility.

3.1.6. Area D The numbers caught per tows are smaller than in the other areas. The fish density seems to be lesser in this area compared to areas A-C. This may reflect either naturally smaller fish or an impact of fishing. Therefore, there is a need to contrast this result with the history of fisheries per area (basically looking at cumulated catch other time) and at oceanography data to derive some indication of the possible productivity per area. There is a temporal trend in N1 and Δ, other indices are mostly stable (Figure 11). Then as indices reflecting mostly the taxonomic relatedness (Δ*, Δ+ and Λ+) and the species richness (N0) are stable and diversity indices are increasing, the change might come from the dominance structure in the community. This may suggest an effect of fishing that reduced the abundance of some dominant (possible large commercial) species.

14 15 there is a minimum in diversity indices on the shelf break. This is visible for areas B and C and not for area D (depths shallower than 200 m were not sampled in area A).

3.3. Model of depth, temporal and spatial effects

3.3.1. Choice of the best model The fit of models 1-3 by area (i.e. for all the data with an area factor) showed that the area factor was significant. Then all models were fitted for every area and results are analysed area by area because an overall picture make little sense owing to different histories of fisheries and regulations measures between areas. The models confirmed the depth effect for all indices. For Δ, the year effect improved the model fit in area C only while for but year effect was not.

Models with a spatial effect always explained a higher percentage of deviance, quality of fit (i.e. better residual distribution) and AIC were also improved (see appendix 2). Although some improvement was achieved with a factor for rectangle, the use of a smooth for latitude and longitude was most often better. The best model selected according to AIC (appendix 2) varied between areas and indices (Table 5). (result for model without latitude and longitude shown for the index Δ and Δ+ only). In two out of the four areas (C and D), a model with a smooth for depth and area and another for longitude and latitude was evaluate the best, all other model having significantly larger AIC.

Table 5. Best model (selected from AIC) and percentage deviance explained by area and index. Models 1 to 13 (see section 2.5) are denoted M1-M13.

Model A B C D B - C N0 M8 (38.8) M5 (33.6) M5 (51.3) M8 (35.7) M5 (47) Δ1 M12 (64.9) M12 (61.5) M10 (66.4) M10 (72.5) M10 (58) Δ M12 (63.8) M7 (66.7) M10 (75) M10 (71.7) M10 (67.1) Δ* M10 (54.3) M7 (91.2) M9 (70.3) M10 (55.6) M7 (83.5) Δ+ M12 (36.5) M8 (62.3) M1 (51.3) M8 (54.1) M9 (56.7) Λ+ M12 (40.6) M7 (30.2) M1 (28.6) M7 (15.7) M7 (32)

In area A the distribution of indices was better modelled with model 12, including a depth and spatial effect both combined with year (i.e. there is a depth effect and a spatial effect that both change with year) for 4 out of 6 indices modelled. For the two other indices model 10 and 8 were better. Therefore in area A, a spatial effect was detected for all indices, for the species richness there was a depth effect but no temporal effect for the other indices a depth effect in addition to the spatial effect improved the model fit. In area B the different indices were best fitted by different model. There was a spatial effect in all models. For the species richness (N0) model 5 (with a rectangle effect) was the best, in other word the rectangle effect represented the spatial effect as well as a model with a finer spatial effect (i.e. latitude and longitude).Only for the probability of interspecific encounter (Δ1), model 12 with depth year and latitude, longitude effects was the best, for other indices the model ignoring one of the depth or year effects (models 7 and 8) were better. In area C, the species richness was also best fitted by model 5. Indices Δ1, Δ and Δ* were best fitted by models 9 and 10 including depth, yea and latitude longitude while Δ+ and Λ+ were best fitted by model 1, i.e. only a depth effect.

16 In area D model 8, with year and latitude longitude without depth, was the best for N0 and Δ+. For Δ1, Δ and Δ*, model 10 (with a depth factor) was the best and model 7 with depth and latitude, longitude for Δ+. Combining areas B and C did not lead in general to select more complex models than in areas B and C separate. For N0, Δ and Λ+ the most complex of the models for the areas separate was selected for area combined and for Δ1 and Δ* the simplest was selected, only for Δ+ a more complex one was selected (Table 5). All residual plots were good, with the exception of that of model 8 for Δ+ in area D. Figure 12a provides an example for all residual plots except model 8 for Δ+ in area D presented in figure 12b. The deviance explained was generally high, most often over 50% and it was generally higher for Δ and Δ1 and lower for N0 and Λ+ (Table 5). Across all areas and indices model 13 was never the best fit (Appendix 2) so that all explanation variables available in the data were used to model the observed indices and there is no option for more complexity of the model base upon available data.

3.3.2. Estimation of the depth and year effects Using model 9 with separated effects for depth, year and geographical coordinates to assess the depth and year effects on the Δ index, shows a significant year effect and area A and D and a not significant year effect in areas B and C (Figure 13). In both areas A and D the year effect in an increase over time of the estimated index. The delta index was chosen for this investigation because it account for relative abundance (like the Shannon index) and a taxonomic distinctness. The depth effect is significant in all 4 areas, it is an increasing diversity down to 800 to 1000 m and then a decrease in estimated in areas A and B where deeper waters were sampled. Using model 10 which includes an interaction for depth and year, the year effect appears to be stronger by 750 to 1100 meters in area A and below 600 meters in area D (Figure 14). Note than in these two areas the sampling intensity at these depth was quite high so that this year effect is well estimated. In area B, although the separate year was not significant the smooth suggest a year effect in the 800-1000 meters depth range. However, this depth range was not much sampled (one or two hauls per year since 2000, table 2) so that this is only indicative. In area C no year effect is seen and the depth effect clear appear as contours parallel to the depth variable. These results are consistent with the plots of raw data (Figure 7-11) but the modelling allows a much better evaluation of the changes mainly because the different effects can be separated. Part of the dispersion observed in figures 7-11 to spatial and depth effects within depth strata. Taking the example of area A where there was more data, for index Δ, the deviance explained increases from about 30% with models with depth and/or year effects only, to about 40% and 60 % when space is included as a rectangle effect and as smooth for latitude and longitude respectively (Table 6). Although year alone does not explain anything, the addition of a term for year in the smooth for space increased the deviance explained by 7% (compare models 7 and 12 in table 6). As most of the variability is due to the depth, no time trend was visible when depth was not accounted for.

Table 6. Explained deviance by the different models for the taxonomic distinctness index Δ in area A s() stand for isotropic smooth and te() for anysotropic smooth.

Model Effects Explained deviance 1 s(depth) 34.1 2 s(year) 0.1 3 s(depth)+s(year) 34.2

17 4 te(depth,year) 32.3 5 s(depth)+s(year)+factor(rect) 42.2 6 te(depth,year)+factor(rect) 41.9 7 s(depth)+s(lat,lon) 57.6 8 s(year)+s(lat,lon) 56.6 9 s(year)+s(depth)+s(lat,lon) 58.5 10 te(depth,year)+s(lat,lon) 61.7 11 te(year,lat,lon) 60.1 12 s(depth)+te(year,lat,lon) 64.9 13 te(depth,year)+te(lat,lon,year) 64.1

3.3.3. Estimation of the spatial effect Again model 10 on the Δ index was used to represent the geographical because in this model the smooth for year and depth is separated from the smooth for latitude and longitude (see section 2.6). In area A the spatial effect is mainly latitudinal with higher diversity find to the north limit of the area, in areas B and C it is mainly longitudinal and in area d it is both (Figure 14). In area D there seems to be a gradual pattern all over the area with higher diversity to the southeast and lower to the northwest. In the three other areas there seem to be mainly a particular area that is different from the rest (northern limit in area A, longitudes around 24° West in area B and 18°30' to 19°30' West in area C). In these cases the pattern in diversity need to be compare with habitat pattern to identify whether they correspond to overall latitudinal or longitudinal pattern or if areas of higher fish diversity are associated with a particular habitat feature.

(a)

Normal Q-Q Plot Resids vs. linear pre 40 40 20 20 Sample Quantiles residuals 0 0 -20 -20 -40 -40

-3 -2 -1 0 1 2 3 0 20 40 60 80

Theoretical Quantiles linear predictor

Histogram of residua Response vs. Fitted 80 200 60 150 Frequency Response 40 100 20 50 0 0

-40 -20 0 20 40 60 0 20 40 60 80

Residuals Fitted Values

(b)

Normal Q-Q Plot Resids vs. linear pre 5 5 0 0 -5 -5 Sample Quantiles residuals -10 -10 -15 -15 -20 -20

-3 -2 -1 0 1 2 3 75 80 85

Theoretical Quantiles linear predictor

Histogram of residua Response vs. Fitted 90 150 80 100 Frequency Response 70 50 0 60

-20 -15 -10 -5 0 5 10 75 80 85

Residuals Fitted Values

Figure 12. Residual plots of GAM models of diversity indices (a) model 12 for Δ in area A as an example of residual plot for all retained models (see table 5) except model 8 for Δ+ in area D (b) model 8 for Δ+ in area D.

19 8 6 4 2 0 -2 -4 -6 1996 2000 2004 2008

20 Area A Area B

40 45 70 10 2008 50 2008 10 35 20

2006 2006 65

45 15 2004 40 25 2004 ea

15 35 Year 30 60 2002 30 2002 20 20 25 15 2000 2000 55 55

25 35 45

1998 1998 25

40 50

20 50 25 30 40 50

35 1996 1996 45

400 600 800 1000 1200 200 400 600 800 1000 1200 Area C Area D

70 10 70 10 2008 2008 10 10

2006 2006 65 65

15 15 2004 2004 ea

15 15 Year 60 60 2002 20 2002 20

2000 2000 55 55

30 30 25 25 35 45 35 45

1998 25 1998 25

40 40 50 50

30 40 20 30 40 20 25 25

50 50 1996 45 35 1996 45 35

200 400 600 800 1000 1200 200 400 600 800 1000 1200

Figure 14. Interaction of depth (x-axis) and year (y-axis) effects by area using a model with a separate smooth for depth and year (model 10).

Area A Area B

65.6 80 90

63.2 25 50

60 40 35 20 65.4 70

30 63.0 30 15 50 40

45 10 65.2 40 Latitude 62.8 35

65.0 30

Latitude 62.6

25 25

64.8 62.4

-26.5 -26.0 -25.5 -25.0 -24.5 -24.0 -23.5 64.6 Longitude

64.4

-28.5 -28.0 -27.5 -27.0 Longitude Area D

60 Area C 70

64.0

63.2

63.1 40 60 30 80 63.0 63.5

Latitude 50 70 Latitude 50 62.9 20 20 40 62.8 60 30 62.7 20 63.0 40 20 10 62.6 50

20 -22 -21 -20 -19 -18 -17 -16

62.5 30

80 40 50 60 70 90

Longitude -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 Longitude

Figure 15. Spatial effect by area using a model with a separate smooth for latitude and longitude (model 10), plots dimensioned to represented roughly the relative extent in latitude and longitude of the for areas. latitude and longitudes in decimal degrees.

4. Discussion

Although this analysis of diversity indices was carried out in four relatively small areas expectedly without geographical trends inside them, most of the models revealed a geographical effect in the data. This suggests that these areas are indeed not spatially homogeneous and include geographical variations. Only in the case of area C, two indices were as well fitted with model without geographical terms. This may simply come from the quantity of data available ass there was a smaller number of hauls in area C possibly preventing to assess all effects. This seems likely because area C is larger than the three other areas so that it is unexpected that is includes less geographical variations but this requires further investigation of hydrology and habitat variations as well as fishery management e.g. does the sampling cover some fishing grounds and some areas closed to fishing. Moreover the plot of the modelled spatial distribution for area C suggests some pattern (but the model with the spatial smooth did not explained to data better than models without). In most areas indices were also better modelled with and explicit depth effect. This was expected because depth is well known to be the main structuring factor for deep-water fish communities (Gordon and Bergstad 1992; Lorance 1998) but depth and location could have been confounded, as they are fully correlated in the real world. Finding that the best model includes both a depth and a geographical term might imply that the depth is better represented by an explicit factor. The smooth for latitude and longitude, or in a few cases the factor for rectangles, might represent geographical effects partly independent of depth. Nevertheless, depth and location may not be always well identified by these models and in 4 out of 36 cases the best fit was obtained from model 8, which does not include a depth term. In these cases it is likely that the depth effect was well represented by the latitude and longitude smooth.

22 Most retained models (28 out of 36) included a year effect, suggesting some temporal changes in the diversity. Diversity indices have often been used to describe the structure and spatial variations in communities, they have been less used to detect temporal trends. Diversity metrics are not thought to be efficient at tracking time changes at least for fish communities. Nevertheless, they were used for changes in benthic communities. The results observed here suggest that changes in deep-water fish communities may be tracked with these indices. The observed changes needs to be compared to the history of fishing activities, existence of protected areas, regulations at the scale of the 4 small areas studied.

When there is enough data a depth effect was identified, the year effect may be combined with the depth or the spatial effect. In practical terms, this mean that the temporal changes in the index varied with depth or spatially. In some area, no depth effect was identified, most probably because it is comprised in the geographical effect, i.e. provided the resolution and accuracy of the data is sufficient, the depth is fully correlated with the location. Only if the geographical coordinates are no accurate the depth may convey an additional information to the geographical effect. Then, observing the best fit for a model with no term for depth does not mean the absence of a depth distribution of the diversity. Therefore, these investigations suggests that: (i) the diversity of the fish community in Icelandic waters is structured spatially, (ii) the main factor is depth but there is an additional geographical structuring; (iii) there has been temporal changes over the past 10 to 15 years. This latter conclusion is a new finding on diversity indices applied to fish communities, which are informative in terms of communities organisation but rarely show temporal variations. The reason for the temporal variations observed here deserve further analyses in term of history of fishing in every area and the species driving the observed change in diversity (which species increased/decreased over time ?). Further research needs include: o Developing these investigations by analysing (i) the consistency of the depth and year interaction for all indices and (ii) the consistency of the spatial effect (here these were looked at for the index Δ only) o Analysing whether the observed change in diversity index are due to particular species or group of species or if they involve most of species occurring in the fish community o Analysing how these results match with other community metrics such as those chosen in the Data collection Framework, e.g. proportion of large fish, mean maximum length of fish (Commission decision 2008/949 of 16 November 2008) or in the Marine Strategy Framework Directive o Investigating if (turn-over) diversity (Gray 2000) can provided further information on the spatial aspect (in other word compare the GAM modelling approach developed here to other approaches of the spatial pattern). In addition to this the diversity in the sense of Gray (2000) includes non strictly speaking spatial aspect by also aspect such as the contribution of one station to the total diversity in a given stratum, area or biogeographical province.

The work presented here has some important implication. As deep-water fish diversity varies by depth and spatially, this offer options for a spatial management in an approach to put more protection or reduce human impacts in areas where there is more diversity. As some increase of fish diversity over time is identified here, this implies that environmental and/or management changes might have an effect of the diversity. Although this is a rather trivial statement, it is not well recognised for deep-water communities and ecosystems which are often presented as the last area in which human impacts developed and which are so sensitive that no sustainable activity can be carried out. The observation of some increases of fish

23 diversity made here should then be further analysed to explain their causes and assess to which extent they result from management of deep-water fishing.

5. References

Campbell, N., Neat, F., Burns, F., Kunzlik, P. (in press). Species richness, taxonomic diversity, and taxonomic distinctness of the deep-water demersal fish community on the Northeast Atlantic continental slope (ICES Subdivision VIa). ICES Journal of Marine Science, fsq070. http://icesjms.oxfordjournals.org/cgi/content/abstract/fsq070v1

Gordon, J. D. M., Bergstad, O. A. (1992). Species composition of demersal fish in the Rockall Trough, north-eastern Atlantic, as determined by different trawls. Journal of the Marine Biological Association of the United Kingdom 72, 213-230.

Gray, J. S. (2000). The measurement of marine species diversity, with an application to the benthic fauna of the Norwegian continental shelf. Journal of Experimental Marine Biology and Ecology 250(1-2), 23-49.

Koslow, J. A. (1993). Community structure in north Atlantic deep-sea fishes. Progress in Oceanography 31(3), 321-338.

Lorance, P. (1998). Structure du peuplement ichtyologique du talus continental a l'ouest des îles Britanniques et impact de la pêche. Cybium 22(4), 309-331.

Lorance, P., Souissi, S., Uiblein, F. (2002). Point, alpha and beta diversity of carnivorous fish along a depth gradient. Aquat. Living Resour. 15, 263-271.

R Development Core Team. (2008). "R: A language and environment for statistical computing." from http://www.R-project.org.

24 CHAPTER 3

Spatial and temporal patterns in fish diversity in the Eastern Ionian Sea (Case study 3b)

Contents

1.1. Introduction ...... 2 1.2. Material and methods ...... 3 1.2.1. Survey data ...... 3 1.2.2. Species included in the indices ...... 3 1.2.3. Diversity indices ...... 4 1.2.4. Sharks, rays and chimaeras ...... 6 1.3. Results ...... 7 1.3.1. Species sets ...... 7 1.3.2. Species diversity and species distinctness metrics ...... 10 1.3.3. Chondrichthyans ...... 11 1.4. Discussion ...... 22 2. References ...... 33

1 Spatial and temporal patterns in fish diversity in the Eastern Ionian Sea (Case study 3b) 1.1. Introduction

The Eastern Ionian Sea is exploited by small-medium scale fisheries including trawling conducted mainly in shallow waters and fixed gears (longlines and nets) operating in shallow as well as deep waters. To date, few of the exploited stocks have been assessed from quantitative stock assessment (e.g. Machias, 2001; Tserpes et al., 2003; Antonakakis et al., 2011). In the context of the deep-water survey data, they seem to be the most reliable data for both stock and community assessment. One of the main deep-water species of interest to fisheries in this area is the blackspot seabream (Pagellus bogaraveo) but a number of other species are exploited in both shallow and deep-waters including hake (Merluccius merluccius) and Monkfish (Lophius spp.). The area is also exploited for deep-wtaer shrimps such as Aristomeorpha foliacea and Aristeus antennatus. Catch and effort statistics are considered poorly reliable for a number of reasons. The fleet includes a large number of small vessels that lands in numerous harbours. The catch may be marketed directly to final consumers (including local population, restaurants, tourists). Last but not least, the contribution of recreational fisheries to the landings (and therefore fishing mortality) is unknown but may be high for some species. Recreational fisheries may also be diverse including angling from the coastline, diving and fishing with small vessels. These fisheries exploit primarily coastal and shelf populations but may also impact those deep-water populations, which juveniles occur in shallow waters such as the backspot seabream (Priol 1932, Lorance 2011). As a consequence, survey data seem to be the most reliable data to assess both fish populations and communities trends. Based upon a literature review, temporal trends in the fishing pressure and ecosystem production in this area were identified. Fishing pressure decreased over 1995-2006 and ecosystem productivity increased (Rochet et al. 2010). Such a combination of direction of changes was identified only for two areas in the Ionian Sea amongst ten Mediterranean and four North-East Atlantic areas. In the Eastern Ionian sea, changes in populations and communities were identified and their causes could be inferred (Rochet et al. 2010). Therefore the context in this area is one of decreasing fishing pressure and increasing ecosystem productivity inducing a most probably increased recruitment in populations and communities and decreasing total mortality, multiples causes of changes in both populations and communities may also have occurred. This study uses species diversity and species distinctness metrics to evaluate changes with depth and over time. The use of these metrics complement analyses at population and community levels which allow appraising some biodiversity aspects (e.g. trends in vulnerable and/or key populations) by the estimation of the species diversity and how it is linked to environmental factors. The evaluation of changes in diversity with depth may also be useful in identifying depth with higher diversity that can be of management interest as management measures taken in areas with higher diversity may be more efficient for biodiversity conservation. Because this study is mainly focussed on the deep-water (here the upper slope 200-800 m), sharks, rays and chimaeras received a particular attention because they are thought to be especially sensitive to fishing pressure. In particular, a number of deep-water sharks species has been classified as threatened by IUCN (International Union for the Conservation of Nature) in the North east Atlantic. Nevertheless, because of small number caught and relatively moderate number of tows only a preliminary study was carried out.

2 1.2. Material and methods

1.2.1. Survey data Data used are the time-series 1994-2008 of the MEDITS survey in the Eastern Ionian Sea (Bertrand et al. 2000, 2002). Sampling scheme and gear are described in detail by Bertrand et al. (2002). Tow duration was 30 minutes for shallow tows by 0-200 m and 1 hour for deeper tows (Bertrand et al. 2000, 2002). In the analyses presented here, tow duration was not standardised because such a standardisation is not straightforward for the estimation of diversity. The reason for longer tow duration for deep-water tows is that the catch rates in number of fish tend to be smaller than on the shelf. Operation time (shooting and hauling the trawl) is also longer so that the total cost of a deep-water tow is higher for lesser data collected. Because towing time is only a fraction of the total time of the fishing operation, there is a trade-off between the amount of biological data collected and the number of sampled station (number of tows per day). For the calculation of diversity indices, the standardisation for towing time (therefore samples surface, usually denoted as swept area in survey data analyses) may not be appropriate because diversity metrics and density are correlated (Buckland et al. 2005). More individuals in the sample, higher is the estimated diversity. Therefore, a standardisation to the number caught or to the density may be more appropriate.

Number and weight per species were used to calculate the diversity indices. The method was applied to fish only. Diversity indices were calculated by year and by depth strata (all years combined). Analysing the time-series of year aims to detect possible temporal trends while the analysis by depth (depth as a continuous variable) and by depth strata (using here two depth strata: 0-200 m for the shelf and >200 m for the slope) aims to explore whether some depth range or one of the strata include more fish diversity.

1.2.2. Species included in the indices The objective is to assess the diversity of the deep-water demersal and benthic fish community. This "benthodemersal" fish community is further simply denoted demersal fish community. The categorisation of fish as pelagic, demersal or benthic is straightforward for most species but may be debatable for some. In the deep-water a bentho-pelagic component was identified and species are further termed bentho-demersal. To give an example, Macrorhamphosus scolopax is difficult to categorise as demersal or pelagic. In fishbase (Froese and Pauly 2011), this species is categorised as demersal, but it is "Found between the seabed and midwater on the lower continental shelf", it is also gregarious, and juveniles "are found in oceanic surface water". Lastly, juveniles feed on pelagic invertebrates and adults on benthic invertebrates. Blue whiting (Micromesistius poutassou) is another species which category is debatable. In the North East Atlantic, adult blue whiting are mesopelagic, forming large pelagic schools and exploited by large offshore pelagic trawlers (ICES 2010). Nevertheless, in the Bay of Biscay and Celtic Sea, juvenile blue whiting occur on the shelf and are caught in high numbers in fishery surveys using bottom otter trawl. They are abundant in the diet of demersal fish (Pinnegar et al. 2003; Trenkel et al. 2005). Blue whiting is then in trophic and spatial interaction with demersal fish. In Greek waters, blue whiting is mainly caught by bottom trawl. Individual caught in the bottom trawl in the MEDITS survey could then be considered as representing the bottom dwelling

3 component of the population. Numerous such examples could be given and one might question whether the results are sensitive to the selection of species considered "demersal".

The time series 1994-2008 included 188 fish species or taxons as a few individuals were not identified to species level (Appendix 1), 32 fish taxons were excluded as there were clearly pelagic (e.g. sardines, anchovy and mackerel) or meso-pelagic (e.g. and Myctophidae, Sternoptychinae, Paralepididae) and the diversity indices were calculated using the 156 remaining fish taxons, treated as "Species set 1" (Appendix 1).

In order to assess the effect of species which may not be (or fully be) part of the demersal fish community but to the pelagic community, the effect of sequentially excluding additional species was explored. Species were removed when the background knowledge did not allow for a clear allocation to a category, as in the example above, or when they appeared highly aggregative in the data. In the latter case, an aggregated distribution may suggest moving shoals that may impact the indices for a small proportion of the hauls. The gregariousness of species was assessed for all species which more than 50 individuals were caught all years combined. For each of these, the hauls were ordered in decreasing number (or biomass) of the species and the curve of the cumulative catch vs number of cumulated hauls was plotted. The proportion of the haul producing 80% of the species catch was also calculated using both the positive hauls and all hauls as divisor. Using the positive haul was considered more appropriate as it might restrict the haul taken into account to suitable species habitats. The remaining list a species after excluding species of debatable status and aggregative distribution is further demoted "Species set 2".

1.2.3. Diversity indices A number of diversity indices were computed. The diversity of demersal and benthopelagic fish was assessed using indices from the series of Hill (Hill 1973) and taxonomic distinctness indices (Clarke and Warwick 1998, 1999, 2001). In the series from Hill, N0, N1 et N2 are respectively the species richness, an exponentiation of the Shannon index and the inverse of the Simpson index (Simpson 1949).

The general expression of Hill indices is:

1 S (1−a)  a  Na =∑pi  (1)  i=1  where: a is the order of the diversity index pi : is the relative abundance of the species i in the sample S : is the number of species in the sample Here, samples are trawl hauls.

It can be easily seen that:

NO =S (2)

N0 is the species richness.

4 1 = N 2 n 2 ∑ pi i=1 (3)

N2 is then the inverse of the Simpson index. Remind that the Simpson index is expressed as (Simpson 1949): S D=∑[ni(ni −1)]/[n(n−1)] (4) i=1

Where ni and n are respectively the number of individuals of species i and the total number. 2 2 Compared to pi =(ni/n) , terms with ni-1 et n-1 are corrected for finite populations, which can be neglected when samples are large enough.

Lastly, 1 N ∞ = p d (5)

Where d stands for the most abundant (i.e. dominant) species. N∞ is the inverse of the frequency of the dominant species. N∞ is not be used in the present analysis, only indices of low order (0 to 2) are used. In equation 1, N1s a is undefined, but when a approaches 1, Na approaches:

 n  N1 = exp− ∑ pi ln( pi ) = exp(H )  i=1  (6) Where H is the Shannon index.

As described by Hill (1973), all indices of orders from -∞ à +∞ can be calculated, but all are not useful. For example, N-∞ is the reciprocal of the frequency of the rarest species, which is of no practical use.

Diversity indices reduce the distribution of individuals in a sample to a single number. It is therefore useful to consider more than one single index to have an appropriate view of the diversity. In addition, diversity indices described above do no take into account the species relatedness in taxonomic or phylogenetic words. Let's consider two samples of 100 fish, the first including 30 cod, 30 pollacks and 40 whiting and the second 30 cod, 30 lesser spotted catsharks and 40 plaice. Intuitively, the second sample is more diverse because the species are more different from each other.

Diversity indices that capture the species relatedness have been defined (Clarke and Warwick 1998, 1999, 2001). For the same reason as with Hill's indices, it is useful to consider several indices, those used here are:

S j−1 S ∑∑ωijnin j +∑0.ni(ni −1) ∆= j=1 i=1 i=1 S j−1 S (7) ∑∑nin j +∑ni(ni −1)/2 j=1 i=1 i=1

5 S j−1 ∑∑ωijnin j ∆ ∗ = j=1 i=1 S j−1 (8) ∑∑nin j j=1 i=1

S ∑∑ωij = < ∆+=2 i 1 j i (9) S(S−1)

S S 2 2 ∑∑(ωij−ω) ∑∑(ωij−ω) + i=1 j≠i i=1 j≠i 2 = = −ω (10) Λ S(S−1) S(S−1)

Where:

S is the number of species ni is the number of individuals of species i n is the total number of individuals in the sample (trawl haul) ωij is a weighting for the co-occurrence in a sample of species i and j. ωij is first defined according to taxonomical levels as: 1 for species of the same genus; 2 for species of the same family (but different genus); 3 for species of the same order; 4 for for species of different orders.

Then, Warwick and Clarke, (1999) showed that it is convenient to standardise ωij so that the largest weight for to species amongst all studied samples is 100. As a consequence, Δ, Δ* and Δ+ can take values between 0 and 100.

Lastly, the probability of interspecific encounter was used, because it has been calculated in some studies and may therefore allow to comparison with literature. This index also has a direct ecological interpretation and it variance can be calculated (Hurlbert 1971; Rochet and Trenkel 2003; Trenkel and Rochet 2003).

S 2 n  ni  ∆1= 1−∑( )  (11) n−1 i=1 n  Δ1 is similar to Simpson index (i.e. N2 index in the Hill's series).

In addition to diversity indices, standard populations and community indices were calculated and used to interpret the results, in particular to assess whether changes in diversity indices could be due to one single or a few species (see e.g. Rochet and Trenkel 2003; Rochet et al. 2005; Bertrand et al. 2009).

1.2.4. Sharks, rays and chimaeras For simplicity, sharks rays and chimaeras are further denoted Chondrichthyans, although this group is no longer recognised as a valid taxonomic level. In the current

6 taxonomy, the superclass Gnathostomata includes four classes of which sharks and rays from the Elasmobranchii and chimaeras the Holocephalii (World register of Marine Species; http://www.marine species.org). It was not possible to investigated temporal trends in diversity of Chondrichthyans because of the small numbers available. The diversity indices on the shelf and on the slopes were estimated separately and the species richness was analysed according to depth.

1.3. Results

1.3.1. Species sets Eighty species were caught in a total number over 50 individuals all years combined, these were ordered by decreasing proportion of proportion of hauls cumulated to make up 80% of the total catch (Table 1). Some species appeared clearly aggregative with 80% of the total catch taken in less than 20% of the positive hauls for this species. The cumulative curves of the number of individuals per haul are displayed for the 20 most abundant species in figure 1. Micromesistius poutassou, A sphryraena, Gadiculus argenteus argenteus argenteus, Spicara smaris and capros aper appeared to be highly aggregative but some demersal predators, including the hake, Merluccius merluccius were also quite aggregative. For comparison, the 20 less abundant species are shown in figure 2 and display a clearly dispersed distribution pattern. Is it worth noting that Chondrichthyans appear to have dispersed distributions, with Galeus melastomus and Squalus blainville being the most aggregative (ranking 30 and 33 in table 1). M. poutassou,, A. Sphyraena, M. scolopax, Capros aper, S. smaris and G. a. argenteus were excluded from the Species set 2. The number of positive hauls for these species was also high, about one quarter to one half of all hauls. Epigonus telescopus and E. constanciae were also aggregative (Table 1) and excluded from Species set 2. Additionally, S. maenas and E. denticulatus were excluded because they were assumed to have similar distributions. Chlorophthalmus agassizi, which was somewhat aggregative with very high number caught were also removed. All these species are mostly small prey species. Caranx ronchus, which 192 individuals were caught in one single haul was also excluded. Contrarily, clearly demersal species such Pagellus acarne, were not excluded although some appeared to have an aggregative distribution (Table 1). The trichiurid Lepidopus caudatus was also kept because all trichiurids are considered deep-water benthopelagic dwellers and are large predators. As a result, the species set 2 included 144 species (compared to 156 for the species set 1).

7 Table 1. Number of fish caught, number of haul where the species was caught(positive hauls), number of cumulated hauls which catch make more than 80% of the total catch, proportion of these hauls in positive hauls and all hauls. Hauls which cumulated catch is more than 80% of the total catch Number of Positive Proportion of Proportion of all Species fish hauls Number positive haul hauls (n=340) Micromesistius poutassou 8671 75 5 0.07 0.01 Argentina sphyraena 49897 192 23 0.12 0.07 Macroramphosus scolopax 25425 92 12 0.13 0.04 Capros aper 5956 97 14 0.14 0.04 Pagellus acarne 1734 62 9 0.15 0.03 Lepidopus caudatus 4053 82 12 0.15 0.04 Spicara smaris 32035 143 21 0.15 0.06 Dentex maroccanus 2473 46 7 0.15 0.02 Epigonus telescopus 443 11 2 0.18 0.01 Pagellus bogaraveo 1537 81 16 0.20 0.05 Epigonus constanciae 68 5 1 0.20 0.00 Chelidonichthys lastoviza 899 85 19 0.22 0.06 Peristedion cataphractum 6175 84 19 0.23 0.06 Merluccius merluccius 18154 350 81 0.23 0.24 Diplodus annularis 5633 82 19 0.23 0.06 Boops boops 8495 168 40 0.24 0.12 Deltentosteus quadrimaculatus 611 25 6 0.24 0.02 Gobius spp. 8314 186 45 0.24 0.13 Trisopterus minutus 8637 151 37 0.25 0.11 Chlorophthalmus agassizi 64598 94 24 0.26 0.07 Scorpaena notata 380 56 15 0.27 0.04 Aspitrigla cuculus 2909 121 33 0.27 0.10 Gadiculus argenteus argenteus 77168 73 20 0.27 0.06 Coelorinchus caelorhincus 4446 79 22 0.28 0.06 Pagellus erythrinus 3988 151 44 0.29 0.13 Mullus barbatus 11912 197 59 0.30 0.17 Hoplostethus m. mediterraneus 2303 30 9 0.30 0.03 Lepidotrigla dieuzeidei 10798 82 25 0.30 0.07 Serranus cabrilla 958 133 42 0.32 0.12 Galeus melastomus 2121 69 22 0.32 0.06 Helicolenus d. dactylopterus 3349 112 36 0.32 0.11 Arnoglossus rueppelii 728 49 17 0.35 0.05 Squalus blainville 1082 71 25 0.35 0.07 Lepidorhombus boscii 827 82 29 0.35 0.09 Etmopterus spinax 58 11 4 0.36 0.01 Arnoglossus thori 773 41 15 0.37 0.04 Lepidorhombus whiffiagonis 124 27 10 0.37 0.03 Bothus podas 77 8 3 0.38 0.01 Arnoglossus laterna 4154 181 68 0.38 0.20 Eutrigla gurnardus 375 74 28 0.38 0.08 Phycis blennoides 1414 131 50 0.38 0.15 Hymenocephalus italicus 5184 52 20 0.38 0.06 Raja clavata 942 178 71 0.40 0.21 Lesueurigobius friesii 132 5 2 0.40 0.01 Microchirus variegatus 119 25 10 0.40 0.03 Scyliorhinus canicula 1664 92 37 0.40 0.11 Citharus linguatula 1226 148 60 0.41 0.18 Lophius budegassa 1223 206 84 0.41 0.25 Spicara maena 16247 169 69 0.41 0.20 Gobius niger 88 22 9 0.41 0.03 Cepola macrophthalma 546 120 50 0.42 0.15 Sparus aurata 150 24 10 0.42 0.03 Squalus acanthias 159 7 3 0.43 0.01 Scorpaena scrofa 127 35 15 0.43 0.04 Serranus hepatus 12203 180 78 0.43 0.23 Nezumia sclerorhynchus 1000 27 12 0.44 0.04 Lepidotrigla cavillone 11597 197 90 0.46 0.26 Mullus surmuletus 114 37 18 0.49 0.05 Trigla lyra 143 42 21 0.50 0.06 Epigonus denticulatus 81 2 1 0.50 0.00 Dentex macrophthalmus 70 6 3 0.50 0.01 Centrophorus uyato 66 6 3 0.50 0.01

8 Hauls which cumulated catch is more than 80% of the total catch Number of Positive Proportion of Proportion of all Species fish hauls Number positive haul hauls (n=340) Callionymus maculatus 64 24 12 0.50 0.04 Molva macrophthalma 58 20 10 0.50 0.03 Notacanthus bonaparte 53 12 6 0.50 0.02 Gaidropsarus mediterraneus 51 18 9 0.50 0.03 Zeus faber 261 102 53 0.52 0.16 Nettastoma melanurum 66 25 13 0.52 0.04 Dipturus oxyrinchus 153 63 33 0.52 0.10 Scorpaena porcus 109 34 18 0.53 0.05 Blennius ocellaris 198 81 44 0.54 0.13 Raja miraletus 161 65 36 0.55 0.11 Solea solea 163 74 42 0.57 0.12 Trachinus draco 87 42 25 0.60 0.07 Chelidonichthys lucerna 57 29 18 0.62 0.05 Uranoscopus scaber 135 72 45 0.63 0.13 Carapus acus 81 46 30 0.65 0.09 Conger conger 131 76 50 0.66 0.15 Torpedo marmorata 90 61 43 0.70 0.13 Caranx rhonchus 192 1 1 1.00 0.00

0 10 30 50 70 0 20 40 60 80 0 50 100 150 0 20 60 100 140 Gadiculus argenteus argenteus Chlorophthalmus agassizi Argentina sphyraena Spicara smaris

0 20 40 60 80 0 50 150 250 350 0 50 100 150 0 50 100 150 Macroramphosus scolopax Merluccius merluccius Spicara maena Serranus hepatus

0 50 100 150 200 0 50 100 150 200 0 20 40 60 80 0 20 40 60 Mullus barbatus Lepidotrigla cavillone Lepidotrigla dieuzeidei Micromesistius poutassou

0 50 100 150 0 50 100 150 0 50 100 150 0 20 40 60 80 Trisopterus minutus Boops boops Gobius Peristedion cataphractum

0 20 40 60 80 100 0 20 40 60 80 0 10 20 30 40 50 0 20 40 60 80 Capros aper Diplodus annularis Hymenocephalus italicus Coelorinchus caelorhincus Figure 1. Cumulative number of individuals caught per haul for the 20 most abundant species in the catch from the MEDITS survey in the Eastern Ionian Sea. The x axis depicts the number of hauls and the y axis the number of individuals caught. The straight line is the fictive curve for a species caught in equal numbers in all hauls.

0 5 10 15 20 25 5 10 15 20 25 0 5 10 20 30 0 5 10 15 20 25 30 35 Lepidorhombus whiffiagonis Microchirus variegatus Mullus surmuletus Scorpaena porcus

0 10 20 30 40 50 60 5 10 15 20 0 10 20 30 40 0 10 20 30 40 Torpedo marmorata Gobius niger Trachinus draco Carapus acus

1.0 1.2 1.4 1.6 1.8 2.0 1 2 3 4 5 6 7 8 1 2 3 4 5 6 1 2 3 4 5 Epigonus denticulatus Bothus podas Dentex macrophthalmus Epigonus constanciae

1 2 3 4 5 6 5 10 15 20 25 5 10 15 20 2 4 6 8 10 Centrophorus uyato Nettastoma melanurum Callionymus maculatus Etmopterus spinax

5 10 15 20 0 5 10 15 20 25 30 2 4 6 8 10 12 5 10 15 Molva macrophthalma Chelidonichthys lucerna Notacanthus bonaparte Gaidropsarus mediterraneus Figure 1. Cumulative number of individuals caught per haul for the 20 less abundant species (caught in total number>50) in the catch from the MEDITS survey in the Eastern Ionian Sea. The x axis depicts the number of hauls and the y axis the number of individuals caught. The straight line is the fictive curve for a species caught in equal numbers in all hauls.

1.3.2. Species diversity and species distinctness metrics Diversity indices in Species set 1 Figures 3 and 4 display the raw data (plots of diversity indices in number and biomass for 340 trawl stations against fishing depth). The number of individuals and biomass caught per tow do not show a strong shift at 200 m (Figure 3 and 4 upper left panel). As tows are longer (1 hour) below 200 m that on the shelf (30 min) this implies that the fish density and biomass is lower on the slope than on the shelf. In terms of diversity studies, this implies that, we are calculating diversity indices based on similar numbers and biomass on the shelf and slope. In other words, the change of tow duration at 200 m provides a rough standardisation of samples and this is appropriate for diversity studies. Suggesting that the impact of the difference in tow duration might not be high in this data set. Indeed, the change from half an hour to one hour towing duration, roughly compensate the lower density and biomass below 200 m and therefore provide a

10 rough standardisation of the data to the catch per tow. Nevertheless, in the deeper range there are both tows with low and high catches both in numbers and biomass. Diversity indices of the Hill's series tend to be lower on the slope, differences are not significant owing to overlap of the distribution (Figures 5,6). Λ+ give a different result, which suggests that on the slope the taxonomic distance between pairs of species is less variable than on the shelf (Figure 7). Taxonomic distances are higher on the slope (higher Δ+) but less variable (lower Λ+). The distribution of Δ, Δ* and Δ+ are higher on the slope and that of Λ+ is shifted to the right (Figures 8,9). The times series in number and weight (Figure 10,11) suggest some increase in N0 (species richness) and N1 (Shannon index) over time. Nevertheless, the interpretation of N0 and N1 should be cautious because numbers of stations per year are small and these indices may be sensitive to observation effect, i.e. they may artificially increase if more care is given to species identification. Taxonomic distinctness indices show no change. They might be less sensitive to the observation effect described above, as more attention to species identification might mainly result in closely related species to be identified separately instead of being lumped together and similar species contribute lesser to these indices. Similar conclusion can be drawn about the difference between shelf and slope strata and the time series from diversity indices calculated in biomass. However, absolute value of taxonomic distinctness indices tends to be higher when calculated in biomass (e.g. Figures 5 and 6) suggesting that a few large individuals from rare species contribute to the diversity in the Eastern Ionian Sea.

Diversity indices in Species set 2 Excluding species down to species set 2 did not change the general distribution of the indices by depth, between depth strata and by year (results not shown). The total number of individual fish taken into account was ca 440,000 for Species set 1 and 160,000 for set 2. The range of number of individuals caught per haul was 2-11672 with most haul having a catch of less than 2000 fish for Species set 1 and 1-3692 with most haul having a catch of less than 1000 fish for Species set 2. Nevertheless, results described above for species set 1 are all valid for species set 2. Therefore, these results are not sensitive to the integration or not of small, abundant prey species and species, which may be considered pelagic rather than demersal, although they make up close to 2/3 of the total abundance.

1.3.3. Chondrichthyans Overall 0 to 6 sharks, rays and chimaeras species were caught by haul. Chondrichthyans were presents in 298 over 340 hauls (77%). Occasionally, (14 hauls over 340) more that 100 individuals were caught by haul. Four species (Galeus melastomus, Squalus blainville, Scyliorhinus canicula and Raja clavata) were caught in numbers over 50 individuals per haul (only 1 time for R clavata). There was never more than 6 chondrichthyans species per haul, the interpretation of diversity indices with such low species numbers might be cautious. Because of frequent low numbers of individuals, the interpretation of taxonomic distinctness indices may be misleading (see Figure 12). At slope depth, there is on average more species per haul (Figure 13). Chondrichthyans were present in all hauls between depth of 200 m and 400 m, where there was often 3 to 4 species. On the shelf, most haul had 0 to 2 species and in deeper waters 0 to 6 species per haul were caught (Figure 13).

11 n  12000 10000 60 8000 6000 40 4000 20 2000 0 0 0 200 400 600 800 0 200 400 600 800 N0 * 30 100 25 90 20 80 15 70 10 60 5 50 0 40 0 200 400 600 800 0 200 400 600 800 N1 + 100 14 12 90 10 8 80 6 70 4 2 60 0 200 400 600 800 0 200 400 600 800 1 + 0.8 300 0.6 200 0.4

0.2 100

0.0 0 0 200 400 600 800 0 200 400 600 800 Depth (m) Depth (m)

Figure 3. Number caught per haul (n, top, left) and diversity indices (in numbers) vs depth in the Eastern Ionian Sea (MEDITS Greek survey), all years combined (1994-2008). From top to bottom and left to right: n: number of individual, Hill's N0 (species richness), Hill N1 (Shannon Index), Δ1 proportion of interspecific encounter, and taxonomic distinctness indices Δ, Δ*, Δ+, Λ+.

12 Biomass  200 80

150 60

100 40

50 20

0 0 0 200 400 600 800 0 200 400 600 800 N0  100 * 30 25 90 20 80 15 70 10 60 5 50 0 0 200 400 600 800 0 200 400 600 800 N1  90 + 15 85 80 10 75 70 5 65 60 55 0 200 400 600 800 0 200 400 600 800 1 + 350 0.8 250 0.6

0.4 150

0.2 50 0.0 0 200 400 600 800 0 200 400 600 800 Depth (m) Depth (m)

Figure 4. Biomass (kg) per haul (top, left) and diversity indices (in biomass) per haul vs depth in the MEDITS Greek survey, Eastern Ionian Sea, all years combined (1994-2008). From top to bottom and left to right: biomass of all fish of Species set 1 Hill's N0 (species richness), Hill N1 (Shannon Index), Δ1 proportion of interspecific encounter, and taxonomic distinctness indices Δ, Δ*, Δ+, Λ+.

13 14 15 A) B) Class

Order

Family

Genus

Species + + Δ+ low and Λ+ high Δ high and Λ low

Figure 7. Schematic representation of two taxonomic trees with A) several species from the same genus (low Δ+) and varying length of the taxonomic connexion between pairs of species (high Λ+) and B) species being highly different taxonomically, here from different families (high Δ+), but all with the same taxonomic relatedness (low Λ+).

16 60

N0 30 20 10 0 0

0 5 10 15 20 25 30 0 5 10 15 20 25 30 40 80  20 40 0 0

0 20 40 60 80 100 0 20 40 60 80 100 60 80 * 40 20 0 0

20 40 60 80 100 20 40 60 80 100 60 80 + 40 40 20 0 0

50 60 70 80 90 100 50 60 70 80 90 100

60 + 40 20 20 0 0 0 50 100 150 200 250 300 350 0 100 200 300 400

Figure 8. Distribution of diversity indices of Species set 1, in numbers in shelf (0-200 m, left) and slope (> 200 m, right) strata.

17 50 75 

50 25 25 0 0 0 20 40 60 80 100 0 20 40 60 80 100

100 75 75 * 50 50 25 25 0 0 20 40 60 80 100 20 40 60 80 100

75 100 75 + 50 50 25 25 0 0 50 60 70 80 90 100 50 60 70 80 90 100

+ 60 50 40 25 20 0 0 0 50 100 150 200 250 300 350 0 100 200 300 400

Figure 9. Distribution of diversity indices of Species set 1, in biomass caught in shelf (0-200 m, left) and slope (> 200 m, right) strata (index N0 not represented, it would be the same as N0 in number, see figure 8).

Biomass  200 80

150 60

100 40

50 20

0.4 150

0.2 50 0.0 0 200 400 600 800 0 200 400 600 800 Depth (m) Depth (m)

Figure 11. Time-series (1994-2008) of diversity indices in biomass in the Eastern Ionian Sea (MEDITS Greek survey). From top to bottom, left:biomass (kg) of all fish of Species set 1, Hill's N0 (species richness), Hill N1 (Shannon Index), Hill N2 (Simpson index), and, from top to bottom right taxonomic distinctness indices Δ, Δ*, Δ+, Λ+.

20 21 1.4. Discussion

The species and taxonomic diversity of the demersal and benthopelagic fish was analysed with a particular emphasis on the comparison of the diversity on the shelf and on the slope. This comparison relied upon similar number or biomass of fish caught by station from both communities. Under the assumption that the catch process is a random draw in each community, our results represent the species and taxonomic diversity in both shelf and slope fish communities. Taxonomic distinctness indices calculated here suggest higher fish diversity at slope depth than on the shelf. Species diversity indices (here N0 and N1 corresponding to species richness and Shannon index) do not show this difference. Therefore, taking the species identity into account appears important for diversity analyses of slope fish. The higher diversity at the slope is consitenct with results from D'Onghia et al. (2003) who analysed the whole fauna (fish and invertebrates) caught during trawl surveys with a few indices.

Species to retain or exclude from diversity and communities analyses may be debatable. There are some clear pelagic (such as mackerels, Scomber spp.) and benthic (such as sole, Solea spp.) but others are intermediate, so that it is a permanent burden to do classification in ecological continuums). There are ecological interactions between pelagic species and bentho-demersal species. Pelagic species are part of the catch of bottom trawl so they are actually part of the fish diversity near the seabed. However, as they may be caught in large number, display large temporal variations at population level if pelagic species are included in community and diversity analyses they may mask variations in demersal species. This was the main reason to restrict our analyses to demersal species.

There is a further problem with species classification as the results could be sensitive to one or a few species which demersal or pelagic status is unclear. Here, this question was handle by calculating the indices with and without such species so as to get a sort of sensitivity study. There was some changes when species were removed, trivially the species richness and biomass per haul decreased. However, no impact was found on the difference between the shelf and slope and on the distributions of indices. Therefore, the results can be considered robust to the categorisation of species as pelagic and bentho-demersal and should be regarded as informative in terms of diversity of the demersal fish community. Lastly, As the analysis was carried out at the scale of trawl hauls, i.e. points diversity sensus Gray (2000), the impact of species caught in small numbers in a few hauls only cannot be important.

A few fish were not identified to the species level but this might not impact our results either. Although diversity analyses may be sensitive to this problem, our results here are not impacted because all the analysis builds on diversity per station, i.e. point or α-diversity (Gray 2000). In this case, if in one haul, the genus "Genus" was reported and in another haul the species was identified as "Genus species", these would play the same role in the diversity indices in the two hauls. If Genus and Genus species were reported in the same haul, they were assumed to be two species of the same genus, therefore inducing only a small increase in taxonomic distinctness indices.

The calculation of diversity indices in both weight and number seem essential because they give different relative contributions to small species with high number of individuals and large species with small numbers. In the case at hand, both gave similar view of differences between shelf and slope depths and both suggested no temporal trends.

22 In terms of temporal trends, only the species richness (here termed N0) suggested some increase over time and other indices suggest no change. Temporal trends may be difficult to detect in this dataset because of low number of tow per years with respect to a significant overall variability of diversity per station. Moreover, there was changes in the sampling plan, with an increased number of tows from 1998 and there is no visible changes in diversity over years 1998-2008. So that the increase in species richness may be a sampling artifact and the diversity of the demersal fish community may be mostly stable over the past decade in the Eastern Ionian Sea. Nevertheless, the available time series is still short, and diversity indices may not reflect changes in the short term as a number of populations should change for the diversity index of a community to be changed. In the case studied here, some populations increased, which was interpreted as resulting from a reduced fishing pressure and favourable environmental conditions (Rochet et al. 2010; see also chapter 4).

Further analyses are required for a number of questions. First, for some technical aspects: a standardisation the data or using indices accounting for the effect of density (Buckland et al. 2005) might be useful. This might not change much the results obtained here as the change of duration of towing time between the shelf and the slope generates a rough standardisation but standardising might allow analysing the relationship between diversity and density or biomass in the study area. A second aspect is that the present study was restricted to point diversity and the other components of the diversity such as the β- and γ-gamma diversity (Gray 2000) remain to be studied. Then, the result obtained are to be related to ecosystem state and functioning and human pressures, in particular fishing. Biodiversity is of course related to ecosystem functioning and result presented here offer opportunity to compared the observed trends (in particular the increase in diversity indices over time) with trends in metric at population and community levels.. It seems of particular relevance to compare these trends to trends in fishing pressure, ecosystem productivity and community metrics recently identified (Rochet et al. 2010). The chapter 4 of this deliverable also identified some relationship between changes in populations and communities and environmental factors. Such changes at populations level may not change diversity indices. Further analyses are needed to relate populations, community and diversity indices. Lastly, the MEDITS survey data also include catch of megafaunal invertebrates and these might require the same analysis as done here for the fish community. However, the number of invertebrate species is lower because the sampling gear is primarily designed to catch fish. Invertebrates were studied at populations level (see chapter 4). They include benthic invertebrate such as Nephrops norvegicus, pelagic species such as squids and more demersal species such as deep-water shrimps. How these should be taken into account to obtain meaningful invertebrates diversity indices still require investigation. The analysis of invertebrate is of interest as both cephalopods and deep-water shrimps are exploited in the Ionian Sea. A further aspect is the linkage between fish and invertebrate abundance diversity in relation to habitat this work was initiated for a number of fish species (Damalas et al. 2010).

Chondrichthyans species appeared to have a higher occurrence (number of species per haul) on the upper slope. Although the number of hauls in the data set and the number of chondrichthyans caught are moderate, this result is robust as in the depth range 200-400 m there were chondrichthyans in all hauls. The number of shark species per haul was lower on the shelf and deeper than 700 m. This lower number of sharks at the deep end of the studied depth distribution might be a natural pattern because there is little fishing deeper than 400 m in this area (D'Onghia et al., 2003).

23 The temporal trend in chondrichthyans diversity was not analysed because of small total number caught per year and small number of species. Population indicators revealed changes in some sharks and rays species: Raja miraletus, Raja clavata, Scyliorhinus canicula, Squalus blainville and Galeus melastomus. Increases in abundance of S. canicula and G. melastomus have been observed in a number of marine regions and these species may feed on fisheries discards. It is more noticeable to detect increase in population growth rate for Raja clavata and Raja miraletus, two ray species. This suggests that chondrichthyans populations in the area might continue to be monitored and in the case of the eastern Ionian Sea the MEDITS survey may be the most reliable monitoring tool. So far, population indicators do not suggest serious problems for the chondrichthyan species sampled by the survey. However, a number of chondrychthyan species are at low level in the whole Mediterranean Sea, some of which are deep-water or are distributed from the shelf to the slope (Table 2). Some other species are not assessed threatened by IUCN but are however subject of concern and are protected by the Convention for the Protection of the Marine Environment and the Coastal Region of the Mediterranean better known as Barcelona convention (http://www.unep.ch/regionalseas/regions/med/t_barcel.htm).

Table 2. Chondrichthyan populations of the Mediterranean Sea assessed in the threatened categories of the IUCN red list (http://www.iucnredlist.org/) and distributed in deep-water or from the shelf to the slope. Populations listed below are assessed in the threatened categories of IUCN (VU: vulnerable, EN: endangered, CR: critically endangered) or protected by the Barcelona convention*.

Species IUCN Centrophorus granulosus (Bloch & Schneider, 1801) VU Dipturus batis Linnaeus, 1758 CR Galeorhinus galeus (Linnaeus, 1758) VU Leucoraja circularis (Couch, 1838) VU Leucoraja melitensis (Clark, 1926) CR Mustelus asterias Cloquet, 1819 LC Mustelus punctulatus Risso, 1827 DD Odontaspis ferox (Risso, 1810) VU Oxynotus centrina (Linnaeus, 1758) VU Rostroraja alba (Lacepède, 1803) EN Squalus acanthias Linnaeus, 1758 VU Squatina aculeata Dumeril, in Cuvier, 1817 CR Squatina oculata Bonaparte, 1840 CR Heptranchias perlo (Bonnaterre, 1788) NT * Mustelus asterias, Mustelus punctulatus and Heptranchias perlo that are not in the threatened categories are listed here because of their inclusion in the Barcelona convention. (2) CR: critically endangered, EN: endangered, VU: vulnerable; NT: Near Threatened; LC: least concern, DD data deficient.

Therefore, the upper slope in the Eastern Ionian Sea appears as an area of current higher chondrychthyans abundance and as a potential habitat for a number of threatened chondrychthyan species. This is of interest to fisheries and biodiversity management. In the context of the Marine Strategy Framework Directive (MSFD) programmes of measures are required to conserve biodiversity, managing fishing and other human activities. Our results suggest that the upper slope may be an important habitat for chondrychthyan species.

24 However, the level of fishing mortality generated by upper slope fisheries is not known so that it impossible to known whether threatened species would benefit or not from some protection measures at the upper slope that. The Greek fleet comprise mainly small vessels that may not fish deep but the few large vessels that fish deeper may generate a significant fishing effort. data available to the project are not sufficient to assess whether the fishing intensity is higher or lower on the slope than on the shelf.

The main result of this analysis is the observation of higher fish diversity on the slope than on the shelf. This is the first observation of this pattern in this area. Chondrychthyan have also a higher occurrence (proportion of haul where they are caught) on the slope in particular between 200 and 400 m. As Chondrychthyans are more vulnerable to fishing than bony fishes (Actinopterygii) the habitats where they are more abundant may be of interest for management but the fishing pressure and it spatial distribution is poorly known so that spatial fisheries management measures may be difficult to define in this area. Lastly, is it unknown, whether the higher diversity and chondrychthyan abundance at the upper slope (200-400 m) are natural pattern or a consequence of different fishing pressure but the decreasing abundance of chondrychthyan below 400 m is likely to be natural.

25 Appendix 1. Species list of fish caught in MEDITS 1994-2008

Included in species sets (YES/N0) Taxon Classe Set 1 Set 2 Sardina pilchardus Actinopterygii N N Sardinella aurita Actinopterygii N N Sardinella maderensis Actinopterygii N N Sprattus sprattus sprattus Actinopterygii N N Alosa fallax Actinopterygii N N Engraulis encrasicolus Actinopterygii N N Vinciguerria Actinopterygii N N Vinciguerria attenuata Actinopterygii N N Argyropelecus hemigymnus Actinopterygii N N Argyropelecus aculeatus Actinopterygii N N Maurolicus muelleri Actinopterygii N N Chauliodus sloani Actinopterygii N N Argentina sphyraena Actinopterygii Y N Aulopus filamentosus Actinopterygii Y Y Synodus saurus Actinopterygii Y Y Chlorophthalmus agassizi Actinopterygii Y N Myctophidae Actinopterygii N N Myctophum punctatum Actinopterygii N N Benthosema glaciale Actinopterygii N N Ceratoscopelus maderensis Actinopterygii N N Symbolophorus veranyi Actinopterygii N N Diaphus Actinopterygii N N Diaphus holti Actinopterygii N N Diaphus metopoclampus Actinopterygii N N Diaphus rafinesquii Actinopterygii N N Hygophum benoiti Actinopterygii N N Lampanyctus crocodilus Actinopterygii N N Lobianchia dofleini Actinopterygii N N Notoscopelus elongatus Actinopterygii N N Paralepis speciosa Actinopterygii N N Muraena helena Actinopterygii Y Y Nettastoma melanurum Actinopterygii Y Y Conger conger Actinopterygii Y Y Gnathophis mystax Actinopterygii Y Y Echelus myrus Actinopterygii Y Y Ophichthus rufus Actinopterygii Y Y Notacanthus bonaparte Actinopterygii Y Y Macroramphosus scolopax Actinopterygii Y N Syngnathus Actinopterygii Y Y Syngnathus acus Actinopterygii Y Y Hippocampus hippocampus Actinopterygii Y Y Nezumia sclerorhynchus Actinopterygii Y Y Coelorinchus caelorhincus Actinopterygii Y Y Hymenocephalus italicus Actinopterygii Y Y Merluccius merluccius Actinopterygii Y Y Gadiculus argenteus argenteus Actinopterygii Y N Merlangius merlangus Actinopterygii Y Y Micromesistius poutassou Actinopterygii Y N Trisopterus minutus Actinopterygii Y Y Molva macrophthalma Actinopterygii Y Y Phycis phycis Actinopterygii Y Y Phycis blennoides Actinopterygii Y Y

26 Included in species sets (YES/N0) Taxon Classe Set 1 Set 2 Gaidropsarus Actinopterygii Y Y Gaidropsarus mediterraneus Actinopterygii Y Y Gadella maraldi Actinopterygii Y Y Hoplostethus mediterraneus mediterraneus Actinopterygii Y Y Zeus faber Actinopterygii Y Y Capros aper Actinopterygii Y N Serranus cabrilla Actinopterygii Y Y Serranus hepatus Actinopterygii Y Y Serranus scriba Actinopterygii Y Y Epinephelus Actinopterygii Y Y Epinephelus aeneus Actinopterygii Y Y Epinephelus marginatus Actinopterygii Y Y Callanthias ruber Actinopterygii Y Y Epigonus constanciae Actinopterygii Y N Epigonus denticulatus Actinopterygii Y N Epigonus telescopus Actinopterygii Y N Cepola macrophthalma Actinopterygii Y Y Caranx rhonchus Actinopterygii Y N Trachurus trachurus Actinopterygii N N Trachurus mediterraneus Actinopterygii N N Trachurus picturatus Actinopterygii N N Mullus barbatus Actinopterygii Y Y Mullus surmuletus Actinopterygii Y Y Sparus aurata Actinopterygii Y Y Pagrus pagrus Actinopterygii Y Y Boops boops Actinopterygii Y Y Diplodus annularis Actinopterygii Y Y Diplodus vulgaris Actinopterygii Y Y Dentex dentex Actinopterygii Y Y Dentex gibbosus Actinopterygii Y Y Dentex macrophthalmus Actinopterygii Y Y Dentex maroccanus Actinopterygii Y Y Pagellus erythrinus Actinopterygii Y Y Pagellus acarne Actinopterygii Y Y Pagellus bogaraveo Actinopterygii Y Y Centracanthus cirrus Actinopterygii Y Y Spicara maena Actinopterygii Y N Spicara smaris Actinopterygii Y N Acantholabrus palloni Actinopterygii Y Y Symphodus Actinopterygii Y Y Coris julis Actinopterygii Y Y Trachinus draco Actinopterygii Y Y Trachinus araneus Actinopterygii Y Y Trachinus radiatus Actinopterygii Y Y Uranoscopus scaber Actinopterygii Y Y Lepidopus caudatus Actinopterygii Y Y Scomber scombrus Actinopterygii N N Scomber japonicus Actinopterygii N N Gobius Actinopterygii Y Y Gobius niger Actinopterygii Y Y

27 Included in species sets (YES/N0) Taxon Classe Set 1 Set 2 Gobius paganellus Actinopterygii Y Y Deltentosteus quadrimaculatus Actinopterygii Y Y Lesueurigobius friesii Actinopterygii Y Y Callionymus Actinopterygii Y Y Callionymus lyra Actinopterygii Y Y Callionymus maculatus Actinopterygii Y Y Callionymus risso Actinopterygii Y Y Synchiropus phaeton Actinopterygii Y Y Blenniidae Actinopterygii Y Y Blennius ocellaris Actinopterygii Y Y Benthocometes robustus Actinopterygii Y Y Carapus acus Actinopterygii Y Y Centrolophus niger Actinopterygii Y Y Sphyraena sphyraena Actinopterygii N N Mugil cephalus Actinopterygii Y Y Scorpaena Actinopterygii Y Y Scorpaena elongata Actinopterygii Y Y Scorpaena porcus Actinopterygii Y Y Scorpaena loppei Actinopterygii Y Y Scorpaena notata Actinopterygii Y Y Scorpaena scrofa Actinopterygii Y Y Helicolenus dactylopterus dactylopterus Actinopterygii Y Y Trigla lyra Actinopterygii Y Y Aspitrigla cuculus Actinopterygii Y Y Chelidonichthys obscurus Actinopterygii Y Y Lepidotrigla cavillone Actinopterygii Y Y Lepidotrigla dieuzeidei Actinopterygii Y Y Eutrigla gurnardus Actinopterygii Y Y Chelidonichthys lucerna Actinopterygii Y Y Chelidonichthys lastoviza Actinopterygii Y Y Peristedion cataphractum Actinopterygii Y Y Dactylopterus volitans Actinopterygii Y Y Citharus linguatula Actinopterygii Y Y Scophthalmus rhombus Actinopterygii Y Y Psetta maxima Actinopterygii Y Y Lepidorhombus whiffiagonis Actinopterygii Y Y Lepidorhombus boscii Actinopterygii Y Y Arnoglossus laterna Actinopterygii Y Y Arnoglossus imperialis Actinopterygii Y Y Arnoglossus rueppelii Actinopterygii Y Y Arnoglossus thori Actinopterygii Y Y Bothus podas Actinopterygii Y Y Solea Actinopterygii Y Y Solea solea Actinopterygii Y Y Solea impar Actinopterygii Y Y Synaptura kleinii Actinopterygii Y Y Microchirus ocellatus Actinopterygii Y Y Microchirus variegatus Actinopterygii Y Y Monochirus hispidus Actinopterygii Y Y Symphurus Actinopterygii Y Y

28 Included in species sets (YES/N0) Taxon Classe Set 1 Set 2 Symphurus ligulatus Actinopterygii Y Y Symphurus nigrescens Actinopterygii Y Y Lophius piscatorius Actinopterygii Y Y Lophius budegassa Actinopterygii Y Y Arnoglossus kessleri Actinopterygii Y Y Sphoeroides Actinopterygii Y Y Stomias boa Actinopterygii N N Heptranchias perlo Elasmobranchii Y Y Scyliorhinus canicula Elasmobranchii Y Y Galeus melastomus Elasmobranchii Y Y Galeorhinus galeus Elasmobranchii Y Y Mustelus mustelus Elasmobranchii Y Y Oxynotus centrina Elasmobranchii Y Y Squalus acanthias Elasmobranchii Y Y Squalus blainville Elasmobranchii Y Y Etmopterus spinax Elasmobranchii Y Y Centrophorus granulosus Elasmobranchii Y Y Centrophorus uyato Elasmobranchii Y Y Dalatias licha Elasmobranchii Y Y Torpedo torpedo Elasmobranchii Y Y Torpedo marmorata Elasmobranchii Y Y Torpedo nobiliana Elasmobranchii Y Y Raja brachyura Elasmobranchii Y Y Raja clavata Elasmobranchii Y Y Raja montagui Elasmobranchii Y Y Dipturus oxyrinchus Elasmobranchii Y Y Leucoraja naevus Elasmobranchii Y Y Raja asterias Elasmobranchii Y Y Raja miraletus Elasmobranchii Y Y Raja polystigma Elasmobranchii Y Y Raja radula Elasmobranchii Y Y Raja undulata Elasmobranchii Y Y Dasyatis pastinaca Elasmobranchii Y Y Myliobatis aquila Elasmobranchii Y Y Raja rondeleti Elasmobranchii Y Y Chimaera monstrosa Holocephali Y Y

29 Table 2. Species list of invertebrates caught in the MEDITS survey 1994-2008

Taxon Class Sepia officinalis Cephalopoda Sepia elegans Cephalopoda Sepia orbignyana Cephalopoda Rossia macrosoma Cephalopoda Neorossia caroli Cephalopoda Heteroteuthis dispar Cephalopoda Rondeletiola minor Cephalopoda Sepiola Cephalopoda Sepiola affinis Cephalopoda Sepiola intermedia Cephalopoda Sepiola ligulata Cephalopoda Sepiola rondeleti Cephalopoda Sepietta Cephalopoda Sepietta neglecta Cephalopoda Sepietta oweniana Cephalopoda Loligo vulgaris Cephalopoda Loligo forbesi Cephalopoda Alloteuthis subulata Cephalopoda Alloteuthis media Cephalopoda Abralia veranyi Cephalopoda Histioteuthis bonnellii Cephalopoda Histioteuthis reversus Cephalopoda Illex coindetii Cephalopoda Todaropsis eblanae Cephalopoda Todarodes sagittatus Cephalopoda Octopus vulgaris Cephalopoda Octopus macropus Cephalopoda Octopus salutii Cephalopoda Scaeurgus unicirrhus Cephalopoda Pteroctopus tetracirrhus Cephalopoda Eledone moschata Cephalopoda Eledone cirrhosa Cephalopoda Bathypolypus sponsalis Cephalopoda Lophogaster typicus Malacostraca Squilla mantis Malacostraca Rissoides desmaresti Malacostraca Rissoides pallidus Malacostraca Euphausiidae Malacostraca Aristaeomorpha foliacea Malacostraca Aristeus antennatus Malacostraca Gennadas elegans Malacostraca Parapenaeus longirostris Malacostraca Solenocera membranacea Malacostraca Sergestidae Malacostraca Sergestes Malacostraca Sergestes arcticus Malacostraca Sergia robusta Malacostraca

30 Taxon Class Sergestes sargassi Malacostraca Pasiphaea sivado Malacostraca Pandalina profunda Malacostraca Plesionika acanthonotus Malacostraca Plesionika antigai Malacostraca Plesionika gigliolii Malacostraca Plesionika heterocarpus Malacostraca Plesionika martia martia Malacostraca Chlorotocus crassicornis Malacostraca Alpheus glaber Malacostraca Processa canaliculata Malacostraca Crangonidae Malacostraca Pontophilus spinosus Malacostraca Pontophilus norvegicus Malacostraca Nephrops norvegicus Malacostraca Polycheles typhlops Malacostraca Scyllarides latus Malacostraca Calocaris macandreae Malacostraca Paguridae Malacostraca Munida Malacostraca Munida rugosa Malacostraca Munida intermedia Malacostraca Paromola cuvieri Malacostraca Homola barbata Malacostraca Dromia personata Malacostraca Calappa granulata Malacostraca Bathynectes maravigna Malacostraca Macropipus tuberculatus Malacostraca Liocarcinus depurator Malacostraca Parthenope macrochelos Malacostraca Parthenope massena Malacostraca Geryon longipes Malacostraca Pilumnus hirtellus Malacostraca Monodaeus couchi Malacostraca Goneplax rhomboides Malacostraca Majidae Malacostraca Maja squinado Malacostraca Maja crispata Malacostraca Pisa armata Malacostraca Eurynome aspera Malacostraca Ergasticus clouei Malacostraca Inachus communissimus Malacostraca Inachus dorsettensis Malacostraca Inachus thoracicus Malacostraca Macropodia Malacostraca Macropodia longirostris Malacostraca Macropodia rostrata Malacostraca Medorippe lanata Malacostraca Ethusa mascarone Malacostraca

31 Taxon Class Penaeus Malacostraca Maja goltziana Malacostraca Stenopus spinosus Malacostraca Latreillia elegans elegans Malacostraca Lysmata seticaudata Malacostraca Nematocarcinus ensifer Malacostraca Palicus caroni Malacostraca Bathynectes longipes Malacostraca Calappa pelii Malacostraca Ebalia granulosa Malacostraca Plesionika edwardsii Malacostraca

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35 Chapter 4: populations and community in the Eastern Ionian Sea

Changes and trends in population and community metrics in the Eastern Ionian Sea based on survey data: information for assessment and ecosystem approach to fisheries management

Μytilineou Ch1., J. Haralabous1, Politou C.-Y1., Lorance P2. and Dokos I1.

1Hellenic Centre for Marine Research (HCMR). Institute of Marine Biological Resources (IMBR) 2 IFREMER, Departement Ecologie et Modeles pour l'Halieutique, Nantes

SOMMAIRE

INTRODUCTION ...... 2 MATERIAL AND METHODS ...... 3 SURVEY DATA ...... 3 SELECTION OF STUDIED SPECIES...... 3 METRICS AND ANALYSES ...... 4 FISHERIES DEPENDENT DATA ...... 6 RESULTS ...... 6 POPULATION METRICS IN THE WHOLE AREA ...... 6 POPULATION METRICS BY DEPTH STRATUM...... 13 COMMUNITY METRICS ...... 18 FISHERIES DEPENDENT DATA ...... 20 DISCUSSION ...... 21 REFERENCES ...... 26

1 Changes and trends in population and community metrics in the Eastern Ionian Sea based on survey data: information for assessment and ecosystem approach to fisheries management

INTRODUCTION Stock assessment has for many decades been based on official landings statistics; however, several weaknesses are related to this approach reported by Cotter et al. (2009 and references therein). As a consequence, attention was paid to scientific survey data, which presents two advantages: a) they can be derived from a standardized methodology and b) they can offer more global information for the ecosystem since commercial and non-commercial species can be assessed. On the other hand, management has for many decades been practiced as a single species management; however, another management approach appeared the last decade (FAO, 2003), the ecosystem approach to fisheries management (EAFM), adopted nowadays by governmental organizations (EC, 2008; FAO, 2009), and which puts emphasis on a management regime that maintains the health of the ecosystem alongside appropriate human use of the marine environment for the benefit of current and future generations (ICES, 2005). EAFM requires valid data for its implementation, which is the case of the scientific survey data (Cotter et al., 2009). To realize an ecosystem approach to fisheries management, selected indicators have to be aggregated and combined. The term “indicator” has been defined in many works (e.g. Garcia et al., 2003; Jennings, 2005; Rice et al., 2005). Indicators are tools to help make clear assessments of and comparisons between fisheries, through time (FAO, 1999). There is a wide range of indicators either to assess the status of stocks (e.g. INDECO: Anon., 2005a, INDENT: Anon., 2006a; FISBOAT: Anon., 2007) or the efficiency of different fisheries management strategies (e.g. Marchal et al., 2006). However, the objective is not to find the best indicator, but rather a relevant suite of indicators with known properties (Cury & Christensen, 2005). It is widely accepted that indicators are tools to monitor changes in ecosystems and that several indicators are needed to identify multiple pressures on them (Rochet et al., 2005; 2010). Jennings (2005) suggests that indicators should guide the management of fishing activities that have led to or are most likely to lead to, unsustainable impacts on ecosystem components. However, Rice et al. (2005) mentioned that this is true, only if the performance characteristics of the indicators are understood, and if trends and current values can be interpreted correctly. European Union has funded several projects aimed at establishing lists of potential indicators for fisheries management (e.g. INDECO: Anon., 2005a; INDENT: Anon., 2006a). A list of ten indicators has been included in the Data Collection Regulation of 2008 (EC 199/2008) to be examined. Quantitative metrics (such as average length, 95% percentile of the length distribution, proportion of large fish, maximum length, average weight, abundance and biomass indices of individual populations, community total abundance and biomass, proportion of non-commercial species etc) are appropriate to describe the status and dynamics of populations and communities and consequently to be used as indicators (e.g. Rochet and Trenkel, 2003; Trenkel and Rochet, 2003; Daan et al., 2005; Shin et al., 2005; Jennings, 2005). Evaluation of changes and trends in these metrics and combination of these results may lead to a comprehensive diagnostic of fisheries impacts on populations and communities and therefore be useful tools to give advice for management, particularly if these changes are related to fishing and if there is an interest in ecosystem-based fisheries management (Rochet et al., 2005; 2007). Important steps into this diagnostic are the knowledge of the initial status of the community, the monitoring of target and non-target species and the assessment of changes at both population and community level (Rochet et al., 2005 and references therein). The primary purpose of the study presented here is to assess the status and of populations and community in the Eastern Ionian Sea with emphasis in the deep-waters of this area. In the Eastern Ionian Sea, increase in surface temperature and biological production have been reported recently (Souvermezoglou and Krasakopoulou, 2005). In addition, signs of increased recruitment at both population and community level, increased community total abundance and decreased total mortality in three populations had been detected during 1995-2006 period (Rochet et al., 2007). Politou (2007)

2 found an increasing fishing impact on the demersal community of the Eastern Ionian Sea for the period 1994-2004, although indication of its decrease has been detected after 2000. Changes in Mediterranean communities were currently found, consistent more with environmental than fishing impact changes (Rochet et al., 2010). In Greece, fisheries-dependent data present several problems. The fishing fleet in the Eastern Ionian Sea consists of 35 trawlers, 39 purse seiners and about 4100 small-scale boats using longlines, nets and traps (NDCP data, 2008). The majority of these vessels operates in shallow waters (<200 m depth), some of them down to 500 m and very few in waters deeper than 500m (Mytilineou & Machias, 2007). To date, the national catch and effort statistics were inaccurate. Effort data from small vessels with multiple gears might be worse than catch data. Effort reports require explicit guidelines and therefore are less reliable than catch reports, which are more straightforward (Lorance et al., 2010). The great number of vessels and metiers, the absence of logbooks and the absence of an effective data collection system enhanced the problems of the Greek fisheries statistics. In theory, most national statistical systems cover a wide spectrum of information, but in practice the quality and utility of data is poor (Papaconstatinou et al., 1998). In addition, different areas of interest and statistical objectives have shaped these systems, and the output results fail both in reliability and completeness (Papaconstantinou et al., 2002). Furthermore, recreational fishery has not been accurately assessed up to now and may represent a significant proportion of the fishing mortality. In fact, a high number of recreational fishermen (96,075) with annual production 18,639 tons has been estimated for Greece the period 1996-1997 in the framework of a research project (Anon., 1999). No similar estimations exist nowadays. Another problem until now was the absence of stock assessment; in most cases conducted occasionally and for a few species only (e.g. Stergiou et al., 1997; Papaconstantinou and Farrugio, 2000, Tserpes et al., 2003; Maravelias, 2007, Maravelias et al., 2007, 2010; Damalas et al., 2010; Antonakakis, 2011). The above problems are nowadays addressed by EU regulations (1543/2000, 199/2008) under the Fisheries Data National Collection Programs (DCNP). On the other hand, in all Greek seas (the Eastern Ionian Sea included) scientific trawl surveys are conducted in the framework of MEDITS program from 1994 to 2002 and of NDC program from 2002 onwards. These survey data, collected with a standardized methodology, seem to be the most valid data to assess populations and community status in the area. Here we examine changes and trends in various quantitative metrics, suggested by the literature as indicators of the ecosystem effect of fishing (e.g. Rochet and Trenkel, 2003; Shin et al., 2005; Daan et al., 2005; Rochet et al., 2005; 2007; http://www.ifremer.fr/Medits_indices/) for the megafauna populations and community in the Eastern Ionian Sea using the MEDITS surveys. We try to interpret these changes by combining the results from the metrics analysis and identify causes of impacts in the ecosystem state.

MATERIAL AND METHODS Survey data Data used are the time-series of the MEDITS surveys. The MEDITS surveys are carried out every year since 1994 during the spring/early summer period. These surveys cover all the trawlable areas over the shelves and the upper slopes from 10 to 800 m depth. A stratified sampling scheme was used with random drawing stations inside each stratum. The same sampling gear (trawl) was used for the whole surveys. Its cod-end mesh size is 20 mm (stretched mesh). Tow duration was 30 minutes for shallow tows by 10-200 m and 1 hour for deeper tows. The detailed sampling scheme and methods applied are described in Bertrand et al. (2002). In the present work, only the surveys from 1998 to 2008 in the Eastern Ionian Sea (Fig. 1) were used. The previous years, the studied area was smaller, the stations differed form year to year and the number of stations and particularly those of the deeper stratum was very low. Since 1998 onwards, 32 stations were selected and sampled in the Eastern Ionian Sea every year. During this 11 years period, the surveys of 2002 and 2007 were not carried out because of financial and administrative problems. Selection of studied species During the MEDITS surveys, all macrofauna species are identified, counted and weighted by species. Special attention is given to 56 species of fish, cephalopods and crustaceans. For 36 of these species,

3 length frequency distribution, sex and maturity stages were recorded (Bertrand et al., 2002). However, in the Eastern Ionian Sea surveys, these data were recorded for more species.

Fig. 1. Map of the GFCM (number) and MEDITS (coloured) study areas. Eastern Ionian Sea is represented by the number 20.

All demersal species caught during the MEDITS surveys in the Eastern Ionian Sea from 1998 to 2008, were taken into consideration. Pelagic, mesopelagic and bathypelagic species were excluded because they were considered to have a low catchability to the gear used. Then, from the 244 remaining demersal species caught, 70 were selected for the analyses using the following criteria: a) constant presence across the years, b) percentage of mean occurrence (termed as the presence in the total number of stations across years) >5% for each species. Thus, rare or poorly sampled species were removed. This is necessary in order to avoid unreliable results (Trenkel and Rochet, 2005). Length, sex and maturity data were continuously recorded for 44 of the selected species and were used for length-based metrics. The 70 taxa selected for the analyses include 69 species and one genus, Gobius (Annex 1). The analysis was also performed for two depth strata (10-200 m: shelf & 200-800 m: slope) since special interest is given to deep waters. The same criteria, mentioned above, were applied for the selection of species to be analysed per depth stratum. The analysis for the shelf and the slope included 45 and 42 species (29 and 27 with length data), respectively. The selected species by depth stratum are shown in Annex 1. Metrics and analyses The metrics selected here for the analyses, mainly size-based metrics, are most of the metrics proposed by the MEDITS working group because of their capacity to give some information on fisheries impact (Rochet and Trenkel, 2005) (http://www.ifremer.fr/medits_-indices and references therein). The proportion of non-commercial fish, proposed as indicator by Rochet and Trenkel (2003) has also been analysed. The following metrics (see Annex II for their description) were used: a) For each selected species • Natural logarithm of population abundance: Ln(N) • Population growth rate r (the slope of ln(N) against time) b) For the selected species with measured individual length

• Mean length in the population: Lbar (cm) • Sampling variance of length: Lvar • Slope of time trend in mean length Lbar • Length at the fifth percentile of the length distribution: L0.05 (cm) • Length at the twenty- fifth percentile of the length distribution: L0.25 (cm)

4 • Length at the seventy- fifth percentile of the length distribution: L0.75 (cm) • Length at the ninety- fifth percentile of the length distribution: L0.95 (cm) • Length at first maturity L50 • Slope of time trend in L50 • Total mortality rate Z c) For the whole community • Natural logarithm of total abundance in the area ln(N) • Natural logarithm of total biomass in the area W (kg) • Community average length lbar (cm) • Community average length percentiles l0.95 (Large fish index in DCF) • Proportion of large individuals plarge larger than lbig = 15, 20, 25, 30 cm • Community average weight Wbar (g) The size-based indicators are widely used because they are expected to be sensitive to fishing and other human impacts (e.g. Shin et al., 2005; Rochet et al., 2007). Decrease of population mean size is expected and observed under the effect of fishing (Beverton and Holt, 1957; Haedrich and Barnes, 1997; Babcock et al., 1999); it can be easily estimated, but it is difficult to define a reference point for it (Rochet and Trenkel 2003). Mean size in community (measured in length or weight) is expected to decrease in exploited populations as do mean lengths in individual populations (Rochet and Trenkel, 2003). The proportion of large individuals is agreed as an indicator at the community level, but there is no consensus how to define what a large individual is (Rochet et al., 2007). Thus, “larger” is interpreted as larger than a threshold length at 15, 20, 25 and 30 cm. For Mediterranean populations, 25 cm has been proposed as the best value for threshold (Rochet et al., 2010). Estimated ln(abundance) is used to help interpretation of length-based indicators (Rochet et al., 2007) and the calculation of population growth rate r (Rochet and Trenkel, 2003). Population growth rate is expected to decrease by undesired effect of fishing. It is considered decreasing (or increasing) when it is significantly lower (respectively higher) than 0 (Rochet and Trenkel, 2003). Total biomass or abundance is related to the productivity of dependent fisheries, but the effects of fishing on them are difficult to predict, because of indirect effects along food webs (Rochet and Trenkel, 2003). Since fishing decreases length (or age) at maturity, this could be considered as indicator in a community perspective, when it can be monitored for a sufficient number of populations; no reference points are currently available (Trippel, 1995; Rochet, 2000; Rochet and Trenkel, 2003). The average weight of an individual in the community is expected to decrease as a result of fishing, but this indicator is sensitive to recruitment variations (Rochet and Trenkel, 2005). Total mortality Z rate has been suggested as a robust indicator for exploited populations (Die and Caddy, 1997). It is well founded as it has a clear meaning and predictable effects of fishing, including reference points, but it is clearly not exclusive to fishing impacts (Rochet and Trenkel, 2003). The proportion of non-commercial species should increase as a result of exploitation. It is an easily understood and measurable indicator, rather exclusive to fishing, but foundation of reference points is difficult (Rochet and Trenkel, 2003). The selected metrics were analysed for the whole time period (1998-2008) as well as for the last five years (2004-2008) using the MEDITS R code as explained in Rochet and Trenkel (2005) and Trenkel and Rochet (2005). Trends were estimated by linear regressions. The significance level of the probability for the slope coefficients was tested for both at α=0.1 and α=0.05. However, the second level was considered very severe and few statistically significant trends were identified (results not shown here). Thus, the level α=0.1 was selected for the analyses in the present study to increase the possibility of detecting any trend. This has also been proposed by Rochet et al. (2007) to increase the power of detecting effects. Total mortality Z analysis in the MEDITS code requires the von Bertalanffy parameters and for crustaceans a relationship to transform carapace length to total length (any information available from literature). Since this information was not available for all species, the analysis for this metric was performed on 23 populations. Moreover, the analysis for length at first maturity could be realized only for 14 species. MEDITS surveys are carried out in one season per year only. As a result, mature and immature proportions, necessary for L50 calculations, could not be estimated in all cases. To identify changes in community, combining of indicator trends was done according to Rochet et al. (2005, 2007). The first step into this diagnostic consists of assessing the initial status of the community (impacted-non impacted by fisheries) using published information. Then, moving from

5 single-stock to ecosystem-based management, assessment should be performed at two levels: populations and community as a whole (Rochet et al., 2005 and references therein). According to the results, the populations can be grouped into eight categories (Annex III), identified as follows: a) populations whose combined metric trends suggest increased (or decreased) individual growth g, recruitment, or mortality, b) those with no detectable change, and c) those with no simple interpretable trend combination, probably reflecting the influence of several, or other non considered, factors (Rochet et al., 2007). The effect of these factors at the community level will depend on whether they affect the most abundant populations (dominant populations), or/and whether many populations in the community were affected by the same process changes (Trenkel and Rochet, 2005; Rochet et al., 2007). Fisheries dependent data Landings, effort and fleet data available from the Fisheries Data Collection National Program (DCNP) during the period 2003-2008 (Anon., 2004; 2005; 2006, 2009) were analysed to identify trends across the years that would possibly help our interpretations based on the fisheries independent metrics. Landings and effort are estimations (calculated under the DCNP), whereas the number of vessels has been derived from census. Particular attention was given to landings, effort and fleet data related to the gears targeting demersal resources, since our work was mainly based on the MEDITS trawl surveys that concern basically demersal species. Linear regression was used to detect trends in the variables across the years. All the above results were combined with available published information on human pressures and environmental changes within the studied area.

RESULTS

Population metrics in the whole area Changes in the population growth rate r of the abundance (Ln N), detected in the populations of the Eastern Ionian Sea, are shown in Figure 2A and Table 1. From the 70 studied populations, 51 (~92% of N) showed signs of increasing trend in abundance, followed by those indicating decreasing trend. However, taking into account the statistical significance of this trend, most of populations (47 constituting 63% of N) were stationary; only 22 (~37% of N) showed significant increasing trend (Munida iris, Gadiculus argenteus, Trigla lyra, Trachurus trachurus, Zeus faber, Todaropsis eblanae, Merluccius merluccius, Rossia macrosoma, Pagellus bogaraveo, Lepidotrigla dieuzeidei, Boops boops, Trigloporus lastoviza, Lepidorhombus boscii, Plesionika giglioli, Parapenaeus longirostris, Aspitrigla cuculus, Raja miraletus, Helicolenus dactylopterus and Lepidotrigla cavillone, Raja clavata, Serranus hepatus ) and 1 significantly decreasing trend (Nephrops norvegicus) (Table 1). The last five years (2004-2008), the number of populations with increasing r diminished (39 populations: ~42% of N). In contrast, the number of populations with signs of decreasing rate rose to 30 (~57% of N) (Fig. 2B; Table 1). Similarly, the number of statistically significant increasing trends was reduced to 5 (R. macrosoma, Trisopterus minutus capelanus, P. bogaraveo, Raja oxyrhynchus, R. clavata) and that of significantly decreasing trends made up 7 (Goneplax rhomboides, Medorippe lanata, Liocarcinus depurator, Argentina sphyraena, N. norvegicus, Torpedo marmorata, Spicara flexuosa); the latter constituted an important proportion of the community in terms of numbers (~15%). However, as in the whole period analysis, if we take into consideration the significance of the slope coefficients, the vast majority of populations did not presented any significant trend (58 populations: 84% of N) (Table 1).

The analysis of the mean length Lbar revealed that half of the populations showed signs of increasing trend (~34% of N); slightly less than half of them revealed decreasing trend (~18% of N) (Fig. 3A, Table 1). However, only 7 of the increasing trends (Scyliorhynus canicula, T. trachurus, T. lastoviza, Loligo vulgaris, Plesionika giglioli, Plesionika martia, M. lanata) and 2 of the decreasing trends (H. dactylopterus, Plesionika heterocarpus) were statistically significant (Fig. 3A, Table 1). As a result, the amount of populations without significant trend in Lbar made up 40% of the total abundance (Table 1).

The mean length Lbar trend for the last five years indicated a slight increase in the percentage of populations with increasing trend (~57%) compared with that of the whole studied period; however,

6 these populations constituted a lower proportion of the community in terms of numbers (~25%) (Table 1). This is related to the numerous population of S. smaris, whose trend changed from increasing to decreasing (Fig. 3A, B). Only 5 from the populations (~4% of N) showed statistically significant increasing trend (Z. faber, Plesionika edwardsii, S. canicula, P. bogaraveo, S. flexuosa) and only 3 of the populations (~0.6% of N) decreasing trend (R. clavata, L. depurator, Plesionika antigai). It should be noticed that most populations were stationary representing almost 44% of the community total abundance (Fig. 3B, Table 1). th The analysis of the length at 95 percentile of the size distribution L0.95 during the whole studied period showed that 26 populations (32% of N) presented signs of increasing trend, whereas 18 of decreasing (21% of N) (Table 1). No stationary populations were found (Fig. 4A). However, when significance of trend is taken into account, most of the populations were stationary (33) constituting 27% of the total abundance (Table 1). Only 8 populations indicated a statistically significant increasing trend (A. cuculus, T. trachurus, Pagellus erythrinus, L. vulgaris, Eledone moschata, Pagellus acarne, S. flexuosa, M. lanata) and 3 a decreasing trend (Squalus blanville, Spicara smaris, P. heterocarpus) (Fig. 4A); they represented approximately 15% and 10% of the total abundance, respectively (Table 1).

From 2004 onwards, the number of populations with stationary trend in L0.95 increased to 3, whereas that of the populations with a decreasing trend reduced to 13 (Fig., 4B, Table 1). The increasing trend was statistically significant in 1 population only (S. canicula). The same was observed for the decreasing trend for another population (L. boscii) (Fig. 4B). As a result, almost all (38) studied populations were classified to the category “stationary”, representing ~48% of the total abundance (Table 1).

The total mortality Z trend, estimated for 23 populations for the time period of the last five years (2004-2008), indicated that approximately half of the examined populations showed an increasing trend (Micromesistius poutassou, Trachurus mediterraneaus, Illex coindetii, P. acarne, S. smaris, S. canicula, H. dactylopterus, Solea vulgaris, M. merluccius, P. erythrinus, R. clavata, Aspitrigla cuculus) and half a decreasing trend (Mullus barbatus, Citharus linguatula, T. capelanus, P. longirostris, T. lastoviza, P. bogaraveo, Lophius budagassa, T. trachurus, L. boscii, S. flexuosa, Z. faber). The same proportions remained when significance of the trend was considered. The populations with increasing trend represented ~19% of the community abundance, those with decreasing trend ~22% (Fig. 5, Table 1).

The length at first maturity L50 analysis, based on 14 populations, revealed 4 populations with statistically significant increased trend (S. blanville, N. norvegicus, R. clavata and Galeus melastomus); their contribution to the community was very low in terms of abundance (0.6% of N), but much higher in terms of biomass (~8% of W) (Fig. 6, Table 1). The remaining 10 populations showed signs of decreasing trend, 7 of them (11% of N) statistically significant (L.budegassa, C. linguatula, H. dactylopterus, P. longirostris, M. merluccius, P. erythrinus and Aristaeopmorha foliacea) (Fig. 6A, Table 1). The analysis of the last five years indicated only signs of increasing trend, 3 of them statistically significant (R. clavata, G. melastomus, P. longirostris) (Fig. 6B, Table 1). They constituted a greater portion of the community (~6% of N) compared to the other 3 populations with stationary trend (~2% of N) (Table 1).

The results from the combination of the trends of the three populations metrics Ln(N), Lbar and L0.95 for the whole studied period and for the last five years, according to the scheme proposed by Rochet et al. (2007) (see ANNEX III), are summarized in Table 2. Considering the significance level α = 0.1, most populations (19 out of 44) representing an important proportion of the community (~13% of N) were characterized by “No change”. Less populations (9, 8 and 4) concerning also important proportions of the total abundance (~12%, ~9% and ~8%) showed changes explained by “Other causes”, “increasing individual growth g” and “decreasing total mortality Z”. Although, only 2 populations were related with “decreasing individual growth g”, their proportion to the community was quite important (~10% of N) (Table 2), explained by the presence of the numerous population of S. smaris in this category. From 2004 onwards, a greater number of stationary populations appeared, which reached ~43% of the community total abundance (Table 2). Few changes, observed this period, were related to various factors representing a very low part of the community, except in the case of 1 population (S. flexuosa) characterized by “decreasing recruitment R” (3.5% of N). The only population that was interpreted by “decreasing Z” was the blackspot seabream P. bogaraveo and by increasing Liocarcinus depurator (Table 2).

Ionian Sea 1998 - 2008 Population growth rate r 0.5 A

PLESEDWPLESANTGONERHOALPHGLALEPICAUPAGEACADORILANNEPRNORMACRLONPLESACAMCPIDEPTRACMEDLOLIFORSOLEVULDIPLANNTORPMARGOBISPPNEZUSCLSEPIORB 0.0

MULLBARSERAHEPGALUMELPAGEERYSCYOCANELEDCIROCTOVULRAJACLAPERICATSPICFLEHYMEITALEPTCAVLOLIVULMACOSCORAJAOXYCAPOAPECLORAGAELEDMOSARNOLATCITHMACCOELCOEHELIDACPLESHETSQUABLAARISFOLLOPHBUDRAJAMIRSPICSMATRISCAPARGESPYASPICUCPAPELONPHYIBLEPLESGIGPLESMARHOPLMEDLEPMBOSILLECOIMICMPOUTRIPLASBOOPBOOLEPTDIEPAGEBOGROSSMACMERLMERTODIEBLZEUSFABTRACTRATRIGLYRGADIARGMUNIIRI Slopes -0.5 -1.0

Ionian Sea 2004 - 2008 Population growth rate r

4 B 2

GONERHOPLESANTPLESEDWALPHGLADORILANPLESHETMCPIDEPMICMPOULEPICAUTRACMEDMACRLONARGESPYGOBISPPNEPRNORLOLIFORSOLEVULTORPMARCAPOAPEDIPLANNCOELCOEMACOSCOCITHMACSCYOCANMERLMERPAPELONSPICFLEILLECOIASPICUCGADIARGLEPTCAVPAGEACA 0

ARNOLATLOPHBUDNEZUSCLCLORAGAPLESGIGTRIGLYRPLESMARLEPMBOSLEPTDIESERAHEPMULLBARSQUABLAELEDMOSSPICSMARAJACLASEPIORBTRACTRATRIPLASOCTOVULHELIDACRAJAMIRPAGEERYBOOPBOORAJAOXYZEUSFABPERICATPAGEBOGTODIEBLHYMEITAMUNIIRI TRISCAPROSSMACPHYIBLEHOPLMEDELEDCIRGALUMELARISFOLLOLIVULPLESACA Slopes -2 -4

Fig. 2. Slopes (cm.y-1) of time trends in population average population growth rate r using MEDITS data from 1998-2008 surveys, and ranked by increasing order. Analysis performed for the whole period (A) and for the surveys of the last five years (B). Coloured bars are significant slopes at α=0.1. Species codes are listed in Annex I.

8 Ionian Sea 1998 - 2008 Slope of time trend in mean length

A 0.5

SQUABLA HELIDAC LOPHBUD SOLEVUL MERLMERRAJACLA PHYIBLE TRISCAP NEPRNOROCTOVUL BOOPBOO PLESEDW PLESHET GALUMEL LEPMBOS PAPELON CITHMAC ELEDMOS MULLBAR PLESANT MACRLON PLESACA 0.0

ARISFOL MCPIDEP MUNIIRI DORILAN SPICSMA TRACMEDELEDCIR GONERHOPLESMAR ILLECOI PLESGIG PAGEERYSPICFLE ASPICUC PAGEACAPAGEBOG LOLIVUL MICMPOU ZEUSFAB TRIPLAS TRACTRA SCYOCAN Slopes -0.5 -1.0

Ionian Sea 2004 - 2008 Slope of time trend in mean length 3

B 2 1 Slopes

RAJACLA GALUMEL SOLEVUL HELIDAC PHYIBLE ARISFOL NEPRNOR MERLMER LOLIVUL TRIPLAS MCPIDEP PAGEERY SPICSMA TRISCAP PLESANT PAPELON MACRLON 0

DORILAN MUNIIRI PLESHET ILLECOI MULLBAR ELEDMOS PLESMAR PLESGIG LOPHBUD CITHMAC OCTOVUL TRACTRA ASPICUC SPICFLE PAGEACA PAGEBOG TRACMED ELEDCIR LEPMBOS SCYOCAN PLESEDW MICMPOU ZEUSFAB -1

-1 Fig. 3. Slopes (cm.y ) of time trends in population mean length (Lbar) using MEDITS data from 1998-2008 surveys, and ranked by increasing order. Analysis performed for the whole period (A) and for the surveys of the last five years (B). Coloured bars are significant slopes at α=0.1. Species codes are listed in Annex I.

9 Ionian Sea 1998 - 2008 Slope of time trend in L0.95

1.0 A 0.5

SQUABLA ZEUSFAB PHYIBLE MERLMER NEPRNORCITHMAC PLESEDW SPICSMA OCTOVULPLESHET RAJACLA LOPHBUD PAPELON SOLEVUL MCPIDEP ELEDCIR MACRLON PLESANT 0.0

MUNIIRI TRIPLAS DORILAN MULLBAR PLESACA PLESMAR ILLECOI ARISFOL PLESGIG GONERHO TRISCAP HELIDAC SPICFLE SCYOCAN TRACMEDBOOPBOOPAGEACA ELEDMOS GALUMEL PAGEBOG LEPMBOS LOLIVUL PAGEERYTRACTRA ASPICUC MICMPOU -0.5 Slopes -1.0 -1.5 -2.0 -2.5

Ionian Sea 2004 - 2008 Slope of time trend in L0.95

3 B 2 Slopes 1

MERLMER PHYIBLE LEPMBOS NEPRNOR TRISCAP ARISFOL TRIPLAS PAGEBOG SPICSMA MCPIDEP PAGEACA PLESANT PLESHET DORILAN MACRLON MUNIIRI PLESGIG PLESMAR 0

ELEDMOS LOLIVUL RAJACLA MULLBAR SPICFLE PAPELON CITHMAC ILLECOI ASPICUC PAGEERY TRACTRA HELIDAC SOLEVUL SCYOCAN GALUMEL PLESEDW TRACMED ELEDCIR OCTOVUL LOPHBUD MICMPOU ZEUSFAB -1

-1 Fig. 4. Slopes (cm.y ) of time trends in population length at 95% percentile of size distribution (L0.95) using MEDITS data from 1998-2008 surveys, and ranked by increasing order. Analysis performed for the whole period (A) and for the surveys of the last five years (B). Coloured bars are significant slopes at α=0.1. Species codes are listed in Annex I.

10 Ionian Sea 2004 - 2008 Slope of time trend in Z 6 5 4 3 Slopes 2 1

MULLBAR CITHMAC TRISCAP PAPELON TRIPLAS PAGEBOG LOPHBUD TRACTRA LEPMBOS SPICFLE ZEUSFAB 0

ASPICUC RAJACLA PAGEERY MERLMER SOLEVUL HELIDAC SCYOCAN SPICSMA PAGEACA ILLECOI TRACMED MICMPOU -1

Fig. 5. Slopes (cm.y-1) of time trends in population total mortality Z using MEDITS data from 2004-2008 surveys, and ranked by increasing order. Analysis performed for the whole period (A) and for the surveys of the last five years (B). Coloured bars are significant slopes at α=0.1. Species codes are listed in Annex I.

Ionian Sea 1998 - 2008 Ionian Sea 2004 - 2008 Slope of time trend in L50 Slope of time trend in L50

3 A B 1.5 2 1 1.0 Slopes Slopes

LOPHBUD CITHMAC PHYIBLE HELIDAC PAPELON MERLMER PAGEERY ARISFOL SCYOCAN ILLECOI 0

GALUMEL RAJACLA NEPRNOR SQUABLA 0.5 -1 0.0

ARISFOL PAPELON ILLECOI SCYOCAN GALUMEL RAJACLA -2

Fig. 6. Slopes (cm.y-1) of time trends in population mean length at first maturity (L50) using MEDITS data from 1998-2008 surveys, and ranked by increasing order. Analysis performed for the whole period (A) and for the surveys of the last five years (B). Coloured bars are significant slopes at α=0.1. Species codes are listed in Annex I.

11 Table 1. Number (n) and percentage (%) of populations, their proportion in the total biomass (%W) and abundance (%N) with increasing, stationary or decreasing trend in various population metrics across the whole studied period (1998-2008) and the last five years (2004-2008). Ln(N): Ln(abundance), Lbar: mean length, L0.95: length at 95% percentile, Z: total mortality, L50: length at first maturity). Results shown considering the trend with and without taking into account its statistical significance (p≤0.1). Z was not possible to be calculated for the whole period. Based on indication Significant at α=0.1 1998-2008 n % %W %N n % %W %N Ln(N) Increasing 51 72.86 91.84 92.23 22 31.43 38.34 36.80 Stationary 3 4.29 0.33 1.82 47 67.14 61.50 63.11 Decreasing 16 22.86 7.83 5.95 1 1.43 0.16 0.08 TOTAL 70 100.00 100.00 100.00 70 100.00 100.00 100.00

Lbar Increasing 22 50.00 35.73 34.16 7 15.91 7.18 10.52 Stationary 2 4.55 0.00 0.10 35 79.55 63.13 40.11 Decreasing 20 45.45 36.70 18.38 2 4.55 2.12 2.02 TOTAL 44 100 72.43 52.64 44 100.00 72.43 52.64

L0.95 Increasing 26 59.09 39.97 31.93 8 18.18 14.03 14.85 Stationary 0 0.00 0.00 0.00 33 75.00 46.33 27.54 Decreasing 18 40.91 32.46 20.71 3 6.82 12.07 10.25 TOTAL 44 100.00 72.43 52.64 44 100.00 72.43 52.64

L50 Increasing 4 28.57 7.99 0.56 4 28.57 7.99 0.56 Stationary 0 0.00 0.00 0.00 3 21.43 3.92 1.42 Decreasing 10 71.43 21.70 12.60 7 50.00 17.78 11.18 TOTAL 14 100.00 29.69 13.16 14 100.00 29.69 13.16

2004-2008 n % %W %N n % %W %N Ln(N) Increasing 39 55.71 52.49 42.73 5 7.14 5.56 1.31 Stationary 1 1.43 0.24 0.16 58 82.86 86.73 84.11 Decreasing 30 42.86 47.27 57.11 7 10.00 7.71 14.58 TOTAL 70 100.00 100.00 100.00 70 100.00 100.00 100.00

Lbar Increasing 23 57.50 37.13 25.35 5 12.50 6.61 4.03 Stationary 1 2.50 0.00 0.05 32 80.00 54.19 43.88 Decreasing 16 40.00 26.36 23.14 3 7.50 2.70 0.64 TOTAL 40 100.00 63.50 48.54 40 100.00 63.50 48.54

L0.95 Increasing 24 60.00 44.45 31.03 1 2.50 0.85 0.15 Stationary 3 7.50 0.12 1.03 38 95.00 62.23 48.31 Decreasing 13 32.50 18.93 16.48 1 2.50 0.42 0.08 TOTAL 40 100.00 63.50 48.54 40 100.00 63.50 48.54

L50 Increasing 6 100.00 9.04 8.45 3 50.00 5.36 6.24 Stationary 0 0.00 0.00 0.00 3 50.00 3.68 2.21 Decreasing 0 0.00 0.00 0.00 0 0.00 0.00 0.00 TOTAL 6 100.00 9.04 8.45 6 100.00 9.04 8.45

Z Increasing 12 52.17 34.97 19.43 12 52.17 34.97 19.43 Stationary 0 0.00 0.00 0.00 0 0.00 0.00 0.00 Decreasing 11 47.83 22.21 22.13 11 47.83 22.21 22.13 TOTAL 23 100.00 57.18 41.57 23 100.00 57.18 41.57

12 On the other hand, the analysis without taking into account the significance of the trends indicated several reasons related to the changes occurring in the populations (Table 2). In the whole studied period, most of the examined populations (14), constituted the greater part of the community (~21% of N), presented changes explained by “decreasing Z”. An important number of populations (11, 9 and 5) with changes interpreted by “Other causes”, “increasing R”, and “decreasing R” constituted also an important part of the community (16%; ~11% and ~4% of N). From 2004 onwards, the most important reasons interpreting the changes were “Other causes” and “decreasing Z” (12 and 10 populations, respectively) (~11% and ~13% of N, respectively). They were followed by “decreasing and increasing R” (each one ~10% of N). During this period, “increasing mortality Z” made up ~5% of the total abundance (Table 2).

Table 2. Interpretation of the status of populations combining three of the populations metrics (Ln(N): Ln(abundance), Lbar: mean length, L0.95: length at 95% percentile of the length composition) according to the scheme proposed by Rochet et al. (2005, 2007). Results for the whole studied period (1998-2008) and the last five years (2004-2005) are shown. Z: total mortality, R: recruitment, g: individual growth, Other: several or other processes not examined.

Based on indication Significant at α=0.1 Process n % %W %N n % %W %N 1998-2008 g decreasing 0 0.00 0.00 0.00 2 4.55 8.65 10.13 g increasing 0 0.00 0.00 0.00 8 18.18 9.14 8.58 R decreasing 5 11.36 3.26 3.52 0 0.00 0.00 0.00 R increasing 9 20.45 21.86 11.09 2 4.55 5.38 0.53 Z decreasing 14 31.82 22.51 21.38 4 9.09 6.20 7.75 Z increasing 5 11.36 0.65 0.33 0 0.00 0.00 0.00 No Change 0 0.00 0.00 0.00 19 43.18 25.45 13.22 Other 11 25.00 24.14 16.33 9 20.45 17.62 12.43 TOTAL 44 100.00 72.43 52.64 44 100.00 72.43 52.64

2004-2008 g decreasing 0 0.00 0.00 0.00 2 5.00 0.42 0.10 g increasing 0 0.00 0.00 0.00 3 7.50 1.41 0.30 R decreasing 9 22.50 19.04 10.23 1 2.50 3.82 3.51 R increasing 5 12.50 9.13 9.62 1 2.50 2.46 0.10 Z decreasing 10 25.00 15.94 12.99 1 2.50 1.38 0.22 Z increasing 4 10.00 7.65 4.72 1 2.50 0.24 0.52 No Change 0 0.00 0.00 0.00 28 70.00 53.06 42.74 Other 12 30.00 11.74 10.99 3 7.50 0.71 1.05 TOTAL 40 100.00 63.50 48.54 40 100.00 63.50 48.54

Population metrics by depth stratum Different changes in the population metrics were observed from the analysis by depth stratum. A summary of the results is presented in Table 3 for the shelf (10-200m) and in Table 4 for the slope (200-800m). Most of the populations in shelf during 1998-2008 were stationary in ln(N), Lbar and L0.95 (34, 27 and 25 representing ~68%, ~64% and ~45% of the total biomass in numbers, respectively). The remaining populations showed a statistically significant increasing trend in all three metrics. No population with significant decreasing trend was observed (Table 3). On the other hand in the slope, most of the populations were stationary in ln(N), Lbar and L0.95 (27, 25and 22 representing ~49%, ~28% and ~24% of the total biomass in numbers, respectively). However, although almost all the remaining populations indicated a statistically significant increasing trend in ln(N) (except the decreasing N. norvegicus), a decreasing trend was detected for the remaining 2 populations (R. clavata and P. heterocarpus) in Lbar and for 3 populations (M. merluccius, P. heterocarpus and S. blainville) in

13 L0.95 (Table 4). Regarding L50, stationary or significant decreasing (shelf: C. lingulatula, P. longirostris, P. eryhtrinus; slope: Phycis blennoides, P. longirostris, H. dactylopterus, Aristaeomorpha foliacea) was the trend in most populations in both depth strata (Table 3, Table 4). The analysis of the metrics in the last five years of the studied period (2004-2008) revealed a stationary status in ln(N), Lbar and L0.95 in most of populations and for the greater portion of the community in both depth strata (Table 3 & Table 4). However, in shelf, 3 crab populations and a fish (S. flexuosa), constituting ~7% of the community in numbers, showed statistically significant decreasing trend in ln(N) and one in L0.95 (T. lastoviza) (Table 3). In slope, N. norvegicus continued to indicate significant decreasing trend this period (Table 4). Regarding L50, in shelf, 2 populations shifted from increasing to decreasing trend (M. merluccius, P. longirostris), but maybe we should not take into account this result, since both populations are distributed in both depth strata and therefore the entire population is not included in the analysis of one stratum. Furthermore, in the analysis of the whole area, P. longirostris showed an increasing trend in this period (Fig. 6B). In the slope, most of the examined populations (among them P. longirostris) showed an increasing trend (Table 4). The analysis of total mortality Z in shelf indicated statistically significantly increasing (Trachurus mediterraneus, P. acarne, S. smaris, Illex coindetii, P. longirostris, T. lastoviza, M. merluccius) and decreasing trend in a similar number of populations and in a similar percentage (~28% of N) (Table 3). In slope, the number of population was similar for both trends. However, the populations with increasing Z constituted a much lower percentage (~2%) of the total slope community in numbers than those with decreasing trend (~13%) (Table 4).

The results from the combination of the populations metrics Ln(N), Lbar and L0.95, according to the scheme proposed by Rochet et al. (2007) (see ANNEX III), are summarized in Table 5 for both depth strata. In the shelf, during 1998-2008, most populations (19 constituted ~35% of N) were stable. From the remaining populations, changes were explained by “Other” in 5 of them (~10% of N) and by “increasing individual growth g” in 3 of them (~8% of N). Only 2 populations were related with “decreasing Z”, however, representing ~11% of the shelf community in numbers (Table 5A) because of the T. trachurus contribution. Similarly, in the slope, almost half of the populations examined were stationary (14 constituted ~9% of the community in numbers). From the remaining populations, 7 populations were interpreted by “Other” (among them N. norvegicus). They represented the greatest percentage of the slope community (~14% of N), due to the contribution of P. longirostris and Munida iris. Changes in 2 more populations were explained by “decreasing individual growth g” and in another 2 (~5% of N) by “increasing recruitment R” (~3% of N) (Table 5B). The combination of the metrics for the shelf in the last five years of the studied period (2004- 2008), revealed a stationary status for most populations (19 representing ~47% of the community in numbers). Changes, explained by “Other”, were related to 3 populations, which constituted a small part of the community (~1% of N). In opposite, 2 populations, interpreted by “increasing individual growth g” and 1 population related with “decreasing recruitment R” constituted ~5% and ~6% of the total shelf community abundance, respectively (table 5A). The first case involves M. merluccius and the second one S. flexuosa. Almost all slope populations were stationary from 2004 onwards (Table 5B).

14 Table 3. Number (n) and percentage (%) of populations, their proportion in the total biomass (%W) and abundance (%N) with increasing, stationary or decreasing trend in various population metrics across the whole studied period (1998-2008) and the last five years (2004-2008) in the shelf. Ln(N): Ln(abundance), Lbar: mean length, L0.95: length at 95% percentile, Z: total mortality, L50: length at first maturity) Results shown considering the trend with and without taking into account its statistical significance (p≤0.1). Z was not possible to be calculated for the whole period. Depths: 0-200m Indication Significant at α=0.1 1998-2008 n % %W %N n % %W %N Ln(N) Increasing 33 73.33 90.00 91.13 11 24.44 35.95 32.28 Stationary 1 2.22 0.27 0.37 34 75.56 64.05 67.72 Decreasing 11 24.44 9.73 8.50 0 0.00 0.00 0.00 TOTAL 45 100.00 100.00 100.00 45 100.00 100.00 100.00

Lbar Increasing 16 55.17 40.00 40.92 2 6.90 0.27 0.38 Stationary 2 6.90 0.81 0.61 27 93.10 81.89 63.86 Decreasing 11 37.93 41.35 22.71 0 0.00 0.00 0.00 TOTAL 29 100.00 82.16 64.24 29 100.00 82.16 64.24

L0.95 Increasing 14 48.28 42.97 40.80 4 13.79 17.84 18.96 Stationary 1 3.45 0.00 0.12 25 86.21 64.32 45.28 Decreasing 14 48.28 39.19 23.33 0 0.00 0.00 0.00 TOTAL 29 100.00 82.16 64.24 29 100.00 82.16 64.24

L50 Increasing 2 33.33 6.49 5.95 0 0.00 0.00 0.00 Stationary 0 0.00 0.00 0.00 3 50.00 14.59 10.01 Decreasing 4 66.67 12.97 9.54 3 50.00 4.86 5.48 TOTAL 6 100.00 19.46 15.49 6 100.00 19.46 15.49

2004-2008 Ln(N) Increasing 24 53.33 62.89 49.77 2 4.44 2.68 0.15 Stationary 1 2.22 0.01 0.02 39 86.67 89.90 92.74 Decreasing 20 44.44 37.10 50.22 4 8.89 7.42 7.11 TOTAL 45 100.00 100.00 100.00 45 100.00 100.00 100.00

Lbar Increasing 17 65.38 51.16 36.47 1 3.85 6.87 5.92 Stationary 0 0.00 0.00 0.00 25 96.15 66.26 53.98 Decreasing 9 34.62 21.97 23.43 0 0.00 0.00 0.00 TOTAL 26 100.00 73.13 59.90 26 100.00 73.13 59.90

L0.95 Increasing 16 61.54 53.98 41.37 2 7.69 10.12 5.42 Stationary 2 7.69 0.67 0.26 23 88.46 62.58 54.26 Decreasing 8 30.77 18.48 18.27 1 3.85 0.44 0.23 TOTAL 26 100.00 73.13 59.90 26 100.00 73.13 59.90

L50 Increasing 0 0.00 0.00 0.00 0 0.00 0.00 0.00 Stationary 0 0.00 0.00 0.00 0 0.00 0.00 0.00 Decreasing 2 100.00 9.59 8.71 2 100.00 9.59 8.71 TOTAL 2 100.00 9.59 8.71 2 100.00 9.59 8.71

Z Increasing 7 41.18 29.52 27.60 7 41.18 29.52 27.60 Stationary 0 0.00 0.00 0.00 0 0.00 0.00 0.00 Decreasing 10 58.82 38.57 28.51 10 58.82 38.57 28.51 TOTAL 17 100.00 68.09 56.12 17 100.00 68.09 56.12

15 Table 4. Number (n) and percentage (%) of populations, their proportion in the total biomass (%W) and abundance (%N) with increasing, stationary or decreasing trend in various population metrics across the whole studied period (1998-2008) and the last five years (2004-2008) in the slope. Ln(N): Ln(abundance), Lbar: mean length, L0.95: length at 95% percentile, Z: total mortality, L50: length at first maturity) Results shown considering the trend with and without taking into account its statistical significance (p≤0.1). Z was not possible to be calculated for the whole period. Depth: 200-800m Indication Significant at α=0.1 1998-2008 n % %W %N n % %W %N Ln(N) Increasing 33 78.57 93.33 97.46 14 33.33 36.67 51.04 Stationary 1 2.38 0.00 0.39 27 64.29 62.86 48.76 Decreasing 8 19.05 6.67 2.15 1 2.38 0.48 0.20 TOTAL 42 100.00 100.00 100.00 42 100.00 100.00 100.00

Lbar Increasing 13 48.15 16.19 12.85 0 0.00 0.00 0.00 Stationary 2 7.41 0.00 0.32 25 92.59 50.00 27.63 Decreasing 12 44.44 37.62 19.21 2 7.41 3.81 4.76 TOTAL 27 100 53.81 32.39 27 100.00 53.81 32.39

L0.95 Increasing 16 59.26 23.33 9.94 2 7.41 4.29 0.67 Stationary 1 3.70 0.48 3.96 22 81.48 33.81 23.95 Decreasing 10 37.04 30.00 18.48 3 11.11 15.71 7.76 TOTAL 27 100.00 53.81 32.39 27 100.00 53.81 32.39

L50 Increasing 5 45.45 20.95 1.88 2 18.18 5.24 0.89 Stationary 0 0.00 0.00 0.00 5 45.45 24.29 4.67 Decreasing 6 54.55 20.95 15.85 4 36.36 12.38 12.17 TOTAL 11 100.00 41.90 17.73 11 100.00 41.90 17.73

2004-2008 Ln(N) Increasing 26 61.90 54.14 35.59 3 7.14 4.43 0.91 Stationary 0 0.00 0.00 0.00 38 90.48 95.48 99.06 Decreasing 16 38.10 45.86 64.41 1 2.38 0.09 0.04 TOTAL 42 100.00 100.00 100.00 42 100.00 100.00 100.00

Lbar Increasing 11 47.83 11.96 8.97 1 4.35 2.20 0.12 Stationary 1 4.35 0.43 2.30 22 95.65 43.66 29.95 Decreasing 11 47.83 33.47 18.80 0 0.00 0.00 0.00 TOTAL 23 100.00 45.86 30.07 23 100.00 45.86 30.07

L0.95 Increasing 12 52.17 28.15 13.47 0 0.00 0.00 0.00 Stationary 2 8.70 0.69 4.76 23 100.00 45.86 30.07 Decreasing 9 39.13 17.02 11.84 0 0.00 0.00 0.00 TOTAL 23 100.00 45.86 30.07 23 100.00 45.86 30.07

L50 Increasing 5 100.0 15.77 13.26 4 80.0 14.13 11.23 Stationary 0 0.0 0.00 0.00 1 20.0 1.64 2.03 Decreasing 0 0.0 0.00 0.00 0 0.0 0.00 0.00 TOTAL 5 100.0 15.77 13.26 5 100.0 15.77 13.26

Z Increasing 4 50.00 12.91 2.35 4 50.00 12.91 2.35 Stationary 0 0.00 0.00 0.00 0 0.00 0.00 0.00 Decreasing 4 50.00 16.63 13.14 4 50.00 16.63 13.14 TOTAL 8 100.00 29.54 15.48 8 100.00 29.54 15.48

16 Table 5. Interpretation of the status of populations in the two depth strata (A: shelf and B: slope) combining three of the populations metrics (Ln(N): Ln(abundance), Lbar: mean length, L0.95: length at 95% percentile of the length composition) according to the scheme proposed by Rochet et al. (2005, 2007). Results for the whole studied period (1998-2008) and the last five years (2004-2005) are shown. Z: total mortality, R: recruitment, g: individual growth, Other: several or other processes not examined. A. 0-200m (shelf) Based on Indication Significant at α=0.1 Process n % %W %N n % %W %N 1998-2008 g- 0 0.00 0.00 0.00 0 0.00 0.00 0.00 g+ 0 0.00 0.00 0.00 3 10.34 10.27 7.84 R- 3 10.34 0.54 1.01 0 0.00 0.00 0.00 R+ 7 24.14 20.81 6.95 0 0.00 0.00 0.00 Z- 7 24.14 21.89 23.59 2 6.90 7.84 11.33 Z+ 1 3.45 0.81 0.06 0 0.00 0.00 0.00 No Change 0 0.00 0.00 0.00 19 65.52 45.68 35.43 Other 11 37.93 38.11 32.63 5 17.24 18.38 9.64 TOTAL 29 100.00 82.16 64.24 29 100.00 82.16 64.24

2004-2008 g- 0 0.00 0.00 0.00 1 3.85 0.44 0.23 g+ 1 3.85 0.01 0.02 2 7.69 10.12 5.42 R- 8 30.77 25.14 17.59 1 3.85 6.87 5.92 R+ 5 19.23 17.71 17.04 0 0.00 0.00 0.00 Z- 7 26.92 25.67 18.59 0 0.00 0.00 0.00 Z+ 2 7.69 0.43 0.97 0 0.00 0.00 0.00 No Change 0 0.00 0.00 0.00 19 73.08 52.89 47.17 Other 3 11.54 4.16 5.69 3 11.54 2.82 1.17 TOTAL 26 100.00 73.13 59.90 26 100.00 73.13 59.90 B. 200-800m (slope) 1998-2008 g- 0 0.00 0.00 0.00 2 7.41 10.00 4.95 g+ 0 0.00 0.00 0.00 1 3.70 2.86 0.44 R- 2 7.41 0.95 0.28 0 0.00 0.00 0.00 R+ 7 25.93 29.52 17.99 2 7.41 9.05 2.98 Z- 11 40.74 14.76 12.67 1 3.70 1.43 0.23 Z+ 2 7.41 0.00 0.38 0 0.00 0.00 0.00 No Change 0 0.00 0.00 0.00 14 51.85 17.14 9.33 Other 5 18.52 8.57 1.08 7 25.93 13.33 14.46 TOTAL 27 100.00 53.81 32.39 27 100.00 53.81 32.39

2004-2008 g- 0 0.00 0.00 0.00 0 0.00 0.00 0.00 g+ 0 0.00 0.00 0.00 1 4.35 2.20 0.12 R- 3 13.04 4.88 0.56 0 0.00 0.00 0.00 R+ 2 8.70 2.55 2.19 0 0.00 0.00 0.00 Z- 3 13.04 0.99 2.54 0 0.00 0.00 0.00 Z+ 2 8.70 8.37 3.79 0 0.00 0.00 0.00 No Change 0 0.00 0.00 0.00 19 82.61 39.24 29.08 Other 13 56.52 29.06 20.98 3 13.04 4.42 0.87 TOTAL 23 100.00 45.86 30.07 23 100.00 45.86 30.07

17 Community metrics Few changes were identified in the community across the years in the Eastern Ionian Sea. They were related more to the total abundance and total biomass, increasing significantly during 1998- 2008, but not in the last five years (Fig. 7A, B, C, D). Community mean length fluctuated without any clear trend across the years as well as in the last 5 years period (Fig. 8B). This was also the case for the community mean individual weight (Fig. 9A, B). The shelf community metrics for the total abundance and biomass and the mean length and mean individual weight presented similar results as the total community in the Eastern Ionian Sea from 1998 to 2008 as well as from 2004 to 2008 (results not shown here). Thus, no statistically significant changes were detected. The same was also found for the slope (results not shown).

Ionian Sea Ionian Sea Total abundance: p= 0.007 Total abundance: p= 0.681 25.0 25.0 A B 24.8 24.5 24.6 ln(Abundance) ln(Abundance) 24.0 24.4 24.2 23.5 24.0 23.0

1998 2000 2002 2004 2006 2008 2004 2005 2006 2007 2008 Year Year

Ionian Sea Ionian Sea Total biomass: p= 0.001 Total biomass: p= 0.462 21.0 C D 20.8 20.5 20.6 ln(Biomass) ln(Biomass) 20.4 20.0 20.2 20.0 19.5

1998 2000 2002 2004 2006 2008 2004 2005 2006 2007 2008 Year Year Fig. 7. Total community abundance and biomass in the Eastern Ionian Sea using MEDITS data collected during 1998-2008 surveys and analysed for the whole period (A and C, respectively) and for the last five years (B and D, respectively). Probability p of the trend is also shown.

18 Ionian Sea Ionian Sea Community mean length Community mean length 13.0 13.0 12.5 12.5 12.0 11.5 Community mean length mean Community Community mean length mean Community 12.0 11.0 10.5 11.5

10.0 A B

1998 1999 2000 2001 2002 2003 2004 2005 2006 2008 2004 2005 2006 2007 2008

Year Year slope = 0.066 , P = 0.57 slope = 0.31 , P = 0.295

Fig. 8. Community mean length in the Eastern Ionian Sea using MEDITS data collected during 1998-2008 surveys and analysed for the whole period (A) and for the surveys of the last five years (B). Probability p of the trend is also shown in both cases.

Ionian Sea Ionian Sea Mean weight: p= 0.691 Mean weight: p= 0.109

A B 30 30 25 25 Mean weight (g) Mean Mean weight (g) Mean 20 20 15 15 10 10

1998 2000 2002 2004 2006 2008 2004 2005 2006 2007 2008 Year Year Fig. 9. Community individual mean weight in the Eastern Ionian Sea using MEDITS data collected during 1998- 2008 surveys and analysed for the whole period (A) and for the surveys of the last five years (B). Probability p of the trend is also shown in both cases.

The proportion of large fish, when using the 25 cm threshold, did not show any significant trend in both depth strata and in total during 1998-2008 as well as during 2004-2008 (Fig. 10A, B). Finally, the analysis of the proportion of non-commercial species in numbers (Fig. 11A) was stationary across the years without any significant trend for the whole community as well as for the shelf (10-200m) and slope (200-800m). The trend of the proportion in weight was in all cases decreasing, although statistically significant for the total community and the slope community (Fig. 11B).

19 Ionian Sea Ionian Sea threshold 25 cm p= 0.183 threshold 25 cm p= 0.705 0.12

A 0.08 0.10 0.06 0.08 B 0.06 0.04 Proportion large Proportion large 0.04 0.02 0.02 0.00 0.00 1 4 Total 1 4 Total -0.02 -0.02

1998 2000 2002 2004 2006 2008 2004 2005 2006 2007 2008

Year Year Fig. 10. Proportion of large individuals in the Eastern Ionian Sea using MEDITS data collected during 1998- 2008 surveys and analysed for the whole period (A) and for the last five years (B). The length of 25 cm was used as threshold in the analyses. Probability p of the trend is also shown. Red line (1): shelf (10-200 m), green line (4): slope (200-800m) and bold black line (Total): all the studied area.

80 60 0-200m A y = -0.1514x + 372.82 B 55 200-800m R2=0.0123 70 Total p=0.356 50 Linear (200-800m) 60 45 Linear (0-200m) Linear (Total) y = -0.2115x + 473.95 40 50 R2=0.0276 p=0.711 35 y = -0.6142x + 1263.2 R2=0.0889 40 30 p=0.006

25 30 y = -0.685x + 1395.5 y = -0.5261x + 1090.1 20 2

N % of non-commercial % of N R =0.3355 2

0-200m R =0.061 non-commercial % of Weight p=0.0169 15 20 200-800m p=0.950 Total 10 y = -0.5213x + 1059.1 Linear (0-200m) 10 R2=0.2555 Linear (200-800m) 5 p=0.573 Linear (Total) 0 0 1996 1998 2000 2002 2004 2006 2008 2010 2012 1996 1998 2000 2002 2004 2006 2008 2010 2012 Year Year Fig. 11. Proportion of non-commercial species in the community by depth stratum (0-200m & 200-800 m) and overall in the Eastern Ionian Sea using MEDITS data collected during 1998-2008 surveys (A: in number, B: in weight). Linear regressions and probability p of the trend are also shown.

Fisheries dependent data In the Eastern Ionian Sea, from 2003 to 2008, the total number of fishing vessels registered and the registered vessels related with fishing of demersal resources (trawlers, netters, long-liners, trappers, potters) (Fig. 12A) as well as total effort and effort related with demersal resources (Fig. 12B) were decreasing across the years with statistically significant trends. On the other hand, total landings in the area and landings of demersal resources (Fig. 12C) indicated signs of slightly increasing trend, marginally significant only in the latter case. Therefore, even though fishing impact decreases significantly during 2003-2008, landings appeared almost stationary or weakly increasing.

20 4600 900 A B

4500 850

y = -64.629x + 134023 y = -57.057x + 118612 R2 = 0.9995 4400 800 y = -31.571x + 64082 R2 = 0.9991 p=0.000 R2 = 0.8286 p=0.000 p=0.032

Vessels N Vessels 4300 750

Total Effort 4200 700

Effort (Vess*days) x10³ Dem Effort T ot al V ess Linear (T ot al Effort ) Dem Vess y = -31.558x + 64042 Linear (Dem Effort) 2 4100 Linear (T ot al V ess) 650 R = 0.834 Linear (Dem Vess) p=0.030

4000 600 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 Year Year

40000 C y = 1000x - 2E+06 35000 R2 = 0.5311 p=0.163

30000 Fig. 12. (A) Total number of registered vessels and vessels related with demersal resources 25000 y = 766.28x - 2E+06 ('Dem Vess'), (B) total effort and 'Dem Effort' Landings (tn) R2 = 0.6477 and (C) total landings and 'Dem Land' in the p=0.100 20000 Total Land Eastern Ionian Sea, based on NDCP data from Dem Land 2003 to 2008. Linear regressions and probability Linear (Total Land) 15000 Linear (Dem Land) p of the trend are also shown.

10000 2002 2003 2004 2005 2006 2007 2008 2009 Year

DISCUSSION In the present work, an attempt to provide information necessary for an ecosystem approach to fisheries management (EAFM) is made using fisheries independent data. For many decades, stock assessment was based on fisheries dependent data targeting toward a single species management. This approach presented several weaknesses, problems and implications (Cotter et al., 2009). The EAFM, adopted in the last decade by several scientific organisations (FAO, 2003; 2009; ICES, 2005; EC, 2008), requires valid data for its implementation, which is the case of fisheries independent data from scientific surveys (Cotter et al., 2009). Surveys data present the advantages of standardized methodology and of more global information for the ecosystem. The MEDITS trawl surveys data, used in the present study, offer these advantages, since they are collected with a standard methodology for many years and they concern commercial and non-commercial species. In order to best accomplish the requirement of a standard methodology, the data of the time period from 1998 to 2008 were only used. A disadvantage in our case is the gap of data in 2002 and 2007, which mainly affected the total mortality analysis. To implement an EAFM, several indicators have to be aggregated and combined (Cury & Christensen, 2005; Rochet et al., 2005; 2010). Those selected in the present work indicators are in most cases size-based, and are among those proposed by the MEDITS working group (http://www.ifremer.fr/medits_indices and references therein) because of their capacity to give information on fishery impact (Rochet and Trenkel, 2005). One more indicator proposed by Rochet and Trenkel (2003), the proportion of non-commercial species, has also been included in the analyses. Size based metrics are expected to be sensitive to fishing and other human impacts. Thus, they are appropriate to describe the status and dynamics of populations and communities, and consequently to be used as indicators. Their strengths and weaknesses have also been discussed (e.g. Trippel, 1995; Die and Caddy, 1997; Rochet and Trenkel, 2003; 2005; Daan et al., 2005; Shin et al., 2005; Jennings, 2005; Rochet et al., 2007; 2010). They are easily estimated (e.g. mean size, proportion of non-

21 commercial species) or not (e.g. community mean trophic level), they are easily defined (e.g. total mortality, proportion of non-commercial species) or not (e.g. proportion of large individuals), they could easily be defined by a reference point (e.g. Z) or not (e.g. mean size, size at first maturity, proportion of non-commercial species.), they can easily interpret fishing impact (e.g. proportion of non-commercial species), whereas others are not exclusive to fishing (e.g. mean individual weight, total abundance and biomass, diversity indices). Other type of indicators describing the trophic level of populations and communities and diversity indices (their study not falling within the scope of this work) may also be effective and perhaps they should also be examined under certain conditions (but see Rochet and Trenkel, 2003). Vassilopoulou et al. (2009) reviewed the different types of indicators used in Mediterranean studies, and highlighted the power of the trophic level of catches indicator and of size-based metrics. The selection of the species used for the metrics analysis was based on criteria that ensure the reliability of results. Since MEDITS data concern mainly demersal resources, pelagic species were excluded. Moreover, rare or poorly sampled species were also removed, since indicators cannot be reliably estimated for these species. The number of examined populations was decreasing as we went from abundance to the length at first maturity analysis (e.g. for the whole area Ln(N): 70 populations, Lbar: 44, L0.95: 44, Z: 23 populations, L50: 14 populations) following the availability of data and the possibility of obtaining reliable results. In order to achieve the maximum available information, we preferred to examine the maximum available population number for each metric and not the same number of populations for all metrics. The same rule was followed in the analysis by depth stratum. Since demand for environmental approach to fisheries management is always increasing, scientific surveys should provide the maximum possible information required for the best implementation of EAFM. In this context, recording of data from a greater number of species is proposed for the scientific surveys by the present work. Problems arising from the data during the analysis concerned mainly L50 and Z. The results from L50 analysis should be treated with caution. MEDITS maturity data, deriving from one season, present many gaps and some years the model could not be fitted. They should be supported by more data (maybe from other studies) in the future to reinforce their validity. Z analysis is based on the von Bertalanffy growth parameters of species and on a transformation of carapace to total length in crustaceans. This information is not available for many species, and therefore Z could not be estimated for them. Moreover, the gap of two years (2002, 2007) in our time series prevented Z estimation during the whole period. Another problem arising from the analysis was the statistical significance level that should be used for the interpretation of the results. We tried both 0.05 and 0.1 significance levels; however, the first one seemed to be very severe for the existing trends. As a result, the probability 0.1 was selected to interpret the results. Rochet et al., (2007) proposed also the 0.1 significance level to increase the possibility of detecting trends. More discussion is needed on this topic. The results from the analysis of the DCNP 2003-2008 fisheries data showed that for both total and demersal category fishing impact (fleet and effort) decreases significantly, whereas landings appeared almost stationary or weakly increasing. The decreasing of the registered fleet may be the result of the decommissioning of many small and not very active vessels. In order to reduce this bias, the effort estimations presented here are based on the active registered fleet. The effect of modernization and efficiency of the fleet on the effort across the years could not be taken into account. It could be suggested that it is compensated by the effect of EC regulation 1626/94 for the increase of the trawl cod-end mesh size from 28 mm to 40 mm. Thus, decreasing of fishing effort could be considered as a reliable estimation. Moreover, published work mentioned a considerable increase in the fishing effort from 1964 to 1989 (Stergiou et al., 1997), starting to decrease from 1993 onwards (Politou, 2007). Furthermore, overexploitation has been reported for some fisheries resources in the area (e.g. Stergiou et al., 1997; Papaconstantinou et al., 1988; Papaconstantinou & Farrugio, 2000; Petrakis et al., 2001). No information is available on the recreational fishery, but it should be noted that several restrictions have been legislated ultimately to control it. Based on all the above information, the initial status of the whole community in the Eastern Ionian Sea at the beginning of this study was considered as impacted by fisheries. The situation between the two depth strata (shelf-slope) may be different. No data on fleet, effort and landings exist by depth stratum; only information from questionnaires and grey literature. Most of the fishing vessels operate in waters <400 m depth. Nevertheless, last decade, few trawlers started to extend occasionally their activities in deeper waters targeting red shrimps (Mytilineou and Machias, 2007). On the other hand, small scale vessels with bottom nets and long lines targeting P. bogaraveo,

22 Polyprion americanus and sharks in deep waters in late 90’s, reduced their activities because of collapse of the stocks (Petrakis et al., 2001) or the decrease of their catches (Mytilineou and Machias, 2007; unpublished information). Furthermore, after 2003, some Italian trawlers started fishing systematically in the deep waters of the Eastern Ionian Sea; they send their catch to Italy. No information on their activity and catch exist. Since the active fleet targets shallow waters mainly, the initial status of the shelf populations and community could be considered impacted by fisheries. Consequently, the reduction of the fishing impact concerns primarily the shelf. In contrast, the initial state of the deep-water populations in the area was considered pristine at the beginning of the decade 2000 (e.g. D’ Onghia et al., 2003; 2005; Politou et al., 2003; 2008; Mytilineou et al., 2001; 2003; 2006) seems to move towards exploitation last years. The results from the population metrics analysis for the period 1998-2008, taking into account the significance level 0.1 (Table 1), showed that most of populations were stationary in their abundance, mean length and L0.95. An important part demonstrated increasing trend in these metrics, whereas much less populations indicated decreasing trends. From the analysis per depth stratum, it was found that the increasing trend in the length-based metrics referred to the shelf populations, whereas the decreasing to the slope populations. This may indicate that the reduction of the fishing impact, found by the analysis of the DCNP data, and which is exercised mainly in the shallow waters, has a positive effect at least for some populations of the shelf. In contrary, the increase of fishing activity in the deep waters may be the reason of mean length and L0.95 decreasing trend in some populations of the slope (among them N. norvegicus, R. clavata, P. heterocarpus, M. merluccius, S. blainvillei). From 2004 onwards, almost all populations were characterized by a stable condition in these metrics. Z trend was equally increasing and decreasing among the examined populations, but with higher statistically significant slopes for the increasing trends for both depth strata and in total. Among the populations with increasing Z were P. longirostris and M. merluccius for the shelf populations and H. dactylopterus, S. canicula, P. bogaraveo and Lepidorhombus boscii for the slope. Combination of the results of the population metrics for the period 1998-2008 (Table 2) showed that all populations in the Eastern Ionian Sea showed changes interpreted by various reasons. From the 44 examined populations (~53% of the community total abundance or ~73% of total biomass), these changes were statistically significant in 25 (~39% of the community abundance or ~47% of biomass). The remaining 19 populations were in a stationary condition (~13% of the community in numbers or ~25% in weight). However, when depth stratum was taken into account, the number of populations with significant changes were less than the stationary ones in the shelf and much less in the slope. The most important reasons of changes were “Other”, followed by “increasing and decreasing individual growth g”. “Other” includes several or other processes not examined in the present study, like environmental factors. “Individual growth g” is environmentally and maybe more density (Walters and Post, 1993; Lorenzen and Enberg, 2002; Lorenzen, 2008) dependent. Total mortality Z and recruitment R, factors affecting density, were of low importance in our results. In addition, all the populations related with the “increasing individual growth g” presented stationary abundance. These facts let us suppose that the environment affects more than the fisheries the “individual growth g” in the studied populations. In the Eastern Ionian Sea, after 1998, a thermohaline circulation reversal and an increase in water column temperature and productivity have been reported by Souvermezoglou & Krasakopoulou (2005). Such factors could affect biological features of populations like individual growth (g), recruitment and length at first maturity. In fact, this was observed in the squid Illex coindetii, which showed an increasing trend in the frequency of occurrence, the density index, the mean length and the length at first maturity mainly between 1999 and 2006, changes well linked to environmental conditions of this time period (Lefkaditou et al., 2000). In agreement with these, our results support the hypothesis that most changes in populations in the Eastern Ionian Sea during 1998- 2008 are related more to environmental factors (such as water warming and productivity), than the decrease of fisheries impact. During the decade 1998-2008 in both depth strata and in total, “decreasing Z” was related with many populations, but it was statistically significant in only few of them. In opposite, “increasing Z” was not significant for any population. This is consistent with the decrease of fishing impact reducing Z in some populations. However, the status changed in the last five years, when most populations were stationary. In the analysis of the total community, “decreasing Z” appeared only for the blackspot red seabream (P. bogaraveo) and “increasing Z” started to appear as significant process.

23 Our findings suggest that some species are more vulnerable to fishery than others. N. norvegicus, S. blainville, R. clavata are some of them indicating decreasing trends in their abundance and/or length-metrics and/or increasing trend in total mortality. N. norvegicus in our study indicated a statistically significant decreasing trend in abundance. Although the trends in mean length and L0.95 were not significant, they could be considered decreasing as shown by the slope value (Fig. 2, 3, 4). In this case, “increasing Z” explains the changes in this population, whereas if significance of the trend is taken into account, “Other” is the main reason. Spawning stock biomass, L0.05, L0.25 and L0.75 of N. norvegicus were also found to decrease (results not shown here). We could suggest that both processes, environment and fisheries impact, are related with N. norvegicus dynamics. This benthic crustacean with burrowing behaviour, one of the most commercially important targets in fisheries, is easily accessible particularly by trawl fishery, which catches it and destroys its habitat (e.g. Mytilineou et al., 1998a; Smith and Papadopoulou, 2003). Moreover, it is a long-living species with a low growth rate (e.g. Tully et al., 1989; Mytilineou et al., 1998b). Burrowing and low growth rate make N. norvegicus a vulnerable species. S. blainville was found with increasing abundance, but with decreasing mean length and L0.95, although not statistically significant. Decreasing trend was also found in L0.25 and L0.75 (not shown here). These changes could be explained by “Other” processes and fisheries impact. This ovoviviparous chondrichthyan, distributed in slope, is particularly vulnerable because of its k-selection life strategy, its low growth rate, low fecundity and large size at first maturity (Sion et al., 2003). R. clavata showed also similar changes. As most chondrichthyan species, it is also chatacterized by a k-selection strategy, long-living and low growth rate and fecundity (Stevens et al., 2000) and therefore it could be considered a vulnerable species. Environmental and fisheries factors have been proposed to be the reasons of disappearance of S. blainville and R. clavata from the Italian Ionian Sea (D’ Onghia et al., 2003). Similar results were observed for M. merluccius during 1998-2008, although they became worse after 2004 with signs of increasing Z. Hake is not considered a vulnerable species, but since it is one of the main target species, in shelf and slope, particular management to prevent undesirable effects in the future is necessary. Finally, P. bogaraveo is also a vulnerable species of the slope, because the juveniles encounter the shallow waters and they are easily caught by coastal (Mytilineou and Papaconstantinou, 1995) and recreational fishery (Priol, 1932). As a protandrous hermaphrodite species, female P. bogaraveo are more vulnerable to fishery and their size at first maturity is very large. Petrakis et al. (2001) and Chilari et al. (2006) mentioned that after an intensive exploitation of the species in the Eastern Ionian Sea in 90’s, the stock as well as gillnet and longline fishery related with this resource collapsed at the beginning of 2000. Interviews of the fishermen in the studied area during 2010 confirmed this information as well as the recovering of the stock (unpublished data). This coincides with our results for the red blackspot seabream, which showed an increasing trend in abundance, mean length and L0.95, although the last two metrics were not statistically significant. The combination of the metrics indicated “Other” processes as reason of changes in the status of the blackspot seabream population; “deceasing Z” was also involved when significance of the trend was not taken into account. A. foliacea and H. dactylopterus, considered as vulnerable species (e.g. D’ Onghia et al., 1994; 1998; 2005; Mytilineou et al., 2001; 2003; 2006; Politou et al., 2003; 2004; Kapiris, 2004; Carlucci et al., 2007), revealed a stable status in the studied area across the years. Changes in the community metrics in the Eastern Ionian Sea during 1998-2008, are not easily explained. The abundance and biomass increased, while all other metrics remained stationary. This corresponds to the process “Other” (according to Annex III, i.e. several or other processes not examined here). Environmental factors, maybe combined with changes in Z, recruitment and/or individual growth of populations, could interpret the status of the community. The analysis of the population metrics revealed the intervention of all these processes, dominated by the environmental ones. Therefore the status of the community is consistent, as expected, with the status of most populations examined. This was also observed from the analysis of the last five years, when the status of the community remained stationary, as detected for most populations. The stationary proportion of large fish and the lack of changes in mean length and l0.95 could not support the suggestion of a lower fisheries impact, unless this affect all the size spectrum of the populations and the community by increasing them in numbers and consequently in weight. This could be then related to the reduced fishing effort as well as to the increase of the trawl codend mesh size, which let small fish to escape. Nevertheless, Z, expressing in a way fishing impact, was not found to be very important in the population changes. We could suggest therefore, that the results from the community metrics reinforce the hypothesis that environmental factors combined with other processes are related with the changes

24 in the populations and the community status in the Eastern Ionian Sea during 1998-2008. These changes seemed to occur more at the beginning of the decade, since no changes were detected for most populations and the community from 2004 onwards. Changes in the populations and community of the Eastern Ionian Sea have been reported by Politou (2007) and Rochet et al., (2007; 2010), related more to recruitment than Z (Rochet et al., 2007). Politou (2007) used the MEDITS data from 1994 to 2004, whereas Rochet et al. (2007, 2010) used the surveys from 1994 to 2006. We believe that 1994-1997 data should not be included in such an analysis, since they do not have comparable methodology with the following years. Our study was based on data from 1998 to 2008 and on a greater number of populations. However, the gaps in 2002 and 2007 made our work difficult in some cases (particularly for Z). Certainly, a longer time series is needed to arrive to safer conclusions. The analysis of the shelf and slope community metrics for abundance, biomass, mean length and mean individual weight and proportion of large individuals showed similar results with those for the whole community. The stable status of the proportion in numbers of the non-commercial species, although their proportion decreased in weight, particularly for the slope community (Fig. 11B), could be related to the greater codend mesh size applied the last decade. Because of this, small species may escape, maintaining the number of individuals stationary, although fishing impacts the larger and heavier individuals that frequent the deeper waters. The decrease of larger individuals was proved by the analysis of the mean length and L0.95 in the slope. This could be supported by the fact that of trawling began as intensive fishing activity in the deep waters of the Eastern Ionian by Italian fishers after 2003 and by an increasing use of gill nets during and particularly at the end of 90’s. In summary, in the Eastern Ionian Sea the community showed changes related to environment and to a lesser extend to fisheries processes. The community status could be considered improving compared to the initial situation in the shelf, but this improvement stopped after 2004. On the other hand, slope seems more impacted and management should take into account this condition. Reducing the fishing effort or implementation of new technical measures may help more the shelf community, but this is doubtful for the slope community. No particular management for deep waters exists in the area. Information concerning landings, fleet and effort related with deep-water resources is lacking. This requirement could be included in the DCNP regulations irrespectively of the amount of deep landings, but more because of the vulnerability of the species in this environment. In fact, deep-water species exhibit clear “k-selected” life-history characteristics: extreme longevity, late age at maturity, slow growth and low fecundity, and as a consequence they are notably unproductive, highly vulnerable to overfishing and sensitive to any change that occurs over a few generations (Koslow et al., 2000). At this point, Italian vessels activity (effort, landings etc), which constitute the main trawl fishing in the deep-waters of the Eastern Ionian Sea (international waters but not far from the Greek coast) should be recorded, controlled and managed. As a second step, commercial and vulnerable deep-water species could be selected for monitoring and studying their biological traits and fisheries. Vulnerable species, like N. norvegicus, A. foliacea, H. dactylopterus, S. blainiville, R. clavata, M. merluccius and P. bogaraveo should be included in this list. Monitoring should focus on them to identify the most important features for their conservation (e.g. essential habitats, nursery, feeding and spawning grounds, maturity, recruitment, behavior, selectivity etc) and to continuously detect their dynamics. The assessment of these stocks could lead to the integrated scheme for their sustainable management. In the mean time, precautionary approach to management could include new technical measures for the minimum landing size of these species (smaller than size at first maturity in most of cases - Mytilineou and Machias, 2007), increase of trawl codend and gillnet mesh size (Mytilineou and Machias, 2007 and references therein) and closure of areas or periods to fishing (Carlucci et al., 2007). Determination of MPAs could also help in the conservation and protection of this fragile ecosystem.

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28 Rochet, M.-J., Trenkel, V.M., Forest, A., Lorance P. and Mesnil B., 2007. How could indicators be used in an ecosystem approach to fisheries management? ICES CM 2007/: 05. Rochet, M.-J., Trenkel, V.M., Carpentier, A., Coppin, F., Gil de Sola, L., Léauté, J.-P., Mahé, J.C., Maiorano, P., Mannini, A., Murenu, M., Piet, G., Politou, C.-Y., Reale, R., Spedicato., M.-T., Tserpes, G. and J. Bertrand, 2010. Do changes in environment and fishing pressures impact marine communities ? An empirical assessment. J. Appl. Ecology, 47: 741-750. Shin, Y-J., Rochet, M-J., Jennings, S., Field, J., and H. Gislason, 2005. Using size-based indicators to evaluate the ecosystem effects of fishing. ICES Journal of Marine Science, 62: 384-396. Sion, L., D’ Onghia G., Matarrese A. and Ch., Mytilineou, 2003. First data on distribution and biology of Squalus blainvillei (Risso, 1826) from the eastern Mediterranean Sea. J. Northw. Atl. Fish. Sci., vol. 31 : 431-440. Smith, C. J. and K.-N. Papadopoulou, 2003. Burrow density and stock size fluctuations of Nephrops norvegicus in a semi-enclosed bay ICES Journal of Marine Science, 60: 798–805. Souvermezoglou, A. and E. Krasakopoulou, 2005. Nutrients in deep seas. In: State of the Hellenic Marine environment. Papathanasiou & Zenetos (eds), pp. 137-145. HCMR Publ., Athens. Stergiou, K.I., Christou, E.D., Georgopoulos, D., Zenetos, A. and A. Souvermezoglou, 1997. The Hellenic Seas: physics, chemistry, biology, and fisheries. Oceanography and Marine Biology Annual Review, 35: 415-538. Stevens, J. D., R. Bonfil, N.K. Dulvy and P.A.Walker, 2000. The effects on fishing on sharks, rays and chimaeras (chondrichthyans), and the implications for marine ecosystem. ICES Journal of marine Science, 54: 774-786. Trenkel, V.M. and M.-J. Rochet, 2003. Performance of indicators derived of abundance estimates for detecting the impact of fishing on a fish community. Can. J. Fish. Aquat. Sci., 60: 67-85. Trenkel, V.M. and M.-J. Rochet, 2005. How to interpret indicators results. MEDITS working group, Nantes, March 2005. http://www.ifremer.fr/medits_indices. Trippel, E.A., 1995. Age at maturity as a stress indicator in fisheries Bioscience, 45:759-771. Tserpes G., C. Darby, A. Di Natale, P. Peristeraki, and A. Mangano, 2003. Assessment of the Mediterranean swordfish stock based on Greek and Italian fisheries data. Collective volume of scientific papers ICCAT, 55(1): 94-106. Vassilopoulou, V., Maravelias, C.D., Haralabous, J., Katsanevakis, S. and D. Damalas, 2009. Review of the bibliography referring to using indicators as advisory tools for fisheries management in the Mediterranean Sea. Proceedings of the 9th Panhellenic Symposium of Oceanography and Fisheries, Patra, May 13-16, 946-951. Walters, C. and J. R. Post. 1993. Density-dependent growth and competitive asymmetries in size- structured fish populations: a theoretical model and recommendations for fi eld experiments. Trans. Am. Fish. Soc., 122: 34-45.

29 ANNEX I. List of the selected species, caught during MEDITS 1998-2008 surveys, for the analysis in the whole area. •: species selected for the length-based metrics in the whole area, *: species selected for the analysis in the shelf (10-200 m), ■: species selected for the analysis in the slope: (200-800m). Group Species_Code Species name Shelf Slope Length ARGESPY Argentina sphyraena * ■ Osteichthyes ARNOLAT Arnoglossus laterna * ASPICUC Aspitrigla cuculus * ■ • BOOPBOO Boops boops * • CAPOAPE Capros aper * ■ CLORAGA Chlorophthalmus agassizii ■ CITHMAC Citharus linguatula (macrolepidotus) * • COELCOE Coelorhynchus coelorhynchus ■ DIPLANN Diplodus annularis * GADIARG Gadiculus argenteus ■ GOBISPP Gobius spp * HELIDAC Helicolenus dactylopterus * ■ • HOPLMED Hoplostethus mediterraneus ■ HYMEITA Hymenocephalus italicus ■ LEPICAU Lepidopus caudatus * ■ LEPMBOS Lepidorhombus boscii ■ • LEPTCAV Lepidotrigla cavillone * LEPTDIE Lepidotrigla dieuzeidei * LOPHBUD Lophius budegassa * ■ • MACOSCO Macrorhamphosus scolopax * MERLMER Merluccius merluccius * ■ • MICMPOU Micromesistius poutassou ■ • MULLBAR Mullus barbatus * • NEZUSCL Nezumia sclerorhynchus ■ PAGEACA Pagellus acarne * • PAGEBOG Pagellus bogaraveo * ■ • PAGEERY Pagellus erythrinus * • PERICAT Peristedion cataphractum ■ PHYIBLE Phycis blennoides ■ • SERAHEP Serranus hepatus * SOLEVUL Solea vulgaris * • SPICFLE Spicara flexuosa * • SPICSMA Spicara smaris * • TRACMED Trachurus mediterraneus * • TRACTRA Trachurus trachurus * ■ • TRIGLYR Trigla lyra ■ TRIPLAS Trigloporus lastoviza * • TRISCAP Trisopterus minutus capelanus * ■ • ZEUSFAB Zeus faber * • GALUMEL Galeus melastomus ■ • Chondrichthyes RAJACLA Raja clavata * ■ • RAJAMIR Raja miraletus * RAJAOXY Raja oxyrinchus ■ SCYOCAN Scyliorhinus canicula ■ • SQUABLA Squalus blainvillei ■ • TORPMAR Torpedo marmorata * ELEDCIR Eledone cirrhosa * ■ • Cephalopoda ELEDMOS Eledone moschata * • ILLECOI Illex coindetii * ■ • LOLIFOR Loligo forbesi * ■ LOLIVUL Loligo vulgaris * • OCTOVUL Octopus vulgaris * • ROSSMAC Rossia macrosoma ■ SEPIORB Sepia orbignyana * TODIEBL Todaropsis eblanae * ■ ALPHGLA Alpheus glaber * Crustacea ARISFOL Aristaeomorpha foliacea (only for slope analysis) ■ • ARITANT Aristeus antennatus ■ DORILAN Dorippe lanata * • GONERHO Goneplax rhomboides (= angulata) * • MCPIDEP Liocarcinus (Macropipus) depurator * • MACRLON Macropodia longipes * • MUNIIRI Munida iris ■ • NEPRNOR Nephrops norvegicus ■ • PAPELON Parapenaeus longirostris * ■ • PLESACA Plesionika acanthonotus ■ • PLESANT Plesionika antigai ■ • PLESEDW Plesionika edwardsii ■ • PLESGIG Plesionika gigliolii ■ • PLESHET Plesionika heterocarpus ■ • PLESMAR Plesionika martia ■ •

30 Annex II. Definition of population metrics used in the analysis. Population metrics Definition Required data Estimator

lnNi Abundance Catch n j n j index haul k Ni =∑Ni, j =∑Aj∑yk, j ∑ak, j j j k =1 k =1 for stratum j yk,j lnNi =ln(Ni)−Var(lnNi)/2 species i Swept area ak,j n 2 Stratum area Aj  j  2  y  n j ∑ k, j Aj  yk, j  Var(N ) = − k =1 i ∑ ∑ n j  n −1 = a j j k 1  k, j   ∑ ak, j   k =1 

Var(Ni )  i Var(lnN )= ln 2 +1  Ni  r Population i Ni (t) log(Nˆ (t)) = δ + r t + ω Growth rate i i i i,t 2 ωi,t ~ N(0,σ i ) L Average Catch per L bari length in length ∑ yl,i l L l =1 population L = with yi = yl,i class yl,i bari ∑ yi l=1

L  2   ∑ yl,i l   l=1 2  Var[L ]= − L y bari  y bari  i  i   

95% Catch l Lq,i q percentile per y q = 0.95 ∑ l,i l=1 of the length Lq,i = lq,i = q population y class yl,i i length distribution q(1− q) Var[Lq,i ]= y (y y )2 i lq ,i i Total Zi Nl,i (t) , t 0i ki , mortality  a _ max−1 a _ max  L∞ i = −  −  Z(t) log ∑ Ni (t 1) / ∑ Ni (t)  i=a _ min i=a _ min+1  Var(Z) by bootstrap

L50 length at 1) Fitting a logistic model for the probability of Nm,i (t) which 50% being mature pl as a function of body length (l), year (t): of the population i  p  (p )= log l,t  = µ + a + b l + ε are mature, l,t   t t 1− pl,t  year t. 2) Estimating L50 from the parameters:

(0.5)− µ − at L50 = bt

31 Annex III. Definition of community metrics used in the analysis. Community Definition Required input Estimator Metrics Ln(N) Ln(community Catch haul k n j n n j total abundance N) N = Ni, j = Aj yikj ak, j stratum j yk,j ∑ ∑ ∑∑ ∑ j j k =1 i k =1 Swept area ak,j 2 n j n Stratum area Aj    i,kj i,,  2 n j ∑y ∑∑y ( )= Aj  i − k =1 i  Var N ∑ ∑ n j j n j −1 k =1 ak, j   ∑ak, j   k =1 

ln(W) Ln(community W (t) Wˆ (t) = W (t) total biomass W) ∑ i i variance by bootstrap W Community mean W (t) , N(t) Wˆ (t) individual weight W (t) = Nˆ (t) variance by bootstrap

lbar Community Catch L average length per y l ∑ l L length l=1 lbar = with y = ∑ yl class yl y l=1 L  2   ∑ yl l  Var[l ]=  l=1 − L 2  y bar  y bar     

Proportion of large catch per yl (t) plarge (t) = ∑ yl (t) y(t) > individuals plarge length class l l lbig larger than lbig = y(t) total catch p (1− p ) Var[p ]= large large 15, 20, 25, 30 cm (measured species) large y(t) Large size threshold lbig

ANNEX IV. Combinations of time trends in three population metrics suggesting major influence of changes in individual growth (g), recruitment (R), or adult total mortality (Z) for population (species or taxon) i.  : significant metric trend in direction of arrow, ↔: no detectable trend. (): increase (decrease) in process or consistently high pressure enhancing (impeding) this process. Other: several or other processes not examined here. Population metrics: L0.95,i : 95% length percentile, lnNi: log- transformed population abundance and L : mean length (Rochet et al., 2005). bari

lnNi  lnNi ↔ lnNi  L  L ↔ L  L  L ↔ L  L  L ↔ L  bari bari bari bari bari bari bari bari bari

L0.95,i  Z  Z  Other g  g  Other R  R  Other       L0.95,i ↔ Z Other R g No change g R Other Z L0.95,i  Other R  R  Other g  g  Other Z  Z 

CHAPTER 5

PORTUGUESE BENTHIC FAUNA - THE FIRST LEVEL OF AGGREGATION

PORTUGUESE BENTHIC FAUNA - THE FIRST LEVEL OF AGGREGATION Introduction Material and methods 2.3. Results Monthly variations in community and species indicator variables Depth stratum: 200-400 m (Figs. 8 a to 14 a) Depth stratum: 400-600 m (Figs 8 b to 14 b) Depth stratum: 600-800 m (Figs 8 c to 14 c) Depth stratum: over 800 m (Figs 8 d to 14 d) Community and species indicator variables between different years Depth stratum: 200-400 m (Figs. 15 a to 21 a) Depth stratum: 400-600 m (Figs. 15 b to 21 b) Depth stratum: 600-800 m (Figs. 15 c to 21 c) Depth stratum: over 800 m (Figs. 15 d to 21 d) Indices of abundance, biomass and diversity along the latitudinal gradient Depth stratum: 200-400 m (Figs. 15 a to 21 a) Depth stratum: 400-600 m (Figs. 15 b to 21 b) Depth stratum: 600-800 m (Figs. 15 c to 21 c) Depth stratum: over 800 m (Figs. 15 d to 21 d) Indices of abundance, biomass and diversity along the longitudinal gradient Depth stratum: 200-400 m (Figs. 8 a to 14 a) Depth stratum: 400-600 m (Figs. 8 b to 14 b) Depth stratum: 600-800 m (Figs. 8 c to 14 c) Depth stratum: over 800 m (Figs. 8 d to 14 d) Study of benthic fauna - multivariate statistical approach Discussion

PORTUGUESE BENTHIC FAUNA - THE FIRST LEVEL OF AGGREGATION

Introduction

The IPIMAR-INIAP deep-water trawl surveys carried out in 1994-1998 sampled the deep-water populations and community. Species composition varied between fishing hauls. Initially species diversity, intra- and inter- fishing haul, constitute the basic information for community-based bioecological studies. At more advanced stages such studies aim to model the underlying community structure, as well as the spatio-temporal dynamics. Spatio and temporal approaches in community ecology have a higher interest rather than conceptual viewpoint or methodology approaches (Osmond, Bjorkman, and Anderson, 1980). The present work aims to compile and analyse IPIMAR-INIAP deep- water survey data in order to identify the spatio-temporal underlying structure and dynamics of the bentho-pelagic community off Portugal. As the sampling was carried out in different years, in different times of the year and in different geographic areas and depth, a high level of variance is expected for the whole data set. However these factors were used as explanatory variables to assess their contribution to the whole variance and to address the following questions: - Do community and species indicators vary seasonally? - Do they change from year to year? - What are the latitudinal, longitudinal and depth effects on the populations an community indicators et the scale of the Portuguese continental slope

Even if some factors prove to have an important role in the behaviour of some of the population aggregation measures used, the whole of the available information can be studied in order to obtain a picture of the underlying organization of the benthic fauna. Though it is a priori known that the true underlying structure and its dynamics will always remain unknown, the approximation attained can be evaluated in terms of its robustness and consistency, and by this approach recognise that picture as the most probable

representation of reality. Indirect strategies that take into consideration simple basic principles are commonly used to perform such an evaluation.

Material and methods

From 1994 through 1998 eight surveys were carried out along the Portuguese continental coast (May 1994; August 1994; January 1995; June 1995, August 1998, December 1995, August 1997 and August 1998). The whole set of surveys comprises a total of 516 fishing hauls. For each fishing haul date, the shooting and hauling times (UTM), latitude, longitude (decimal degrees) and depth (m) were recorded. The whole catch was identified to species level and the number and weight per species was recorded. In addition, for some species length composition, individual weight, sex and maturity stage were also recorded. Aggregation measures, such as community and species indicators, were computed for each haul. Community indicators measures include total CPUE in abundance and biomass, which correspond, respectively, to the sum of numbers and weights from a fairly large set of variables, the species, caught per hour at each observational unit, the fishing haul. Species richness and equitability were also determined. Species richness is a measure directly related to the total number of species present while equitability expresses how evenly the individuals are distributed among different species within the community: - Shannon-Wiener diversity index H´

H´  pi ln(pi ) i th where pi is the proportion of the total count (or weight) arising from the i species; - Species richness given simply as the total number of species present (S); - Margalef´richness index (d) which includes a weighting of the number of species present by the total number of individuals caught (N) according to S 1 the following expression d  ; lnN - Pielou´s evenness index (equitability) J

H´ Equitability  J  obs H´max

where H´max is the maximum possible diversity (=ln(S)).

An exploratory data analysis of CPUE in biomass and CPUE in abundance estimates is initially followed. This analysis includes histograms and normal probability plots of estimates from each variable and through which of their empirical one-dimensional distributions are established. Scatterplot matrices constructed for each CPUE variable also include data from other variables, such as year, month, latitude, longitude and depth. Through this strategy it is possible to investigate the existence of relationships between pairs of variables. In this analysis the information from fishing hauls presenting extremely high values of CPUE variables are excluded. This will avoid their overweighing influence, and therefore arrive at a clearer picture on the influence of single factors and on two-factor interactions. As expectedly, depth proved to be an important factor explaining a great proportion of the variance several depth intervals were designed to further investigate the effect of the other factors within strata where the depth effect was minor. Each of them is designated as a depth stratum. Their limits are visually determined through the analysis of scatterplots of CPUE abundance versus depth stratum. The estimates of CPUE variables are then grouped accordingly to those strata. Boxplots of CPUE in abundance estimates by depth stratum are constructed, which provides further information about the distribution and spread of the data as well as their skewness. Each boxplot shows the limit of the middle half of the data (the line inside the box represents the median); the whiskers are drawn to the nearest value not beyond a standard span from the quartiles. In this case, the span corresponds to 1.5*(Inter-Quartile Range). The points beyond the span, possible outliers, are drawn individually. After the exploratory data analysis the study proceeds to gather information for answering the questions raised. Some difficulties are immediately identified since sampling effort between surveys is highly unbalanced. To partially overcome such deficiencies, subsets of data are adopted for addressing each question. Subsets are established in such way as

to eliminate the introduction of additional sources of variability other than the main factors under study. Coplots allow the simultaneous representation of two different variables against each other conditioned by the ranges of values of a third and/or a fourth variable. With this graphical approach it is possible to get information on the behaviour of the variable conditionally to other variables. To get a first insight on the distribution of both community indicator variables over the different times of the year boxplots of each CPUE variables versus month are constructed for each defined depth strata. A more restricted analysis of both community and species indicator variables is then carried out by considering the data available for the Algarve region using the four surveys carried out in 1995. Excluding the August survey, the surveyed area of the remaining ones is mainly performed in Algarve. Consequently and in order to avoid the confounding effect due to latitude, the study is carried on by restricting the latitude range with the 37º N parallel as the northern limit. In addition since no differences in the distribution of the variables between 1994 and 1995 are detected, the data collected in August 1994 in the Algarve region is used instead of that obtained in 1995 which did not include that region. Coplots of community and species variables versus month conditioned by similar pre-established series of longitude and latitude are constructed for the following depth strata: a) 200 to 400 m; b) 400 to 600 m; c) 600 to 800 m and d) over 800 m. In the last depth stratum the number of fishing hauls available in each latitude*longitude series is reduced. Because of this the analysis of temporal behaviour of variables at this depth stratum is made by only conditioning the variables to non-overlapped longitude series. Longitude ranges are the same as those used for the other depth strata. The study of the behaviour of community and species indicator variables among the different years is done based on data from August surveys because it is the month in which more surveys were performed over the range of years available. Coplots of each community and species indicator variables versus year, conditioned by pre-established series of latitude and longitude are constructed for each depth stratum.

To further understand the structure of the benthic fauna, particularly in terms of the underlying aggregation pattern, a multivariate statistical approach is used. In this analysis the relative importance of species, in number and in weight, as well as the fishing haul characteristics are considered. So in this context each fishing haul is considered as an observational unit and the whole set of fishing hauls is thus used to define the structure. To conveniently perform such a multivariate approach and since there are too many variables, especially a high number of species and a relative low number of fishing hauls, the number of variables needs to be initially reduced. PCA is used as a reduction tool and is applied to transformed species datasets. One of these datasets comprises the log abundance values by species in all fishing hauls and the other the log biomass by species. A pre-selected level of 80% of explained variance is adopted and the corresponding number of components is thereby identified. As result of PCA adjustment and of the restrictions imposed, scores obtained for the first 18 components and for the first 15 components by fishing haul provide the summary inputs of species abundance and species biomass respectively. To avoid a differential influence of variables due to scale discrepancies among them, simple transformations are applied. As a result of those transformations all the variables have similar magnitudes and comparable ranges. Hartigan's K-Means Clustering Method is applied to the final data set and used to establish the clusters. The results from a preliminary hierarchical clustering, using the Euclidean distance as a dissimilarity measure, are applied as initial guesses for the cluster centres. To decide on the number of clusters Hartigan's (1975) rough rule of thumb is followed: If k is the result of k-means with k groups and kplus1 is the result with k+1 groups, then it is justifiable to add the extra group when

 SSk   1 * (n  k 1)  SSk 1  is greater than 10. where:

SSk - sum of squares of cluster when k groups are considered;

SSk+1 - sum of squares of clusters when k+1 groups are considered; n - number of observations, i.e. number of fishing hauls.

The defined clusters are further studied taking into consideration the initial data information. At the first stage, the absolute frequencies of fishing hauls by month and by year in each cluster are tabulated. Boxplots of fishing hauls characteristics such as depth, latitude and longitude versus cluster are constructed to crudely evaluate the contribution of those factors in cluster differentiation. The role of species in determining the dissimilarity between different clusters is also analysed, using of the analytical procedure proposed by Clarke (1993). According to this, the average dissimilarity δ between all pairs of inter-cluster fishing hauls is computed and then the average is broken into the separate

th contribution from each species to δi . The contribution of the i species for the

Bray-Curtis dissimilarity between fishing hauls j and k, δ jk(i) is defined as

100  yij  yik δ jk(i)  yij  yik i1 where: th yij - score for the i species at the j cluster; th yik - score for the i species at the k cluster.

δ jk(i) is then averaged over all the pairs (j, k) with j in the first and k in the

th second cluster to give an average contribution δi from the i species to the overall dissimilarity between clusters. Standard deviation of δi was used as a measure to evaluate how consistently a species contributes to δi across all δ pairs of clusters. Ratio i values higher than 1.96 were considered s.d.(δi ) significant. According to this criterion high values of that ratio are indicative that the ith species not only contributes to the dissimilarity between clusters j and k but it also does so consistently in all fishing haul inter-comparisons between the two clusters. In this sense significant species can be considered as discriminants of cluster pairs. The robustness of the initial clusters is investigated following a simple strategy that consists of forcing higher levels of data aggregation, which implies

further reductions in the initial number of clusters. To partially circumvent the problem of subjectivity involved in cluster selection and to decide on the number of cluster at a higher level of aggregation both Hartigan’s (1975) rough rule of thumb and results from the test proposed by Beale (1969 in Everitt, 1993) were considered. Under Beale's test, the null hypothesis to be tested is that a subdivision of the data into g2 clusters gives no improvement over the subdivision into a smaller number g1. The test statistic is defined as:

Rg1  Rg 2 R F(g ,g )  g 2 1 2 2  n  g  g  p   1  2  1  n  g  g    2  1   where:

2 Rg  n  g Sg ;

2 Sg is the mean square deviation from cluster centers in the sample; p is the number of variables.

Under the null hypothesis this test statistic follows a F-Snedecor distribution with p(g2-g1) and p(n-g2) degrees of freedom, in the numerator and denominator respectively. A significant result means that subdivision into g2 is significantly better than subdivision into the smaller number of cluster g1.

2.3. Results

One-dimensional distribution of CPUE in abundance based on estimates available for 516 fishing hauls reveals the existence of extremely high values (Fig 1 a) over 60,000 individuals, which correspond to two shallow fishing hauls. Excluding these values, the distribution of CPUE values is asymmetric (Fig. 1 b) with numerous haul catching 200-1200 individuals per hour and decreasing number of hauls up to 6000 individuals per hour.

Extreme CPUE in abundance values are excluded Frequency Frequency 0100200300 0 102030405060

0 20000 40000 60000 0 1000 2000 3000 4000 5000 6000 CPUE in abundance CPUE in abundance

a) b)

Figure 1 - Histograms of CPUE in abundance using the whole data set a) and by using a subset in which extremely high values of this variable were excluded b).

Applying the same procedure to CPUE in biomass also detects extremely high values of this variable, which represent 5% of the total number of observations (Fig. 2 a). Most of these high estimates are derived from fishing hauls at depths deeper than 600 m (64%) and are responsible for the long right tail in the empirical distribution of CPUE in biomass (Fig. 2 b). The CPUE in weight is reasonably adjusted by a log normal distribution (Fig. 3).

Extreme CPUE in biomass values are excluded Frequency Frequency 0 100 200 300 400 0 102030405060

0 2000 4000 6000 0 100 200 300 400 500 CPUE in biomass CPUE in biomass

a) b)

Figure 2 - Histograms of CPUE in biomass using the whole data set a) and by using a subset in which extremely high values of this variable are excluded b)

Logarithm of CPUE in biomass vs standard normal quantiles log (CPUE in biomass) 2468

-3 -2 -1 0 1 2 3 Quantiles of Standard Normal

Figure 3 - CPUE in biomass values versus lognormal quantiles, using the whole data set (extreme values excluded).

CPUE in abundance and CPUE in biomass have similar distributions and both are characterised by a large dispersion. In the scatterplots matrices built to relate CPUE in abundance and other factors it is difficult to individualise patterns (Fig.4). This suggests that higher order interactions may be more adequate. Despite this fact, it can be observed that: - CPUE in abundance estimates are less variable at deeper depths; - CPUE in abundance estimates are lower and less variable at the more northern latitudes; - the ranges of CPUE in abundance estimates are different according to longitude; - the variability on CPUE in abundance estimates is different among months of the year; - ranges of CPUE in abundance estimates are different among years; with an apparent decreasing trend in magnitude from earlier to more recent years; - there are some extremely high estimates of CPUE in abundance.

24681012 -10.0 -9.0 -8.0 0 10000 30000

Year 94 95 96 97 98

Month 2468 12

Lat 37 39 41

Long -10.0 -8.5

Depth 200 600

CPUE in abundance 0 20000 94 95 96 97 98 37 38 39 40 41 42 200 400 600 800

Figure 4 – Scatterplots matrices for CPUE in abundance and for fishing haul variables: year, month, latitude (on a decimal scale), longitude (on a negative decimal scale, since the fishing hauls were held west of Greenwich) and depth of the bottom (in meters) hauls with CPUE in abundance >=60000 excluded.

Adecreasing trend in the magnitude of CPUE in abundance from 1994 to 1998 is detected (Fig.4). However, depth, latitude and longitude also influence the CPUE and the sampling was shifted towards greater depth in 1997-98 compared to 1994-95. A decreasing trend in CPUE in abundance with depth is evident (Fig. 5).

Figure 5 – Boxplots of CPUE in abundance by depth strata for the whole time period under analysis; values of CPUE over 60000 are CPUE in abundance excluded. 0 10000 20000 30000

0+ thru 200 200+ thru 400 400+ thru 600 600+ thru 800 800+ thru 1000

depth stratum(m)

Similar results are obtained from scatterplot matrices of the CPUE in biomass (Fig. 6).

2 4 6 8 10 12 -10.0 -9.0 -8.0 0 100 200 300 400

Year 94 95 96 97 98

Month 2468 12

Lat 37 39 41

Long -10.0 -8.5

Depth 200 600

CPUE in biomass 0 100 300 94 95 96 97 98 37 38 39 40 41 42 200 400 600 800 Figure 6 – Scatterplots matrices for CPUE in biomass and for fishing haul variables: year, month, latitude (on a decimal scale), longitude (on a negative decimal scale, since the fishing hauls were held west of Greenwich) and depth of the bottom (in meters). Fishing hauls with CPUE in biomass values higher than 470 Kg are excluded.

The distribution of CPUE in biomass in relation to explanatory variables can be summarized as: - at shallow fishing hauls the values of CPUE in biomass do not attain magnitude levels as high as those registered at deep areas; - the variability of CPUE in biomass is higher at more western longitude; - CPUE in biomass decreases from south to North; - ranges of CPUE in biomass are similar between years; - there are fluctuations of CPUE in biomass estimates between months.

These results suggest the existence of seasonal changes in the benthic fauna. At depths greater than 400-800 m, CPUE in abundance estimates and their variability in January and in December are higher than in June and in August (Fig. 7 a). For that depth range a different pattern for CPUE in biomass estimates is observed between months (Fig. 7 b). At depth strata 400-600 and 600-800m, CPUE in biomass median estimates in December are higher than at other months.

200 – 400m 400 - 600m 200 – 400m 400 - 600m

CPUE in WeightCPUE in CPUE in Number

0 20000 40000 60000 0 2000 4000 6000 0 100 200 300 400 500 600 0 200 400 600 168 16812 168 16812

600 – 800m over 800m 600 – 800m over 800m

CPUE in Weight CPUE in Number

100 200 300 400

50 100 150 200 250 200 400 600 800 1000 1200 1400 0 1000 2000 3000 4000 5000 16812 16812 16812 16812 Month Month Month Month a) b)

Figure 7 – CPUE in abundance versus month a) and CPUE in biomass versus month b) by depth stratum.

Monthly variations in community and species indicator variables

Monthly variations in community and species indicator variables are next presented by depth stratum and latitude/longitude (Figs 8 to 14).

Depth stratum: 200-400 m (Figs. 8 a to 14 a)

Relative few fishing hauls are available for this depth stratum. At the southern latitude there is only one fishing haul held in January, that presented an extremely high value of CPUE in biomass, medium magnitude levels of CPUE in abundance and of diversity in weight and by low values of diversity in number and equitability. The remaining fishing hauls from this depth stratum were made more to the north in June and August at two longitude ranges (7.0 - 8.0º W and 8.0 - 9.0 W). At 7.0 - 8.0°W, the values of CPUE variables are more homogeneous than at 8.0 - 9.0º W. Excluding the highest value of CPUE in biomass registered in June at the 8.0 - 9.0º W longitude range, the values of the two CPUE variables in August are slightly higher than in June. In those months, the diversity index in number and equitability are highly variable, particularly in August at the 8.0 - 9.0º W longitude series. In this

longitude series diversity in weight is quite heterogeneous and its lowest values are registered in August. Species richness is also highly variable. At the more eastern longitude and in June the values of richness are higher than in August while the contrary is observed for the 8.0 - 9.0º W longitude series.

Depth stratum: 400-600 m (Figs 8 b to 14 b)

At the most southern latitude there are two atypical values of CPUE in abundance: the lowest value is registered in August and the highest one in December. Excluding these two cases, June and August present high values of CPUE in abundance. Also the heterogeneity of CPUE in biomass in these two months is high. In June and in August the heterogeneity of this variable decreases and in June CPUE in biomass is higher than in December. In January, June and August the values of diversity in number, equitability and diversity in number have similar ranges and magnitudes. In December equitability is higher than in the other months but diversity in weight is lower. Species richness has its lowest values in June but no differences were observed between the remaining months. At the intermediate latitude range (36.4 - 36.7º N) the two highest values of CPUE in abundance are observed in January and in August although both are derived from fishing hauls performed in a restricted area of the Algarve region. Excluding these fishing hauls, the values of CPUE in abundance and of CPUE in biomass in January are lower than in the other months. In December the range of CPUE in abundance values is broader and their magnitude higher than in June and in August. This contrasts with the higher variability but similar magnitude levels in the values of CPUE in biomass registered in June and August in comparison to December. Despite the high levels of heterogeneity, the magnitude of diversity indexes and equitability are lower in January and December than in June and August. In August the values of richness are lower than in January, June and December, which, in turn, have similar magnitudes. At the northern latitude there is information for three different longitude ranges. At the furthermost east longitude range (7.0 - 8.0º W) there is

information for June, August and for one fishing haul held in December. In the first two months, the distributions of CPUE in abundance and CPUE in biomass values are homogeneous. In June the magnitude of CPUE in abundance is higher than in August. An opposite distribution is observed for CPUE in biomass. Excluding the extremely low values of diversity, both in number and weight and equitability detected in August, the magnitude of diversity in number and equitability is higher in August than in June. On the contrary the value of diversity in weight is higher in June. In relation to richness it is observed that in June the values are lower than in August, suggesting the occurrence of changes at the species level between these months, but which are not explained by these descriptor variables. In December CPUE variable estimates present a medium values while those of diversity indexes, equitability and richness is high. At the 8.0 – 9.0º W longitude series there is information for June and August. Between these two months the differences in the magnitude of the two CPUE variables are small; in June the values of CPUE in abundance and of CPUE in biomass are slightly higher than in August. However the ranges of diversity indexes and of equitability in June are included in the corresponding August ranges. In August the values of richness are lower than in June. For the western subset (9.0 - 9.5º W) there is only information in August. The magnitude of CPUE in abundance and diversity in weight is similar to the lower limit of their ranges at the 8º and 9º W longitude series. On the contrary the magnitude of CPUE in biomass, diversity in number and equitability are similar to the upper part of their ranges in that subset.

Depth stratum: 600-800 m (Figs 8 c to 14 c)

At the southern latitude series there is information for one fishing haul in January and several fishing hauls in June, August and December. The longitude ranges of all of these fishing hauls belong to the 7.0º and 8.0º W series. In January CPUE in abundance and CPUE in biomass estimates are the lowest. The values of diversity in number, equitability and richness are at an

intermediate level and that of diversity in weight presents the second highest value observed. In June the ranges of the two CPUE variables are wider than in August, which, in turn has a smaller magnitude. In June the values of diversity indexes, equitability and richness are also more variable than in August. In August diversity in number, equitability and richness are higher than in June but the magnitude of diversity in weight does not differ. In December the values of the two CPUE variables and of diversity in weight are more heterogeneous and higher than in June. December is also the month with the highest values of richness. In this month the values of diversity in number are highly variable, and are similar to the lower limit of the range of this variable in June. At the southern latitude series at the longitudes between 8.0 and 9.0º W there is information for one fishing haul performed in December. At this fishing haul the values registered for most of the variables differ from those observed for the same month at the previous and more eastern longitude series. Their magnitudes are, however, similar to those registered at the same longitude range but at the intermediate latitude series (36.5 - 36.6º N). For the range of latitude between 36.5º and 36.6º N there is information for two longitude ranges: 7.0 - 8.0º W and 8.0 - 9.0º W. At the most eastern subset there is information for all the months. As observed for the same latitude*longitude series but at the 400 – 600 m depth stratum there is a great heterogeneity in the two CPUE variables which is due to the occurrence of extremely high values in January, August and December. Excluding those fishing hauls, the values of those variables, of diversity index in number and of equitability in January are very heterogeneous and the magnitude of CPUE in biomass is low. On the contrary the values of diversity index in weight are more homogeneous and high in magnitude. Richness has values of medium magnitude. In June the values of the two CPUE variables present a low level of heterogeneity; CPUE in abundance has values at a medium level while the values of CPUE in biomass are high. The two diversity indices, equitability and richness estimates are also high.

In August the values of CPUE in abundance are heterogeneous, with a range wider than in June. The values of CPUE in biomass, diversity, in number and in weight, equitability and richness are quite homogeneous but their magnitude levels are lower than in June. In December the estimates of CPUE in biomass and richness are higher than at the other months. The values of CPUE in abundance, of diversity indices are rather heterogeneous and their range widths are similar to those registered in August. At longitude range 8.0 - 9.0º W there is information for January (one fishing haul only), June, August and December. January presents the lowest value of CPUE in abundance and an extremely low value of CPUE in biomass. Diversity indices and equitabilty are high and richness has a moderate magnitude level. In June the values of the two CPUE variables present a medium magnitude level but are quite heterogeneous, particularly in the case of CPUE in biomass. Diversity indices and equitability also show a high level of heterogeneity. Excluding one fishing haul with an extremely low value of diversity in weight, the remaining values of this variable vary from medium to high levels of magnitude. On the contrary, low values of diversity in number predominate. Richness is quite heterogeneous and the values show a moderate magnitude level. In August the values of CPUE in abundance have a range similar to that of June. The estimates of CPUE in biomass are homogeneous, with a magnitude similar to the lower limit of the August range. Diversity in number and equitability are quite heterogeneous while diversity in weight is more homogeneous and presents a high magnitude level. The values of richness are also homogeneous but of medium magnitude. In December the variability of the two CPUE variables and of diversity indices are high. In this month the values of richness are quite high. For the range of latitude comprised between 36.5º and 37.0º N there is information for all three longitude ranges. At the easternmost subset (7.0 - 8.0º W) information is available for June (one fishing haul) and August. In June, the values of the two CPUE variables are higher than in August. The magnitude of diversity in number, equitability and

richness match the upper limit of the ranges of those variables in August. On the contrary, diversity in weight is lower in June. In August the values of CPUE in abundance, diversity in number, equitability and richness are quite heterogeneous and two subgroups of values with different magnitudes can be identified. At longitudes between 8.0º and 9.0º W, there is only one fishing haul in January and several in June and August. In those two months CPUE variables are homogeneous and the values of CPUE in abundance are higher than in August. No differences on the magnitude of CPUE in biomass are registered between the two months. The values of diversity index in number and equitability in August are higher than in June, but diversity in weight and richness are higher in June. In January the magnitude of community and species indicator variables have an intermediate level. Finally, at the most western longitude (9.0 – 9.5º W) there is information for January, June and August. In January CPUE in abundance, diversity in number and equitability are highly variable while CPUE in biomass is more homogeneous. The values of diversity in weight and of richness are high. In June CPUE in abundance is quite variable and two subgroups with different magnitudes are evident. A similar pattern is observed for this variable in August, although its range is narrower than in June. Diversity in number and equitability are more homogeneous in August. In both June and August, the values of CPUE in biomass and of diversity in weight are heterogeneous. In June the values of CPUE in biomass are slightly higher than in August although the values of diversity in weight have similar magnitude in these two months. Richness is also higher in June than in August.

Depth stratum: over 800 m (Figs 8 d to 14 d)

At this depth stratum, data are only conditioned by longitude. In January at the westernmost longitude series (9.0 - 9.5º W) the magnitude of CPUE variables are low and the magnitude of diversity in number corresponds to the lowest registered at this depth stratum. On the contrary, at the fishing haul performed at the most eastern longitude range (7.0 - 8.0º W)

the values of CPUE variables and of diversity indices are high. No differences in richness are detected between longitude ranges. In June the variability of CPUE variables decreases from the east to the west. Excluding one fishing haul, that presents the lowest value of diversity in weight, no differences in this variable between longitude ranges are evident. Also in all fishing hauls the values of this variable are high. Diversity in number differs between longitudes; the lowest values are registered at the most western fishing hauls. In August the variability of CPUE in abundance and diversity indices is high. The values of CPUE in biomass are high and those of richness are medium. December is the month with more homogeneity in all the variables. Excluding CPUE in biomass, the other variables present high magnitude levels.

LatitudeGiven : LatitudeGiven : 36.4 36.6 36.8 36.4 36.6 36.8

2468 2468 12 0 20000 40000 60000 0 2000 4000 6000 Given : Given : Given Longitude Longitude 0 20000 40000 60000 CPUE in abundance CPUE in abundance 0 2000 4000 6000 -9.0 -8.5 -8.0 -7.5 -9.0 -8.5 -8.0 -7.5 0 20000 40000 60000 0 2000 4000 6000 2468 2468 2468 12 2468 12 Month Month

a) b)

LatitudeGiven : LongitudeGiven : 36.4 36.6 36.8 -9.0 -8.5 -8.0 -7.5

2468 12 0 1000 3000 5000

200 600 1000 1400 2 4 6 8 10 12 Given : Longitude CPUE in abundance 0 1000 3000 5000 CPUE in abundance -9.0 -8.5 -8.0 -7.5 200 600 1000 1400 0 1000 3000 5000 2468 12 2468 12 24681012 Month Month

c) d) Figure 8 - Distribution of CPUE in abundance versus month by longitude (7.5º - 9.5º W, negative values) and latitude (36.2 º - 37º N) series at 200-400m a); 400-600m b) 600-800 c) and more than 800m d) depth strata.

LatitudeGiven : LatitudeGiven : 36.4 36.6 36.8 36.4 36.6 36.8

2468 2468 12 0 100 200 300 400 500 0 100 300 500 Given : Given Given : Longitude Longitude CPUE in biomass CPUE CPUE in biomass 0 100 200 300 400 500 0 100 300 500 -9.0 -8.5 -8.0 -7.5 -9.0 -8.5 -8.0 -7.5 0 100 200 300 400 500 0 100 300 500 2468 2468 2468 12 2468 12 Month Month

a) b)

LatitudeGiven : LongitudeGiven : 36.4 36.6 36.8 -9.0 -8.5 -8.0 -7.5

2468 12 0 100 200 300 400 500

0 100 200 300 400 500 2 4 6 8 10 12 Given : Longitude CPUE in biomass 0 100 200 300 400 500 CPUE in biomass CPUE -9.0 -8.5 -8.0 -7.5 0 100 200 300 400 500 0 100 200 300 400 500 2468 12 2468 12 24681012 Month Month

c) d)

Figure 9 – Distribution of CPUE in biomass versus month by longitude (7.5º - 9.5º W, negative values) and latitude (36.2 º - 37º N) series at 200-400m a); 400-600m b) 600-800 c) and more than 800m d) depth strata.

LatitudeGiven : LatitudeGiven : 36.4 36.6 36.8 36.4 36.6 36.8

2468 2468 12 0.5 1.0 1.5 2.0 0.61.01.41.8 Given : Given : Given Longitude H´ in Number H´ in Number Longitude 0.5 1.0 1.5 2.0 0.6 1.0 1.4 1.8 -9.0 -8.5 -8.0 -7.5 -9.0 -8.5 -8.0 -7.5 0.5 1.0 1.5 2.0 0.61.01.41.8 2468 2468 2468 12 2468 12 Month Month

a) b)

LatitudeGiven : LongitudeGiven : 36.4 36.6 36.8 -9.0 -8.5 -8.0 -7.5

2468 12 1.0 1.5 2.0 2.5

2 4 6 8 10 12 Given : Given 1.0 1.4 1.8 2.2 H´ in Number Longitude 1.0 1.5 2.0 2.5 H´ in Number -9.0 -8.5 -8.0 -7.5 1.0 1.5 2.0 2.5 1.0 1.4 1.8 2.2 2468 12 2468 12 24681012 Month Month

c) d)

Figure 10 – Distribution of diversity (Shannon index) in number versus month by longitude (7.5º - 9.5º W, negative values) and latitude (36.2 º - 37º N) series at 200-400m a); 400-600m b) 600- 800 c) and more than 800m d) depth strata.

LatitudeGiven : LatitudeGiven : 36.4 36.6 36.8 36.4 36.6 36.8

2468 2468 12 0.2 0.4 0.6 0.8 0.2 0.3 0.4 0.5 0.6 Given : Given : Given Equitability Equitability Equitability Longitude Longitude 0.2 0.4 0.6 0.8 0.2 0.3 0.4 0.5 0.6 -9.0 -8.5 -8.0 -7.5 -9.0 -8.5 -8.0 -7.5 0.2 0.4 0.6 0.8 0.2 0.3 0.4 0.5 0.6

2468 2468 2468 12 2468 12 Month Month

a) b)

LatitudeGiven : LongitudeGiven : 36.4 36.6 36.8 -9.0 -8.5 -8.0 -7.5

2468 12 0.4 0.6 0.8 0.4 0.5 0.6 0.7

2 4 6 8 10 12 Given : Given Equitability 0.4 0.6 0.8 Longitude Equitability -9.0 -8.5 -8.0 -7.5 0.4 0.5 0.6 0.7 0.4 0.6 0.8

2468 12 2468 12 24681012 Month Month

c) d)

Figure 11 – Distribution of Pielou´s evenness index (Equitability) versus month by longitude (7.5º - 9.5º W, negative values) and latitude (36.2 º - 37º N) series at 200-400m a); 400-600m b) 600-800 c) and more than 800m d) depth strata.

LatitudeGiven : LatitudeGiven : 36.4 36.6 36.8 36.4 36.6 36.8

2468 2468 12 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 Given : Given : Given H´ in Weight H´ in Weight Longitude Longitude 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 -9.0 -8.5 -8.0 -7.5 -9.0 -8.5 -8.0 -7.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0

2468 2468 2468 12 2468 12 Month Month a) b)

LatitudeGiven : LongitudeGiven : 36.4 36.6 36.8 -9.0 -8.5 -8.0 -7.5

2468 12 0.5 1.0 1.5 2.0 2.5

1.2 1.6 2.0 2.4 2 4 6 8 10 12 Given : Given H´ in Weight Longitude H´ in Weight 0.5 1.0 1.5 2.0 2.5 -9.0 -8.5 -8.0 -7.5 1.2 1.6 2.0 2.4 0.5 1.0 1.5 2.0 2.5 2468 12 2468 12 24681012 Month Month

c) d)

Figure 12 –Distribution of diversity (Shannon index) in weight versus month by longitude (7.5º - 9.5º W, negative values) and latitude (36.2 º - 37º N) series at 200-400m a); 400-600m b) 600- 800 c) and more than 800m d) depth strata.

LatitudeGiven : LatitudeGiven : 36.4 36.6 36.8 36.4 36.6 36.8

2468 2468 12 10 15 20 25 5 1015202530 Given : Given Given : Richness Richness Longitude Longitude 10 15 20 25 5 1015202530 -9.0 -8.5 -8.0 -7.5 -9.0 -8.5 -8.0 -7.5 10 15 20 25 5 1015202530

2468 2468 2468 12 2468 12 Month Month

a) b)

LatitudeGiven : LongitudeGiven : 36.4 36.6 36.8 -9.0 -8.5 -8.0 -7.5

2468 12 10 15 20 25 30 35

12 14 16 18 20 22 24 2 4 6 8 10 12 Given : Given Richness Longitude Richness 10 15 20 25 30 35 -9.0 -8.5 -8.0 -7.5 10 15 20 25 30 35 12 14 16 18 20 22 24 2468 12 2468 12 24681012 Month Month

c) d)

Figure 13 – Distribution of richness versus month by longitude (7.5º - 9.5º W, negative values) and latitude (36.2 º - 37º N) series at 200-400m a); 400-600m b) 600-800 c) and more than 800m d) depth strata.

LatitudeGiven : LatitudeGiven : 36.4 36.6 36.8 36.4 36.6 36.8

2468 2468 12 1.0 2.0 3.0 4.0 1.0 1.5 2.0 2.5 3.0 d d Given : : Given : Given Sp Richness Sp Richness Longitude Longitude 1.0 2.0 3.0 4.0 1.0 1.5 2.0 2.5 3.0 -9.0 -8.5 -8.0 -7.5 -9.0 -8.5 -8.0 -7.5 1.0 2.0 3.0 4.0 1.0 1.5 2.0 2.5 3.0 2468 2468 2468 12 2468 12 Month Month

a) b)

LatitudeGiven : LongitudeGiven : 36.4 36.6 36.8 -9.0 -8.5 -8.0 -7.5

2468 12 1.5 2.5 3.5 2.0 2.5 3.0 3.5 d

Given : : Given 24681012 Sp Richness Sp d Longitude 1.5 2.5 3.5 Sp Richness Sp -9.0 -8.5 -8.0 -7.5 2.0 2.5 3.0 3.5 1.5 2.5 3.5

2468 12 2468 12 Month 24681012 Month

c) d)

Figure 14 – Distribution of Margalef´richness index (d) versus month by longitude (7.5º - 9.5º W, negative values) and latitude (36.2 º - 37º N) series at 200-400m a); 400-600m b) 600-800 c) and more than 800m d) depth strata.

Community and species indicator variables between different years

Data from surveys held in August are used to make this analysis.

Depth stratum: 200-400 m (Figs. 15 a to 21 a)

At the southernmost latitude series (36 - 37º N) that corresponds to the Algarve region, there is only information for 1994 and 1998 and in the last year only two fishing hauls are available. In 1998 the values of the two CPUE variables correspond to the lower limit of the range registered in 1994. The number of species caught in 1998 is higher than in 1994. Diversity in number and equitability are highly variable in both years. In 1998, the values of diversity in weight are higher than in 1994 while those of diversity in number and equitability are lower. At the intermediate latitude series (37 - 38.5º N), which corresponds to the Alentejo region, there is information for 1994 and 1995. Fishing hauls at this latitude range are restricted to longitudes between 9.5º and 10.0º W. Excluding one fishing haul performed in 1994, the values of CPUE in abundance and CPUE in biomass variables are more homogeneous in 1994 than in 1995. In 1994 the ranges of the two CPUE variables are wider and higher in magnitude than in 1995. In 1994 diversity indices, of equitability and of richness are less heterogeneous than in 1995. At the northern latitude series (38.5 - 42º N), corresponding to the Northwest region, there is also information for 1994 and 1995 and in the last year there is information for two different longitude series (9.0 - 9.5º W and 9.5 - 10º W). In 1994 there is only information for the furthermost west longitude series (9.0 - 9.5º W). In both years extremely high values of the two CPUE variables are observed. Excluding those high values, at longitude series 9.5 - 10º W the ranges of the two CPUE variables in 1994 are narrower and lower in magnitude than in 1995. Diversity in number and equitability in 1994 are higher than in 1995. A similar pattern is observed for diversity in weight. Richness values are more heterogeneous in 1995.

At 9.0 - 9.5º W longitude series the two CPUE variables in 1995 have a magnitude similar to the lower limit of the ranges of these variables observed in this year at the further west longitude series ( 9.5 - 10º W ). Diversity indices, equitability and richness are highly variable, but their ranges are similar to those observed in 1994 at the 9.5º - 10º W longitude series.

Depth stratum: 400-600 m (Figs. 15 b to 21 b)

At Algarve region there is information for fishing hauls performed at the furthermost east longitude series (7.0 - 9.0º W) in 1994, 1997 and 1998 and also for a group of fishing hauls held in 1994 at the furthermore west (9.0 - 9.5 º W) longitude. Considering the information available for the 7.0 - 9.0º W longitude series it is observed that due to the existence of extremely high values, the ranges of the two CPUE variables are wider in 1994 than in the two most recent years (1997 and 1998). Excluding those high values, the magnitudes of CPUE in abundance and CPUE in biomass in 1997 are higher than in 1994 and 1998. In addition 1994 present higher values of CPUE in abundance and lower values of CPUE in biomass than 1997. In 1994 the values of diversity indices in number and in weight and of equitability are highly heterogeneous and are lower than those from 1997 and 1998. Richness is also lower in 1994. Excluding the lowest values of diversity indices and of equitability registered in 1997, this year is more homogeneous with respect to those variables than 1998. In 1997 richness is also more homogeneous and its magnitude is higher than in 1998. The group of fishing hauls belonging to the 9.0 - 9.5º W longitude and made only in 1994 are characterized by low values of CPUE variables, high heterogeneity and low magnitude in values of diversity, in number and in weight, and of equitability and of richness. In the Alentejo region there is information for one longitude series (9.0 - 9.5º W), which is derived from fishing hauls performed in 1994, in 1995 and in 1997. In 1994 and in 1995 the values of CPUE in abundance are higher than in 1997 while those of CPUE in biomass are higher in 1995 than in 1994 and 1997

In 1994 the values of diversity in number and of equitability are more heterogeneous, reaching higher values than those registered in 1995 and 1997. Ranges of diversity in weight are similar between 1994 and 1995. In these years the magnitude of this variable is higher than 1997. Richness shows a lower magnitude in 1994 than in 1995 and 1997. In the two last years the ranges of this variable are similar. In the Northwest region there is information for two longitude series (9.0 - 9.5º W and 9.5 - 10.0º W). At furthermost west group the fishing hauls are from 1994, 1995 and 1997. Only two hauls are performed in 1994 and the ranges of all the variables are found in those from 1995. So for annual comparison purposes only 1995 and 1997 will be considered. Between 1995 and 1997 the behaviour of CPUE variables is different. Excluding extremely high values of CPUE in abundance and CPUE in biomass, which are in 1997, the remaining values of these variables in this year are significantly lower than in 1995. In 1995 the values of diversity indices and of equitability are more homogeneous, reaching higher magnitude levels than 1997. Nevertheless, in 1997 richness values are higher than in 1995. For the longitude range comprised between 9.0º and 9.5º W there is information for 1995 and 1997. Despite the low number of fishing hauls no special differences in the magnitude of the two CPUE variables are detected between those years. Additionally, all species indicator variables present higher magnitude levels in 1995 than in 1997.

Depth stratum: 600-800 m (Figs. 15 c to 21 c)

The values of CPUE in biomass are rather homogeneous in all three years. Excluding the highest value registered in 1998, the range and magnitude of values CPUE in biomass in this year are lower than in 1994 and in 1997. In 1997 the values of CPUE in biomass are higher than in 1994. No special differences are detected in the magnitude and range sizes of species indicator variables between 1997 and 1998. In 1994 the ranges of those variables reach the inferior range limits determined for 1997 and 1998. Data for the 9.0 - 9.5º W longitude series are available for 1994, 1995, 1997 and 1998. Excluding one fishing haul performed in 1995, which presents a high value of CPUE in abundance, no special differences either in ranges and magnitudes are evident between 1994, 1995 and 1997. In 1998 the range of CPUE in abundance is wider and has a higher magnitude level than in previous years. A similar behaviour is observed for CPUE in biomass. The values of diversity in number and equitability are quite heterogeneous within years, particularly in 1994 and 1998. In 1998 the values of equitability are lower than in the remaining years. There are no great differences in the values of diversity in weight between years and in all years the magnitude of this variable is high. The values of richness are quite different between 1994 and 1998; 1994 registers the lowest values while 1998 has the highest ones. The ranges of richness in 1995 and in 1997 are contained in those from 1994 and 1998. In Alentejo there is information on only one longitude series (9.0 - 9.5º W). This subset includes fishing hauls from 1994, 1995, 1997 and 1998. In all those years, the values of CPUE in abundance are highly heterogeneous, particularly in 1997 for which an extremely high value is registered. The distribution of CPUE in biomass values is more homogeneous than that of CPUE in abundance, especially in 1995 and in 1998. As formerly observed for CPUE in abundance, in 1997 there is a very high value of CPUE in biomass. Excluding that value, a decreasing trend in the magnitude of this variable is evident from earlier to more recent years. In 1994, 1995 and 1998 both the ranges and the magnitudes of diversity in number and equitability are similar. In 1997 these variables are highly heterogeneous, attaining very low values.

Despite the fact that values of diversity in weight are highly variable in all the years, an increasing trend in magnitude of this variable is detected from earlier to more recent year. A similar temporal pattern is evident for the magnitude of richness. In the Northwest region there is information for two longitude series (9.0 - 9.5º W and 9.5 - 10.0º W). Further west, there are fishing hauls in 1994, 1995 and 1997. The two CPUE variables show a large heterogeneity and two subgroups with different magnitudes can be identified in all three years. The remaining variables also show a great heterogeneity within each year and, as observed before, for some variables two subgroups of values with different magnitudes can be identified. Despite the heterogeneity in all the variables, the magnitude of the two CPUE variables in 1995 is lower than in 1997. No comparison can be done between 1994 and 1995 and 1997. In effect, although the fishing hauls performed in 1994 belong to same depth stratum, most of them are quite close to the lower limit of this depth stratum while in the two other years fishing hauls are more widely spread according to depth. At the further east series of longitude from the northern latitude series there are data available for 1995 and 1997, although the number of fishing hauls is small in both years. The fishing haul performed 1995 presents such unusual features for the variables under study, particularly an extremely low value of richness, that no comparisons are done between this year and 1997. In 1997 the values of the variables are comparable to those obtained for the same year at the 9.5 - 10.0º W longitude series. The two CPUE variables have a magnitude similar to the lower limits of ranges obtained for that longitude series. On the contrary the values of diversity indices and of richness are similar to the upper part of range obtained for the 9.5 - 10.0º W longitude series.

Depth stratum: over 800 m (Figs. 15 d to 21 d)

At the deepest stratum the information is restricted to a reduced number of fishing hauls, and most of them are in the Algarve and Alentejo regions.

In the Algarve region there is information for two series of longitudes (7.0 - 9.0º W and 9.0 - 9.5º W). At the furthermost east longitude series fishing hauls are in 1994, 1997 and 1998. In this region there is a clear decreasing trend in the magnitude of the two CPUE variables from earlier to more recent years. In all those years, diversity indices and equitability present high magnitude levels. Richness has a high heterogeneity between years. In 1994 the values of this variable tend to be lower than at the remaining, and more recent, years. At the further west longitude series (9.0 - 9.5º W) data are available for 1995, 1997 and 1998. In each of all these years there is only information for two fishing hauls. In 1995 and 1998, the values of the two CPUE variables are quite variable, particularly in 1995 but in 1997 these variables are homogeneous. Due to such high levels of heterogeneity no temporal trends can be identified. Despite this, in 1997 the values have magnitudes similar to that registered for this year at the prior longitude series. In 1995 and 1997 the magnitudes of diversity indices, equitability and richness are similar to those observed for 7.0-9.0º W longitude series. The high level of heterogeneity in these variables observed in 1994 does not allow any further comparison. In the Alentejo region there is information for fishing hauls held at one longitude series (9.0 - 9.5º W) in 1997 and 1998. Excluding two fishing hauls held in 1997 and one fishing haul performed in 1998, that present very high values of CPUE in abundance and CPUE in biomass, the ranges and magnitudes of these two variables at the remaining fishing hauls are similar between 1997 and 1998. The extremely high values of CPUE variables are due to the predominance of one species, which in turn has resulted in low values of diversity in number, equitability and richness. At the remaining fishing hauls there are small differences in diversity indices and equitability between years. Major differences in richness are observed among years. In the Northwest region there is only information for 1997. In this year the range and the magnitude of the two CPUE variables, particularly CPUE in abundance, are similar to those obtained at the (7.0 - 9.0º W) longitude series of the Algarve region. Excluding diversity in weight, which presents very low

values, that behaviour is also observed for diversity in number, equitability and richness. The study of the behaviour of community and species indicator variables in terms of north/south and west/east effects is based on visual inspection of coplots constructed for analyzing temporal changes on those variables. As in the study of temporal changes on variables, the investigation of latitudinal and longitudinal changes is best discriminated by depth stratum.

Indices of abundance, biomass and diversity along the latitudinal gradient

Depth stratum: 200-400 m (Figs. 15 a to 21 a) The magnitude of both CPUE in abundance and CPUE in biomass variables is higher in the Alentejo region than the Algarve and Northwest regions. In the Northwest region these two variables present the highest levels of heterogeneity. No special differences in the magnitude of species indicator variables are detected between different latitude ranges. But the variability on species indicator variables is high at all the latitude ranges.

Depth stratum: 400-600 m (Figs. 15 b to 21 b)

The values of CPUE in abundance and CPUE in biomass are higher at Alentejo than at Algarve and the Northwest. The heterogeneity on the values of the two CPUE variables is higher in the Northwest region than in Algarve, which in turn presents relative higher magnitude levels. In general, the values of species indicator variables in Algarve and Alentejo are wider than at the Northwest region, where the lowest values of these variables are registered.

Depth stratum: 600-800 m (Figs. 15 c to 21 c) The values of CPUE in abundance and CPUE in biomass are more homogeneous in Algarve than in the Northwest and Alentejo regions. However, in Alentejo the ranges of these variables are wider than at the other two regions. In addition at Algarve there is a difference in the magnitude of CPUE variables between the two longitude ranges; at the more eastern range the values are lower. In all three regions, the values of species indicator are high variable even within the same latitude range.

Depth stratum: over 800 m (Figs. 15 d to 21 d)

Despite the reduced number of fishing hauls available for the Northwest region, the values of CPUE in abundance and CPUE in biomass are lower and more homogeneous than the values from both Alentejo and Algarve. The magnitude of species indicator variable is also lower in the Northwest region. In the Alentejo region the heterogeneity in the values of both CPUE in abundance and CPUE in biomass is quite high, and two subsets with different magnitude levels of CPUE variables can be recognised. This pattern is also evident for diversity in number and equitability. In the Algarve region the values of the two CPUE variables are low and relatively homogeneous while those of species indicator variables are quite high and heterogeneous.

Indices of abundance, biomass and diversity along the longitudinal gradient

Depth stratum: 200-400 m (Figs. 8 a to 14 a)

At this depth stratum the information is derived from a relatively reduced number of fishing hauls from two different longitude ranges. At the further east longitude range (7.5 - 9.0º W) the values of CPUE in abundance and CPUE in biomass have lower magnitudes than at 8.0 - 9.0º W. On the contrary, diversity indices, richness and equitability present higher magnitudes at the 7.5 - 9.0º W longitude series.

Depth stratum: 400-600 m (Figs. 8 b to 14 b)

For latitudes comprised between 36.7º N and 37.0º N there is information for three different longitude ranges. At the most eastern longitude range (7.0 - 7.5º W) there is also information for 36.0 - 36.5 N and 36.5 - 36.7 N latitude intervals. Concentrating the analysis in the 36.7 - 37.0º N latitude interval, no special differences in the magnitude of CPUE in abundance and CPUE in biomass are detected between different longitudes. Nevertheless the values of these two variables are slightly lower at the 8.0 - 9.0º W longitude range.

In all the three longitude ranges the variability of species indicator variables is high. In addition, the lowest values of these variables are mainly derived from fishing hauls from the further west longitude range, which however is only represented by two fishing hauls.

Depth stratum: 600-800 m (Figs. 8 c to 14 c)

At the intermediate longitude range (8.0 - 9.0º W) the values of CPUE in abundance and CPUE in biomass are lower and more homogeneous than at further east (7.0 - 8.0º W) and further west (9.0 - 9.5º W) longitude ranges. Despite this pattern in the values of CPUE variables at different longitude ranges, no special differences in magnitude of diversity indices, richness and equitability are detected.

Depth stratum: over 800 m (Figs. 8 d to 14 d) The values of both community and species indicator variables show a great dispersion within all three longitude ranges. This variability causes difficulties in regard to drawing consistent conclusions about east/west behaviour of those variables.

Study of benthic fauna - multivariate statistical approach

From the multivariate statistical analysis id observed that when a high level of aggregation is imposed on the data six clusters are identified. The species or group of species with a major contribution for the clustering are: Aristeus antennatus, Capros aper, Chelidonichthys lucerna, Cyttopsis rosea,, Gadiculus argenteus argenteus, Galeus melastomus,, Helicolenus dactylopterus dactylopterus, Hoplostethus mediterraneus mediterraneus,, Lepidorhombus boscii, Macroramphosus scolopax,, Micromesistius poutassou, Nephrops norvegicus, Parapenaeus longirostris,, Phycis blennoides, Plesionika heterocarpus, Plesionika martia martia, Scyliorhinus canicula, Trachurus trachurus, Trachyrhynchus scabrus , Nezumias pp., Raja spp.

With this level of aggregation there is no independence between the two factors, clusters and year; the 2 statistic has a value of 75.1 which is greater than the corresponding 95% quantile value of the 2 distribution (Fig. 23). For instance, cluster 1 is only formed by fishing hauls performed in the two first years while cluster 3 receives a quite important contribution from fishing hauls performed in 1995 and 1997 (Tab. 1).

Figure 23 - Spatial distribution of fishing hauls at each cluster identified at a higher aggregation level.

Table 1 – Number of fishing hauls included in each cluster by year.

Cluster Year 123456 1994 18 48 23 45 19 22 1995 24 28 50 37 31 6 1997 18 38 30 21 1 1998 11 16 23 5 2

There is also an unbalanced distribution of the number of fishing hauls by month in each cluster (Tab. 2), which translates into a lack of independence

between the two factors cluster and month. The 2 statistic has the value 68.13. This lack of independence suggests that the aggregation pattern partially reflects the time of year. Clusters 2 and 4 are examples; in both the fishing hauls from spring months (May and June) are overemphasized relative to the remaining months, except August.

Table 2 – Number of fishing hauls included in each cluster by month.

Cluster Month 123456 January 26761 May 20 7 23 7 11 June 1 13 11 25 8 5 August416690764814 December 4 13 4 7

The distribution of depth values by cluster shows a clear pattern (Fig. 25); there are two shallow clusters (clusters 1 and 6), two intermediate depth clusters (clusters 2 and 5) and two deep depth clusters (clusters 3 and 4). The depth ranges of these groups are in agreement with the first three depth ranges formerly adopted in the analysis of community and species indicator variables. The depth stratum over 800 m used in that analysis it is not recognized at this stage, probably due to the reduced number of fishing hauls included in that stratum. With the exception of the two shallow clusters, a high level of latitude overlap is detected for the remaining groups of clusters (Fig. 24 b), which reinforces the role played by depth in the organization of the benthic fauna off the Portuguese slope. Depth's role largely exceeds the north-south effect. In the case of longitude the level of overlap between clusters is also very high (Fig. 24 c). As observed in the case of latitude, excluding the shallow group, the ranges of longitude do not greatly differ between groups.

a) b)

Depth (m) Depth Latitude (ºN) Latitude 37 38 39 40 41 42 200 400 600 800

123456 123456 cluster number cluster number c) Figure 24 - Boxplots of depth versus cluster number a), of latitude versus cluster number b) and of longitude versus cluster number c) Longitude (ºW) Longitude 7.5 8.0 8.5 9.0 9.5 10.0

123456 cluster number

The values of CPUE in abundance in shallow clusters are higher than the deep ones (Fig. 25). Excluding the extremely high values on CPUE in biomass registered in shallow clusters 1 and 6, the magnitude of this variable increases with depth. The highest value from cluster 1 is derived from a fishing haul assigned to cluster 5 at the previous aggregation level while that registered at cluster 6 is due to a fishing haul previously allocated to cluster 9. This last fishing haul is also atypical in terms of species composition.

a) b) CPUE in biomass in CPUE CPUE in abundance in CPUE 0 5000 10000 15000 20000 25000 0 200 400 600 800 1000 1200 1400

123456 123456 cluster number cluster number

Figure 25 – Boxplot of CPUE in abundance versus cluster a) and of CPUE in biomass versus cluster b).

The values of diversity indices and richness in all the six clusters present high levels of variability (Fig. 26). The overlap of the ranges of these variables between different clusters is also high. a) b) H' in weight H' in number in H' 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 2.5

123456 123456 cluster number cluster number c) Figure 26 – Boxplots of diversity in number versus cluster a); of diversity in weight versus cluster and of richness versus cluster c).

Richness 5 101520253035

123456 cluster number

Discussion

Depth plays an important role of depth in benthic fauna organization, as well as the existence of differences in that organization both in space and in time. The high level of heterogeneity in the data creates difficulties in the discrimination of such changes and their influence on how variables behave. In addition to the two-way factor influences, visually identified, the analysis of coplots suggests that higher order factor interactions are important. The study of changes in community and species indicator variables in different months of the year was restricted to the Algarve region, to avoid the introduction of additional influence by other factors. Although temporal changes both in species and community variables are detected, the direction of those changes, commonly varying over space, is difficult to identify and because of this clear patterns are seldom evident. In some months the high level of heterogeneity observed in variable values suggests that the spatial scale adopted is inadequate to detect any changes. As a consequence a finer spatial resolution may be required, however, the number of fishing hauls available is insufficient to proceed in such a direction. Examples of this situation are observed for 400 – 600 m and 600 – 800 m depth strata from the intermediate latitude range (36.5 - 36.7º N). The variables within each of these subsets present a high variability, suggesting a more regionalized behaviour. At the 200 – 400 m depth stratum temporal changes in species and community indicator variables is difficult to assess. This may be partially related with the geographical closeness of the cluster to the coast with which unknown interrelations and interdependencies could occur. The distribution of variable values for the 400 – 600 m depth stratum at several latitude and longitude ranges suggests some interesting features concerning community temporal dynamics. These should be explored at the species level by considering additional information, such as relative species composition, and the biological aspects of feeding and reproduction. In the case of feeding it is important to remember that food supply ultimately originates from surface production and that virtually all trophic input to the ocean is derived from solar energy (Gordon, Merrett and Haedrich, 1995).

At the 600 – 800 m depth stratum, the number of fishing hauls is unequally distributed between months. For instance the low number of fishing hauls held in January hampers the temporal comparison of variable behaviour other than stating some general comments. At the deepest stratum community and species indicator variable analysis suggests the existence of changes between months. However, the underlying direction of those changes is hard to recognise due to the low magnitudes of variables and to their high variability. Despite such deficiencies for the majority of situations, January is a month characterised by great levels of variability in the values of CPUE variables. Such high variability may even occur between geographically close fishing hauls, as detected for the two fishing hauls in the 400 – 600 m depth stratum at the southern latitude range (36.0 - 36.5º N). One presents high values of the CPUE variables while at the other the values of these variables are low. In January the values of diversity indices are very heterogeneous in all the depth strata and there are differences in their magnitude over space. For instance at the 400 – 600 m depth stratum from the intermediate latitude series (36.5 - 36.7º N) values are commonly low. This fact together with the high value of specific mean weight (i.e., the ratio between CPUE in biomass and CPUE in abundance) suggests the occurrence of dynamic processes, probably related to reproduction. At the same latitude range but at the 600 – 800 m depth stratum the relatively low levels of diversity in number and the high level of diversity in weight suggests a predominance of small size species, probably juveniles. In January the different behaviour may also be associated with other community processes. For instance shallow strata seem to be mainly related with reproduction, but at deeper strata there may be more complex community phenomena at work. At the deepest stratum, there is a predominance of low values of CPUE variables, high levels of magnitude of diversity in number and equitability and moderate values of richness. These features may reflect migration processes, which, in turn, result in a reduction of the magnitude of abundance and biomass levels. Despite the considerable variation in the vertical distribution of slope species, some have a wide range and show a well marked “bigger deeper”

distribution (Gordon and Bergstad, 1992). In many species, the relatively low occurrence of juveniles in comparison with that of adults may indicate that in their early life is in the midwater and are therefore unavailable to bottom trawls (Gordon et al., 1996). Between June and August the behaviour of the variables does not greatly differ. Moderate to high magnitude levels and homogeneity in the values of CPUE in abundance and high variability of CPUE in biomass indicate some instability in the community, which is probably linked with the simultaneous occurrence of small size and large size species. Additionally the occurrence of relative high values of diversity in weight should result from the presence of small size species or small size specimens in relatively high numbers. So it might be hypothesised either an entrance (in the present context “entrance” might not mean migration but instead a recent availability of younger stages to the fishing gear) of small size species or an escapement of large size species to other areas, probably to deeper depths. Some exceptions to this general pattern are detected, as is the case in June at the 600 – 800 m depth stratum from the southernmost latitude range (36.0 - 36.5º N). Fishing hauls from this subset are characterised by high magnitude levels of CPUE in biomass and species indicator variables. These features together with the occurrence of moderate values of CPUE in abundance imply the predominance there of large size species. In December the behaviour of variables suggest the occurrence of a large variety of phenomena taking place in different regions and depths. In the 400 – 600 m depth stratum from the intermediate latitude series (36.5 - 36.7º N) the relative homogeneity of CPUE in biomass values, high heterogeneity of CPUE in abundance and low magnitude of species diversity indices probably reflect the existence of migratory processes. At the same depth stratum but at the southernmost latitude range (36.0 - 36.5º N) the values of the two CPUE variables are both high, probably due to a major occurrence of medium size species. This may also indicate that some species are in an active reproductive state, which is further corroborated by the high values of diversity in number and in weight. This hypothesis is in accordance with the winter reproductive season common to most shelf species from Northern Atlantic areas.

At the deepest stratum in December most variables particularly species indicator variables such as richness, present, high values, suggesting the occurrence of important migratory processes, some perhaps related to reproduction. However the magnitude levels of community indicator variables are not consistent with such an hypothesis. This inconsistency may reveal a lack of synchronism in reproduction not only between species but also between individuals from the same species. This last case is evident for deep-water sharks, which do not show a preferential copulation time period (Girard, 2000). At this depth stratum and most likely at deeper ones a yearly periodicity in biological processes, frequently observed for shelf species is not necessarily expected. There is evidence suggesting that in some deepwater species, females may not have enough energetic resources to guarantee annual breeding. Reproductive periodicity constraints may be greater among species of large size feeding at high trophic levels. The logarithmic decline in food supply determines that semelparity (spawning once in a lifetime) be a possible strategy for many larger slope species (Gordon, Merrett and Haedrich, 1995). Differences in the magnitude levels of community and species indicator variables between years are also detected, especially between earlier and more recent years. Although a decreasing trend with time appears to be frequent, its strength is not sufficient to clearly differentiate it from other factors. Also the spatial scale used is occasionally too wide to prevent the confounding effect of other, more regionalised, factors, as exemplified by the high levels of heterogeneity in most variables. Number of fishing hauls available is clearly insufficient, namely at the furthermost northern latitude range, to proceed with an analysis on a finer spatial scale that would guarantee detecting the influence of such factors. These different annual patterns can be easily exemplified. For instance at the shallow depth stratum (200 – 400 m) and in the Algarve region the values of two CPUE variables present a remarkable decrease in magnitude from 1994 to 1998. Excluding the variable diversity in number, the remaining species indicator variables do not show any trend. At the same depth stratum but at Alentejo, although differences in community and species indicator variables are detected between 1994 and 1995, their high level of heterogeneity prevents further clarification of those

changes. On the contrary the differences in variables between those two years are clear for fishing hauls held at the same depth stratum but at rather northern latitudes (Northwest region). In 1994 there was a higher specific mean weight and a greater homogeneity both in the distribution of individuals, in number and in weight, by species and in the total number of species than in 1995. In addition in 1995 at the westernmost longitude range (9.5 - 10.0º W) the behaviour of variables is different from that of the previous longitude range. This inconsistency stresses the importance of spatial factors masking the role played by temporal factors. At 400 – 600 m depth stratum and in the Algarve region there are high differences in the magnitude levels and range of two community indicators between 1994 and the two more recent years; at the more recent years the values of those variables are higher than in 1994. These results may suggest an improvement in community status in recent years. However such a conclusion should be taken with caution, because the results may reflect changes in the area surveyed between those time periods since in more recent years the sampling effort has been oriented towards the exploration of new areas. Actually in the Algarve there are two different areas with depths belonging to the 400 – 600 m depth stratum: one extending parallel to the coastline and the other located far from the coast in a restricted area in the southeast off Algarve. At the 400 – 600 m depth stratum in the Alentejo region variables show a slight decrease from earlier years to more recent ones. In contrast to the Algarve region, the bathymetry of the bottom in the Alentejo region runs parallel to the coastline so no drastic changes in the surveyed area can occur. Due to this the results obtained at this last latitude range better reflect differences between years. At the 400 - 600 m depth stratum in the Northwest region and for the longitude range (9.5 - 10.0º W) there are indications of a decrease in the magnitude CPUE variables from earlier to more recent years. However at the 9.0 - 9.5º W latitude range, the values of CPUE variables in 1995 and 1997 do not differ although all the species indicator variables are higher in 1995. At the 600 – 800 m depth stratum in the Algarve region and at the most eastern longitude range 7.5 - 9.0º W changes on CPUE variables between

years are evident. In 1998 the values of these variables are lower than in 1995 and 1997. An opposite trend is observed between 1995 and 1997, which seems to reflect the additional influence of other factors. High levels of variability, not only in the total number of individuals caught by species but also on the relative number of individuals of a species between different hauls, are registered in fishing hauls from different years at 600 – 800 m and in the Alentejo region. Despite such heterogeneity and if two extremely values of CPUE in biomass registered in 1997 are excluded, a decreasing trend in magnitude of this variable from earlier to more recent years can be perceived. In addition since at recent years the range of diversity in weight is narrow and high in magnitude, it appears that temporal changes are mainly reflected in terms of biomass. Finally at the deepest depth stratum the number of fishing hauls is insufficient to proceed with inter-year comparisons. The contour of the Portuguese coast and the pattern of bottom bathymetry are factors influencing the longitudinal and latitudinal gradients in community and species indicator variables. In addition the unbalanced number of fishing hauls along the Portuguese continental slope, especially at the northern latitude range, is a constraint for the understanding of those gradients. Despite such sampling deficiencies some general statements can be made. In relation to the north/south gradient, the values of community and species indicator variables are more homogeneous and higher in the Alentejo region. In the Northwest region the values of community variables are very low while in the Algarve region both community and species indicator variables show the highest levels of heterogeneity. Such behaviour in the Algarve region seems to be closely related to bottom topography but could also reflect spatial differences in fishing pressure across this region. In more recent years, the Portuguese commercial crustacean fishery that takes mainly place along the continental shelf and slope off south and southwest Portugal (Algarve and south of Alentejo regions) has been very intensive (Figueiredo, Figueiredo and Bordalo-Machado, 2001). The Nephrops norvegicus stock targetted by that fishery is actually in state of over exploitation (ICES, 1998). The study of east/west effects is restricted to the Algarve region where the results obtained are not very conclusive. Nevertheless it seems that at

shallow depths and at intermediate longitude range the values of community indicator variables are lower than at the further eastern and western ranges. This pattern may also be related to fishing activities that take place mainly at this area. Although the study of longitudinal effects has not been extended to the occidental coast off Portugal, the bottom bathymetry there, running parallel to the coastline, determines that east/west effects should reflect the patterns observed for depth. This hypothesis is further supported by the important role this variable plays in respect to the spatial distribution of benthic fauna. Fishing haul characteristics such as depth, latitude and longitude play important roles in cluster differentiation, particularly in regard to depth. Two shallow clusters, two intermediate depth clusters and two deep depth clusters are identified. Excluding the two shallow clusters, there is a high level of overlap in latitude ranges between the remaining clusters from the same depth stratum. In the case of longitude the level of overlap between clusters identified at the higher aggregation level continues to be very high. The behaviour of both species and community indicator variables are in agreement with the one identified for those variables at the first level of aggregation.

Community and species indicator variables between different years

Data from surveys held in August are used to make this analysis.

Depth stratum: 200-400 m (Figs. 15 a to 21 a)

LatitudeGiven : LatitudeGiven : 36 37 38 39 40 41 42 36 37 38 39 40 41 42

94 95 96 97 98 94 95 96 97 98

0 20000 40000 60000 0 4000 8000 12000

Given : Given :

Longitude Longitude

CPUE in abundance CPUE in abundance 0 20000 40000 60000 0 4000 8000 12000

-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 0 20000 40000 60000 0 4000 8000 12000 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 Year Year a) b)

LatitudeGiven : LatitudeGiven : 36 37 38 39 40 41 42 36 37 38 39 40 41 42

94 95 96 97 98 94 95 96 97 98

0 2000 4000 6000 8000 0 1000 3000

Given : Given :

Longitude

Longitude CPUE in abundance CPUE in abundance 0 2000 4000 6000 8000 0 1000 3000

-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 0 2000 4000 6000 8000 0 1000 3000 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 Year Year c) d)

Figure 15 - Distribution of CPUE in abundance versus year by restricted longitude (7.5º - 10º W, negative values) and latitude (36 º - 42º N) series at 200-400m a); 400-600m b); 600-800m c) and more than 800m d) depth strata.

LatitudeGiven : LatitudeGiven : 36 37 38 39 40 41 42 36 37 38 39 40 41 42

94 95 96 97 98 94 95 96 97 98

0 400 800 1200 0 200 400 600 800

Given : Given : Given Longitude Longitude CPUE in biomass CPUE in biomass

0 400 800 1200 0 200 400 600 800

-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 0 200 400 600 800 0 400 800 1200

94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 Year Year a) b)

LatitudeGiven : LatitudeGiven : 36 37 38 39 40 41 42 36 37 38 39 40 41 42

94 95 96 97 98 94 95 96 97 98

0 400 800 1200 0 200 400 600 800

Given : Given : Longitude Longitude CPUE in biomass CPUE in biomass 0 400 800 1200 0 200 400 600 800

-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 0 400 800 1200 0 200 400 600 800 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 Year Year c) d)

Figure 16 - Distribution of CPUE in biomass versus year by restricted longitude (7.5º - 10º W, negative values) and latitude (36 º - 42º N) series at 200-400m a); 400-600m b); 600-800m c) and more than 800 m d) depth strata.

LatitudeGiven : LatitudeGiven : 36.4 36.6 36.8 36.4 36.6 36.8

2468 2468 12

0.5 1.0 1.5 2.0 0.6 1.0 1.4 1.8

Given : Given : Given

Longitude H´ in Number H´ in Number Longitude

0.5 1.0 1.5 2.0 0.6 1.0 1.4 1.8

-9.0 -8.5 -8.0 -7.5 -9.0 -8.5 -8.0 -7.5

0.51.01.52.0 0.6 1.0 1.4 1.8 2468 2468 2468 12 2468 12 Month Month a) b)

LatitudeGiven : LongitudeGiven : 36.4 36.6 36.8 -9.0 -8.5 -8.0 -7.5

2468 12

1.0 1.5 2.0 2.5

2 4 6 8 10 12 Given : Given 1.0 1.4 1.8 2.2

H´ in Number Longitude 1.0 1.5 2.0 2.5

H´ in Number

-9.0 -8.5 -8.0 -7.5

1.0 1.5 2.0 2.5 1.0 1.4 1.8 2.2 2468 12 2468 12 24681012 Month Month c) d)

Figure 17 - Distribution of H´ in number versus year by restricted longitude (7.5º - 10º W, negative values) and latitude (36 º - 42º N) series at 200-400m a); 400-600m b); 600-800m c) and more than 800m d) depth strata.

LatitudeGiven : LatitudeGiven : 36.4 36.6 36.8 36.4 36.6 36.8

2468 2468 12

0.2 0.4 0.6 0.8 0.20.30.40.50.6

: Given : Given Equitability Equitability Longitude Longitude 0.2 0.4 0.6 0.8

0.2 0.3 0.4 0.5 0.6

-9.0 -8.5 -8.0 -7.5 -9.0 -8.5 -8.0 -7.5

0.2 0.4 0.6 0.8 0.20.30.40.50.6 2468 2468 2468 12 2468 12 Month Month a) b)

LatitudeGiven : LongitudeGiven : 36.4 36.6 36.8 -9.0 -8.5 -8.0 -7.5

2468 12

0.4 0.6 0.8

0.4 0.5 0.6 0.7 2 4 6 8 10 12 Given : Given

Equitability 0.4 0.6 0.8 Longitude Equitability

-9.0 -8.5 -8.0 -7.5 0.4 0.5 0.6 0.7

0.4 0.6 0.8

2468 12 2468 12 24681012 Month Month c) d)

Figure 18 - Distribution of Pielou´s evenness index (Equitability) versus year by restricted longitude (7.5º - 10º W, negative values) and latitude (36 º - 42º N) series at 200- 400m a); 400-600m b); 600-800m c) and more than 800m d) depth strata.

LatitudeGiven : LatitudeGiven : 36 37 38 39 40 41 42 36 37 38 39 40 41 42

94 95 96 97 98 94 95 96 97 98

0.51.01.52.02.5 0.5 1.0 1.5 2.0 2.5

Given : Given : HWeight HWeight H´ Weight H´ Weight

Longitude Longitude 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 2.5

-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 2.5 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 Year Year a) b)

LatitudeGiven : LatitudeGiven : 36 37 38 39 40 41 42 36 37 38 39 40 41 42

94 95 96 97 98 94 95 96 97 98

0.5 1.0 1.5 2.0 2.5 1.0 1.5 2.0 2.5

Given : Given : HWeight HWeight H´ Weight H´ Weight Longitude Longitude

0.5 1.0 1.5 2.0 2.5 1.0 1.5 2.0 2.5

-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 0.5 1.0 1.5 2.0 2.5 1.0 1.5 2.0 2.5 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 Year Year c) d)

Figure 19 - Distribution of H´ in weight versus year by restricted longitude (º W, negative values) and latitude (º N) series at 200-400m a); 400-600m b); 600-800m c) and more than 800 m d) depth strata.

LatitudeGiven : LatitudeGiven : 36 37 38 39 40 41 42 36 37 38 39 40 41 42

94 95 96 97 98 94 95 96 97 98

10 15 20 25 30 5 101520253035

Given : Given : Richness Richness Longitude Longitude 10 15 20 25 30

5 101520253035

-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 10 15 20 25 30 5 101520253035

94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 Year Year a) b)

LatitudeGiven : LatitudeGiven : 36 37 38 39 40 41 42 36 37 38 39 40 41 42

94 95 96 97 98 94 95 96 97 98

10 15 20 25 30 35

5 101520253035

Given : Given :

Richness Richness Longitude Longitude

10 15 20 25 30 35 5 101520253035

-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 10 15 20 25 30 35

5 101520253035 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 Year Year c) d)

Figure 20 - Distribution of richness versus year by restricted longitude (º W, negative values) and latitude (º N) series at 200-400m a); 400-600m b) 600-800m c); and more than 800 m d) depth strata.

LatitudeGiven : LatitudeGiven : 36 37 38 39 40 41 42 36 37 38 39 40 41 42

94 95 96 97 98 94 95 96 97 98

1234 12345

d d

Given : Given : Longitude SpRichness SpRichness Longitude 1234

12345

-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 1234 12345 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 Year Year a) b)

LatitudeGiven : LatitudeGiven : 36 37 38 39 40 41 42 36 37 38 39 40 41 42

94 95 96 97 98 94 95 96 97 98

1.5 2.5 3.5 4.5 12345

d d Given : Given : Longitude Longitude SpRichness SpRichness

1.5 2.5 3.5 4.5 12345

-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 1.5 2.5 3.5 4.5

12345 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 94 95 96 97 98 Year Year c) d)

Figure 21 - Distribution of Margalef´richness index (d) versus year by restricted longitude (º W, negative values) and latitude (º N) series at 200-400m a); 400-600m b) 600- 800m c) and more than 800 m d) depth strata

Depth stratum: 400-600 m (Figs. 15 b to 21 b)

lower magnitude in 1994 than in 1995 and 1997. In the two last years the ranges of this variable are similar. In the Northwest region there is information for two longitude series (9.0 - 9.5º W and 9.5 - 10.0º W). At furthermost west group the fishing hauls are from 1994, 1995 and 1997. Only two hauls are performed in 1994 and the ranges of all the variables are found in those from 1995. So for annual comparison purposes only 1995 and 1997 will be considered. Between 1995 and 1997 the behaviour of CPUE variables is different. Excluding extremely high values of CPUE in abundance and CPUE in biomass, which are in 1997, the remaining values of these variables in this year are significantly lower than in 1995. In 1995 the values of diversity indices and of equitability are more homogeneous, reaching higher magnitude levels than 1997. Nevertheless, in 1997 richness values are higher than in 1995. For the longitude range comprised between 9.0º and 9.5º W there is information for 1995 and 1997. Despite the low number of fishing hauls no special differences in the magnitude of the two CPUE variables are detected between those years. Additionally, all species indicator variables present higher magnitude levels in 1995 than in 1997.

Depth stratum: 600-800 m (Figs. 15 c to 21 c)

In the Algarve region fishing hauls belong to two different longitude series (7.0 - 9.0º W and 9.0 - 9.5º W). For the furthermost east subset there is information for 1994, 1997 and 1998. The values of the two CPUE variables are more homogeneous in 1994 than 1997 and 1998. Excluding two fishing hauls performed in 1997, that present relatively high values of CPUE in abundance, the range of this variable is similar to that observed in 1994. In 1998 and in spite of the occurrence of two fishing haul with high values of CPUE in abundance, this variable has a lower magnitude than in 1994 and 1997. The values of CPUE in biomass are rather homogeneous in all three years. Excluding the highest value registered in 1998, the range and magnitude of values CPUE in biomass in this year are lower than in 1994 and in 1997. In 1997 the values of CPUE in biomass are higher than in 1994.

No special differences are detected in the magnitude and range sizes of species indicator variables between 1997 and 1998. In 1994 the ranges of those variables reach the inferior range limits determined for 1997 and 1998. Data for the 9.0 - 9.5º W longitude series are available for 1994, 1995, 1997 and 1998. Excluding one fishing haul performed in 1995, which presents a high value of CPUE in abundance, no special differences either in ranges and magnitudes are evident between 1994, 1995 and 1997. In 1998 the range of CPUE in abundance is wider and has a higher magnitude level than in previous years. A similar behaviour is observed for CPUE in biomass. The values of diversity in number and equitability are quite heterogeneous within years, particularly in 1994 and 1998. In 1998 the values of equitability are lower than in the remaining years. There are no great differences in the values of diversity in weight between years and in all years the magnitude of this variable is high. The values of richness are quite different between 1994 and 1998; 1994 registers the lowest values while 1998 has the highest ones. The ranges of richness in 1995 and in 1997 are contained in those from 1994 and 1998. In Alentejo there is information on only one longitude series (9.0 - 9.5º W). This subset includes fishing hauls from 1994, 1995, 1997 and 1998. In all those years, the values of CPUE in abundance are highly heterogeneous, particularly in 1997 for which an extremely high value is registered. The distribution of CPUE in biomass values is more homogeneous than that of CPUE in abundance, especially in 1995 and in 1998. As formerly observed for CPUE in abundance, in 1997 there is a very high value of CPUE in biomass. Excluding that value, a decreasing trend in the magnitude of this variable is evident from earlier to more recent years. In 1994, 1995 and 1998 both the ranges and the magnitudes of diversity in number and equitability are similar. In 1997 these variables are highly heterogeneous, attaining very low values. Despite the fact that values of diversity in weight are highly variable in all the years, an increasing trend in magnitude of this variable is detected from earlier to more recent year. A similar temporal pattern is evident for the magnitude of richness.

In the Northwest region there is information for two longitude series (9.0 - 9.5º W and 9.5 - 10.0º W). Further west, there are fishing hauls in 1994, 1995 and 1997. The two CPUE variables show a large heterogeneity and two subgroups with different magnitudes can be identified in all three years. The remaining variables also show a great heterogeneity within each year and, as observed before, for some variables two subgroups of values with different magnitudes can be identified. Despite the heterogeneity in all the variables, the magnitude of the two CPUE variables in 1995 is lower than in 1997. No comparison can be done between 1994 and 1995 and 1997. In effect, although the fishing hauls performed in 1994 belong to same depth stratum, most of them are quite close to the lower limit of this depth stratum while in the two other years fishing hauls are more widely spread according to depth. At the further east series of longitude from the northern latitude series there are data available for 1995 and 1997, although the number of fishing hauls is small in both years. The fishing haul performed 1995 presents such unusual features for the variables under study, particularly an extremely low value of richness, that no comparisons are done between this year and 1997. In 1997 the values of the variables are comparable to those obtained for the same year at the 9.5 - 10.0º W longitude series. The two CPUE variables have a magnitude similar to the lower limits of ranges obtained for that longitude series. On the contrary the values of diversity indices and of richness are similar to the upper part of range obtained for the 9.5 - 10.0º W longitude series.

Depth stratum: over 800 m (Figs. 15 d to 21 d)

At the deepest stratum the information is restricted to a reduced number of fishing hauls, and most of them are in the Algarve and Alentejo regions. In the Algarve region there is information for two series of longitudes (7.0 - 9.0º W and 9.0 - 9.5º W). At the furthermost east longitude series fishing hauls are in 1994, 1997 and 1998. In this region there is a clear decreasing trend in the magnitude of the two CPUE variables from earlier to more recent years. In all those years,

diversity indices and equitability present high magnitude levels. Richness has a high heterogeneity between years. In 1994 the values of this variable tend to be lower than at the remaining, and more recent, years. At the further west longitude series (9.0 - 9.5º W) data are available for 1995, 1997 and 1998. In each of all these years there is only information for two fishing hauls. In 1995 and 1998, the values of the two CPUE variables are quite variable, particularly in 1995 but in 1997 these variables are homogeneous. Due to such high levels of heterogeneity no temporal trends can be identified. Despite this, in 1997 the values have magnitudes similar to that registered for this year at the prior longitude series. In 1995 and 1997 the magnitudes of diversity indices, equitability and richness are similar to those observed for 7.0-9.0º W longitude series. The high level of heterogeneity in these variables observed in 1994 does not allow any further comparison. In the Alentejo region there is information for fishing hauls held at one longitude series (9.0 - 9.5º W) in 1997 and 1998. Excluding two fishing hauls held in 1997 and one fishing haul performed in 1998, that present very high values of CPUE in abundance and CPUE in biomass, the ranges and magnitudes of these two variables at the remaining fishing hauls are similar between 1997 and 1998. The extremely high values of CPUE variables are due to the predominance of one species, which in turn has resulted in low values of diversity in number, equitability and richness. At the remaining fishing hauls there are small differences in diversity indices and equitability between years. Major differences in richness are observed among years. In the Northwest region there is only information for 1997. In this year the range and the magnitude of the two CPUE variables, particularly CPUE in abundance, are similar to those obtained at the (7.0 - 9.0º W) longitude series of the Algarve region. Excluding diversity in weight, which presents very low values, that behaviour is also observed for diversity in number, equitability and richness. The study of the behaviour of community and species indicator variables in terms of north/south and west/east effects is based on visual inspection of coplots constructed for analyzing temporal changes on those variables. As in

the study of temporal changes on variables, the investigation of latitudinal and longitudinal changes is best discriminated by depth stratum.

Indices of abundance, biomass and diversity along the latitudinal gradient

Depth stratum: 400-600 m (Figs. 15 b to 21 b)

Depth stratum: over 800 m (Figs. 15 d to 21 d) Despite the reduced number of fishing hauls available for the Northwest region, the values of CPUE in abundance and CPUE in biomass are lower and more homogeneous than the values from both Alentejo and Algarve. The magnitude of species indicator variable is also lower in the Northwest region. In the Alentejo region the heterogeneity in the values of both CPUE in abundance and CPUE in biomass is quite high, and two subsets with different magnitude levels of CPUE variables can be recognised. This pattern is also evident for diversity in number and equitability. In the Algarve region the values of the two CPUE variables are low and relatively homogeneous while those of species indicator variables are quite high and heterogeneous.

Indices of abundance, biomass and diversity along the longitudinal gradient

Depth stratum: 200-400 m (Figs. 8 a to 14 a)

Depth stratum: 400-600 m (Figs. 8 b to 14 b)

Concentrating the analysis in the 36.7 - 37.0º N latitude interval, no special differences in the magnitude of CPUE in abundance and CPUE in biomass are detected between different longitudes. Nevertheless the values of these two variables are slightly lower at the 8.0 - 9.0º W longitude range. In all the three longitude ranges the variability of species indicator variables is high. In addition, the lowest values of these variables are mainly derived from fishing hauls from the further west longitude range, which however is only represented by two fishing hauls.

Depth stratum: 600-800 m (Figs. 8 c to 14 c)

Study of benthic fauna - multivariate statistical approach

heterocarpus, Plesionika martia martia, Scyliorhinus canicula, Trachurus trachurus, Trachyrhynchus scabrus , Nezumias pp., Raja spp.

Figure 23 - Spatial distribution of fishing hauls at each cluster identified at a higher aggregation level.

Table 1 – Number of fishing hauls included in each cluster by year.

Cluster Year 123456 1994 18 48 23 45 19 22 1995 24 28 50 37 31 6 1997 18 38 30 21 1 1998 11 16 23 5 2

There is also an unbalanced distribution of the number of fishing hauls by month in each cluster (Tab. 2), which translates into a lack of independence between the two factors cluster and month. The 2 statistic has the value 68.13. This lack of independence suggests that the aggregation pattern partially reflects the time of year. Clusters 2 and 4 are examples; in both the fishing hauls from spring months (May and June) are overemphasized relative to the remaining months, except August.

Table 2 – Number of fishing hauls included in each cluster by month.

Cluster Month 123456 January 26761 May 20 7 23 7 11 June 1 13 11 25 8 5 August416690764814 December 4 13 4 7

a) b)

Depth (m) Latitude (ºN) Latitude 37 38 39 40 41 42 200 400 600 800

123456 cluster number

a) b) CPUE in biomass in CPUE CPUE in abundance in CPUE 0 5000 10000 15000 20000 25000 0 200 400 600 800 1000 1200 1400

123456 123456 cluster number cluster number

Figure 25 – Boxplot of CPUE in abundance versus cluster a) and of CPUE in biomass versus cluster b).

123456 123456 cluster number cluster number c) Figure 26 – Boxplots of diversity in number versus cluster a); of diversity in weight versus cluster and of richness versus cluster c).

Richness 5 101520253035

123456 cluster number

Discussion