The importance of deep-sea vulnerable marine ecosystemsfor demersal fish in the Azores

Christopher K. Pham a,b,n, Frederic Vandeperre a,b, Gui Menezes a,b, Filipe Porteiro a,b,c,Eduardo Isidro a,b, Telmo Morato a,b

a Centro do IMAR da Universidade dos Açores, Departamento de Oceanografia e Pescas & Laboratório Associado LARSyS, 9901-862 Horta, Portugalb Marine and Environmental Sciences Centre (MARE), Universidade dos Açores, 9901-862 Horta, Portugalc Direção Regional dos Assuntos do Mar, Secretaria Regional do Mar, Ciência e Tecnologia, Governo dos Açores, 9901-140 Horta, Portugal

a r t i c l e i n f o

Article history:Received 29 July 2014Received in revised form5 November 2014Accepted 9 November 2014Available online 25 November 2014

Keywords:Vulnerable marine ecosystemsCold-water coralsDemersal fishHabitat associationGeneral additive modelsDeep-sea

a b s t r a c t

Cold-water corals and sponges aggregations are important features of the deep sea, recently classified asvulnerable marine ecosystems (VMEs). VMEs increase habitat complexity, believed to act as feeding,reproductive, nursery and refuge areas for a high number of invertebrates and fish species. In the Azoresarchipelago (NE Atlantic), VMEs are prevalent but their ecological role has not received much attention.The objective of this study was to investigate the importance of VMEs in influencing the distribution ofdemersal fish in the Azores. With data collected during experimental longline surveys , we modeled thecatch of six demersal fish species of commercial value (Helicolenus dactylopterus, Pagellus bogaraveo,Mora moro, Conger conger, Phycis phycis, Pontinus kuhlii) in relation to the presence of VMEs and otherenvironmental factors using General Additive Models (GAMs). Our study demonstrated that total fishcatch was higher inside VMEs but the relationship between fish and VMEs varied among fish species.Species specific models showed that catch was strongly influenced by environmental factors, mainlydepth, whilst the presence of VMEs was only important for two rockfish species; juvenile and adult P.kuhlii and juvenile H. dactylopterus. Although the association between deep-sea demersal fish and VMEsmay be an exception to the rule, we suggest that VMEs act as an important habitat for two commerciallyimportant species in the Azores.

& 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Understanding fish-habitat relationships is a fundamentaltheme in marine ecology with considerable implications for anecosystem-based approach to fisheries management and thesustainable exploitation of demersal fish stocks (Johnson et al.,2013). Habitat complexity and heterogeneity has been shown tohave a positive relationship with species diversity and abundancein a variety of terrestrial and aquatic settings (Hixon and Menge,1991). In coral reefs (Almany, 2004), estuaries (Humphries et al.,1992), freshwater systems (Diehl, 1992) or forests (MacArthur andMacArthur, 1961), habitats of higher structural complexity havebeen found to host greater species diversity and abundance thanin less complex habitats. Similarly, in the vast deep-sea environ-ment, biological structures offered by cold-water corals (CWC) or

sponges increase habitat complexity, providing habitats for a widevariety of species (Buhl-Mortensen et al., 2010). CWC ecosystemshave been identified as biodiversity hotspots, comparable to thatof tropical coral reefs (Watling et al., 2011), of considerableecological and economic value (Foley et al., 2010).

These habitats have been classified as vulnerable marineecosystems (VMEs) by the United Nations General Assembly(UNGA) Resolution 61/105 (FAO, 2009). The functional significanceof the habitats along with their fragility and rarity is among themost important criteria used to designate VMEs (FAO, 2009). As aresult, there has been an expanding literature investigating theimportance of VMEs for commercially important deep-sea fishspecies, comparing biodiversity, species composition and abun-dance between VMEs and non VME areas using in situ imageryinformation (Auster, 2005; Baker et al., 2012; Beazley et al., 2013;Biber et al., 2014; Costello et al., 2005; D’Onghia et al., 2012; DuPreez and Tunnicliffe, 2011; Freese and Wing, 2003; Harter et al.,2009; Marliave et al., 2009; Parrish, 2006; Quattrini et al., 2012;Ross and Quattrini, 2007; Stone, 2006; Sulak et al., 2007; Tissotet al., 2006) and fisheries surveys (Colloca et al., 2004; D’Onghia

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Deep-Sea Research I

http://dx.doi.org/10.1016/j.dsr.2014.11.0040967-0637/& 2014 Elsevier Ltd. All rights reserved.

n Corresponding author at: Centro do IMAR da Universidade dos Açores,Departamento de Oceanografia e Pescas & Laboratório Associado LARSyS, 9901-862 Horta, Portugal. Tel.: þ351 292 200 400; fax: þ351 292 200 411.

E-mail address: phamchristopher@uac.pt (C.K. Pham).

Deep-Sea Research I 96 (2015) 80–88

et al., 2010; Edinger et al., 2007; Kenchington et al., 2013; Kutti et al.,2014; Parker and Mormede, 2009).

The extent of the association between fish and VMEs differsbetween studies and geographical areas. For example, in the NEAtlantic, analysis of video footage collected across eight distinctlocations did not reveal any species to be restricted to Lopheliapertusa reefs (Costello et al., 2005) but found greater fish richnessand abundance in CWC habitats than in surrounding areas. Resultsobtained in the Norwegian margin suggested that although CWCseem to be preferred habitats for some fish species, there is noevidence to support the hypothesis that CWC form key habitats forfish (Kutti et al., 2014). Some researchers suggest that it is thecomplexity of the habitat that attracts certain fish and not thecoral itself (Auster, 2005; Harter et al., 2009; Soffker et al., 2011).Yet, investigation in CWC reefs off the southeastern United Statesfound a unique and obligate fish assemblage (Ross and Quattrini,2007). Furthermore, catches of various commercially importantfish were significantly higher in CWC habitats in the MediteraneanSea (D’Onghia et al., 2012), off Norway (Husebø et al., 2002), or inthe Aleutian Islands, Alaska (Stone, 2006).

Several hypotheses have been proposed to explain higher fishabundance in VMEs, including provision of shelter from predators,nursery areas (Costello et al., 2005; D’Onghia et al., 2012; Krieger andWing, 2002), spawning grounds (Costello et al., 2005; D’Onghia et al.,2010; Freese and Wing, 2003; Koenig et al., 2005; Marliave et al.,2009) and enhanced prey availability (Costello et al., 2005; Husebøet al., 2002). However, with the exception of a study demonstratingthe direct use of CWC by fish larvae (Baillon et al., 2012) and deep-water sharks (Henry et al., 2013), there is little evidence supportingany functional relationship between VMEs and fish (Auster, 2007).Regardless of the exact nature of the relationship, VMEs are increas-ingly recognized to be important features of the deep-sea (Robertset al., 2009) and the evidence presented by many authors, stronglysuggest that CWC influence fish distribution.

In Azorean waters, the numerous seamounts and island shelveshost a high diversity of CWC and sponges, revealed to be keycomponents of deep benthic communities in this region (Braga-Henriques et al., 2013; Tempera et al., 2013). CWC are commonbycatch in the demersal longline fishery suggesting that there is anoverlap between CWC distribution and commercial fish species (Phamet al., 2014; Sampaio et al., 2012). ROV footage of gorgonian aggrega-tions (or coral gardens) on Condor seamount showed that some fishspecies are associatedwith these habitats but their abundances are notsignificantly higher than in surrounding soft sediments (Porteiro et al.,2013). However, this may not hold true at a larger scale. If suchhabitats promote fish occurrence or abundance, we therefore expectfish catches of certain species or life history stages to be higher in VMEareas. In this study, we used catch data from deep-sea bottom longlinesurveys across the entire Azorean EEZ to assess the importance ofVMEs for commercial demersal fish species.

2. Materials and methods

2.1. Study site and bottom fishery

The study was conducted in the Azores, an archipelago com-posed of nine islands, with a maritime territory encompassingnearly a million square kilometres (Fig. 1). The area is characterizedby steep slopes, numerous seamounts and reduced island shelves(Menezes et al., 2006; Morato et al., 2008, 2013). Today, fishing takesplace on most of the seamounts in the economic exclusive zone(EEZ) at depths varying between 150 and 750 m. Deep-sea fisheriesresources in the Azores are a significant component of the totalfishery, targeting principally alfonsinos (Beryx splendens and Beryxdecadactylus), black belly rosefish (Helicolenus dactylopterus),

blackspot seabream (Pagellus bogaraveo), common mora (Moramoro), European conger (Conger conger), forkbeard (Phycis phycis),offshore rockfish (Pontinus kuhlii) and wreckfish (Polyprion amer-icanus) (Menezes et al., 2006; Pham et al., 2013a).

The Azores EEZ harbours a mixed coral fauna with highestdiversity found between 300 and 900 m depths with gorgonians(Order Alcyonacea) being the most diverse and abundant group(Braga-Henriques et al., 2013). CWC are principally abundant aroundisland slopes and on the edges and peaks of seamounts where theyform “coral gardens”. Coral gardens host high densities of corals thatincrease habitat complexity and are classified as VMEs (Auster et al.,2013; Bullimore et al., 2013; Gomes-Pereira et al., 2013; Porteiroet al., 2013). Other emergent epifauna present in coral gardens includesponges, hydroids, bryozoans, ascidians and actiniarians (Pham et al.,2014).

2.2. Data collection

We used data from 130 longline surveys (done between 2007and 2011) collected onboard the R/V Arquipélago (Fig. 1) tocompare the catch of six commercially important fish species(H. dactylopterus, P. bogaraveo,M. moro, C. conger, P. phycis, P. kuhlii)between areas designated as VMEs and as non-VMEs (see below).We selected these species based on their high catch rate, benthic/benthopelagic life style, and representativeness of different depthranges (Menezes et al., 2006).

The bottom longline surveys followed a standardized metho-dology designed to monitor Azorean demersal and deep-waterfish species (Menezes et al., 2006). The fishing sets were carriedout on the slopes of each island and on most seamounts foundwithin the EEZ (Fig. 1). The longline fishing gear was identical tothe one typically used in Azorean commercial fisheries, equippedwith J type hooks (gape of approx. 12 mm) and baited with saltedand chopped sardine.

During gear deployment, the start and end of each 50 m depthstratum was registered. For all depth intervals, we recorded theinitial and final position of the stratum, total fish catch (numbersand total weight), the number of VME indicator organisms and thenumber of hooks deployed. All fish and epibenthic organisms wereidentified to species level. For the purpose of this study, VMEindicator organisms are defined as all sessile epibenthic organismscaptured because longlining is known to be selective for large andcomplex epibenthic organisms that increase habitat complexity(Mytilineou et al., 2014; Pham et al., 2014). Therefore, all epi-benthic organisms caught by longline were assumed to be VMEindicator organisms.

The total or fork length of each individual fish was measured tothe nearest cm. In order to examine the association of fish and VMEsfor juveniles and adults, we separated the two groups based on the1st quartile of the size distribution (Fig. S1). For most species, thelength at the 1st quartile corresponded to the reported size atmaturity (Table S1). It is important to note that sexual dimorphismin size for M. moro, P. kuhlii and P. bogaraveo implies that juvenilesand adults were biased towards a certain gender in those species.

A total of 30,622 fish were caught during the longline surveys,with a corresponding weight of 27,330 kg and belonging to 110different species. Average nominal catch per unit effort (CPUE) forthe species of interest (H. dactylopterus, P. bogaraveo, P. phycis, P.kuhlii, C. conger and M. moro) can be found in Table 1. Bycatch ofVME indicator organisms was registered in 69.2% of the longlinesets (or 12% of the longline sections) with an average nominalCPUE of 1.5 (0.13 S.E.) ind. 1000 hooks�1 and dominated by CWC(comprising the Anthozoa, Actiniaria, Antipatharia, Scleractinia,Zoantharia, Alcyonacea and the Hydrozoa Leptothecata, and Sty-lasteridae). When considering solely CWC, average bycatch levelswere 1.03 (0.11 S.E.) CWC 1000 hooks�1. Bycatch of sponges was

C.K. Pham et al. / Deep-Sea Research I 96 (2015) 80–88 81

0.41 (0.07 S.E.) ind. 1000 hooks�1. Within CWC, soft corals(Alcyonacea) were dominant bycatch organisms (43% of total)with an average bycatch rate of 0.61 (0.09 S.E.) ind. 1000 hook s�1,followed by hydrozoans 0.19 (0.03 S.E.) ind. 1000 hooks�1. A moredetailed description of the taxonomic composition of the bycatchcan be found elsewhere (Pham et al., 2014).

2.3. Identifying areas of vulnerable marine ecosystems (VMEs)

Locations were identified as VMEs when the average bycatchrates of VME indicator organisms obtained from the fishing sets ina 5�5 km cells exceeded a predefined threshold value. Such athreshold is also commonly used as a criterion in VME encounterprotocols of “move-on” rules, which are widely used for themanagement of deep-sea fisheries in response to the UNGAresolution 61/105, calling for Regional Fisheries ManagementOrganizations (RFMOs) to prevent Significant Adverse Impacts toVMEs (Auster et al., 2011). The organisms classified as VMEindicators and the threshold value vary greatly between RFMOSand fishing gears (Kenchington, 2011). For the present study, wedeveloped a new threshold, because no VME encounter protocolhas currently been set for the demersal longline fleet operating inthe Azores region. The threshold was set as the average

standardized bycatch rate of VME indicator organisms in a wellknown VME site, the Condor Seamount. ROV surveys on thisseamount revealed the presence of dense coral gardens (definedas areas where octocorals are dominant; occurring at densitieshigher than surrounding patches (Bullimore et al., 2013) on thesummit and extensive sponge fields on its slopes (Gomes-Pereiraet al., 2013; Pham et al., 2013b; Porteiro et al., 2013). The averagestandardized bycatch rate (from 10 longline sets) on this VME sitewas 0.4 (0.1 S.E) VME indicator organisms per 1000 hooks, andwas used as a threshold value for VME encounter.

Bycatch levels for the study area used for identifying VMEs,were obtained from the experimental research fishing setsdescribed above along with additional bycatch data collected byobservers onboard commercial vessels (389 longline sets). Allbycatch data were standardized to adjust for differences in bycatchrates between vessels using Generalised Additive Models (GAMs)with a negative binomial distribution and a log-link function (Rpackage mgcv). Further details on the model selection and valida-tion for this data set can be found elsewhere (Pham et al., 2014).Average standardized bycatch rates of VME indicator organismswere computed for 5�5 km cells, classified as VMEs when theaverage standardized bycatch rates were equal to or exceeded thedefined threshold (Fig. 2).

Fig. 1. The location of the 130 bottom longline fishing surveys performed in the Azores EEZ between 2007 and 2011.

Table 1Average nominal CPUE (and standard error) of the main species captured during the longline surveys in the Azores.

Species Common name Depth range (m) Mean (n 1000 hooks�1) Mean (kg 1000 hooks�1) Juveniles (%) Adults (%)

H. dactylopterus Black belly rosefish 380–700 23.071.1 10.070.5 50 50P. kuhlii Offshore rockfish 150–400 2.770.3 1.270.1 78 22P. bogaraveo Blackspot seabream 80–500 12.270.6 7.770.4 76 24C. conger European conger 150–525 1.570.1 5.170.5 76 24P. phycis Forkbeard 50–320 2.870.2 3.770.3 76 24M. moro Common mora 420–1300 7.870.4 13.470.8 75 24Other 25–2500 16.170.5 21.671.1 – –

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2.4. Data analysis

We used GAMs to explore the importance of VMEs for sixdemersal fish species while controlling for other likely effects,including (i) bottom depth, (ii) time (year and month), (iii) seafloorslope, (iv) aspect (direction of slope), (v) sediment type divided inmud, sand, gravel, and rock and (vi) habitat type (seamounts vs.island shelf). Slope, aspect and substrate type were derived fromacoustic bathymetric data, for which the details can be foundelsewhere (Vasquez et al., in press). Variance Inflation Factor (VIF)analysis (cut-off value set at 3) along with multi-panel scatterplots(Zuur et al., 2007) revealed no collinearity between these vari-ables. Depth and slope were included in the models as thin-plateregression splines. Aspect, being a circular covariate, was includedas a cyclic cubic regression spline (type “cc” in the R-package mgcv).

We computed the length weighted mean values of eachcontinuous environmental variable (slope and aspect) per longlinesection using the software Geospatial Modelling Environment0.6.0.0 (Beyer, 2012). For categorical variables (substrate type),we calculated the percentage contribution of each sediment typethroughout the longline segment. When the longline segment

crossed different types of substrate, we assigned the longlinesection to the dominant type of substrate.

Separate models were developed for catch rates (number ofindividuals) of juveniles and adults of each fish species (H.dactylopterus, P. bogaraveo, M. moro, C. conger, P. phycis, P. kuhlii)and for all species pooled together. Data used in the models wererestricted to the depth range (90–10 inter-percentile) in which thespecies is reported (Menezes et al., 2006) to avoid a large amountof false negatives (Zuur et al., 2009). The numbers of fish in eachset were modeled as counts, using a negative binomial distributionwith a log link function, due to the high level of dispersion presentin the data. The logarithm of the effort measured as number ofhooks was included as a model offset to account for variations insampling effort (Zuur et al., 2009).

Selection of explanatory variables for each model was carriedout using a stepwise forward selection process based on theAkaike’s Information Criteria (AIC). All models were fit usingthe ‘mgcv’ package in the R version 2.14.0 environment (Wood,2006). Model validation was performed by visual inspection ofstandard QQ plots, plots of the model residuals (Pearson) againstfitted values, plots of model residuals against all explanatoryvariables (including the ones not retained during model selection)

Fig. 2. Location of vulnerable marine ecosystems (VMEs) based on average standardized by catch of VME indicator organisms per 25 km2 cells obtained from research andcommercial longline fishing sets.

Table 2Analysis of deviance table of final GAM model (Negative Binomial (3.164) with the logarithm as link function) for the catch of all demersal fish species pooled together.

Parametric terms Approximate significance of smooth terms

Variable df Chi.sq p-Value edf Ref.df Chi.sq p-Value

Habitat type 1 64.70 8.71e�16 s(depth) 8.32 8.87 143.98 o2e�16Month 5 28.01 3.64e�05 s(aspect) 3.41 4.37 33.27 1.64e�06Year 4 38.38 9.34e�08Substrate type 3 8.04 0.04VME area 1 6.08 0.01

C.K. Pham et al. / Deep-Sea Research I 96 (2015) 80–88 83

and plots of residuals against spatial coordinates (Zuur et al.,2009).

3. Results

Pooling all species and life stages together, the final modelexplained 22.8% of the variation in the data and included sevensignificant explanatory variables: habitat type, month, year, sedi-ment type, depth, aspect and the presence of VME (Table 2; Fig. 3).The final GAM fitted to the data revealed significantly higher catchinside VMEs compared to outside VMEs (Fig. 3).

Individual GAMs fitted separately for the six fish species and life-history stages showed some differences in the number and type ofenvironmental variables explaining catch rates. Percent devianceexplained by the models varied between 10.6% (for juvenilesC. conger) and 60.9% (for adults M. moro) (Table 3 and Table S2).Depth was the only significant variable common to all species andlife history stages (Table 3), highlighting divergence in depth dis-tribution between species but also between juveniles and adults of H.dactylopterus, P. bogaraveo, C. conger, P. phycis and M. moro (Fig. 4).

Aspect was significant for P. kuhlii (adults), H. dactylopterus (bothjuveniles and adults), P. bogaraveo (both juveniles and adults), P. phycis

(both juveniles and adults) and M. moro (only juveniles) (Fig. S2).Slope was only significant for two species; M. moro (both juvenilesand adults) and C. conger (adults) (Fig. S3). Habitat type wassignificant in explaining the catch rates of P. kuhlii (only juveniles),H. dactylopterus (both juveniles and adults), P. bogaraveo (onlyjuveniles) and C. conger (both juveniles and adults) (Fig. S4). Substratetype was a significant variable for P. kuhlii and H. dactylopterus (bothjuveniles and adults) and C. conger and M. moro (only juveniles) (Fig.S5). Year was significant for P. kuhlii (juveniles), H. dactylopterus(both juveniles and adults), P. bogaraveo (both juveniles and adults)and M. moro (adults) (Fig. S6). Month was significant for P. kuhlii(adults), H. dactylopterus (both juveniles and adults), P. bogaraveo(both juveniles and adults) and M. moro (adults) (Fig. S6).

Vulnerable marine ecosystems revealed to influence the catchrates of four species (Table 3). For H. dactylopterus, catch rates ofjuveniles was higher in VME compared to non VME areas but wasnot a significant variable for the catch of adults. On the other hand,for P. kuhlii, catch rate of both juveniles and adults was higherinside VME areas (Fig. 5). In contrast, final model for adults of C.conger suggested that catch was higher outside of VMEs comparedto inside, but this was not the case for adults. ForM. moro, the finalmodel fitted for adults did not retain VME as an explanatoryvariable. However, for juveniles M. moro, the VME variable was

Fig. 3. Additive fits of the explanatory variables aspect, depth, habitat type, month, substrate type, the presence of VMEs and year in the GAM models for the catch of allspecies pooled together with longline. Dashed lines indicate 95% confidence intervals and the thick marks on the x-axis indicate the distribution of observations.

C.K. Pham et al. / Deep-Sea Research I 96 (2015) 80–8884

retained in the final GAM, suggesting catch rates were higheroutside VME areas. For the remaining two species (P. bogaraveoand P. phycis), VME classification variable was not retained in thefinal models for either juveniles or adults.

4. Discussion

The explanatory power of the models developed for the sixdemersal species showed that for some species, large-scale spatial

Table 3Explanatory variables retained in final generalized additive models (GAMs) for juveniles and adults of six species of deep-sea fish in the Azores. AIC¼Akaike’s informationcriteria; DE¼deviance explained. x denotes which variable was retained in final model.

Models for juveniles P. kuhlii H. dactylopterus P. bogaraveo P. phycis C. conger M. moro

AIC¼538.9,DE¼47.4

AIC¼4241.9DE¼39

AIC ¼1728.4,DE¼50

AIC¼700.8,DE¼47.4

AIC¼600.36DE¼10.6

AIC ¼1276.4,DE¼38.6

Variable GAM ΔAIC GAM ΔAIC GAM ΔAIC GAM ΔAIC GAM ΔAIC GAM ΔAIC

Substrate type x 12.2 x 8.1Habitat type x 15.8 x 118.7 x 99 x 6.4Year x 8.7 x 34.6 x 11.8Month x 68.9 x 47.3VME x 1.8 x 11.2 x 6s(depth) x 71.9 x 218.6 x 205.5 x 166.2 x 15 x 40s(aspect) x 51.7 x 6.9 x 17.9 x 5.5s(slope) x 4.5

Models for adults AIC¼1371.9,DE¼35.7

AIC ¼4247.8,DE¼38.3

AIC¼3662.5,DE¼24.5

AIC ¼1462.8,DE¼54.7

AIC¼1452.3,DE¼11.2

AIC¼2110.1,DE¼60.9

Variable GAM ΔAIC GAM ΔAIC GAM ΔAIC GAM ΔAIC GAM ΔAIC GAM ΔAIC

Substrate type x 27.6 x 31.6 x 8.8 x 72.4Habitat type x 49.6 x 26.76Year x 4 x 8.2 x 12.4Month x 5.6 x 14.1 x 21.2 x 20.2VME x 7.6 x 8.71s(depth) x 85.2 x 346.74 x 118.2 x 462.42 x 13.74 x 684.8s(aspect) x 9 x 47.2 x 42.53 x 2.9s(slope) x 15.6 x 21.6

Fig. 4. Additive fits of the explanatory variable depth in the GAM model for adults and juveniles of H. dactylopterus, P. kuhlii, P. bogaraveo, C. conger, P. phycis and M. moro.Dashed lines represent 95% confidence intervals and marks along the lower axis represent a single observation.

C.K. Pham et al. / Deep-Sea Research I 96 (2015) 80–88 85

patterns in catches are strongly related to environmental factors.Identifying the habitat requirements for fish species is verychallenging because of the multiple biotic and abiotic factorsaffecting habitat associations, the range of scales over which theyfunction and the general difficulties of obtaining high resolutionenvironmental data for marine systems (Johnson et al., 2013).GAMs are a powerful method to identify the influence of environ-mental variables on the distribution of marine organisms(Maravelias, 1999; Stoner et al., 2001; Walsh and Kleiber, 2001;Yee and Mitchell, 1991) and have been used to predict fishdistribution over large geographical areas (Drexler andAinsworth, 2013). In this study, GAMs showed that a large portionof the variability in fish catch could be attributed to depth. Manystudies have demonstrated the importance of depth on the spatialdistribution of demersal fish (Damalas et al., 2010; Katsanevakisand Maravelias, 2009; Kenchington et al., 2013; Macpherson,2003; Maravelias et al., 2007; Menezes et al., 2006; Moore et al.,2011; Priede et al., 2010). Therefore, it is not surprising that depthexplained the greatest portion of the variability in the catch of thedemersal species. Depth is an important physiological limitationthat influences the distribution of fish in conjunction with otherenvironmental (e.g. salinity, temperature, light, substrate) andbiological factors (e.g. availability of food, predation or competi-tion). Therefore, depth is a proxy of many biotic and abioticvariables for which the direct effects are difficult to elucidate.

The importance of deep-sea vulnerable marine ecosystems fordemersal fish in the Azores was demonstrated for all species pooledtogether. However, the results obtained for species-specific modelsshowed that the presence of VMEs was significant in explaining theabundance for only two (H. dactylopterus and P. kuhlii) out of the sixdemersal fish species considered in this study. The use of VMEs bythese two rockfish species was previously expected, based on ROVimages showing both species sitting close to gorgonians gardens andother 3D structures (Gomes-Pereira et al., 2012; Porteiro et al., 2013).

However, at a larger spatial scale, our results suggest that onlyjuveniles H. dactylopterus were associated to VMEs, whereas for P.kuhlii, both juveniles and adults were found in higher abundanceinside VMEs. In fact, we found that the distribution of P. kuhlii andVME indicator organisms overlap to a great extent. As demonstratedby another study (Menezes et al., 2006), our results showed that P.kuhlii has a narrow depth distribution, ranging between 100 and350 m and coinciding with the depth where bycatch of VMEindicator taxa is highest (Pham et al., 2014). Furthermore, similarlyto VME indicator taxa, P. kuhlii is more abundant on seamountscompared to islands slopes (Catarino et al., 2013). Our data cannotexplain the reasons for an overlapping distribution but it is probablethat VMEs serve as a feeding area for P. kuhlii since it has beenobserved feeding on zooplanktivorous fish (Gomes-Pereira et al.,2013), known to be particularly abundant within coral areas(Porteiro et al., 2013). It is also likely that this species utilizes theincreased complexity created by coral aggregations to seek refugefrom predators (Fosså et al., 2002) or as a spawning ground (Baillonet al., 2012).

In the case of H. dactylopterus, only juveniles were found to bemore abundant in VMEs. This observation was similar to resultsobtained from longline surveys in the Santa Maria di Leuca cold-water coral province (Mediterranean) that suggested small-medium H.dactylopterus to be more abundant in coral habitats (D’Onghia et al.,2012). Equally, on the shelf-break off the central western coasts ofItaly, the abundance of juveniles H. dactylopteruswas higher in crinoidbeds than in surrounding muddy areas (Colloca et al., 2004). In light ofthese results, both studies suggested aggregations of epibenthicorganisms to be important nursery areas for this species, a possibilityfor H. dactylopterus in the Azores since little is known about early lifestages. On the other hand, this species increases distribution over awider depth range during growth (Massuti et al., 2001) and adultshave been observed on various bottom types away from coral areas inmany locations (Gordon et al., 1996; Haedrich and Merrett, 1988; Reedet al., 2006; Uiblein et al., 2003). Looking specifically at H. dactylopterusin various locations in the North Atlantic, (Biber et al., 2014) did notdetect any significant differences between their abundance insidecompared to outside Lophelia reefs ecosystems. Similarly, in the coralbanks of southeastern United States, H. dactylopterus was occasionallyobserved sitting under gorgonians but was also found away from thereef habitat and overall, no strong association with any particular zonewas detected (Quattrini et al., 2012; Ross and Quattrini, 2007). Theseresults further demonstrate that H. dactylopterus can be found in awide range of habitats and that it is not restricted to VMEs. However,the above studies did not distinguish juveniles from adults, preventingontogenetic changes in habitat requirements from being exposed, asrevealed by our results.

Co-occurrence does not necessarily imply the existence of afunctional relationship essential for sustaining a fish population(Auster, 2007). Analogous distribution patterns could be a con-sequence of habitat characteristics benefiting both fish and corals,but with no direct association. For example, both fish and coralsmight favor areas of increased flow speed because of the enhanceddelivery of drifting particulates (detritus and zooplankton) neces-sary to sustain their dietary requirements (Parrish, 2006).

Although many studies report a higher species richness andabundance in coral aggregations (Biber et al., 2014; Costelloet al., 2005; D’Onghia et al., 2012, 2010; Harter et al., 2009;Husebø et al., 2002; Reed et al., 2006; Ross and Quattrini, 2007;Soffker et al., 2011; Stone, 2006; Sulak et al., 2007) and spongehabitats (Beazley et al., 2013; Du Preez and Tunnicliffe, 2011;Kenchington et al., 2013; Marliave et al., 2009), the exact relation-ship is far from being clear and varies between areas (Auster, 2007).Some authors proposed that it is the complexity of the habitat thatattracts certain fish and not the coral itself (Auster, 2005; Harter etal., 2009; Soffker et al., 2011) while others suggests that fishes and

Fig. 5. The effect of the presence of VMEs on juveniles and adult of H. dactylopterus,P. kuhlii, P. phycis, P. bogaraveo, M. moro and C. conger. Dashed lines indicate 95%confidence intervals and the thick marks on the x-axis indicate the distribution ofobservations.*Indicates when the presence of VME was retained in the GAM fit.

C.K. Pham et al. / Deep-Sea Research I 96 (2015) 80–8886

octocoral aggregations co-occur in the same types of habitats butwithout any functional relationship (Parrish, 2006; Tissot et al.,2006). Analysis of longline surveys off Norway showed some speciesto be associated with coral reefs but the spatial extent of suchhabitats is very small to play a major role in sustaining fishpopulations (Kutti et al., 2014). However, in some areas, there ismore direct evidence of a relationship between fish and corals(Baillon et al., 2012; Henry et al., 2013).

In the Azores region, our understanding of the ecological rele-vance of VMEs is mainly limited by incomplete knowledge of thespatial distribution of VMEs. Most of the data on coral and spongedistribution comes from bycatch obtained from commercial longlinefishery and research surveys (Braga-Henriques et al., 2013; Sampaioet al., 2012). In the future, it is important to improve our under-standing of VME distributions by obtaining further in situ dataon the occurrence of VME organisms but also by obtaining in situdata on fish behavior, small-scale patterns of movements and VMEhabitat use. Considering ontogenic shifts in habitat use and potentialdifferences between sexes, it is essential for future studies toincorporate such factors when studying habitat associations.

Collectively, our data suggest that VMEs in the Azores influencethe abundance of two rockfish species but the nature of thisassociation remains to be determined. Understanding fish-habitatrelationships would be of great relevance since unregulated long-line fishing may eventually result in habitat homogenization andfragmentation (Pham et al., 2014) with dramatic consequencesto associated fauna. Hence, comprehending how demersal fishpopulations are linked to VMEs has direct implications for fisheriesmanagement and conservation.

Acknowledgements

This research received funding from the European Community7th Framework Programme under the CoralFISH (213144) andHERMIONE (226354) projects, the Fundação para a Ciência e aTecnologia (FCT), Fundo Regional da Ciência, Tecnologia (FRCT),through the research project 2020-M2.1.2/I/026/2011 (Pro-Conver-gência), the Project CONDOR (EEA grants—PT0040/2008) and theproject “Annual demersal fish monitoring research cruises” fundedby the Azores Government. The authors also acknowledge fundsprovided by FCT-IP/MEC to LARSyS Associated Laboratory and IMAR-University of the Azores (R&DU #531), Thematic Area E, through theStrategic Project (PEst-OE/EEI/LA0009/2011–2014, COMPETE, QREN)and by the Government of Azores FRCT multiannual funding. CKPwas supported by an FCT doctoral grant (SFRH/BD/66404/2009,COMPETE/QREN). TM, a Ciência Researcher, is co-funded by IMARand FCT/Portuguese Ministry for Education and Science (POPH,COMPETE/QREN European Social Fund). We thank the scientistsand crew aboard the RV Arquipélago in particular Alexandra Rosa,Diana Catarino and Valentina Matos. We would like to thank PålBuhl-Mortensen, Lea-Anne Henry and an anonymous reviewerwhose comments greatly improved the manuscript.

Appendix A. Supporting information

Supplementary data associated with this article can be found inthe online version at http://dx.doi.org/10.1016/j.dsr.2014.11.004.

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