Layout 1 INTRODUCTION The giant red shrimp Aristaeomorpha foliacea (Risso, 1827) is one of the key species in Mediterranean deep-sea benthic communities and it is also one of the most impor- tant target species for Mediterranean bottom trawling (Cau et al., 2002; Guillen et al., 2012; Rinelli et al., 2013, Palmas et al., 2017a). The species is exploited on muddy bottoms, mainly at depths of 400-800 m, using traditional trawl nets (Ragonese et al., 2002; Sbrana et al., 2003; Sabatini et al., 2011; Sala et al., 2015). In 2016, declared landings of red shrimp (A. foliacea jointly with Aristeus antennatus (Risso, 1816)), amounted to 5500 t across the entire Mediterranean Sea (STECF, 2015). Nowadays, deep-water shrimps account for about 5% of the total pro- fessional fishing income in the Mediterranean Sea (STECF, 2019), with increasing catches in Italy and Spain in particular, which are the main producers in Europe (Eu- mofa, 2019). In the Mediterranean Sea, giant red shrimp distribu- tion differs among western and eastern basins according to a geographical gradient (Cau et al., 2002; Politou et al., 2004; Cartes et al., 2011a). A. foliacea is predominant in Sardinia, northern and Central Tyrrhenian Sea, Strait of Sicily, Ionian Sea, whereas it is nearly absent in the Lig- urian and Catalan Sea, Balearic Islands and eastern Mediterranean basin (Ragonese and Bianchini, 1995; Pa- paconstantinou and Kapiris, 2003). Several authors have reported a strong correlation be- tween the geographical distribution of red shrimp and en- vironmental factors, including the geomorphological characteristics of the fishing grounds (e.g. presence of canyons and underwater reliefs) (Abellò et al., 2002, Sabatini et al., 2007, 2011), trophic factors (Cartes et al., 2008), hydrological factors (Ghidalia and Bourgois, 1961; Sardà et al., 2004; Carney, 2005; Company et al., 2008; Guijarro et al., 2008; Massuti et al., 2008; Canals et al., 2009; Cartes et al., 2011a; Cartes et al., 2011b) and fish- ing pressure (Relini and Orsi Relini, 1987; Bianchini and Ragonese, 1994; Blanchard, 2001; D’Onghia et al., 2005). Despite these studies, many aspects of the interactions among environmental variables and spatial distribution of the species remain unclear, yet. The aim of this study is to provide further information about the possible relationships between environmental factors and the spatio-temporal distribution of giant red shrimp in Sardinian waters (western Mediterranean), using multivariate models (GAMs and Regression Trees). METHODS Study area The seas around Sardinia represent a particularly in- teresting environment, for their central position in the western Mediterranean basin, its geomorphologic hetero- geneity and the presence of extended fishing bottoms (Cau et al., 1994). These features (i.e., submarine canyons ARTICLE Exploring relationships between the distribution of giant red shrimp Aristaeomorpha foliacea (Risso, 1827) and environmental factors in the Central-Western Mediterranean Sea Cinzia Podda,*§ Francesco Palmas,§ Serenella Cabiddu, Paola Pesci, Andrea Sabatini Department of Life and Environmental Sciences, University of Cagliari, Via Fiorelli 1, 09126 Cagliari, Italy §Cinzia Podda and Francesco Palmas contributed equally to this work. ABSTRACT Mediterranean giant red shrimp Aristaeomorpha foliacea (Risso, 1827) is one of the dominant species in deep-sea megafaunal assemblages, plays a key role in deep-sea communities and it is considered one of the most important targets of deep-water trawl fishing. Although a large number of studies have analysed the spatial distribution of epibenthic crustaceans in bathyal habitats with respect to environmental, geomorphological and hydrological factors, as well as fishing pressure, the manner in which these vari- ables synergistically affect the spatio-temporal changes of giant red shrimp is unclear. To analyse the possible effects of abiotic predictors on the spatio-temporal distribution of giant red shrimp, Generalized Additived Models (GAMs) and Regression Trees were produced. Biological data were collected during the MEDITS trawl surveys carried out in the Sea of Sardinia (2009-2014), during which environmental data were obtained with a multiparametric probe. A longitudinal (west-east) trend was found, with higher abundances at depths of 400-600 m, corresponding to salinity values of 38.1-38.5 psu and temperatures of 13.6-13.8°C. Our results confirm the existence of a tight linkage between the distribution of the Levantine Intermediate Water (LIW) from the eastern Mediterranean Sea and the preferential habitat characteristics of the giant red shrimp. We suggest that a deeper knowledge of the relationships between abiotic (hydrological) factors in the water column and the distribution of Mediterranean resources, such as the giant red shrimp, can provide valuable support for their better management, at the local scale (Sardinia) and across the whole Mediterranean Sea. No n- co mm er cia l u se on ly Relationships between distribution of giant red shrimp and environmental factors 85 and seamounts) determine local hydrographic conditions that can facilitate species movement, thus contributing to the peculiar spatial distributions observed in this area (Orrù and Ulzega, 1988; Sabatini et al., 2007). Data come from the MEDITS survey programme, an international bottom trawl survey, carried out in the Mediterranean since 1994, every year, during the spring and the beginning of summer. This survey has been de- signed to sample all trawlable areas along coasts from 10 to 800 m depth. The application of a common standard- ized protocol allowed to produce biological data on dem- ersal resources (Spedicato et al., 2019). According to the MEDITS protocol, the seas around Sardinia were subdivided into seven zones: two located in the eastern coast (SE-Area 1, NE-Area 2), one in the northern (N-Area 3), three in the western coast (NW-Area 4, CW-Area 5, SW-Area 6) and one in the southern coast (S-Area 7) (Fig. 1). Data collection Fishing data were collected from the Sea of Sardinia during the summer months from 2009 to 2014; trawl sur- veys were performed according to Bertrand et al., 2002 using a stratified random sampling design, with the number of experimental hauls proportional to the surface of each depth stratum. A total of 103 experimental hauls were analysed (at depths of 400-700 m), corresponding to the sets in which potentially giant shrimp are found. Trawl sen- sors (SIMRAD) were connected to the net mouth to record the functioning and opening of the wings. Data about hor- izontal opening net were used to evaluate the swept area (Sparre and Venema, 1998) and to compute standardized density index (di: number of individuals km–2). To investigate the effect of hydrological conditions on species abundance, data of both vertical and longitudinal profiles of temperature (°C), salinity (psu) and depth (m) were recorded using a multi-parameter probe CTD (SBE- 37 IM Microcat) mounted on the experimental net (in the otter of the trawler, GOC73 net). For each longitudinal profile, we calculated the average temperature (Bot_Temp) and salinity (Bot_Sal) values at the bottom. For each vertical profile, we compiled the temperature (LiwCT) and salinity (LiwCS) at the LIW (Levantine In- termediate Water) core, between 250 and 500 m, the av- erage depth at the bottom (Depth), the longitude and latitude coordinates (lat, lon), the spatio-temporal vari- ables (Year and sampling areas, Areas 1-7) and fishing ef- fort (n.A: number of bottom trawlers that operate in the sampling areas) (Tab. 1). Data on the trawling fleets active in the main ports of Sardinia for the period 2009–2014 were obtained from the European Fleet Register (https://webgate.ec.europa.eu/fleet-europa/search_en). Statistical analyses For the environmental variables, the Zuur et al. (2010) protocol was followed, whereby collinearity was examined by computing pairwise scatter plots to compare continuous covariates; combinations with relevant Spearman’s rho co- efficients (ρ>0.7) were discarded prior to modelling. Data exploration revealed non-linear patterns among the re- sponse variables, as such, giant red shrimp abundance and its relationship with environmental and spatio-temporal variables were described using Generalized Additive Mod- els (GAMs) (Hastie and Tibshirani, 1990; Maunder and Punt, 2004) and Regression Trees (Walsh et al., 2001). GAMs (Hastie and Tibshirani, 1990; Wood, 2006) are Fig. 1. Map of the study area. Tab. 1. Variables and acronyms used for the analysis. Variable Name of the variables Bottom temperature Bot_Temp Bottom salinity Bot_Sal LIW core temperature LiwCT LIW core salinity LiwCS Depth Depth Latitude lat Longitude lon Year of sampling Year Sampling areas Areas Fishing effort n.A No n- co mm er cia l u se on ly C. Podda et al.86 non-parametric regressions in which part of the linear pre- dictor is specified as a sum of the smoothing functions (smooth function, s) of the predictor variables; the chal- lenge is to find suitable parametric representations for the smoothing functions and appropriately control the degree of smoothness (Wood and Augustin, 2002). A stepwise backward selection procedure was implemented to identify the best fitting model, based on minimizing the Akaike’s Information Criterion (AIC) (Akaike, 1973) values. Model performances were evaluated by obtaining the total ex- plained deviance. Further approaches were based on the representation of Generalized Additive Mixed Models (GAMMs) as an extension of GAMs; GAMMs suggest a more complex structure than ordinary additive model and include smoothing terms as random effects (Wood, 2006). The prediction of the spatial aggregations of species was obtained by means of Gaussian process kriging model im- plemented in mgcv R package (Wood, 2006). Regression Trees (Morgan and Sonquist, 1963), based on a recursive partitioning regression, were used to vali- date the obtained results. These models break data into left and right branches, whereby the splitting rules are de- fined by the predictor variable values. Splitting continues until the ‘terminal’ nodes, wherein response values be- come similar within a node or data are too sparse for ad- ditional splitting. At the terminal node, the predicted response is the average or majority of the response values within that node for continuous or discrete variables. The sizes of the regression tree structure were examined be- cause the vertical position of the nodes is an important function that reflects the degree of the relationship be- tween variables (Clark and Pregibon, 1992). All statistical analyses were performed using R 3.3.1, with a significance level of P<0.05 (R Core Team, 2019). The GAM approach, as proposed by Wood (2006), was performed using the library mgcv and the Regression Tree with rpart. RESULTS Density of giant red shrimp, sampled between 399 and 711 m depth, showed marked inter-annual fluctuations in all investigated areas. Highest average densities were recorded in the southern (S-Area 7), northern (N-Area 3) and north-eastern areas of Sardinia (NE-Area 2), while the lowest density was recorded in north western area (NW-Area 4) (Tab. 2). The exploratory analysis of environmental data showed a significant correlation (ρ=0.93) between bottom salinity (Bot_Sal) and salinity at LIW strata (LiwCS). As such, Bot_Sal and LIWCS were considered redundant and were then included separately in the predictive models. The best density model for A. foliacea was a GAMM (AIC=346.9) without random effect, that explains the 25% of the total deviance. The final model included geo- graphical coordinates (lat, lon), depth (Depth), bottom temperature (Bot_Temp), bottom salinity (Bot_Sal) and fishing effort (n.A) (Tab. 3), according to the equation: (1) (di ~ s(lat, lon) + s(Depth) + s(Bot_Temp) + s(Bot_Sal) + s(lat, lon, n.A)) (eq. 1) The cumulative effect of the covariates are illustrated in Fig. 2, where the giant red shrimps’ aggregation areas are reported. Highest abundances were recorded in south- eastern and northern areas (Area 3 and 7). Important areas of aggregation were also observed throughout the eastern areas (Areas 1, 2), where A. foliacea showed intermediate densities. Depth, bottom temperature (Bot_Temp) and bot- tom salinity (Bot_Sal) showed a negative correlation. Species density increased at depths between 400 and 600 m where bottom temperatures reach values between 13.6 °C and 13.8 °C and bottom salinity between 38.1 and 38.55 psu (Fig. 2). The Regression Tree showed similar result with a sig- nificant relationship with environmental variables as se- lected in GAMM models. The density was mainly influenced, at its first branch, by longitude values ≥4278000. Later, a secondary branch was observed at depths ≥580.2 m (94 record). This node splits into two branches, which were influenced by latitude values ≥460000 (50 records) and latitude values <600000 (44 records), respectively, and average depth <565.6 m (20 records) and bottom salinity ≥38.55 psu, respectively (24 Tab. 2. Density index (average value ± SE) of the investigated species in each one of the seven zones selected and each year. 2009 2010 2011 2012 2013 2014 Area 1 639.4±27.6 2239.2±40.3 431.5±30.9 509±28.5 404.3±24.2 71.5±10 Area 2 1053.75±41.8 178.2±18.2 758.3±34.7 10.5±4.6 199.7±15.4 455.8±22.8 Area 3 1009±29.1 499±13.1 364.7±11.7 1965.7±45.4 1190±21.3 0 Area 4 0 23±0 1364±0 1540±0 106±0 2736±0 Area 5 78.5±4.1 231±0 12±4.9 0 870±46.9 104.5±14.4 Area 6 256±0 148±0 315±0 115±0 24±1.2 103±0 Area 7 2409.3±67.4 1646.8±59.6 202±0 1621.3±52.9 1013±38.2 800.6±44.2 No n- co mm er cia l u se on ly Relationships between distribution of giant red shrimp and environmental factors 87 records). Our data confirm the significance of the results obtained from the GAMM model: density was affected by the depth, longitude, latitude and bottom salinity (Fig. 3). DISCUSSION AND CONCLUSIONS Changes in environmental conditions can influence the life traits of marine organisms in different ways: by impacting spawning, growth and recruitment (Pankhurst and Munday, 2011; Beggs et al., 2013), prey availability and prey-predator relationships (Fanelli and Cartes, 2010) or by altering their spatial distribution (Perry et al., 2005). These impacts can be exacerbated by the effects of human activities, such as fishing pressure, which may impair the resistance and resilience of marine populations against en- vironmental changes (Anderson et al., 2008). Due to the importance of A. foliacea in the deep- water communities of the Mediterranean Sea, many studies focused on the biology, ecology and fishery (Pal- mas et al., 2017a and references therein). Other authors have analysed the effects of different hydrological con- ditions (i.e., depth, temperature and salinity) on the species’ distribution (Yahiaoui, 1994; Cau et al., 2002; Politou et al., 2004; Sardà et al., 2004; Company et al., 2008; Eumofa, 2019). Spatial distribution has also been related to the cascading of dense shelf waters along the slope (Company et al., 2008), geomorphology (presence Tab. 3. GAMM summary results for the abundance of giant red shrimps. Variables p-value % of variation explained s(lat, lon) 0.000565*** 25% s(Detph) 3.62e-06*** s(Bot_Temp) 0.003380** s(Bot_Sal) 0.019274* s(lat, lon, n/A) 1.86e-05*** Fig. 2. Generalized additive model (GAM)-derived effect of covariate modelling for the density index of giant red shrimps. Shaded areas and dashed lines indicate 95% confidence bands. No n- co mm er cia l u se on ly C. Podda et al.88 of canyons and seamounts) (Sabatini et al., 2007, 2011), bottom type (Cartes et al., 2008), oceanographic features (Guijarro et al., 2008) and fishing activity (D’Onghia et al., 2005; Carlucci et al., 2006). For instance, the abun- dance of the giant red shrimp would result high patchy as a consequence of several intermingling factors (Rinelli et al., 2013, Masnadi et al., 2018), which could act as drivers in shaping the spatial distribution of the species. The available literature reports a longitudinal gradient of the spatial distribution of A. foliacea, with abundances in the central and easternmost areas higher than those in the westernmost areas of the Mediterranean Sea (Cau et al., 2002; Politou et al., 2004; Cardinale et al., 2017). In the Mediterranean Sea, giant red shrimp hotspots were observed: i) in the Sardinian Sea, where the species was more abundant in the southern grounds (Cau et al., 2002); ii) in the central Tyrrhenian Sea, where the species reach greater numbers in the southern sector; iii) in southern Sicily and in the western sector of the Ionian Sea where this species showed a consistent southern aggregation (Ragonese et al., 1994; D’Onghia et al., 2003). Giant red shrimps occur at depths of 160-1330 m, and are prevalently caught at depths of 500–800 m (Maiorano et al., 2010; Bayhan et al., 2015; Deval et al., 2016; Eu- mofa, 2019, Guijarro et al., 2019), although populations can be found also at shallower depths, typically between 100 and 160 m in the the Ionian Sea and in southern Italy canyons (Relini and Relini-Orsi, 1987; Matarrese et al., 1995; D’Onghia et al., 1996, Sabatini et al., 2007). Such a wide vertical distribution is due to the fact that the species is capable of wide daily movements along the water column (Kapiris et al., 2010; Fernández et al., 2013), ascending to shallower depths during the night (Cau and Deiana, 1982), particularly in winter. This phe- nomenon is more evident in canyons and seamounts, where bottom climbing on the continental slope edge can be observed (Matarrese et al., 1995, Sabatini et al., 2007, 2011; Palmas et al., 2015, 2017b). A. foliacea prefers the warmer and more saline waters of the eastern Mediter- ranean basin than the western basin. In particular, the species appreciates seawater temperatures close to 13°C and salinities of 38.5 psu (Ghidalia and Bourgois, 1961; Yahiaoui, 1994; Cartes et al., 2002; Politou et al., 2004; Sardà et al., 2004; Company et al., 2008; Noël, 2015), corresponding to the typical hydrological features of the Levantine Intermediate Waters (LIW) arriving from the eastern Mediterranean basin. In detail, for the Catalan Sea and the Balearic Islands, red shrimps are abundant be- tween 12.8 and 13.9°C. Nevertheless, peak densities occur Fig. 3. Regression Tree model on density index of A. foliacea. No n- co mm er cia l u se on ly Relationships between distribution of giant red shrimp and environmental factors 89 at ca. 12.8°C at depths between 900 and 1000 m (Demestre and Martín, 1993, Sardà et al., 1998, Tudela et al., 2003). This species can be found also from 80 to 600 m depth off Algeria and Tunisia at temperatures ranging from 12.8 to 14°C (Yahiaoui, 1994). In the Ionian Sea, red shrimps have been reported at different depths, but the highest abundances were found at 600-800 m depths (Deval et al., 2016), both in the Western (at 13.3 and 13.7°C) and the Eastern (at up 13.9°C) basin (Politou et al., 2004). The hypothetical distribution range of this species could extend down to 2800 m depth (Sardà et al., 2004). The spatio-temporal variability of the species abun- dance would seem also related to large-scale climatic in- dices, such as the North Atlantic Oscillation (NAO) (D’Onghia et al., 2012) even if the variability can differ among even nearby ports (Hidalgo et al., 2015). All of the above delineated abiotic constraints are associated with an intense and prolonged fisheries exploitation, resulting in concurring effects which make difficult the interpreta- tion of the whole picture of the species’ distribution (Rinelli et al., 2013; Sabatini et al., 2013). Overall, our study confirms either the general assump- tion by which the abundance of A. foliacea follows a lon- gitudinal eastern-western gradient or the influence of environmental variables in its spatial distribution and abundance in the seas surrounding Sardinia (Murenu et al., 1994; Cau et al., 2002; Rinelli et al., 2013). The high- est abundances of giant red shrimp were observed in the southern (S-Area 7) and northern (N-Area 3) areas, con- firming a longitudinal trend for the distribution of the species, with an increasing western-eastern pattern (Gui- jarro et al., 2019). The particular variability in the hydro- graphic conditions of the Sardinian seas determine the presence of different habitats that provide a complex sys- tem of environmental patches, which, in turn, are reflected in the distribution and abundance of the deep-water red shrimps along the Sardinian slopes. Our data confirm also that giant red shrimp, preferring depths of 400-600 m, salinity levels between 38.1 and 38.5 psu and tempera- tures between 13.6°C and 13.8°C, seem to concentrate in Levantine Intermediate Waters (LIW). In conclusion, due to response complexity, it is not al- ways easy to establish unique relationships between a sin- gle environmental (abiotic or biotic) factor and a biological response. The multitude of pathways through which hydrological features affect marine populations often makes it difficult to establish univocal, significant and non-spurious connections between the climate and ecological responses (Ottersen et al., 2010). In this work, the use of in situ environmental observa- tions helped to clarify the role of some key environmental process on giant red shrimp abundance that can be extend across the whole Mediterranean distributive scenario. Corresponding author: cpodda@unica.it Key words: Aristaeomorpha foliacea; giant red shrimp; abundance; distribution models; environmental effects; Mediterranean Sea. Received: 7 November 2020. Accepted: 15 December 2020. This work is licensed under a Creative Commons Attribution Non- Commercial 4.0 License (CC BY-NC 4.0). ©Copyright: the Author(s), 2020 Licensee PAGEPress, Italy Advances in Oceanography and Limnology, 2020; 11:9271 DOI: 10.4081/aiol.2020.9471 REFERENCES Abelló P, Carbonell A, Torres P, 2002. 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