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INTRODUCTIONTop

The deep-water rose shrimp, Parapenaeus longirostris, and the Norway lobster, Nephrops norvegicus, are two of the most important target species of demersal fisheries on the Mediterranean continental shelf and upper slope. Their biology and population dynamics have been thoroughly studied (García-Rodríguez et al. 2009García-Rodríguez M., Pérez Gil J.L., Barcala E. 2009. Some biological aspects of Parapenaeus longirostris (Lucas, 1846) (Decapoda, Dendrobranchiata) in the gulf of Alicante (S.E. Spain). Crustaceana 82: 293-310., Johnson and Johnson 2013Johnson M.L, Johnson M.P. 2013. Advances in marine biology. The ecology and biology of Nephrops norvegicus. Academic Press, 325 pp., Kapiris et al. 2013Kapiris K., Markovic O., Klaoudatos D., et al. 2013. Contribution to the Biology of Parapenaeus longirostris (Lucas, 1846) in the South Ionian and South Adriatic Sea. Turkish J. Fish. Aquat. Sci. 13: 647-656.) and the exploitation status of their stocks has been assessed in several areas of the Mediterranean Sea (GFCM 2017GFCM. 2017. Report of the Working Group on Stock Assessment of Demersal Species (WGSAD), Rome, Italy, 7-12 November 2016. FAO., STECF 2017Scientific, Technical and Economic Committee for Fisheries (STECF). 2017. Mediterranean assessments 2016- part 2 (STECF-17-06); Publications Office of the European Union, Luxembourg; EUR 28359 EN.). Because the life histories of the two species show different characteristics, especially concerning their lifespan and growth rate, the study of the spatiotemporal evolution of their abundance is of valuable interest for understanding the dynamics of deep-water ecosystems (Company et al. 2008Company J.B., Puig P., Sardà F., et al. 2008. Climate influence on deep sea populations. Plos ONE 3: e1431., Ligas et al. 2011Ligas A., Sartor P., Colloca F. 2011. Trends in population dynamics and fishery of Parapenaeus longirostris and Nephrops norvegicus in the Tyrrhenian Sea (NW Mediterranean): the relative importance of fishery and environmental variables. Mar. Ecol. 32: 25-35.).

Along the NW coasts of the Mediterranean, the species forms small and highly fluctuating populations and shows the largest abundances in warmer waters, with a decreasing south-north abundance gradient (Abelló et al. 2002Abelló P., Abella A., Adamidou A., et al. 2002. Geographical patterns in abundance and population structure of Nephrops norvegicus and Parapenaeus longirostris (Crustacea: Decapoda) along the European Mediterranean coasts. Sci. Mar. 66: 125-141., Sobrino et al. 2005Sobrino I., Silva C., Sbrana M., et al. 2005. Biology and Fisheries of Deep Water Rose Shrimp (Parapenaeus longirostris) in European Atlantic and Mediterranean waters. Crustaceana 78: 1153-1184.). A positive correlation between sea surface temperature (SST) and abundance of deep-water rose shrimp was observed in Morocco (Benchoucha et al. 2008Benchoucha S., Berraho A., Bazairi H., et al. 2008. Salinity and temperature as factors controlling the spawning and catch of Parapenaeus longirostris along the Moroccan Atlantic Ocean. Hydrobiologia 612: 109-123.). In the Ligurian and northern Tyrrhenian Seas, Ligas et al. (2010Ligas A., De Ranieri S., Micheli D., et al. 2010. Analysis of the landings and trawl survey series from the Tyrrhenian Sea (NW Mediterranean). Fish. Res. 105: 46-56., 2011)Ligas A., Sartor P., Colloca F. 2011. Trends in population dynamics and fishery of Parapenaeus longirostris and Nephrops norvegicus in the Tyrrhenian Sea (NW Mediterranean): the relative importance of fishery and environmental variables. Mar. Ecol. 32: 25-35. and Colloca et al. (2014)Colloca F., Mastrantonio G., Jona Lasinio G., et al. 2014. Parapenaeus longirostris (Lucas, 1846) an early warning indicator species of global warming in the central Mediterranean Sea. J. Mar. Syst. 138: 29-39. underpinned the positive effect of SST on the temporal trend of stock abundance and commercial catches of P. longirostris. The deep-water rose shrimp in the Ligurian and northern Tyrrhenian Seas are one of the few stocks in the western and central Mediterranean to be exploited sustainably, exhibiting a sharp increase in abundance in the last few years (Colloca et al. 2014Colloca F., Mastrantonio G., Jona Lasinio G., et al. 2014. Parapenaeus longirostris (Lucas, 1846) an early warning indicator species of global warming in the central Mediterranean Sea. J. Mar. Syst. 138: 29-39., GFCM 2017GFCM. 2017. Report of the Working Group on Stock Assessment of Demersal Species (WGSAD), Rome, Italy, 7-12 November 2016. FAO., STECF 2017Scientific, Technical and Economic Committee for Fisheries (STECF). 2017. Mediterranean assessments 2016- part 2 (STECF-17-06); Publications Office of the European Union, Luxembourg; EUR 28359 EN.). This increasing trend does not appear to be affected by fishing activities, because the exploitation pattern has remained the same over the last decade, as is also demonstrated by the lack of recovery of other important stocks (Colloca et al. 2014Colloca F., Mastrantonio G., Jona Lasinio G., et al. 2014. Parapenaeus longirostris (Lucas, 1846) an early warning indicator species of global warming in the central Mediterranean Sea. J. Mar. Syst. 138: 29-39.).

Due to these contrasting characteristics, it was hypothesized that the two species show different behaviours in relation to changes in environmental factors. The aim of the present paper is to analyse the distribution pattern of both P. longirostris and N. norvegicus in the European Mediterranean waters. All data used in the present study were collected from different areas of the Mediterranean with identical fishing gear and sampling procedures, thus allowing combined analysis of data from the different areas.

MATERIALS AND METHODSTop

The data analysed originate from a total of 23144 hauls carried out during daylight hours in the second/third quarter of the year between 10 and 800 m depth under the framework of the EU Project MEDITS for the period 1994-2015. The surveys took place in the European waters of the Mediterranean Sea and cover the FAO-GFCM geographic sub-areas (hereafter GSAs) reported in Figure 1. The sampling procedures were standardized according to a common protocol; details on the survey methodology and defined geographical sectors are described in the MEDITS handbook (http://www.sibm.it/MEDITS%202011/principaledownload.htm).

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Fig. 1. – GSAs included in the study.

The area swept in each haul was calculated from direct measurements of the horizontal opening of the net detected by ScanMar (or SIMRAD) equipment. If such instruments were not available, the measuring procedures reported in the MEDITS handbook were applied. Biomass and density indices of deep-water rose shrimp and Norway lobster were calculated from each haul as biomass and number of individuals per swept area (kg km^–2 and N km^–2, respectively) (Sparre and Venema 1998Sparre P., Venema S.C. 1998. Introduction to tropical fish stock assessment. Part I. Manual. FAO Fish. Tech. Pap. 306/1 (Rev. 2): 407 pp.). Stratified mean biomass and density indices by species and year were also calculated for each GSA, as well as the frequency of occurrence per depth stratum (10-200 m, 200-800 m). The Spearman Rho non-parametric test was applied to the biomass and density indices to evaluate statistical significance in the trends of the various GSA data series (Hamed 2016Hamed K.H. 2016. The distribution of Spearman’s rho trend statistic for persistent hydrologic data. Hydrol. Sci. J. 61: 214-223.). Mean depth (m) and mean geographical position (latitude and longitude) were recorded at each station during the surveys.

The effects of abiotic variables on the spatiotemporal variations in the abundance of deep-water rose shrimp and Norway lobster were determined using generalized additive models (GAMs) (Hastie and Tibshirani 1990Hastie T.J., Tibshirani R.J. 1990. Generalized additive models. Chapman & Hall, Boca Raton, 335 pp.). The density index of shrimps was modelled on the log scale. By means of an exploratory analysis, a change in spatial distribution of the two species over the years was observed, suggesting a non-separable effect of space and time that was used as a basis for the modelling approach. Explanatory variables in deep-water rose shrimp and Norway lobster abundance models were latitude and longitude, SST, depth, and time (years). Satellite data at a fine spatial scale (0.063×0.063 degrees) for SST (degrees Celsius) were collected (MyOcean follow-on project, http://marine.copernicus.eu); monthly data were averaged to obtain the mean SST for the first quarter of each year from 1994 to 2015. Mean depth (m) and geographical position (latitude and longitude) were recorded at each station during the surveys.

Multicollinearity between the explanatory variables was analysed by means of pair-plots, a matrix of scatter plots that shows the bivariate relationships of variables, and variance inflation factor (VIF) values (Zuur et al. 2007Zuur A.F., Ieno E.N., Smith G.M. 2007. Analysing Ecological Data. Springer, London, 680 pp.). Variables with a high Pearson correlation coefficient (r) (>0.8, absolute value) and a high VIF value (>3) were considered correlated, and one of the pair was removed.

GAM models with Gaussian distribution (and identity link) were used to describe the relationship between the abundance of deep-water rose shrimp and Norway lobster and explanatory variables.

The initial model included a smooth term accounting for spatial (latitude and longitude) and temporal (years) interaction. Later, a smooth term for depth and for the interaction between depth and time was added. The best model was used to introduce a smooth term for SST. Finally, the models were compared using the Akaike information criterion (AIC) and deviance explained. The model with the lowest AIC was selected as the best model, that is, the most parsimonious model with the greatest explanatory power (which best fits the data). The significance of each variable in the GAM was determined by means of analysis of variance (F-test).

Data exploration and analysis were carried out with R 3.4.1 (R Core Team 2017R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/), and the associated mgcv package (Wood 2006Wood S.N. 2006. Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press, Boca Raton: 410 pp.). An assumed significance level of 5% was used in all the statistical analyses.

The spatial analysis predictions were performed using a 0.03° resolution grid, which was created through the sampling of the informative layers used in the model by means of the QGIS software (QGIS Development Team 2017QGIS Development Team. 2017. Geographic Information System. Open Source Geospatial Foundation Project [WWW Document]. Qgis. http://www.qgis.org/). In particular, the bathymetric data were derived from the EMODnet Bathymetry portal, while the SST data were derived from MyOcean follow-on project (http://marine.copernicus.eu). The resulting model prediction maps were produced using the R software (R Core Team 2017R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/). In order to assess changes in the abundance and spatial distribution of the species, maps for six MEDITS surveys carried out in the years 1994, 1998, 2003, 2007, 2011 and 2015 are shown.

RESULTSTop

The frequency of occurrence per depth stratum of the studied species in all the investigated GSAs is shown in Table 1. Deep-water rose shrimp was caught in 43.5% of the hauls performed in the MEDITS survey from 1994 to 2015. Its frequency of occurrence was higher on the continental slope than on the shelf (61.9% and 31.3%, respectively). The highest frequency of occurrence was on the continental slope in GSA 23 (Crete, 94.6%). In general, the species was more frequent in the eastern and central Mediterranean, from GSA 25 to GSA 8, while the lowest occurrences were recorded in the westernmost part of the Mediterranean (GSAs 1-7).

Table 1. – Percentage of occurrence of deep-water rose shrimp (P. longirostris) and Norway lobster (N. norvegicus) in the investigated GSAs.

	Deep-water rose shrimp			Norway lobster
GSA	Shelf	Slope	Total	Shelf	Slope	Total
1	19.9	37.3	30.0	1.4	48.4	28.7
2	0.0	20.0	17.1	0.0	63.3	54.3
5	1.6	33.6	14.4	0.0	58.4	23.3
6	13.6	35.5	19.9	5.7	78.0	26.4
7	11.0	32.5	14.6	11.1	77.5	22.2
8	0.5	75.3	44.0	1.6	97.8	57.5
9	47.0	65.5	55.8	3.4	87.6	43.6
10	58.9	63.4	61.6	0.7	62.5	37.0
11	7.1	58.3	27.2	0.3	70.8	27.9
15	60.9	88.7	76.3	1.4	78.5	44.1
16	45.5	78.6	63.7	2.9	77.5	44.0
17	29.4	89.8	33.3	25.4	77.8	28.7
18	58.9	69.6	62.2	15.6	62.4	30.0
19	34.0	46.9	42.0	4.7	46.1	30.5
20	49.1	72.5	58.3	4.3	35.6	16.6
22	35.0	72.3	53.5	12.0	46.7	29.2
23	17.8	94.6	42.4	0.0	18.9	6.1
25	24.2	84.9	41.3	0.0	0.0	0.0
All GSAs	31.3	61.9	43.5	9.4	66.4	32.0

Norway lobster was mostly present on the continental slope (200-800 m depth). Its frequency of occurrence was higher than 10% on the shelf only in GSAs 7, 17, 18, and 22 (Table 1), with the highest value in GSA 17 (25.4%, northern and central Adriatic Sea). The highest occurrences were recorded on the continental slope of the central part of the Mediterranean, from GSA 6 to 17, with frequencies higher than 70% (97.8% in GSA 8).

During the MEDITS surveys (1994-2015), deep-water rose shrimp showed the highest biomass and density indices in GSA 23 in 2003 (45.128 kg km^–2 and 9866.32 N km^–2. Supplementary material, Table S1). In the case of the Norway lobster, the highest abundance indices were observed in GSA 8 in 2004 (52.43 kg km^–2 and 1326.26 N km^–2, respectively; Supplementary material, Table S2). Although characterized by the presence of large fluctuations, the deep-water rose shrimp biomass and density indices showed an increasing trend in most of the investigated areas, as shown by the positive values of the Spearman rho coefficient (Supplementary material, Table S1). In contrast, the Norway lobster biomass and density indices showed generally a decreasing pattern (Supplementary material, Table S2).

Data exploration highlighted high collinearity between the biomass and density indexes for the two species (r=0.9; VIF values >3). Therefore, GAM models were fitted only on the biomass index as a response variable. For the explanatory variables, no high collinearity was detected by both pair-plots (Pearson correlation coefficient. Supplementary material, Fig. S1) and VIF.

Based on the model selection procedure, the best GAM model for both species was the one containing as explanatory terms the interaction between coordinates (longitude and latitude) and time, SST, and the interaction of depth and time (Tables 2 and 3). The summary of the outputs of the best GAM models for the two species are shown in Tables 4 and 5, respectively.

Table 2. – GAMs for deep-water rose shrimp (P. longirostris); j is the year and i is the i-observation in the j-year; β₀ is the intercept; s(Lon_ij, Lat_ij, Year_j) is the smoother for the interaction of coordinates (longitude and latitude) and year; s(SST_ij) is the smoother for sea surface temperature; s(Depth_ij) and s(Depth_ij, Year_j) are the smoothers for depth and the interaction of depth and year; ε_ij ~ N(0, σ²) is the error term; AIC is the Akaike information criterion and Dev. expl. (%) is the deviance explained. The best model is given in bold.

Model	AIC	Dev. expl. (%)
β₀ + s(Lon_ij, Lat_ij, Year_j) + ε_ij	36695.7	25.9
β₀ + s(Lon_ij, Lat_ij, Year_j) + s(SST_ij) + ε_ij	35942.2	29.9
β₀ + s(Lon_ij, Lat_ij, Year_j) + s(Depth_ij) + ε_ij	29791.4	55.3
β₀ + s(Lon_ij, Lat_ij, Year_j) + s(Depth_ij, Year_j) + ε_ij	29629.1	55.9
β₀ + s(Lon_ij, Lat_ij, Year_j) + s(SST_ij) + s(Depth_ij, Year_j) + ε_ij	29596.8	56.1

Table 3. – GAMs for Norway lobster (N. norvegicus); j is the year and i is the i-observation in the j-year; β₀ is the intercept; s(Lon_ij, Lat_ij, Year_j) is the smoother for the interaction of coordinates (longitude and latitude) and year; s(SST_ij) is the smoother for sea surface temperature; s(Depth_ij) and s(Depth_ij, Year_j) are the smoothers for depth and the interaction of depth and year; ε_ij ~ N(0, σ²) is the error term; AIC is the Akaike information criterion and Dev. expl. (%) is the deviance explained. The best model is given in bold.

Model	AIC	Dev. expl. (%)
β₀ + s(Lon_ij, Lat_ij, Year_j) + ε_ij	37895.4	13.8
β₀ + s(Lon_ij, Lat_ij, Year_j) + s(SST_ij) + ε_ij	36593.0	21.6
β₀ + s(Lon_ij, Lat_ij, Year_j) + s(Depth_ij) + ε_ij	25597.0	64.9
β₀ + s(Lon_ij, Lat_ij, Year_j) + s(Depth_ij, Year_j) + ε_ij	25477.3	65.3
β₀ + s(Lon_ij, Lat_ij, Year_j) + s(SST_ij) + s(Depth_ij, Year_j) + ε_ij	25377.3	65.6

Table 4. – Summary of the outputs of the best GAM model for deep-water rose shrimp. Explanatory variables included longitude-latitude-time interaction, depth-time interaction and sea surface temperature (SST); SE, standard error; edf, estimated degrees of freedom; df, degrees of freedom.

Parametric coefficients:	Estimate	SE	t value	Significance level
Intercept	0.73	0.01	136.10	p<0.05
Smooth terms:	edf	df	F
Longitude, latitude, year	103.73	107.65	61.92	p<0.05
Depth, year	26.81	28.67	431.42	p<0.05
SST	4.75	4.97	16.91	p<0.05

Table 5. – Summary of the outputs of the best GAM model for Norway lobster. Explanatory variables included longitude-latitude-time interaction, depth-time interaction, and sea surface temperature (SST); SE: standard error; edf: estimated degrees of freedom; df: degrees of freedom.

Parametric coefficients:	Estimate	SE	t value	Significance level
Intercept	0.55	0.01	119.10	p<0.05
Smooth terms:	edf	df	F
Longitude, latitude, year	99.91	106.79	23.15	p<0.05
Depth, year	27.02	28.73	746.88	p<0.05
SST	4.28	4.79	10.17	p<0.05

Deep-water rose shrimp showed a widening bathymetric range in time, from 200-400 m in depth at the beginning of the time series to 150-600 m depth in the last few years. As regards temperature, the highest abundance of the species was associated with SSTs at around 10°C, while a second peak was observed at around 14°C (Fig. 2). The maps in Figure 3 present the highest abundances of deep-water rose shrimp in the southeastern Mediterranean.

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Fig. 2. – Effects of explanatory variables on the biomass of deep-water rose shrimp, as estimated by generalized additive model (GAM). Left panel, effect of the interaction between depth and time (years); right panel, effect of SST (dashed lines represent GAM confidence intervals).

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Fig. 3. – Maps of deep-water rose shrimp distribution.

Norway lobster showed no particular temporal trend in bathymetric distribution over the years. There was a negative relationship between its biomass and SST. The highest biomass indexes of this species were associated with low temperatures, while biomass decreased with increasing SST (Fig. 4). Figure 5 shows the spatiotemporal distribution of Norway lobster, indicating a slight decrease in biomass indices over time.

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Fig. 4. – Effects of explanatory variables on the biomass of Norway lobster, as estimated by generalized additive model (GAM). Left panel, effect of the interaction between depth and time (years); right panel, effect of bottom temperature (shaded areas represent GAM confidence intervals).

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Fig. 5. – Maps of Norway lobster distribution.

An inspection of the residuals of the best models for deep-water rose shrimp and Norway lobster shows that there was no evidence of a pattern within the model residuals or deviation from the assumptions of homogeneity and normality of variance (Supplementary material, Figs S2 and S3).

DISCUSSIONTop

Since ecosystems have changed over time due to anthropogenic and environmental factors, fisheries management should not be based only on recent studies of populations (Ligas et al. 2013Ligas A., Osio G.C., Sartor P., et al. 2013. Long-term trajectory of some elasmobranch species off the Tuscany coasts (NW Mediterranean) from 50 years of catch data. Sci. Mar. 77: 119-127.). While long time series of data have been collected in the Atlantic Ocean and the North Sea, fisheries time series in the Mediterranean are available only for the last few decades. Current management advice in most cases is based on data starting in the early 1990s (Sartor 2011Sartor P. (coord.) 2011. The 20th Century evolution of Mediterranean exploited demersal resources under increasing fishing disturbance and environmental change (EVOMED, Contract. N° SI2 539097). European Commission, Final Report. 515 pp., Colloca et al. 2013Colloca F., Cardinale M., Maynou F., et al. 2013. Rebuilding Mediterranean fisheries: a new paradigm for ecological sustainability. Fish Fish. 14: 89-109.). The availability of 22 years of standardized data obtained from the MEDITS surveys of two of the most important decapods of the demersal communities in the Mediterranean offers a unique opportunity to evaluate trends in abundance at a large scale. According to Cook (1997)Cook R.M. 1997. Stock trends in six North Sea stocks as revealed by an analysis of research vessel surveys. ICES J. Mar. Sci. 54: 924-933., data collected by means of trawl surveys are a more accurate source of information for estimating stock abundance than observations from commercial landing data. In addition, the latter could be biased by the heterogeneous distribution of fishing effort and strategy, the selectivity of the gears, and discarding procedures influenced by market demands (Fox and Starr 1996Fox D.S., Starr R.M. 1996. Comparison of commercial fishery and research catch data. Can. J. Fish. Aquat. Sci. 53: 2681-2694., Quirijns et al. 2008Quirijns F.J., Poos J.J., Rijnsdorp A.D. 2008. Standardizing commercial CPUE data in monitoring stock dynamics: accounting for targeting behaviour in mixed fisheries. Fish. Res. 89: 1-8.).

A decrease in the fishing fleet size has been observed during the last years in the Mediterranean EU waters, mainly due to the EU Common Fishery Policy, although this decrease has been partially compensated by an increasing catch efficiency and changes in fleet characteristics (Rijnsdorp et al. 2006Rijnsdorp A.D., Daan N., Dekker W. 2006. Partial fishing mortality per fishing trip: a useful indicator of effective fishing effort in mixed demersal fisheries. ICES J. Mar. Sci. 63: 556-566.). This reduction in fishing effort has not yet produced the desired effects, as is also demonstrated by the lack of recovery of the main exploited stocks in the area (GFCM 2017GFCM. 2017. Report of the Working Group on Stock Assessment of Demersal Species (WGSAD), Rome, Italy, 7-12 November 2016. FAO., STECF 2017Scientific, Technical and Economic Committee for Fisheries (STECF). 2017. Mediterranean assessments 2016- part 2 (STECF-17-06); Publications Office of the European Union, Luxembourg; EUR 28359 EN.). In addition, it seems that the abundance of some stocks is closely linked to climate change. For example, Colloca et al. (2014)Colloca F., Mastrantonio G., Jona Lasinio G., et al. 2014. Parapenaeus longirostris (Lucas, 1846) an early warning indicator species of global warming in the central Mediterranean Sea. J. Mar. Syst. 138: 29-39. reported that the stock trend of deep-water rose shrimp in the central Mediterranean Sea did not appear to be driven by fishing effort, since the exploitation pattern has remained the same in the last two decades.

Since the 1950s, a warming process has occurred in the Mediterranean basin, demonstrated by both environmental (Vargas-Yánez et al. 2009Vargas-Yánez M., Moya F., Tel E., et al. 2009. Warming and salting in the western Mediterranean during the second half of the 20th century: inconsistencies, unknowns and the effect of data processing. Sci. Mar. 73: 7-28.) and biological changes (CIESM 2008CIESM. 2008. Climate warming and related changes in Mediterranean marine biota. CIESM Workshop Monographs, 35: 1-152.). Skliris et al. (2012)Skliris N., Sofianos S., Gkanasos A., et al. 2012. Decadal scale variability of sea surface temperature in the Mediterranean Sea in relation to atmospheric variability. Ocean Dyn. reported that for the period 1985-2008, the SST in the Mediterranean Sea has increased by about 0.037°C per year, causing a northward expansion of native tropical and sub-tropical species (Raitsos et al. 2010Raitsos D.E., Beaugrand G., Georgopoulos D., et al. 2010. Global climate change amplifies the entry of tropical species into the eastern Mediterranean Sea. Limnol. Oceanogr. 55: 1478-1484.) and promoting the tropicalization of the Mediterranean (Azzurro et al. 2011Azzurro E., Moschella P., Maynou F. 2011. Tracking signals of change in Mediterranean fish diversity based on local ecological knowledge. PLoS ONE 6: e24885.).

According to the different life cycles and behavioural strategies, the two studied species showed different reactions in relation to changes in environmental factors. Long-term changes in abundance of deep-water rose shrimp and Norway lobster correlated significantly with the latitudinal and longitudinal distribution, SST and bathymetric range distribution.

The core area of deep-water rose shrimp, occurring between 200 and 400 m, expanded progressively in a wider depth range. This process clearly indicated an enhancement of the habitat suitability in the central-western Mediterranean for the deep-water rose shrimp. The main mechanism driving the spatiotemporal dynamics of this species is the increase in water temperature (Ligas et al. 2011Azzurro E., Moschella P., Maynou F. 2011. Tracking signals of change in Mediterranean fish diversity based on local ecological knowledge. PLoS ONE 6: e24885., Colloca et al. 2014Colloca F., Mastrantonio G., Jona Lasinio G., et al. 2014. Parapenaeus longirostris (Lucas, 1846) an early warning indicator species of global warming in the central Mediterranean Sea. J. Mar. Syst. 138: 29-39.). Moreover, this species has expanded its distribution further north, up to the northwestern Mediterranean areas. It is one of the few species showing increasing trends in both commercial catches and scientific surveys (Colloca et al. 2014Colloca F., Mastrantonio G., Jona Lasinio G., et al. 2014. Parapenaeus longirostris (Lucas, 1846) an early warning indicator species of global warming in the central Mediterranean Sea. J. Mar. Syst. 138: 29-39.). In the Catalan Sea, Cartes et al. (2009)Cartes J.E., Maynou F., Fanelli E., et al. 2009. Long-term changes in the composition and diversity of deep-slope megabenthos and trophic webs off Catalonia (western Mediterranean): are trends related to climatic oscillations? Prog. Oceanogr. 82: 32-46. hypothesized that the increasing temperature produced low rainfall regimes and river discharges, and consequently a reduction in the flux of organic matter. These environmental conditions lead to an increased production of suprabenthos (e.g. Lophogaster typicus), which represents the main prey of the deep-water rose shrimp. Therefore, a minor increase in temperature observed in recent years may have favoured the increase in abundance of this shrimp throughout the Mediterranean basin. In the northern Tyrrhenian Sea, Bartolino et al. (2008)Bartolino V., Colloca F., Sartor P., et al. 2008. Modelling recruitment dynamics of hake, Merluccius merluccius, in the central Mediterranean in relation to key environmental variables. Fish. Res. 93: 277-288. demonstrated that high temperature and low wind circulation negatively affect the recruitment of European hake (Merluccius merluccius). It is known that hake juveniles prey upon juveniles of deep-water rose shrimp (Carpentieri et al. 2005Carpentieri P., Colloca F., Cardinale M., et al. 2005. Feeding habits of European hake (Merluccius merluccius) in the central Mediterranean Sea. Fish. Bull. 103: 411-416.), so the lower predation pressure could have further enhanced the recruitment success of the shrimp.

In contrast, Norway lobster showed a negative relationship between biomass and SST, with the highest indexes associated with low temperatures. The results confirmed the preference of this species for cold waters, with highest abundances in the northern part of the western Mediterranean. Unlike the deep-water rose shrimp, this species showed no particular trend in bathymetric distribution. The decreasing abundance related to higher temperatures is again explained by Cartes et al. (2009)Cartes J.E., Maynou F., Fanelli E., et al. 2009. Long-term changes in the composition and diversity of deep-slope megabenthos and trophic webs off Catalonia (western Mediterranean): are trends related to climatic oscillations? Prog. Oceanogr. 82: 32-46.: benthic feeders and predators such as the Norway lobster are influenced by the reduction of the organic matter flux resulting from the decreased rainfall and river discharge. In the Atlantic Sea, in areas where minor changes in fishing pressureFariña A.C., González Herraiz I. 2003. Trends in catch-per unit-effort, stock biomass and recruitment in the North and Northwest Iberian Atlantic Nephrops stocks. Fish. Res. 65: 351-360.s were observed, Fariña and González Herraiz (2003)Fariña A.C., González Herraiz I. 2003. Trends in catch-per unit-effort, stock biomass and recruitment in the North and Northwest Iberian Atlantic Nephrops stocks. Fish. Res. 65: 351-360. and González Herraiz et al. (2009)González Herraiz I., Torres M.A., Fariña A.C., et al. 2009. The NAO index and the long-term variability of Nephrops norvegicus population and fishery off West of Ireland. Fish. Res. 98: 1-7. recorded a decline in Norway lobster abundance associated with higher temperatures.

GAM models adequately described the effects of sea temperature on the two decapod species at a large geographical scale. In a future scenario of increasing temperature, we can expect a further expansion of deep-water rose shrimp even in areas where the species is not yet abundant, such as the Gulf of Lions and the Catalan Sea. For Norway lobster, we could expect an opposite effect, which has already been demonstrated for some species of cold or temperate waters (e.g. Merlangius merlangius and Sprattus sprattus) distributed in the northern areas of the Mediterranean Sea, such as the Gulf of Lions, the northern Adriatic Sea and the northern Aegean Sea) (Daskalov et al. 2008Daskalov G.M., Prodanov K., Zengin M. 2008. The Black Seas fisheries and ecosystem change: discriminating between natural variability and human-related effects. In: Nielsen J., Dodson J., et al. (eds), Proceedings of the Fourth World Fisheries Congress: Reconciling Fisheries with Conservation. American Fisheries Society Symposium 49, AFS, Bethesda, MD, pp. 1649-1664., Fortibuoni et al. 2010Fortibuoni T., Libralato S., Raicevich S., et al. 2010, Coding Early Naturalists’ Accounts into Long-Term Fish Community Changes in the Adriatic Sea (1800-2000). PLoS ONE 5: e15502.). However, these general trends may vary at smaller scale, since environmental features may influence species’ regional preferences, leading to changes in the general trend. Further analyses are therefore indispensable to understand better the relationships between variations in the abundance of the species and environmental and anthropogenic factors. Additional information on sea-floor topography, sediment composition and hydro-geographical characteristics would be a useful tool for describing further temporal changes in species abundance.

ACKNOWLEDGEMENTSTop

This study was conducted in the framework of the MEDITS Project. We wish to thank all participants in the MEDITS project and the Coordination Committee for providing the data for the analyses.

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SUPPLEMENTARY MATERIAL

The following supplementary material is available through the online version of this article and at the following link: http://scimar.icm.csic.es/scimar/supplm/sm04858esm.pdf

Table S1. – Stratified mean biomass and density indices (kg km^–2and N km^–2 ± standard deviation, SD) per year (1994-2015) of deep-water rose shrimp (P. longirostris) in the investigated GSAs. Information on the results of Spearman Rho coefficient analysis is also shown.

Table S2. – Stratified mean biomass and density indices (kg km^–2and N km^–2 ± standard deviation, SD) per year (1994-2015) of Norway lobster (N. norvegicus) in the investigated GSAs. Information on the results of Spearman Rho coefficient analysis is also shown.

Fig. S1. – Pair-plots for all the explanatory variables in the data set used for the analysis. The upper diagonal panel shows the Pearson correlation coefficient, and the lower diagonal panel shows the scatterplots with a smoother added to visualize the pattern. The font size of the correlation coefficient is proportional to its estimated value.

Fig. S2. – Graphs of the validation of the best GAM model for deep-water rose shrimp. From top left, residuals versus depth, residuals versus longitude, residuals versus latitude, residuals versus SST, and residuals versus time (year) to assess homogeneity; bottom right, histogram of residuals to assess normality.

Fig. S3. – Graphs of the validation of the best GAM model for Norway lobster. From top left, residuals versus depth, residuals versus longitude, residuals versus latitude, residuals versus SST, and residuals versus time (year) to assess homogeneity; bottom right, histogram of residuals to assess normality.