Structure, functioning, and cumulative stressors of Mediterranean deep-sea ecosystems

Progress in Oceanography 135 (2015) 156–167

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Progress in Oceanography journal homepage: www.elsevier.com/locate/pocean

Structure, functioning, and cumulative stressors of Mediterranean deep-sea ecosystems Samuele Tecchio a,b,⇑, Marta Coll b,c,d, Francisco Sardà b a

Normandie Université UNICAEN, UMR BOREA (MNHN, UPMC, CNRS-7208, IRD-207), CS 14032, Caen, France Institut de Ciències del Mar (CSIC), Pg. Marítim de la Barceloneta 37-49, 08003 Barcelona, Spain c Institut de Recherche pour le Développement, UMR EME 212, Centre de Recherche Halieutique Méditerranéenne et Tropicale, Avenue Jean Monnet, BP 171, 34203 Sète cedex, France d Ecopath International Initiative Research Association, Barcelona, Spain b

a r t i c l e

i n f o

Article history: Received 4 September 2014 Received in revised form 6 May 2015 Accepted 6 May 2015 Available online 14 May 2015

a b s t r a c t Environmental stressors, such as climate fluctuations, and anthropogenic stressors, such as fishing, are of major concern for the management of deep-sea ecosystems. Deep-water habitats are limited by primary productivity and are mainly dependent on the vertical input of organic matter from the surface. Global change over the latest decades is imparting variations in primary productivity levels across oceans, and thus it has an impact on the amount of organic matter landing on the deep seafloor. In addition, anthropogenic impacts are now reaching the deep ocean. The Mediterranean Sea, the largest enclosed basin on the planet, is not an exception. However, ecosystem-level studies of response to varying food input and anthropogenic stressors on deep-sea ecosystems are still scant. We present here a comparative ecological network analysis of three food webs of the deep Mediterranean Sea, with contrasting trophic structure. After modelling the flows of these food webs with the Ecopath with Ecosim approach, we compared indicators of network structure and functioning. We then developed temporal dynamic simulations varying the organic matter input to evaluate its potential effect. Results show that, following the west-to-east gradient in the Mediterranean Sea of marine snow input, organic matter recycling increases, net production decreases to negative values and trophic organisation is overall reduced. The levels of food-web activity followed the gradient of organic matter availability at the seafloor, confirming that deep-water ecosystems directly depend on marine snow and are therefore influenced by variations of energy input, such as climate-driven changes. In addition, simulations of varying marine snow arrival at the seafloor, combined with the hypothesis of a possible fishery expansion on the lower continental slope in the western basin, evidence that the trawling fishery may pose an impact which could be an order of magnitude stronger than a climate-driven reduction of marine snow. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction World ecosystems are changing, and so are the oceans (Levitus et al., 2000). Climate-determined alterations, driving increases in sea water temperatures, are among the most worrisome of those changes (Hoegh-Guldberg and Bruno, 2010). While ample resources are being allotted to the study and modelling of climate influences on marine coastal ecosystems because of their direct connection with human activities, the relations between atmosphere, open-ocean, and deep-water regions are still mostly unknown (Smith et al., 2008).

⇑ Corresponding author at: Normandie Université UNICAEN, UMR BOREA (MNHN, UPMC, CNRS-7208, IRD-207), CS 14032, Caen, France. Tel.: +33 2 31 56 51 02. E-mail address: [email protected] (S. Tecchio). http://dx.doi.org/10.1016/j.pocean.2015.05.018 0079-6611/Ó 2015 Elsevier Ltd. All rights reserved.

Deep-sea ecosystems need allochthonous energy for their functioning. Continuous sinking streams of particles and occasional point-like pulses of organic matter are the processes performing the required energy input (Gage, 2003). Productivity of the deep-sea benthos is related with food availability or, more specifically, with marine snow input. As ocean waters have undergone a constant warming over the past century, the levels of primary productivity (and, by consequence, the marine snow input) have been fluctuating with different patterns across the oceans (Levitus et al., 2000; Boyce et al., 2010, 2014). Food availability is a modulating factor of benthic diversity and community structure, at both spatial and temporal scales (Danovaro et al., 1999; Corliss et al., 2009; Tecchio et al., 2011; Pape et al., 2013b). An ecosystem with higher species diversity is usually more buffered against external stressors and it is, thus, less adaptable and less vulnerable to impact (Tilman, 1996; Worm

S. Tecchio et al. / Progress in Oceanography 135 (2015) 156–167

et al., 2006). Therefore, if an increment of seawater temperature would reduce primary productivity and thus reduce the amount of organic matter input to the deep sea, ecosystems may see their diversity reduced, may respond to environmental drivers with wider fluctuations and thus face a higher risk of collapse or regime shift (Ruhl and Smith, 2004). The Mediterranean Sea, the largest enclosed basin on the planet, is not an exception. In addition, this basin is more strongly influenced by river input and evaporation than open ocean, without possibilities of neither large-scale intra-oceanic currents nor significant upwelling events that may perform a constant renewal of deep water masses (Sardà et al., 2004). The Mediterranean deep-water fauna is based on a region-wide species pool, from which assemblages are drawn on each basin (western, central, and eastern) according to basin-specific environmental drivers, which include surface primary production and food availability at the seafloor (Sardà et al., 2004; Tecchio et al., 2011; Pape et al., 2013a). Its particular geographical setting, the presence of dense human population along the coast, and a closed water mass circulation, concur to pose high impacts to Mediterranean ecosystems compared to other regions (Costello et al., 2010). However, the Mediterranean Sea is unique in that below a depth of 1000 m trawl fishing is not permitted (EC Regulation 1967/2006). For this reason, and as suggested by recent works (Sardà et al., 2009; Ramírez-Llodra et al., 2011), the deep Mediterranean can be considered the last wild marine ecosystem. Mediterranean deep-water masses have undergone a warming trend in the past decades, a process which might be ascribed to local climate change (Bethoux et al., 1990; Rixen et al., 2005). Furthermore, the higher water temperatures of the Mediterranean intermediate and deep waters (13–14 °C), compared to the Atlantic Ocean, enhance the degradation of organic matter by prokaryotic metabolism (Tyler, 2003). Future warming will amplify the impoverishment of carbon to the deep seafloor ecosystems, with effects that still have to be completely clarified (Danovaro et al., 2001). Overall, the Mediterranean Sea is an excellent ‘‘miniature ocean’’ (Bethoux et al., 1999) and natural laboratory for studying the effects of global change due to its peculiar environmental conditions, its broad-scale gradients, and its deep-sea fauna. An ecosystem food-web model of the deep western Mediterranean Sea was recently parameterised, considering data from the Catalan continental margin at mid-lower continental slope depths (Tecchio et al., 2013a). Here, we expand the study developing three food-web models representing the longitudinal axis of the Mediterranean Sea from west to east, at the same depth ranges. We then perform a comparative network analysis across basins and simulate the effects of an increase in sea surface temperature on primary productivity and, thus, on the amount of marine snow input. Our working hypothesis is that following a decrease in organic matter input to the deep sea (going from west to east) due to a natural gradient of productivity, the food webs would present traits of increased simplification and decreased resilience. In addition, and due to the deep Mediterranean Sea having important commercial resources, such as the highly commercial red shrimp Aristeus antennatus that could be exploited in the future if the above mentioned trawling ban is not properly enforced, we also include a simulation of fishing expansion to the deep in the Western Deep Mediterranean model. By comparing both the effects of an increase in sea surface temperature on primary productivity and fishing impacts we evaluate the possible interaction of the two drivers (be it additive, synergistic or antagonistic).

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2. Methods 2.1. Modelling approach Ecopath with Ecosim (EwE) is a food-web modelling approach and software widely used to describe aquatic ecosystems (Heymans et al., 2014). EwE includes a snapshot mass-balanced model (Ecopath) and temporal (Ecosim) and temporal–spatial (Ecospace) dynamic models (Christensen and Walters, 2004). Ecopath is a linear model that estimates values of the unknown flows between ecosystem compartments, solving two linear equations for each functional group parameterised from their biomass stock (B, t km2), their production rate over biomass (P/B, yr1), their food consumption rate over biomass (Q/B, yr1) and the predator–prey interactions in the form of a diet matrix. A detailed explanation of the Ecopath basic equations and its constraints is given by Christensen and Walters (2004). The temporal–dynamic model Ecosim integrates the foraging arena theory to recalculate the consumption flows at each time step (Walters et al., 1997). A parameter called ‘vulnerability’, linked to the carrying capacity for predators, models the degree to which an increase in predator biomass will cause mortality on a prey. Low values of vulnerability indicate a ‘bottom-up’-controlled interaction, while high values indicate that prey mortality is controlled by the predator biomass, as in a ‘top-down’ control (Ahrens et al., 2012). Using the same methods of data collection, treatment, and model balancing procedures as in Tecchio et al. (2013a), we expanded the modelled area developing three different food webs in the deep Mediterranean Sea: western deep sea (WDS), central deep sea (CDS) and eastern deep sea (EDS) (Fig. 1). The model representative of the central Mediterranean basin was built integrating data of the western Ionian sea, while the model for the eastern basin was developed integrating data from the southern Cretan sea. With the exception of the Risso’s smooth-head fish (Alepocephalus rostratus), which is only present in the WDS, all functional groups defined to describe the three deep Mediterranean Sea were represented in the three models. Therefore, the functional groups of each model were defined in similar terms to ensure the comparability of the results. In all models, a second detritus group (apart from the sediment detritus) was included to simulate the particulate organic matter input provided by marine snow. Biomass (B) estimates to parameterise the three models came from deep-sea trawl surveys, conducted in June 2009 during a 1-month trans-Mediterranean sampling cruise (Tecchio et al., 2011) and in May 2009 during a cruise in the Catalan continental margin in the western Mediterranean Sea (Tecchio et al., 2013b). Different sampling gears were used depending on the organisms to sample: an Otter-trawl Maireta system (OTMS) was used to sample the demersal compartment, an Agassiz dredge was applied for the strictly benthic species, and remotely-operated mid-water trawl nets (RMT) were used for the mesopelagic macroplankton. Data from these cruises were used to calculate the biomass per functional group and unit of surface and was complemented with information from the literature. Production and consumption rates (P/B and Q/B) were estimated using empirical equations (Pauly, 1980; Palomares and Pauly, 1998), were obtained from literature or from already balanced food-web models. The diet matrices were constructed using literature data on stomach contents analyses, giving preference to studies from the same or similar areas (Table 2). A complete description of data sources is given in Appendix A. Mass balance was achieved by modifying input parameters until: (a) no violations of the mass-balance rule were present (EE

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Fig. 1. Map of the Mediterranean Sea, with positions of the three modelled ecosystems (WDS: Western, CDS: Central, and EDS: Eastern deep Mediterranean sea).

was 61), and (b) physiological rates were within the known limits for each functional group (e.g. respiration/biomass ratio of 1–10 for fishes, and food-conversion efficiencies in the range 0.1–0.3 for consumers in general). As biomass estimates were collected locally and with high-precision sampling, they were considered of high quality and were not modified if possible, while diet composition data were modified first as they were usually available only for the western basin and we assumed that they had the least precision. 2.2. Ecological network analysis Once the three models were developed and balanced, we performed ecological network analysis (ENA) on their estimated flows, using the calculations included in EwE (Christensen and Walters, 2004). General flow statistics were calculated, including the sum of Consumption (Q, t km2 y1), Exports (E, t km2 y1), Respiratory flows (R, t km2 y1), Production (P, t km2 y1), and all Flows to Detritus (FD, t km2 y1). The total system throughput (T.., t km2 y1) was calculated as the sum of all flows occurring in the system (Latham, 2006). We also calculated the System Omnivory Index (SOI), defined as the average of the omnivory of the consumer groups, weighted by the logarithm of their consumption (Pauly et al., 1993; Libralato, 2008). The Finn’s Cycling Index (FCI) was calculated as the percentage of all fluxes generated by cycling, as well as the Average Path Length (APL), defined as the average number of steps along the shortest paths for all possible pairs of functional groups (Finn, 1980). The ratio of Redundancy (overhead of internal flows) over the total Development Capacity was also calculated (R/DC) and interpreted as the fraction of inefficient network processes (Ulanowicz, 2001). The species that occupy keystone roles in each ecosystem were identified calculating the Keystoneness index according to Libralato et al. (2006). The trophic spectrum of biomass for each ecosystem was also calculated. This analysis visualises the biomass kinetics between trophic levels as a smoothed estimator and is interpreted as an indicator of trophic efficiency along the food chain (Gascuel et al., 2005). Flows and biomasses were aggregated into discrete trophic levels to calculate the Lindeman spine breakdown plot and to

quantify the total transfer efficiency, the transfer efficiencies between successive trophic levels and the flow contributions to detritus (Lindeman, 1942). The transfer efficiency of a discrete trophic level is calculated as the ratio between the exiting flows from that level (exports and flows to the next level) and its throughput (total flows connected to the level). 2.3. Dynamic simulations We used the temporal dynamic module Ecosim (Walters et al., 1997; Christensen and Walters, 2004) to study the effects of variations in primary productivity in the three food webs. Before performing the simulations, the vulnerability parameters in Ecosim were set at 1.1 for the top predator groups (bluntnose sixgill shark, demersal sharks, and monkfish), and at 2.0 for all the other groups following previous work (Tecchio et al., 2013a). This setup was adopted considering the initial absence of fishing pressure in the deep Mediterranean, which would mean that organisms in the ecosystem were close to carrying capacity. We established forcing time series for the biomass of the Marine Snow (POC) group using as basis the simulations of chlorophyll change recently conducted for the north-eastern Atlantic Ocean by Boyce et al. (2014). In that work, a regional estimate of chlorophyll change of 0.84% increase per year is found for the north-eastern Atlantic region, as well as indications that at a global scale, phytoplankton biomass would decrease (Boyce et al., 2014). Three linear scenarios were considered: (A) an annual regional increase of +0.84% of primary productivity would fully land on the seafloor, (B) half of the annual regional increase would arrive to the seafloor, i.e. +0.42% per year, and (C) a global yearly decrease of 0.63% in marine snow input at the seafloor would take place due to the increase in temperature and the consequent increased degradation of organic matter in the water column (estimated as a negative mean between the previous two scenarios, to provide a contrasting hypothesis). In addition, for the WDS model we ran temporal simulations with and without an additional fishery scenario described in Tecchio et al. (2013a), which models the possible arrival of the red shrimp trawl fishery to 1000–1400 m depth, currently operating at shallower depths. To perform this simulation we used the current benthic trawler fishery operating in the

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shallower area of the WDS model, down to 800 m depth approximately. The targeted groups of this fishery are, aside from the Mediterranean red shrimp, the monkfish (Lophius piscatorius), greater forkbeard (Phycis blennoides), common mora (Mora moro), cephalopods, and mesopelagic crustaceans. The groups Risso’s smooth-head, benthic crustaceans, and other benthic invertebrates were also considered as discards. During the simulations, fishing effort was increased from zero to a level so that the red shrimp would receive a fishing mortality of 1.5 year1 over 10 years (Tecchio et al., 2013a). After reaching this maximum level, fishing effort was left stable for the rest of the simulation, which overall were run for 50 years. To analyse the results of the dynamic simulations, we compared the proportion of biomass of fish and invertebrate groups (Bfish/Binv) between the start and the end of the simulation period. The Kempton’s Q index was calculated as an indicator of ecosystem change and plotted against time (Ainsworth and Pitcher, 2006; Christensen et al., 2009). This index is proportional to the inverse slope of the species-abundance curve – here calculated by functional groups – and is thus a proxy of system biodiversity. A decrease of Q has been interpreted previously as a signal of increasing impact (Coll et al., 2009). 3. Results 3.1. Ecosystem structure and functioning The three balanced food webs included 19–20 functional groups, with trophic levels up to 4.3 (Table 1). The highest TL value was always occupied by the bluntnose sixgill shark (Hexanchus griseus), with the other demersal sharks and monkfish always following (Fig. 2). In terms of biomasses, the groups that substantially changed between models were fish groups, such as Mediterranean codling (Lepidion lepidion) and macrourids that decreased from west to east, and the common mora (M. moro) which showed the highest accumulation of biomass in the CDM. The mesopelagic crustaceans and benthopelagic fish varied widely between basins, as they belong to the pelagic compartment which is naturally subject to wider biomass fluctuations. Biomasses of

non-crustacean benthic invertebrates and zooplankton, which were estimated by the model due to lack of complete data, decreased with longitude, from the WDM to the EDM. The structure of the food webs, defined by the keystoneness of functional groups and their relative biomass, varied between the three modelled systems (Fig. 3). The group with the highest keystoneness was always the non-crustacean benthic invertebrates; however, with its high biomass it is unlikely to be a keystone group in the system but rather a key structuring group. In addition, possible keystone groups were identified as demersal sharks and monkfish in all three systems, and gelatinous zooplankton specifically in the WDM. An increase of relative contribution of biomass of crustacean groups (both benthic and mesopelagic) was identified in the central (CDM) and eastern (EDM) ecosystems. The total production, consumption, respiration, export, flows to detritus, and the total system activity (T..) all decrease from west to east (Table 3). The ratio between primary input (in our case represented by marine snow) and total biomass also decreases with latitude, indicating an increasing trophic limitation and competition for resources from west to east. This is also confirmed by the decreasing values of the net system production, which reach slightly negative values in the EDM. The System Omnivory Index (SOI) was low considering the likely values of this index, indicating that all three deep-sea basins possess a structure that is more chain-like than web-like (Libralato, 2008). At the same time, the SOI in all three basins was at the higher end of the range of the published Mediterranean food-web models, which is from 0.19 to 0.36 (Coll and Libralato, 2012; Corrales et al., 2015). System omnivory also remained partially stable with longitude, with a slight increase in the EDM. The Finn’s Cycling Index (FCI), being highest in the EDM and generally higher where starting biomasses were lower, indicated that as we move from west to east, system stress increases and thus biomass is being recycled at a higher rate through the food web. At the same time, the fraction of inefficient flows, expressed by R/DC, remained stable in all models. The biomass trophic spectrum evidenced a decline in global transfer efficiencies of the system following the longitudinal axis from west to east (Fig. 4). The majority of biomass was

Table 1 Main input parameters for the three food-web models (WDS: Western, CDS: Central, and EDS: Eastern deep Mediterranean sea), and calculated trophic levels and omnivory indices as output. Values of biomass indicated in bold were estimated by the Ecopath model. Note that the Risso’s smooth-head group was only present in the western basin model. P/B: Production/Biomass, Q/B: Consumption/Biomass, U/Q: Unassimilated consumption ratio. Biomass (t km2)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Bluntnose sixgill shark Demersal sharks Monkfish Greater forkbeard Mediterranean codling Macrourids Common mora Risso’s smooth-head Other demersal fish Cephalopods Mediterranean red shrimp Mesopelagic crustaceans Benthopelagic fish Benthic invertebrates, crustaceans Benthic invertebrates, other Zooplankton, gelatinous Zooplankton, BBL Meiobenthos Marine snow (POC) Detritus

P/B (year1)

WDS

CDS

EDS

0.128 0.200 0.050 0.100 0.140 0.225 0.250 0.285 0.080 0.105 0.150 0.305 0.540 0.255 0.632 0.080 0.381 0.012 35.800 –

0.110 0.113 0.032 0.030 0.018 0.025 0.424 – 0.064 0.059 0.065 0.510 0.329 0.278 0.445 0.012 0.214 0.020 18.000 –

0.090 0.120 0.015 0.034 0.010 0.019 0.125 – 0.025 0.021 0.010 0.330 0.101 0.065 0.191 0.018 0.103 0.011 4.850 –

0.148 0.250 0.430 0.508 0.736 0.516 0.283 0.265 1.050 2.190 3.000 3.115 1.300 2.768 3.190 22.000 18.000 60.000 – –

Q/B (year1)

1.14 1.90 2.70 2.75 2.91 3.35 2.45 2.03 3.60 7.30 8.59 8.90 7.40 10.40 16.20 56.00 50.00 240.00 – –

U/Q

0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.30 0.40 0.40 0.35 – –

Trophic level

Omnivory index

WDS

CDS

EDS

WDS

CDS

EDS

4.32 4.02 3.90 3.33 3.08 2.79 3.20 3.39 3.05 3.13 2.77 2.94 2.76 2.47 2.11 2.62 2.05 2.00 1.00 1.00

4.31 3.72 3.82 3.24 3.36 2.75 3.27 – 3.02 3.09 3.10 2.69 2.76 2.34 2.12 2.62 2.05 2.00 1.00 1.00

4.29 3.82 3.89 3.30 3.41 2.93 3.23 – 3.07 3.38 3.15 2.77 2.93 2.37 2.18 2.61 2.05 2.00 1.00 1.00

0.80 0.16 0.12 0.39 0.44 0.51 0.40 0.26 0.23 0.34 0.36 0.45 0.49 0.44 0.10 0.38 0.05 0.00 0.29 0.48

0.92 0.20 0.16 0.34 0.31 0.44 0.49 – 0.19 0.31 0.35 0.42 0.49 0.32 0.11 0.38 0.05 0.00 0.62 0.42

0.97 0.21 0.17 0.38 0.32 0.51 0.72 – 0.27 0.14 0.39 0.45 0.63 0.37 0.16 0.38 0.06 0.00 1.01 0.29

160 Table 2 Diet composition matrix used as input for the three food-web models. Diet was kept as consistent as possible during balancing. In the CDS and EDS models, contribution of Risso’s smooth-head in its predators’ diets was re-distributed over the other fish prey groups.

Bluntnose sixgill shark Demersal sharks Monkfish Greater forkbeard Mediterranean codling Macrourids Common mora Risso’s smooth-head Other demersal, small Cephalopods Mediterranean red shrimp Mesopelagic crustaceans Benthopelagic fish Benthic invertebrates, crustaceans Benthic invertebrates, other Zooplankton, gelatinous Zooplankton, BBL Meiobenthos Marine snow (POC) Benthic detritus Import

1

2

3

4

0.130 0.020 0.040 0.060 0.080 0.090 0.080 0.050 0.150

0.030 0.015 0.015 0.050 0.090 0.050 0.070 0.100 0.200 0.050 0.140 0.050 0.030 0.030

0.030 0.080 0.080 0.085 0.095 0.090 0.080 0.100 0.080 0.180

0.010

0.100

0.300

0.080

5

6

0.020 0.010 0.010 0.010 0.005 0.030 0.200 0.100 0.350 0.100 0.060

0.060 0.090 0.005 0.070 0.380 0.040 0.080

0.150

0.220

0.010 0.000 0.050 0.000 0.250 0.100 0.040 0.070 0.060 0.150 0.270

7

0.030 0.030 0.085 0.085 0.080 0.110 0.410

8

9

0.040 0.070

0.080

0.010 0.180 0.600

0.100 0.420

10

0.020 0.100 0.050 0.160 0.420

0.215 0.050 0.100

0.135 0.100

12

13

0.040 0.030

14

15

16

17

0.060 0.050 0.430 0.460

0.380

0.050

0.270

0.950

18

0.010 0.030 0.050

0.200 0.300

0.150 0.170

11

0.150 0.050 0.300

0.020 0.100 0.030 0.350

0.400

0.130

0.150

0.300

0.400

0.150 0.100 0.050 0.010 0.200 0.450

0.350

0.300 0.700

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Preynpredator 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

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3.2. Simulations of global change The initial values of system diversity in the three models, expressed by the Kempton’s Q index, were 8.19 in the WDS, followed by the EDS with 4.80 and the CDS with 3.85. Considering climate simulations alone, the relation between marine snow input and diversity (analysed using the Kempton’s Q index) were linear, showing either positive or negative effects depending on whether the food input would increase or decrease with time (Fig. 6a). However, the variation of food input, both as increase or decrease, did not significantly change the proportion of fish over invertebrate biomass (Bfish/Binv) in neither the western, central nor eastern deep-sea food webs after 50 years of simulation (Table 4). Fishing effect alone reduced the biomass of the most targeted species, the red shrimp Aristeus antennatus, to 47.0% of the initial value after 50 years. Marine snow increases (both full and half) mitigated this reduction, while the combination of fishing and marine snow reduction would decrease its biomass down to 35.4% of the initial stock. Considering climate-only scenarios, A. antennatus biomass was linearly responsive to marine snow changes, with the widest variations found in the western basin, and the reverse was true in the central basin. Total biomass was always increased at the end of the ‘‘full increase’’ and ‘‘half increase’’ simulations, while it was always decreased at the end of the ‘‘snow decrease’’ scenario. Worth noting are the biomasses of monkfish, greater forkbeard, common mora, and Risso’s smooth-head, which in the WDS showed the strongest changes, especially in the ‘‘fishing and snow decrease’’ scenario where they collapsed to 11.1%, 6.7%, 0.4%, and 17.5% of their initial value, respectively. In the combined temporal simulations of fishing and climate performed on the WDS, the reduction in system diversity caused by fishing was an order of magnitude stronger than the reduction caused by food input variations (Fig. 6b). Accordingly, the Bfish/Binv ratio decreased to 0.799 after 50 years. By comparison, fishing effects alone shifted the ratio from 1.04 to 0.85, evidencing that the cumulative effects on the western deep Mediterranean Sea of these two stressors would be additive. 4. Discussion

Fig. 2. Flows and biomasses of the three modelled habitats (WDS: Western, CDS: Central, and EDS: Eastern deep Mediterranean sea). Flows values by diet are indicated by the width of the line, while group biomasses are logarithmically proportional to the area of their respective circles. Groups are vertically positioned by their calculated trophic level.

concentrated over TL of 2.5–3.5. In addition, the contribution of higher trophic levels to the total system biomass was lower in the CDM and EDM, suggesting an increase of importance of mid-web functioning as the food availability decreases. Transfer efficiencies along the discrete trophic chain, shown by the Lindeman spine breakdown, were the highest in the CDM and the lowest in the EDM, thus not concordant with the longitudinal gradient (Fig. 5). However, transfer efficiencies declined with TL at approximately the same steep rate in the three basins, indicating that the compartments, although exposed to different levels of food input, functionally behave consistently across the Mediterranean deep sea.

The deep ocean is currently under human impacts and climate change, and few studies have addressed how its structure and functioning may change under these human and environmental pressures. Here, we have presented new results in this sense, considering three ecosystems in the Mediterranean Sea that follow a longitudinal decreasing gradient of energy input. Our main focus was the comparison of ecosystem traits across basins with contrasting trophic availability. Then, we performed a direct – but nevertheless preliminarily informative – simulation on how the food webs would respond to a future variation of food input. We are aware that marine snow fluctuations are not the only factor that would impart changes on deep-sea communities in the future; however, they remain the most straightforward to interpret, and the data by Boyce et al. (2014) provided the possibility for this linear approach. 4.1. Varying structure with longitude Resilience of marine ecosystems is defined as the facility to return to their initial state after a perturbation, while resistance is the capacity of reducing the consequences on functioning when a variable is permanently changed (Pimm, 1991). In our study, resilience can be evaluated when considering the possible arrival of fishing effort to the deep sea, and resistance when considering

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S. Tecchio et al. / Progress in Oceanography 135 (2015) 156–167 Table 3 Comparative network analysis for the three modelled ecosystems of the Mediterranean deep sea.

Sum of all consumption (Q) Sum of all exports (E) Sum of all respiratory flows (R) Sum of all flows to detritus (FD) Total system throughput (T..) Sum of all production (P) Marine snow (POC) input Total snow input/total respiration Net system production Total snow input/total biomass Total biomass/total throughput Total biomass (excluding detritus) Total transfer efficiency Relative redundancy (R/DC) System Omnivory Index (SOI) Finn’s Cycling Index (FCI) Average Path Length (APL) Pedigree index

Western Med.

Central Med.

Eastern Med.

51.64 9.43 20.17 65.98 158.00 50.78 35.80 1.78 15.63 9.14 0.025 3.92 15.8 0.455 0.288 4.20 3.91 0.54

33.50 2.20 14.02 34.35 88.65 27.47 18.00 1.28 3.98 6.55 0.031 2.75 14.5 0.479 0.281 7.52 4.26 0.54

15.46 0.89 6.04 10.34 32.73 4.72 4.85 0.74 1.68 3.66 0.028 1.29 18.3 0.470 0.330 10.78 4.78 0.54

t km2 y1 t km2 y1 t km2 y1 t km2 y1 t km2 y1 t km2 y1 t km2 y1 t km2 y1

t km2 %

% of T..

Fig. 3. Food-web structure of the three modelled systems of the Mediterranean deep sea, defined by the biomass of each group (proportional to the area of circles) and by the keystoneness index. Values of keystoneness lower than 1.0 are not included in the plot.

local food input variations driven by global climate change. The Finn’s Cycling Index has been interpreted in various and conflicting ways, with its classical interpretation of system maturity and resilience (Finn, 1980) being challenged by contrasting results of increased cycling and self-reliance with stress (Scharler and Baird, 2005; Pranovi and Link, 2009). The proportion of flows generated by cycling increased from west to east, following the gradient of increasing oligotrophy, in parallel with a decrease of system activity (expressed by T..). Also, the clear decreasing pattern of net system production with longitude, becoming negative in the Eastern basin, confirms that the Levantine deep-sea ecosystem is extremely impoverished in organic matter input, to a level of reaching energy deficit (Azov, 1991). These two factors combined indicate that resilience of deep-sea Mediterranean ecosystems decreases from west to east due to an increased self-reliance of the ecosystems to internal flows. The proportion of inefficient network flows (shown by R/DC) slightly increased in the central and eastern basins, compared to the western, but in general this index did not capture the variations expressed by the other indices, suggesting that its interpretation should be further discussed.

Fig. 4. Trophic spectra of biomass for the three modelled ecosystems of the Mediterranean deep sea showing the continuous and smoothed distribution of group biomass across trophic levels.

The trophic spectrum and the analysis of transfer efficiencies sensu Lindeman (1942) are complementary in the sense that they both consider the trophic pyramid concept, excluding discussion at taxon level. System transfer efficiencies evidenced by the biomass spectrum declined with longitude, following the reduction of total system throughput (i.e. sum of all flows in the system, or T..), but it is also evident that the bottom-up effect proportionally declines with the reduction of food availability. However, due to the food limitation and heterotrophicity of these ecosystems, it is unlikely that top-down control gradually takes over. As expected, the highest total transfer efficiencies were recorded in the EDM, where extreme reductions in biomasses are present, combined with food web compression and simplification. Combining these results with those from both the biomass spectra and system omnivory, we can assume that the systems become less organized

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163

Fig. 5. Lindeman spine plots of flows and biomasses, aggregated by discrete trophic levels, for the three modelled ecosystems of the Mediterranean deep sea. The POC box represents the input reservoir of organic matter which is feeding the detritus compartments.

as resources become scarcer following the longitudinal gradient from west to east in the Mediterranean Sea. However, the interactions between a decreased resilience and this oversimplification are still unknown. And more importantly, we cannot know if this condition is only present in the Mediterranean Sea due to its increased temperature from west to east and its enclosed status. To date, basic ecological studies are lacking in this sense for deep-sea ecosystems in the oceans as a whole. Considering our initial hypothesis, we can observe a partial relationship between food input and system resilience. Deep-water ecosystems of the Mediterranean Sea follow a pattern of reduced efficient flows and increased recycling, in accordance to the longitudinal gradient of decreasing marine snow input (Danovaro et al., 1999). In addition, the eastern Mediterranean ecosystem, probably due to it reduced basal diversity and standing

stock, shows a negative net production that might indicate an increased vulnerability to natural fluctuations, even without the man-driven impact of fishing. 4.2. Ecosystems response to climate and fishing Climate processes are already known to exert multi-faceted influences on deep-sea populations and ecosystems, both at human (Danovaro et al., 2004; Company et al., 2008; Smith et al., 2009) and geological time scales (Yasuhara et al., 2008). However, the direction at which climatic change will impact marine ecosystems is still under discussion in the deep sea as both deep-water warming and cooling have been identified as possible future scenarios driving ecosystem functioning (Danovaro et al., 2001).

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Fig. 6. System diversity, expressed by Kempton’s Q index, and its temporal changes under different marine snow (MS) variations scenarios (a) in the three basins of the Mediterranean deep sea, and (b) specifically in the Western basin with the addition of fishery effects. The solid line represents the scenario with fishing simulation only.

It is known that the Mediterranean deep sea hosts a higher proportion of crustaceans (both in terms of species and densities) than fishes, compared to the connected Atlantic ocean (Massutí et al., 2004; Tecchio et al., 2013b). This is explained by the fact that the Mediterranean Sea is less productive and thus the slower metabolism of decapod crustaceans and their higher adaptability give them an advantage over fish species in this enclosed basin. In this study, we indeed observe a Bfish/Binv ratio of 0.71–1.04 across the

Mediterranean, lower if we compared it to an approximate 1.26 calculated from another ecosystem model of the northern Atlantic ocean (Vasconcellos and Watson, 2004). However, this ratio did not change substantially with the simulations of marine snow variation in the CDM and the EDM. In the WDM, the response of the Bfish/Binv ratio to variations of marine snow was linear, while the introduction of a trawl fishery would cause a change of up to 20% of the initial value. The arrival of the fishing activity to the lower-slope depths may thus convey an impact an order of magnitude stronger than a climate-driven reduction of marine snow landing. However, the biomasses present in the continental slope of the central and eastern Mediterranean Sea are at present not considered viable for fishing exploitation, and this is why fishing simulations were conducted only in the western basin model. The relation between food input and system diversity is unimodal, that is, the highest levels of diversity can be found where food input stands at medium levels, while with a further increase, competitive exclusion and physiological stress would have a detrimental effect of diversity (Levin et al., 2001). In our study, the response of system diversity to variations in food input is fairly linear, indicating that in all three deep-water basins we are observing the lower half of the unimodal curve. In this situation, resource limitation is the dominant factor, driving ecosystem functioning and increasing facilitation processes that are indeed profuse in deep-sea environments (Levin et al., 2001; Loreau, 2008). Concluding, climate and fisheries showed a simple additive interaction, without any mutual amplification of ecosystem effects. Increases in marine snow would only partially mitigate the biomass collapse of the targeted species, should fishing activity arrive at lower continental slope depths in the western deep Mediterranean. Our results confirm that trawling is the strongest shaper of benthic communities over human temporal scales (Puig et al., 2012). The negative effects of trawl fishing on deep-sea ecosystems, which have been well studied both in simulations and in real scenarios (Roberts, 2002; Norse et al., 2012; Tecchio et al., 2013a), might be avoided if the current trawling ban in the deep Mediterranean Sea is maintained and enforced. Following the increasing concerns for management of the deep ocean (Mengerink et al., 2014), the imminent regulation changes on mining and gas explorations in the Mediterranean Sea may impart a pressure similar to that brought by other anthropogenic activities, such as trawling. Our results suggest that this may have strong detrimental effects on deep-sea Mediterranean ecosystems. Acknowledgements This work was supported by the BIOFUN (CTM2007-28739-E) and PROMETEO (CTM2007-66316-C02/MAR) research projects, funded by the European Science Foundation and the Spanish MICYT, respectively. We thank the officers and crew of the CSIC research vessels García del Cid and Sarmiento de Gamboa, for their assistance at sea. We also thank Daniel Boyce for his advice on

Table 4 Biomass changes in proportion of fish biomass over invertebrates’ biomass (Bfish/Binv ratio) and in red shrimp biomass at the end of simulated changes in marine snow (MS) input in the three modelled ecosystems of the Mediterranean deep sea. Western Med.

Bfish/Binv ratio

A. antennatus biomass (% change)

Start (year 2009) After 50 years of Full MS increase Half MS increase MS decrease Full MS increase Half MS increase MS decrease

Central Med.

Eastern Med.

Climate

Climate + fishing

1.040

1.040

0.714

0.713

1.152 1.091 0.969

0.915 0.875 0.787

0.701 0.707 0.727

0.721 0.719 0.727

128.6 111.7 87.2

67.6 56.2 35.4

114.9 105.7 96.6

123.7 109.9 91.1

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Appendix A Source data and references for all functional groups included in the three deep-sea Mediterranean models. When ‘‘d.s.’’ is indicated, biomass data came from direct sampling programs conducted for this study. Source data for applying empirical equations came primarily from FishBase. Dietary preferences for multi-species groups were always weighted by the relative biomass contribution of each species. Group

Biomass

1. Bluntnose sixgill shark 2. Demersal sharks 3. Monkfish

d.s. FishBase d.s. FishBase d.s./estimated for FishBase CDS and EDS d.s./estimated for FishBase EDS d.s. FishBase d.s. FishBase

4. Greater forkbeard 5. Mediterranean codling 6. Macrourids 7. Common mora 8. Risso’s smooth-head

d.s. d.s. (present only in the WDS) 9. Other demersal fish d.s./estimated for CDS 10. Cephalopods d.s./estimated for CDS 11. Mediterranean red shrimp d.s.

P/B and Q/B

Diet Ebert (1994) Cortés (1999), Valls et al. (2011) Stergiou and Karpouzi (2002) Macpherson (1978)

FishBase Morales-Nin et al. (1996) FishBase Coll et al. (2006)

García-Rodriguez and Esteban (1999),Company et al. (2003) 12. Mesopelagic crustaceans d.s. FishBase 13. Benthopelagic fish d.s. FishBase 14. Benthic invertebrates, d.s./estimated for Brey (2001) crustaceans CDS 15. Benthic invertebrates, Estimated with Vasconcellos and Watson (2004), other EE = 0.99 corrected for temperature (Opitz, 1996) 16. Zooplankton, gelatinous d.s. Coll et al. (2006), Pauly et al. (2009) 17. Zooplankton, BBL Estimated with Morato and Pitcher (2005). Corrected for EE = 0.95 temperature (Opitz, 1996) 18. Meiobenthos Gambi et al. Heip et al. (1990), Van Oevelen et al. (2010) (2011) 19–20. Sediment detritus and Danovaro et al. – marine snow (POC) (1999)

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