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Carbon flows in the benthic food web at the deep-sea
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observatory HAUSGARTEN (Fram Strait)
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Dick van Oevelen1,*, Melanie Bergmann2, Karline Soetaert1, Eduard Bauerfeind2, Christiane
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Hasemann2, Michael Klages2, Ingo Schewe2, Thomas Soltwedel2, Nataliya E. Budaeva3
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140, 4400 AC Yerseke, The Netherlands
Centre for Estuarine and Marine Ecology, Netherlands Institute of Ecology (NIOO-KNAW), PO Box
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Bremerhaven, Germany
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Moscow, Russia
Alfred Wegener Institute for Polar and Marine Research, Am Handelshafen 12, D-27570
P.P. Shirshov Institute of Oceanology, Russian Academy of Sciences, Nakhimovsky Pr., 36, 117997
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Corresponding author: d.vanoevelen@nioo.knaw.nl
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ABSTRACT
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The HAUSGARTEN observatory is located in the eastern Fram Strait (Arctic Ocean) and used as
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long-term monitoring site to follow changes in the Arctic benthic ecosystem. Linear inverse modelling
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was applied to decipher carbon flows among the compartments of the benthic food web at the central
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HAUSGARTEN station (2500 m) based on an empirical data set consisting of data on biomass,
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prokaryote production, total carbon deposition and community respiration. The model resolved 99
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carbon flows among 4 abiotic and 10 biotic compartments, ranging from prokaryotes up to megafauna.
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Total carbon input was 3.78±0.31 mmol C m-2 d-1, which is a comparatively small fraction of total
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primary production in the area. The community respiration of 3.26±0.20 mmol C m-2 d-1 is dominated
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by prokaryotes (93%) and has lower contributions from surface-deposit feeding macro- (1.7%) and
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suspension feeding megafauna (1.9%), whereas contributions from nematode and other macro- and
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megabenthic compartments were limited to <1%. The high prokaryotic contribution to carbon
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processing suggests that functioning of the benthic food web at the central HAUSGARTEN station is
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comparable to those of abyssal plain sediments that are characterised by strong energy limitation.
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Faunal diet compositions suggest that labile detritus is important for deposit-feeding nematodes (24%
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of their diet) and surface-deposit feeding macrofauna (~44%), but that semi-labile detritus is more
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important in the diets of deposit-feeding macro- and megafauna. Dependency indices on these food
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sources were also calculated as these integrate direct (i.e. direct grazing and predator – prey
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interactions) and indirect (i.e. longer loops in the food web) pathways in the food web. Projected sea-
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ice retreats for the Arctic Ocean typically anticipate a decrease in the labile detritus flux to the already
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food-limited benthic food web. The dependency indices indicate that faunal compartments depend
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similarly on labile and semi-labile detritus, which suggests that the benthic biota may be more
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sensitive to changes in labile detritus inputs than when assessed from diet composition alone.
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Species-specific responses to different types of labile detritus inputs, e.g. pelagic algae versus
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sympagic algae, however, are presently unknown and are needed to assess the vulnerability of
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individual components of the benthic food web.
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Keywords: Food web – Modelling – Sediment – Benthos – Arctic Ocean – Carbon processing
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1. Introduction
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The Earth is warming rapidly due to anthropogenic inputs of CO2 into the atmosphere (IPCC,
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2007). While research is mainly directed at the terrestrial consequences of global warming the
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changes in the deep oceans, especially those in the vulnerable Polar regions receive less attention.
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Climate change is expected to affect Arctic marine ecosystems in various direct and indirect ways.
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One direct effect is that seawater temperatures will rise and this will directly affect organisms
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physiology (Pörtner et al., 2001). However, observed temperature changes in the deep Arctic ocean
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are still limited to <0.01°C y-1 (Glover et al., 2010). A more profound and faster impact is to be
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expected through an indirect mechanism: the retreat of the ice-edge and the continuous loss of multi-
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year ice will lead to a decreased flux of fast-sinking sympagic algae and fauna (Hop et al., 2006). The
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dominant primary producers in the upper water column may therefore shift from sympagic algae to
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pelagic phytoplankton, which may be retained in the twilight zone (Buesseler et al., 2007). This change
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could shift an ecosystem characterized by strong benthic-pelagic coupling to one characterized by a
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water column – dominated food web (Grebmeier et al., 2006; Hop et al., 2006).
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It may not be easy to detect changes in the quantity and composition of primary producers in
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the upper water column directly because algal blooms and ice cover are erratic and difficult to sample
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at appropriate temporal resolution (Bauerfeind et al., 2009; Forest et al., 2010). The benthic
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ecosystem, which depends directly on phytodetritus produced in the euphotic zone and which
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integrates patterns in the overlying productivity over longer time periods, may yield a more consistent
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signal.
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In this context, the Alfred Wegner Institute for Polar and Marine Research (Germany)
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established the deep-sea observatory HAUSGARTEN west of Svalbard (Soltwedel et al., 2005) to
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provide a long-term monitor of changes in the Arctic benthic ecosystem (Fig. 1). The observatory
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comprises nine sampling stations along a bathymetric transect (1000 – 5500 m). A latitudinal transect
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crosses the bathymetric transect at the central HAUSGARTEN station (2500 m), which serves as an
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experimental area for biological long-term experiments (Gallucci et al., 2008; Kanzog et al., 2009).
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Repeated sampling and deployments of moorings and long-term landers has been conducted on an
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annual basis since 1999 and has yielded a unique time-series dataset on mega-, macro- and
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meiobenthic, prokaryotic, biogeochemical and geological properties as well as on hydrography and
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sedimentation patterns (Bauerfeind et al., 2009; Bergmann et al., 2009; Hoste et al., 2007). This time-
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series has revealed decreases in the proportions of fresh phytodetrital matter at the seafloor and in the
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concentration of sediment-bound organic matter in the period 2001 – 2005 (Soltwedel et al., 2005).
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Changes in the quality and quantity of detrital input can affect the structure of the benthic food
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web profoundly (Billett et al., 2010; Ruhl et al., 2008; Smith et al., 2009). Indeed, Hoste et al. (2007)
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showed a decline in the microbial biomass of sediments and changes in nematode community
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structure at HAUSGARTEN. These changes, however, operate in a food web context, in which biota
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are linked through consumption and predation processes. Data sets on benthic food webs are typically
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restricted to biomass estimates of large functional groups and occasional rate measurement (Soetaert
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and Van Oevelen, 2009), rendering knowledge based on field measurements alone insufficient to
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derive a coherent picture of carbon flows in these systems. Recent advances in the use of so-called
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‘inverse modelling’ techniques, however, enable us not only to quantify food web flows based on
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limited data sets, but also to assess the uncertainty associated with this quantification (Van Oevelen et
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al., 2010). These techniques allow us to analyse even complex deep-sea food webs quantitatively
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(Van Oevelen et al., 2009). The basic advantage is that site-specific field data on carbon processing
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and carbon biomass are combined with more uncertain data from the literature to collectively constrain
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the magnitudes of the food web flows.
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In this paper, we combine the comprehensive set of available empirical data to quantify the
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carbon flows in the benthic food web of the central HAUSGARTEN station (2500 m). Detrital input to
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the food web is divided into three classes of lability to do justice to the heterogeneity of natural detritus
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and assess differences in diet contributions of these different detritus classes. Moreover, we determine
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partitioning of respiration and secondary production to identify which food web compartments are
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important pathways in the benthic food web. Trophic levels of the faunal compartments are calculated
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and compared with trophic level position based on δ15N isotope data (Bergmann et al., 2009), to verify
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the resulting food web structure. Finally, dependency indices of biotic compartments on the basal
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detritus and prokaryotic resources are calculated. Dependency indices quantify the dependence of a
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biotic compartment on other compartments via direct (i.e. consumption) and indirect (transfer via
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longer pathways) interactions (Ulanowicz, 2004). The model results will be used to speculate on
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changes that can be anticipated in the benthic food web under a scenario of receding sea ice.
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2. Material and methods
2.1 Data collection
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An overview of the field data and references that were used in the food web model is given in
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Table 1 and 2, with a brief summary of the sampling methodology given here. Most samples were
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taken during expedition ARK XIX/3c (July–August 2003) with the German research ice breaker
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Polarstern at the central HAUSGARTEN station (2500 m water depth).
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The deposition of particulate organic carbon (POC) represents an important input parameter of
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the inverse model, since it determines the total carbon processing by the benthic food web. Long-term
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deployments of deep sediment traps provide important constraints on the POC input. The sedimenting
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particles were sampled by modified automatic Kiel sediment traps (see Bauerfeind et al., 2009 for
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details). The sediment traps were installed in bottom-tethered moorings at different depths, but here
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only the data from the deepest sediment trap (170 mab) are considered. The traps were programmed
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to collect at 15 day intervals. POC input data from the productive spring-summer season are used in
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the model, since this depositional flux is most relevant for the benthic food web compartments that
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were sampled in July 2003. The collector cups were filled with sterile water, adjusted to a salinity of 40
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and poisoned with mercury chloride (0.14% final solution) and kept refrigerated till further processing
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after recovery. Sub-samples were analyzed for, amongst other parameters, particulate organic carbon
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(see Bauerfeind et al., 2009 for details). POC deposition showed little variation during March – May
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(range 0.77 to 1.44 mmol C m -2 d-1), but rose substantially to 3.99 mmol C m -2 d-1 in June (Fig. 2).
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Because of this range in deposition rates, it was decided to include the full range as input for the
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model (Table 2).
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Sediment samples were taken by a multiple corer and the top 5 cm of the sediment analyzed
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at 1-cm intervals for organic carbon content, pigment concentration, prokaryotic biomass, hydrolytic
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activity, meio- and macrofaunal biomass. Sediment porosity was estimated by measuring the weight
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loss of wet sediment samples dried at 60 °C (average of 0.60 for the top 5 cm). Total organic carbon
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content was determined as the ash-free dry weight after combustion and converted to total organic
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carbon in the sediment using sediment porosity and assuming a density of 2.5 g cm -3 for the sediment
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fraction. Particulate proteins were analyzed photometrically following 0.5 N NaOH extraction (Greiser
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and Faubel, 1988). Chloroplastic pigments were extracted and the chlorophyll a content was
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determined with a fluorometer. Prokaryotic cell volume was determined with the Porton grid
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(Grossmann and Reichardt, 1991) after staining with acridine orange and converted to prokaryotic
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biomass using a conversion factor of 3.0×10−13 g C μm−3 (Borsheim et al., 1990). Prokaryotic
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enzymatic turnover rates were measured as an indicator of the potential hydrolytic activity of
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prokaryotes using fluorescein-di-acetate as fluorogenic substrate (Köster et al., 1991), hydrolysis rates
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were converted to carbon units assuming that one mole fluorescein is equivalent to four moles of
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carbon (i.e. 2 acetate molecules). Sediment samples for nematode enumeration were sieved through
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a 1 mm sieve and nematodes that were retained on a 32 μm were extracted by Ludox centrifugation
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(Hoste et al., 2007). Macrofaunal density estimates were based on box-core samples (Budaeva et al.,
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2008). Megafaunal density estimates were acquired by analysis of still images of the seafloor taken by
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a towed camera system during RV Polarstern expedition ARK XVIII/1 in 2002 (Soltwedel et al., 2009).
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Total oxygen uptake by the benthic community was determined from the decrease in oxygen
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concentration in sediment cores that were incubated in situ during the RV Polarstern cruise ARK XVI/2
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(2002) and RV Maria S. Merian expedition 2-4 (2006) (Winkler titration were done on-board)
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(Soltwedel, unpublished).
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2.2 Food web model
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The food web model was set up as a linear inverse model (LIM). The term linear refers to the
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food web model being described as a linear function of the flows, inverse means that the food web
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flows are derived from observed data. The model itself is the topology of the food web, which is
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determined a priori by delineating the compartments and connecting them with flows.
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Several reviews on linear inverse modelling have been recently published and contain simple
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models to exemplify the setup and solution of benthic food web LIMs (Soetaert and Van Oevelen,
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2009; Van Oevelen et al., 2010). Here, we therefore limit our methodological discussion on linear
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inverse models. A LIM contains a mass balance for each food web compartment and a set of
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quantitative data constraints. A LIM is captured by two matrix equations:
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Equality equation:
𝐀𝐱 = 𝐛
(1)
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Inequality equation:
𝐆𝐱 ≥ 𝐡
(2)
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in which vector 𝐱 contains the unknown flows. Each row in the equality equation (1) imposes a strict
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constraint: a linear combination of the flows must match the corresponding value in vector 𝐛. The
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inequality equation (2) imposes lower and upper bounds on flows or on linear combinations of flows. A
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default set of inequalities is the condition 𝐱 ≥ 0, which ensures that flows have directions that are
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consistent with the imposed food web topology.
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For the HAUSGARTEN station, the compartments of the benthic food web were defined as: labile
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detritus (lDet), semi-labile detritus (sDet), refractory detritus (rDet), dissolved organic carbon (DOC),
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prokaryotes (Pro), deposit-feeding nematodes (NemDF), predatory+omnivore nematodes (NemPO),
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surface deposit-feeding macrofauna (MacSDF), deposit-feeding macrofauna (MacDF), suspension-
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feeding macrofauna (MacSF), predatory+scavenging macrofauna (MacPS), deposit-feeding
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megafauna (MegDF), suspension-feeding megafauna (MegSF) and predatory+scavenging megafauna
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(MegPS).
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Carbon stocks were available for all compartments, except DOC (Table 1). Labile detritus was defined
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as all carbon associated with chlorophyll a. Chlorophyll a concentrations were summed in the top 5 cm
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and were converted to carbon units by assuming a carbon to chlorophyll a ratio of 40 that is typical for
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diatoms (Allen et al., 2005). Semi-labile detritus was defined as the carbon equivalents of particulate
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proteins (converted to carbon equivalents by the conversion factor 0.49, Pusceddu et al., 2010) in the
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top 5 cm (Hoste et al., 2007) minus the labile detritus stock. Refractory detritus was defined as the
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total OC stock in the top 5 cm of the sediment minus the labile and semi-labile detritus stocks.
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Prokaryotic carbon stocks were inferred from cell volumes (see above). The biomass of nematodes (>
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85% of the meiobenthic community, Hoste et al., 2007) was partitioned among feeding modes based
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on the following nematode feeding types (Wieser, 1953): deposit-feeding nematodes (Wieser type 1A,
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1B and 2A) and predatory+omnivore nematodes (Wieser type 2B). Macrobenthic and megabenthic
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species were divided into feeding types using specialized literature, natural abundance stable isotope
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values (Bergmann et al., 2009) and expert judgement.
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Carbon inputs into the food web are deposition and/or feeding on suspended labile (lDet_w),
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semi-labile (sDet_w) and refractory detritus (rDet_w). Carbon outputs from the food web are
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respiration to dissolved inorganic carbon (DIC), burial of rDet, DOC efflux to the water column and
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export by the macro- and megafaunal compartments (e.g. consumption by fish).
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Within the food web, the labile and semi-labile detritus pools in the sediment can be
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hydrolysed to DOC, or are grazed upon by nematodes (NemDF and NemPS) and MacSDF, MacDF,
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MacPS, MegSDF, MegDF and MegPS. Refractory detritus is only hydrolysed to DOC. The DOC is
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taken up by prokaryotes or effluxes to the water column. Predatory feeding links are primarily defined
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based on size class; prokaryotes are consumed by the nematode and non-suspension-feeding macro-
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and megafaunal compartments, deposit-feeding nematodes are consumed by predatory nematodes,
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both nematode compartments are consumed by non-suspension-feeding macro- and megafaunal
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compartments, the macrofaunal compartments MacSDF, MacDF and MacSF are preyed upon by
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predatory macro- and megafauna and predatory macrofauna is predated upon by predatory
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megafauna.
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Part of the sources ingested by the faunal compartments is not assimilated but instead
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expelled as faeces. The non-assimilated labile (e.g. labile detritus, prokaryotic and faunal
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compartments) and semi-labile (semi-labile detritus) carbon enter the semi-labile and refractory
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detritus, respectively. Respiration by faunal compartments is defined as the sum of maintenance
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respiration (biomass-specific respiration) and growth respiration (overhead on new biomass
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production). Prokaryotic mortality is defined as a flux to DOC and faunal mortality is defined as a flux
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to labile detritus.
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2.3 Data constraints
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The range in POC fluxes, as measured with deep sediment traps, was included in the
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inequality equation (Table 2). There were two measurements of sediment oxygen consumption rates
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and these were quite variable, and were therefore also included in the inequality equation (Table 2).
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Esterase activity reflects potential hydrolysis rates rather than in situ hydrolysis rates (Gumprecht et
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al., 1995) and the measured hydrolysis rate was therefore imposed as upper bound on total hydrolysis
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(Table 2).
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In addition to the site-specific data, a set of general constraints from the literature were
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included in the inequality equation. These constraints were used to set bounds on degradation rates of
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the labile, semi-labile and refractory detritus pools, burial efficiency, prokaryotic growth efficiency, viral-
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induced prokaryotic lysis, release of DOC from the sediment, grazing of prokaryotes by nematodes,
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assimilation efficiency of all faunal compartments, net growth efficiency of all faunal compartments,
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production and mortality rates of all faunal compartments (Table 2). The biomass-specific production
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and mortality rates in combination with the biomass values of the faunal stocks constrain the total
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carbon demand by the faunal compartments. Since measurements of assimilation and growth
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efficiencies of deep-sea benthos are very rare, an extensive literature review (Van Oevelen et al.,
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2006) of temperate benthos was used as basis for these constraints. Assimilation efficiencies for semi-
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labile carbon were set to half the values of the assimilation efficiencies of labile carbon for the macro-
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and megafaunal compartments. Faunal maintenance respiration was defined as 0.01 d-1 at 20°C (see
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references in Van Oevelen et al., 2006) and is corrected with a temperature-correction factor (Tlim)
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based on the Q10 formulation with a doubling of rates for every 10°C increase (Table 2). The bottom
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water temperatures at HAUSGARTEN were ca. -0.8°C.
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Both surface-deposit and deposit-feeding holothurians and other echinoderms ingest organic
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matter with higher than ambient chlorophyll a and total hydrolysable amino acid concentrations
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(Ginger et al., 2001; Witbaard et al., 2001), although selectivity differs between feeding modes with
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surface-deposit feeders typically exhibiting stronger selectivity than deposit feeders (Wigham et al.,
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2003). Selectivity between labile detritus and semi-labile detritus for these organisms was defined as
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the ratio of chlorophyll a concentrations in the gut with respect to the ambient surface sediment. The
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level of selectivity varies from 1 to 10 for deposit feeding holothurians at the Porcupine Abyssal Plain
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to >500 for the surface-deposit-feeding holothurian Amperima rosea (Wigham et al., 2003). Selectivity
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at the Antarctic peninsula was less evident (selectivity of 2 to 7), possibly because of the existence of
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a food bank, but there was a clear separation between deposit and surface-deposit feeders (Wigham
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et al., 2008). Therefore, zero to moderate (1 to 10) selectivity for deposit feeders and strong selectivity
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(50 to 100) for surface deposit feeders was assumed in the model (Table 2). Since no comparable
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data are available for macrofauna, similar selectivity ranges were defined for these communities
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(Table 2). Finally, the predatory nematodes and macro- and megafaunal compartments were assumed
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to ingest a minimum of 75% through predatory feeding (Table 2).
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2.4 Model solution
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The complete food web model consists of 99 flows, 16 compartments and mass balances, 99
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inequalities of ≥ 0 and 123 data inequalities. It is clear that the total number of flows in a food web
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greatly outnumbers the equations in the LIM (99≫16). As a result, a food web LIM is mathematically
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under-determined, which implies that an infinitely large set of solutions fits the matrix equations. Since
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no unique solution can be found for an under-determined model, a recently developed likelihood
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approach was followed (Van den Meersche et al., 2009; Van Oevelen et al., 2010). In short, a large
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set of 50,000 solutions is sampled from the infinitely large set of solutions. Each solution represents a
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different food web configuration and is consistent with the matrix equations 𝐀𝐱 = 𝐛 and 𝐆𝐱 ≥ 𝐡. The
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mean and standard deviation for each food web flow is calculated from this set of sampled solutions
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and represents a central estimate (i.e. the mean) of the flow value and its associated uncertainty (i.e.
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standard deviation) (Van Oevelen et al., 2010). This will be noted as mean ± standard deviation.
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Trophic levels of the biotic compartments and dependency indices were calculated for each solution in
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the set of 50,000 solutions using the R-package NetIndices (Kones et al., 2009). By running the model
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50,000 times, the uncertainty in the empirical data (indicated by the flow ranges in Table 2) is
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propagated onto an uncertainty estimate of the carbon flows as indicated by its standard deviation.
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Convergence of the mean and standard deviation of the flows was checked visually to confirm that the
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set of 50,000 model solutions was sufficiently large. Generally, model convergence (within 10% of the
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final mean and standard deviation for each flow value) was achieved after <5,000 solutions. In the
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calculation of trophic levels, the three detritus and dissolved organic carbon compartments were fixed
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to a trophic level of one. The model code is made available in the R-package LIM (Soetaert and Van
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Oevelen, 2008).
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2.5 Sensitivity analysis
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The data set included in the inverse model is inherently uncertain. The uncertainty of the flux data and
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rate parameters is included in the model by incorporating them as lower and upper bounds on their
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values (Table 2). In this way, this uncertainty propagates onto the final model solution as standard
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deviation for each flow value (see description of sampling methodology above). The stock data,
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however, are also uncertain, but this uncertainty cannot be directly included by lower and upper
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bounds using this sampling methodology. This is because, with a perturbation of the stock inputs, the
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core equations 𝐀𝐱 = 𝐛 and 𝐆𝐱 ≥ 𝐡 are not guaranteed to be valid when all solutions are averaged to
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obtain the final model solution. Henceforth, a sensitivity analysis was performed in which the stock
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values were perturbed one-by-one by increasing or decreasing a stocks value with 15% of its default
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value. With the perturbed stock value a new set of 500 solutions was sampled. The number of 500
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was chosen to save computing time, while at the same time it was large enough to reasonably
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approach the final model solution. The set of solutions was subsequently averaged to obtain a
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perturbed model solution. These perturbed model solutions were compared with the default model
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solution to assess the sensitivity of our model results for changes in the stock values.
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3. Results
A complete overview of the mean and standard deviation for each food web flow is given in
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the Appendix.
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3.1 Carbon flows inferred by the inverse model
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Total carbon input to the food web was 3.78±0.31 mmol C m-2 d-1 and is partitioned among
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labile detritus deposition (30%), semi-labile detritus deposition (31%), refractory detritus deposition
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(32%) and suspension feeding (8%). Total respiration is 3.26±0.20, burial is 0.32±0.08 and export from
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the food web is 0.02±0.006 mmol C m-2 d-1. Total respiration is dominated by prokaryotes (93%) with
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contributions that are <2% for each of the faunal compartments (Table 3). The contributions to total
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respiration by the individual compartments are well-constrained, given the small standard deviations
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(Table 3).
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Largest carbon flows in the food web at the central HAUSGARTEN station is the deposition of
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the three classes of detritus, which subsequently dissolve into DOC that is taken up by prokaryotes
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and then respired by prokaryotes (Fig. 3A). All carbon flows in this pathway are >1 mmol C m-2 d-1.
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Prokaryotic production is 1.84±0.12 mmol C m-2 d-1 and the prokaryotic growth efficiency is 0.38±0.03.
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Much of the prokaryotic production (92±4%) undergoes cell lysis after viral infection (Danovaro et al.,
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2008), and this carbon cycles back to DOC (Appendix). Other important flows are carbon burial
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(0.31±0.08 mmol C m-2 d-1) and efflux of DOC from the sediment (0.19±0.10 mmol C m -2 d-1) (Fig. 3B).
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Important faunal flows (>0.1 mmol C m-2 d-1) are uptake by surface-deposit feeding macrofauna and
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suspension-feeding macro- and megafauna (Fig. 3B). Most carbon flows related to faunal
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compartments, however, are between 0.005 and 0.05 mmol C m -2 d-1 (Fig. 3C). Finally, export flows
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and carbon flows associated with the predatory+omnivore nematodes, predatory macrofauna and
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megafauna are typically <3·10-3 mmol C m-2 d-1 (Fig. 3D).
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Faunal secondary production is highest for macrofauna (0.10±0.004 mmol C m -2 d-1), followed
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by megafauna (0.07±0.003 mmol C m-2 d-1) and nematodes (0.04±0.004 mmol C m-2 d-1) (Fig. 4). The
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fate of the secondary production by the non-predatory faunal compartments shows that 83% of the
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deposit-feeding nematode production is grazed, but only to a small extent (6%) by predatory
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nematodes, most production is predated upon by macro- (40%) and megafauna (38%) (Fig. 4B). The
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maintenance costs are relatively higher for the macro- (22%) and megafauna (77%) compared to the
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nematodes, because maintenance costs are a fixed fraction of the biomass per day, whereas
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biomass-specific production rates decrease with faunal size (Table 2). For macrofauna, a total of 56%
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is grazed by predatory macro- (18%) and megafauna (38%) (Fig. 4C). Finally, for non-predatory
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megafauna, a similar proportion (6-10%) of the secondary production is grazed by predatory
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megafauna, lost through mortality and exported from the food web (Fig. 4D).
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The model results suggest that faunal diets are typically dominated by labile and semi-labile
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detritus, with variable contributions among the compartments (Fig. 5). Despite the fact that deposit-
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feeding nematodes form the principle carbon source of predatory nematodes (>80%, Fig. 5), this
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represents only 6% of the fate of secondary production by deposit-feeding nematodes (Fig. 4A). The
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surface-deposit feeding macrofauna and deposit-feeding macro- and megafauna derive carbon mainly
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from three principle sources: labile detritus, semi-labile detritus and prokaryotes. Semi-labile detritus
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dominates the diets of the deposit-feeding compartments (52±9% for MacDF and 52±12% for MegDF),
321
whereas prokaryotes (50±28%) are of similar importance for surface-deposit feeding macrofauna as
322
labile detritus (44±28%) with a lower contribution from semi-labile detritus (4±1%). Diets of suspension
323
feeding macro- and megafauna diets are dominated by semi-labile detritus with a lower contribution of
324
labile detritus (67±23% and 33±23% for MacSF, respectively and 59±17% and 41±17% for MegSF,
325
respectively) (Fig. 5). The diets of predatory macro- and megafauna are diverse and seem to be
326
similar among the two predatory compartments. Important contributions (>50%) are from the
327
macrofaunal compartments, most notably surface-deposit feeding macrofauna (>24%), nematodes
328
(>11%), with labile (<5%) and semi-labile (<13%) detritus representing a much lower contribution.
329
3.2 Trophic levels and dependencies on primary resources
330
The trophic level (TL) of suspension feeding macro- and megafauna is fixed at two (Fig. 6),
331
because the two suspended detritus sources are presumed to have a fixed trophic position of one (see
332
Material and Methods). The TL of deposit feeding nematodes is fairly well-determined and is slightly
333
higher than 2 because of a small contribution of prokaryotes in their diet. The TL of deposit-feeding
334
macro- and megafauna is similar and fairly well-determined with lower and upper quartiles of 2.2 and
335
2.4 (Fig. 6), corresponding with the similarity in their diet compositions (Fig. 5). The TL of surface-
336
deposit feeding macrofauna, however, is much more uncertain and has lower and upper quartiles of
337
2.3 and 2.8 with a median of 2.5 (Fig. 6). This uncertainty is due to the uncertain diet contributions
338
described above of labile detritus (TL of 1) and prokaryotes (TL of 2). The TL of predatory+omnivore
339
nematodes is well constrained between 2.8 and 2.9, because of their predominant feeding on deposit-
340
feeding nematodes. The predatory macro- and megafauna have similar and highest TL with lower and
341
upper quartiles between 2.8 – 3.0, respectively, but with large excursions to lower and higher values
342
for their trophic level (Fig. 6). The high uncertainty is a result of the uncertainty in the diet composition
343
and the diets of its preys, but overall the higher TLs are expected for these predatory compartments.
344
The direct and indirect dependence on refractory detritus is lowest for all biotic compartments
345
in the food web (Fig. 7), with lower and upper quartiles between 0.15 and 0.77. Dependence of
346
prokaryotes is highest on semi-labile detritus (median of 1.5) and prokaryotes (median 2.5) (Fig. 7A).
347
Dependence on labile and semi-labile detritus is comparable for most biotic compartments with lower
348
quartiles between 0.98 and 1.15 and upper quartiles between 2.18 and 2.28 (Fig. 7B-H). The level of
349
uncertainty is high for the dependency values, particularly with respect to the upper levels of
350
dependency that can be >8, which is substantially higher than the median values (Fig. 7).
351
Overall it is clear that the standard deviations are fairly limited for respiration rates (Table 3)
352
and secondary production (see above) by the various compartments, which are measures of total
353
carbon processing. There is, however, a much higher variability in trophic levels (Fig. 6) and
354
dependencies (Fig. 7), which indicates that the uncertainty on the flows between the compartments is
355
substantially higher than the uncertainty on the total carbon processing by the compartments.
356
3.3 Sensitivity analysis
357
The perturbations of the stock values with ±15% of their default value in the sensitivity analysis
358
gave following results: for 46% of the flow values, the deviation in the perturbed solution was between
359
0 and 10% of the default flow value, for 37% this deviation was between 10 and 25% of the default
360
flow values, for 13% the deviation was between 25 and 50%, for 4% it was between 50 and 100% and
361
for <1% of the flow values a deviation of more than 100% of its default flow value was found. The
362
maximum deviation for a flow was 169%, which involved the export flow of surface-deposit feeding
363
macrofauna and occurred under a reduced stock value of the predatory megafauna. The reduced
364
stock of predatory megafauna involved a reduction in its predation pressure on surface-deposit
365
feeding macrofauna due to which the export flow increased. Overall, however, the sensitivity analysis
366
revealed that in the model results were generally insensitive to perturbations in the stock values.
367
4. Discussion
368
Ecosystem dynamics in the Arctic Ocean are regulated by the strong seasonality in the light
369
and temperature regime and the cover of sea-ice (Grebmeier and Barry, 1991; Honjo et al., 2010;
370
Wassmann et al., 2006). The Arctic Ocean is surrounded by landmasses with extensive shallow
371
continental shelves with strong benthic-pelagic coupling (Grebmeier and Barry, 1991), particularly in
372
the ice-edge and ice-free regions such as the Bering Sea (Grebmeier et al., 2006), Barents Sea (De
373
Laender et al., 2010; Wassmann et al., 2006) and Chukchi Sea (Moran et al., 2005). This benthic-
374
pelagic coupling on the shallow shelves results in a comparatively high fraction (>15%) of the primary
375
production being processed by the benthos, sustaining high levels of macrofaunal biomass
376
(Grebmeier et al., 1988; Renaud et al., 2007). Compared to the shallow continental shelves, the deep
377
sediments of the Arctic Ocean are much less studied (e.g. Bergmann et al., 2009; Clough et al., 1997;
378
Iken et al., 2005; Kröncke, 1994; Vanreusel et al., 2000; Wlodarska-Kowalczuk and Pearson, 2004),
379
especially at the integration level of the whole food web.
380
Fram Strait is located between Spitsbergen and Greenland and forms a deep (>2000 m) and
381
narrow connection between the Arctic Ocean and the Atlantic Ocean (Fig. 1). In this region, the
382
amount of organic matter processed in the sediment decreases as allometric functions of water depth
383
and primary production (Schluter et al., 2000). To detect and track the impact of large-scale
384
environmental changes in the transition zone between the northern North Atlantic and the central
385
Arctic Ocean, the long-term observatory HAUSGARTEN was established (Budaeva et al., 2008; Hoste
386
et al., 2007; Soltwedel et al., 2009). Export fluxes from the euphotic zone at the HAUSGARTEN
387
observatory are restricted to <10% of the primary production suggesting an efficient processing by the
388
pelagic food web (Bauerfeind et al., 2009). Although information on biomass of various compartments
389
(Budaeva et al., 2008; Hoste et al., 2007; Soltwedel et al., 2009) and organic carbon deposition
390
(Bauerfeind et al., 2009) is well-documented, no inferences have been made on how the organic
391
carbon that arrives at the seafloor is processed within the benthic community. To address this
392
deficiency, the available data were merged to a food web model using linear inverse methodology
393
(Soetaert and Van Oevelen, 2009). Since the resulting food web structure depends heavily on the
394
model assumptions and data quality, it is essential to start with a critical appraisal of these.
395
4.1 Model assumptions
396
The inherent heterogeneity of sedimentary detritus implies that various detritus fractions have
397
degradation rates differing over orders of magnitude (Middelburg, 1989; Moore et al., 2004; Westrich
398
and Berner, 1984). On the one hand, it is impossible to do justice to this continuum of degradation
399
rates within a food web context, firstly because too many detritus compartments would need to be
400
defined and secondly because no data are available to constrain their dynamics. On the other hand,
401
mass-balance models of sediment food webs typically merge all dead organic matter into one
402
homogeneous “detritus” compartment (e.g. Rowe et al., 2008), which may be a too crude
403
simplification. Here, three detritus compartments were defined based on empirical data using a similar
404
approach as Van Oevelen et al. (2011). Labile detritus was defined as all carbon associated with
405
chlorophyll a. Chlorophyll a deposition is typically linked to the input of fresh phytodetritus, both in
406
coastal (Sun et al., 1991), canyon (Van Oevelen et al., 2011) and abyssal plain (Stephens et al., 1997;
407
Witbaard et al., 2000) sediments. Henceforth, the particulate organic carbon that is associated with
408
chlorophyll a, using a carbon : chlorophyll a ratio for living phytoplankton (following Stephens et al.,
409
1997), represents a natural choice to define the labile detritus compartment. In addition to a labile
410
detritus fraction, it is readily established that there is a refractory detritus pool in marine sediments that
411
is only degradable by prokaryotes (Benner et al., 1986; Deming and Baross, 1993; Pfannkuche,
412
2005). Therefore, total particulate organic carbon in the sediment minus the labile and semi-labile
413
detritus was defined refractory and hence not degradable by benthic fauna. The most ambiguous
414
detritus pool to define was semi-labile detritus, which was here defined as the sum of extractable
415
particulate proteins in the top 5 cm. Amino acids are frequently used as indicator for the lability of
416
detritus (Dauwe et al., 1999; Kiriakoulakis et al., 2001; Mayer et al., 1995) and termed are semi-labile
417
(Fabiano et al., 2001), because they do not degrade in short time scales and proteins have
418
intermediate degradation rates in experimental decays studies (Harvey et al., 1995). Since the
419
extraction and hydrolyzation methods used for the characterization of proteins are harsher (e.g. low
420
pH, high temperature) than those present in digestive tracts (Mayer et al., 1995), it is likely that this
421
definition represents an upper limit on the semi-labile detritus stock. Van Oevelen et al. (2011)
422
followed a similar approach, although they defined semi-labile detritus as the sum of lipids,
423
carbohydrates and proteins. However, proteins comprise ~50% of this total pool. To account for the
424
uncertainty in classifying the detritus classes, the lower and upper bounds on the degradation rates of
425
semi-labile detritus encompasses two orders of magnitude (Table 2). Also, our sensitivity analysis
426
showed that the model results presented are insensitive to the stock values ± 15% (see Results).
427
Admittedly, our separation into various detritus classes is an operational definition. However, it does
428
better justice to the natural detritus heterogeneity than considering simply one pool and it is linked to
429
measurable quantities. Moreover, there is roughly an order of magnitude increase in the different
430
detritus stocks with decreasing lability (Table 2), suggesting a reasonable coverage using these
431
metrics.
432
Some biotic compartments are missing from the food web topology (Fig. 3). Microfauna or
433
nanobenthos (i.e. flagellates and ciliates) are not included because of a lack of biomass data. This is a
434
problem in most deep-sea studies such that the role of the nanofauna in carbon cycling of deep-sea
435
food webs remains an open question. However, their limited biomass compared to, for example
436
Foraminifera (Alongi, 1992), may suggest a limited role in carbon processing. Meiofauna were
437
represented by two nematode compartments (Fig. 3). Hoste et al. (2007) showed that nematodes
438
strongly dominated the metazoan meiofauna (85-99%) at HAUSGARTEN, such that the omission of
439
other metazoan meiofauna is probably not significant, at least when it comes down to carbon
440
processing rates. Foraminifera, i.e. protozoan meiofauna, had to be omitted from the Hausgarten food
441
web model because no biomass data were available. Therefore their role in carbon processing could
442
not be assessed and this represents a shortcoming of the present model. Foraminifera can have
443
comparable or higher biomass levels compared to the metazoan meiofauna in deep sediments
444
(Bernhard et al., 2008; Gooday, 1986; Witte et al., 2003) and have been shown to be important
445
contributors to short-term processing of fresh phytodetritus in deep sediments (Moodley et al., 2002).
446
A specific study on the Foraminifera showed that their contribution to total respiration was limited to
447
0.5 – 2.5% only (Geslin et al., 2010) and the uptake of 13C-phytodetritus by the larger (>300 μm)
448
Foraminifera is generally limited (Woulds et al., 2007). Moreover, if biomass of Foraminifera were of
449
comparable magnitude as the nematodes at the Hausgarten, then their role will be limited considering
450
the limited role of nematodes in carbon processing (Fig. 3 and 4 and Table 3). The absence of
451
nanobenthos and Foraminifera as specific compartments in essence implies that their role in carbon
452
processing is included in the prokaryotes. This latter compartment acts as a closure term on
453
respiration, because specific information of the prokaryotic respiration or production was unavailable.
454
Overall, however, very detailed benthic biomass data were available ranging from prokaryotes to
455
megafauna, that could be split among feeding types using taxonomic information and stable isotope
456
studies (Bergmann et al., 2009). Carbon flows could therefore be inferred at a high resolution,
457
especially considering the fact that we are dealing with a deep-sea food web.
458
The resulting standard deviations on the carbon flows are limited for prokaryotic and faunal
459
respiration rates and secondary production (see above), which are all measures of total carbon
460
processing. These carbon flows are predominantly constrained by biomass data and literature
461
constraints (Table 2). There is, however, higher variability in diet compositions (see ‘Results’), trophic
462
levels (Fig. 6) and food dependencies (Fig. 7), indicating that the uncertainty on the flows between the
463
compartments is substantially higher than the uncertainty on the total carbon processing by the
464
compartments (see also Appendix). This higher uncertainty on flows between compartments is
465
undoubtedly the result of limited data that are available to constrain these flows, a situation that is
466
typical for benthic food web reconstructions. This may be improved by more detailed information on
467
diet composition using for example fatty acid composition data (Iverson et al., 2004; Meziane et al.,
468
1997) or trophic level indicatios using δ15N values of specific amino acids (Chikaraishi et al., 2009).
469
4.2 Carbon budget
470
Respiration by the total community was estimated at 3.26±0.20 mmol C m-2 d-1. This respiration
471
rate is 3 to 10 times lower than the range of 10.3 – 35.6 mmol C m-2 d-1 that can be inferred from an
472
empirical relation based on in-situ sediment oxygen consumption rates from open slope sediments
473
(Andersson et al., 2004), although substantially higher than the 0.12 – 0.25 mmol C m-2 d-1 measured
474
at similar depth at an Arctic continental slope of the Laptev Sea (Boetius and Damm, 1998).
475
Bauerfeind et al. (2009) report low export from the pelagic food web (<10% of primary production) at
476
the central HAUSGARTEN station and explain this by effective recycling within the pelagic community.
477
However, short-term sedimentation events like ice-edge blooms or detached sympagic algae may
478
cause a temporary decoupling from the pelagic food web. The food web model resolves carbon fluxes
479
in spring/summer 2003, during which carbon export was comparatively large compared to other years
480
and was composed mostly of diatomaceous material (Bauerfeind et al., 2009). This decoupling in the
481
pelagic food web renders total respiration rates of the sediment comparatively low compared to other
482
open slopes.
483
Respiration was clearly dominated by prokaryotes (93±0.6%) with contributions of less than 2% by
484
different faunal compartments (Table 3). Piepenburg et al. (1995) conducted an extensive study in the
485
north-eastern Barents Sea, in which sediment community oxygen consumption (SCOC) rates,
486
including micro-, meio and macrofaunal respiration, were amended with respiration rates of
487
megafauna and fish. These authors also report a microbial dominance (57%) for slope sediments (200
488
– 550 m), with more limited contributions of meio- (7%), macro- (21%) and megafauna (16%).
489
Ambrose et al. (2001) found contributions of up to 25% at shallow (<50 m) stations, with respiration
490
rates of >1 mmol C m-2 d-1 for epibenthic echinoderms. These faunal contributions and rates are
491
consistently higher than estimated here for the deeper HAUSGARTEN station (2500 m). This pattern
492
is also seen in other regions were faunal contributions can be as high as 50% at the shelf and shelf
493
break sediments compared to continental slope and abyssal plain environments (Heip et al., 2001;
494
Woulds et al., 2009). The shift towards an increased microbial contribution to carbon processing
495
possibly relates to energy limitation at greater depths, such that population densities of large
496
organisms simply become too low to remain reproductively viable (Rex et al., 2006). In all, the
497
respiration partitioning at the central HAUSGARTEN station is more comparable to abyssal plain food
498
webs that are under strong energy limitation, compared to shallower sediments where the faunal
499
contribution is typically larger.
500
4.3 Faunal carbon flows and position in food web
501
The nematode community contributed surprisingly little to total respiration (<1%, Table 3),
502
especially when compared to a global estimate of around 7.5% for nematodes (Soetaert et al., 2009).
503
This limited contribution of nematodes is also found in two isotope pulse-chase experiments
504
conducted in Arctic sediments. Ingels et al. (2010) inferred that <1% of the added organic matter
505
sources (bacteria and diatoms) at the central HAUSGARTEN station were processed by nematodes.
506
Urban-Malinga and Moens (2006) conducted 13C-phytodetritus tracer experiments in two Arctic beach
507
sediments and reported meiofaunal processing of <5%. The diet composition of deposit-feeding
508
nematodes indicates that a substantial 24% of their carbon requirements is derived from labile detritus
509
(Fig. 5). This seems to contradict the findings of Ingels et al. (2010), who report a higher uptake of
13C-
510
labelled bacteria compared to13C-labelled diatoms. However, the total uptake of labelled bacteria was
511
limited to <6·10-5 mmol C m-2 d-1, which is less than 0.1% of the carbon assimilation of 0.06±0.007
512
mmol C m-2 d-1 inferred here. This indicates that although uptake rates of labelled bacteria were higher
513
than labelled diatoms, these labelled carbon sources were insignificant in the total carbon
514
requirements of the nematodes. Whether this is related to the reduced food availability because of
515
reduced mixing of the carbon sources into the sediment or methodological bias because of freeze-
516
drying of the carbon sources, as Ingels et al. (2010) note, is presently unclear. The limited uptake of
517
bacterial carbon, however, is in agreement with experimental results from Guilini et al. (2010), who
518
injected different 13C enriched dissolved organic compounds into the top 5 cm of the sediment of a
519
shallower HAUSGARTEN station and traced the 13C into prokaryotes and subsequently nematodes
520
(henceforth the constraints in Table 2). We lack sufficient evidence to more precisely quantify labile
521
and semi-labile diet contributions for the nematode community (Fig. 5 and Fig. 7B-C), but modelling
522
results indicate a dominance of semi-labile detritus. Hoste et al. (2007) found inter-annual changes in
523
nematode biomass in the years 2001 – 2004 at the central HAUSGARTEN site, but these could not be
524
related directly to export fluxes from the water column. The authors indicated that the absence of this
525
relation may have been related to uncertainties in exact timing of export flux and the amount actually
526
arriving at the seafloor. Present results, however, indicate that this may also be due to a dependence
527
on a semi-labile pool that are much less dynamic given the degradation rate of 0.0018 d-1
528
corresponding to a half-life of 385 days. Ruhl et al. (2008) report response times of the benthic
529
community to a POC deposition event of 4 – 6 months for abundance and up to 10 months for
530
biomass. It is not clear, however, to what extent the benthos were directly acquiring their carbon from
531
the freshly deposited carbon compared to the more stable semi-labile detritus pools. Their figures also
532
show that benthic biomass does not decrease monotonically with POC fluxes approaching zero,
533
indicating at least partial reliance on a less fraction detritus pool.
534
Only a limited fraction of secondary production (~6%) of the deposit-feeding nematodes is
535
transferred to predatory nematodes, despite the fact that deposit-feeding nematodes represent 80% of
536
the diet of predatory nematodes. This indicates that predation may not control their biomass at this
537
station. Gallucci et al. (2008) used cage experiments at HAUSGARTEN to investigate the impact of
538
presence/absence of megafauna on the community structure of nematodes. Total nematode densities
539
were higher within the cages, probably related to higher food abundance, although the percentage of
540
predatory nematodes was low (~3%) and similar within and outside the cages. Similarly low
541
abundances of predatory nematodes were reported in sediments of the Laptev Sea (Vanaverbeke et
542
al., 1997), suggesting that the ‘incomplete’ utilisation of secondary production within the nematode
543
sub-food web may be a more general phenomenon in Arctic sediments.
544
The non-predatory macrofaunal compartments account for half of the faunal secondary
545
production, which is efficiently transferred up the food web to the macro- (18%) and megabenthic
546
(38%) predators. The transfers also result in high contributions of non-predatory macrofauna in diets of
547
predatory macro- and megafauna. Only few studies are available for comparison. Rowe et al. (2008)
548
determined predation rates by megafauna in the Gulf of Mexico, but a priori assumed that organisms
549
preferentially feed on larger prey items. Predation rates ranged from 2·10-4 – 0.03 mmol C m-2 d-1,
550
depending on station depth. Total predation by the megafauna at the HAUSGARTEN station amounts
551
to 0.055±0.007 mmol C m-2 d-1 and is comparable to the upper slope stations (500 – 1000 m) of the
552
Gulf of Mexico. Total megafaunal biomass at HAUSGARTEN (6 mmol C m-2) is somewhat higher
553
compared to these shallower stations (2.2 – 2.6 mmol C m-2) despite the lower organic matter input at
554
HAUSGARTEN (3.78 vs. 4.2 – 9.5 mmol C m-2 d-1). This implies that the Arctic HAUSGARTEN
555
megafauna appear to take more advantage of the organic matter flux than megafaunal assemblages
556
from the tropical Gulf of Mexico.
557
The diet contribution of surface-deposit feeding macrofauna seems to be dominated by
558
prokaryotes (50±28%), although there is substantial uncertainty in these diet reconstructions as
559
evidenced by the large standard deviation (see also ‘Results’). There is no experimental evidence on
560
the importance of prokaryotes in the diets of deposit-feeding macro- and megafauna, but the model
561
results suggest 15 to 20%. Constraints on deep-sea feeding strategies based on optimal foraging
562
theory imply that deposit feeders may use a strategy in which oxygen and ammonium is supplied to
563
prokaryotes to facilitate (pre-)degradation of detritus and the prokaryotes are subsequently grazed
564
(Jumars et al., 1990). Although this theory has not been rigorously tested for benthic food webs, our
565
results here show that prokaryotes may indeed contribute to carbon demands, but further experimental
566
work is required to substantiate these findings.
567
The trophic levels (TL) that are inferred from the model are not directly comparable with those
568
estimated at species-level with δ15N measurements (e.g. Bergmann et al., 2009; Iken et al., 2005; Iken
569
et al., 2001). Here, the trophic level is calculated for benthic compartments from a large set of model
570
solutions that are feasible within the current data set. As such, these results can be interpreted as the
571
range of TLs that are feasible within the different biotic compartments, with two restrictions. (1) The
572
ranges in the model results are based on a compartment in which species are lumped into functional
573
groups and will therefore be more limited than those based on species-specific δ15N, in which more
574
extreme values can be found. (2) The δ15N of detritus may increase during progressive degradation
575
resulting in a δ15N difference between labile and semi-labile detritus (Altabet, 1996). This fractionation
576
effect is not included in the TL calculation, because it does not influences an organism’s TL. Moreover,
577
the estimates of TL based on δ15N may not be accurate if there is a large difference in δ15N of the
578
primary food sources.
579
Overall, the model results are consistent with the δ15N results that Bergmann et al. (2009)
580
obtained at the HAUSGARTEN stations, in that deposit feeders mostly occupy the second and most
581
predators the third TL. In addition, the model results show that the largest range of trophic levels is
582
found for the predatory compartments (TL range of 2.5 – 3.3, respectively), which is qualitatively
583
consistent with the results from Bergmann et al. (2009) but their δ15N data indicate a larger range in
584
TLs of up to 3.5. The largest discrepancy is found for suspension feeders that are at TL of 2 in the
585
model, because in the model setup they are assumed to feed exclusively on suspended detritus (with
586
fixed TL of 1). Bergmann et al. (2009), however, find a much larger variation in the suspension feeders
587
(range of 3 TLs), which they attribute to starvation effects, feeding selectivity and the uncertain
588
classification of sponges and anthozoans (that are potentially carnivorous or rely on microbial
589
farming).
590
The spread in TLs also agrees with a study of δ15N signatures of benthic fauna in the Canada
591
Basin (Iken et al., 2005), where benthic fauna are found mainly at a TL of 2 – 3. Moreover, they infer
592
that deposit feeders rely to a large extent on less labile detritus because of the large difference in δ15N
593
between benthic deposit feeders and fresh detritus (i.e. sympagic algae and pelagic algae). Our model
594
results also indicate that semi-labile detritus is an important (>50%) component of the diets of deposit-
595
feeding macro- and megafauna.
596
Trophic dependency quantifies the dependence of a consumer on a resource through direct (i.e.
597
grazing) and indirect (i.e. longer loops in the food webs) pathways in the food web, thereby giving a
598
more complete view on trophic interactions than when looking at direct interactions only (Ulanowicz,
599
2004). Overall, dependence on refractory detritus is low for all biotic compartments. The dependencies
600
that are inferred for the other basal resources show an interesting feature: although labile detritus is
601
generally substantially less important in the diet compositions than semi-labile detritus, the
602
dependence on labile detritus is comparable or even slightly higher than dependence on semi-labile
603
detritus. This is due to the effect of combining all pathways, and not only the direct interactions, into
604
the dependency indices. As such, this may indicate that the benthic food web is more sensitive to
605
changes in labile detritus input as may be inferred from their diet compositions alone.
606
4.4 Speculations on future conditions
607
Based on the results of this food web reconstruction, we conclude that carbon mineralisation at
608
the central HAUSGARTEN station (2500 m) is strongly dominated by prokaryotes with limited
609
contributions of the faunal compartments. The limited vertical export of particulate organic matter
610
imposes energy limitation on the benthic food web such that carbon processing resembles that of an
611
abyssal plain food web. The Arctic Ocean is a region where major shifts in the ecosystem are
612
expected due to climate change. Grebmeier et al. (2006) describe a shift in the Bering Sea, where
613
high production of sympagic algae favoured a high export to the benthos and resulted in a high
614
benthic production. This situation changed with a receding ice-edge to a pelagic dominated food web
615
with limited export fluxes and decreasing benthic production.
616
How the benthic food web at HAUSGARTEN will change under projected climate change will
617
mainly depend on the changes in the pelagic food web structure and export of organic matter. It could
618
be argued that ice-free conditions promote phytoplankton growth, as projected for some Arctic regions
619
due to temperature and light penetration increases as a result of shrinking sea ice (Arrigo et al., 2008;
620
Slagstad et al., 2010). However, primary production may rise only slightly if increased thermal or
621
haline stratification limits mixing and upward nutrient transport (Carmack et al., 2006; Slagstad et al.,
622
2010). In addition, mesozooplankton abundance may increase in the Fram Strait, since Atlantic
623
species extend their range as more Atlantic water masses prevail and sea surface temperatures rise
624
(Hirche and Kosobokova, 2007). This would amplify the grazing pressure and lead to increased
625
retention in the water column (Carroll and Carroll, 2003). The retreat of the ice edge and the
626
continuous loss of multi-year ice will lead to a lower flux of fast-sinking sympagic algae and ice-related
627
POM (Forest et al., 2010; Hop et al., 2006), which may affect megafaunal deposit feeders such as
628
holothurians (Bergmann et al., 2011). In sum, all this would lead to a decreased carbon deposition at
629
the deep seafloor, which is already characterised by food limitation.
630
Based on the results from our model, we do not expect that shifts in the overall functioning of the
631
benthic food web will occur rapidly, because semi-labile detritus plays an significant role in the benthic
632
food web. The semi-labile detritus is a stock with a comparatively low degradation rate (0.0018±0.0007
633
d-1 and corresponding half-life of 382 days) such that changes will be slower to observe than expected
634
from sole differences in detritus deposition rates. The dependency indices of the benthic fauna on
635
labile and semi-labile detritus were, however, of comparable magnitude such that that the benthic food
636
web may be more sensitive to changes in labile detritus input as may be inferred from their diet
637
compositions alone. Unfortunately, there were not enough data to allow better discrimination between
638
pelagic and sympagic plankton inputs; if some species specifically select sympagic phytodetritus, as
639
seen for some pelagic fauna (Hop et al., 2006), these species will be especially vulnerable. It will be
640
important to study the species-specific feeding preferences in detail to assess the vulnerability of
641
individual components of the benthic food web.
642
5. Acknowledgements
643
We thank the officers and crews of RVs Polarstern and Maria S. Merian and the team of the
644
remotely operated vehicle ‘‘Victor 6000’’ for their support. We also acknowledge the work of our
645
technicians and student workers in the laboratory and at sea. Three anonymous reviewers and Andy
646
Gooday are thanked for constructive comments that considerably improved an earlier version of this
647
manuscript. This research was supported by the HERMES project (contract GOCE-CT-2005-511234),
648
funded by the European Commission’s Sixth Framework Programme under the priority “Sustainable
649
Development, Global Change and Ecosystems”, and HERMIONE project (grant agreement n°
650
226354") funded by the European Community's Seventh Framework Programme (FP7/2007-2013).
651
This is publication **** from the Netherlands Institute of Ecology (NIOO-KNAW), Yerseke and
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publication awi-n**** of the Alfred Wegener Institute for Polar and Marine Research.
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654
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