Canyon conditions impact carbon flows in food webs of three sections of the Nazaré canyon



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Methods

2.1 Nazaré canyon characteristics


The Nazaré canyon, one of the largest submarine canyons in Europe, intersects the Portuguese continental shelf and has been intensively studied in the framework of different European projects such as OMEX-II, EUROSTRATAFORM and HERMES. Expeditions carried out within these projects have resulted in comparatively high data availability on different physical, chemical and biological aspects of the canyon system. De Stigter et al. (2007) proposed a division of the canyon into three sections based on hydrographic and physical characteristics. The upper canyon is characterized by a V-shaped valley that is deeply incised in the continental shelf and starts at 50 m water depth and runs down to a depth of 2700 m. The middle canyon (2700 – 4000 m) is a broad meandering valley with terraced slopes and the lower canyon is a flat floored valley that gently descends from 4000 to 5000 m depth. The water column along the Western Iberian Margin is stratified, with relatively warm (14 to 18ºC) and saline (35.4 to 35.8) water at the surface (North Atlantic Central Water) to cold (2ºC) and less saline (34.8) water at 5000 m depth (North Atlantic Deep Water). The upper and middle canyon sections capture suspended particulate matter from the adjacent shelf and are affected by internal tide circulation of water with high bottom current speeds (de Stigter et al., 2007).

The seabed of the Nazaré canyon is heterogeneous and consists of a highly dynamic thalweg filled with coarse sandy and gravelly deposits, steep sloping canyon walls with rocky outcrops, and terraces with thick accumulations of soft muddy sediments (Tyler et al., 2009). The hard substrata in the thalweg and on steep walls and outcrops are covered in places with a thin, centimeter-thick drape of soft mud, where it is impossible to sample with box- or multicorer to estimate biomass. Moreover, to avoid large heterogeneity in the data set due to seabed differences, the focus of this manuscript is on soft-sediments outside the thalweg, which were split into the three sections as described above. The depth range of the upper section was here limited to 300 – 700 m.

Chemical and biological data were available on the concentration of total carbohydrates, lipids and proteins in the sediment (Pusceddu et al., 2010), sedimentary chl a content (Garcia and Thomsen, 2008), sediment diagenesis (Epping et al., 2002), prokaryotic heterotrophic carbon production (Danovaro, unpub. data), nematode trophic structure (Danovaro et al., 2009) and the macro- and megafaunal community structure (Cunha et al., this issue and unpub. data). Such data on biotic and abiotic carbon stocks and transformation rates are perfectly suited to quantify food webs of the three sections of the Nazaré canyon using linear inverse modeling.

2.2 Linear inverse models


The food web models developed for the Nazaré canyon are constructed using linear inverse modeling (Van Oevelen et al., 2010). In an inverse model, the food web compartments and flows between them are fixed a priori (see ‘Food web structure’ below). The flow magnitudes are constrained within the boundaries that are defined by the inclusion of empirical data on standing stocks, flux data and physiology into the model. The food web topology and empirical data are included in a matrix equation with equalities and in a matrix equation with inequalities. These matrix equations are solved simultaneously to recover quantitative values for the flow values, such that the flow values in a model solution are within the boundaries defined by the matrix equations. The model was run 10,000 times and each time a different solution is generated to allow estimating the mean and standard deviation of each unknown flow. It is important to note that by running the model 10,000 times, the uncertainty in the empirical data (see ‘Data availability’ below) is propagated onto an uncertainty estimate of the carbon flows as indicated by its standard deviation. Convergence of the mean and standard deviation of the flows was used to verify whether the set of 10,000 model solutions was sufficiently large.

Several reviews on the technical and methodological aspects of linear inverse modeling have been published and will therefore not be repeated here (Soetaert and Van Oevelen, 2009; Van Oevelen et al., 2010). These reviews contain simple models to exemplify the setup and solution of linear inverse food web models using the software packages LIM (Soetaert and Van Oevelen, 2008; Van Oevelen et al., 2010) and limSolve (Soetaert et al., 2008) that run in the R software (R Development Core Team, 2008). The Nazaré food web models are made publically available in the LIM package.


2.3 Food web structure


The compartments in the food web models were chosen based on the classical size distribution of prokaryotes (Pro), meiofauna (Mei), macrofauna (Mac) and megafauna (Meg). The faunal compartments were further subdivided based on the feeding classification for nematodes (Wieser, 1953) and feeding types for macro- and megafauna were surface deposit-feeder (SDF), deposit-feeder (DF), suspension feeder (SF) and predator+scavenger (PS) (see below). The sedimentary organic matter was divided into dissolved organic carbon (DOC) and labile (lDet), semi-labile (sDet) and refractory detritus (rDet).

Inputs to the food web are deposition and/or suspension feeding of suspended labile (lDet_w), semi-labile (sDet_w) and refractory detritus (rDet_w). Outputs from the food web are respiration to dissolved inorganic carbon (DIC), burial of rDet, DOC efflux to the water column and export by the macro- and megafaunal compartments (e.g. consumption by fish).

The detritus pools in the sediment can be hydrolyzed to DOC and the labile and semi-labile detritus pools are grazed upon by meiofauna and MacSDF, MacDF, MacPS, MegSDF and MegDF. DOC is taken up by prokaryotes or fluxes out of the sediment to the water column. Predatory feeding links are primarily defined based on size class; prokaryotes are consumed by all meiofaunal and non-suspension feeding macro- and megafaunal compartments, meiofaunal compartments are consumed by non-suspension feeding macro- and megafaunal compartments, the macrofaunal compartments MacSDF, MacDF and MacSF are preyed upon by MacPS.

Part of the ingested matter by the faunal compartments is not assimilated but instead expelled as feces, the non-assimilated labile (e.g. labile detritus, prokaryotes and faunal compartments) and semi-labile (semi-labile detritus) carbon, flows into semi-labile and refractory detritus, respectively. Respiration by faunal compartments is defined as the sum of maintenance respiration (biomass-specific respiration) and growth respiration (overhead on new biomass production). Prokaryotic mortality is represented here as a flux to DOC and faunal mortality is defined as a flux to labile detritus.


2.4 Data availability


The Nazaré canyon is one of the best studied canyons in Europe, with studies on sediment transport and/or fate of organic matter (e.g. de Stigter et al., 2007; Epping et al., 2002; García et al., 2008), concentration of total carbohydrates, lipids and proteins in the sediment (Pusceddu et al., 2010) heterotrophic prokaryotic C production (Danovaro unpub. data), nematode community structure (Garcia et al., 2007; Danovaro et al., 2009; Ingels et al., 2009), meiofaunal abundance (Bianchelli et al., 2010), macro- and megafaunal community structure (Tyler et al., 2009, Cunha et al., this issue and unpub. data). As stated above, empirical data were only included if they were collected from the soft-sediments of the upper, middle or lower section of the canyon.

Detritus stocks were delineated as follows (Table 1): the stock of labile detritus was defined as all carbon associated with chlorophyll a. Chlorophyll a concentrations were taken from the top 5 cm in sediments of the off-thalweg stations (Garcia and Thomsen, 2008), which were converted to carbon units by assuming a carbon to chl a ratio of 40. Semi-labile detritus was defined as the sum of the carbohydrates, lipids and proteins (i.e. biopolymeric carbon) that were converted to carbon equivalents (Pusceddu et al., 2010). Biopolymeric carbon concentrations were measured only in the top 1 cm and were linearly extrapolated to 5 cm depth under the assumption that all semi-labile detritus is degraded in the top 5 cm. The latter assumption is supported by Epping et al. (2002) who showed that carbon degradation occurs primarily in the top 5 cm of the sediment. Refractory detritus was defined as the degradable fraction of the particulate organic carbon in the top 5 cm of the sediment (derived from organic carbon content profiles in Epping et al., 2002), minus the labile and semi-labile detritus pools.

Biomass data were available for prokaryotes and all faunal compartments (i.e., meiofaunal, macrofauna and megafauna; Table 1). Nematodes dominated the metazoan meiofauna (on average 90% of total abundance) and the Wieser feeding classification based on nematode mouth morphology was used to designate biomass to selective feeding (Wieser type 1A + 2A), non-selective feeding (Wieser type 1B) and omnivore/predatory (Wieser type 1B). Polychaetes dominated the macrofaunal compartments and these were grouped into surface-deposit, deposit, suspension and predatory+scavenging feeding compartment based on standard feeding type classification from Fauchald and Jumars (1979). Biomass-dominant polychaete families in the upper section are Onuphidae (57%) and Sigalionidae (36%), in the middle section Spionidae (61%), Fauveliopsidae (9%) and Ampharetidae (8%), and in the lower section Spionidae (40%), Goniadidae (15%) and Siboglinidae (12%). Other contributions to the macrofaunal biomass from Mollusca, Bivalvia and Crustacea are low (< 3%) in the upper section, higher in the middle section with 48%, 14% and 19%, and negligible in the lower section (<1%), respectively. Finally, the megafaunal surface-deposit feeding community consists of Ypsilothuria bitentaculata (Holothuroidea) and deposit feeding community of Molpadia musculus (Holothuroidea).

Since there were no data available on the temporal variability in benthic biomass, these were neglected and it was assumed that the mass balances of all compartments are in steady-state, i.e., . This assumption introduces only limited bias in the model solution (Vézina and Pahlow, 2003), primarily because net biomass increases (e.g. for the fauna and bacteria) are small as compared to the other flows in the food web.

In addition to the standing stock measurements, a variety of data on process rates were available for the different sections of the Nazaré canyon (Table 2). These data were implemented as inequalities by setting the minimum and maximum value found in each section as lower and upper bounds, respectively.

The determination of prokaryotic C production in sediment samples was carried out according to the procedure described for marine sediments by Danovaro et al. (2002). Sediment subsamples from the top 1 cm were mixed with a solution of 3H-leucine (final concentration 0.2 mmol L-1), were incubated at in situ temperature for 1 hour in the dark. After incubation, samples were supplemented with ethanol (80%) and processed according to Van Duyl and Kop (1994) before scintillation counting. Sediment blanks were made adding ethanol immediately after 3H-leucine addition. The incorporated radioactivity in all samples was measured by a liquid scintillation counter. The following equation was used for calculating prokaryotic C production:

PCP ~ LI · 131.2 · (%Leu) – 1 · (C: protein) · ID

where PCP is prokaryotic C production, LI is the leucine incorporation rate (mol ml-1 h-1), 131.2 is the molecular weight of leucine, %Leu is the fraction of leucine in protein (0.073), C:protein is the ratio of cellular carbon to protein (0.86), and ID is the isotope dilution assuming a value of 2.

The prokaryotic C production was determined in the top 1 cm and this value was taken as lower bound on prokaryotic production (Table 2). Prokaryote production typically decreases with depth in the sediment due to reduced availability of degradable detritus and electron acceptors (e.g. Nodder et al., 2003; Glud and Middelboe, 2004). The upper bound on prokaryotic C production for the top 5 cm was set to five times the prokaryotic C production of the top 1 cm. As such, we impose that the integrated prokaryotic C production does not increase within the top 5 cm of the sediment, because the model solution is found between the lower bound (production in top 1 cm layer) and the upper bound (5 times the production in the top 1 cm layer). Carbon burial rates, total respiration rates, total carbon deposition and burial efficiencies for each section were taken from the diagenetic modeling work of Epping et al. (2002) (Table 2). We imposed that total respiration and carbon deposition in Epping et al. (2002) did not include the respiration and uptake by megafauna, respectively, because the activity of these large burrowing or surface-dwelling organisms is missed in a diagenetic modeling approach that is based on small cores incubations and oxygen profiles in the sediment.

An additional number of general inequality constraints were taken from the literature to constrain degradation rates of the labile, semi-labile and refractory detritus pools, prokaryote growth efficiency, release of DOC from the sediment, assimilation efficiency of all faunal compartments, net growth efficiency of all faunal compartments, production and mortality rates of all faunal compartments (Table 2). Since measurements of assimilation and growth efficiencies of deep-sea benthos are very rare, we decided to use an extensive literature review (Van Oevelen et al., 2006b) of temperate benthos as basis for these constraints. Biomass-specific maintenance respiration of all faunal compartments was defined as 0.01 d-1 at 20°C (see references in Van Oevelen et al., 2006b) and is corrected with Q10 of 2, giving a temperature-correction factor (Tlim) for each canyon section (Table 2).



Benthic organisms do not feed indiscriminately on the available food sources. Both surface-deposit and deposit-feeding holothurians and echinoderms ingest organic matter with higher than ambient chlorophyll a and total hydrolysable amino acid concentrations (Ginger et al., 2001; Witbaard et al., 2001; Amaro et al., 2010), though selectivity differs between feeding modes with surface-deposit feeders typically exhibiting stronger selectivity than deposit feeders (Wigham et al., 2003). Selectivity between labile detritus and semi-labile detritus for megafauna was defined as the ratio of chlorophyll a concentrations in the gut with respect to the ambient surface sediment. The level of selectivity varies from 1 to 10 for deposit feeding holothurians to >500 for the surface deposit feeding holothurians Amperima rosea (Porcupine Abyssal Plain, Wigham et al., 2003). Selectivity at the Antarctic Peninsula was less evident (selectivity of 2 to 7), possibly because of the existence of a food bank, but there was a clear separation between deposit and surface deposit feeders (Wigham et al., 2008). Therefore, no to moderate selectivity of 1 to 10 for deposit feeders and strong selectivity (50 to 100) for surface-deposit feeders was assumed in the model (Table 2). Since no comparable data are available for macrofauna, similar selectivity ranges were defined for these compartments (Table 2). Finally, few organisms in benthic food webs can be considered as sole predators (Fauchald and Jumars, 1979), therefore the predatory meio-, macro- and megafaunal compartments were assumed rely between 75% and 100% through predatory feeding to account for this (Table 2).

2.5 Network indices


The network indices ,  and  were directly calculated from the output of the sampling algorithm in R using the newly developed R-package NetIndices (Kones et al., 2009). Details on the calculation of the indices can be found in Ulanowicz (2004) and Kones et al. (2009), but a summary of the nomenclature (Table 3) and calculation algorithms (Table 4) are included in this manuscript.

Network indices were calculated for the complete set of food web solutions (10,000 for each section). The network indices were compared between canyon sections by calculating the fraction of which the randomized set of indices of one canyon section is larger than that of another section. For example, when this fraction is 0.90, this implies that 90% of the values of section 1 are larger than the ones of section 2 (and consequently 10% of the values are lower). We define differences of >90% and <10% as significant difference and >95% and <5% as highly significant difference.




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