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This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG) Stratmann, T., Lins, L., Purser, A., Marcon, Y., Rodrigues, C. F., Ravara, A., Cunha, M. R., Simon-Lledó, E., Jones, D. O. B., Sweetman, A. K., Köser, K., and van Oevelen, D.: Faunal carbon flows in the abyssal plain food web of the Peru Basin have not recovered during 26 years from an experimental sediment disturbance, Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-167, in review, 2018.
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1
Faunal carbon flows in the abyssal plain food web of the Peru Basin
have not recovered during 26 years from an experimental sediment
disturbance
Tanja Stratmann1*, Lidia Lins2†, Autun Purser3, Yann Marcon3, Clara F. Rodrigues4, Ascensão Ravara4,
Marina R. Cunha4, Erik Simon-Lledó5, Daniel O. B. Jones5, Andrew K. Sweetman6, Kevin Köser7, Dick 5 van Oevelen1
1NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine and Delta Systems, and Utrecht University,
P.O. Box 140, 4400 AC Yerseke, The Netherlands.
2Marine Biology Research Group, Ghent University, Krijgslaan 281 S8, 9000 Ghent, Belgium.
†present address: Senckenberg Research Institute, Senckenberganlage 25, 60325 Frankfurt am Main, Germany 10 3Deep Sea Ecology and Technology, Alfred Wegener Institute, Am Handelshafen 12, 27570 Bremerhaven, Germany.
MARUM-Center for Marine Environmental Sciences, General Geology - Marine Geology, University of Bremen, D-28359
Bremen, Germany
4Departamento de Biologia & Centro de Estudos do Ambiente e do Mar (CESAM), Departamento de Biologia, Universidade
de Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal 15 5National Oceanography Centre, University of Southampton Waterfront Campus, European Way, Southampton SO14 3ZH,
UK.
6Marine Benthic Ecology, Biogeochemistry and In-situ Technology Research Group, The Lyell Centre for Earth and Marine
Science and Technology, Heriot-Watt University, Edinburgh EH14 4AS, UK.
7GEOMAR Helmholtz Centre for Ocean Research, FE Marine Geosystems, Wischhofstr 1-3, 24148 Kiel, Germany. 20
Correspondence to: Tanja Stratmann (tanja.stratmann@nioz.nl)
Abstract
Future deep-sea mining for polymetallic nodules in abyssal plains will impact the benthic ecosystem, but it is largely unclear
whether this ecosystem will be able to recover from mining disturbance and if so, at what time scale and to which extent. In
1989, during the ‘DISturbance and reCOLonization’ (DISCOL) experiment, a total of 22% of the surface within a 10.8 km2 25
large circular area of the nodule-rich seafloor in the Peru Basin (SE Pacific) was ploughed to bury nodules and mix the surface
sediment. This area was revisited 0.1, 0.5, 3, 7, and 26 years after the disturbance to assess macrofauna, megafauna and fish
density and diversity. We used this unique abyssal faunal time series to develop carbon-based food web models for disturbed
(sediment inside the plough tracks) and undisturbed (sediment inside the experimental area, but outside the plough tracks)
sites. We developed a linear inverse model (LIM) to resolve carbon flows between 7 different feeding types within macrofauna, 30
megafauna and fish. The total faunal biomass was always higher at the undisturbed sites compared to the disturbed sites and
26 years post-disturbance the biomass at the disturbed sites was only 54% of the biomass at undisturbed sites. Fish and sub-
surface deposit feeders experienced a particularly large temporal variability in biomass and model-reconstructed respiration
rates making it difficult to determine disturbance impacts. Deposit feeders were least affected by the disturbance, with
respiration, external predation and excretion levels only reduced by 2.6% in the sediments disturbed 26-years ago compared 35
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with undisturbed areas. In contrast, the respiration rate of filter and suspension feeders was still 79.5% lower after 26 years
when comparing the same sites. The total system throughput(T..), i.e. the total sum of carbon flows in the food web, was
always higher at undisturbed sites compared to the corresponding disturbed sites and was lowest at disturbed sites directly
after the disturbance (8.63 ×10-3±1.58×10-5 mmol C m-2 d-1). Therefore, 26 years after the DISCOL disturbance, the
throughput discrepancy between the undisturbed and the disturbed sediment was still 56%. From these results we conclude 5
that C cycling within the fauna compartments of an abyssal plain ecosystem remains reduced 26 years after physical
disturbance, and that a longer period of time is required for the system to recover from such a simulated small scale deep-sea
mining experimental disturbance.
1 Introduction
Abyssal plains cover approximately 50% of the world’s surface and 75% of the seafloor (Ramirez-Llodra et al., 2010). The 10
abyssal seafloor is primarily composed of soft sediments consisting of fine-grained erosional detritus and biogenic particles
(Smith et al., 2008). Occasionally, hard substrate occurs occasionally in the form of clinker from steam ships, glacial drop
stones, outcrops of basaltic rock, whale carcasses, and marine litter (Amon et al., 2017; Kidd and Huggett, 1981; Radziejewska,
2014; Ramirez-Llodra et al., 2011; Ruhl et al., 2008). In some soft sediment regions, islands of hard substrate are provided by
polymetallic nodules, authigenically formed deposits of metals, which grow at approximate rates of 2 to 20 mm per million 15
years (Guichard et al., 1978; Kuhn et al., 2017). These nodules have the shape and size of cauliflower, cannon balls or potatoes,
and are found on the sediment surface and in the sediment at depths between 4000 and 6000 m in areas of the Pacific, Atlantic
and Indian Ocean (Devey et al., 2018; Kuhn et al., 2017).
Polymetallic nodules are rich in metals, such as nickel, copper, cobalt, molybdenum, zirconium, lithium, yttrium and rare earth 20
elements (Hein et al., 2013), and occur in sufficient densities for potential exploitation by the commercial mining industry in
the Clarion-Clipperton Fracture Zone (CCFZ; equatorial Pacific), around the Cook Islands (equatorial Pacific), in the Peru
Basin (E Pacific) and in the Central Indian Ocean Basin (Kuhn et al., 2017). Extracting these polymetallic nodules during
deep-sea mining operations will have severe impacts on the benthic ecosystem, such as the removal of hard substrate (i.e.
nodules) and the food-rich surface sediments from the seafloor, physically causing the mortality of organisms within the mining 25
tracks and re-settlement of resuspended particles (Levin et al., 2016; Thiel and Tiefsee-Umweltschutz, 2001). Defining
regulations on deep-sea mining requires knowledge on ecosystem recovery from these activities, but to date information on
these rates is not extensive (Gollner et al., 2017; Jones et al., 2017; Stratmann et al., 2018; Stratmann et al., in review; Vanreusel
et al., 2016). Especially the recovery of ecosystem functions, such as food web structure and carbon (C) cycling, from deep-
sea mining is understudied. 30
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In the Peru Basin (SE Pacific), small-scale deep-sea mining activities were simulated during the ‘DISturbance and
reCOLonization’ experiment (DISCOL) in 1989. A 10.8 km2 large circular area was ploughed diametrically 78 times with a
8 m-wide plough-harrow to bury the surface nodules into the sediment (Thiel and Schriever, 1989). This experimental
disturbance resulted in a heavily disturbed centre and a less affected periphery of the DISCOL area (Bluhm, 2001; Foell et al.,
1990; Foell et al., 1992). Over 26 years the region was re-visited five times to assess the Post-Disturbance (PD) situation: 5
directly after the disturbance event, March 1989: (hereafter referred to as ‘PD0.1’); half a year later, September 1989: ‘PD0.5;
three years later, January 1992: ‘PD3; seven years later, February 1996: ‘PD7’; 26 years later, September 2015: ‘PD26.
Following the original definition by Bluhm (2001), we denote sites within the DEA (DISCOL Experimental Area), but not
directly disturbed by the plough harrow as ‘undisturbed sites’ and sites that were directly impacted by the plough harrow as
‘disturbed sites(Bluhm, 2001). During subsequent visits, densities of macrofauna and megafauna were assessed, but data on 10
meiofauna and microbial communities were only sparsely collected. Therefore, the food web models presented in this work
cover a period of 1989 to 2015 and contain macrofauna, megafauna and fish.
Linear inverse modelling (LIM) is an approach that has been developed to disentangle carbon flows between food web
compartments for data-sparse systems (Klepper and Van de Kamer, 1987; Vézina and Platt, 1988). It has been applied to assess 15
differences in C and nitrogen (N) cycling in various ecosystems, including the abyssal plain food web at Station M (NE Pacific)
under various particulate organic carbon (POC) flux regimes (Dunlop et al., 2016), and a comparison of food web flows
between abyssal hills and plains at the Porcupine Abyssal Plain (PAP) in the north-eastern Atlantic (Durden et al., 2017). LIM
is based on the principle of mass balancing various data sources (Vézina and Platt, 1988), i.e. faunal biomasses and
physiological constraints, that are implemented in the model, either as equality or inequality equations, and these are solved 20
simultaneously (van Oevelen et al., 2010). A food web model almost always includes more inequalities than equalities, i.e. it
is mathematically under-determined, which implies that an infinite number of solutions will solve the models. In this case, a
likelihood approach can be used to generate a large dataset of possible solutions for the model (van Oevelen et al., 2010), from
which the mean and standard deviations for each flow is calculated. Food web models from different sites and/or points in
time can be compared quantitatively by calculating network indices, such as the total system throughput’ (T..) that sums all 25
carbon flows in the food web (Kones et al., 2009). Hence, a decrease in the difference of T.. between the food webs from
undisturbed and corresponding disturbed sites (ΔT..) over time is taken as a sign of ecosystem recovery following disturbance.
In this study, benthic food-web models were developed for undisturbed sites and disturbed sites at DISCOL to assess whether
faunal biomass and trophic composition of the food webs varied and/or converged between the two sites over time. The model 30
outcomes were compared with conceptual and qualitative predictions on benthic community recovery from polymetallic
nodule mining published by Jumars (1981). Additionally, it was investigated how ΔT.. developed over time to infer the
recovery rate of C flows from experimental deep-sea disturbance in the Peru Basin.
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2 Methods
2.1 Data availability
Macrofauna, megafauna and fish density data (mean±std; ind. m-2) for the first four cruises (PD0.1 to PD7) were extracted from
the original papers (Bluhm, 2001 annex 2.8; Borowski, 2001; Borowski and Thiel, 1998) and methodological details can be
found in those papers. In brief, macrofauna samples (>500 μm size fraction) were collected with a 0.25 m-2 box-corer and 5
densities of megafauna and fish were assessed on still photos and videos taken with a towed “Ocean Floor Observation System”
(OFOS) underwater camera system. During the PD26 cruise (RV Sonne cruise SO242-2; Boetius, 2015), macrofauna were
collected with a square 50 × 50 × 60 cm box-corer (disturbed sites: n = 3; undisturbed sites: n = 7) and the upper 5 cm of
sediment was sieved on a 500 μm sieve (Greinert, 2015). All organisms retained on the sieve were preserved in 96% un-
denaturated ethanol on board (Greinert, 2015) and were sorted and identified ashore to the same taxonomic level as the previous 10
cruises under a stereomicroscope. Megafauna and fish density during the PD26 cruise was acquired by deploying the OFOS
(Boetius, 2015). Every 20 s, the OFOS automatically took a picture of the seafloor at an approximate altitude of 1.5 m above
the seafloor (Boetius, 2015; Stratmann et al., in review) resulting in 1,740 images of plough marks (disturbed sites) and
6,624 images from undisturbed sites (Boetius, 2015). A subset of 300 pictures from the disturbed sites (surface area:
1,440.6 m2) and 300 pictures from the undisturbed sites (surface area: 1,420.4 m2) were randomly selected from the original 15
set of pictures and annotated using the open-source annotation software PAPARA(ZZ)I (Marcon and Purser, 2017). Megafauna
were identified to the same taxonomic levels as for the previous megafauna studies conducted within the DEA (Bluhm, 2001),
whereas fish were identified to genus level using the CCZ-species atlas (www.ccfzatlas.com).
The above-mentioned density data collected for macrofauna, megafauna and fish were used to build food web models to 20
resolve carbon fluxes; hence, all faunal density data needed conversion into carbon units before they can be used in the food
web model. Converting density data to carbon biomass values was challenging in the current study, as few to no conversion
factors for deep-sea fauna are available in the literature. Below, we describe the approach we used to tackle this hurdle for
macrofauna, megafauna and fish.
In case of a macrofaunal specimen, measuring the carbon content requires its complete combustion, which means that the 25
specimen cannot be kept as voucher specimen in scientific collections. The macrofauna samples collected for this study are
part of the Biological Research Collection of Marine Invertebrates (Department of Biology & Centre for Environmental and
Marine Studies, University of Aveiro, Portugal) and were therefore not sacrificed. Instead, we used the C conversion factors
of macrofauna specimens previously collected within the framework of a pulse-chase experiment in the Clarion-Clipperton
Zone (CCZ, NE Pacific), in which a deep-sea benthic lander (3 incubation chambers à 20 × 20 × 20 cm) was deployed at water 30
depths between 4050 and 4200 m (Sweetman et al., in review). The upper 5 cm of the sediment of the incubation chambers
was sieved on 300 μm sieve and preserved in 4% buffered formaldehyde solution. Ashore, the samples were sorted and
identified under a dissecting microscope and the biomass of individual freeze-dried, acidified specimens was determined with
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at Thermo Flash EA 1112 elemental analyser (EA; Thermo Fisher Scientific, USA) to give the individual carbon content in
mmol C ind-1. The macrofauna density data (ind. m-2) from all cruises were converted to macrofauna biomass (mmol C m-2)
by multiplying each taxon-specific density (ind. m-2) with the mean taxon-specific individual biomass value for macrofauna
(mmol C ind-1; Table 1). Subsequently, the biomass data of all taxa with the same feeding type (Table 1) were summed to
calculate the biomass of each macrofaunal compartment (mmol C m-2; Supplement 1, Figure 2). 5
The megafauna density data (ind. m-2) of the time series was converted to biomass (mmol C m-2) by multiplying the taxon-
specific density with a taxon-specific mean biomass per megafauna specimen (mmol C ind-1; Table 1). To determine this
taxon-specific biomass per megafauna specimen, size measurements were used as follows. The ‘AUV Abyss‘ (Geomar Kiel)
equipped with a Canon EOS 6D camera system with 8-15 mm f4 fisheye zoom lens and 24 LED arrays for lightning 10
(Kwasnitschka et al., 2016) flew approximately 4.5 m above the seafloor at a speed of 1.5 m s-1 and took one picture every
second (Greinert, 2015). Machine vision processing was used to generate a photo-mosaic (Kwasnitschka et al., 2016). A
subsample covering an area of 16,206 m2 of the mosaic was annotated using the web-based annotation software ‘BIIGLE 2.0’
(Langenkämper et al., 2017). The length of all megafauna taxa for which data were available from previous cruises was
measured using the approach presented in Durden et al. (2016). Briefly, depending on the taxon, either body length, the 15
diameter of the disk, or the length of an arm were measured on the photo-mosaic and converted into biomass per individual
(g ind-1) using the relationship between measured body dimensions (mm) and preserved wet weight (g ind-1) (Durden et al.,
2016). Subsequently, the preserved wet weight (g ind-1) was converted to fresh wet weight (g ind-1) using conversion factors
from Durden et al. (2016) and to organic carbon (g C ind-1 and mmol C ind-1) using the taxon-specific conversion factors
presented in Rowe (1983). For the taxa Cnidaria and Porifera no conversion factors were available. Therefore, taxon-specific 20
individual biomass values were extracted from a study from the CCZ (Tilot, 1992). The individual biomass of Bryozoa and
Hemichordata were calculated as the average biomass of an individual deep-sea megafauna organism (B, mmol C ind-1) at
4100 m depth following from the ratio of the regression for total biomass and abundance by Rex et al. (2006):
=(..×)
(..×). (1)
Following the approach applied to the macrofauna dataset, individual biomasses of taxa with similar feeding types (Table 1) 25
were summed to determine the biomass of the megafauna food-web compartments (mmol C m-2; Supplement 1; Figure 1).
Individual biomass of fish was calculated using the allometric relationship for Ipnops agassizii:
wet weight = a × lengthb, (2)
where a = 0.0049 and b = 3.03 (Froese and Pauly, 2017; Froese et al., 2014), as Ipnops sp. was the most abundant deep-sea 30
fish observed at the DEA (60% of total fish density at undisturbed and 40% of total fish density at disturbed sites). The length
(mm) of all Ipnops sp. specimens was measured on the annotated 600 pictures (300 pictures from undisturbed site, 300 pictures
from disturbed site) in PAPARA(ZZ)I (Marcon and Purser, 2017) using three laser points captured in each image (distance
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between laser points: 0.5 m (Boetius, 2015)). The wet weight (g) was converted to dry-weight and subsequently to carbon
content (mmol C ind-1) using the taxon-specific conversion factors presented in Brey et al. (2010).
2.2 Food web structure
The faunal biomass was further divided into feeding guilds in order to define the food web compartments of the model. Fish
(Osteichthyes) were classified as scavenger/ predator and invertebrate macrofauna and megafauna were divided into 5
filter/suspension feeders (FSF), deposit feeders (DF), carnivores (C) and omnivores (OF) (Figure 2). Since feeding types are
well described for polychaetes (Jumars et al., 2015), we made a further detailed classification of the macrofaunal polychaetes
into suspension feeders (PolSF), surface deposit feeders (PolSDF), subsurface deposit feeders (PolSSDF), carnivores (PolC),
and omnivores (PolOF).
10
External carbon sources that were considered in the model included suspended detritus in the water column (Det_w), labile
(lDet_s) and semi-labile detritus (sDet_s) in the sediment. Suspended detritus was considered a food source for polychaete,
macrofaunal and megafaunal suspension feeders. Labile and semi-labile sedimentary detritus was a source for deposit-feeding
and omnivorous polychaetes, macrofauna and megafauna. Omnivores and carnivores of each size class preyed upon organisms
of the same and smaller size classes, i.e. MegC and MegOF preyed upon MegDF, MegFSF, MacFSF, MacDF, MacC, MacOF, 15
PolSDF, PolSSDF, PolSF, PolOF, and PolC. Furthermore, MacC, PolC, MacOF, and PolOF preyed upon MacFSF, MacDF,
PolSDF, PolSSDF, and PolSF. Fish preyed upon all fauna and the carcass pool. This carcass pool consisted of all fauna
(macrofauna, megafauna and fish) that died in the food web and was also the food source of omnivores.
Carbon losses from the food web were respiration to dissolved inorganic carbon (DIC), predation on macrofauna, megafauna
and fish by pelagic/ benthopelagic fish, scavenging on carcasses by pelagic/ benthopelagic scavengers and faeces production 20
by all faunal compartments.
2.3 Literature constraints
The carbon flows between faunal compartments are constrained by the implementation of various minimum and maximum
process rates and conversion efficiencies as inequalities in all models, which are described here. Assimilation efficiency (AE)
is calculated as: 25
AE = (I-F) / I, (3)
where I is the ingested food and F are the faeces (Crisp, 1971). The min-max range was set from 0.62 to 0.87 for macrofauna
and polychaetes (Stratmann et al., in prep.), from 0.48 to 0.80 for megafauna (Stratmann et al., in prep.) and from 0.84 to 0.87
for fish (Drazen et al., 2007).
Net growth efficiency (NGE) is defined as: 30
NGE = P / (P + R), (4)
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with P being secondary production and R being respiration (Clausen and Riisgård, 1996). The min-max ranges are set to 0.60
to 0.72 for macrofauna and polychaetes (Clausen and Riisgård, 1996; Navarro et al., 1994; Nielsen et al., 1995), from 0.48 to
0.60 for megafauna (Koopmans et al., 2010; Mondal, 2006; Nielsen et al., 1995) and from 0.37 to 0.71 for fish (Childress et
al., 1980). The secondary production P (mmol C m-2) is calculated as:
P = P/B-ratio × biomass, (5) 5
with the P/B-ratios for macrofauna and polychaetes (8.49 × 10-4 to 4.77 × 10-3 d-1; (Stratmann et al., in prep.)), megafauna
(2.74 × 10-4 to 1.42 × 10-2 d-1; (Stratmann et al., in prep.)) and fish (6.30×10-4 d-1; (Collins et al., 2005; Randall, 2002)). The
respiration rate R (mmol C m-2) was calculated as:
R = bsFR × biomass, (6)
where bsFR is the biomass-specific fauna respiration rate (d-1) and ranges were fixed between 7.12 × 10-5 to 2.28 × 10-2 d-1 for 10
macrofauna and polychaetes (Stratmann et al., in prep.), 2.74 × 10-4 to 1.42 × 10-2 d-1 for megafauna (Stratmann et al., in prep.)
and 2.3×10-4 and 3.6×10-4 d for fish (Mahaut et al., 1995; Smith and Hessler, 1974).
2.4 Linear inverse model solution and network index
A food web model with all compartments present in the food web, like e.g. the PD26 food web model for the undisturbed site,
consists of 147 carbon flows with 14 mass balances, i.e. food-web compartments, and 76 data inequalities leading to a 15
mathematically under-determined model (14 equalities vs. 147 unknown flows). Therefore, the LIMs were solved with the R
package ‘LIM’ (van Oevelen et al., 2010) in R (R-Core-Team, 2016) following the likelihood approach (van Oevelen et al.,
2010) to quantify the mean and standard deviations of each of the carbon flows from a set of 100,000 solutions. This set was
sufficient to guarantee the convergence of mean and standard deviation within a 2.5% deviation.
The network index ‘total system throughput’ (T..) was calculated with the R-package ‘NetIndices’ (Kones et al., 2009) for 20
each of the 100,000 model solutions and subsequently summarized as mean ± standard deviation.
2.5 Statistical analysis
Statistical differences between compartment biomasses of the undisturbed vs. disturbed sites for the same sampling event
(PD0.1, PD0.5, PD3, and PD7; PD26 was omitted due to a lack of megafauna replicates) were assessed by calculating Hedge’s d
(Hedges and Olkin, 1985a), which is especially suitable for small sample sizes (Koricheva et al., 2013): 25
d=(
E-
C)/(((nE-1)(sE)2+(nC-1)(sC)2)/(nE+nC-2))0.5×J (7)
with J=1-(3/(4(nE + nC-2)-1)), (8)
where
E is the mean of the experimental group (i.e. the biomass at disturbed sites of a particular year),
C is the mean of the
control group (i.e. the biomass at undisturbed sites of the respective year), sE and sC are the standard deviations with
corresponding groups, nE and nC are the sample sizes of the corresponding groups. The variance of Hedge’s d σd2 (Koricheva 30
et al., 2013) is estimated as:
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σd2=(nE+nC)/(nEnC)+d2/(2(nE+nC)). (9)
The weighted Hedge’s d and the estimated variance (Hedges and Olkin, 1985b) of the total biomass of all compartments of
the same sampling event were calculated as:
d+=sum(di/ σdi2)/sum(1/σdi2), (10)
with σd+2=1/sum(1/σdi2). 5
Following Cohen (1988)’s rule of thumb for effect sizes, Hedge’s d=|0.2| signifies a small experimental effect, implying that
the biomass of the food-web compartments is similar between the disturbed and undisturbed sites. When Hedge’s d=|0.5|, the
effect size is medium, hence there is moderate difference, and when Hedge’s d=|0.8|, the effect size is large, i.e. there is a large
difference between the biomass of the compartments between sites.
The network index T.. was compared between the undisturbed and disturbed sites of the same sampling event by assessing the 10
fraction of the T.. values of the 100,000 model solutions of the undisturbed food web that were larger than the T.. values of the
100,000 model solutions of the disturbed food web. When this fraction is >0.95, the difference in ‘total system throughput
between the two food-webs from the same sampling event is considered significantly different (van Oevelen et al., 2011),
indicating that the carbon flows in the food web from that specific sampling event have not recovered from the experimental
disturbance. 15
3 Results
3.1 Food-web structure and trophic composition
Total faunal biomass was always higher at the undisturbed sites as compared to the disturbed sites from the same sampling
year (Figure 1, Supplement 1), and ranged from a minimum of 5.45±1.27 mmol C m-2 (PD0.1) to a maximum
22.33±3.40 mmol C m-2 (PD3) at the undisturbed sites and from minimum of 1.36±1.24 mmol C m-2 (PD0.1) to maximum 20
15.82±1.99 mmol C m-2 (PD3) at the disturbed sites. At PD0.1 the total faunal biomass at the disturbed sites was only 25% of
the total faunal biomass at the undisturbed sites, whereas at PD3 the total faunal biomass at the disturbed sites was 71% of the
total faunal biomass at the undisturbed sites. At PD26, the faunal biomass at the disturbed sites was 54% of the biomass at the
undisturbed sites. The absolute weighted Hedge’s d |d+| of all faunal compartment biomasses for PD0.1 to PD7 ranged from
0.053±0.019 at PD0.5 to 0.075±0.019 (Supplement 2), indicating a strong experimental effect and therefore that biomasses of 25
all faunal compartment did not recover over the period analysed (PD0.1 to PD7).
The faunal biomass at both the undisturbed and disturbed sites from PD0.1 to PD7 was dominated by deposit feeders (from 63%
at undisturbed PD0.1 to 83% at disturbed PD0.5 and disturbed PD3) (Figure 3). In contrast, at the undisturbed sites of PD26, the
largest contribution to total faunal biomass was from filter- and suspension feeders (44%), whereas deposit feeders only
contributed 35%. At the disturbed sites of PD26, deposit feeders had the highest biomass (61%), followed by carnivores (19%) 30
and filter- and suspension feeders (14%).
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3.2 Carbon flows
The total faunal C ingestion (mmol C m-2 d-1) ranged from 8.63×10-3±1.58×10-5 at the disturbed sites at PD0.1 to 1.47×10-
1±8.55×10-4 at the undisturbed sites at PD3 and was always lower at the disturbed sites compared to the undisturbed sites
(Figure 4A; Supplement 3). The ingestion consisted mainly of the sedimentary detritus (labile and semi-labile) that contributed
between 56.97% (undisturbed sites, PD26) and 99.50% (disturbed sites, PD0.1) to the total carbon ingestion. 5
Faunal respiration (mmol C m-2 d-1) ranged from 6.02×10-3±6.75×10-5 (disturbed sites, PD0.5) to 3.92×10-2±3.69×10-4
(undisturbed sites, PD3). During the twenty-six years after the DISCOL experiment, modelled faunal respiration was always
higher at undisturbed sites as compared to disturbed sites (Table 2, Figure 4). Over time, non-polychaete macrofauna
contributed least to total faunal respiration (Table 2), except at the disturbed sites of PD0.5 and at both sites of PD3. During this
PD3 sampling campaign, macrofauna contributed 49.97% at the undisturbed sites and 58.35% at the disturbed sites to the total 10
faunal respiration. Polychaetes respired between 18.59% of the total fauna respiration at the undisturbed sites at PD26 and
77.61% of the total fauna respiration at the disturbed sites at PD0.5. The megafauna respiration contribution was highest at
PD26, where they respired 64.95% of the total faunal respiration at the disturbed sites and 78.67% of the total faunal respiration
at the undisturbed sites. The contribution of fish to total faunal respiration was always <2%. Besides respiration, faeces
production contributed between 20.07% at disturbed PD3 and 34.65% at disturbed PD0.1 to total carbon outflow from the food 15
web (Figure 4). The contribution of the combined outflow of predation by external predators and scavengers on carcasses to
the total C loss from the food web ranged from 50.48% at disturbed PD7 to 65.33% at disturbed PD0.1.
The fraction of T.. values that were larger for the food webs at the undisturbed sites than for the disturbed sites from the same
sampling event was 1.0 at PD0.1, PD0.5, PD3, PD7 and PD26. No decreasing trend in ΔT.. over time was visible (Figure 5), in
fact, the largest Δ T.. were calculated for PD3 (7.87×10-2±1.97×10-3 mmol C m-2 d-1) and PD26 (7.67×10-2±9.41×10-
20
4 mmol C m-2 d-1).
4 Discussion
This study assessed the evolution of the food web structure and ecosystem function faunal C cyclingin an abyssal nodule-
rich soft-sediment ecosystem following an experimental sediment disturbance. By comparing a time-series over 26 years with
food web models (undisturbed vs. disturbed sites), we show that the total faunal biomass at the disturbed site was still only 25
about half of the total faunal biomass at the undisturbed sites 26 years after the disturbance. Furthermore, the role of the various
feeding types in the carbon cycling differs and thetotal system throughputT.., i.e. the sum of all carbon flows in the food
web, was still significantly lower at the disturbed sediment compared to the undisturbed sediment after 26 years.
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10
4.1 Model limitations
Our results are unique as it allowed for the first time to assess the recovery of C cycling in benthic deep-sea food webs from a
small-scale sediment disturbance in polymetallic nodule rich areas. However, the models come with limitations. The standard
procedures to assess megafauna densities have evolved during the 26 years of post-disturbance monitoring. The OFOS system
used 26 years after the initial DISCOL experiment took pictures automatically every 20 s from a distance of 1.5 m above the 5
seafloor (Boetius, 2015; Stratmann et al., in review). By contrast, the OFOS system used in former cruises was towed
approximately 3 m above the seafloor and pictures were taken selectively by the operating scientists (Bluhm and Gebruk,
1999). Therefore, the procedure used in the former cruises very likely led to an overestimation of rare and charismatic
megafauna, and probably to an underestimation of dominant fauna and organisms of small size (<3 cm) for PD0.1 to PD7 as
compared to PD26. 10
Previous cruises to the DEA focused on monitoring changes in faunal density and diversity, but not on changes in biomass.
Hence, a major task in this study was to find appropriate conversion factors to convert density into biomass. However, no
individual biomass data for macrofauna taxa were available for the Peru Basin, so we used data from sampling stations of
similar water depths in the eastern Clarion-Clipperton Zone (CCZ, NE Pacific; Sweetman et al., in review). As organisms in
deep-sea regions with higher organic carbon input are larger than their counterparts from areas with lower organic carbon input 15
(McClain et al., 2012), using individual biomass data from the CCZ, a more oligotrophic region than the Peru Basin (Haeckel
et al., 2001; Vanreusel et al., 2016) might have led to an underestimation of the biomass for macrofauna. However, this has
likely limited impact on the interpretation of the comparative results within the time series, because the same methodology
was applied throughout the time series dataset. Moreover, the determination of megafauna biomass was also difficult as no
size measurements were taken from megafauna individuals during the PD0.1 to PD7 cruises. Consequently, it was not possible 20
to detect differences in size classes between disturbed and undisturbed sediments or recruitment events in e.g. echinoderms
(Ruhl, 2007) following the DISCOL experiment. Instead, we used fixed conversion factors for the different taxa for the entire
time series.
4.2 Feeding-type specific differences in recovery
Eight years before the experimental disturbance experiment was conducted at the DISCOL area, Jumars (1981) qualitatively 25
predicted the response of different feeding types in the benthic community to polymetallic nodule removal. Although several
seabed test mining or mining simulations were performed since then (Jones et al., 2017), no study compared or verified these
conceptual predictions on feeding-type specific differences in recovery from deep-sea mining. As few comparative studies are
available, we compare here our food-web model results with those of the conceptual model predictions for scavengers, surface
and subsurface deposit feeders and suspension feeders by Jumars (1981). 30
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Jumars (1981) predicted that organisms inside the mining tracks would be killed either by the fluid shear of the dredge/ plough
or by abrasion and increased temperatures inside the rising pipe with a mortality rate of >95%. In contrast, the impact on
mobile and sessile organisms in the vicinity of the tracks would depend on their feeding type (Jumars, 1981).
The author also predicted that the density of mobile scavengers, such as fish and lysianassid amphipods would rise shortly
after the disturbance in response to the increased abundance of dying or dead organisms within the mining tracks. Indeed, 5
when plotting the respiration of fish (in mmol C m-1 d-1) normalized to the fish respiration at the undisturbed sediment at PD0.1
over time, the respiration for the undisturbed sediment increased steeply until PD3 and dropped subsequently (Figure 6).
However, experiments with baits at PAP and the Porcupine Seabight (NE Atlantic) showed that the scavenging deep-sea fish
Coryphaenoides armatus intercept bait within 30 min (Collins et al., 1999) and stayed at the food fall for 114±55 min (Collins
et al., 1998). Hence, it is very likely that this rise in fish respiration at the undisturbed sediment 0.5 years after the DISCOL is 10
a result of natural variability as opposed to the predicted rise in scavenger density and/ or biomass caused by the mining
activity. At the disturbed sediment, no fish were detected at PD0.1 or PD0.5, which could be related to lack of prey in a potential
predator-prey relationship (Bailey et al., 2006). However, because of the relatively small area of disturbed sediment (only 22%
of the 10.8 km2 of sediment were ploughed (Thiel and Schriever, 1989)), the low density of deep-sea fish (e.g. between 7.5
and 32 ind. ha-1 of the dominant fish genus Coryphaenoides sp. at Station M (Bailey et al., 2006)) and the high motility of fish, 15
this observation may be coincidental.
Jumars (1981) predicted that, on a short term, subsurface deposit feeders outside the mining tracks would be the least impacted
feeding type, because of their relative isolation from the re-settled sediment, and their relative independence of organic matter
on the sediment surface, whereas subsurface deposit feeders inside the mining tracks would experience high mortality. For the
long-term recovery, the author pointed to the dependence of subsurface deposit feeders on bacterial production in the sediment 20
covered with re-resettled sediment. In our food web model, sub-surface and surface deposit feeders were grouped into the
deposit feeder category, except for polychaetes, for which we kept the surface-subsurface distinction. The biomass of PolSSDF
fluctuated by one order of magnitude over the 26-year time series and had high biomass values at the undisturbed PD0.1 site,
the disturbed PD3 sites and at both sites at PD7. The normalized respiration of PolSSDF also showed strong fluctuations at the
undisturbed and disturbed sites over time (Figure 6) indicating a large natural variability or variable sampling results. Such 25
temporal dynamics in deep-sea macrofauna were detected at Station M, where the density of several dominating metazoan
macrofauna increased eight months after a peak in POC flux was measured at 50 and 600 m above the seafloor (Drazen et al.,
1998). Hence, Jumars (1981) predictions for sub-surface deposit feeders could not be tested, provided the natural fluctuations
in PolSSDF densities that were used to calculate biomass.
Jumars (1981) anticipated that surface deposit feeders would suffer more strongly from deep-sea mining activities compared 30
to sub-surface deposit feeders because the rate of sediment deposition would increase inside and beyond mining tracks, with
this newly settling sediment altering the sediment composition and food concentration in the sediment. Indeed, the recovery
of holothurian densities at the DEA was probably delayed owing to unfavourable food conditions (Stratmann et al., in review).
Nevertheless, deposit feeders seem to have advantages during the recovery from the DISCOL disturbance experiment. When
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comparing the contribution of deposit feeders from all size classes (macrofauna, polychaetes, megafauna) to respiration,
predation by external predators and faeces production to the contribution of omnivores, filter- and suspension feeders and
carnivores, their contribution was always higher at the disturbed site compared to the undisturbed site of the same sampling
event. However, owing to the overall lower biomass inside the disturbed area compared to the undisturbed area, the absolute
carbon respiration (in mmol C m-2 d-1) remained lower for deposit feeders at the disturbed site compared to the corresponding 5
undisturbed site, even after 26 years when this difference was 2.6%.
Jumars (1981) expected that the suspension feeders outside the mining tracks would be negatively affected during the presence
of the sediment plumes and/ or as long as their filtration apparatus was clogged by sediment. This “clogging” hypothesis could
not be tested here, because the models did not resolve these unknown changes in faunal physiology, but could only assess
carbon cycling differences associated with differences in biomass. Furthermore, Jumars (1981) anticipated that the recovery 10
of nodule-associated organisms, such as filter and suspension feeding Porifera, Antipatharia or Ascidiacea (Vanreusel et al.,
2016) would require more than 10,000 years, owing to the slow growth rate of polymetallic nodules (Guichard et al., 1978;
Kuhn et al., 2017) and the removal and/ or burial of the nodules. Directly after the initial DISCOL disturbance event, the
respiration rate of filter and suspension feeders at the disturbed sediment was only 1% of the respiration rate of this feeding
type at the undisturbed sediment. After 26 years, the relative difference in the filter and suspension feeding respiration rate 15
was still 80%. Part of this difference at PD26 resulted from the presence of a single specimen of Alcyonacea with a biomass of
4.71 mmol C m-2 at the undisturbed site. However, even if we ignore this Alcyonacea specimen in the model, the respiration
of suspension and filter feeding in the disturbed site would still be 71% lower compared to the undisturbed site, indicating a
slow recovery of this feeding group.
To summarize the comparison of modelled potential recovery of the different feeding types with the predictions by Jumars 20
(1981), scavenging and predatory fish at the undisturbed sediment followed first the predicted density pattern, though this
might also have been related to natural variability. After three years, however, the fish contribution to carbon cycling was
lower than expected from the predictions. Owing to an apparently strong natural variability in polychaete subsurface deposit
feeder biomass, the recovery prognosis for subsurface deposit feeders could not be tested. Furthermore, it could not be assessed
whether surface deposit feeders were more strongly affected by the mining activity than subsurface deposit feeders. In general, 25
the time series analysis showed that deposit feeders likely benefited from the disturbance experiment in comparison to other
feeding types. Confirming Jumars (1981) prediction, the activity of filter and suspension feeders in the food web did not
recover within 26 years.
5 Conclusion
Deep-sea mining will negatively impact the benthic ecosystem of abyssal ecosystems. It is therefore important to be able to 30
estimate how long the recovery of the ecosystem after a deep-sea mining operation will take. This study used the linear inverse
modelling technique to compare the carbon flows between different food web compartments at undisturbed and disturbed sites
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13
at the DISCOL experimental area in the Peru Basin over a period of 26 years. Even after 26 years, the total faunal biomass and
the total food-web activity (i.e. summed carbon cycling) at the disturbed sites was only approximately half (54% and 56%
respectively) of the total faunal biomass and food-web activity at the undisturbed sites. Deposit feeders were the least impacted
by the sediment disturbance, with less than 3% relative difference in total carbon loss (i.e. respiration, external predation and
feces production) between undisturbed and disturbed sites after 26 years. In contrast, filter and suspension feeders did not 5
recover at all and the relative difference in respiration rate was 79%. Overall, it can be concluded that ecosystem functioning
(as measured by total carbon cycling) within the macrofauna, megafauna and fish has not recovered 26 years after the
experimental disturbance.
Data availability
Data on biomass of the different food web compartments are presented in Supplement 1. Data on Hedge’s d, the corresponding 10
standard deviations, weighted Hedge’s d and weighted standard deviation are presented in Supplement 2. The mean and
standard deviations calculated for each carbon flux over 100,000 iterations for all food webs from the undisturbed and disturbed
site for all time steps is presented in Supplement 3. All OFOS images associated with this article are available from the
PANGAEA storage archive.
Authors contribution 15
TS went through the published literature for data input to the model, LL, AP, YM, CR, AR, MRC, ESL, AKS, DOBJ and KK
contributed data, TS and DvO developed the food web models, TS and DvO wrote the manuscript with input from all co-
authors.
Acknowledgements
We thank the chief scientists Jens Greinert (SO242-1) and Antje Boetius (SO242-2) as well as captain and crew of RV Sonne 20
for their excellent support during both legs of cruise SO242. We also thank the ‘AUV Abyss‘ team from Geomar, Kiel
(Germany) and Daniëlle de Jonge (Groningen University, The Netherlands) for identifying the fish species. The research
leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013)
under the MIDAS project, grant agreement n° 603418 and by the JPI Oceans Ecological Aspects of Deep Sea Mining project
(NWO-ALW grant 856.14.002) and the Bundesministerium für Bildung und Forschung (BMBF) grant n° 03F0707A-G. 25
Further financial support was granted to CESAM (UID/AMB/50017 - POCI-01-0145-FEDER-007638), to FCT/MCTES by
national funds (PIDDAC), and by co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020.
CFR was supported by Fundação para a Ciência e a Tecnologia (FCT) grant (SFRH/ BPD/107805/2015).
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14
References
Amon, D. J., Hilario, A., Arbizu, P. M., and Smith, C. R.: Observations of organic falls from the abyssal Clarion-Clipperton Zone in the
tropical eastern Pacific Ocean, Mar. Biodivers., 47, 311-321, 2017.
Bailey, D. M., Ruhl, H., and Smith, K.: Long‐term change in benthopelagic fish abundance in the abyssal northeast Pacific Ocean, Ecology,
87, 549-555, 2006.
5 Bluhm, H., and Gebruk, A.: Holothuroidea (Echinodermata) of the Peru Basin‐Ecological and Taxonomic Remarks Based on Underwater
Images, Mar. Ecol., 20, 167-195, 1999.
Bluhm, H.: Re-establishment of an abyssal megabenthic community after experimental physical disturbance of the seafloor, Deep-Sea Res.
II, 48, 3841-3868, 2001.
Boetius, A.: RV SONNE Fahrtbericht/Cruise Report SO242-2: JPI OCEANS Ecological Aspects of Deep-Sea Mining, DISCOL Revisited,
10 Guayaquil-Guayaquil (Equador), 28.08.-01.10. 2015., 2015.
Borowski, C., and Thiel, H.: Deep-sea macrofaunal impacts of a large-scale physical disturbance experiment in the Southeast Pacific, Deep-
Sea Res. II, 45, 55-81, 1998.
Borowski, C.: Physically disturbed deep-sea macrofauna in the Peru Basin, southeast Pacific, revisited 7 years after the experimental impact,
Deep-Sea Res. II, 48, 3809-3839, 2001.
15 Brey, T., Müller-Wiegmann, C., Zittier, Z. M., and Hagen, W.: Body composition in aquatic organismsa global data bank of relationships
between mass, elemental composition and energy content, J. Sea Res., 64, 334-340, 2010.
Childress, J. J., Taylor, S. M., Cailliet, G. M., and Price, M. H.: Patterns of growth, energy utilization and reproduction in some meso-and
bathypelagic fishes off southern California, Mar. Biol., 61, 27-40, 1980.
Clausen, I., and Riisgård, H.: Growth, filtration and respiration in the mussel Mytilus edulis: no evidence for physiological regulation of the
20 filter-pump to nutritional needs, Mar. Ecol. Prog. Ser., 141, 37-45, 1996.
Cohen, J.: Statistical power analysis for the behavioral sciences, 2nd ed. ed., Lawrence Erlbaum Associates, Publishers, Mahwah, NJ, 1988.
Collins, M., Priede, I. G., Addison, S., Smith, A., and Bagley, P. M.: Acoustic tracking of the dispersal of organic matter by scavenging
fishes in the deep-sea, Hydrobiologia, 371/372, 181-186, 1998.
Collins, M., Bailey, D., Ruxton, G., and Priede, I.: Trends in body size across an environmental gradient: a differential response in scavenging
25 and non-scavenging demersal deep-sea fish, Proc. R. Soc. B, 272, 2051-2057, 2005.
Collins, M. A., Priede, I. G., and Bagley, P. M.: In situ comparison of activity in two deep-sea scavenging fishes occupying different depth
zones, Proc. R. Soc. Lond. B Biol. Sci., 266, 2011-2016, 1999.
Crisp, D.: Energy flow measurements, in: Methods for the study of marine benthos, 1 ed., edited by: Holme, N. A., and McIntyre, A. D.,
Blackwell Scientific Publications, Oxford, 284-367, 1971.
30 Devey, C. W., Augustin, N., Brandt, A., Brenke, N., Köhler, J., Lins, L., Schmidt, C., and Yeo, I. A.: Habitat characterization of the Vema
Fracture Zone and Puerto Rico Trench, Deep-Sea Res. II, 2018.
Drazen, J. C., Baldwin, R. J., and Smith Jr, K. L.: Sediment community response to a temporally varying food supply at an abyssal station
in the NE Pacific, Deep-Sea Res. II, 45, 893-913, 1998.
Drazen, J. C., Reisenbichler, K. R., and Robison, B. H.: A comparison of absorption and assimilation efficiencies between four species of
35 shallow-and deep-living fishes, Mar. Biol., 151, 1551-1558, 2007.
Drazen, J. C., and Sutton, T. T.: Dining in the deep: the feeding ecology of deep-sea fishes, Ann. Rev. Mar. Sci., 9, 337-366, 2017.
Dunlop, K. M., van Oevelen, D., Ruhl, H. A., Huffard, C. L., Kuhnz, L. A., and Smith, K. L.: Carbon cycling in the deep eastern North
Pacific benthic food web: Investigating the effect of organic carbon input, Limnol. Oceanogr., 61, 1956-1968, 2016.
Durden, J. M., Bett, B. J., Horton, T., Serpell-Stevens, A., Morris, K. J., Billett, D. S., and Ruhl, H. A.: Improving the estimation of deep-
40 sea megabenthos biomass: dimension to wet weight conversions for abyssal invertebrates, Mar. Ecol. Prog. Ser., 552, 71-79, 2016.
Durden, J. M., Ruhl, H. A., Pebody, C., Blackbird, S. J., and van Oevelen, D.: Differences in the carbon flows in the benthic food webs of
abyssal hill and plain habitats, Limnol. Oceanogr., 62, 1771–1782, 10.1002/lno.10532, 2017.
Foell, E., Thiel, H., and Schriever, G.: DISCOL: a long-term, large-scale, disturbance-recolonization experiment in the abyssal eastern
tropical South Pacific Ocean, Offshore Technology Conference, 1990, 1990.
45 Foell, E., Thiel, H., and Schriever, G.: DISCOL: a long-term, large-scale, disturbance-recolonization experiment in the abyssal eastern
tropical South Pacific Ocean. , Min. Eng., 44, 90-94, 1992.
Fox, R., Barnes, R. D., and Ruppert, E. E.: Invertebrate Zoology: A Functional Evolutionary Approach, 7 ed., Brooks/ Cole Thompson
Learning, Belmont, California, 2003.
Froese, R., Thorson, J. T., and Reyes, R.: A Bayesian approach for estimating length‐weight relationships in fishes, J. Appl. Ichthyol., 30,
50 78-85, 2014.
FishBase www.fishbase.org, access: 2018/02/21, 2017.
Gage, J. D., and Tyler, P. A.: Deep-sea biology: a natural history of organisms at the deep-sea floor, Cambridge University Press, 1991.
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-167
Manuscript under review for journal Biogeosciences
Discussion started: 9 April 2018
c
Author(s) 2018. CC BY 4.0 License.
15
Gollner, S., Kaiser, S., Menzel, L., Jones, D. O., Brown, A., Mestre, N. C., van Oevelen, D., Menot, L., Colaço, A., and Canals, M.: Resilience
of benthic deep-sea fauna to mining activities, Mar. Environ. Res., 129, 76-101, 10.1016/j.marenvres.2017.04.010, 2017.
Greinert, J.: RV SONNE Fahrtbericht/ Cruise Report SO242-1 - JPI OCEANS Ecological Aspects of Deep-Sea Mining - DISCOL Revisited,
GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, 290, 2015.
Guichard, F., Reyss, J. L., and Yokoyama, Y.: Growth rate of manganese nodule measured with 10Be and 26Al, Nature, 272, 155-156,
5 10.1038/272155a0, 1978.
Haeckel, M., König, I., Riech, V., Weber, M. E., and Suess, E.: Pore water profiles and numerical modelling of biogeochemical processes
in Peru Basin deep-sea sediments, Deep-Sea Res. II, 48, 3713-3736, 2001.
Hedges, L. V., and Olkin, I.: Estimation of a single effect size: Parametric and nonparametric methods, in: Statistical methods for meta-
analysis, Academic Press, Inc., Orlando, 75-106, 1985a.
10 Hedges, L. V., and Olkin, I.: Parametric estimation of effect size from a series of experiments, in: Statistical methods for meta-analysis,
Academic Press, Inc., Orlando, 107-145, 1985b.
Hein, J. R., Mizell, K., Koschinsky, A., and Conrad, T. A.: Deep-ocean mineral deposits as a source of critical metals for high-and green-
technology applications: Comparison with land-based resources, Ore Geol. Rev., 51, 1-14, 2013.
Jones, D. O., Kaiser, S., Sweetman, A. K., Smith, C. R., Menot, L., Vink, A., Trueblood, D., Greinert, J., Billett, D. S., Martinez Arbizu, P.,
15 Radziejewska, T., Singh, R., Ingole, B., Stratmann, T., Simon-Lledo, E., Durden, J. M., and Clark, M. R.: Biological responses to disturbance
from simulated deep-sea polymetallic nodule mining, PloS One, 12, e0171750, 10.1371/journal.pone.0171750, 2017.
Jumars, P. A.: Limits in predicting and detecting benthic community responses to manganese nodule mining, Mar. Min., 3, 213-229, 1981.
Jumars, P. A., Dorgan, K. M., and Lindsay, S. M.: Diet of worms emended: an update of polychaete feeding guilds, Ann. Rev. Mar. Sci., 7,
497–520, 2015.
20 Kidd, R., and Huggett, Q.: Rock debris on abyssal plains in the northeast Atlantic-a comparison of epibenthic sledge hauls and photographic
surveys, Oceanol. Acta, 4, 99-104, 1981.
Klepper, O., and Van de Kamer, J.: The use of mass balances to test and improve the estimates of carbon fluxes in an ecosystem, Math.
Biosci., 85, 37-49, 1987.
Kones, J. K., Soetaert, K., van Oevelen, D., and Owino, J. O.: Are network indices robust indicators of food web functioning? A monte carlo
25 approach, Ecol. Model. , 220, 370-382, 2009.
Koopmans, M., Martens, D., and Wijffels, R. H.: Growth efficiency and carbon balance for the sponge Haliclona oculata, Mar. Biotechnol.,
12, 340-349, 2010.
Koricheva, J., Gurevitch, J., and Mengersen, K.: Handbook of meta-analysis in ecology and evolution, Princeton University Press, Princeton,
2013.
30 Kuhn, T., Wegorzewski, A., Rühlemann, C., and Vink, A.: Composition, Formation, and Occurrence of Polymetallic Nodules, in: Deep-Sea
Mining, Springer, 23-63, 2017.
Kwasnitschka, T., Köser, K., Sticklus, J., Rothenbeck, M., Weiß, T., Wenzlaff, E., Schoening, T., Triebe, L., Steinführer, A., and Devey, C.:
DeepSurveyCama deep ocean optical mapping system, Sensors, 16, 164, 2016.
Langenkämper, D., Zurowietz, M., Schoening, T., and Nattkemper, T. W.: BIIGLE 2.0-Browsing and Annotating Large Marine Image
35 Collections, Front. Mar. Sci., 4, 83, 2017.
Levin, L. A., Mengerink, K., Gjerde, K. M., Rowden, A. A., Van Dover, C. L., Clark, M. R., Ramirez-Llodra, E., Currie, B., Smith, C. R.,
Sato, K. N., Gallo, N., Sweetman, A. K., Lily, H., Armstrong, C. W., and Brider, J.: Defining "serious harm" to the marine environment in
the context of deep-seabed mining, Mar. Policy, 74, 245-259, 2016.
Mahaut, M.-L., Sibuet, M., and Shirayama, Y.: Weight-dependent respiration rates in deep-sea organisms, Deep-Sea Res. I, 42, 1575-1582,
40 1995.
Marcon, Y., and Purser, A.: PAPARA (ZZ) I: An open-source software interface for annotating photographs of the deep-sea, SoftwareX, 6,
69-80, 2017.
McClain, C. R., Allen, A. P., Tittensor, D. P., and Rex, M. A.: Energetics of life on the deep seafloor, PNAS, 109, 15366-15371, 2012.
Menzies, R. J.: On the food and feeding habits of abyssal organisms as exemplified by the Isopoda, Int. Rev. Hydrobiol., 47, 339-358, 1962.
45 Mondal, S. K.: Effect of temperature and body size on food utilization in the marine pearl oyster Pinctada fucata (Bivalvia: Pteridae), Indian
J. of Mar. Sci., 35, 43-49, 2006.
Navarro, E., Iglesias, J. I. P., Ortega, M. M., and Larretxea, X.: The Basis for a Functional Response to Variable Food Quantity and Quality
in Cockles Cerastoderma edule (Bivalvia, Cardiidae), Physiol. Zool., 67, 468-496, 1994.
Nielsen, A. M., Eriksen, N. T., Iversen, J. J. L., and Riisgard, H. U.: Feeding, growth and respiration in the polychaetes Nereis diversicolor
50 (facultative filter-feeder) and N. virens (omnivorous) - a comparative study, Mar. Ecol. Prog. Ser., 125, 149-158, 10.3354/meps125149,
1995.
R-Core-Team: R: A language and environment for statistical computing R Foundation for Statistical Computing, Vienna, 2016.
Radziejewska, T.: Characteristics of the sub-equatorial North-Eastern Pacific Ocean’s abyss, with a particular reference to the Clarion-
Clipperton Fracture Zone, in: Meiobenthos in the sub-equatorial Pacific abyss, Springer, Heidelberg, 13-28, 2014.
55
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Manuscript under review for journal Biogeosciences
Discussion started: 9 April 2018
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Author(s) 2018. CC BY 4.0 License.
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Ramirez-Llodra, E., Brandt, A., Danovaro, R., De Mol, B., Escobar, E., German, C., Levin, L., Arbizu, P., Menot, L., and Buhl-Mortensen,
P.: Deep, diverse and definitely different: unique attributes of the world's largest ecosystem, Biogeosciences, 7, 2851-2899, 2010.
Ramirez-Llodra, E., Tyler, P. A., Baker, M. C., Bergstad, O. A., Clark, M. R., Escobar, E., Levin, L. A., Menot, L., Rowden, A. A., and
Smith, C. R.: Man and the last great wilderness: human impact on the deep sea, PLoS One, 6, e22588, 2011.
Randall, R.: Using allometry with fish size to estimate production to biomass (P/B) ratios of salmonid populations, Ecol. Freshw. Fish, 11,
5 196-202, 2002.
Rex, M. A., Etter, R. J., Morris, J. S., Crouse, J., McClain, C. R., Johnson, N. A., Stuart, C. T., Deming, J. W., Thies, R., and Avery, R.:
Global bathymetric patterns of standing stock and body size in the deep-sea benthos, Mar. Ecol. Prog. Ser., 317, 1, 2006.
Rowe, G. T.: Biomass and production of the deep-sea macrobenthos, in: Deep-Sea Biology, edited by: Rowe, G. T., The Sea, John Wiley &
Sons, Inc., New York, 1983.
10 Ruhl, H. A.: Abundance and size distribution dynamics of abyssal epibenthic megafauna in the northeast Pacific, Ecology, 88, 1250-1262,
2007.
Ruhl, H. A., Ellena, J. A., and Smith, K. L.: Connections between climate, food limitation, and carbon cycling in abyssal sediment
communities, P. Natl. Acad. Sci. USA, 105, 17006-17011, 2008.
Smith, A. B., and Stockley, B.: The geological history of deep-sea colonization by echinoids: roles of surface productivity and deep-water
15 ventilation, Proc. R. Soc. B, 272, 865-869, 2005.
Smith, C. R., De Leo, F. C., Bernardino, A. F., Sweetman, A. K., and Arbizu, P. M.: Abyssal food limitation, ecosystem structure and climate
change, Trends Ecol. Evol., 23, 518-528, 2008.
Smith, K., and Hessler, R.: Respiration of benthopelagic fishes: in situ measurements at 1230 meters, Science, 184, 72-73, 1974.
Stratmann, T., Mevenkamp, L., Sweetman, A. K., Vanreusel, A., and van Oevelen, D.: Has phytodetritus processing by an abyssal soft-
20 sediment community recovered 26 years after an experimental disturbance?, Front. Mar. Sci., 5, 10.3389/fmars.2018.00059, 2018.
Stratmann, T., Soetaert, K., Wei, C.-L., and van Oevelen, D.: Global patterns of energy partitioning within the seafloor ecosystems show
longitudinal and depth gradient, Global Ecol. Biogeogr., in prep.
Stratmann, T., Voorsmit, I., Gebruk, A., Brown, A., Purser, A., Marcon, Y., Sweetman, A. K., Jones, D. O., and van Oevelen, D.: Recovery
of Holothuroidea population density, community composition and respiration activity after a deep-sea disturbance experiment, Limnol.
25 Oceanogr., in review.
Sweetman, A. K., Smith, C. R., Shulse, C. N., Maillot, B., Lindh, M., Church, M. J., Meyer, K., van Oevelen, D., Stratmann, T., and Gooday,
A. J.: Key role of bacteria in the short-term cycling of carbon at the abyssal seafloor, Limnol. Oceanogr., in review.
Thiel, H., and Schriever, G.: Cruise report DISCOL 1, Sonne-Cruise 61, Institut für Hydrobiologie und Fischereiwissenschaft der Universität
Hamburg, Hamburg, 1989.
30 Thiel, H., and Tiefsee-Umweltschutz, F.: Evaluation of the environmental consequences of polymetallic nodule mining based on the results
of the TUSCH Research Association, Deep-Sea Res. II, 48, 3433-3452, 2001.
Tilot, V.: La structure des assemblages mégabenthiques d'une province à nodules polymétalliques de l'Océan Pacifique tropical Est, PhD,
Department de l’Environnement Profund, Direction des Recherches Oceaniques, IFREMER/ Centre de Brest, Universite de Bretagne
occidentale, Plouzane, 1992.
35 van Oevelen, D., Van den Meersche, K., Meysman, F. J., Soetaert, K., Middelburg, J. J., and Vézina, A. F.: Quantifying food web flows
using linear inverse models, Ecosystems, 13, 32-45, 2010.
van Oevelen, D., Soetaert, K., García, R., De Stigter, H. C., Cunha, M. R., Pusceddu, A., and Danovaro, R.: Canyon conditions impact
carbon flows in food webs of three sections of the Nazaré canyon, Deep-Sea Res. II, 58, 2461-2476, 2011.
Vanreusel, A., Hilario, A., Ribeiro, P. A., Menot, L., and Martinez Arbizu, P.: Threatened by mining, polymetallic noduels are required to
40 preserve abyssal epifauna, Sci. Rep., 6, 26808, 10.1038/srep26808, 2016.
Vézina, A. F., and Platt, T.: Food web dynamics in the ocean. I. Best-estimates of flow networks using inverse methods, Mar. Ecol. Prog.
Ser., 269-287, 1988.
45
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Table 1. Taxon-specific biomass per individual (mmol C ind-1) for macrofauna and megafauna including the specific feeding
types. Macrofauna biomass data are based on macrofauna specimen collected in the abyssal plains of the Clarion-Clipperton
Zone (NE Pacific) (Sweetman et al., in review). In contrast, megafauna biomass was estimated by converting size-
measurements of specific body parts of organisms from DEA that were acquired using photo-annotation into preserved wet
weight per organism using the relationships presented in Durden et al. (2016). Subsequently the preserved wet weight was 5
converted into fresh wet weight and biomass following the conversions presented in Durden et al. (2016) and Rowe (1983).
Whenever no conversion factors for a specific taxon were reported in Durden et al. (2016) mean taxon-specific biomass data
per individual were extracted from Tilot (1992) for the CCZ.
The abbreviation are: C = carnivores, DF = deposit feeders, FSF = filter/ suspension feeders, O = omnivores, PolC =
carnivorous polychaete, PolOF = omnivorous polychaete, PolSF = suspension feeding polychaete, PolSDF = surface deposit 10
feeding polychaete, PolSSDF = subsurface deposit feeding polychaete, S = scavengers.
References: 1(Fox et al., 2003), 2(Menzies, 1962), 3(McClain et al., 2012), 4(Smith and Stockley, 2005), 5(Gage and Tyler,
1991), 7(Jumars et al., 2015), 8(Bluhm, 2001), 9(Drazen and Sutton, 2017)
Size
class
Taxon
Feeding type
n
Biomass (mmol C ind-1)
(Mean±Std)
Macro-
fauna
Bivalviaa
FSF1
7
1.41×10-3±8.29×10-4
Cumaceaa
DF1
2
3.09×10-3±6.22×10-4
Echinoideab
85% O, 15% DF4
64
9.66×10-3±2.84×10-2
Gastropodaa
90% DF, 10% C3
2
8.56×10-2±3.98×10-2
Isopodaa
93% DF, 7% C2
4
1.33×10-3±1.06×10-3
Ophiuroideab
C1
64
9.66×10-3±2.84×10-2
Polychaetaa
PolSF, PolSDF,
PolSSDF, PolC,
PolOF7
26
1.33×10-2±3.68×10-2
Scaphopodab
C1
64
9.66×10-3±2.84×10-2
Tanaidaceaa
DF1
5
5.48×10-3±1.04×10-2
Mega-
fauna
Actiniaria
FSF1
301
2.95×10-1±8.75×10-1
Antipatharia
FSF1
3
177.30±68.23
Ascidiacead
FSF1
8.30×10-1
Asteroidea
C1
53
139.23±43.56
Bryozoag
FSF1
22.38
Cephalopoda
C1
7
46.85±27.88
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Cerianthariad
FSF1
1923.17
Cnidariac
FSF1
2.35×10-1
Crinoidead
FSF1
5.33
Crustacea
C1, 8
541
2.56±10.05
Echinoidead
15% DF, 85% OF4
59.17
Alcyonacead
FSF1
21.67
Hemichordatag
DF5, 8
22.38
Holothuroideae
DF1
450
154.32±332.51
Ophiuroidea
C1
527
16.05±10.15
Pennatulariad
FSF1
21.67
Polychaeta
PolSF, PolSDF,
PolSSDF, PolC,
PolOF7
62
5.30×10-1±1.20×10-2
Poriferac
FSF1
6.74
Fish
Osteichthyesf
S, C9
10
73.36±41.12
aTaxon-specific individual biomass; bIndividual biomass calculated based on all other macrofauna data; cMedian taxon-specific
individual biomass for individuals from the Porcupine Abyssal Plain where Durden et al. (2016) did not have reliable
dimension measurements; dMean taxon-specific biomass data per individual were extracted from Tilot (1992) for the CCZ;
eIndividual biomass of Benthodytes sp., one of the most abundant holothurian morphotype at the DISCOL site (Stratmann et
al., in review); fIndividual biomass of Ipnops sp., the most abundant deep-sea fish at the PD26 undisturbed site; gIndividual 5
biomass calculated for mean benthos megafauna at 4100 m depth based on the biomass-bathymetry and abundance-bathymetry
relationships presented in Rex et al. (2006).
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Table 2. Faunal respiration rate (mmol C m-2 d-1) and contribution (%) of the size classes macrofauna, polychaetes, megafauna and fish to the
respiration for the undisturbed (Undist.) and disturbed (Dist.) sites directly after the disturbance event in March 1989 (PD0.1), 0.5 years post-
disturbance (September 1989, PD0.5), 3 years post-disturbance (January 1992, PD3), 7 years post-disturbance (February 1996, PD7) and 26 years
post-disturbance (September 2015, PD26).
PD
0.1
,
Undist.
PD
0.1
,
Dist.
PD
0.5
,
Undist.
PD
0.5
,
Dist.
PD
3
,
Undist.
PD3, Dist.
PD7,
Undist.
PD7, Dist.
PD26,
Undist.
PD26,
Dist.
respiration
1.02×
10-2±
1.17
×
10-4
2.72×
10-3±
5.23
×
10-6
1.07×
10-2±
5.73
×
10-5
6.02×
10-3±
6.75
×
10-5
3.92×
10-2±
3.68
×
10-4
2.99×
10-2±
2.33
×
10-4
2.14×
10-2±
2.50
×
10-4
1.54×
10-2±
1.49
×
10-4
2.00×
10-2±
1.50
×
10-4
1.13×
10-2±
1.04
×
10-4
8.63
7.34
9.73
14.35
49.97
58.35
6.50
4.51
2.64
1.19
61.59
77.80
62.69
77.61
27.09
30.03
67.08
83.51
18.52
32.43
29.47
14.85
27.06
8.04
22.30
11.54
25.75
11.63
78.67
64.95
3.02×10-1
0.00
5.29×10-1
0.00
6.43×10-1
7.75×10-2
6.64×10-1
3.53×10-1
1.73×10-1
1.44
5
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Figure 1. Mean biomass (mmol C m-2) of the food web compartments for the undisturbed and disturbed sites inside the
DISCOL experimental area (Peru Basin, SE Pacific) 0.1 years post-disturbance (PD0.1), for 0.5 years post-disturbance (PD0.5),
for three years post-disturbance (PD3), for seven years post-disturbance (PD7), and for 26 years post-disturbance (PD26). The
error bars represent 1 standard deviation. 5
The abbreviation are: MacC = macrofauna carnivores, MacDF = macrofauna deposit feeders, MacFSF = macrofauna filter/
suspension feeders, MacO = macrofauna omnivores, MegC = megafauna carnivores, MegDF = megafauna deposit feeders,
MegFSF = megafauna filter/ suspension feeders, MegOF = megafauna omnivores, PolC = polychaete carnivores, PolOF =
polychaete omnivores, PolSDF = polychaete surface deposit feeders, PolSF = polychaete suspension feeders, PolSSDF =
polychaete subsurface deposit feeders. 10
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Figure 2. Simplified schematic representation of the food web structure that forms the basis of the linear inverse model (LIM).
All compartments inside the box were part of the food web model, whereas compartments outside the black box were only
considered as carbon influx or efflux, but were not directly modelled. In order to simplify the graph, for macrofauna,
polychaetes and megafauna, only feeding types were presented and no size classes. Solid black arrows represent the carbon 5
flux between food-web compartments and black dashed arrows represent the influx of carbon to the model. Blue-dotted arrows
show the loss of carbon from the food web via respiration to DIC. The red dashed arrows indicate the loss of carbon from the
food web as faeces and as predation by pelagic/ benthopelagic fish and the yellow-dashed arrow indicate the reduction of the
carcass pool due to scavenging by pelagic/ benthopelagic fish.
10
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Figure 3. Proportional contribution (in %) of the feeding types C = carnivores, DF = deposit feeders, FSF = filter and
suspension feeders, OF = omnivores to the total biomass for the undisturbed and disturbed sites inside the DISCOL
experimental area (Peru Basin, SE Pacific) 0.1 years post-disturbance (PD0.1), for 0.5 years post-disturbance (PD0.5), for 3 years
post-disturbance (PD3), for 7 years post-disturbance (PD7) and for 26 years post-disturbance (PD26). 5
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Figure 4. A) Mean faunal carbon ingestion (mmol C m-2 d-1) as suspended detritus, sedimentary labile and sedimentary semi-
labile detritus for the undisturbed and disturbed sites the DISCOL experimental area (Peru Basin, SE Pacific) 0.1 years post-
disturbance (PD0.1), 0.5 years post-disturbance (PD0.5), 3 years post-disturbance (PD3), 7 years post-disturbance (PD7) and
26 years post-disturbance (PD26). B) Mean carbon losses (mmol C m-2 d-1) from the food webs as predation, faeces, scavenging
on the carcass, and faunal respiration for the undisturbed and disturbed sites at PD0.1, PD0.5, PD3, PD7 PD26. In both figures, the 5
error bars represent 1 standard deviation.
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Figure 5. Development of ΔT.. (mmol C m-2 d-1), i.e. the difference in total system throughputT.. from the undisturbed
compared to the disturbed sites, over time. PD0.1 corresponds to 0.1 years post-disturbance, PD0.5 is 0.5 years post-disturbance,
PD3 is 3 years post-disturbance, PD7 is 7 years post-disturbance and PD26 is 26 years post-disturbance.
5
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Figure 6. Feeding-type related differences in the recovery of faunal respiration (mmol C m-2 d-1) over time following the
DISCOL disturbance experiment. Due to a lack of pre-disturbance respiration rates (T0), the respiration rate for each feeding
type (filter and suspension feeders=FSF, surface deposit feeders=SDF, subsurface deposit feeders=SSDF, fish) is standardized
to the respective feeding type specific respiration rate at the undisturbed sediment of 0.1 years post-disturbance. The respiration 5
rate for filter and suspension feeders includes the respiration of macrofaunal, polychaete and megafaunal filter and suspension
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feeders. The surface deposit feeders are the polychaete surface deposit feeders and the subsurface deposit feeders correspond
to the polychaete subsurface deposit feeders. Fish are the scavengers and predators.
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... Only recently food web models were first used to study ecosystem-wide e ects of experimental seabed mining. It concerned a non-quantitative binary food web (Stratmann, Amptmeijer, et al. submitted) and a food web time-series with a relatively low resolution (14 compartments; Stratmann, Lins, et al. (2018a)) focusing on the DISCOL experimental area (DEA) at 4100 meters depth in the Peru Basin (Pacific Ocean) where the sediment was ploughed experimentally in 1989 (Thiel et al. 2001). A quantitative food web of higher resolution, including processes that were omitted by previous research, might increase the representation of the real trophic relations. ...
... During the RV Sonne cruise SO424-2 (Boetius 2015) the Ocean Floor Observation System (OFOS) was towed 1.5 m above the seafloor. A photograph was taken every 10 seconds (Boetius 2015;Stratmann, Lins, et al. 2018a;Stratmann, Voorsmit, et al. 2018). In total, 2805 photographs of inside the plough tracks, 6624 photographs of outside the plough tracks, and 5763 photographs of the reference sites were taken (Boetius 2015). ...
... The megafauna densities were converted to carbon stocks using a taxon-specific biomass (Table 2.4). The taxonspecific biomasses of megafauna, except fish, were calculated using the method from Stratmann, Lins, et al. (2018a) by Simon-Lledo and Jones (unpublished). Fish taxon-specific biomass was calculated by the author, by converting the annotated length of each individual to wet weight using Equation 2.1, with W being weight in grams, L being length in cm, and a and b are parameters based on growth and body shape. ...
Thesis
The increased interest in polymetallic nodule mining has sparked research into the ecological eects of seabed mining. At the DISCOL experimental area (DEA) a plough harrow was used to mimic polymetallic nodule mining in 1989, and several studies have looked into the ecological effects of this disturbance. However, these studies have mainly focused on the effects on specific faunal groups (e.g. Bluhm 2001; Ingole et al. 2001; Rodrigues et al. 2001; Ingole et al. 2005; Ingole, Pavithran, et al. 2005; Miljutin et al. 2011; Vanreusel et al. 2016), and have not looked at the overall effect on the system. In this study a detailed Linear Inverse Model of the food web at three different DEA sites was prepared: 1) inside the plough tracks, 2) outside the plough tracks, and 3) of reference sites 4 km away. It is the best resolved food web model for this area to date. It specifically includes the microbial loop, a scavenging pathway, and fish. The food web model was investigated using network indices, inspection of certain carbon flows and pathways, an interaction strength plot, and a stability analysis. The total carbon throughput is a measure of total system activity and was found to be significantly lower (-26%) at the ploughed areas compared to the reference sites. As expected from an abyssal system, the microbial loop dominated the food web (58.7-68.6% of the total carbon throughput). The microbial loop was found to be significantly impaired at the ploughed areas compared to the reference sites (-37%), thereby being the main cause of the overall total carbon throughput reduction. Faunal activity was highest inside the plough tracks, but this trend differs among faunal groups. The interaction strengths and stability analysis suggest the benthic disturbance has not significantly influenced food web stability. The results indicate that benthic disturbance due to polymetallic nodule mining adversely impacts the microbial loop even after 26 years.
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Mining polymetallic nodules on abyssal plains will have adverse impacts on deep-sea ecosystems, but it is largely unknown whether the impacted ecosystem will recover, and if so at what rate. In 1989 the "DISturbance and reCOLonization" (DISCOL) experiment was conducted in the Peru Basin where the seafloor was disturbed with a plough harrow construction to explore the effect of small-scale sediment disturbance from deep-sea mining. Densities of Holothuroidea in the region were last investigated 7 yr post-disturbance, before 19 yr later, the DISCOL site was re-visited in 2015. An "ocean floor observatory system" was used to photograph the seabed across ploughed and unploughed seafloor and at reference sites. The images were analyzed to determine the Holothuroidea population density and community composition, which were combined with in situ respiration measurements of individual Holothuroidea to generate a respiration budget of the study area. For the first time since the experimental disturbance, similar Holothuroidea densities were observed at the DISCOL site and at reference sites. The Holothuroidea assemblage was dominated by Amper-ima sp., Mesothuria sp., and Benthodytes typica, together contributing 46% to the Holothuroidea population density. Biomass and respiration were similar among sites, with a Holothuroidea community respiration of 5.84 3 10 24 6 8.74 3 10 25 mmol C m 22 d 21 at reference sites. Although these results indicate recovery of Holothuroidea, extrapolations regarding recovery from deep-sea mining activities must be made with caution: results presented here are based on a relatively small-scale disturbance experiment as compared to industrial-scale nodule mining, and also only represent one taxonomic class of the megafauna.
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Inputs of detritus from the surface ocean are an important driver of community dynamics in the deep sea. The assessment of the flow of carbon through the benthic food web gives insight into how the community is sustained, and its resilience to fluctuations in food supply. We used a linear inverse model to compare the carbon flow through the food webs on an abyssal hill and the nearby plain at the Porcupine Abyssal Plain sustained observatory (4850 m water depth; northeast Atlantic), to examine the partitioning of detrital input in these substantially different megafaunal communities. We found minimal variation in carbon flows at the plain over two years, but differences in the detrital inputs and in the processing of that carbon input between the hill and plain habitats. Suspension feeding dominated metazoan carbon processing on the hill, removing nearly all labile detritus input to the system. By contrast, half of all labile detritus was deposited and available for deposit feeders on the abyssal plain. This suggests that the biomass on the hill is dependent on a more variable carbon supply than the plain. The presence of millions of abyssal hills globally suggests that the high benthic biomass and respiration, and reduced deposition of detritus may be pervasive, albeit with varying intensity.
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