MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 667: 1–13, 2021https://doi.org/10.3354/meps13727

Published June 10

1. INTRODUCTION

Nitrogen loading is commonly associated witheutro phication and the degradation of coastal envi-ronments, as nitrogen typically limits algal growthand primary production (Howarth & Marino 2006),but in excess, can lead to toxic algal blooms (Paerl1997, Anderson et al. 2002) and hypoxic conditions(Diaz & Rosenberg 2008). Anthropogenic nitrogen

© The authors 2021. Open Access under Creative Commons byAttribution Licence. Use, distribution and reproduction are un -restricted. Authors and original publication must be credited.

Publisher: Inter-Research · www.int-res.com

*Corresponding author: msea579@aucklanduni.ac.nz

FEATURE ARTICLE

Environmental predictors of sediment denitrification rates within restored green-lipped

mussel Perna canaliculus beds

Mallory A. Sea*, Simon F. Thrush, Jenny R. Hillman

Institute of Marine Science, University of Auckland, Auckland 1142, New Zealand

ABSTRACT: Benthic processes in coastal marine en -vironments can enhance the natural removal of bio -reactive nitrogen through denitrification (DNF), avaluable ecosystem service as nutrient over-enrich-ment intensifies globally. Enhancing ecosystem serv-ices is an important justification for restoring coastalecosystems, and while it is known that epifaunalbivalves (oysters or mussels) are capable of influenc-ing nitrogen cycling, empirical measurement of therole of particular species across a range of environ-mental conditions is missing. Bivalves within shell-fish beds are not uniformly distributed and thusclumps and empty patches within restored beds maydifferentially impact DNF given the importance oflocal biogeochemistry. This study reports DNF andrespiration rates in 4 restored beds that vary in sedi-ment composition, while identifying which ecologi-cal and biogeochemical factors best explain the ob -served variability in measured fluxes. We deployedbenthic chambers in sediments with and withoutmussels at these sites, and measured net N2 fluxesthrough membrane inlet mass spectroscopy. Sedi-ment organic matter proved to be the most signifi-cant predictor of DNF rates in regression tree andrandom forest models, suggesting that biodepositionby green-lipped mussels enhances nitrogen removalat these sites and that these effects occur across bedsdespite differences in mussel density. Greatest DNFrates corresponded to lower sediment chlorophyll aconcentrations and higher nitrate/nitrite effluxesmeasured within chambers. Determining the influ-ence of subtidal mussel restoration on significantsediment processes informs future restoration effortsaiming to maximise this nitrogen removal service,while providing insights on underrepresented oligo-trophic systems of the southern hemisphere.

KEY WORDS: Soft sediments · Benthic−pelagic coupling · Benthic fluxes · Nitrogen · Subtidal · Restoration · Bivalves

Nitrogen removal rates are consistent despite patchiness inrestored beds of green-lipped mussels, highlighting theupscaling-role of mussels in seafloor biogeochemistry.

Photo: Jenny Hillman

OPENPEN ACCESSCCESS

Mar Ecol Prog Ser 667: 1–13, 2021

enrichment is currently accelerating at alarmingrates (Galloway et al. 2004, 2008, Kuypers et al.2018), and therefore has growing consequences forcoastal oceans and the services they provide (Nixon1998, Howarth & Marino 2006). As humans continueto generate bioreactive nitrogenous compounds (e.g.ammonium and nitrate available for biological up -take) and fail to control excessive nitrogen inputs toecological systems, the capacity of coastal environ-ments to remove excess nitrogen becomes of evengreater value to humans. However, this process iscurrently not well understood. The fate of over 60%of global reactive nitrogen is unknown (Galloway etal. 2008), but a significant portion of this nitrogen isthought to be denitrified in coastal to continentalshelf sediments (Middelburg et al. 1996, Seitzinger etal. 2006). It is thought that coastal marine environ-ments are capable of removing half (Seitzinger 1988)to over 80% (Galloway et al. 2003) of terrestrial nitro-gen inputs through the denitrification (DNF) process.Driven by facultative anaerobic bacteria, DNF con-verts bioavailable, inorganic nitrogen (in the forms ofnitrite and nitrate) to molecular dinitrogen gas (N2)and nitrous oxide (N2O), resulting in the net removalof this nutrient from the system (Thrush et al. 2013,Humphries et al. 2016). It is estimated that estuariesand coastal shelf regions currently remove 24 Tg ofreactive nitrogen each year (Galloway et al. 2004),making DNF a valuable service provided by coastalsoft sediments.

Previous work has shown that bivalves are capableof enhancing the DNF process (e.g. Christensen et al.2003, Piehler & Smyth 2011, Carlsson et al. 2012, Kel-logg et al. 2013, Humphries et al. 2016). However, bi -valve populations are under pressure in many coastalecosystems as a result of multiple stressors, includingover-harvesting, physical disturbance, sedimenta-tion, contamination, and eutrophication (e.g. Peter-son et al. 1994, Cook et al. 2000, Kirby 2004, McLeodet al. 2012, Thrush et al. 2021). Restoration projectsare underway worldwide to restore these popula-tions and regain ecosystem services associated withshellfish reef formation (zu Ermgassen et al. 2020),and shellfish restoration could be an effective tool toenhance this nitrogen removal service limitingdegradation in coastal ecosystems. In short, subtidalepifaunal bivalve populations remove resuspendedmicrophytes and phytoplankton from the water col-umn as a result of their filter feeding processes(Dame 2012). Bivalves then deposit this organic mat-ter, sourced across a range of spatial scales depend-ing on hydrodynamic regimes, onto the seafloor inthe form of faeces and pseudofaeces (herein referred

to as biodeposits; Newell 2004). The remineralisationof these biodeposits results in am monium drivingnitrification in surface-layer aerobic sediments (Kel-logg et al. 2013). The nitrate produced from this pro-cess typically drives DNF at oxic/ anoxic interfacesand results in the conversion of biologically reactiveforms of nitrogen into inert N2 gas, ef fectively remov-ing nitrogen from the system. While enhanced sedi-ment DNF is predicted as a result of bivalve restora-tion projects in highly eutrophic systems (e.g. Cerco& Noel 2007), studies in oligotrophic systems are rare(Vieillard et al. 2020), and there is a severe lack of insitu studies involving bivalves and DNF.

Laboratory studies (e.g. Kellogg et al. 2013, Smythet al. 2018) and one known in situ study (Humphrieset al. 2016) have shown that bivalve restoration pro-jects on the seafloor can enhance sediment DNFrates as compared to nearby sediments without anybivalve presence. While these studies determine hownitrogen removal varies spatially and/or temporallyin sediments with and without the presence of shell-fish, the complex interactions between environmen-tal factors that drive enhanced DNF within thesebeds remain less well understood. In addition, thespatial structure of mussels within mussel beds isinfluenced by a multitude of factors, including loca-tion within an estuary, position along tidal heightgradients, differential predation, local hydrodynam-ics, etc. (Commito & Dankers 2001). The role of theseheterogeneous spatial arrangements of clumps andbare patches within mussel beds has yet to be ex -plored, but is important in estimating the overall con-tribution of restored beds to DNF. We have observedthis patchiness in the distribution of mussels at ourrestoration sites, which led us to question how thequantity of trapped sediment organic matter (SOM)from both allochthonous sources and local biode-posits influences DNF rates within individual beds.

Bivalves involved in benthic−pelagic coupling andthe resultant organic material accumulated in re -stored beds are predicted to enhance sediment DNF,but the effect of organic matter deposits on nitrogenmineralisation pathways ultimately depends on mul-tiple factors, including the type and quantity of mate-rial deposited (Eyre et al. 2013), as well as localhydro dynamics and benthic mineralisation rates incoastal waters (Prins et al. 1998). In addition, differ-ing sediment characteristics will likely influence theaccumulation of organic-rich biodeposits and trans-port of nutrients, as changes in suspended sedimentconcentration (associated with a grain size gradient)affect bivalve clearance rates (Hawkins et al. 1999)and the quality of biodeposits produced (Iglesias et

2

Sea et al.: Denitrification rates within restored mussel beds

al. 1996). Species diversity and varying abundancesof benthic macrofauna (typically polychaetes andmolluscs) will also affect particle movement andwater transport as a result of feeding activity and bio-turbation (Quintana et al. 2007, Douglas et al. 2017,O’Meara et al. 2020). Together, these environmentalvariables and the complex interactions among themaffect sediment biogeochemistry and likely regulateDNF in different restoration sites. A greater under-standing of factors that significantly influence nitro-gen removal rates will aid in our selection of futuremussel bed restoration locations that seek to enhancethis service.

These gaps in our understanding together with apaucity of research on restored mussel beds providean opportunity to explore the complexities of nitrogencycling in coastal ecosystems. In this study, we meas-ured a suite of environmental factors associated withsediment DNF rates in 2 muddy and 2 sandy subtidalrestoration sites of New Zealand’s endemic green-lipped mussel Perna canaliculus. To determine ifDNF rates varied within the larger mussel bed com-plex, benthic chambers were deployed at each siteenclosing mussel clumps or bare sediments betweenbi valve patches. Net gas fluxes across the sediment−water interface were measured using ratios of di -nitro gen to argon (N2/Ar) through membrane inletmass spectroscopy (MIMS). The importance of a rep-resentative suite of environmental factors in explain-ing flux variability was then explored using decisiontrees and random forest algorithms. We hypothesisedthat organic-rich biodeposits trapped within musselbeds would be most important in stimulating micro-bial activity associated with enhanced sediment DNF,but that the strength of these effects would vary withsite characteristics.

2. MATERIALS AND METHODS

2.1. Study area

Restored mussel beds were created by transplantingroughly 10 t of adult green-lipped mussels sourcedfrom a mussel farm to desired soft-sediment locationsat similar depths (5−10 m), forming beds approxi-mately 10 m2 in size. At the time of this study, bedswere 2−3 yr old and exhibited different spatial patterns (see Text S1 in the Supplement at www. int-res. com/ articles/ suppl/ m667 p001_ supp. pdf). Resto ra -tion sites were initially identified to capture environ-mental variation associated with sediment grain size,SOM, and macrofaunal assemblages. Two of these re-

stored beds were located near the mouth of Mahu-rangi Harbour (Lagoon Bay and Pukapuka), and 2were outside the harbour adjacent to nearshore is-lands (Motuora and Motoketekete; Fig. 1). Our 2 har-bour sites were characterised by finer, siltier sedi-ments more easily resuspended in the water column,whereas the island sites exhibited a sandy substratewith less resuspended material. Chosen sites thereforeprovided differences in both a suite of environmentalcharacteristics and the effect of mussel clumps.

2.2. Sample collection

This study was conducted in March and April 2019.Using SCUBA, benthic chambers (e.g. Lohrer et al.2004, O’Meara et al. 2020) were placed within re -stored beds over sediments containing mussel clumps.To capture spatial variability and account for bedpatchiness, a number of chambers without mussels(but within bed boundaries) were also deployed inthis study. Chambers (0.25 m2 each) were pushed intothe sediment to a pre-marked line 7 cm from thechamber base, so that all chambers held 41 l of near-bed seawater at the start of the incubation. A total of12 chambers were deployed at 3 sites (Motuora,

3

Fig. 1. Location of study sites in Mahurangi Harbour andKawau Bay, New Zealand. LB: Lagoon Bay; PP: Pukapuka;

MR: Motuora; MK: Motoketekete

Mar Ecol Prog Ser 667: 1–13, 2021

Moto kete kete, and Pukapuka; beds roughly 10 m2

each), while 9 chambers were deployed at the smallerLagoon Bay bed (~7 m2). Photos were taken of eachchamber, and the number of mussels was recorded(Table S1). One site was sampled per day.

Each benthic chamber was equipped with aminiDOT optical oxygen logger (Precision Measure-ment Engineering), which recorded dissolved oxygenin the chamber water every minute, and a pumpwhich recirculated chamber water for 5 s at 30 s inter-vals. Chamber lids were carefully sealed while under-water to ensure there were no gas bubbles. Chamberswere then covered with black polyethylene to omit in-coming light. These dark incubations mean we omitthe role of microphyte production, but this avoidedcomplications associated with bubble formation inlight chambers due to photosynthesis as well as com-plicating comparisons due to variation in photosyn-thetically active radiation due to changes in weatherand water clarity.

To determine net fluxes across the sediment−waterinterface, samples for MIMS and dissolved inorganicnutrient analysis (nitrate/nitrite, reactive phospho-rus, and ammonium concentrations, herein referredto as NOx

−, PO43−, and NH4

+) were taken from thechamber port using 2 syringes (60 ml capacity) im -mediately after covering chamber lids, taking care toremove any air bubbles from the syringes before-hand. Water samples were taken again from eachchamber at the end of the incubation period (approx-imately 3 h). At the end of the incubation period, thechamber lids were removed and 2 sediment syringecores (3 cm length, 1.9 cm diameter) were taken fromwithin each chamber for chlorophyll a (chl a), SOM,and grain size analysis. Avoiding mussel clumps, anadditional 10 cm diameter, 10 cm deep sediment corewas taken from within each chamber to assess domi-nant macrofauna present at each site.

Immediately after collection, water samples for nu -trient analysis were filtered through Whatman GF/Ffilters (0.7 μm pore size) and stored on ice beforebeing transferred to the lab and frozen. Each unfil-tered MIMS sample was transferred into 2 gas-tight12 ml exetainer vials (for a total of 4 per chamber)and poisoned with ZnCl2 to stop biological activity.MIMS samples were stored below collection temper-ature, but above freezing. These samples were trans-ferred to the lab and kept at 4°C pending analysis.Sediment samples for SOM, grain size, and chl a con-tent were kept on ice in the dark and frozen at the labuntil later analysis. Macrofauna cores were sieved(500 μm mesh) with seawater and contents trans-ferred into storage containers with 70% isopropyl

alcohol stained with rose bengal. Macrofauna werelater sorted and classified to the lowest practical tax-onomic group. For each core, the total number ofmacrofauna present, total number of known biotur-bators present (total bioturbators), and total numberof species present (species richness) were recordedfor use in later analyses.

2.3. Sediment characterisation

SOM was determined by loss on ignition (Ball1964). Sediments were left in a 60°C oven, dried for48 h, and then weighed before and after combustionat 500°C for 10 h.

To determine sediment grain size, homogenisedsediment samples (~20 g) from each chamber weredigested with 50 ml of 6% H2O2 and periodicallymixed over a 48 h time period. Samples were thenrinsed of hydrogen peroxide and sat overnight in~15 ml of 5% Calgon (a dispersing agent for sedi-ment particles) prior to grain size analysis with aMalvern Mastersizer 3000 (ATA Scientific).

To assess the standing stock of the microphytoben-thos (MPB), chl a was extracted from 1 g of freeze-dried sediment samples with 3 ml of 90% acetone.Optical densities of extracts were measured at 664,665, and 750 nm with a UV-Vis spectrophotometer(Thermo Scientific, Multiskan Sky) before and afteracidification with hydrochloric acid (0.1 ml of 0.1MHCl). Values at 750 nm were subtracted from valuesat 664 and 665 nm to correct for turbidity in samples.Sediment chl a content was calculated using equa-tions from Lorenzen (1967) based on 90% acetoneextraction:

(1)

where E664b is the corrected absorbance at 664 nmbefore acidification, E665a is the corrected absorbanceat 665 nm after acidification, Ve is the volume of theextractant (ml),andD is thedryweightofsediment (g).

2.4. Flux analysis

To determine if net DNF rates varied betweenmussel clumps and the spaces between them, sea-water samples were analysed on a quadrupole mem-brane inlet mass spectrometer (with Pfeiffer VacuumPrisma Plus QMG220 M1 QMS, Bay Instruments)using the MIMS technique (Kana et al. 1994). This

( )( )

=× × − ×

−Chl   content  μg chl   g  sediment

11 2.43  

1

664 665

a a

E E VD

b a e

4

Sea et al.: Denitrification rates within restored mussel beds

high precision (<0.05%) method uses N2/Ar ratiosto determine a net flux of N2 from all processes thatform N2 (DNF, anaerobic ammonium oxidation) oruse N2 (nitrogen fixation). With this technique, a netpositive N2 flux indicates that DNF dominates,while a net negative N2 flux in di cates that nitrogenfixation dominates. It should be noted that, whileanaerobic ammonium oxidation produces N2 gas,the contribution of this process to overall nitrogenremoval in estuarine environments is quite minorglobally (5− 14% on average; Hou et al. 2015) andgoes undetected at high-salinity, low-nutrient sites(<6 μM NO3

−; Rich et al. 2008) similar to those stud-ied here (see Table S2 for a summary of additionalenvironmental characteristics re corded). We there-fore assume that all nitrogen re moval measured inthis study is the direct consequence of DNF, but thatfurther nitrogen removal (not estimated) may beoccurring as a result of burial within sediments.

With known incubation times for each chamber,fluxes were calculated as the difference betweeninitial and final concentrations and corrected forvolume and surface area of the chamber to obtain aflux in μmol m−2 h−1 (e.g. Eyre et al. 2002, O’Mearaet al. 2020). This was done for both N2 and O2

fluxes. O2 fluxes calculated from the MIMS tech-nique were then compared to data obtained fromthe oxygen loggers placed in benthic chambers.Dis solved oxygen data from these loggers were sim-ilarly graphed, and the slope of the linear portion ofthe line was used to calculate a flux rate that wasalso corrected for volume and area of the chamberspace. Even though O2 fluxes were found to becomparable, the manual truncation method de -scribed above to calculate O2 fluxes has been cri-tiqued for introducing subjectivity into analysis (seeOlito et al. 2017), and data from MIMS was insteadused in further calculations. All chambers with mus-sels were examined for hypoxia (<2.0 mg O2 l−1) atthe end of the 3 h incubation period, and chamberswith oxygen concentrations be low this thresholdwere discarded from further analysis.

Nutrient samples were analysed using a LachatAuto sampler (ASX-260 Series) flow injection ana -lyser (method detection limits of 0.032, 0.05, and0.071 μmol l−1 for PO4

3−, NH4+, and NOx

−, respec-tively), with standard methods for analysis of seawa-ter nutrients. Values were corrected using calibrationstandards, and net fluxes were calculated by takingthe difference between final and initial concentrationvalues and dividing by chamber incubation timewhile accounting for volume and area of sedimentenclosed by the chamber.

2.5. Statistical analysis

The effects of site, mussel presence/absence, andtheir interaction on measured fluxes and varying en -vironmental characteristics were investigated using2-way ANOVA, followed by Tukey’s tests for posthoc comparisons (results summarised in Table S3). As -sumptions of normality and homogeneity of variancewere checked using the statistical package ‘s20x’(Balemi et al. 2021) in R (version 3.6.1; R Core Team2019). Q-Q plots, Shapiro-Wilk tests, and Levene’stests were used to check assumptions, and valueswere log-transformed where necessary. Significancelevels were set to α = 0.05, and standard errors (SE)were used to assess the precision of mean values,unless otherwise stated.

To determine which of the measured environ -mental variables explained the most variation inmeasured fluxes within mussel beds, we usedmachine learning statistical techniques involvingregression trees and random forest models. Thesenon-para metric approaches have gained popularityin ecological sciences (e.g. Evans & Cushman 2009,Ray et al. 2020, Smith DiCarlo et al. 2020) and arevalued for their ability to cope with complex inter-actions, non-linear relationships, and unbalancedexperimental designs, while producing graphicaloutputs that are easily interpreted and reveal rela-tionships that often go unrecognised using moretraditional statistical techniques (De’ath & Fabricius2000).

We used the ‘rpart’ package (Therneau & Atkin-son 2019) in R to create a decision tree which splitthe full data set into multiple groups (each splitcreating the lowest possible within-group variation)until predetermined end points were met (in thiscase, 4 minimum observations required in a nodefor a split to be attempted and at least 2 obser -vations in any terminal node; complexity param-eter = 0.014). Eleven explanatory variables wereused to predict net N2 fluxes in regression treesand random forests: SOM, oxygen fluxes, nutrientfluxes (NOx

−, PO43−, NH4

+), total macrofauna abun-dance, total number of bioturbators, species rich-ness, chl a content, total number of mussels perchamber, and percentage mud content. Variablesnear the root (top) of the tree were consideredmost influential in classifying the response variableinto various terminal nodes (in this case, net N2

fluxes). Interactions between explanatory variableswere conceptualised as subsequent splits, depend-ent on the results of decisions at nodes higher inthe tree.

5

Mar Ecol Prog Ser 667: 1–13, 2021

It is recognised that regression trees and relatedpredictive mapping methods typically undergo a 2-step process: first, modelling the relationship be -tween a response variable and a suite of explanatoryvariables from field measurements or observations;and second, using the generated model to predictthe response variable at new sites given knownvalues for the explanatory variables. As the goal ofthis study was to determine which predictor vari-ables could best explain N2 flux variability in thesingle data set, it was only necessary to conduct themodel creation process. We advise against the useof this descriptive model in predicting fluxes out-side our wider study system, although conclusionsgleaned from this investigation regarding the gen-eral importance of various environmental and bio-geochemical factors can be used to inform the designof future experiments involving predictive modellingtechniques.

A random forest algorithm (‘randomForest’ pack-age; Liaw & Wiener 2002) was used to rank the rela-tive importance of explanatory variables used in thisstudy. Random forest is an ensemble method result-ing from the growth of hundreds to thousands ofunique regression trees, each of which is createdusing a bootstrapped sample of the dataset and ran-dom subsets of explanatory variables at each node(Breiman 2001, Diesing et al. 2017). Random forestoutputs a list of variable importance, determined bypermuting each predictor variable and calculatingthe percentage error increase associated with thechange. Higher values correspond to greater vari-able importance. Variable importance from the ran-dom forest was then compared to regression tree out-puts. We used a forest with 500 trees and tested 8randomly selected variables at each split.

3. RESULTS

3.1. Environmental variables

Differences in mud content (% <63 μm) were ap-parent between sites (2-way ANOVA; F3,24 = 333.43,p < 0.001), with Lagoon Bay (LB) and Pukapuka (PP)characterised by significantly muddier sedimentsthan the sandier Kawau Bay sites (Motuora [MR] andMotoketekete [MK]; Table 1). A significant differencein percentage mud content was also detected be -tween Kawau Bay sites, with MK exhibiting less siltand clay than MR. Moderate SOM was observed atall locations, with mean SOM ranging from 2.3 ±0.02% at LB to 3.4 ± 0.36% at PP. Significant differ-ences in chl a content were observed between sites(2-way ANOVA; F3,24 = 5.31, p < 0.01), with LBhaving higher chl a content than PP and MR.

We observed differences in macrofaunal abun-dance between sites (2-way ANOVA; F3,24 = 8.35,p < 0.001), with sandier sediments at MR and MKexhibiting significantly higher macrofaunal abun-dance per core than PP and LB (Table 1). The totalnumber of species identified per core increasedfrom 10 ± 0.9 and 9.5 ± 1.2 at the muddier sites LBand PP to 14.5 ± 1.2 and 14.5 ± 1.0 species at thesandier sites MR and MK, respectively, indicatingan increase in species richness associated withsandier sites. While there was no significant differ-ence in abundance of individual macrofauna pres-ent between the 2 sandier sites, a distinct separationin community structure was apparent, with highernumbers of gastropods and amphipods separatingMK from MR (Fig. S1). Polychaetes were less abun-dant and more similar in species composition withinthe harbour sites.

6

Site Chamber Mud Medium Coarse SOM Chl a content Total number of contents content (%) sand (%) sand (%) (%) (μg g−1) macrofauna core−1

PP Mussels 22.4 ± 0.37a 1.32 ± 0.13a 1.87 ± 0.78a 3.2 ± 0.33a 4.1 ± 0.9a 34.0 ± 2.5a

No mussels 24.0 ± 0.27a 1.13 ± 0.16a 2.31 ± 0.65a 3.4 ± 0.36a 3.8 ± 1.1a 36.7 ± 24.2a

LB Mussels 22.9 ± 1.61a 0.18 ± 0.03a 0.18 ± 0.03a 2.7 ± 0.38b 5.6 ± 2.5bc 34.0 ± 11.4a

No mussels 20.0 ± 1.56a 0.42 ± 0.10a 0.71 ± 0.18a 2.3 ± 0.02b 8.5 ± 1.1bc 30.0 ± 6.3a

MR Mussels 7.0 ± 0.83b 12.28 ± 1.04b 0.89 ± 0.39a 3.4 ± 0.30a 4.9 ± 0.5ac 107.3 ± 13.6b

No mussels 4.6 ± 0.10b 12.14 ± 0.28b 1.45 ± 0.69a 2.9 ± 0.17a 3.2 ± 2.7ac 84.0 ± 1.0b

MK Mussels 3.3 ± 0.29c 35.81 ± 0.67c 11.17 ± 0.89b 2.7 ± 0.08b 6.1 ± 0.4c 76.1 ± 10.6b

No mussels 3.1 ± 0.38c 36.86 ± 0.45c 10.98 ± 0.71b 2.7 ± 0.11b 6.8 ± 0.3c 107.3 ± 28.1b

Table 1. Summary of sediment characteristics between sites, separated by chambers with and without mussels. Mud is com-prised of silt + clay (<63 μm). Medium sand is between 250 and 500 μm, and coarse sand is >500 μm. SOM: sediment organicmaterial. PP: Pukapuka; LB: Lagoon Bay; MR: Motuora; MK: Motoketekete. Different superscript letters denote significant

differences between sites (post hoc Tukey’s test, p < 0.05). Data represent the mean ± SE

Sea et al.: Denitrification rates within restored mussel beds

3.2. Net DNF and nutrient fluxes

The magnitude of NOx− fluxes measured over the

incubation period varied by roughly 100 μmol m−2 h−1

(Fig. 2A), with a net efflux measured in the majorityof chambers. PO4

3− fluxes (not shown) varied consid-erably across chambers, ranging from −2.26 ± 8.9 to37.61 ± 16.2 μmol m−2 h−1, and no clear pattern re -lated to the presence of mussels or location. Two-wayANOVA suggested that the effect of mussels on NH4

+

fluxes was dependent on location (F3,24 = 4.82, p <0.01), although post hoc pairwise comparisons werenot powerful enough to detect significant pairs ofmeans (Fig. 2B; Table S3). Sediment oxygen demandwas significantly higher in chambers with musselsthan in chambers without mussels (2-way ANOVA;F1,24 = 24.02, p < 0.001), and this pattern was ob -served across sites (Fig. 2C).

Net N2 fluxes were highly variable, but were ob -served on a scale similar to other coastal marinesediments globally (e.g. Seitzinger 1988, O’Mearaet al. 2020 and references therein). Net N2 fluxes inchambers without mussels were close to 0 at PP,

MR, and MK (33.86 ± 25.8, 42.66 ± 42.6, and 94.33 ±124.1 μmol N m−2 h−1, respectively). Net nitrogenfixation was apparent in chambers with (−625.36 ±292.8 μmol N m−2 h−1) and without (−768.24 ±414.3 μmol N m−2 h−1) mussels at site LB (Fig. 2D).In chambers containing mussels, highest averagesediment DNF rates were recorded at PP (56.82 ±40.6 μmol N m−2 h−1). Log-transformed N2 fluxes atLB were significantly different from all other sites(2-way ANOVA; F3,24 = 10.23, p < 0.001), and thepresence or absence of mussels did not significantlyaffect these rates (2-way ANOVA; F1,24 = 0.02, p =0.883). While significant differences were not de -tected due to the high variance in measurementsbetween chambers, DNF appeared higher in cham-bers without mussels at the sandiest site (MK), anda net decrease in nitrogen fixation (potentiallyindicative of DNF) was seen in chambers with mus-sels at the muddiest site (LB; Fig. 2D).

Regression trees and the random forest algorithmused data from 32 benthic flux chambers that weredeemed to have acceptable oxygen levels (non-hypoxic) and small changes in Ar concentration at

7

–1200

–900

–600

–300

0

300

No musselsMussels

PP MRLB MK

–8000

–6000

–4000

–2000

0

–100

–50

0

50

100

150

200 A B

C–400

–200

0

200

400

600

800

1000

D

PP MRLB MK

*

* **

ab b b

NO x– (

µmol

m–2

h–1)

O2 (

µmol

m–2

h–1)

NH

4+ (µm

ol m

–2 h

–1)

N2 (

µmol

m–2

h–1)

Fig. 2. Net fluxes measured in chambers containing no mussels (white) and mussels (dark) across 4 field sites. (A) Nitrate + ni-trite (NOx

−) fluxes; (B) NH4+ fluxes; (C) benthic oxygen consumption; (D) denitrification (positive) and nitrogen fixation (nega-

tive) rates. Note scale differences between graphs. Asterisks indicate significant differences (p < 0.05) between chambers withand without mussels, while letters denote significant differences in mean flux rates between sites. Error bars represent SE.Sites are arranged across a decreasing mud gradient. LB: Lagoon Bay; PP: Pukapuka; MR: Motuora; MK: Motoketekete

Mar Ecol Prog Ser 667: 1–13, 2021

the end of the incubation period. Regression treeana lysis of N2 fluxes produced a tree with 5terminal nodes (Fig. 3A). The first branching pointsplit relative to the percentage of SOM present,with chambers containing lower organic material(<2.3%) having lower DNF rates (alternatively,higher net nitrogen fixation). From this split, themagnitude of predicted fluxes varied depending onmud content, with greater nitrogen fixation levelsap parent in muddier sediments (mud content≥20%). In chambers with organic material >2.3%,greatest DNF rates corresponded to higher NOx

− ef -fluxes. Chambers with lower NOx

− values showedadditional branching relative to the concentration ofchl a, with nitrogen fixation occurring in chamberswith higher chl a (≥7 μg g−1), and DNF occurring inchambers with chl a below this threshold. The ran-dom forest was able to explain 27.2% of the vari-ance in observed nitrogen fluxes and ranked SOMas the most important environmental predictor ofnitrogen fluxes, in alignment with the regressiontree model. Site, oxygen demand, and total macro-faunal abundance were also identified as influentialexplanatory variables (Fig. 3B).

4. DISCUSSION

In this study, we used benthic flux chambers toshow that DNF occurs within restored mussel beds,and that the magnitude and extent of nitrogen re-moval is dependent on a variety of environmentaland biogeochemical factors. As coastal systems arehighly heterogeneous in nature, ecosystem servicesare often generated by complex and interrelated eco-system processes that vary over space and time; thismakes the quantification of nitrogen cycling andother ecosystem services provided by restored musselbeds very challenging. However difficult, it is impor-tant to recognise and assign value to these services,as more traditional management schemes have com-monly failed to do so and have consequently permit-ted the degradation of ecosystems in ways thatgreatly reduce the value of services they provide (e.g.Costanza et al. 1997). The historic collapse of musselbeds in New Zealand (Paul 2012) is one such exam-ple, where services other than food provision did notinfluence the regulation of the fishery.

Multiple studies (e.g. Kellogg et al. 2013, Hum -phries et al. 2016, Smyth et al. 2018) have shown that

8

SOM < 2.3 %noyes

NOx– < 98

μmol m–2 h–1

Mud content ≥ 20 %

–79816%

4084%

–91100%

–11419%

–2826%

7.475%

3009%

Chl a ≥ 7 μg g–1

3662%

–13712%

SOM

Oxygen

NOx–

Total macrofauna

Chlorophyll a

NH4+

PO43–

Mud content

Species richness

Total bioturbators

Number mussels

Variation explained = 27.2%

A B

IncNodePurity0 4×106

Fig. 3. (A) Single best regression tree predicting denitrification rates in restored mussel beds. Each node highlights the se-lected environmental variable at its split point (bold), with positive responses separating left, and negative responses separat-ing right further down the tree. Terminal nodes show mean N2 flux (μmol m−2 h−1) and the percentage of total samples thatcontribute to the predicted rate. (B) List of decreasing variable importance (from top to bottom) assigned by the random forest,

as determined by increase in node purity (IncNodePurity)

Sea et al.: Denitrification rates within restored mussel beds

DNF rates are significantly enhanced in restoredbivalve beds; our study is first in building on thatknowledge to determine if DNF rates vary at thepatch scale within individual beds, and which envi-ronmental factors are most significant in explainingvariability in measured rates. This in situ study wasconducted entirely within restored mussel beds of anoligotrophic coastal ecosystem. Net nitrogen fluxeswere similar be tween chambers with and withoutmussels (Fig. 2D), suggesting that mussels influenceDNF on scales larger than individual clumps, imply-ing a bed-scale phenomenon rather than more loca -lised effects around smaller patches. Measured envi-ronmental factors associated with restoration (e.g.enhanced SOM) are better predictors of sedimentDNF rates in these systems.

This study highlights the importance of SOM on N2

fluxes, with greater DNF associated with moderateorganic matter loading in mussel beds. The impor-tance of SOM in partitioning N2 fluxes in our models,across both chambers with and without mussels, sug-gests that carbon-rich biodeposits generated by theenhanced benthic−pelagic coupling of restored mus-sel populations can provide a source of organic mate-rial necessary for remineralisation, the products ofwhich (NH4

+, NO2−, and NO3

−) go on to fuel DNF inlocalised regions of hypoxic sediment (Herbert 1999,Nizzoli et al. 2006). Stimulation of DNF as a result ofbivalve biodeposits and increased SOM has been re -ported elsewhere (e.g. Newell et al. 2002, Newell2004, Kellogg et al. 2013) and likely contributed toin creased nitrogen removal observed in chamberswith higher SOM in this study. While we did not de -tect statistical differences in percentage SOM be -tween chambers with and without mussels, studiesusing other bivalves in this harbour have docu-mented increased sedimentation rates and organiccarbon inputs closer to individuals of the large pinnidbi valve Atrina zelandica (Norkko et al. 2001), reaf-firming the idea that bivalve beds retain organicmaterial enriched in carbon and nitrogen. It is possi-ble that the biodeposits created by the smaller green-lipped mussels are not trapped locally but are insteadspread over the entire bed. This increased SOM dis-persion would account for potential effects at thescale of mussel beds rather than individual clumpsand could help explain the similarities in DNF ob -served between chambers with and without musselsin this study.

Nitrification and DNF are tightly coupled pro-cesses at low nitrate concentrations (e.g. Gongol &Savage 2016, Vieillard et al. 2020), suggesting thatthe enhanced nitrogen removal capacity resulting

from increased SOM observed here may have anupper limit. However, this was not observed at orga -nic matter loads present in our beds. In our oligo -trophic system, we conclude that highest DNF rateswill be achieved with moderate organic matter en -richment, as insufficient SOM will limit ammonifica-tion and nitrate production, and excessive SOM willresult in higher oxygen demand and the potentialproduction of sulfides (e.g. Christensen et al. 2003)which would further inhibit these processes (Joye &Hollibaugh 1995). These relationships are mirroredin the conceptual models of others (Hoellein & Zar -noch 2014) who predicted that the role of bivalvebiodeposition on DNF will have the greatest effect inoligo trophic systems, as bivalves likely stimulatecoupled nitrification−DNF (as opposed to ammoniumimmobilisation in eutrophic systems high in sedi-ment organic carbon), and competition for nitrate(shown to be important in regression tree outputs) isreduced in low-nutrient environments. Even if com-petition is reduced in these systems, both regressiontree and random forest models indicate the impor-tance of NOx

− effluxes from chamber incubations onDNF rates, with higher NOx

− availability correspon-ding to maximum nitrogen removal. It is likely thatthe nitrogenous products of organic matter minerali-sation (resulting from biodeposits and mussels them-selves) supply the NOx

− necessary to fuel eventualnitrogen removal, and that the coupling of nitrifica-tion and DNF is important in these epifaunal musselbeds.

When NOx− levels measured during incubations

were low, chl a content influenced whether nitrogenfixation or DNF took place, with higher chlorophylllevels associated with nitrogen fixation and lowerchl a levels associated with DNF. This suggests aweak yet negative influence of local MPB communi-ties on sediment DNF possibly due to competition fornitrogen (also noted by O’Meara et al. 2020). MPB areknown to reduce nutrient fluxes across the sediment−water interface as a result of nutrient uptake neces-sary for their growth and survival, and the stickypolymers produced by MPB can alter sediment cohe-sion (Newell et al. 2002, Hope et al. 2020); together,these actions result in the reduction of solute trans-port and could help explain lower predicted DNFrates in regression tree outputs. The influence ofMPB was not expected as experiments were carriedout under dark conditions; however, others haveshown that nutrient uptake by MPB continues in thedark (Rysgaard et al. 1993, Evrard et al. 2008, NiLongphuirt et al. 2009), and this dark ammoniumuptake was observed at 3 of our field sites. MPB are

9

Mar Ecol Prog Ser 667: 1–13, 2021

capable of outcompeting nitrifiers and denitrifiers fordissolved inorganic nitrogen (Newell et al. 2002,Sundbäck & Miles 2002, Cook et al. 2004), and ap -pear to do so when found at higher concentrations inthese systems. As all fluxes in our study were meas-ured under dark conditions, it should be noted thatthese results reflect an approximation of maximumDNF potential under no-light conditions. The pres-ence of light would likely enhance nutrient uptakeby MPB, but would also increase oxygen availabilityas a result of photosynthesis (potentially available forfurther nitrification which is then coupled to DNF); itwould be useful in the future to measure and balancethese fluxes over a diel cycle in order to upscalenitrogen removal rates on restored mussel beds. Inaddition, active epibenthos such as bivalves can re -spond rapidly to changes in their environment (e.g.fluctuations in suspended sediment and phytoplank-ton concentrations; Higgins 1980, Ward & MacDon-ald 1996). Future studies linking changes in environ-mental conditions to Perna canaliculus behaviourcould further our understanding of how bivalves af -fect nitrogen dynamics at larger spatial and temporalscales.

Our flux measurement incubation period repre-sents one point in time. While ambient inorganicnitro gen concentrations and measured fluxes havenot shown substantial seasonality in northern NewZealand (Jones et al. 2011, Tay et al. 2012), temporalvariation was not addressed in the current study.These scale constraints are common with field-basedresearch and suggest that any information used fromthis study to inform the placement of future beds benested in an adaptive management framework to re-evaluate the success of mussel restoration projects onnitrogen removal over longer timescales.

In the present study, net DNF increased with de -creasing mud content, and these results are mirroredby others who have carried out benthic chamberexperiments in the same harbour (O’Meara et al.2020). The regression tree suggests that percentagemud content becomes most important at low levels ofSOM (<2.3%). It should be noted that the terminalnodes associated with this split both result in appar-ent nitrogen fixation, yet this rate decreases with de -creasing mud content. Grain size affects the trans-port of nutrients into and out of marine sediments,and larger particle size typically corresponds tohigher porosity associated with increased porewateradvection and the transport of nutrients (Santos et al.2012). At low organic matter loads, mussel biode-posits make a greater contribution to DNF in morepermeable, nutrient-poor sediments, increasing the

availability of bioreactive nitrogen while creatinganoxic microsites required for DNF (Seitzinger et al.2006). It is also well documented that macrofaunalabundance and species richness increase with de -creasing mud content (e.g. Mannino & Montagna1997, Thrush et al. 2003, 2004, Pratt et al. 2014) andthat macrofauna stimulate increased nitrogen re -moval (reviewed by Stief 2013). Given these relation-ships, it follows that DNF increases with decreasingmud content, as the abundance of these sediment-dwelling species also increases under these circum-stances. While not emphasised by regression treeanalysis, the significance of macrofaunal abundanceas an important variable was reflected in random for-est outputs.

Of notable interest are the net fluxes at site LB,where net nitrogen fixation was apparent in allchambers. Nitrogen fixation (breaking the dinitrogentriple bond) is an energetically costly process. Evenin the presence of bivalves that emit nitrogenouscompounds, it is clear that a demand for bioavailablenitrogen exists at LB; this nitrogen demand is per-haps not surprising for a low-nutrient system, andnitrogen fixation has previously been recorded inother bivalve studies (Humphries et al. 2016) andunder dark conditions (Fulweiler et al. 2007). Cyano-bacteria known to produce nitrogenase (the enzymeresponsible for reducing N2 to ammonia) have beenfound in local estuarine sediments of this region(Hicks & Silvester 1985) and could account for ob -served nitrogen fixation. At LB, NOx

− and NH4+ emis-

sions in chambers without mussels possibly reflectthe products of microbial nitrogen fixation; lowerNOx

− and NH4+ fluxes observed in chambers with

mussels suggest tight recycling/high turnover ofnitrogen in these places. In many oligotrophic envi-ronments, a larger percentage of total bioreactivenitrogen is assimilated rather than removed as N2

gas; this increased nitrogen retention, however, cansupport increased secondary and tertiary production(Cook et al. 2004, Vieillard et al. 2020).

Understanding nitrogen dynamics in oligotrophicenvironments is crucial, as these systems are highlyunderrepresented in the literature (Vieillard et al.2020) and restoration projects in these places likelydiffer in their capacity to support enhanced nitrogenremoval services (Hoellein & Zarnoch 2014). In sucha system, we conclude that the effects of mussel res-toration on sediment DNF are not restricted to thepatch-scale, and that dispersed SOM is an importantpredictor of net N2 fluxes at restoration sites. Thisstudy contributes to the small but growing literaturebase on nitrogen cycling in oligotrophic estuaries of

10

Sea et al.: Denitrification rates within restored mussel beds

the southern hemisphere, with project insights help-ing us better understand ecosystem functionality inrestored bivalve systems.

Acknowledgements. We thank Al Alder, Brady Doak, CaitlinBlain, Craig Norrie, Eliana Ferretti, Kaiwen Yang, and Ste-fano Schenone for their help with field work, and Maria Mug-ica and Julie Hope for their assistance in the laboratory. Spe-cial thanks to Amanda Vieillard, Stefano Bonaglia, TeriO’Meara, and Rebecca Gladstone-Gallagher for content-specific advice and to Judi Hewitt and Marco Colossi Brus-tolin for help with statistical analysis. We greatly appreciaterecommendations from 3 anonymous reviewers whichimproved the overall quality of the manuscript. This projectwas supported by Fulbright New Zealand, George MasonCentre for the Natural Environment, Gulf Innovation FundTogether at Foundation North, and a University of AucklandDoctoral Scholarship to M.A.S.

LITERATURE CITED

Anderson DM, Glibert PM, Burkholder JM (2002) Harmfulalgal blooms and eutrophication: nutrient sources, com-position, and consequences. Estuaries 25: 704−726

Balemi A, Chandra D, Curran J, Deppa B and others (2021)s20x: Functions for University of Auckland CourseSTATS 201/208 Data Analysis. R package version 3.1-30.https://CRAN.R-project.org/package=s20x

Ball D (1964) Loss-on-ignition as an estimate of organic mat-ter and organic carbon in non-calcareous soils. J Soil Sci15: 84−92

Breiman L (2001) Random forests. Mach Learn 45: 5−32Carlsson MS, Engström P, Lindahl O, Ljungqvist L, Petersen

JK, Svanberg L, Holmer M (2012) Effects of mussel farmson the benthic nitrogen cycle on the Swedish west coast.Aquacult Environ Interact 2: 177−191

Cerco CF, Noel MR (2007) Can oyster restoration reversecultural eutrophication in Chesapeake Bay? EstuariesCoasts 30: 331−343

Christensen PB, Glud RN, Dalsgaard T, Gillespie P (2003)Impacts of longline mussel farming on oxygen and nitro-gen dynamics and biological communities of coastal sed-iments. Aquaculture 218: 567−588

Commito JA, Dankers NMJA (2001) Dynamics of spatial andtemporal complexity in European and North Americansoft-bottom mussel beds. In: Reise K (ed) Ecological com-parisonsofsedimentaryshores.EcologicalStudies(Analy-sis and Synthesis), Vol 151. Springer, Berlin, p 39−59

Cook AE, Shaffer JA, Dumbauld BR (2000) A plan for re -building stocks of Olympia oysters (Ostreola concha -phila, Carpenter 1857) in Washington state. J ShellfishRes 19: 409−412

Cook PLM, Revill AT, Butler ECV, Eyre BD (2004) Carbonand nitrogen cycling on intertidal mudflats of a temper-ate Australian estuary. II. Nitrogen cycling. Mar EcolProg Ser 280: 39−54

Costanza R, d’Arge R, de Groot R, Farber S and others (1997)The value of the world’s ecosystem services and naturalcapital. Nature 387: 253−260

Dame RF (2012) Ecology of marine bivalves: an ecosystemapproach. Taylor & Francis, Boca Raton, FL

De’ath G, Fabricius KE (2000) Classification and regression

trees: a powerful yet simple technique for ecological dataanalysis. Ecology 81: 3178−3192

Diaz RJ, Rosenberg R (2008) Spreading dead zones and con-sequences for marine ecosystems. Science 321: 926−929

Diesing M, Kröger S, Parker R, Jenkins C, Mason C, WestonK (2017) Predicting the standing stock of organic carbonin surface sediments of the North−West European conti-nental shelf. Biogeochemistry 135: 183−200

Douglas EJ, Pilditch CA, Kraan C, Schipper LA, Lohrer AM,Thrush SF (2017) Macrofaunal functional diversity pro-vides resilience to nutrient enrichment in coastal sedi-ments. Ecosystems 20: 1324−1336

Evans JS, Cushman SA (2009) Gradient modeling of coniferspecies using random forests. Landsc Ecol 24: 673−683

Evrard V, Cook PLM, Veuger B, Huettel M, Middelburg JJ(2008) Tracing carbon and nitrogen incorporation andpathways in the microbial community of a photic sub-tidal sand. Aquat Microb Ecol 53: 257−269

Eyre BD, Rysgaard S, Dalsgaard T, Christensen PB (2002)Comparison of isotope pairing and N2: Ar methods formeasuring sediment denitrification — assumptions, mod-ifications, and implications. Estuaries 25: 1077−1087

Eyre BD, Maher DT, Squire P (2013) Quantity and quality oforganic matter (detritus) drives N2 effluxes (net denitrifi-cation) across seasons, benthic habitats, and estuaries.Global Biogeochem Cycles 27: 1083−1095

Fulweiler RW, Nixon SW, Buckley BA, Granger SL (2007)Reversal of the net dinitrogen gas flux in coastal marinesediments. Nature 448: 180−182

Galloway JN, Aber JD, Erisman JW, Seitzinger SP, HowarthRW, Cowling EB, Cosby BJ (2003) The nitrogen cascade.Bioscience 53: 341−356

Galloway JN, Dentener FJ, Capone DG, Boyer EW and oth-ers (2004) Nitrogen cycles: past, present, and future. Bio-geochemistry 70: 153−226

Galloway JN, Townsend AR, Erisman JW, Bekunda M andothers (2008) Transformation of the nitrogen cycle: re -cent trends, questions, and potential solutions. Science320: 889−892

Gongol C, Savage C (2016) Spatial variation in rates of ben-thic denitrification and environmental controls in fourNew Zealand estuaries. Mar Ecol Prog Ser 556: 59−77

Hawkins AJS, James MR, Hickman RW, Hatton S, Weather-head M (1999) Modelling of suspension-feeding andgrowth in the green-lipped mussel Perna canaliculus ex -posed to natural and experimental variations of sestonavailability in the Marlborough Sounds, New Zealand.Mar Ecol Prog Ser 191: 217−232

Herbert RA (1999) Nitrogen cycling in coastal marine eco-systems. FEMS Microbiol Rev 23: 563−590

Hicks BJ, Silvester WB (1985) Nitrogen fixation associatedwith the New Zealand mangrove (Avicennia marina(Forsk.) Vierh. var. resinifera (Forst. f.) Bakh.). Appl Env-iron Microbiol 49: 955−959

Higgins PJ (1980) Effects of food availability on the valvemovements and feeding behavior of juvenile Crassostreavirginica (Gmelin). I. Valve movements and periodicactivity. J Exp Mar Biol Ecol 45: 229−244

Hoellein TJ, Zarnoch CB (2014) Effect of eastern oysters(Crassostrea virginica) on sediment carbon and nitrogendynamics in an urban estuary. Ecol Appl 24: 271−286

Hope JA, Paterson DM, Thrush SF (2020) The role of micro-phytobenthos in soft-sediment ecological networks andtheir contribution to the delivery of multiple ecosystemservices. J Ecol 108: 815−830

11

Mar Ecol Prog Ser 667: 1–13, 2021

Hou L, Zheng Y, Liu M, Li X and others (2015) Anaerobicammonium oxidation and its contribution to nitrogenremoval in China’s coastal wetlands. Sci Rep 5: 15621

Howarth RW, Marino R (2006) Nitrogen as the limiting nutri-ent for eutrophication in coastal marine ecosystems: evolving views over three decades. Limnol Oceanogr 51: 364−376

Humphries AT, Ayvazian SG, Carey JC, Hancock BT andothers (2016) Directly measured denitrification revealsoyster aquaculture and restored oyster reefs removenitrogen at comparable high rates. Front Mar Sci 3: 74

Iglesias JIP, Urrutia MB, Navarro E, Alvarez-Jorna P, Lar-retxea X, Bougrier S, Heral M (1996) Variability of feed-ing processes in the cockle Cerastoderma edule (L.) inresponse to changes in seston concentration and compo-sition. J Exp Mar Biol Ecol 197: 121−143

Jones HF, Pilditch CA, Bruesewitz DA, Lohrer AM (2011)Sedimentary environment influences the effect of aninfaunal suspension feeding bivalve on estuarine ecosys-tem function. PLOS ONE 6: e27065

Joye SB, Hollibaugh JT (1995) Influence of sulfide inhibitionof nitrification on nitrogen regeneration in sediments.Science 270: 623−625

Kana TM, Darkangelo C, Hunt MD, Oldham JB, Bennett GE,Cornwell JC (1994) Membrane inlet mass spectrometerfor rapid high-precision determination of N2, O2, and Ar inenvironmental water samples. Anal Chem 66: 4166−4170

Kellogg ML, Cornwell JC, Owens MS, Paynter KT (2013)Denitrification and nutrient assimilation on a restoredoyster reef. Mar Ecol Prog Ser 480: 1−19

Kirby MX (2004) Fishing down the coast: historical expan-sion and collapse of oyster fisheries along continentalmargins. Proc Natl Acad Sci USA 101: 13096−13099

Kuypers MM, Marchant HK, Kartal B (2018) The microbialnitrogen-cycling network. Nat Rev Microbiol 16: 263−276

Liaw A, Wiener M (2002) Classification and regression byrandomForest. R News 2:18–22

Lohrer AM, Thrush SF, Gibbs MM (2004) Bioturbatorsenhance ecosystem function through complex biogeo-chemical interactions. Nature 431: 1092−1095

Lorenzen CJ (1967) Determination of chlorophyll andpheo-pig ments: spectrophotometric equations. LimnolOceanogr 12: 343−346

Mannino A, Montagna PA (1997) Small-scale spatial varia-tion of macrobenthic community structure. Estuaries 20: 159−173

McLeod IM, Parsons DM, Morrison MA, Le Port A, TaylorRB (2012) Factors affecting the recovery of soft-sedimentmussel reefs in the Firth of Thames, New Zealand. MarFreshw Res 63: 78−83

Middelburg JJ, Soetaert K, Herman PMJ, Heip CHR (1996)Denitrification in marine sediments: a model study.Global Biogeochem Cycles 10: 661−673

Newell RIE (2004) Ecosystem influences of natural and cul-tivated populations of suspension-feeding bivalve mol-luscs: a review. J Shellfish Res 23: 51−61

Newell RIE, Cornwell JC, Owens MS (2002) Influence ofsimulated bivalve biodeposition and microphytobenthoson sediment nitrogen dynamics: a laboratory study. Lim-nol Oceanogr 47: 1367−1379

Ni Longphuirt S, Lim JH, Leynaert A, Claquin P, Choy EJ,Kang CK, An S (2009) Dissolved inorganic nitrogenuptake by intertidal microphytobenthos: nutrient con-centrations, light availability and migration. Mar EcolProg Ser 379: 33−44

Nixon SW (1998) Enriching the sea to death. Sci Am 9: 48−53Nizzoli D, Welsh DT, Fano EA, Viaroli P (2006) Impact of

clam and mussel farming on benthic metabolism andnitrogen cycling, with emphasis on nitrate reductionpathways. Mar Ecol Prog Ser 315: 151−165

Norkko A, Hewitt JE, Thrush SF, Funnell T (2001) Benthic−pelagic coupling and suspension-feeding bivalves: linkingsite-specific sediment flux and biodeposition to benthiccommunity structure. Limnol Oceanogr 46: 2067−2072

O’Meara TA, Hewitt JE, Thrush SF, Douglas EJ, Lohrer AM(2020) Denitrification and the role of macrofauna acrossestuarine gradients in nutrient and sediment loading.Estuaries Coasts 43: 1394−1405

Olito C, White CR, Marshall DJ, Barneche DR (2017) Esti-mating monotonic rates from biological data using locallinear regression. J Exp Biol 220: 759−764

Paerl HW (1997) Coastal eutrophication and harmful algalblooms: importance of atmospheric deposition andground water as ‘new’ nitrogen and other nutrientsources. Limnol Oceanogr 42: 1154−1165

Paul LJ (2012) A history of the Firth of Thames dredge fish-ery for mussels: use and abuse of a coastal resource. NewZealand Aquatic Environment and Biodiversity Report94. Ministry of Agriculture and Forestry, Wellington

Peterson CH, Irlandi EA, Black R (1994) The crash in sus-pension-feeding bivalve populations (Katelysia spp.) inPrincess Royal Harbour: an unexpected consequence ofeutrophication. J Exp Mar Biol Ecol 176: 39−52

Piehler MF, Smyth AR (2011) Habitat-specific distinctions inestuarine denitrification affect both ecosystem functionand services. Ecosphere 2: art12

Pratt DR, Lohrer AM, Pilditch CA, Thrush SF (2014)Changes in ecosystem function across sedimentary gra-dients in estuaries. Ecosystems 17: 182−194

Prins TC, Small AC, Dame RF (1998) A review of the feed-backs between bivalve grazing and ecosystem pro-cesses. Aquat Ecol 31: 349−359

Quintana CO, Tang M, Kristensen E (2007) Simultaneousstudy of particle reworking, irrigation transport and reac-tion rates in sediment bioturbated by the polychaetesHetero mastus and Marenzelleria. J Exp Mar Biol Ecol 352: 392−406

R Core Team (2019) R: a language and environment for sta-tistical computing. R Foundation for Statistical Comput-ing, Vienna

Ray DG, Cahalan GD, Lendemer JC (2020) Factors influenc-ing the persistence of reindeer lichens (Cladonia sub-genus Cladina) within frequent-fire environments of theMid-Atlantic coastal plain, USA. Fire Ecol 16: 1

Rich JJ, Dale OR, Song B, Ward BB (2008) Anaerobic ammo-nium oxidation (anammox) in Chesapeake Bay sedi-ments. Microb Ecol 55: 311−320

Rysgaard S, Risgaard-Petersen N, Nielsen LP, Revsbech NP(1993) Nitrification and denitrification in lake and estuar-ine sediments measured by the 15N dilution technique andisotope pairing. Appl Environ Microbiol 59: 2093−2098

Santos IR, Eyre BD, Huettel M (2012) The driving forces ofporewater and groundwater flow in permeable coastalsediments: a review. Estuar Coast Shelf Sci 98: 1−15

Seitzinger SP (1988) Denitrification in freshwater andcoastal marine ecosystems: ecological and geochemicalsignificance. Limnol Oceanogr 33: 702−724

Seitzinger S, Harrison JA, Böhlke JK, Bouwman AF andothers (2006) Denitrification across landscapes andwaterscapes: a synthesis. Ecol Appl 16: 2064−2090

12

Sea et al.: Denitrification rates within restored mussel beds

Smith DiCarlo LA, DeBano SJ, Burrows S (2020) Arid grass-land bee communities: associated environmental variablesand responses to restoration. Restor Ecol 28: A54−A64

Smyth AR, Murphy AE, Anderson IC, Song B (2018) Differ-ential effects of bivalves on sediment nitrogen cycling ina shallow coastal bay. Estuaries Coasts 41: 1147−1163

Stief P (2013) Stimulation of microbial nitrogen cycling inaquatic ecosystems by benthic macrofauna: mecha-nisms and environmental implications. Biogeosciences10: 7829−7846

Sundbäck K, Miles A (2002) Role of microphytobenthos anddenitrification for nutrient turnover in embayments withfloating macroalgal mats: a spring situation. Aquat MicrobEcol 30: 91−101

Tay HW, Bryan KR, Pilditch CA, Park S, Hamilton DP (2012)Variations in nutrient concentrations at different timescales in two shallow tidally dominated estuaries. MarFreshw Res 63: 95−109

Therneau T, Atkinson B (2019). rpart: Recursive Partitioningand Regression Trees. R package version 4.1-15. https://CRAN.R-project.org/package=rpart

Thrush SF, Hewitt JE, Norkko A, Nicholls PE, Funnell GA,Ellis JI (2003) Habitat change in estuaries: predictingbroad-scale responses of intertidal macrofauna to sedi-ment mud content. Mar Ecol Prog Ser 263: 101−112

Thrush S, Hewitt J, Cummings V, Ellis J, Hatton C, LohrerA, Norkko A (2004) Muddy waters: elevating sedimentinput to coastal and estuarine habitats. Front Ecol Envi-ron 2: 299−306

Thrush SF, Townsend M, Hewitt JE, Davies K and others(2013) The many uses and values of estuarine ecosys-tems. Ecosystem services in New Zealand — conditionsand trends. Manaaki Whenua Press, Lincoln

Thrush SF, Hewitt JE, Gladstone-Gallagher RV, Savage Cand others (2021) Cumulative stressors reduce the self-regulating capacity of coastal ecosystems. Ecol Appl 31: e02223

Vieillard AM, Newell SE, Thrush SF (2020) Recovering frombias: a call for further study of underrepresented tropicaland low-nutrient estuaries. J Geophys Res Biogeosci 125: e2020JG005766

Ward EJ, MacDonald BA (1996) Pre-ingestive feeding be -haviors of two sub-tropical bivalves (Pinctada imbricataand Arca zebra): responses to an acute increase in sus-pended sediment concentration. Bull Mar Sci 59: 417−432

zu Ermgassen PSE, Thurstan RH, Corrales J, Alleway H andothers (2020) The benefits of bivalve reef restoration: aglobal synthesis of underrepresented species. AquatConserv 30: 2050−2065

13

Editorial responsibility: Robinson Fulweiler, Boston, Massachusetts, USA

Reviewed by: G. Banta, F. Roth and 1 anonymous referee

Submitted: August 27, 2020Accepted: April 13, 2021Proofs received from author(s): May 29, 2021