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Ecological Indicators 39 (2014) 134– 143

Contents lists available at ScienceDirect

Ecological Indicators

jou rn al hom epage: www.elsev ier .com/ locate /eco l ind

hysiological indices as indicators of ecosystem status in shellfishquaculture sites

. Filgueiraa,∗, T. Guyondeta, L.A. Comeaua, J. Grantb

Department of Fisheries and Oceans, Gulf Fisheries Centre, P.O. Box 5030, Science Branch, Moncton, Canada NB E1C 9B6Department of Oceanography, Dalhousie University, Halifax, Canada NS B3H 4R2

r t i c l e i n f o

rticle history:eceived 5 July 2013eceived in revised form 17 October 2013ccepted 3 December 2013

eywords:ytilus edulis

hysical–biogeochemical modelhlorophyll depletionhell growth ratequaculture

a b s t r a c t

The filtration activity of cultured mussels may exert a strong control on phytoplankton populations. Giventhat phytoplankton constitutes the base of marine food webs, carrying capacity in shellfish aquaculturesites has been commonly studied in terms of phytoplankton depletion. However, spatial and temporalvariability of phytoplankton concentration in coastal areas present a methodological constraint for usingphytoplankton depletion as an indicator in monitoring programs, and necessitates intensive field cam-paigns. The main goal of this study is to explore the potential of different bivalve performance indices foruse as alternatives to phytoplankton depletion as cost-effective indicators of carrying capacity. For that, afully spatial hydrodynamic–biogeochemical coupled model of Tracadie Bay, an intensive mussel cultureembayment located in Prince of Edward Island (Canada), has been constructed and scenario building hasbeen used to explore the relationship between phytoplankton depletion and bivalve performance. Ourunderlying premise is that overstocking of bivalves leads to increased competition for food resources,i.e. phytoplankton, which may ultimately have a significant effect on bivalve growth rate and perfor-mance. Following this working hypothesis, the relationships among bay-scale phytoplankton depletionand three bivalve physiological indices, one static, condition index, and two dynamic, tissue mass and

shell length growth rates, have been simulated. These three metrics present methodological advantagescompared to phytoplankton depletion for incorporation into monitoring programs. Although significantcorrelations among phytoplankton depletion and the three physiological indices have been observed,shell length growth rate is shown as the most sensitive indicator of carrying capacity, followed by tissuemass growth rate and then by condition index. These results demonstrate the potentiality of using bivalvephysiological measurements in monitoring programs as indicators of ecosystem status.

. Introduction

Legislative frameworks for ocean governance based on ancosystem perspective are an important development in the poli-ies of coastal nations (e.g. Oceans Act, 1996 – Canada). Suchrameworks instigate ocean management policies meant to safe-uard ecosystem services that humans require or value. One ofhe key requirements for the success of these policies is to mon-tor their effectiveness over time, i.e. evidence-based managementSutherland et al., 2004). Therefore, the development of appro-riate metrics and indicators constitutes a major and worldwide

riority in ocean management (e.g. Science for an Ocean Nation,013 – United States). This context provides the scientific com-unity with a strong incentive to develop robust, simple, and

∗ Corresponding author at: Department of Oceanography, Dalhousie University,alifax, Canada NS B3H 4R2. Tel.: +1 613 404 9683.

E-mail address: ramonf@dal.ca (R. Filgueira).

470-160X/$ – see front matter © 2013 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.ecolind.2013.12.006

© 2013 Elsevier Ltd. All rights reserved.

pragmatic indicators that can capture the effects of several environ-mental variables into a single metric (Borja et al., 2000). However,a challenging aspect is the precise definition of the limits at whichecosystem health is not compromised (Fisher et al., 2009). Froman ecosystem perspective, the definition of thresholds requires anunderstanding of ecosystem resilience, i.e. the capacity of a systemto absorb disturbance and reorganize while retaining essentiallythe same functions, structure, identity, and feedbacks (Walker et al.,2004).

Among coastal resource developments, aquaculture has becomeprominent worldwide, and concerns about its impact on bays andestuaries are widespread. Bivalve aquaculture in particular relieson phytoplankton as food for suspension-feeding clams, oysters,and mussels. Extensive farming of bivalves thus has potential toaffect the base of coastal food chains at the ecosystem level by

reducing the expected phytoplankton availability based on nutrientavailability (Meeuwig et al., 1998). Consequently, ecosystem-basedmanagement (EBM) in the context of bivalve aquaculture has beenassessed by estimating ecological carrying capacity, that is, the

R. Filgueira et al. / Ecological Indi

Ff

swTeracviaaacpsoTti

ib2tisaras

is a sub-basin located at the southwest side of Tracadie Bay where a

Fs

ig. 1. Scheme of natural variation in the context of ecological resilience (see textor further explanation).

tocking density at which some measure of ecosystem health isithin the bounds of natural variation (Grant and Filgueira, 2011).

his framework (Fig. 1) assumes that the natural variation ofcosystem variables is within tipping points beyond which theesilience of the system is exceeded and it reorganizes (Crowdernd Norse, 2008), compromising ecosystem health. Therefore, pre-autionary thresholds based on natural variation of environmentalariables are needed to guarantee ecosystem sustainability, thats, the capacity of the system to maintain its essential functionsnd processes in the long term. This framework has already beenpplied to the assessment of the ecological carrying capacity inn intensive mussel culture embayment; the framework and pre-autionary thresholds were based on the natural variability ofhytoplankton biomass, with idea that that cultivated musselshould not be allowed to graze primary producers down to a levelutside their natural variability range (Filgueira and Grant, 2009).his approach is attractive given that phytoplankton constituteshe primary step in marine food webs and that their preservations an important tenet of EBM (Crowder and Norse, 2008).

More recently, phytoplankton depletion has been incorporatednto a set of standards to demonstrate the environmental sustaina-ility of farming operations (Aquaculture Stewardship Council,012, Formerly WWF Bivalve Dialog, 2010). The monitoring of phy-oplankton depletion, however, does raise a series of pragmaticssues. In bivalve aquaculture areas phytoplankton concentrationhows (1) spatial variability that mainly depends on water currentsnd allocation of bivalves (Duarte et al., 2008) as well as (2) tempo-

al variability in the short and long term due to, for example, tidalnd seasonal effects, respectively (e.g. Grant et al., 2008). Theseources of variability become a methodological constraint, such

ig. 2. Map of Tracadie Bay including (A) location map in Eastern Canada and (B) locatiotations (inner and outer stations).

cators 39 (2014) 134– 143 135

that using phytoplankton depletion as an indicator in monitoringprograms requires extensive and high-resolution synoptic surveysto detect a meaningful depletion at the farm to bay scale (Cranfordet al., 2012). For these reasons, phytoplankton depletion is not agood operational indicator of ecosystem status and its applicationis limited to general bay-scale indices of depletion such as the DameIndex (Dame, 1996) or modeling exercises (e.g. Ferreira et al., 2007;Gibbs, 2007; Duarte et al., 2008) in which theoretical analyses arecarried out to determine the optimal sustainable standing stock ofcultivated biomass.

In the present study, we focussed our attention on the commonunderstanding that phytoplankton depletion has a direct effecton bivalve performance (Smaal et al., 2013). Since a reduction inphytoplankton availability depresses bivalve growth (Grant et al.,1993; Bacher et al., 2003), we hypothesized that bivalve perfor-mance could be used as an indicator of phytoplankton depletionand perhaps of ecosystem health. This hypothesis was investigatedby developing an ecosystem model of an extensive mussel cultureembayment, in which scenario building was used to explore therelationship of three bivalve performance metrics (condition index,tissue mass growth, and shell length growth) with phytoplanktondepletion. The ultimate goal of this study is to provide the scientificframework for using bivalve performance metrics as cost-effectiveindicators of ecosystem health.

2. Methods

2.1. Study area

Blue mussel (Mytilus edulis) farming in Prince Edward Island(PEI) Canada (Fig. 2A) is carried out using a longline system of sus-pended polyethylene ropes (Scarratt, 2000). Tracadie Bay (46◦23′N60◦59′W, Fig. 2B) is a small (13.8 km2 at low tide), shallow (max-imum depth 6 m) barrier beach inlet with predominantly diurnaltides with a range of 0.6 m. The embayment is located on the northshore of PEI and is open to the Gulf of St. Lawrence through twochannels, a main one located on the West side of the bay and asmall breach in the central part of the sand barrier. Winter Harbor

small river empties (≈1 m3 s−1; see also Cranford et al., 2007). Win-ter Bay and the inner part of Tracadie Bay are primarily used for spatcollection and adult mussel biomass is considered negligible. The

n of leases, sampling stations (L: tide gauge, C: current meter) and groundtruthing

136 R. Filgueira et al. / Ecological Indicators 39 (2014) 134– 143

F RMA,

d that r

ptaMiBscitvts

2

rtfecaca

AotwOsiwDPwP

ig. 3. Coupling scheme developed for Tracadie Bay: triangular finite grid created ineveloped in Simile, which combines the biogeochemical model and the submodel

recise distribution of mussel biomass within the bay is difficulto determine but the most current estimation establishes that theverage total biomass is around 5000 tons (Comeau et al., 2008).ussel density in the innermost area is estimated to be half that

n the central and northern parts (see Filgueira and Grant, 2009).ased on these estimates, the densities in the current aquaculturecenario were assumed to be 250 and 125 individuals per m2 in theentral/northern and innermost parts of the bay, respectively. Thenitial mussel shell length in the model was set at 35 mm with aotal wet weight of 3.33 g, which, in combination with the densityalues, equates to a total initial biomass of 3479 tons. On average,he final biomass within the bay was 5236 tons at the end of theimulation period.

.2. Hydrodynamic model

The finite element model RMA-10 (King, 1982) was used toeproduce water circulation within Tracadie Bay in response toidal, meteorological (wind and atmospheric pressure) and riverorcing. RMA-10 solves the Reynolds form of the Navier–Stokesquations for momentum, the continuity equation and aonvection–diffusion equation for transport of heat, salinity andny dissolved or suspended matter. The triangular mesh for Tra-adie Bay contained 2288 triangles, and 6434 connections betweendjacent triangles.

Instruments were moored during summer 2011 (June 2 tougust 3) at different locations both outside and inside the bay inrder to collect the necessary data to respectively force and validatehe model. Sea level fluctuations forcing the hydrodynamic modelere recorded using a tide gauge (Water Level Data HOBO Logger,nset Computer Corporation Inc., Bourne, MA, USA) at an outside

tation located 1.2 km north of the main inlet. Inner stations shownn Fig. 2B were equipped with HOBO tide gauges and two of them

ith current meters (Sontek Argonaut-XR, YSI Inc./Xylem Inc., San

iego, CA, USA and Workhorse Sentinel, Teledyne RD Instruments,oway, CA, USA) used for validation purposes. Meteorological dataere retrieved from the Environment Canada station located in St

eter’s, 25 km east of the study site. Freshwater discharge rates for

example of water exchange file delivered by Matlab and description of the structureeads the spatial topology and executes the hydrodynamics.

Winter River were also provided by Environment Canada (station01CC002).

For the present application, a two-dimensional vertically aver-aged representation of the system was used to capture the mainfeatures of water circulation under low freshwater discharges. Thevalidated model was then run under tidal and river forcing only toderive information on long term circulation.

2.3. Ecosystem model

The hydrodynamic model developed in RMA-10 was coupledto a biogeochemical model following a first order upwind schemeas described in Filgueira et al. (2012). The biogeochemical modelwas constructed using a configurable GUI-based software (Simile,http://www.simulistics.com) that allows explicit coupling betweenelements representing regions of the bay. The general scheme ofthis coupling process is presented in Fig. 3. The biogeochemicalmodel is based on a classical PNZ model (Kremer and Nixon (1978):Phytoplankton (P)–Nutrients (N)–Zooplankton (Z)) with the addi-tion of mussel (M) and detritus (D) submodels (Fig. 3). Given theminimal effect of zooplankton on the ecology of Tracadie Bay, thissubmodel was not considered in the simulations (Filgueira andGrant, 2009).

The model is characterized in terms of mg C m−3, with theexception of dissolved nutrients, which are expressed mg N m−3.The general model follows Grant et al. (1993, 2007, 2008), Dowd(1997, 2005) and Filgueira and Grant (2009) but the Scope ForGrowth mussel submodel used in these papers was substitutedby the Dynamic Energy Budget (DEB) model described in Roslandet al. (2009) and Filgueira et al. (2011). DEB is a mechanistictheory based on the assumption that assimilated energy is firststored in ‘reserves’ which in turn are used to fuel other metabolicprocesses, describing energy flow through organisms from assim-ilation to allocation to growth, reproduction and maintenance

(Filgueira et al., 2011). The direct interaction between musseland phytoplankton is carried out by the DEB ingestion submodel,which accounts for variations of temperature and it is estimatedas the maximal ingestion for a given mussel size multiplied by

R. Filgueira et al. / Ecological Indicators 39 (2014) 134– 143 137

Table 1Ecosystem model terms.

Term Definition Reference

dP/dt Phytoplantkton change rate (mgC m−3 d−1)Pgrowth Phytoplankton growth Eq. (7) in Grant et al. (2007)Pmortality Phytoplankton mortalityMgrazing Mussel grazing on phytoplanktonPmixing Exchange of phytoplankton with adjacent elements and/or far field

dN/dt Nitrogen change rate (mgN m−3 d−1)Nriver Nitrogen river discharge River discharge x River Nitrogen concentrationDremineralization Detritus reminiralization See Dowd (2005)Mexcretion Mussel nitrogen excretion Eq. (17) in Grant et al. (2007)Puptake Phytoplankton nitrogen uptake Eq. (15) in Grant et al. (2007)Nmixing Exchange of nitrogen with adjacent elements and/or far field

dD/dt Detritus change rate (mgC m−3 d−1)Dresuspension Detritus resuspension forced by wind See Filgueira and Grant (2009)Mfeces Mussel feces production Eq. (5) in Grant et al. (2007)Pmortality Phytoplankton mortality See aboveDsinking Detritus removal by sinking Eq. (5) in Grant et al. (2007)Dremineralization Detritus remineralization See textDmixing Exchange of detritus with adjacent elements

dM/dt Mussel change rate (mgC m−3 d−1)

ats1rtf

ai(aode

2

desDab4st

Mgrazing Mussel grazing on phytoplankton

Mexcretion Mussel nitrogen excretionMfeces Mussel feces production

Michaelis–Menten term. The Michaelis–Menten term includeshe half-saturation coefficient, XK, the only parameter that isite-specific in this version of mussel DEB, which was set up as.3 �g chla l−1. A sensitivity test was carried out to evaluate theesponse of the model to a change in this parameter. The differen-ial equations of the ecosystem model are as follows (see Table 1or a detailed description of the terms):

dP

dt= +Pgrowth − Pmortality − Mgrazing ± Pmixing (1)

dN

dt= +Nriver + Dremineralization + Mexcretion − Puptake ± Nmixing (2)

dD

dt= + Dresuspension + Mfeces + Pmortality − Dsinking

− Dremineralization ± Dmixing (3)

dM

dt= +Mgrazing − Mexcretion − Mfeces (4)

Given that this version of DEB only considers chlorophylls a food source for mussels, the detritus compartment onlynteracts with the other submodels via nutrient remineralizationDremineralization). Therefore the detritus submodel was simplifiednd prescribed as a forcing function to deliver Dremineralization basedn field measurements of seston rather than a dynamic balance asepicted in Eq. (3). A sensitivity test was performed to evaluate theffect of remineralization on the model performance.

.4. Boundary conditions and field data

The model was run from 1 August 2012 to 1 November 2012 (93ays) in order to avoid spawning periods commonly observed inarly summer. Chlorophyll and temperature time series were con-tructed for the simulation period using satellite remote sensing.aily time series of 4 km MODIS-Aqua chlorophyll were aver-ged within a region located just outside of Tracadie Bay defined

y the coordinates 62◦57′54′′W to 63◦4′49′′W and 46◦30′45′′N to6◦25′44′′N. Missing data were extrapolated using linear regres-ion. The same methodology was used to generate temperatureime series. Chlorophyll concentration was converted to carbon

DEB model (Rosland et al., 2009; Filgueira et al., 2011)

units assuming a carbon:chl of 50:1. Nutrient data for far field andriver were taken from Cranford et al. (2007) using an average valueof two years (June to November for 2002 and 2003). River flowwas obtained from the Environment Canada hydrometric database(http://www.wsc.ec.gc.ca). Wind data were taken from Dowd et al.(2001) and the time series was completed with data from CanadianWeather Office (http://www.climate.weatheroffice.ec.gc.ca) afterconfirming that the modulus of wind velocity was similar betweenthe two sources of data over a common period.

Chlorophyll and seston time series were also collected attwo sampling stations (Fig. 2B) during the studied period. Watersamples for chlorophyll analyses were collected in duplicate. Sam-ples were filtered through 25 mm Whatman GF/F filters, keptfrozen (−20 ◦C) until analysis, which was performed followingEPA Method 445.0. Total particulate matter (TPM) and Particulateorganic matter (POM) were measured gravimetrically on pre-ashed(500 ◦C, 4 h) 47 mm Whatman GF/F filters. Two replicates were col-lected at each sampling point. The filters were dried at 70 ◦C for 24 hand weighed to determine TPM. POM was determined after ashingfilters for 6 h at 500 ◦C.

2.5. Phytoplankton depletion

A phytoplankton depletion index (%) was calculated modifyingFilgueira and Grant (2009):

Phytoplankton depletion index = [Chla]i

[Chla]far field× 100 − 100 (5)

where [Chla]i and [Chla]far field are the chlorophyll concentration(�g chla l−1) in the ith element and far field, respectively. Valuesof this ratio below 0% indicate depletion and above 0% indicateenrichment of chlorophyll in the ith element compared to the farfield. An inter-annual coefficient of variation (CV) of chlorophyllconcentration was calculated for the August–November periodusing a multi-year (2002–2011) satellite remote sensing dataset,which resulted in a value of 27.5%. Consequently, a depletionindex of −27.5% (0–27.5%) was used as a threshold for acceptable

ecosystem-level effects. Similarly, a bay-scale depletion index wascalculated as follows:

Bay-scale depletion index =∑

i[Chla]i × Voli/Bay volume

[Chla]far field× 100 − 100 (6)

138 R. Filgueira et al. / Ecological Indicators 39 (2014) 134– 143

1) and

wv

2

(

C

c

�

w0

Fig. 4. Observed and predicted current speed (m s−

here Voli and Bay volume are the volume (l) of element i and theolume of the bay, respectively.

.6. Bivalve performance indices

Condition index was calculated according to Lucas and Beninger1985):

I = dry meat weightdry shell weight

× 100 (7)

Tissue mass and shell length specific growth rates (�, d−1) werealculated according to Clausen and Riisgård (1996):( )

= lnXt

X0× t−1 (8)

here Xt and X0 are the average dry weight or shell length on Day and Day t, respectively.

water level (m) in sampling stations LC1 and LC3.

In order to identify which of these indices was the most sensitivewhen farming intensity approached the system’s carrying capac-ity, the percentage of change of each indicator was calculated fora mussel standing stock biomass ranging from 4250 to 5250 tons.The sustainable 4775 tons is centered within this range (see belowfor specific calculations). The percentage of change was calculatedas follows:

Percentage of change = |X5250 − X4250|X5250 + X4250

× 200 (9)

where X5250 and X4250 are the condition index, tissue mass growth

rate or shell length growth rate for the 5250 and 4250 ton scenarios,respectively.

R. Filgueira et al. / Ecological Indicators 39 (2014) 134– 143 139

Fig. 5. Observed chlorophyll (�g l−1) in the far field (gray area) and groundtruthingstations (red and black dots for inner and outer stations, respectively) as well aspredicted chlorophyll time series in both stations (red and black continuous linesfc

3

3

caorpimrbsautwmMb(aft

ctrv((mgv(becficoritpe

Table 2Tissue mass and shell length growth rates calculated from Waite et al. (2005), MusselMonitoring Program (MMP) and this modeling exercise.

Specific growth rate, �

Tissue mass (d−1) Shell length (d−1)

Waite et al.(2005)

Average 0.0077 0.0017SD 0.0026 0.0008

MMP Average 0.0087 n.a.SD n.a. n.a.

Under the current aquaculture scenario (Fig. 7), there are periods of

or inner and outer stations, respectively). (For interpretation of the references toolour in this figure legend, the reader is referred to the web version of this article.)

. Results

.1. Groundtruthing

A comparison of observations and model results during fouronsecutive tidal cycles is shown on Fig. 4 for the two stations (LC1nd LC3) equipped with both tide gauges and current meters. Anverall good agreement is reached for both currents along theirespective principal axis and water level fluctuations. While thehase of the current time series is well reproduced at both locations,

ts range seems slightly underestimated at LC1. This discrepancyay in part be explained by the strong spatial gradients in cur-

ent velocity that may exist in nature and cannot be accounted fory the model grid. Moreover, the conjunction of sandy sediment,trong tidal currents and wave action in the inlet area contributes to

very dynamic topography/bathymetry. Sufficiently accurate andp to date bathymetric data were not available to better representhis area of the model domain. Nonetheless, observed and predictedater level time series are in good agreement suggesting that theodel captures the main features of the hydrodynamics of the bay.oreover, tidal propagation within the system is well reproduced

y the model as shown by the results of the harmonic analysisForeman, 1977) of observed and predicted water level time seriest all inner stations (Table 3). This result is of particular importanceor the present work as tides were the main forcing considered inhe coupling of the hydrodynamic and biogeochemical models.

Groundtruthing of the biogeochemical model was carried out byomparing modeled and observed values of chlorophyll concentra-ion at two sampling stations (Fig. 2B) and mussel specific growthate within the bay. For chlorophyll, the modeled and observedalues through time at the outer station are in good agreementFig. 5). However, there is a lack of agreement at the inner stationFig. 5), where the modeled values are below the observations in

id September. There is not an obvious explanation for this sin-ular discrepancy but it could be related to the high frequencyariation in chlorophyll concentration driven by tides. Grant et al.2008) showed a significant variation in chlorophyll concentrationetween consecutive low and high tides in Tracadie Bay. How-ver, the ecosystem model is being forced by daily time series andonsequently the outputs are expressed as daily averages but theeld measurements correspond to a precise tidal situation thatannot be simulated using this model. For this reason, the sec-nd groundtruthing process based on bivalve growth rate is moreobust than punctuated chlorophyll measurements. Bivalve growthntegrates the effects of changing environmental conditions over

ime and consequently provides a better assessment of the modelerformance in long-term simulations, avoiding high frequencyvents such as tidal influence on chlorophyll concentration. The

This study Average 0.0085 0.0014SD 0.0011 0.0003

interval of confidence of modeled specific growth rate in weightand length (Table 2) included the values observed by Waite et al.(2005) in a two-year sampling (1998 and 1999) for the same timeof the year. These results confirm that the model is able to accu-rately simulate mussel growth in the bay. The same good agreementbetween model and observations was found with results collectedover the same time frame of this modeling exercise (August toNovember 2012) by the Mussel Monitoring Program carried out bythe Department of Fisheries, Aquaculture and Rural Development(PEI Government, http://www.gov.pe.ca/fard/) (Table 2).

3.2. Sensitivity tests

Sensitivity tests (Table 4) were performed for the followingparameters: XK, bivalve mortality, phytoplankton growth rate, phy-toplankton mortality and detritus remineralization (Dremineralization).Two scenarios were run for each parameter, i.e. by increasingand decreasing the parameter value by 10%. The response ofthe model to these parameters was evaluated by observing therelative change in these simulations compared to current sce-nario values. The following response variables were analyzed: finaltotal bivalve biomass, final bivalve tissue dry weight, length andcondition index as well as phytoplankton depletion. The maxi-mum change observed for these response variables, 6.07%, was forphytoplankton depletion when remineralization was reduced by10%. Phytoplankton depletion was in all cases the most affectedresponse variable. Bivalve biomass and dry weight were only sen-sitive in concert with mineralization (±2.7%). Bivalve length and CIwere the least sensitive variables with a maximum impact of 0.89%on bivalve performance.

3.3. Phytoplankton depletion and associated biological indices

The time averaged spatial distribution of the depletion indexis represented using median values instead of mean values,since depletion tends bias the distribution of values compared toenrichment (Fig. 6). The spatial distribution demonstrates strongdepletion in the northeast part of the bay, where the majority of themussels are located. In contrast, the area close to the main channelconnecting to the open ocean is enriched in chlorophyll. Finally,Winter Bay, as well as the central and inner parts of Tracadie Bay,could be considered as a homogenous body of water with similardepletion index values.

The depletion index changes through time depending on theongoing dynamics inside the bay and the far field conditions. Thisvariation through time can be observed using a bay-scale depletionindex (Eq. (6)), which provides insight about the overall perfor-mance of the bay rather than a single value for each element.

time when the bay-scale chlorophyll depletion is below the criticalthreshold of sustainability based on the −27.5% natural variationstandard (see above). There are also periods of time when the bay

140 R. Filgueira et al. / Ecological Indicators 39 (2014) 134– 143

Table 3Harmonic analysis of observed and predicted water level time series with 95% confidence intervals at all sampled stations inside the model domain. Results are shown forthe four main tidal constituents (O1, K1, M2 and S2).

Amplitude (m) Phase (◦) Amplitude (m) Phase (◦)

Observed Predicted Observed Predicted Observed Predicted Observed Predicted

O1 K1LC1 0.15 ± 0.02 0.15 ± 0.02 241.1 ± 6.8 242.1 ± 5.6 LC1 0.16 ± 0.02 0.16 ± 0.02 267.6 ± 7.0 268.8 ± 4.8L2 0.15 ± 0.02 0.15 ± 0.01 246.6 ± 6.6 249.9 ± 5.6 L2 0.16 ± 0.02 0.16 ± 0.01 273.2 ± 6.4 277.3 ± 5.2LC3 0.15 ± 0.02 0.15 ± 0.01 257.4 ± 6.0 260.0 ± 6.4 LC3 0.16 ± 0.02 0.15 ± 0.02 284.8 ± 6.3 289.7 ± 5.5L4 0.15 ± 0.02 0.15 ± 0.02 260.0 ± 6.2 268.4 ± 6.1 L4 0.16 ± 0.02 0.15 ± 0.02 287.8 ± 6.0 297.2 ± 5.2L5 0.15 ± 0.02 0.14 ± 0.02 256.7 ± 7.1 259.1 ± 7.4 L5 0.16 ± 0.02 0.16 ± 0.02 282.8 ± 6.4 290.6 ± 6.3

M2 S2LC1 0.15 ± 0.00 0.15 ± 0.01 194.2 ± 1.6 194.6 ± 1.8 LC1 0.04 ± 0.00 0.04 ± 0.01 262.5 ± 5.9 266.1 ± 7.2L2 0.14 ± 0.00 0.13 ± 0.01 201.1 ± 1.5 205.8 ± 1.9 L2 0.04 ± 0.00 0.03 ± 0.00 275.5 ± 5.8 284.3 ± 7.2LC3 0.13 ± 0.01 0.12 ± 0.01 218.5 ± 1.8 223.5 ± 2.7 LC3 0.03 ± 0.01 0.03 ± 0.01 308.3 ± 9.2 315.1 ± 14.0L4 0.13 ± 0.01 0.12 ± 0.01 223.8 ± 2.0 238.1 ± 2.5 L4 0.03 ± 0.01 0.03 ± 0.01 314.8 ± 8.1 331.4 ± 13.0L5 0.13 ± 0.01 0.12 ± 0.01 216.9 ± 2.0 222.1 ± 4.2 L5 0.03 ± 0.01 0.03 ± 0.01 305.7 ± 11.7 314.4 ± 16.4

Table 4Sensitivity test of model parameters on bivalve performance and chlorophyll depletion index.

Parameter Parameterchange (%)

Percentage of change in response variable (%)

Bivalvebiomass

Bivalve dryweight

Bivalvelength

Bivalve CI Chlorophylldepletion

XK +10 −0.55 −0.89 −0.27 0.03 2.88−10 0.43 0.85 0.27 −0.09 −4.43

Bivalve mortality +10 −0.42 0.12 0.03 0.04 0.24−10 0.42 −0.12 −0.03 −0.04 −0.24

Phytoplankton growth rate +10 0.83 0.58 0.12 0.22 −0.13−10 −1.16 −0.84 −0.16 −0.35 −2.77

Phytoplankton mortality +10 −0.13 −0.13 −0.04 0.00 −0.41−10 0.13 0.13 0.04 −0.01 0.41

ad−i

i5UomT

Fs

Dremineralization +10 2.70

−10 −2.73

cts as a reservoir of chlorophyll. However, the median bay-scaleepletion index is −34.2%, slightly below the threshold value of27.5%. This result suggests that the current level of farming activ-

ty is very close to the ecological carrying capacity of the system.The mussel standing stock biomass that would cause a depletion

ndex of −27.5% is estimated at 4775 tons (Fig. 8A), which is about00 tons lower than the estimated current scenario (5236 tons).

sing the 4775 tons as a reference point, we calculated the thresh-ld of sustainability in terms of condition index (Fig. 8B), tissueass growth rate (Fig. 8C) and shell length growth rate (Fig. 8D).

he outcome shows that a depletion index of −27.5% would lead

ig. 6. Median values of depletion index (%, see Eq. (5)) for each element over theimulated period in current aquaculture scenario.

2.64 0.68 0.45 5.40−2.72 −0.72 −0.48 −6.07

to a condition of 24.7%, a tissue growth rate of 0.0092 d−1 and ashell length growth rate of 0.0016 d−1. The percentage of change ofthese indices when farming intensity approaches the system’s car-rying capacity is 1.6, 13.7 and 21.3% for condition index, tissue massgrowth rate, and shell length growth rate, respectively. This find-ing suggests that shell growth rate is the most sensitive indicator ofecosystem sustainability because a small change in stocked musselbiomass translates into a large change in the response variable.

4. Discussion

Phytoplankton (or chlorophyll) depletion has been commonlyused to assess carrying capacity and husbandry techniques inbivalve aquaculture sites (e.g. Ferreira et al., 2007; Gibbs, 2007;Duarte et al., 2008), including Tracadie Bay (Dowd, 2003, 2005;

Fig. 7. Bay-scale depletion index (%, see Eq. (6)) through time in current aquacul-ture scenario. Dashed line represents the depletion index sustainable threshold, i.e.−27.5%, and gray area observed far field chlorophyll concentration (�g l−1).

R. Filgueira et al. / Ecological Indicators 39 (2014) 134– 143 141

F omassi culated ex (C

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ig. 8. Median bay-scale depletion index (%, see Eq. (6)) in different standing stock bindex sustainable threshold, i.e. −27.5%, as well as relative biomass production calay); (B, C and D) thresholds of sustainability re-calculated in terms of condition ind

rant et al., 2005, 2008; Comeau et al., 2008; Filgueira and Grant,009). However, to the authors’ knowledge, phytoplankton deple-ion has never been used in a monitoring program. Yet monitoringrograms are key components in the process of marine spa-ial planning in order to evaluate the effectiveness of planning

easures and to provide feedback for new planning stages, i.e.vidence-based management (Sutherland et al., 2004). The strongelationship between phytoplankton depletion and bivalve perfor-ance (Cranford et al., 2012; Smaal et al., 2013) prompted us to

xamine the value of the latter as a proxy of phytoplankton deple-ion and ecosystem health.

As a starting point, we needed to objectively define sustainablehresholds for phytoplankton depletion. Phytoplankton depletionas thus defined as the ratio between the chlorophyll concentra-

ion inside the bay and the concentration in the far field. The usef far field data is motivated by the difficulty in defining base-ine conditions inside the bay, affected not only by aquaculturectivity but by river discharge, which is highly enriched in nutri-nts due to intense agricultural activity in the area. In fact, bivalvelter-feeding activity in the bay could exert a positive effect byitigating eutrophication (Coen et al., 2007), an ecosystem service

hat should also be considered in marine spatial planning (e.g.ee Lindahl, 2011). We used a framework based on ecologicalesilience employing natural variation in chlorophyll as a bench-ark for sustainability. Common to all applications of optimization

n conservation ecology is the quantitative identification of a con-ervation problem, that is, the precise definition of the limits athich ecosystem health is not compromised (Duarte, 2003; Fisher

t al., 2009). The framework used in this study (Fig. 1) objec-ively establishes a threshold assumed to be below the tippingoints beyond which the resilience of the system is exceeded and

t reorganizes (Crowder and Norse, 2008). This approach avoids theeasurement of these tipping points, which are inherently difficult

o determine, but uses a precautionary approach to management.Once the depletion threshold was established, different bivalve

erformance indices were studied. Bivalve physiological mea-urements have commonly been used in monitoring given theirensitivity to environmental conditions, stress and pollution (Baynend Newell, 1983; Widdows and Johnson, 1988). Several static

scenarios and (A) sustainable standing stock biomass calculated based on depletiond as the ratio final biomass (biomass at t = 93 day)/initial biomass (biomass at t = 0I, %), tissue mass growth rate (d−1) and shell length growth rate (d−1), respectively.

measurements such as nucleic acid ratios (e.g. Norkko et al., 2005),scope for growth (e.g. Smaal and Widdows, 1994), and morpho-metric indices (e.g. Sasikumar and Krishnakumar, 2011) have beenused for this purpose. Dynamic indices, such as bivalve growth,are particularly interesting because they integrate the effect ofenvironmental conditions over significant periods of time (Lucasand Beninger, 1985). Intra-specific competition for food and phy-toplankton depletion have direct effects on bivalve performanceand mortality (self-thinning, Fréchette and Lefaivre, 1995). Thiscause–effect relationship is widely recognized in the literature (e.g.Grant et al., 1993; Alunno-Bruscia et al., 2000; Bacher et al., 2003;Cranford et al., 2012) but to our knowledge the present paper pro-vides the first assessment of bivalve performance in terms of itsusefulness as an indicator of carrying capacity. In our assessment,the extent of phytoplankton depletion was calculated for differ-ent stocking stocks of cultivated mussels. The extent of depletionwas then related to three physiological indices: one static (condi-tion index), and two dynamic (tissue mass and shell length growthrates).

Condition index was a priori selected as the ideal indicatorbecause of its common use among farmers as an indicator of prod-uct quality (Orban et al., 2002). Recently, condition index has beenincluded in a monitoring program to evaluate carrying capacityin mussel farms (BAP, 2013), but without connection to any sus-tainability threshold. In a previous meta-analysis, Filgueira et al.(2013) identified a significant relationship between standing stockbiomass and condition index of mussels and oysters. The widerange of conditions included in this meta-analysis allowed for thedetermination of a relationship between biomass and conditionindex, which was also observed in the current study (Fig. 8A).Recently, Smaal et al. (2013) also observed a significant relation-ship between condition index of harvested mussels and bivalvestock in the Oosterschelde estuary. However, according to simula-tions in the present paper, the percentage change in condition indexwith standing stock biomass that matched a sustainable thresh-

old is the lowest (1.6%) of the three tested indicators. In addition,this percentage of change seems insufficient to accommodate intra-specific variation in condition index as well as methodological errorinherent in the measurement. The most sensitive indicator was

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42 R. Filgueira et al. / Ecologic

hell length growth rate (Fig. 8D), showing a 21.3% change nearhe standing stock biomass that matched the sustainable thresh-ld. In terms of monitoring methodology, shell length growth rateequires two simple shell length measurements over a time inter-al, and is non-destructive. Tissue mass growth rate was the secondest indicator with a percentage of change of 13.7% (Fig. 8B). This

ndicator requires two samplings over time followed by laboratoryork (tissue drying, weight measurements).

. Conclusions

In conclusion, this paper demonstrated the potential of usingivalve physiological measurements as indicators of ecosystemtatus. However, further considerations are necessary to standard-ze the use of these indicators. For instance one should take intoccount that the rate of physiological changes vary seasonallyLi et al., 2009; Pogoda et al., 2011) and according to ontogenySukhotin et al., 2002). Perhaps an ideal design for a monitor-ng program uses juveniles and a focus on the season in whichivalve growth is at a maximum. Such a strategy would avoid the

nherent effects of the reproductive cycle and minimize method-logical errors by maximizing shell length growth values. Anothertandardization consideration would be to explore intra-specificariation and confidence limits of bivalve populations in order toetermine the precision of the measurements. Such considerationsay ultimately show that shell length growth is a robust and cost-

ffective indicator of ecosystem status, which is crucial for theiability of monitoring programs (Borja and Elliott, 2013). Suchndicators are essential not only for improving our understandingf the functioning of bivalves in coastal marine ecosystems (Norkkond Thrush, 2006) but also for generating information that cane used as a benchmark for adaptive management (Halpern et al.,008; Polasky et al., 2011).

cknowledgements

The authors are sincerely grateful to Thomas Landry (DFO Gulf)or his valuable feedback in early stages of this project. We thankémi Sonier and Tina Sonier (DFO Gulf) for their field and laboratoryssistance. The work presented in this paper was funded by Depart-ent of Fisheries and Oceans of Canada (Program for Aquaculture

egulatory Research, PARR project 2011-Z-22).

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