Physiological indices as indicators of ecosystem status in shellfish aquaculture sites

Ecological Indicators 39 (2014) 134–143

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Physiological indices as indicators of ecosystem status in shellfish aquaculture sites R. Filgueira a,∗ , T. Guyondet a , L.A. Comeau a , J. Grant b a b

Department of Fisheries and Oceans, Gulf Fisheries Centre, P.O. Box 5030, Science Branch, Moncton, Canada NB E1C 9B6 Department of Oceanography, Dalhousie University, Halifax, Canada NS B3H 4R2

a r t i c l e

i n f o

Article history: Received 5 July 2013 Received in revised form 17 October 2013 Accepted 3 December 2013 Keywords: Mytilus edulis Physical–biogeochemical model Chlorophyll depletion Shell growth rate Aquaculture

a b s t r a c t The filtration activity of cultured mussels may exert a strong control on phytoplankton populations. Given that phytoplankton constitutes the base of marine food webs, carrying capacity in shellfish aquaculture sites has been commonly studied in terms of phytoplankton depletion. However, spatial and temporal variability of phytoplankton concentration in coastal areas present a methodological constraint for using phytoplankton depletion as an indicator in monitoring programs, and necessitates intensive field campaigns. The main goal of this study is to explore the potential of different bivalve performance indices for use as alternatives to phytoplankton depletion as cost-effective indicators of carrying capacity. For that, a fully spatial hydrodynamic–biogeochemical coupled model of Tracadie Bay, an intensive mussel culture embayment located in Prince of Edward Island (Canada), has been constructed and scenario building has been used to explore the relationship between phytoplankton depletion and bivalve performance. Our underlying 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 performance. Following this working hypothesis, the relationships among bay-scale phytoplankton depletion and 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 advantages compared to phytoplankton depletion for incorporation into monitoring programs. Although significant correlations 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 tissue mass growth rate and then by condition index. These results demonstrate the potentiality of using bivalve physiological measurements in monitoring programs as indicators of ecosystem status. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction Legislative frameworks for ocean governance based on an ecosystem perspective are an important development in the policies of coastal nations (e.g. Oceans Act, 1996 – Canada). Such frameworks instigate ocean management policies meant to safeguard ecosystem services that humans require or value. One of the key requirements for the success of these policies is to monitor their effectiveness over time, i.e. evidence-based management (Sutherland et al., 2004). Therefore, the development of appropriate metrics and indicators constitutes a major and worldwide priority in ocean management (e.g. Science for an Ocean Nation, 2013 – United States). This context provides the scientific community with a strong incentive to develop robust, simple, and

∗ Corresponding author at: Department of Oceanography, Dalhousie University, Halifax, Canada NS B3H 4R2. Tel.: +1 613 404 9683. E-mail address: [email protected] (R. Filgueira). 1470-160X/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2013.12.006

pragmatic indicators that can capture the effects of several environmental variables into a single metric (Borja et al., 2000). However, a challenging aspect is the precise definition of the limits at which ecosystem health is not compromised (Fisher et al., 2009). From an ecosystem perspective, the definition of thresholds requires an understanding of ecosystem resilience, i.e. the capacity of a system to absorb disturbance and reorganize while retaining essentially the same functions, structure, identity, and feedbacks (Walker et al., 2004). Among coastal resource developments, aquaculture has become prominent worldwide, and concerns about its impact on bays and estuaries are widespread. Bivalve aquaculture in particular relies on phytoplankton as food for suspension-feeding clams, oysters, and mussels. Extensive farming of bivalves thus has potential to affect the base of coastal food chains at the ecosystem level by reducing the expected phytoplankton availability based on nutrient availability (Meeuwig et al., 1998). Consequently, ecosystem-based management (EBM) in the context of bivalve aquaculture has been assessed by estimating ecological carrying capacity, that is, the

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Fig. 1. Scheme of natural variation in the context of ecological resilience (see text for further explanation).

stocking density at which some measure of ecosystem health is within the bounds of natural variation (Grant and Filgueira, 2011). This framework (Fig. 1) assumes that the natural variation of ecosystem variables is within tipping points beyond which the resilience of the system is exceeded and it reorganizes (Crowder and Norse, 2008), compromising ecosystem health. Therefore, precautionary thresholds based on natural variation of environmental variables are needed to guarantee ecosystem sustainability, that is, the capacity of the system to maintain its essential functions and processes in the long term. This framework has already been applied to the assessment of the ecological carrying capacity in an intensive mussel culture embayment; the framework and precautionary thresholds were based on the natural variability of phytoplankton biomass, with idea that that cultivated mussels should not be allowed to graze primary producers down to a level outside their natural variability range (Filgueira and Grant, 2009). This approach is attractive given that phytoplankton constitutes the primary step in marine food webs and that their preservation is an important tenet of EBM (Crowder and Norse, 2008). More recently, phytoplankton depletion has been incorporated into a set of standards to demonstrate the environmental sustainability of farming operations (Aquaculture Stewardship Council, 2012, Formerly WWF Bivalve Dialog, 2010). The monitoring of phytoplankton depletion, however, does raise a series of pragmatic issues. In bivalve aquaculture areas phytoplankton concentration shows (1) spatial variability that mainly depends on water currents and allocation of bivalves (Duarte et al., 2008) as well as (2) temporal variability in the short and long term due to, for example, tidal and seasonal effects, respectively (e.g. Grant et al., 2008). These sources of variability become a methodological constraint, such

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that using phytoplankton depletion as an indicator in monitoring programs requires extensive and high-resolution synoptic surveys to detect a meaningful depletion at the farm to bay scale (Cranford et al., 2012). For these reasons, phytoplankton depletion is not a good operational indicator of ecosystem status and its application is limited to general bay-scale indices of depletion such as the Dame Index (Dame, 1996) or modeling exercises (e.g. Ferreira et al., 2007; Gibbs, 2007; Duarte et al., 2008) in which theoretical analyses are carried out to determine the optimal sustainable standing stock of cultivated biomass. In the present study, we focussed our attention on the common understanding that phytoplankton depletion has a direct effect on bivalve performance (Smaal et al., 2013). Since a reduction in phytoplankton availability depresses bivalve growth (Grant et al., 1993; Bacher et al., 2003), we hypothesized that bivalve performance could be used as an indicator of phytoplankton depletion and perhaps of ecosystem health. This hypothesis was investigated by developing an ecosystem model of an extensive mussel culture embayment, in which scenario building was used to explore the relationship of three bivalve performance metrics (condition index, tissue mass growth, and shell length growth) with phytoplankton depletion. The ultimate goal of this study is to provide the scientific framework for using bivalve performance metrics as cost-effective indicators 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 suspended polyethylene ropes (Scarratt, 2000). Tracadie Bay (46◦ 23 N 60◦ 59 W, Fig. 2B) is a small (13.8 km2 at low tide), shallow (maximum depth 6 m) barrier beach inlet with predominantly diurnal tides with a range of 0.6 m. The embayment is located on the north shore of PEI and is open to the Gulf of St. Lawrence through two channels, a main one located on the West side of the bay and a small breach in the central part of the sand barrier. Winter Harbor is a sub-basin located at the southwest side of Tracadie Bay where a small river empties (≈1 m3 s−1 ; see also Cranford et al., 2007). Winter Bay and the inner part of Tracadie Bay are primarily used for spat collection and adult mussel biomass is considered negligible. The

Fig. 2. Map of Tracadie Bay including (A) location map in Eastern Canada and (B) location of leases, sampling stations (L: tide gauge, C: current meter) and groundtruthing stations (inner and outer stations).

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Fig. 3. Coupling scheme developed for Tracadie Bay: triangular finite grid created in RMA, example of water exchange file delivered by Matlab and description of the structure developed in Simile, which combines the biogeochemical model and the submodel that reads the spatial topology and executes the hydrodynamics.

precise distribution of mussel biomass within the bay is difficult to determine but the most current estimation establishes that the average total biomass is around 5000 tons (Comeau et al., 2008). Mussel density in the innermost area is estimated to be half that in the central and northern parts (see Filgueira and Grant, 2009). Based on these estimates, the densities in the current aquaculture scenario were assumed to be 250 and 125 individuals per m2 in the central/northern and innermost parts of the bay, respectively. The initial mussel shell length in the model was set at 35 mm with a total wet weight of 3.33 g, which, in combination with the density values, equates to a total initial biomass of 3479 tons. On average, the final biomass within the bay was 5236 tons at the end of the simulation period. 2.2. Hydrodynamic model The finite element model RMA-10 (King, 1982) was used to reproduce water circulation within Tracadie Bay in response to tidal, meteorological (wind and atmospheric pressure) and river forcing. RMA-10 solves the Reynolds form of the Navier–Stokes equations for momentum, the continuity equation and a convection–diffusion equation for transport of heat, salinity and any dissolved or suspended matter. The triangular mesh for Tracadie Bay contained 2288 triangles, and 6434 connections between adjacent triangles. Instruments were moored during summer 2011 (June 2 to August 3) at different locations both outside and inside the bay in order to collect the necessary data to respectively force and validate the model. Sea level fluctuations forcing the hydrodynamic model were recorded using a tide gauge (Water Level Data HOBO Logger, Onset Computer Corporation Inc., Bourne, MA, USA) at an outside station located 1.2 km north of the main inlet. Inner stations shown in Fig. 2B were equipped with HOBO tide gauges and two of them with current meters (Sontek Argonaut-XR, YSI Inc./Xylem Inc., San Diego, CA, USA and Workhorse Sentinel, Teledyne RD Instruments, Poway, CA, USA) used for validation purposes. Meteorological data were retrieved from the Environment Canada station located in St Peter’s, 25 km east of the study site. Freshwater discharge rates for

Winter River were also provided by Environment Canada (station 01CC002). For the present application, a two-dimensional vertically averaged representation of the system was used to capture the main features of water circulation under low freshwater discharges. The validated model was then run under tidal and river forcing only to derive information on long term circulation. 2.3. Ecosystem model The hydrodynamic model developed in RMA-10 was coupled to a biogeochemical model following a first order upwind scheme as described in Filgueira et al. (2012). The biogeochemical model was constructed using a configurable GUI-based software (Simile, http://www.simulistics.com) that allows explicit coupling between elements representing regions of the bay. The general scheme of this coupling process is presented in Fig. 3. The biogeochemical model is based on a classical PNZ model (Kremer and Nixon (1978): Phytoplankton (P)–Nutrients (N)–Zooplankton (Z)) with the addition of mussel (M) and detritus (D) submodels (Fig. 3). Given the minimal effect of zooplankton on the ecology of Tracadie Bay, this submodel was not considered in the simulations (Filgueira and Grant, 2009). The model is characterized in terms of mg C m−3 , with the exception 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 For Growth mussel submodel used in these papers was substituted by the Dynamic Energy Budget (DEB) model described in Rosland et al. (2009) and Filgueira et al. (2011). DEB is a mechanistic theory based on the assumption that assimilated energy is first stored in ‘reserves’ which in turn are used to fuel other metabolic processes, describing energy flow through organisms from assimilation to allocation to growth, reproduction and maintenance (Filgueira et al., 2011). The direct interaction between mussel and phytoplankton is carried out by the DEB ingestion submodel, which accounts for variations of temperature and it is estimated as the maximal ingestion for a given mussel size multiplied by

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Table 1 Ecosystem model terms. Term

Definition

dP/dt Pgrowth Pmortality Mgrazing Pmixing

Phytoplantkton change rate (mgC m−3 d−1 ) Phytoplankton growth Phytoplankton mortality Mussel grazing on phytoplankton Exchange of phytoplankton with adjacent elements and/or far field

Reference

dN/dt Nriver Dremineralization Mexcretion Puptake Nmixing

Nitrogen change rate (mgN m−3 d−1 ) Nitrogen river discharge Detritus reminiralization Mussel nitrogen excretion Phytoplankton nitrogen uptake Exchange of nitrogen with adjacent elements and/or far field

River discharge x River Nitrogen concentration See Dowd (2005) Eq. (17) in Grant et al. (2007) Eq. (15) in Grant et al. (2007)

dD/dt Dresuspension Mfeces Pmortality Dsinking Dremineralization Dmixing

Detritus change rate (mgC m−3 d−1 ) Detritus resuspension forced by wind Mussel feces production Phytoplankton mortality Detritus removal by sinking Detritus remineralization Exchange of detritus with adjacent elements

See Filgueira and Grant (2009) Eq. (5) in Grant et al. (2007) See above Eq. (5) in Grant et al. (2007) See text

dM/dt Mgrazing Mexcretion Mfeces

Mussel change rate (mgC m−3 d−1 ) Mussel grazing on phytoplankton Mussel nitrogen excretion Mussel feces production

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

a Michaelis–Menten term. The Michaelis–Menten term includes the half-saturation coefficient, XK , the only parameter that is site-specific in this version of mussel DEB, which was set up as 1.3 ␮g chla l−1 . A sensitivity test was carried out to evaluate the response of the model to a change in this parameter. The differential equations of the ecosystem model are as follows (see Table 1 for a detailed description of the terms): dP = +Pgrowth − Pmortality − Mgrazing ± Pmixing dt

(1)

dN = +Nriver + Dremineralization + Mexcretion − Puptake ± Nmixing dt

(2)

dD = + Dresuspension + Mfeces + Pmortality − Dsinking dt − Dremineralization ± Dmixing dM = +Mgrazing − Mexcretion − Mfeces dt

Eq. (7) in Grant et al. (2007)

(3)

(4)

Given that this version of DEB only considers chlorophyll as a food source for mussels, the detritus compartment only interacts with the other submodels via nutrient remineralization (Dremineralization ). Therefore the detritus submodel was simplified and prescribed as a forcing function to deliver Dremineralization based on field measurements of seston rather than a dynamic balance as depicted in Eq. (3). A sensitivity test was performed to evaluate the effect of remineralization on the model performance. 2.4. Boundary conditions and field data The model was run from 1 August 2012 to 1 November 2012 (93 days) in order to avoid spawning periods commonly observed in early summer. Chlorophyll and temperature time series were constructed for the simulation period using satellite remote sensing. Daily time series of 4 km MODIS-Aqua chlorophyll were averaged within a region located just outside of Tracadie Bay defined by the coordinates 62◦ 57 54 W to 63◦ 4 49 W and 46◦ 30 45 N to 46◦ 25 44 N. Missing data were extrapolated using linear regression. The same methodology was used to generate temperature time series. Chlorophyll concentration was converted to carbon

units assuming a carbon:chl of 50:1. Nutrient data for far field and river were taken from Cranford et al. (2007) using an average value of two years (June to November for 2002 and 2003). River flow was 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 Canadian Weather Office (http://www.climate.weatheroffice.ec.gc.ca) after confirming that the modulus of wind velocity was similar between the two sources of data over a common period. Chlorophyll and seston time series were also collected at two sampling stations (Fig. 2B) during the studied period. Water samples for chlorophyll analyses were collected in duplicate. Samples were filtered through 25 mm Whatman GF/F filters, kept frozen (−20 ◦ C) until analysis, which was performed following EPA Method 445.0. Total particulate matter (TPM) and Particulate organic matter (POM) were measured gravimetrically on pre-ashed (500 ◦ C, 4 h) 47 mm Whatman GF/F filters. Two replicates were collected at each sampling point. The filters were dried at 70 ◦ C for 24 h and weighed to determine TPM. POM was determined after ashing filters for 6 h at 500 ◦ C. 2.5. Phytoplankton depletion A phytoplankton depletion index (%) was calculated modifying Filgueira and Grant (2009): Phytoplankton depletion index =

[Chla]i × 100 − 100 [Chla]far field

(5)

where [Chla]i and [Chla]far field are the chlorophyll concentration (␮g chla l−1 ) in the ith element and far field, respectively. Values of this ratio below 0% indicate depletion and above 0% indicate enrichment of chlorophyll in the ith element compared to the far field. An inter-annual coefficient of variation (CV) of chlorophyll concentration was calculated for the August–November period using a multi-year (2002–2011) satellite remote sensing dataset, which resulted in a value of 27.5%. Consequently, a depletion index of −27.5% (0–27.5%) was used as a threshold for acceptable ecosystem-level effects. Similarly, a bay-scale depletion index was calculated as follows:



Bay-scale depletion index =

i

[Chla]i × Voli /Bay volume [Chla]far field

× 100 − 100

(6)

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Fig. 4. Observed and predicted current speed (m s−1 ) and water level (m) in sampling stations LC1 and LC3.

where Voli and Bay volume are the volume (l) of element i and the volume of the bay, respectively. 2.6. Bivalve performance indices Condition index was calculated according to Lucas and Beninger (1985): CI =

dry meat weight × 100 dry shell weight

(7)

Tissue mass and shell length specific growth rates (, d−1 ) were calculated according to Clausen and Riisgård (1996):  = ln

X  t

X0

×t

−1

(8)

where Xt and X0 are the average dry weight or shell length on Day 0 and Day t, respectively.

In order to identify which of these indices was the most sensitive when farming intensity approached the system’s carrying capacity, the percentage of change of each indicator was calculated for a mussel standing stock biomass ranging from 4250 to 5250 tons. The sustainable 4775 tons is centered within this range (see below for specific calculations). The percentage of change was calculated as follows: Percentage of change =

|X5250 − X4250 | × 200 X5250 + X4250

(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.

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

Fig. 5. Observed chlorophyll (␮g l−1 ) in the far field (gray area) and groundtruthing stations (red and black dots for inner and outer stations, respectively) as well as predicted chlorophyll time series in both stations (red and black continuous lines for inner and outer stations, respectively). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3. Results 3.1. Groundtruthing A comparison of observations and model results during four consecutive tidal cycles is shown on Fig. 4 for the two stations (LC1 and LC3) equipped with both tide gauges and current meters. An overall good agreement is reached for both currents along their respective principal axis and water level fluctuations. While the phase of the current time series is well reproduced at both locations, its range seems slightly underestimated at LC1. This discrepancy may in part be explained by the strong spatial gradients in current velocity that may exist in nature and cannot be accounted for by the model grid. Moreover, the conjunction of sandy sediment, strong tidal currents and wave action in the inlet area contributes to a very dynamic topography/bathymetry. Sufficiently accurate and up to date bathymetric data were not available to better represent this area of the model domain. Nonetheless, observed and predicted water level time series are in good agreement suggesting that the model captures the main features of the hydrodynamics of the bay. Moreover, tidal propagation within the system is well reproduced by the model as shown by the results of the harmonic analysis (Foreman, 1977) of observed and predicted water level time series at all inner stations (Table 3). This result is of particular importance for the present work as tides were the main forcing considered in the coupling of the hydrodynamic and biogeochemical models. Groundtruthing of the biogeochemical model was carried out by comparing modeled and observed values of chlorophyll concentration at two sampling stations (Fig. 2B) and mussel specific growth rate within the bay. For chlorophyll, the modeled and observed values through time at the outer station are in good agreement (Fig. 5). However, there is a lack of agreement at the inner station (Fig. 5), where the modeled values are below the observations in mid September. There is not an obvious explanation for this singular discrepancy but it could be related to the high frequency variation in chlorophyll concentration driven by tides. Grant et al. (2008) showed a significant variation in chlorophyll concentration between consecutive low and high tides in Tracadie Bay. However, the ecosystem model is being forced by daily time series and consequently the outputs are expressed as daily averages but the field measurements correspond to a precise tidal situation that cannot be simulated using this model. For this reason, the second groundtruthing process based on bivalve growth rate is more robust than punctuated chlorophyll measurements. Bivalve growth integrates the effects of changing environmental conditions over time and consequently provides a better assessment of the model performance in long-term simulations, avoiding high frequency events such as tidal influence on chlorophyll concentration. The

Tissue mass (d−1 )

Shell length (d−1 )

Waite et al. (2005)

Average SD

0.0077 0.0026

0.0017 0.0008

MMP

Average SD

0.0087 n.a.

n.a. n.a.

This study

Average SD

0.0085 0.0011

0.0014 0.0003

interval of confidence of modeled specific growth rate in weight and length (Table 2) included the values observed by Waite et al. (2005) in a two-year sampling (1998 and 1999) for the same time of the year. These results confirm that the model is able to accurately simulate mussel growth in the bay. The same good agreement between model and observations was found with results collected over the same time frame of this modeling exercise (August to November 2012) by the Mussel Monitoring Program carried out by the 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 following parameters: XK , bivalve mortality, phytoplankton growth rate, phytoplankton mortality and detritus remineralization (Dremineralization ). Two scenarios were run for each parameter, i.e. by increasing and decreasing the parameter value by 10%. The response of the model to these parameters was evaluated by observing the relative change in these simulations compared to current scenario values. The following response variables were analyzed: final total bivalve biomass, final bivalve tissue dry weight, length and condition index as well as phytoplankton depletion. The maximum change observed for these response variables, 6.07%, was for phytoplankton depletion when remineralization was reduced by 10%. Phytoplankton depletion was in all cases the most affected response variable. Bivalve biomass and dry weight were only sensitive in concert with mineralization (±2.7%). Bivalve length and CI were 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 index is represented using median values instead of mean values, since depletion tends bias the distribution of values compared to enrichment (Fig. 6). The spatial distribution demonstrates strong depletion in the northeast part of the bay, where the majority of the mussels are located. In contrast, the area close to the main channel connecting 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 similar depletion index values. The depletion index changes through time depending on the ongoing dynamics inside the bay and the far field conditions. This variation through time can be observed using a bay-scale depletion index (Eq. (6)), which provides insight about the overall performance of the bay rather than a single value for each element. Under the current aquaculture scenario (Fig. 7), there are periods of time when the bay-scale chlorophyll depletion is below the critical threshold of sustainability based on the −27.5% natural variation standard (see above). There are also periods of time when the bay

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

Amplitude (m)

Phase (◦ )

Amplitude (m)

Observed

Predicted

LC1 L2 LC3 L4 L5

O1 0.15 0.15 0.15 0.15 0.15

± ± ± ± ±

0.02 0.02 0.02 0.02 0.02

0.15 0.15 0.15 0.15 0.14

± ± ± ± ±

0.02 0.01 0.01 0.02 0.02

241.1 246.6 257.4 260.0 256.7

± ± ± ± ±

6.8 6.6 6.0 6.2 7.1

242.1 249.9 260.0 268.4 259.1

± ± ± ± ±

5.6 5.6 6.4 6.1 7.4

LC1 L2 LC3 L4 L5

M2 0.15 0.14 0.13 0.13 0.13

± ± ± ± ±

0.00 0.00 0.01 0.01 0.01

0.15 0.13 0.12 0.12 0.12

± ± ± ± ±

0.01 0.01 0.01 0.01 0.01

194.2 201.1 218.5 223.8 216.9

± ± ± ± ±

1.6 1.5 1.8 2.0 2.0

194.6 205.8 223.5 238.1 222.1

± ± ± ± ±

1.8 1.9 2.7 2.5 4.2

Observed

Predicted

Observed

Predicted

Observed

Predicted

LC1 L2 LC3 L4 L5

K1 0.16 0.16 0.16 0.16 0.16

± ± ± ± ±

0.02 0.02 0.02 0.02 0.02

0.16 0.16 0.15 0.15 0.16

± ± ± ± ±

0.02 0.01 0.02 0.02 0.02

267.6 273.2 284.8 287.8 282.8

± ± ± ± ±

7.0 6.4 6.3 6.0 6.4

268.8 277.3 289.7 297.2 290.6

± ± ± ± ±

4.8 5.2 5.5 5.2 6.3

LC1 L2 LC3 L4 L5

S2 0.04 0.04 0.03 0.03 0.03

± ± ± ± ±

0.00 0.00 0.01 0.01 0.01

0.04 0.03 0.03 0.03 0.03

± ± ± ± ±

0.01 0.00 0.01 0.01 0.01

262.5 275.5 308.3 314.8 305.7

± ± ± ± ±

5.9 5.8 9.2 8.1 11.7

266.1 284.3 315.1 331.4 314.4

± ± ± ± ±

7.2 7.2 14.0 13.0 16.4

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

Parameter change (%)

Percentage of change in response variable (%)

Bivalve biomass

Bivalve dry weight

Bivalve length

Bivalve CI

Chlorophyll depletion

XK

+10 −10

−0.55 0.43

−0.89 0.85

−0.27 0.27

0.03 −0.09

2.88 −4.43

Bivalve mortality

+10 −10

−0.42 0.42

0.12 −0.12

0.03 −0.03

0.04 −0.04

0.24 −0.24

Phytoplankton growth rate

+10 −10

0.83 −1.16

0.58 −0.84

0.12 −0.16

0.22 −0.35

−0.13 −2.77

Phytoplankton mortality

+10 −10

−0.13 0.13

−0.13 0.13

−0.04 0.04

0.00 −0.01

−0.41 0.41

Dremineralization

+10 −10

2.70 −2.73

2.64 −2.72

0.68 −0.72

0.45 −0.48

5.40 −6.07

acts as a reservoir of chlorophyll. However, the median bay-scale depletion index is −34.2%, slightly below the threshold value of −27.5%. This result suggests that the current level of farming activity is very close to the ecological carrying capacity of the system. The mussel standing stock biomass that would cause a depletion index of −27.5% is estimated at 4775 tons (Fig. 8A), which is about 500 tons lower than the estimated current scenario (5236 tons). Using the 4775 tons as a reference point, we calculated the threshold of sustainability in terms of condition index (Fig. 8B), tissue mass growth rate (Fig. 8C) and shell length growth rate (Fig. 8D). The outcome shows that a depletion index of −27.5% would lead

to a condition of 24.7%, a tissue growth rate of 0.0092 d−1 and a shell length growth rate of 0.0016 d−1 . The percentage of change of these indices when farming intensity approaches the system’s carrying capacity is 1.6, 13.7 and 21.3% for condition index, tissue mass growth rate, and shell length growth rate, respectively. This finding suggests that shell growth rate is the most sensitive indicator of ecosystem sustainability because a small change in stocked mussel biomass translates into a large change in the response variable. 4. Discussion Phytoplankton (or chlorophyll) depletion has been commonly used to assess carrying capacity and husbandry techniques in bivalve aquaculture sites (e.g. Ferreira et al., 2007; Gibbs, 2007; Duarte et al., 2008), including Tracadie Bay (Dowd, 2003, 2005;

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

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

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Fig. 8. Median bay-scale depletion index (%, see Eq. (6)) in different standing stock biomass scenarios and (A) sustainable standing stock biomass calculated based on depletion index sustainable threshold, i.e. −27.5%, as well as relative biomass production calculated as the ratio final biomass (biomass at t = 93 day)/initial biomass (biomass at t = 0 day); (B, C and D) thresholds of sustainability re-calculated in terms of condition index (CI, %), tissue mass growth rate (d−1 ) and shell length growth rate (d−1 ), respectively.

Grant et al., 2005, 2008; Comeau et al., 2008; Filgueira and Grant, 2009). However, to the authors’ knowledge, phytoplankton depletion has never been used in a monitoring program. Yet monitoring programs are key components in the process of marine spatial planning in order to evaluate the effectiveness of planning measures and to provide feedback for new planning stages, i.e. evidence-based management (Sutherland et al., 2004). The strong relationship between phytoplankton depletion and bivalve performance (Cranford et al., 2012; Smaal et al., 2013) prompted us to examine the value of the latter as a proxy of phytoplankton depletion and ecosystem health. As a starting point, we needed to objectively define sustainable thresholds for phytoplankton depletion. Phytoplankton depletion was thus defined as the ratio between the chlorophyll concentration inside the bay and the concentration in the far field. The use of far field data is motivated by the difficulty in defining baseline conditions inside the bay, affected not only by aquaculture activity but by river discharge, which is highly enriched in nutrients due to intense agricultural activity in the area. In fact, bivalve filter-feeding activity in the bay could exert a positive effect by mitigating eutrophication (Coen et al., 2007), an ecosystem service that should also be considered in marine spatial planning (e.g. see Lindahl, 2011). We used a framework based on ecological resilience employing natural variation in chlorophyll as a benchmark for sustainability. Common to all applications of optimization in conservation ecology is the quantitative identification of a conservation problem, that is, the precise definition of the limits at which ecosystem health is not compromised (Duarte, 2003; Fisher et al., 2009). The framework used in this study (Fig. 1) objectively establishes a threshold assumed to be below the tipping points beyond which the resilience of the system is exceeded and it reorganizes (Crowder and Norse, 2008). This approach avoids the measurement of these tipping points, which are inherently difficult to determine, but uses a precautionary approach to management. Once the depletion threshold was established, different bivalve performance indices were studied. Bivalve physiological measurements have commonly been used in monitoring given their sensitivity to environmental conditions, stress and pollution (Bayne and Newell, 1983; Widdows and Johnson, 1988). Several static

measurements such as nucleic acid ratios (e.g. Norkko et al., 2005), scope for growth (e.g. Smaal and Widdows, 1994), and morphometric indices (e.g. Sasikumar and Krishnakumar, 2011) have been used for this purpose. Dynamic indices, such as bivalve growth, are particularly interesting because they integrate the effect of environmental conditions over significant periods of time (Lucas and Beninger, 1985). Intra-specific competition for food and phytoplankton depletion have direct effects on bivalve performance and mortality (self-thinning, Fréchette and Lefaivre, 1995). This cause–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 provides the first assessment of bivalve performance in terms of its usefulness as an indicator of carrying capacity. In our assessment, the extent of phytoplankton depletion was calculated for different stocking stocks of cultivated mussels. The extent of depletion was then related to three physiological indices: one static (condition index), and two dynamic (tissue mass and shell length growth rates). Condition index was a priori selected as the ideal indicator because of its common use among farmers as an indicator of product quality (Orban et al., 2002). Recently, condition index has been included in a monitoring program to evaluate carrying capacity in mussel farms (BAP, 2013), but without connection to any sustainability threshold. In a previous meta-analysis, Filgueira et al. (2013) identified a significant relationship between standing stock biomass and condition index of mussels and oysters. The wide range of conditions included in this meta-analysis allowed for the determination of a relationship between biomass and condition index, which was also observed in the current study (Fig. 8A). Recently, Smaal et al. (2013) also observed a significant relationship between condition index of harvested mussels and bivalve stock in the Oosterschelde estuary. However, according to simulations in the present paper, the percentage change in condition index with standing stock biomass that matched a sustainable threshold is the lowest (1.6%) of the three tested indicators. In addition, this percentage of change seems insufficient to accommodate intraspecific variation in condition index as well as methodological error inherent in the measurement. The most sensitive indicator was

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shell length growth rate (Fig. 8D), showing a 21.3% change near the standing stock biomass that matched the sustainable threshold. In terms of monitoring methodology, shell length growth rate requires two simple shell length measurements over a time interval, and is non-destructive. Tissue mass growth rate was the second best indicator with a percentage of change of 13.7% (Fig. 8B). This indicator requires two samplings over time followed by laboratory work (tissue drying, weight measurements). 5. Conclusions In conclusion, this paper demonstrated the potential of using bivalve physiological measurements as indicators of ecosystem status. However, further considerations are necessary to standardize the use of these indicators. For instance one should take into account that the rate of physiological changes vary seasonally (Li et al., 2009; Pogoda et al., 2011) and according to ontogeny (Sukhotin et al., 2002). Perhaps an ideal design for a monitoring program uses juveniles and a focus on the season in which bivalve growth is at a maximum. Such a strategy would avoid the inherent effects of the reproductive cycle and minimize methodological errors by maximizing shell length growth values. Another standardization consideration would be to explore intra-specific variation and confidence limits of bivalve populations in order to determine the precision of the measurements. Such considerations may ultimately show that shell length growth is a robust and costeffective indicator of ecosystem status, which is crucial for the viability of monitoring programs (Borja and Elliott, 2013). Such indicators are essential not only for improving our understanding of the functioning of bivalves in coastal marine ecosystems (Norkko and Thrush, 2006) but also for generating information that can be used as a benchmark for adaptive management (Halpern et al., 2008; Polasky et al., 2011). Acknowledgements The authors are sincerely grateful to Thomas Landry (DFO Gulf) for his valuable feedback in early stages of this project. We thank Rémi Sonier and Tina Sonier (DFO Gulf) for their field and laboratory assistance. The work presented in this paper was funded by Department of Fisheries and Oceans of Canada (Program for Aquaculture Regulatory Research, PARR project 2011-Z-22). References Aquaculture Stewardship Council, 2012. ACS Bivalve Standard. http://www.asc-aqua.org Alunno-Bruscia, M., Petraitis, P.S., Bourget, E., Frechette, M., 2000. Body size–density relationship for Mytilus edulis in an experimental food-regulated situation. Oikos 90, 28–42. Bacher, C., Grant, J., Hawkins, A., Fang, J., Zhu, P., Duarte, P., 2003. Modeling the effect of food depletion on scallop growth in Sungo Bay (China). Aquat. Living Resour. 16, 10–24. BAP, 2013. Best Aquaculture Practices Standards, Guidelines. Mussel Farms http://www.gaalliance.org Bayne, B.L., Newell, R.C., 1983. Physiological energetics of marine molluscs. In: Wilbur, K.M., Salenddin, A.S.M. (Eds.), The Mollusca, vol. 4, Physiology. Academic Press, London, pp. 407–515 (Part 1). Borja, A., Elliott, M., 2013. Marine monitoring during an economic crisis: the cure is worse than the disease. Mar. Pollut. Bull. 68, 1–3. Borja, A., Franco, J., Pérez, V., 2000. A marine biotic index to establish the ecological quality of soft-bottom benthos within European estuarine and coastal environments. Mar. Pollut. Bull. 40, 1100–1114. Clausen, I.B., Riisgård, H.U., 1996. Growth, filtration and respiration in the mussel Mytilus edulis: no evidence for physiological regulation of the filter-pump to nutritional needs. Mar. Ecol. Prog. Ser. 141, 37–45. Coen, L.D., Brumbaugh, R.D., Bushek, D., Grizzle, R., Luckenbach, M.W., Posey, M.H., Powers, S.P., Tolley, S.G., 2007. Ecosystem services related to oyster restoration. Mar. Ecol. Prog. Ser. 341, 303–307. Comeau, L.A., Drapeau, A., Landry, T., Davidson, J., 2008. Development of longline mussel farming and the influence of sleeve spacing in Prince Edward Island, Canada. Aquaculture 281, 56–62.

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