15 Bivalves

15.1 Contributors

15.2 Overview

Based on work in freshwater and estuarine systems a bivalve module to simulate growth, production, and (optionally) population dynamics has been incorporated into the AED2+ library.

The model is originally based on an application to Oneida Lake and Lake Erie for mussels. One to several size classes of mussels are simulated based on physiological parameters assembled by Schneider (1992) and modified by Bierman et al. (2005) to estimate effect of mussels on Saginaw Bay, Michigan, USA; a formulation also used by Gudimov et al. (2015) to estimate mussel effects in Lake Simcoe, Ontario, Canada. Additionally, some model structure was taken from the Spillman et al. (2008) model of Tapes clams in the Barbamarco Lagoon, Italy, as modified by Bocaniov et al. (2014) for mussels in Lake Erie.

The physiology of mussels are set to be size dependent, and can vary between species (e.g., Zebra vs Quagga)(Hetherington, 2016). Three size classes of mussels can be incorporated in the model, roughly corresponding to age-0, age-1 and age>1 mussels. Physiological parameters are calculated for the weight assigned to each age class, using equations in Table 4. Individual mussel mass is given in mmol C or in the case of calculations of N and P budgets, in mmol N and mmol P. The stoichiometric ratios (C:N:P) are fixed. Group mussel biomass is calculated at each time step by calculating ingestion and subtracting pseudofeces production, standard dynamic action, respiration, excretion, egestion and mortality (expressed in mmol C/mmol C/day, mmol N/mmol N/day; mmol P/mmol P/day). Several of these processes are also functions of temperature, algal+POC concentrations, salinity, suspended solids and mussel density. The effect of salinity, suspended solids and mussel density is incorporated as a multiplier of filtration rate with the multipliers having values between -0 – (no filtering) and 1 (no effect). Mussel nitrogen and phosphorus concentrations are fixed ratios of mussel carbon concentrations. Since the various input and output fluxes have variable C:N:P ratios, the excretion of nutrients is dynamically adjusted each time-step to maintain this ratio at each time step. Reproduction and larval dynamics are not simulated. There is no transfer of biomass between age groups.

15.3 Model Description

15.3.1 Process Descriptions

Ingestion

Ingestion is modelled as a function of filtration rate, food availability, pseudofeces production, density, suspended solids, and salinity (Equation 2) (Bierman et al., 2005; Gudimov et al., 2015; Schneider, 1992; Spillman et al., 2008). Filtration rate is based on maximum ingestion, temperature, food availability, and pseudofeces production according to the following:


\[\begin{equation} FR = \frac{\frac{I_{max}*f(T)_{I}}{K_{A}}}{PF_{min}} \text{ for } [A] < KA \tag{15.1} \end{equation}\]

\[\begin{equation} FR = \frac{\frac{I_{max}*f(T)_{I}}{[A]}}{PF_{min}} \text{ for } [A] > KA \tag{15.2} \end{equation}\]


where \(FR\) is filtration rate (mmol/mmol/d), \(Imax\) is maximum ingestion rate (mmol/mmol/d), \(f(T)_{I}\) is filtration temperature function, \(K_{A}\) is optimum algal concentration (mmol/m3), \([A]\) is algal concentration + particulate organic carbon (POC) concentration (mmol/m3), and \(PFmin\) is minimum pseudofeces production (-). The maximum ingestion rate is based on weight from length according to the following:


\[\begin{equation} I_{max} = (a_{I} * W^{bI}) \tag{15.3} \end{equation}\]

\[\begin{equation} W = \frac{0.071}{1000} * L^{2.8} \tag{15.4} \end{equation}\]


where \(Imax\) is maximum ingestion rate (mmol/mmol/d), \(a_{I}\) is maximum standard ingestion rate (mmol/mmol/d), \(W\) is weight (g), \(bI\) is exponent for weight effect on ingestion, \(L\) in length (mm) (Bierman et al., 2005; Schneider, 1992). The temperature dependence function (Thornton and Lessem, 1978) was fit to zebra and quagga mussel data (Hetherington et al. Submitted) with optimal ingestion from 17°C to 20°C according to the following:


\[\begin{equation} f(T)_{I} = 1 \text{ for } T_{min_{I}} < T < T_{max_{I}} \tag{15.5} \end{equation}\]

\[\begin{equation} f(T)_{I} = (2*\frac{Tmin_{TI}}{Tmin_{I}} - \frac{(Tmin_{TI})^2}{(Tmin_{I})^2}) / (2*\frac{Tmin_{I}-minT_{I}}{Tmin_{I}}-\frac{(Tmin_{I}-minT_{I})^2}{(Tmin_{I})^2}) \text{ for } minT_{I} < T < Tmin_{I} \tag{15.6} \end{equation}\]


\[\begin{equation} f(T)_{I} = -\frac{(T^{2} + 2*Tmax_{I}*T–2*Tmax_{I}*maxT_{I} + maxT_{I}^{2})}{(Tmax_{I}-maxT_{I})^{2}} \text{ for } Tmax_{I}<T<maxT_{I} \tag{15.7} \end{equation}\]


\[\begin{equation} f(T)_{I} = 0 \text{ for } T > maxT_{I} \text{ or } T < minT_{I} \tag{15.8} \end{equation}\]


where \(T\) is temperature (°C), \(minT_{I}\) is lower temperature for no ingestion (°C), \(Tmin_{I}\) is lower temperature for optimum ingestion (°C), \(Tmax_{I}\) is upper temperature for optimum ingestion, \(maxT_{I}\) is upper temperature for no ingestion (°C). Filtration rate is related to food concentration [Walz (1978); Sprung and Rose (1988); Schneider (1992); Bierman et al. (2005)). The filtration rate is maintained at a maximum value for all food values less than saturation food concentration. The filtration rate decreases as food concentrations increase above this value. Pseudofeces production is implicit as the difference between the mass filtered and consumed. According to Walz (1978), pseudofeces production (66%) was approximately double the ingestion rate (34%) at high food concentrations Bierman et al. (2005).

Mussel density limits ingestion above some maximum density according to the following:


\[\begin{equation} $f(D) = 1$ for $D<Dmax$ \tag{15.9} \end{equation}\]


\[\begin{equation} f(D) = \frac{-(D^{2} + 2*Dmax*D – 2*Dmax*maxD + maxD^{2})}{(Dmax – maxD)^{2}} \text{ for } D>Dmax \tag{15.10} \end{equation}\]


\[\begin{equation} f(D) = 0 \text{ for } D>maxD \tag{15.11} \end{equation}\]


where \(D\) is density (mmol/m2), \(Dmax\) is upper density for optimum ingestion (mmol/m2), and \(maxD\) is upper density for no ingestion (mmol/m2). An additional function to reduce ingestion is the suspended solids function which decreases ingestion with high inorganic loads according to the following:


\[\begin{equation} f(SS) = 1 \text{ for } SS<SSmax \tag{15.12} \end{equation}\]


\[\begin{equation} f(SS) = \frac{-(SS^{2} + 2*SSmax*SS – 2*SSmax*maxSS + maxSS^{2})}{(SSmax – maxSS)^{2}} \text{ for } SS>SSmax \tag{15.13} \end{equation}\]


\[\begin{equation} f(SS) = 0 \text{ for } SS>maxSS \tag{15.14} \end{equation}\]


where \(SS\) is suspended solids (mg/L), \(SSmax\) is upper suspended solids for optimum ingestion (mg/L), and \(maxSS\) is upper suspended solids for no ingestion (mg/L) (Spillman et al., 2008). Along with suspended solids, salinity limits ingestion according to the following:


\[\begin{equation} f(S) = 1 \text{ for } Smin < S < Smax \tag{15.15} \end{equation}\]


\[\begin{equation} f(S) = \frac{(2*(S-minS)/Smin)–(S-minS)^{2}/Smin^{2})}{(2*(Smin-minS)/Smin)–(Smin-minS)^{2}/Smin^{2})} \text{ for } minS< S < Smin \tag{15.16} \end{equation}\]

\[\begin{equation} f(S) = \frac{-(S^{2} + 2*Smax*S – 2*Smax*maxS + maxS^{2})}{(Smax-maxS)^{2}} \text{ for } Smax<S<maxS \tag{15.17} \end{equation}\]

\[\begin{equation} f(S) = 0 \text{ for } S > maxS \text{ or } S < minS \tag{15.18} \end{equation}\]


where \(S\) is salinity (psu), \(minS\) is lower salinity for no ingestion (psu), \(Smin\) is lower salinity for optimum ingestion (psu), \(Smax\) is upper salinity for optimum ingestion (psu), \(maxS\) is upper salinity for no ingestion (psu) (Spillman et al., 2008).

Respiration

Respiration is modelled as a base or standard respiration rate based on weight and temperature (Spillman et al., 2008). Respiration rate coefficient at 20°C is based on weight from length according to the following:


\[\begin{equation} R_{20} = (a_{R} * W^{b}_{R}) \tag{15.19} \end{equation}\]


\[\begin{equation} W = \frac{0.071}{1000} * L^{2.8} \tag{15.20} \end{equation}\]


where \(R_{20}\) is respiration rate coefficient at 20°C (mmol/mmol/d), \(a_{R}\) is standard respiration rate (mmol/mmol/d), \(W\) is weight (g), \(b_{R}\) is exponent for weight effect of respiration, and \(L\) is length (mm) (Schneider 1992). The respiration rate coefficient is adjusted for temperature according to the following:


\[\begin{equation} f(T)_{RSpillman} = \Theta_{RSpillman}^{T-20} \tag{15.21} \end{equation}\]


where \(f(T)_{RSpillman}\) is respiration temperature function, \(\Theta_{RSpillman}\) is temperature multiplier for bivalve respiration (-), and \(T\) is temperature (°C) (Spillman et al., 2008). Alternatively, respiration is modelled as a base or standard respiration rate based on weight and temperature in addition to the energetic cost of feeding. Respiration rate coefficient at 30°C is based on weight from length according to the following:


\[\begin{equation} R_{30} = (a^{R} * W^{b}_{R}) \tag{15.22} \end{equation}\]


\[\begin{equation} W = \frac{0.071}{1000} * L^{2.8} \tag{15.23} \end{equation}\]


where \(R_{30}\) is respiration rate coefficient at 30°C (mmol/mmol/d), \(a^{R}\) is standard respiration rate (mmol/mmol/d), \(W\) is weight (g), \(b_{R}\) is exponent for weight effect of respiration, and \(L\) is length (mm) (Schneider, 1992). The temperature function follows Schneider (1992) application of the model of Kitchell et al. (1977) to the data of Jr and McMahon (2004) according to the following:


\[\begin{equation} f(T)_{RSchneider} = V^{X} * e^{X * (1-V)} \tag{15.24} \end{equation}\]


\[\begin{equation} V = \frac{Tmax_{R} – T}{Tmax_{R} – maxT_{R}} \tag{15.25} \end{equation}\]


\[\begin{equation} X = (\frac{W*(1+\sqrt{1 + (\frac{40}{Y})})}{20})^2 \tag{15.26} \end{equation}\]


\[\begin{equation} W = lnQ_{R}*(Tmax_{R} – maxT_{R}) \tag{15.27} \end{equation}\]


\[\begin{equation} Y = lnQ_{R}*(Tmax_{R} – maxT_{R} + 2) \tag{15.28} \end{equation}\]


where \(T\) is temperature (°C), \(Tmax_{R}\) is upper temperature for optimum respiration (°C), \(maxT_{R}\) is upper temperature for no respiration (°C), and \(Q_{R}\) is respiration curve slope estimate (-). The maximum respiration occurs at 30°C with 43°C as the upper lethal temperature. The energetic cost of feeding or specific dynamic action is applied only to the portion of ingestion that is not egested (Bierman et al., 2005; Gudimov et al., 2015; Schneider, 1992).

Excretion

Excretion is modelled as a constant fraction of assimilated food (Bierman et al., 2005; Gudimov et al., 2015; Schneider, 1992). Excretion data for zebra and quagga mussels are limited; therefore, the excretion formulation for Mytilus edulis derived by Bayne and Newell (1983) was used (Bierman et al., 2005; Gudimov et al., 2015; Schneider, 1992).

Egestion

Egestion is modeled as a function of ingestion (Bierman et al., 2005; Gudimov et al., 2015; Schneider, 1992). The model follows the assumption that ingestion is directly proportional to the food content of the water for all food concentrations less than the maximum which can be ingested. For all food concentrations above this saturation value, ingestion remains constant at a maximum value (\(I_{max}\)) (Walz, 1978).

Mortality

Mortality is a function of dissolved oxygen and predation (Equation 7). Mortality increases with low dissolved oxygen concentrations according to the following:


\[\begin{equation} f(DO) = 1 + K_{BDO} * \frac{K_{DO}}{K_{DO} + DO} \tag{15.29} \end{equation}\]


where \(DO\) is dissolved oxygen (mmol/m3), \(K_{BDO}\) is basal respiration rate (mmol/m3), and \(K_{DO}\) is half saturation constant for metabolic response to dissolved oxygen (mmol/m3) (Spillman et al., 2008). A mortality rate coefficient (\(K_{MORT}\)) further influences the dissolved oxygen function. Additionally, mortality from predation is a constant rate added to the effect from dissolved oxygen.

15.3.2 Variable Summary

State Variables

Table 15.1: State variables
Variable Name Description Units Variable Type Core/Optional
BIV_{group} Bivalve group \(mmol C\,m^{-2}\,day^{-1}\) Benthic Core
BIV_filtfrac Filtered fraction of water % Pelagic Optional : biv_tracer = .true.

Diagnostics

Table 15.2: Diagnostics
Variable Name Description Units Variable Type Core/Optional
BIV_tgrz Total rate of food filtration/grazing in the water column \(mmol C\,m^{-3}\,day^{-1}\) Pelagic diagnostic Core
BIV_nmp Net mussel production \(mmol C\,m^{-3}\,day^{-1}\) Benthic diagnostic Core
BIV_tbiv Total bivalve density (all groups) \(mmol C\,m^{-3}\,day^{-1}\) Benthic diagnostic Core
BIV_grz Net bivalve filtration/grazing \(mmol C\,m^{-3}\,day^{-1}\) Benthic diagnostic Core
BIV_resp Net bivalve respiration \(mmol C\,m^{-3}\,day^{-1}\) Benthic diagnostic Core
BIV_mort Net bivalve mortality \(mmol C\,m^{-3}\,day^{-1}\) Benthic diagnostic Core
BIV_excr Net bivalve excretion \(mmol C\,m^{-3}\,day^{-1}\) Benthic diagnostic Core
BIV_egst Net bivalve egestion (faeces) \(mmol C\,m^{-3}\,day^{-1}\) Benthic diagnostic Core
BIV_fT Bivalve temperature limitation Benthic diagnostic Optional: extra_diag=.true.
BIV_fD Bivalve sediment limitation Benthic diagnostic Optional: extra_diag=.true.
BIV_fG Bivalve food limitation Benthic diagnostic Optional: extra_diag=.true.
BIV_fR Filtration rate \(m^{3}\, mmol C\,m^{-2}\,day^{-1}\) Benthic diagnostic Optional: extra_diag=.true.
BIV_pf Pseudofeaces production rate \(day^{-1}\) Benthic diagnostic Optional: extra_diag=.true.

15.3.3 Parameter Summary

Table 15.3: Diagnostics
name ‘zebra’ ‘quagga’ string Name of bivalve group
General Parameters
initial_conc 833 833 real Initial concentration of bivalve (mmolC/m2)
min 8.3 8.3 real Minimum concentration of bivalve (mmolC/m3)
length 15 15 real Length of bivalve (mm)
INC 291.67 291.67 real Ratio of internal nitrogen to carbon of bivalve (-)
IPC 64.58 64.58 real Ratio of internal phosphorus to carbon of bivalve (-)
FILTRATION & INGESTION Parameters
Rgrz 1.9 0.12 real Maximum ingestion rate of bivalve (mmol/mmol/day) (Spillman et al. 2008) Calculated from Schneider 1992 based on 15 mm mussel
Ing 0 0 integer Type of maximum ingestion for bivalve; 0=Enter (mmol/mmol/d) or 1=Calculate based on length
WaI 0 0 real Maximum standard ingestion rate of bivalve (mmol/mmol/day) (Schneider 1992)
WbI -0.39 -0.39 real Exponent for weight effect on ingestion of bivalve (-) (Schneider 1992)
fassim 0.34 0.34 real Minimum proportion of food lost as pseudofeces for bivalve (-)
Cmin_grz 0.05 0.05 real Minimum prey concentration for grazing by bivalve (mmolC/m3)
Kgrz 187.5 187.5 real Optimum algae+POC concentration for ingestion of bivalve (mmol/m3)
minT 4 4 real Lower temperature for no ingestion of bivalve (degC)
Tmin 17 17 real Lower temperature for optimum ingestion of bivalve (degC)
Tmax 20 20 real Upper temperature for optimum ingestion of bivalve (degC)
maxT 32 32 real Upper temperature for no ingestion of bivalve (degC)
Dmax 6333.3 6333.3 real Upper density for optimum ingestion of bivalve (mmol/m2)
maxD 20000 20000 real Upper density for no ingestion of bivalve (mmol/m2)
Ssmax 20 20 real Upper suspended solids for optimum ingestion of bivalve (mg/L)
maxSS 100 100 real Upper suspended solids for no ingestion of bivalve (mg/L)
EXCRETION AND EGESTION Parameters
Rexcr 0 0 real Excretion fraction of ingestion for bivalve(-)
Regst 0 0 real Minimum proportion egested as feces for bivalve (-)
gegst 0 0 real Gamma coefficient for food availability dependence for bivalve
RESPIRATION Parameters
Rresp 0.01 0.01 real Respiration rate coefficient of bivalve (/day) (Calculated from Schneider 1992 based on 15 mm mussel)
saltfunc 0 0 integer Type of salinity function for bivalve; 0=None or 1=Spillman et al. 2008
minS 0 0 real Lower salinity for no ingestion of bivalve (psu)
Smin 0 0 real Lower salinity for optimum ingestion of bivalve (psu)
Smax 0 0 real Upper salinity for optimum ingestion of bivalve (psu)
maxS 0 0 real Upper salinity for no ingestion of bivalve (psu)
fR20 0 0 integer Type of maximum respiration for bivalve; 0=Enter or 1=Calculate based on length (mm)
War 16.759 16.759 real Standard respiration rate of bivalve (mmol/mmol/d) (Schneider 1992)
Wbr -0.25 -0.25 real Exponent for weight effect on respiration of bivalve (-)
fR 1 1 integer Type of respiration function for bivalve; 0=Schneider 1992 or 1=Spillman et al. 2008
theta_resp 1.08 1.08 real Temperature multiplier for respiration of bivalve (-)
TmaxR 30 30 real Upper temperature for optimum respiration of bivalve (degC)
maxTR 43 43 real Upper temperature for no respiration of bivalve (degC)
Qresp 2.3 2.3 real Respiration curve slope estimate for bivalve (-)
SDA 0.285 0.285 real Specific dynamic action of bivalve (-)
MORTALITY Parameters
Rmort 0 0 real Mortality rate coefficient for bivalve (/day)
Rpred 0 0 real Mortality rate from predation of bivalve (/day)
fDO 0 0 integer Type of dissolved oxygen function for bivalve; 0=None or 1=XXX or 2=XXX
K_BDO 160 160 real Basal respiration rate of bivalve (mmol/m3)
KDO 8 8 real Half saturation constant for metabolic response to DO for bivalve (mmol/m3)
GENERAL Parameters
num_prey 3 3 integer Number of state variables for bivalve prey
prey(1)%bivalve_prey PHY_green PHY_green string State variable name of bivalve prey
prey(1)%Pbiv_prey 1 1 real Preference factors for bivalve predators grazing on prey
prey(2)%bivalve_prey PHY_diatom PHY_diatom string State variable name of bivalve prey
prey(2)%Pbiv_prey 1 1 real Preference factors for bivalve predators grazing on prey
prey(3)%bivalve_prey PHY_crypto OGM_poc string State variable name of bivalve prey
prey(3)%Pbiv_prey 1 1 real Preference factors for bivalve predators grazing on prey

15.3.5 Feedbacks to the Host Model

15.4 Setup & Configuration

15.4.1 Setup Example

Table 15.4: Parameters and configuration
Parameter Name Description Units Parameter Type Default Typical Range Comment
num_biv Number of zooplankton groups Integer 0-3
the_biv List of ID’s of groups in aed_zoo_pars.nml (len=num_phyto) Integer 1/3/17
n_zones Number of sediment zones where bivalves will be active Integer
active_zones The vector of sediment zones to include Integer (vector)
biv_tracer Opton to include water column tracer tracking filtration amount Boolean .false.
biv_feedback Switch to enable/disable feedbacks between bivalve metabolism and water column variable concentration Boolean .false.
dn_target_variable Water column variable to receive DON excretion String
pn_target_variable Water column variable to receive PON egestion/mortality String
dp_target_variable Water column variable to receive DOP excretion String
pp_target_variable Water column variable to receive POP egestion/mortality String
dc_target_variable Water column variable to receive DOC excretion String
pc_target_variable Water column variable to receive POC egestion/mortality String
do_uptake_variable Water column variable providing DO concentration String
ss_uptake_variable Water column variable providing SS concentration String
dbase = 'AED2/aed2_bivalve_pars.nml' String aed2_bivalve_pars.nml aed2/aed2_bivalve_pars.nml
extra_diag Switch to enable/disable extra diagnostics to be output Boolean .false.
biv_fixedenv Switch to enable/disable environmental variables to be fixed (for testing) Boolean .false.
fixed_temp Fixed temperature C Float 20
fixed_oxy Fixed oxygen concentration \(mmol O_2\,m^{-3}\) Float 300
fixed_food Fixed food concentration \(mmol C\,m^{-3}\) Float 20


An example nml block for the bivalve module is shown below:

&aed2_bivalve
   num_biv = 2
   the_biv = 1,2
   !
   dn_target_variable=''  ! dissolved nitrogen target variable
   pn_target_variable=''  ! particulate nitrogen target variable
   dp_target_variable=''  ! dissolved phosphorus target variable
   pp_target_variable=''  ! particulate phosphorus target variable
   dc_target_variable=''  ! dissolved carbon target variable
   pc_target_variable=''  ! particulate carbon target variable
   do_uptake_variable='OXY_oxy'  ! oxygen uptake variable
   ss_uptake_variable=''  ! oxygen uptake variable
   !FIX FROM ERIE!
   !n_zones, active_zones, extra_diag,&
   !simBivTracer, simBivFeedback, simStaticBiomass,            &
   !dbase, simFixedEnv, fixed_temp, fixed_sal, fixed_oxy, fixed_food, & initFromDensity
 /

15.5 Case Studies & Examples

15.5.1 Case Study

15.5.2 Publications

References

Bierman, V.J., Kaur, J., Depinto, J.V., Feist, T.J., Dilks, D.W., 2005. Modeling the Role of Zebra Mussels in the Proliferation of Blue-green Algae in Saginaw Bay, Lake Huron, Journal of Great Lakes Research 31, 32–55. https://doi.org/10.1016/s0380-1330(05)70236-7
Bocaniov, S.A., Smith, R.E.H., Spillman, C.M., Hipsey, M.R., Leon, L.F., 2014. The nearshore shunt and the decline of the phytoplankton spring bloom in the Laurentian Great Lakes: insights from a three-dimensional lake model, Hydrobiologia 731, 151–172. https://doi.org/10.1007/s10750-013-1642-2
Gudimov, A., Kim, D.-K., Young, J.D., Palmer, M.E., Dittrich, M., Winter, J.G., Stainsby, E., Arhonditsis, G.B., 2015. Examination of the role of dreissenids and macrophytes in the phosphorus dynamics of Lake Simcoe, Ontario, Canada, Ecological Informatics 26, 36–53. https://doi.org/10.1016/j.ecoinf.2014.11.007
Jr, J.E.A., McMahon, R.F., 2004. Respiratory response to temperature and hypoxia in the zebra mussel Dreissena polymorpha, Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 137, 425–434. https://doi.org/10.1016/j.cbpb.2003.11.003
Kitchell, J.F., Stewart, D.J., Weininger, D., 1977. Applications of a Bioenergetics Model to Yellow Perch ( Perca flavescens ) and Walleye ( Stizostedion vitreum vitreum ), Journal of the Fisheries Research Board of Canada 34, 1922–1935. https://doi.org/10.1139/f77-258
Schneider, D.W., 1992. A Bioenergetics Model of Zebra Mussel, Dreissena polymorpha , Growth in the Great Lakes, Canadian Journal of Fisheries and Aquatic Sciences 49, 1406–1416. https://doi.org/10.1139/f92-156
Spillman, C.M., Hamilton, D.P., Hipsey, M.R., Imberger, J., 2008. A spatially resolved model of seasonal variations in phytoplankton and clam (Tapes philippinarum) biomass in Barbamarco Lagoon, Italy, Estuarine, Coastal and Shelf Science 79, 187–203. https://doi.org/10.1016/j.ecss.2008.03.020
Sprung, M., Rose, U., 1988. Influence of food size and food quantity on the feeding of the mussel Dreissena polymorpha, Oecologia 77, 526–532. https://doi.org/10.1007/bf00377269
Thornton, K.W., Lessem, A.S., 1978. A Temperature Algorithm for Modifying Biological Rates, Transactions of the American Fisheries Society 107, 284–287. https://doi.org/10.1577/1548-8659(1978)107<284:atafmb>2.0.co;2
Walz, N., 1978. The energy balance of the freshwater mussel Dreissena polymorpha Pallas in laboratory experiments and in Lake Constance. I. Pattern of activity, feeding and assimilation efficiency, Archiv für Hydrobiologie 55, 83–105.