Modeling oyster growth rate by coupling oyster population and ...

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Modeling oyster growth rate by coupling oyster population and hydrodynamic models for Apalachicola Bay, Florida, USA Hongqing Wang a,∗ , Wenrui Huang b , Mark A. Harwell a,1 , Lee Edmiston c , Elijah Johnson a , Ping Hsieh d , Katherine Milla d , John Christensen e , Jessica Stewart c , Xiaohai Liu b a

Environmental Cooperative Science Center, Environmental Sciences Institute, Florida A&M University, Tallahassee, FL 32307, USA Civil Engineering Department, College of Engineering, Florida A&M University and Florida State University, Tallahassee, FL 32310, USA c Apalachicola National Estuarine Research Reserve, Florida Department of Environmental Protection, Apalachicola, FL 32320, USA d College of Engineering Sciences, Technology & Agriculture, Florida A&M University, Tallahassee, FL 32307, USA e National Centers for Coastal Ocean Science, National Oceanic & Atmospheric Administration, 1305 East West Highway, Silver Spring, MD 20910, USA b

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Article history:

The eastern oyster (Crassostrea virginica) plays an important role both ecologically and

Received 29 April 2007

economically in Apalachicola Bay, Florida. Oyster population features such as population

Received in revised form

size, age structure, spawning, growth, and reproduction are closely related to bay salinity,

2 August 2007

which is often affected by freshwater flows from the Apalachicola River. Existing model-

Accepted 22 August 2007

ing approaches have used statistical models to examine the effects of changing freshwater

Published on line 24 October 2007

inflow/salinity regime on oyster growth rates in Apalachicola Bay. However, little has been done using population process-based models. In this study, we adapted an oyster population

Keywords:

model that simulates a diversity of population processes (including ingestion, assimila-

Eastern oyster

tion, respiration, reproduction, spawning, recruitment, and mortality) and coupled it with a

Oyster population modeling

hydrodynamic model to examine the effects of changes in freshwater flow/salinity on oyster

Coupled biological–physical models

growth rates. We simulated oyster populations at two sites, Cat Point, a less-freshwater-

Estuaries

influenced oyster reef, and Dry Bar, a more-freshwater-influenced oyster reef. The model

Bay salinity

simulations agree reasonably well with field measurements (r2 = 0.84). Statistical analyses

Freshwater inflow

suggested that oyster growth rates are significantly related to salinity. Lowest oyster growth

Apalachicola Bay

rates tend to occur in mid-spring due to lowest salinity caused by highest Apalachicola

Hydrodynamic model

River freshwater inflows whereas the growth peaks tend to occur in mid-summer because of the warm temperature and high food supply. Meanwhile, changes in freshwater inflows affect oyster growth rates through influencing salinities as well as other environmental factors such as food availability; and the magnitudes of the change in oyster growth rates depend on the difference between salinity and salinity range for optimal growth. The salinity range for optimal growth (≥2.0 mg AFDW oyster−1 day−1 ) is between 20 and 25 ppt at Cat Point and 17–26 ppt at Dry Bar. Only at the optimal salinity level can oysters achieve the maximum growth rates. Our simulations indicate that coupling oyster population process-based

∗ Corresponding author. Current address: Center for Louisiana Inland Water Studies, University of Louisiana at Lafayette, Lafayette, LA 70503, USA. Tel.: +1 850 443 7870; fax: +1 337 482 0698. E-mail address: [email protected] (H. Wang). 1 Current address: Harwell Gentile & Associates, LC, Palm Coast, FL 32164, USA. 0304-3800/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2007.08.018

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models with hydrodynamic models has significant advantage over statistical models in examining the multivariable system and understanding nonlinear relationships between oyster dynamics and environmental factors. © 2007 Elsevier B.V. All rights reserved.

1.

Introduction

The eastern oyster (Crassostrea virginica) is abundant along the coast of the Gulf of Mexico from Florida to Texas (Stanley and Sellers, 1986). Environmental conditions in Apalachicola Bay, Florida, are highly advantageous for oyster propagation and growth (Livingston et al., 1999, 2000). In Apalachicola Bay, growth of oysters is continuous throughout the year, and is considerably more rapid and more extensive than that observed in northern waters (e.g., achieving a harvest of marketable size of 76 mm in ∼18 months) (Ingle, 1950; Ingle and Dawson, 1953). Apalachicola Bay oysters are economically important, comprising more than 90% of Florida’s annual oyster landings and 10% of the catch nationwide (Berrigan, 1990; Wilber, 1992; Livingston et al., 2000). These features make the eastern oyster an important resource both ecologically and economically in the Apalachicola Bay system and to the surrounding communities of the Florida Panhandle. The propagation, growth, reproduction, and survival of oysters as well as the distribution and production of oyster reefs are influenced by multiple factors, including water circulation, salinity, temperature, food, sedimentation, bottom type, predation, disease pollution, commercial harvesting, tropical storms, and hurricanes (Galtsoff, 1964; Berrigan, 1988, 1990; Powell et al., 1995; Shumway, 1996; Christensen et al., 1998; Livingston et al., 1999, 2000; Dekshenieks et al., 2000; Klinck et al., 2002; Apeti et al., 2005). One of the characteristics of the relationships between oyster dynamics and physical factors is that oysters are sensitive to changes in the salinity regime (e.g., Christensen et al., 1998; Livingston et al., 2000; Powell et al., 2003). Variations in freshwater inflow can alter bay salinity, and as a consequence, affect oyster dynamics. It is found that the observed changes in Apalachicola oyster population dynamics are closely related to changes in Apalachicola River flow. For example, oyster landings from 1959 to 1977 were correlated negatively with river flows (Meeter et al., 1979). Wilber (1992) found that low flows were positively correlated with oyster catch per unit effort 2 years later based on oyster data during the period of 1960–1984. Moreover, highest oyster harvests occurred in 1980–1981, coinciding with a major drought and reduced freshwater flows into the bay. More importantly, it was found that one possible explanation for the 2-year time lag between low flows and subsequent poor oyster production is predation by species such as stone crabs (Menippe spp.) and oyster drills (Thais haemastoma) on newly settled spat during periods of high salinity (Wilber, 1992; Livingston et al., 2000). These facts indicate that variations in freshwater inflow tend to affect oyster productivity significantly through changing salinity regime in the bay. Moreover, as the human population grows, freshwater inflow to estuaries tends to be reduced as a result of increased demands from urban and agriculture ecosystems; this is the case for many estuaries throughout the world, including the estuaries along the

coast of the Gulf of Mexico such as Apalachicola Bay. Of particular concern in Apalachicola Bay are the present and anticipated reductions below the historical freshwater flows caused by increasing urban (particularly Atlanta) and agricultural usage (particularly center-pivot irrigation in Georgia). Therefore, studies on the effects of changes in freshwater inflow on oyster population dynamics are crucial to oyster management and ecosystem productivity and health of the Apalachicola Bay system. In examining the relationship between oyster population dynamics, including larvae, spatfall, density, growth and mortality, and bay salinity regime (i.e., range and variability of salinities), in addition to other physical factors such as temperature, turbidity, and water velocity, Christensen et al. (1998) and later Livingston et al. (2000) found that oyster growth in Apalachicola Bay was positively correlated with variation (i.e., standard deviation) in salinity, whereas oyster mortality was negatively related to maximum salinity. Livingston et al.’s (2000) analysis was based on statistical models that linked experimental biological data with physical factors by coupling a hydrodynamic model with field oyster observations. However, the applicability of the regressive models was limited by the linear regression between oyster dynamics and physical factors, which is limited by the ranges and variability of the salinity data used to develop the statistical models. Furthermore, changes in the salinity regime were found to impact not only oyster mortality as a result of changes in predation and diseases, but also oyster processes such as filtration rate and respiratory rate (e.g., Powell et al., 1992). As a consequence, the application of an oyster population model that considers oyster population processes (e.g., ingestion, assimilation, respiration, reproduction, spawning, recruitment, mortality) in relation to changing environmental factors (e.g., freshwater inflows, temperature, food concentration, total seston, water velocity) through coupling to an Apalachicola Bay-specific hydrodynamic models, provides a major advance in examining the multivariable and nonlinear relationships between oyster dynamics and physical factors. Thus, modeling the coupled biological and physical processes adds a considerable degree of realism to the simpler statistical models, such as reported in Livingston et al. (2000). Coupling oyster population models with hydrodynamic models can lead to improved understanding of the mechanisms that control the spatial and temporal variability in oyster population dynamics (e.g., Dekshenieks et al., 2000; Klinck et al., 2002). Additionally, sensitivity analyses on such a coupled modeling system can identify specific hypotheses for further research to advance the scientific understanding necessary for appropriate environmental management and decision-making, a current critical need for the Apalachicola Bay ecosystem. Historical field investigations and analyses of relationships between biological features (e.g., growth, mortality, spawning, predation, disease) and environmental conditions (e.g., salinity, freshwater inputs, storm/hurricanes, harvest-


ing, temperature) for the eastern oyster in Apalachicola Bay have laid a solid foundation for examining the response of oyster dynamics to changing environment using coupled population–hydrodynamic modeling (Ingle, 1950; Ingle and Dawson, 1953; Rockwood, 1973; Berrigan, 1988, 1990; Allen and Turner, 1989; Wilber, 1992; Fisher et al., 1996; Livingston et al., 1997, 1999, 2000; Christensen et al., 1998; Niu et al., 1998; Sun and Koch, 2001). Here we build upon this body of work; our research objectives are to (1) calibrate and validate a population model for the eastern oyster (Hofmann et al., 1992; Klinck et al., 1992; Powell et al., 1992) with modifications as necessary and (2) couple the oyster population model with a well-developed hydrodynamical model of Apalachicola Bay to examine the effects of freshwater inflow/salinity on oyster growth rates at various oyster reefs in the bay.

2.

Methods

2.1.

Study area

The Apalachicola Bay is a wide, shallow barrier island estuarine system located in the Florida panhandle (Fig. 1). The study area is approximately 63 km (39.12 mile) long and 12 km (7.45 mile) wide at the widest point. Apalachicola Bay is the terminus of a 50,500-km2 watershed drained by the Apalachicola-Chattahoochee-Flint (ACF) River System. The ACF system is the largest in Florida in terms of flow and is the third largest river system in the Gulf of Mexico, exceeded only by the Mississippi River and Mobile Bay systems. The bay was formed by deltaic processes of the Apalachicola River, which is one of the last major unpolluted alluvial systems left in the conterminous United States. The Apalachicola River largely controls the salinity of the Apalachicola Bay estuary (Livingston, 1984, 2006; Livingston et al., 2000). Other factors influencing bay salinity include tides, seasonal winds, and the

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shape of the River basin (e.g., Huang et al., 2002). Oyster reefs cover approximately 10 % of the bay bottom (Niu et al., 1998). In this study, we simulated oyster populations at two sites, Cat Point, a less-freshwater-influenced oyster reef, and Dry Bar, a more-freshwater-influenced reef. The two sites are less than 15 km (9.32 mile) apart and located to the southeast (Cat Point) and southwest (Dry Bar) of the mouth of the Apalachicola River (Fig. 1).

2.2.

The Apalachicola Bay hydrodynamic model

We modified a version of the Princeton Ocean Model (POM) to examine the effects of changes in river-inflow on salinity regime in Apalachicola Bay. POM is a semi-implicit, finitedifference model that can be used to determine the temporal and spatial changes of surface elevation, salinity, temperature, and water velocity in response to wind, tide, buoyancy, and Coriolis forces (Blumberg and Mellor, 1987). The model is capable of simulating time-dependent wind and multiple river inputs, and a variety of other forcing conditions. The detail description including the major mathematic equations in the POM is given in Appendix A. Tides at the Gulf boundaries were specified through the harmonic analysis of tide data. Riverflow scenarios (i.e., high-, middle-, and low-flow conditions) were determined by examining historical river flow data at the Sumatra gauge station provided by Northwest Florida Water Management District (NWFWMD). Each flow scenario covered a 13-month duration. The flow in the first 31 days was used as a pre-simulation period in order to establish appropriate initial conditions for the model simulations. The three-dimensional hydrodynamic model of Apalachicola Bay has been calibrated and verified using 6-month field observed dataset in 1993 (Huang and Jones, 1998; Huang et al., 2002), and further validated using dataset in 1985 and 1986 (Livingston et al., 2000). Model coefficients, including bottom-drag coefficient, bottom roughness, hori-

Fig. 1 – Apalachicoia Bay, Florida.

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zontal diffusion and viscosity, surface-wind drag coefficient, time step, and vertical and horizontal grid, were selected to minimize the difference between model predictions and observational data. Simulated salinity values were in good agreement with field observations. For example, the differences in simulated and observed monthly average salinity were