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Determining species distributions and potential for growth involves identifying key drivers and optimal conditions for their growth and survival (Hutchings 1993, Hirzel et al. 2006, Powell et al. 2008, Lowe et al. 2017). Defining the optimal habitat conditions enables the development of accurate distribution, growth, and survival models, along with predictive models based on predicted environmental change (Hirzel et al. 2006, Barnes et al. 2017, Soniat et al. 2012, 2013, 2014, Wang et al. 2017). Numerous studies have examined and quantified optimal growth and survival conditions for the eastern oyster Crassostrea virginica (Nichey & Menzel 1960, Mackin 1961, Paynter & Dimichele 1990, La Peyre et al. 2009, 2013, 2016, Kraeuter et al. 2013, Levinton et al. 2013, Lowe et al. 2017). As an economically important and managed fishery species, these data inform management and harvest models that often occur at a local or estuary-specific level. As such, ensuring models capture critical drivers remains important.

Of all the factors examined, water temperature and salinity drive critical aspects of the eastern oyster's biology, including reproduction, spawning, development, feeding, growth, and mortality (Butler 1954, Galtsoff 1964, Stanley & Sellers 1986, Shumway 1996, La Peyre et al. 2013, Casas et al. 2015, Rybovich et al. 2016). As a eurytopic species with a geographic distribution extending from the Gulf of St. Lawrence to the Caribbean (Galtsoff 1964), oysters are exposed to, and thrive across, a wide range of temperatures (-2[degrees]C to 36[degrees]C) and salinities (0-42.5) (Shumway 1996). Despite this wide tolerance, there is general agreement that lower mortality and higher growth rates occur within narrower ranges of temperature (15[degrees]C-25[degrees]C) and salinity (10-20) (Menzel 1951, Loosanoff 1953, Harding 2007, Kraeuter et al. 2007, Lowe et al. 2017), but less agreement on how these optimal conditions may vary by region, estuary, or population. Recent work has suggested potential regional or populationspecific responses to salinity and temperature (Burford et al. 2014, Casas et al. 2015, Eierman & Hare 2016, Leonhardt et al. 2017, Lowe et al. 2017, Schwarting Miller et al. 2017). Across coastal Louisiana specifically, oysters grow in subtidal locations which experience high variations in mean salinities (Lowe et al. 2017) and differences in potential food supply (Bargu et al. 2010, Roy et al. 2016, Malozzi et al. 2018), resulting in local population differences (Leonhardt et al. 2017, Schwarting Miller et al. 2017). Using a long-term dataset, Lowe et al. (2017) recently demonstrated differences in growth and mortality across Louisiana estuaries, without explicitly accounting for differences in salinity, temperature, or other conditions between the estuaries. Determining whether salinity and temperature were the key drivers among adjacent estuaries helps clarify differences in oyster growth and mortality across a region.

In the present study, oyster growth and mortality rates between two adjacent estuaries [Breton Sound (BS) and Barataria Bay (BA)] were compared during periods of similar temperature and salinity. If growth and mortality rates are found to be similar between estuaries in this analysis, then salinity and temperature are likely driving the differences. Alternatively, if growth and mortality rates differ, given similar temperature and salinity, this provides evidence for the need to further explore the potential for population differences, or other critical environmental factors, such as salinity variance, site history, water quality, food availability, or other factors.


Study Sites

Oyster bottoms in Louisiana encompass approximately 155,800 ha of private leases and 680,000 ha of public oyster grounds, of which about 24,000 ha of the public grounds are oyster reef habitat (Banks et al. 2016). Studies were conducted by the Louisiana Department of Wildlife and Fisheries (LDWF) on public oyster grounds in BA and BS, which include 150 ha and 11,178 ha of reef bottom, respectively (LDWF 2016; Fig. 1). BA is bounded by Bayou Lafourche to the west and the Mississippi River on the eastern edge (Fig. 1). Salinities there are influenced by the Davis Pond freshwater diversion structure (FDS) which was constructed in 2002 to reduce the effects of saltwater intrusion and create habitat for fish and wildlife. The BS estuary is located east of the Mississippi River and south of the Mississippi River Gulf Outlet (MRGO). Local hydrology is influenced by the Bohemia Spillway and the Caernarvon FDS, and main-stem Mississippi River distributaries in the southern areas. Caernarvon FDS was constructed in 1991 by the United States Army Corps of Engineers (USACE) with an initial goal of improving estuarine environments by reducing saltwater intrusion and retarding the rate of coastal land loss (USACE 1984). Breton Sound differs from the BA in that there are fewer barrier islands protecting BS, making it more vulnerable to saltwater intrusion from fronts, tides, and wind-driven advection.

Environmental and Biological Datasets

Environmental and biological data for analyses were available from two sources of fishery-independent data collected by LDWF. The Nestier tray program (NT) collected data from 1998 to 2013 at 12 (BA) and 26 (BS) sites to estimate oyster growth and mortality of oysters (Louisiana Department of Natural Resources 2003). For each site, 40 oysters [greater than 50 mm shell height (SH)] were individually identified with external markings, measured to the nearest 0.1 mm SH, and two trays of 20 oysters each were deployed on the water bottom in January of each year. Mortality, salinity, and temperature were measured monthly, whereas SH was measured quarterly. Furthermore, these discrete salinity and temperature data were supplemented with external environmental data from LDWF fisheries independent monitoring programs (FIMP) from 1998 to 2013 (133 stations) to develop daily time series of salinity and temperature for each NT station. External data within 3 km from each NT station and with a direct hydrological connection (i.e., not separated by a land mass) were included.

Environmental Data

Using both NT and FIMP datasets, daily time series of salinity and temperature for each NT station was constructed. A natural polynomial smoothing spline was fit to the time series of discrete temperature and salinity data points from all data sources. Smoothing splines are series of linear segments connected at points of inflection (i.e., "knots") and are commonly used to smooth data. Smoothing splines were used to interpolate daily temperature and salinity values between sampling dates for each oyster station. To prevent over-fitting the model to the data, a limited number of knots (<20/y) were used to smooth the data. The smoothing spline was forced through or near the discrete data points associated with each NT by weighting the data such that the NTs had a weighting factor of 1.0 and external data a factor of 0.75.

To verify the accuracy of constructed splines, daily temperature and salinity observations from nearby United States Geological Survey (USGS) data recorders (hereafter, "continuous data") were regressed against predicted daily temperature and salinity splines from each study site. Continuous data recorders used were Barataria Estuary (USGS 73,802,512, USGS 292,800,090,060,000, USGS 292,859,090,004,000, USGS 291929089562600, and USGS 73,802,516) and BS (USGS 07,374,527, USGS 07,374,526, and USGS 073,745,258).

Seasonal patterns of temperature variation did not differ between estuaries (Lowe et al. 2017, Sehlinger 2018), and as a result, stations experiencing similar salinity during the same period of time (to control for water temperatures) were considered "synched environmental stanzas." On initial identification of these stanzas, a forward selection stepwise removal of outliers was used to (1) remove stations within each estuary with out-of-range salinity values that could not be reliably paired to a station in the other estuary and (2) equalize sample sizes. Once synched environmental stanzas were identified, biological data were examined to determine if growth and mortality data were available for each station, season, and year.

Biological Data

For identified synched environmental stanzas, NT data were used to calculate growth and mortality for market-sized oysters (greater than or equal to 75 mm SH). Data were removed from analyses when trays were lost due to storm events or theft, when 100% mortality occurred, or when oysters were missing. In some cases, mortality could be calculated because of the more frequent data collection (monthly) compared with SH measurements (quarterly), resulting in more data available for mortality analyses.

Growth was calculated as the difference between successive SH measurements and standardized to monthly growth rates (mm [mo.sup.-1]) by dividing the total growth by the number of days in the measurement interval and then multiplying by the number of days in an average month (30.4 days [mo.sup.-1]; Table 1). A total of 991 measurements were available for this analysis. Similarly, monthly mortality was calculated as the proportion of dead oysters to total oysters for each month ([#dead/(##live-+#dead)]; Table 1) and averaged for each season. A total of 1,214 measurements were available for this analysis.

Statistical Analyses

Assumptions of normality and homogeneity of variance for temperature, salinity, and oyster growth and mortality were checked using Shapiro--Wilk and Levene's tests. As a result, salinity (all P > 0.05 for all seasons) and growth (P > 0.05 for spring, summer, and fall; winter, P = 0.02) data were transformed using the square root transformation to meet these assumptions. A logit transformation was used for the binomial mortality data rather than an arcsin transformation because interpretations were more natural and simpler, and analytical power tended to be higher across all seasons (Warton & Hui 2011). Although the transformed data do not meet the assumptions of normality or heterodescacity, they are much improved (Sehlinger 2018), and both analysis of variance (ANOVA) and analysis of covariance (ANCOVA) are robust to all but the most extreme violations of these assumptions (Olejnik & Algina 1984, 1985, Schmider et al. 2010).

An ANOVA was run for each season to compare salinity and temperature by basin on the synched stanzas. Tukey's HSD was used to further examine significant differences within each independent variable. A full factorial ANCOVA was used to compare the linear relationship between salinity (covariate) and oyster growth or mortality (response variable) between estuaries (independent variable). Models for growth and mortality were developed for each season separately to incorporate the seasonal temperature differences. In models where there was no significant interaction between salinity and estuary, a Tukey's HSD post hoc test was used to compare estimated marginal means (i.e., at a common salinity value) for monthly growth and mortality rates. If there was a significant interaction (i.e., the regression lines were not parallel), comparing estimated marginal means would be inappropriate, and an independent t-test to compare slope values for each level of the independent variable was used. Summary models were created for growth and mortality for each season, and adjusted means were back-transformed for analysis of the differences.

All spatial analyses and products were developed in Qgis (Qgis Development Team 2011). All statistical analyses were performed in R version 3.3.3 (R Core Team 2015). Packages used include "pastecs" (Grosjean 2018) for descriptive statistics, "" (Del Re 2015) for effect size, "effects" (Fox 2018) for adjusted means, "multcomp" (Hothorn 2017) for post hoc tests, "WRS2" (Mair 2018) for robustness tests, "car" (Fox 2018) for Levene's test and Type III sum of squares, and "ggplot2" (Wickman 2018) for graphing.


Environmental Conditions

Water temperature varied following normal seasonal cycles and did not differ between basins, among seasons, or between basins within season (ANOVA, all P values > 0.05). Temperatures for the synched environmental stanzas ranged from 11.1[degrees]C to 32.2[degrees]C, with seasonal means ranging from a winter low of 16.4[degrees]C to a summer high of 29.1[degrees]C. When water temperatures were analyzed during synched environmental stanzas by season, there were no differences between estuaries for the identified data subset of stations, seasons, and years used for either growth or mortality comparisons (Table 2).

Salinity cycles varied temporally over short time scales (Lowe et al. 2017, Sehlinger 2018), but mean salinity did not differ between basins or seasons. Salinity for the synched environmental stanzas ranged from 1.3 to 23.4, with seasonal means ranging from 9.5 to 12.7. For each season, synched environmental stanzas revealed no differences in salinities between estuaries.

Biological Data-Growth

Growth rates during winter were determined from 108 NT samples from nine stations in BA and from 180 samples from 15 stations in BS (Table 1). Growth rate increased significantly with salinity, and basin had a significant effect on oyster growth, but there was no interaction between independent variables (Table 3, Fig. 2A). During the winter, growth rates ranged from 0.12 to 4.5 mm [mo.sup.-1], and oysters consistently grew significantly faster in BA [mean = 1.51 [+ or -] 0.006; mean [+ or -] SE (SE)] than in BS (mean = 1.06 [+ or -] 0.006).

One hundred ten NT samples from 10 stations in BA and

154 samples from 14 stations in BS were used for spring growth analysis (Table 1). Both salinity and basin were significant drivers of market-sized oyster growth, but the interaction was not significant (Table 3, Fig. 2B). Overall, oysters in BA grew faster than those in BS (mean = 1.97 [+ or -] 0.012 and 1.17 [+ or -] 0.0098, respectively), with growth rates ranging from 0.11 to 4.02 mm [mo.sup.-1].

Growth rates during summer were determined from 120 NT samples from 12 stations in BA and from 140 samples from 14 stations in BS (Table 1). Summer growth rates ranged from 0.03 to 5.33 mm [mo.sup.-1]. Neither salinity nor basin had a significant effect on summer oyster growth (Table 3). There was, however, a salinity-basin interaction. At low salinities (<5), oysters grew faster in BA than in BS. For instance, at a salinity of 5, BS oysters grew at a predicted rate of 0.92 mm [mo.sup.-1], whereas oysters in BA grew at a predicted rate of 1.56 mm [mo.sup.-1]. At high salinities (>15), however, oysters grew faster in BS than in BA (Fig. 2C). At a salinity of 15, BS oysters grew at a predicted rate of 2.05 mm [mo.sup.-1] and oysters in BA grew at a predicted rate of 1.49 mm [mo.sup.-1].

In BA, 88 NT samples from eight stations were used for the fall growth analysis, and 91 NT samples from 13 stations were used from BS (Table 1). Salinity was the only significant driver of market-sized oyster growth, and basin and the interaction between the two independent variables were not significant (Table 3, Fig. 2D). Fall growth rates ranged from 0.45 to 6.13 mm [mo.sup.-1], and oysters grew slightly faster in BS (mean = 2.79 [+ or -] 0.03) than in BA (mean = 2.38 [+ or -] 0.024).

Biological Data-Mortality

One hundred ten NT samples from 10 stations in BA and 165 samples from 15 stations in BS were used for the winter mortality analysis (Table 1). Both salinity and basin were significant drivers of market-sized oyster mortality, but the interaction was not significant (Table 4). As salinity increased, mortality rates in both BA and BS decreased (Fig. 3A). Overall, oysters in BA (mean = 0.098 [+ or -] 0.05) had higher mortality rates than those in BS (mean = 0.048 [+ or -] 0.044).

Spring mortality rates were determined from 130 NT samples from 10 stations in BA and from 195 samples from 15 stations in BS (Table 1). Salinity, basin, and the interaction between both independent variables (salinity*basin) had a significant effect on oyster mortality (Table 4), indicating that as salinity increases, mortality rates in BS decline more than those in BA (Fig. 3B). At low salinities (lesser than 5), BS experienced higher mortality rates (0.34) than BA (0.31). As salinities increased (greater than 15), mortality rates in both estuaries decreased, and BS (0.03) had lower mortality rates than BA (0.22).

Summer mortality rates were determined from 156 NT samples from 12 stations in BA and from 182 samples from 14 stations in BS (Table 1). Basin, salinity, and the salinity*basin interaction significantly affected oyster mortality during the summer months (Table 4). At low salinity (5), oysters in BS (0.461) experienced higher mortality rates than those in BA (0.46), and as salinities increased, both mortality rates decreased. At a salinity of 15, mortality rates were higher in BA (0.22) than in BS (0.07) (Fig. 3C).

In BA, 108 NT samples from nine stations were used for the fall mortality analysis and 168 samples from 14 stations were used from BS (Table 1). The independent variable basin and the interaction of salinity*basin had significant effects on oyster mortality (Table 4), indicating that as salinity increases, mortality rates in BS increase whereas BA mortality rates decrease (Fig. 3D). At a low salinity (5), predicted mortality rates were 0.05 in BS and 0.22 in BA. At a salinity of 15, predicted mortality rates in BS would be 0.09, but would still be lower than that of BA (0.16).


Oysters located in adjacent estuaries in coastal Louisiana experienced different growth and mortality rates when compared across similar salinity and temperature conditions. The estuary with the highest growth, BA, also experienced higher mortality rates as compared with the adjacent BS estuary. Understanding population dynamics, and drivers of growth and mortality, of an economically and ecologically important species remains important to inform local models; incorporating and understanding local patterns and resources is also critical. Although not tested in this study, these differences in growth and mortality rates between estuaries may result from other variables, including environmental conditions (i.e., food quality and composition, hydrology, and site history) or localized genetic adaptations to environmental conditions.

Maximum growth and minimal mortality rates for Louisiana oyster populations occur at lower salinities and higher water temperatures than those on the Atlantic coast (Menzel 1951, Loosanoff 1953, Galtsoff 1964, Shumway 1996, Kraeuter et al. 2007, La Peyre et al. 2016, Proestou et al. 2016, Rybovich et al. 2016, Lowe et al. 2017). Louisiana market-sized oysters experience maximum growth between 20[degrees]C and 25[degrees]C and salinities between 10 and 15, and minimum mortality between 12[degrees]C and 18[degrees]C and salinities between 9 and 13 (Butler 1954, La Peyre et al. 2016, Leonhardt et al. 2017, Lowe et al. 2017). Overall growth rates for the two estuaries follow a well-documented seasonal trend (i.e., La Peyre et al. 2009, La Peyre et al. 2015) and generally match previous reports for this size oyster in BS and BA (Owen 1953, Butler 1954, La Peyre et al. 2009, 2016, Rybovich et al. 2016).

Furthermore, in this study, the mean growth rates for winter, spring, and summer are similar to those observed in Lowe et al. (2017), in that BA supported faster growth than BS; however, there are two notable differences. First, the range of observed growth rates (0.1-6.1 mm [mo.sup.-1]) was smaller than the range (0.1-8.7 mm [mo.sup.-1]) observed in the full set of data used in Lowe et al. (2017). Second, Lowe et al. (2017) showed sack-sized (a.k.a., market-sized) oysters growing faster in BA than in BS during the fall, which is markedly different from the pattern presented in this article. These differences are attributed to the truncation of the dataset into "synched environmental stanzas," which removed large portions of data from both BS and BA NT samples, which coincided with low salinity (lesser than 5) and high salinity (greater than 20) conditions, respectively.

In Louisiana, areas of fast growth are associated with increased mortality; thus, commercial production is dependent on oyster bottoms in locations that provide both adequate growth and limited mortality (Owen 1953). In this and other studies in Louisiana, the combination of high water temperatures and high salinities results in higher mortality rates than any other temperature/salinity combination (La Peyre et al. 2016, Lowe et al. 2017). During periods of high temperature and salinity, parasitism (e.g., Dermo, Perkinsus marinus) increases (Soniat 1996) and predators (e.g., the southern oyster drill Stramonita haemastoma) are more prevalent and active (White & Wilson 1996). Mortality of market oysters was greater in BA than in BS, except in spring when they were the same. These results support previous studies of similar sized oysters (Mackin 1961) where mortality is largely a function of temperature and salinity. Lowe et al. (2017) report that the lowest morality for market oysters (greater than or equal to 75 mm) in Louisiana occurs at 17.1[degrees]C and a salinity of 12.4.

During periods of similar temperature and salinity, oysters in the adjacent estuaries had different rates of growth and mortality. Differences in growth and mortality between basins were discernable using synchronized means; however, despite synchronization, BS salinity means were 1.2-2.0 lower than BA salinity means, and variation and ranges reported for each basin differed (Table 2). Field, laboratory, and data studies have shown that the variance of salinity (La Peyre et al. 2009, Livingston et al. 2000) and salinity extremes (La Peyre et al. 2013, Rybovich et al. 2016) influence oyster metabolism, growth, and survival. Diminished growth and elevated mortality may result directly from increased energy costs of osmoconformity (Lombardi et al. 2013), or, indirectly, mortality may result from slow growth (smaller size), which facilitates parasitism (Soniat 1996) and predation (White & Wilson 1996).

The present study parameterizes growth and mortality as a function of oyster size and local environmental temperature and salinity. It indicates that growth and mortality differ at small spatial scales (immediately east and west of the Mississippi River) despite similar environmental conditions, although the proximal cause of the difference is unknown. Although not tested or measured, basin-to-basin differences in growth and mortality rates may be due to differences in other environmental conditions such as turbidity (Loosanoff & Tommers 1948, Tenore & Dunstan 1973, Kiorboe & Mohlenberg 1981, Ward & Shumway 2004), food quality (Widdows et al. 1979, Soniat et al. 1984), oxygen composition (Shumway 1996), or population genetic differences (Leonhardt et al. 2017, Schwarting Miller et al. 2017). Determining the effects of differences in environmental conditions and population responses remains critical to developing accurate models to inform management, particularly in areas experiencing rapid changes, and for significant management and restoration.

Public oyster reefs immediately east and west of the Mississippi River delta are fringed (BR) or encircled (BA) by coastal wetlands, which are rapidly disappearing (Couvillion et al. 2017). To promote wetland building, the state of Louisiana is planning additional freshwater and sediment diversions into the BA and BR estuaries (LDNR 1998). These diversions will increase sediment load, and decrease temperature and salinity in a region that, since 2001, has experienced a precipitous decline in oyster production (LDWF 2016). Furthermore, climate predictions indicate that the northern Gulf of Mexico will experience increased water temperatures, increased hydrologic variation, and increased rates of sea-level rise in the next 100 y (Girvetz et al. 2009). Wang et al. (2017) incorporated the impact of climatically driven temperature and salinity changes on oyster production; outcomes of this work are dependent on appropriate local growth and mortality rates. Localized parameterization of growth and mortality will improve the assessment of the impacts of freshwater diversions. These data will inform HSI models (e.g., Soniat et al. 2013), which relate salinity to habitat quality; such models are used to assess the impacts of freshwater diversions at a given location and predict locations of optimal habitat postdiversion. Estimates of sustainable harvests in Louisiana will also be improved by the incorporation of locally parameterized rates of growth and mortality (Soniat et al. 2012, 2014).


The authors would like to thank the LDWF for providing the data used in these analyses and Drs. Earl Melancon and Eric Powell for their helpful commentary throughout this process. Funding for this research was provided by the National Fish and Wildlife Foundation, Award Agreement #49075, through subcontract #2279 with the Louisiana Department of Wildlife and Fisheries. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. government. Funding from Louisiana Department of Wildlife and Fisheries in support of the U.S. Geological Survey's Louisiana Fish and Wildlife Cooperative Research Unit also supported this research.


Banks, P., S. Beck, K. Chapiesky & J. Isaacs. 2016. Louisiana oyster fishery management plan. Baton Rouge, LA: Louisiana Department of Wildlife and Fisheries, Office of Fisheries. 214 pp.

Bargu, S., E. Smith & K. Ozhan. 201 1. Toxic diatom Pseudo-nitzschia and its primary consumers (vectors). In: Kociolek, P. & J. Seckbach, editors. Spring, The Netherlands: The Diatom World. pp. 493-512.

Barnes, S. R., C. Bond, N. Burger, A. Kate, A. Strong, S. Weilant & S. Virgets. 2017. Economic evaluation of coastal land loss in Louisiana. J. Ocean Coast. Econ. 4:40.

Burford, M. O., J. Scarpa, B. J. Cook & M. P. Hare. 2014. Local adaptation of a marine invertebrate with a high dispersal potential: evidence from a reciprocal transplant experiment of the eastern oyster Crassostrea virginica. Mar. Ecol. Prog. Ser. 505:161- 175.

Butler, P. A. 1954. Summary of our knowledge of the oyster in the Gulf of Mexico. Fish Bull. 89:479-489.

Casas, S. M., J. F. La Peyre & M. K. La Peyre. 2015. Restoration of oyster reefs in an estuarine lake: population dynamics and shell accretion. Mar. Ecol. Prog. Ser. 524:171-184.

Couvillion, B. R., H. Beck, D. Schoolmaster & M. Fischer. 2017. Land area change in coastal Louisiana 1932 to 2016: U.S. Geological Survey Scientific Investigations Map 3381, scale 1: 265,000, 16 pp. pamphlet.

Del Re, A. 2013. Package '': computer effect sizes. R Package Version 0.2-2. Available at:

Eierman, L. E. & M. P. Hare. 2016. Reef-specific patterns of gene expression plasticity in eastern oysters (Crassostrea virginica). J. Hered. 107:90-100.

Fox, J. & S. Weisberg. 2018a. AnR companion to applied regression, 3d edition. Thousand Oaks, CA: Sage Publications. 608 pp.

Fox, J. & S. Weisberg. 2018b. Visualizing fit and lack of fit in complex regression models with predictor effect plots and partial residuals. J. Stat. Software. 87:1-27.

Galtsoff, P. S. 1964. The American oyster. Fish Bull. 64:1-480.

Girvetz, E. H., C. Zganjar, G. T. Raber, E. P. Maurer & P. Kareiva. 2009. Applied climate-change analysis; The Climate Wizard Tool. PLoS One 4:e8320.

Grosjean, P. & F. Ibanez. 2018. pastecs: package for analysis of spacetime ecological series. R Package Version 1.3.21. Available at:

Harding, J. 2007. Comparison of growth rates between diploid DEBY eastern oysters (Crassostrea virginica, Gmelin 1791), triploid eastern oysters, and triploid Suminoe oysters (C. ariakensis, Fugita 1913). J. Shellfish Res. 26:961-972.

Hirzel, A., G. LeLay, V. Helfer, C. Randin & A. Guisan. 2006. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Monogr. 199:142-152.

Hothorn, T., F. Bretz & P. Westfall. 2008. Simultaneous inference in general parametric models. Biometrical J. 50:346-363.

Hutchings, J. A. 1993. Adaptive life histories effected by age-specific survival and growth rate. Ecology 74:673-684.

Kiorboe, T. & F. Mohlenberg. 1981. Particle selection in suspension-feeding bivalves. Mar. Ecol. Prog. Ser. 5:291-296.

Kraeuter, J. N., S. Ford & M. Cummings. 2007. Oyster growth analysis: a comparison of methods. J. Shellfish Res. 26:479-491.

La Peyre, M. K., B. S. Eberline, T. M. Soniat & J. F. La Peyre. 2013. Differences in extreme salinity timing and duration differentially affect eastern oyster (Crassostrea virginica) size class growth and mortality in Breton Sound, LA. Estuar. Coast. Shelf Sci. 135:146-157.

La Peyre, M. K., J. Geaghan, G. Decossas & J. F. La Peyre. 2016. Analysis of environmental factors influencing salinity patterns, oyster growth and mortality in lower Breton Sound Estuary, Louisiana using 20 years of data. J. Coast. Res. 32:519-530.

La Peyre, M. K., K. Serra, A. Joyner & A. Humphries. 2015. Assessing shoreline exposure and oyster habitat suitability maximizes potential success for sustainable shoreline protection using restored oyster reefs. PeerJ 3:e1317.

La Peyre, M. K., B. Grossman & J. F. La Peyre. 2009. Defining optimal freshwater flow for oyster production: effects of freshet rate and magnitude of change and duration on eastern oysters and Perkinsus marinus infection. Estuaries Coasts 32:522-534.

Leonhardt, J. M., S. Casas, J. E. Supan & J. F. La Peyre. 2017. Stock assessment for eastern oyster seed production and field grow-out in Louisiana. Aquaculture 466:9-19.

Levinton, J., M. Doall & B. Allam. 2013. Growth and mortality patterns of the eastern oyster Crassostrea virginica in impacted waters in coastal waters in New York, USA. J. Shellfish Res. 32:417-427.

Livingston, R. J., F. G. Lewis, G. C. Woodsum, X.-F. Niu, B. Galperin, W. Huang, J. D. Christensen, M. E. Monacao, T. A. Battista, C. J. Klein, R. L. Howell, IV & G. L. Ray. 2000. Modelling oyster population response to variation in freshwater input. Estuar. Coast. Shelf Sci. 50:655-672.

Lombardi, S. A., N. P. Harlan & K. T. Paynter. 2013. Survival, acidbase balance, and gaping responses of the Asian oyster Crassostrea ariakensis and the eastern oyster Crassostrea virginica during clamped emersion and hypoxic immersion. J. Shellfish Res. 32:409-415.

Loosanoff, V. L. 1953. Behavior of oysters in water of low salinities. Proc. Natl. Shellfish. Assoc. 43:135-151.

Loosanoff, V. L. & F. D. Tommers. 1948. Effect of suspended silt and other substances on rate of feeding of oysters. Science 107:69-70.

Louisiana Department of Natural Resources. 1998. Coast 2050: toward a sustainable coastal Louisiana. Baton Rouge, LA: Louisiana Department of Natural Resources. 161 pp.

Louisiana Department of Natural Resources. 2003. Caernarvon freshwater diversion project: annual report 2003. Baton Rouge, LA: Louisiana Department of Natural Resources. 41 pp.

Louisiana Department of Wildlife and Fisheries. 2016. Oyster stock assessment report of the public oyster areas of Louisiana: seed grounds and seed reservations. Oyster data report series no. 22. Baton Rouge, LA: Louisiana Department of Wildlife and Fisheries. 130 pp.

Lowe, M. R., T. Sehlinger, T. M. Soniat & M. K. La Peyre. 2017. Interactive effects of water temperature and salinity on growth and mortality of eastern oyster, Crassostrea virginica: a meta-analysis using 40 years of monitoring data. J. Shellfish Res. 36:683-697.

Mackin, J. G. 1961. Mortalities of oysters. Proc. Natl. Shellfish. Assoc. 50:21-40.

Mair, P. & R. Wilcox. 2018. WRS2: Wilcox robust estimation and testing. R Package Version 0.10-0. Available at:

Mallozzi, A. J., R. M. Errera, S. Bargu & A. D. Herrmann. 2019. Impacts of elevated pCO2 on estuarine phytoplankton biomass and community structure in two biogeochemically distinct systems in Louisiana, USA. J. Exp. Mar. Biol. Ecol. 511:28-39.

Menzel, R. W. 1951. Early sexual development and growth of the American oyster in Louisiana waters. Science 113:719-721.

Nichey, F. E. & R. W. Menzel. 1960. Mortality of intertidal and subtidal oysters in Alligator Harbor, Florida. Proc. Natl. Shellfish. Assoc. 51:33-41.

Olejnik, S. F. & J. Algina. 1984. Parametric ANCOVA and the rank transform ANCOVA when the data are conditionally non-normal and heteroscedastic. J. Educ. Stat. 9:129-149.

Olejnik, S. F. & J. Algina. 1985. A review of nonparametric alternatives to analysis of covariance. Eval. Rev. 9:51-83.

Owen, H. M. 1953. Growth and mortality of oysters in Louisiana. Bull. Mar. Sci. 3:44-51.

Paynter, K. T. & L. Dimichele. 1990. Growth of tray-cultured oysters (Crassostrea virginica Gmelin) in Chesapeake Bay. Aquaculture 87:289-297.

Powell, E. N., K. A. Ashton-Alcox, J. N. Kraeuter, S. E. Ford & D. Bushek. 2008. Long-term trends in oyster population dynamics in Delaware Bay: regime shifts and response to disease. J. Shellfish Res. 27:729-755.

Proestou, D. A., B. T. Vinyard, R. J. Corbett, J. Piesz, S. K. Allen, J. M. Small, C. Li, M. Liu, G. DeBrosse, X. Guo & P. Rawson. 2016.

Performance of selectively-bred lines of eastern oyster, Crassostrea virginica, across eastern US estuaries. Aquaculture 464:17-27.

Qgis Development Team. 2011. Quantum GIS geographic information system. Open Source Geospatial Foundation Project, 45. Available at:

R Core Team. 2015. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Available at:

Roy, E. D., E. A. Smith, S. Bargu & J. R. White. 2016. Will Mississippi river diversions designed for coastal restoration cause harmful algal blooms? Ecol. Engineering. 91:350-364.

Rybovich, M., M. K. La Peyre, S. G. Hall & J. F. La Peyre. 2016. Increased temperatures combined with lowered salinities differentially impact oyster size class growth and mortality. J. Shellfish Res. 35:101-113.

Schmider, E., M. Ziegler, E. Danay, L. Beyer & M. Buhner. 2010. Is it really robust? Methodology 6:147-151.

Schwarting Miller, L., J. F. La Peyre & M. K. La Peyre. 2017. Suitability of oyster restoration sites along the Louisiana coast: examining site and site x stock interactions. J. Shellfish Res. 36:341-351.

Sehlinger, T. 2018. Analysis of temperature and salinity effects on growth and mortality of oysters (Crassostrea virginica) in Louisiana. MS thesis. University of New Orleans. 119 pp.

Shumway, S. E. 1996. Natural environmental factors. In: Kennedy, V. S., R. I. E. Newell & A. F. Eble, editors. The eastern oyster Crassostrea virginica. College Park, MD: Maryland Sea Grant College. pp. 467-513.

Soniat, T. M. 1996. Epizootiology of Perkinsus marinus disease of eastern oysters in the Gulf of Mexico. J. Shellfish Res. 15:35-43.

Soniat, T. M., S. M. Ray & L. M. Jeffrey. 1984. Components of the seston and possible available food for oysters in Galveston Bay, Texas. Contrib. Mar. Sci. 27:127-141.

Soniat, T. M., C. P. Conzelmann, J. D. Byrd, D. P. Roszell, J. L. Bridevaux, K. J. Suir & S. B. Colley. 2013. Predicting the effects of proposed Mississippi river diversions on oyster habitat quality; application of an oyster habitat suitability index model. J. Shellfish Res. 32:629-638.

Soniat, T. M., J. M. Klinck, E. N. Powell, N. Cooper, M. Abdelguerfi, E. E. Hofmann, J. Dahal, S. Tu, J. Finigan, B. S. Eberline & J. F. La Peyre. 2012. A shell-neutral modeling approach yields sustainable oyster harvest estimates: a retrospective analysis of the Louisiana state primary seed grounds. J. Shellfish Res. 31:1103-1112.

Soniat, T. M., N. Cooper, E. N. Powell, J. M. Klinck, M. Abdelguerfi, S. Tu, R. Mann & P. D. Banks. 2014. Estimating sustainable harvests of eastern oysters, Crassostrea virginica. J. Shellfish Res. 33:381-394.

Stanley, J. G. & M. A. Sellers. 1986. Species profiles: life histories and environmental requirements of coastal fishes and invertebrates (Gulf of Mexico)-eastern oyster. Report No. Biological-82 (11.64).

National Wetlands Research Center, Slidell, LA: U. S. Army Corps of Engineers Waterways Experiment Station, Vicksburg, MI. Tenore, K. R. & W. M. Dunstan. 1973. Comparison of feeding and biodeposition of three bivalves at different food levels. Mar. Biol. 21:190-195.

United States Army Corps of Engineers. 1984. Louisiana coastal area, Louisiana. Feasibility Report on Freshwater Diversion to Barataria and Breton Sound Basins. New Orleans, LA: U.S. Army Corps of Engineers New Orleans District.

Wang, H., Q. Chen, M. K. La Peyre, K. Hu & J. F. La Peyre. 2017. Predicting the impacts of Mississippi river diversions and sea-level rise on spatial patterns of eastern oyster growth rate and production. Ecol. Modell. 352:40-53.

Ward, J. E. & S. E. Shumway. 2004. Separating the grain from the chaff: particle selection in suspension- and deposit-feeding bivalves. J. Exp. Mar. Biol. Ecol. 300:83-130.

Warton, D. I. & F. K. C. Hui. 2011. The arcsine is asinine: the analysis of proportions in ecology. Ecology 92:3-10.

White, M. E. & E. A. Wilson. 1996. Predators, pests, and competitors. In: Kennedy, V. S., R. I. E. Newell & A. F. Eble, editors. The eastern oyster Crassostrea virginica. College Park, MD: Maryland Sea Grant College. pp. 559-579.

Wickman, H. 2016. ggplot2: elegant graphics for data analysis. New York, NY: Springer-Verlag. 213 pp.

Widdows, J., P. Feith & C. M. Worral. 1979. Relationships between seston, available food and feeding activity in common mussel Mytilus edulis. Mar. Biol. 50:195-207.


(1) Office of Fisheries, Louisiana Department of Wildlife and Fisheries, 2012 Lakeshore Drive, New Orleans, LA 70148; (2) Department of Biological Sciences, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148; (3) U.S. Geological Survey, Great Lakes Science Center, Hammond Bay Biological Station, Millersburg, MI 49759; (4) School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA 70803; (5) U.S. Geological Survey, Louisiana Cooperative Fish and Wildlife Research Unit, School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA 70803; (6) Canizaro Livingston Gulf States Center for Environmental Informatics, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148

(*) Corresponding author. E-mail:

DOI: 10.2983/35.038.0212
Summary of data used to estimate oyster (A) growth and (B) mortality

Basin       Total    Stations  Season    Years included
           stations    used

Barataria     12         9     Winter  1999-2005,2007-2011
              12        10     Spring  1999-2004,2007-2011
              12        12     Summer  1999-2003,2007-2011
              12         8     Fall    1999-2000,2002-2003,
Breton        26        15     Winter  1999-2005,2007-2011
              26        14     Spring  1999-2004,2007-2011
              26        14     Summer  1999-2003,2007-2011
              26        13     Fall    1999-2000,2002-2003,
Barataria     12        10     Winter  1999-2000,2002-2005,
              12        10     Spring  1998-2005,2007-2011
              12        12     Summer  1998-2005,2007-2011
              12         9     Fall    1998-2004,2007-2011
Breton        26        15     Winter  1999-2000,2002-2005,
              26        15     Spring  1998-2005,2007-2011
              26        14     Summer  1998-2005,2007-2011
              26        14     Fall    1998-2004,2007-2011

Basin         Total      Initial shell
           measurements   height (SE)

Barataria      108        70.9 (1.3)
               110        73.9 (1.5)
               120        80.2 (1.9)
                88        84.3 (2.0)
Breton         180        71.5 (1.2)
               154        73.7 (1.3)
               140        78.1 (1.5)
                91        83.7 (1.6)
Barataria      110        70.9 (1.3)
               130        73.7 (1.7)
               156        81.3 (2.1)
               108        85.2 (2.6)
Breton         165        71.5 (1.2)
               195        73.3 (1.4)
               182        75.8 (1.6)
               168        82.5 (1.7)

For each season, the table describes the number of stations available
(Total Station), number of stations used for analysis (Stations Used),
the years included, and the total number of NT used in the analyses.
Initial SH (mm) is the mean size of the 40 oysters in the NT at the
beginning of each year (e.g., winter) and each season.

Mean (range) of environmental conditions for (A) growth and (B)
mortality analysis by season after synchronization of salinity and

Basin             Winter            Spring            Summer

Barataria    16.4 (11.1-25.4)  25.2 (22.7-27.7)  28.3 (15.8-31.6)
Breton       16.9 (11.3-24.9)  24.7 (22.9-28.1)  28.9 (24.9-30.9)
Barataria     9.5 (1.3-22.3)   10.1 (3.0-19.5)   12.2 (4.8-23.4)
Breton        8.8 (1.6-22.0)    8.7 (3.0-20.0)   11.0 (3.9-20.9)
Barataria    16.6 (11.1-30.9)  24.2 (18.1-31.8)  29.1 (26.3-31.6)
Breton       16.5 (11.4-21.6)  24.5 (18.9-27.7)  29.5 (26.5-31.3)
Barataria     9.8 (1.3-22.3)    9.7 (0.7-19.4)   10.7 (1.5-23.4)
Breton        8.4 (2.4-22.0)    8.3 (1.0-20.0)    9.3 (3.1-20.9)

Basin              Fall

Barataria    21.7 (18.0-32.2)
Breton       20.8 (17.2-23.8)
Barataria    12.7 (5.0-18.4)
Breton       10.7 (3.3-17.0)
Barataria    21.9 (16.2-32.2)
Breton       21.0 (17.7-25.0)
Barataria    12.4 (2.2-21.8)
Breton       10.7 (3.8-22.3)

Seasonal analysis of covariance results for square root--transformed
oyster growth rates.

Season  Predictor       df     SSE   MSE  F     P value

Winter  Salinity          1.0   4.4  4.4  40.0  <0.0001
        Basin             1.0   1.4  1.4  12.4   0.0006
        Salinity:basin    1.0   0.1  0.1   0.5   0.5
        Residuals       138.0  15.2  0.1   -     -
Spring  Salinity          1.0   1.3  1.3  11.2   0.001
        Basin             1.0   2.1  2.1  18.5  <0.0001
        Salinity:basin    1.0   0.1  0.1   0.7   0.4
        Residuals        83.0   9.6  0.1   -     -
Summer  Salinity          1.0   0.4  0.4   2.1   0.2
        Basin             1.0   0.0  0.0   0.2   0.7
        Salinity:basin    1.0   0.7  0.7   4.0   0.04
        Residuals        80.0  14.5  0.2   -     -
Fall    Salinity          1.0   1.7  1.7  11.2   0.002
        Basin             1.0   0.2  0.2   1.2   0.3
        Salinity:basin    1.0   0.0  0.0   0.0   0.9
        Residuals        46.0   7.1  0.2   -     -

Predictor variables include square root-transformed salinity and basin
(BS versus BA). Significant effects ([alpha] < 0.05) are highlighted in

Seasonal analysis of covariance results for logit-transformed oyster
mortality rates.

Season  Predictor       df   SSE    MSE   F     P value

Winter  Salinity          1   11.5  11.5  11.4   0.0009
        Basin             1   25.1  25.1  24.9  <0.0001
        Salinity:basin    1    3.0   3.0   3.0   0.09
        Residuals       169  170.3   1.0   -     -
Spring  Salinity          1   22.1  22.1   9.9   0.002
        Basin             1   74.1  74.1  33.2  <0.0001
        Salinity:basin    1   19.2  19.3   8.6   0.004
        Residuals       150  334.3   2.2   -     -
Summer  Salinity          1   64.7  64.7  24.1  <0.0001
        Basin             1   19.6  19.7   7.3   0.008
        Salinity:basin    1   12.3  12.3   4.6   0.03
        Residuals       148  397.9   2.7   -     -
Fall    Salinity          1    1.6   1.6   1.4   0.2
        Basin             1   25.8  25.8  22.9  <0.0001
        Salinity:basin    1    5.4   5.4   4.8   0.03
        Residuals       111  124.8   1.1   -     -

Predictor variables include square root-transformed salinity and basin
(BS versus BA). Significant effects (a <0.05) are highlighted in bold
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Article Details
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Author:Sehlinger, Troy; Lowe, Michael R.; La Peyre, Megan K.; Soniat, Thomas M.
Publication:Journal of Shellfish Research
Geographic Code:1U7LA
Date:Aug 1, 2019

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