Spatial population dynamics of eastern oyster in the Chesapeake Bay, Maryland
Introduction
Incorporating spatial information into stock assessments has become increasingly common (Punt, 2019), but spatial variation in natural mortality has received little attention. Spatial models can provide less biased, more precise estimates and better information for management than non-spatial models when there are differences in population processes (e.g., natural and fishing mortality, growth, recruitment) among areas (e.g., Fu and Fanning, 2004; Goethel et al., 2011; Ying et al., 2011; Guan et al., 2013; Punt et al., 2015; Vincent et al., 2017). Spatially varying natural mortality rates may lead to similar biases in stock assessment models as spatial variation in fishing mortality rates (Cao et al., 2020).
In Chesapeake Bay, the eastern oyster Crassostrea virginica historically supported one of the largest oyster fisheries in the world; however, oyster harvest and abundance have declined substantially during the past 130 years (Rothschild et al., 1994; Wilberg et al., 2011). A variety of factors, such as overfishing, disease, and habitat loss, have been linked to the decline of oysters in Chesapeake Bay (Rothschild et al., 1994; Wilberg et al., 2011; Damiano and Wilberg, 2019). In addition to natural mortality, recruitment, growth, and fishing mortality also vary spatially in Chesapeake Bay, Maryland (Jordan et al., 2002; Damiano and Wilberg, 2019). Differences in recruitment among locations could be due to available habitat for oyster settlement, spatial patterns of adult abundance, environmental factors (e.g., hydrodynamics), and larval oyster swimming behavior (Kimmel and Newell, 2007; North et al., 2008). Once larvae settle, growth rates may differ among locations due to environmental and biological factors such as salinity and food availability (Jordan et al., 2002). Exploitation rate varies spatially due to regulations and unequal distribution of effort by commercial fishers (Damiano and Wilberg, 2019). Lastly, persistent differences in exploitation, recruitment, and natural mortality rates among areas can cause spatial patterns in population dynamics because oysters are sessile as juveniles and adults.
Natural mortality is one of the primary factors that affects the abundance of adult eastern oysters in Chesapeake Bay, Maryland and previous research has documented interannual variation and spatial differences in natural mortality rates within Maryland (Vølstad et al., 2008). The dominant source of natural mortality is two diseases: MSX, which is caused by the single-celled parasite Haplosporidium nelsoni and dermo, which is caused by the protozoan parasite Perkinsus marinus (Ewart and Ford, 1993). Areas of higher salinity typically have higher mortality due to disease than areas of lower salinity (Vølstad et al., 2008). Disease-related mortality also varies over time due to changes in disease prevalence and intensity, which is related to spatial patterns in salinity (Tarnowski, 2007). Although occurring less frequently and typically at a smaller spatial scale, high freshwater discharge from rivers can also cause spatial differences in mortality of oysters due to exposure to fresh or low salinity water (Tarnowski, 2019). Because natural mortality is known to be an important driver of oyster dynamics in Maryland (Vølstad et al., 2008) our goal was to develop a modeling framework that allowed for the estimation of time- and spatially-varying natural mortality.
The spatial variability in population dynamics of oysters in the Chesapeake Bay, Maryland, suggests that treating the population as a single well-mixed unit may not be appropriate. Our objectives were to characterize spatial patterns in natural mortality, fishing mortality, abundance, and recruitment of oysters over time in Chesapeake Bay, Maryland. Given the history of disease mortality events for oysters in Maryland (Vølstad et al., 2008), we were especially interested in accounting for natural mortality that varied over time and space. We implemented stage-structured models that estimated time-varying natural mortality for 36 regions, which was the finest spatial scale that could be supported by the available data.
Section snippets
Model overview
We implemented statistical, stage-structured models for oysters within 36 regions in the Maryland portion of Chesapeake Bay (Fig. 1). The remaining five regions (grey regions in Fig. 1) were not modeled due to a lack of survey data. The models estimated abundance within five stages during 1999–2017. The model year started on October 1, the beginning of the oyster fishing season in Maryland. The model year also coincides with the start of the fall dredge survey, which is one of the
Model fits
Fits of the individual region-specific models to all data sources were acceptable overall with the fishery-dependent data generally fit less well than the fishery-independent data (Figs. S1–S36). The models fit the fall survey indices for all regions across all oyster stages with few outliers and little patterning among the residuals. Additionally, the models fit the index of recent natural mortality relatively well in all regions. Fits to the fishing mortality rate time series were generally
Discussion
The dynamics of oysters in Maryland showed substantial differences regionally such that a spatially aggregated modeling approach would have missed important regional differences in natural mortality, exploitation rates, abundance, and recruitment. For example, northern regions generally had a declining trend in abundance of market oysters during the early 2000s. This was due to a combination of one of the largest recruitment events of the previous 40 years that directly preceded the time period
CRediT authorship contribution statement
Marvin M. Mace: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing - original draft, Writing - review & editing, Visualization. Kathryn L. Doering: Conceptualization, Methodology, Software, Formal analysis, Writing - review & editing, Visualization. Michael J. Wilberg: Conceptualization, Methodology, Software, Formal analysis, Resources, Writing - review & editing, Funding acquisition, Supervision, Project administration. Amy Larimer: Conceptualization,
Declaration of Competing Interest
The authors declare no conflict of interest.
Acknowledgements
We thank the Maryland Department of Natural Resources (MD DNR) and the National Science Foundation under grant number OCE-1427019 for providing funding for this project. We acknowledge the MD DNR Oyster Stock Assessment Team for helpful comments and suggestions. We also thank the numerous individuals at MD DNR who contributed to the collection and maintenance of data we used in our modeling efforts. Two anonymous reviewers provided thoughtful comments that helped improve this manuscript.
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