Roger I. E. Newell’s research while affiliated with University of Maryland Center for Environmental Science and other places

The eastern oyster, Crassostrea virginica , is an important ecological and commercial species along the Eastern Seaboard of North America, although its abundance in Chesapeake Bay (Maryland and Virginia) has declined precipitously since the mid‐1800s. Management activities, including efforts to enhance larval settlement, to rebuild the Chesapeake Bay eastern oyster population have met with limited success. Since 1939, juvenile oyster abundance has been measured at oyster bars in the northern portion of Chesapeake Bay. Prior analyses of those data indicate that juvenile oyster abundance is influenced by environmental conditions, which are related to regional climate. We quantified a regional scale (1,000 km ² ) climate signal using a synoptic climatology approach by identifying predominant weather patterns. We related these weather patterns to Susquehanna River freshwater discharge, a major influencing factor on Chesapeake Bay salinity, and the Palmer Hydrological Drought Index (PHDI), an indicator of the overall wetness or dryness of a given region. We developed path analysis models that linked weather patterns to Susquehanna River discharge and thence to either the PHDI or ambient salinity and water temperature and finally to juvenile oyster abundance in one of four preidentified spatial groupings. The path models indicated that weather patterns that produce wet conditions, leading to a higher PHDI and lower salinity, resulted in reduced juvenile oyster abundance; weather patterns that produce dry conditions that decreased the PHDI and increased salinity resulted in higher juvenile oyster abundance. Our results indicate that regions of higher juvenile oyster abundance may be predicted several months in advance using the path model. These predictions can be used to increase the effectiveness of oyster restoration efforts by indicating where hard substrate added to oyster bars in the four regions is likely to result in greater than average numbers of juvenile oysters. Received January 16, 2013; accepted July 24, 2013 Published online December 3, 2013

Stocks of eastern oysters (Crassostrea virginica, Gmelin 1791) have undergone dramatic declines in the Chesapeake Bay since the mid 1800s. As a result, substantial efforts have been made to try and reverse this decline to provide support for a commercially and socially important fishery and, more recently, to restore the oyster's important ecological role. Since 1939, juvenile oyster abundance has been measured at sentinel oyster bars in the northern portion of Chesapeake Bay, Maryland. We conducted a cluster analysis on these data and detected 4 distinct spatial patterns. These patterns were related to juvenile oyster abundance (i.e., bars that experienced high overall juvenile abundance grouped together) and salinity. Of the sentinel bars sampled since the mid 1980s, our analysis identified 13 bars that were characterized by high juvenile oyster abundance and low variation among years, which makes them prime candidate bars for protection to aid in restoration. A comparison with bars already protected within oyster sanctuaries revealed some overlap (4 total bars in common); however, the 13 bars we identified were found over broader geographical and salinity ranges. Juvenile oyster abundance on this group of 13 prime bars was intercorrelated significantly, suggesting that interannual variability in juvenile oyster abundance affects each region similarly. Significant correlations between the juvenile oyster abundance time series and the Palmer hydrological drought index suggest that variations in wet/dry cycles are the cause of this interannual variability. Our analysis also indicates that the entire oyster population of the northern Chesapeake Bay may respond in similar manner to climate change effects.

Shell growth, survival, and physiology were compared between diploid Suminoe (Crassostrea ariakensis) and eastern oysters (Crassostrea virginica) under conditions simulating a U.S. subtropical estuary. Two age groups (4 mo and 28 mo) of both oyster species were grown for 9 mo (December 2006 through August 2007) in quarantine mesocosms (700 L) supplied with ambient flowing (>= 10 L/min) water (annual temperature range, 18.6-30.4 degrees C; salinity, 28-37.7). There was no difference in overall rates of shell growth between the 2 oyster species over the 9-mo period. Specific growth rates for C. ariakensis did not differ over time, but they did for C. virginica. The growth rate of C. virginica was slowest in the winter (8.9 x 10(-4) mm(2)/day) and fastest in the spring (43.5 x 10(-4) mm(2)/day). Mortality of both species rose abruptly in April 2007, and all (100%) remaining C. ariakensis were dead by the end of the study. Although 28% of the remaining C. virginica died in April 2007, there was little further mortality in this species before the study was terminated, in August 2007. Physiological responses of both species of oysters were compared under seasonal temperate euhaline quarantine conditions to understand better how temperature affects these species without the confounding unexplained mortality encountered in the subtropical mesocosms. The clearance rate of C. ariakensis (1.2 L/g/h) was half that of C. virginica (2.2 L/g/h) during the summer (25 degrees C); however, respiration rates for C. ariakensis (2.6 ml O(2)/g/h) and C. virginica (2.5 ml O(2)/g/h) were similar. The low clearance rate of C. ariakensis during the summer resulted in a negative scope for growth (-36.2 J/g/h) during this season. During the winter, C. ariakensis remained physiologically active when water temperatures were as low as 2 degrees C; C. virginica were quiescent during this time. We conclude that the "Oregon" strain of C. ariakensis tested will not thrive in the warm subtropical waters of the U.S. southeastern coast; however, given its native range in Asia, we do not discount the possibility of an adaptation to warmer temperatures over time.

Anthropogenic carbon dioxide (CO2) emissions reduce pH of marine waters due to the absorption of atmospheric CO2 and formation of carbonic acid. Estuarine waters are more susceptible to acidification because they are subject to multiple acid sources and are less buffered than marine waters. Consequently, estuarine shell forming species may experience acidification sooner than marine species although the tolerance of estuarine calcifiers to pH changes is poorly understood. We analyzed 23years of Chesapeake Bay water quality monitoring data and found that daytime average pH significantly decreased across polyhaline waters although pH has not significantly changed across mesohaline waters. In some tributaries that once supported large oyster populations, pH is increasing. Current average conditions within some tributaries however correspond to values that we found in laboratory studies to reduce oyster biocalcification rates or resulted in net shell dissolution. Calcification rates of juvenile eastern oysters, Crassostrea virginica, were measured in laboratory studies in a three-way factorial design with 3pH levels, two salinities, and two temperatures. Biocalcification declined significantly with a reduction of ∼0.5pH units and higher temperature and salinity mitigated the decrease in biocalcification. KeywordsBiocalcification–Bivalve–Chesapeake Bay–Estuarine acidification–Oyster–pH

Anthropogenic carbon dioxide (CO2) emissions reduce pH of marine waters due to the absorption of atmospheric CO2 and formation of carbonic acid. Estuarine waters are more susceptible to acidification because they are subject to multiple acid sources and are less buffered than marine waters. Consequently, estuarine shell forming species may experience acidification sooner than marine species although the tolerance of estuarine calcifiers to pH changes is poorly understood. We analyzed 23 years of Chesapeake Bay water quality monitoring data and found that daytime average pH significantly decreased across polyhaline waters although pH has not significantly changed across mesohaline waters. In some tributaries that once supported large oyster populations, pH is increasing. Current average conditions within some tributaries however correspond to values that we found in laboratory studies to reduce oyster biocalcification rates or resulted in net shell dissolution. Calcification rates of juvenile eastern oysters, Crassostrea virginica, were measured in laboratory studies in a three-way factorial design with 3 pH levels, two salinities, and two temperatures. Biocalcification declined significantly with a reduction of ~0.5 pH units and higher temperature and salinity mitigated the decrease in biocalcification.

We examined how differing levels of habitat complexity affect interactions among a prey species (grass shrimp), an intermediate predator (white perch), and a top predator (striped bass). In laboratory mesocosms five predator–prey treatment combinations (shrimp only, white perch only, shrimp / white perch, shrimp / striped bass, and shrimp / white perch / striped bass) were paired with each of three habitat complexities (sand control, medium, and high) and replicated five times. Grass shrimp were significantly attracted to the highest complexity habitat in the presence and absence of both fish predators. White perch utilized the sand control least and the high complexity habitat most in the presence and absence of shrimp and striped bass. White perch utilized the medium complexity habitat least when shrimp and striped bass were absent, but usage of this complexity was comparable to high complexity habitat when shrimp and/or striped bass were present. Swimming and schooling activity of white perch decreased with an increase in habitat complexity. Striped bass were most attracted to high complexity habitat and exhibited ambush behavior. We conclude that fish are attracted to structure for food resources, but predatory threat will extend the time that fish spend on structurally complex habitat.

Many of the world's coastal ecosystems are impacted by multiple stressors each of which may be subject to different management strategies that may have overlapping or even conflicting objectives. Consequently, management results may be indirect and difficult to predict or observe. We developed a network simulation model intended specifically to examine ecosystem-level responses to management and applied this model to a comparison of nutrient load reduction and restoration of highly reduced stocks of bivalve suspension feeders (eastern oyster, Crassostrea virginica) in an estuarine ecosystem (Chesapeake Bay, USA). Model results suggest that a 50% reduction in nutrient inputs from the watershed will result in lower phytoplankton production in the spring and reduced delivery of organic material to the benthos that will limit spring and summer pelagic secondary production. The model predicts that low levels of oyster restoration will have no effect in the spring but does result in a reduction in phytoplankton standing stocks in the summer. Both actions have a negative effect on pelagic secondary production, but the predicted effect of oyster restoration is larger. The lower effect of oysters on phytoplankton is due to size-based differences in filtration efficiency and seasonality that result in maximum top-down grazer control of oysters at a time when the phytoplankton is already subject to heavy grazing. These results suggest that oyster restoration must be achieved at levels as much as 25-fold present biomass to have a meaningful effect on phytoplankton biomass and as much as 50-fold to achieve effects similar to a 50% nutrient load reduction. The unintended effect of oyster restoration at these levels on other consumers represents a trade-off to the desired effect of reversing eutrophication.

Restoration of ecologically important marine species and habitats is restricted by funding constraints and hindered by lack of information about trade-offs among restoration goals and the effectiveness of alternative restoration strategies. Because ecosystems provide diverse human and ecological benefits, achieving one restoration benefit may take place at the expense of other benefits. This poses challenges when attempting to allocate limited resources to optimally achieve multiple benefits, and when defining measures of restoration success. We present a restoration decision-support tool that links ecosystem prediction and human use in a flexible "optimization" framework that clarifies important restoration trade-offs, makes location-specific recommendations, predicts benefits, and quantifies the associated costs (in the form of lost opportunities). The tool is illustrated by examining restoration options related to the eastern oyster, Crassostrea virginica, which supported an historically important fishery in Chesapeake Bay and provides a range of ecosystem services such as removing seston, enhancing water clarity, and creating benthic habitat. We use an optimization approach to identify the locations where oyster restoration efforts are most likely to maximize one or more benefits such as reduction in seston, increase in light penetration, spawning stock enhancement, and harvest, subject to funding constraints and other limitations. This proof-of-concept Oyster Restoration Optimization model (ORO) incorporates predictions from three-dimensional water quality (nutrients-phytoplankton zooplankton-detritus [NPZD] with oyster filtration) and larval transport models; calculates size- and salinity-dependent growth, mortality, and fecundity of oysters; and includes economic costs of restoration efforts. Model results indicate that restoration of oysters in different regions of the Chesapeake Bay would maximize different suites of benefits due to interactions between the physical characteristics of a system and nonlinear biological processes. For example, restoration locations that maximize harvest are not the same as those that would maximize spawning stock enhancement. Although preliminary, the ORO model demonstrates that our understanding of circulation patterns, single-species population dynamics and their interactions with the ecosystem can be integrated into one quantitative framework that optimizes spending allocations and provides explicit advice along with testable predictions. The ORO model has strengths and constraints as a tool to support restoration efforts and ecosystem approaches to fisheries management.