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The eastern oyster, Crassostrea virginica, is a foundation species within US Gulf of Mexico (GoM) estuaries that has experienced substantial population declines. As changes from management and climate are expected to continue to impact estuarine salinity, understanding how local oyster populations might respond and identifying populations with adaptations to more extreme changes in salinity could inform resource management, including restoration and aquaculture programs. Wild oysters were collected from four estuarine sites from Texas [Packery Channel (PC): 35.5, annual mean salinity, Aransas Bay (AB): 23.0] and Louisiana [Calcasieu Lake (CL): 16.2, Vermilion Bay (VB): 7.4] and spawned. The progeny were compared in field and laboratory studies under different salinity regimes. For the field study, F1 oysters were deployed at low (6.4) and intermediate (16.5) salinity sites in Alabama. Growth and mortality were measured monthly. Condition index and Perkinsus marinus infection intensity were measured quarterly. For the laboratory studies, mortality was recorded in F1 oysters that were exposed to salinities of 2.0, 4.0, 20.0/22.0, 38.0 and 44.0 with and without acclimation. The results of the field study and laboratory study with acclimation indicated that PC oysters are adapted to high-salinity conditions and do not tolerate very low salinities. The AB stock had the highest plasticity as it performed as well as the PC stock at high salinities and as well as Louisiana stocks at the lowest salinity. Louisiana stocks did not perform as well as the Texas stocks at high salinities. Results from the laboratory studies without salinity acclimation showed that all F1 stocks experiencing rapid mortality at low salinities when 3-month oysters collected at a salinity of 24 were used and at both low and high salinities when 7-month oysters collected at a salinity of 14.5 were used.

Species distribution modeling is the most common method of estimating climate change impacts on biodiversity. In this review, we argue a need for collaboration among physiologists, modelers and conservationists to parameterize models with physiological information in order to increase their accuracy and advance the field of conservation physiology., Climate change conservation planning relies heavily on correlative species distribution models that estimate future areas of occupancy based on environmental conditions encountered in present-day ranges. The approach benefits from rapid assessment of vulnerability over a large number of organisms, but can have poor predictive power when transposed to novel environments and reveals little in the way of causal mechanisms that define changes in species distribution or abundance. Having conservation planning rely largely on this single approach also increases the risk of policy failure. Mechanistic models that are parameterized with physiological information are expected to be more robust when extrapolating distributions to future environmental conditions and can identify physiological processes that set range boundaries. Implementation of mechanistic species distribution models requires knowledge of how environmental change influences physiological performance, and because this information is currently restricted to a comparatively small number of well-studied organisms, use of mechanistic modelling in the context of climate change conservation is limited. In this review, we propose that the need to develop mechanistic models that incorporate physiological data presents an opportunity for physiologists to contribute more directly to climate change conservation and advance the field of conservation physiology. We begin by describing the prevalence of species distribution modelling in climate change conservation, highlighting the benefits and drawbacks of both mechanistic and correlative approaches. Next, we emphasize the need to expand mechanistic models and discuss potential metrics of physiological performance suitable for integration into mechanistic models. We conclude by summarizing other factors, such as the need to consider demography, limiting broader application of mechanistic models in climate change conservation. Ideally, modellers, physiologists and conservation practitioners would work collaboratively to build models, interpret results and consider conservation management options, and articulating this need here may help to stimulate collaboration.