Virginia Aquarium & Marine Science Center

Salt marshes worldwide are threatened by sea-level rise, and their survival is linked to production of belowground biomass and mineral sediment deposition. While hurricanes can pose a significant threat to salt-marsh stability, we show that a single,

The fields of science, technology, engineering, and mathematics (STEM) are rife with inequalities and under-representation that have their roots in childhood. While researchers have focused on gender and race/ethnicity as two key dimensions of inequality, less attention has been paid to wealth. To this end, and drawing from the Social Reasoning Development approach, we examined children's and adolescents' perceptions of STEM ability and access to opportunities as a function of wealth, as well as their desire to rectify such inequalities. Participants (n=234: early childhood, n=70, mean age=6.33, SD=.79; middle childhood, n=92, mean age=8.90, SD=.83 and early adolescence, n=62, mean age=12.00; SD=1.16) in the U.K. (64% White British) and U.S. (40% White/European American) read about two characters, one high-wealth and one low-wealth. In early childhood, participants reported that the high-wealth character would have greater STEM ability and were just as likely to invite either character to take part in a STEM opportunity. By middle childhood, participants were more likely to report equal STEM abilities for both characters and to seek to rectify inequalities by inviting the low-wealth character to take part in a STEM opportunity. However, older participants reported that peers would still prefer to invite the high-wealth character. These findings also varied by ethnic group status, with minority status participants rectifying inequalities at a younger age than majority status participants. Together these findings document that children are aware of STEM inequalities based on wealth and, with age, will increasingly seek to rectify these inequalities.

Abstract Over the last half of the 20th century, the western Antarctic Peninsula has been one of the most rapidly warming regions on Earth, leading to substantial reductions in regional sea ice coverage. These changes are modulated by atmospheric forcing, including the Amundsen Sea Low (ASL) pressure system. We utilized a novel 25-year (1993–2017) time series to model the effects of environmental variability on larvae of a keystone species, the Antarctic Silverfish (Pleuragramma antarctica). Antarctic Silverfish use sea ice as spawning habitat and are important prey for penguins and other predators. We show that warmer sea surface temperature and decreased sea ice negatively impact larval abundance. Modulating both sea surface temperature and sea ice is ASL variability, where a strong ASL is associated with reduced larvae. These findings support a narrow sea ice and temperature tolerance for adult and larval fish. Further regional warming predicted to occur during the 21st century could displace fish populations, altering this pelagic ecosystem.

WAP-1D-VAR (v1.0) is a variational data assimilation 1-D marine ecosystem model combining the forward model simulation and the backward model simulation for model optimization. The backward model simulation is a tangent linear adjoint version of the forward model and is used in a variational adjoint method that optimizes model parameters to adjust model outputs towards observations. The variational adjoint method requires four components for data assimilation that WAP-1D-VAR provides within: 1) a forward model simulated by physical forcings and initial (initial conditions and model parameter guesses) and boundary conditions; 2) a cost function to evaluate misfits between the forward model results and the assimilated observations; 3) a tangent linear adjoint version of the forward model to compute the gradient of the cost function with respect to model parameters; and 4) an optimization procedure (M1QN3 3.1) to determine the direction and the optimal step size by which the model parameters should be modified to reduce the cost function based on the cost function gradient from 3). These four components are iterated sequentially to determine a set of the adjusted model parameters until present criteria are satisfied (e.g., low gradients), which then serves as an optimal numerical solution (equations with the optimized model parameters) for the final model outputs. The model development and validation for version v1.0 of the model for Palmer Station, Antarctica, is presented in: <strong>Kim, H. H., Luo, Y.-W., Ducklow, H. W., Schofield, O. M., Steinberg, D. K., Doney, S. C (2021). WAP-1D-VAR v1.0: Development and Evaluation of a One-Dimensional Variational Data Assimilation Model for the Marine Ecosystem Along the West Antarctic Peninsula, <em>Geoscientific Model Development, Under Review. </em></strong>The Kim <em>et al.</em> manuscript describes in more detail the variables and model equations used in the forward model, physical forcing data sets used for Palmer Station, and the resulting optimized model parameters.

Understanding the spatial distribution of a species is required to enact effective conservation. Complications to effective conservation can arise when the distributions of multiple target species are non-overlapping. Conservation efforts meant to protect one species may shift threats into the distribution of another species. Two species of marine turtle, loggerhead Caretta caretta and Kemp’s ridley Lepidochelys kempii, are common seasonally in Chesapeake Bay, a large estuary on the US east coast. Both species are protected under the US Endangered Species Act and face spatially complex threats in the region. We created habitat suitability models for these 2 species to inform conservation efforts in the region and explore the extent of overlap between their distributions. Argos satellite tags were deployed on 24 Kemp’s ridley and 10 loggerhead turtles to record animal locations within the Bay. Boosted regression tree models were created for each species using presence-only animal locations, predicting suitable habitat within the Bay. Habitat for Kemp’s ridley turtles was predicted in shallow, coastal areas of the southern Bay as well as in brackish areas of rivers. Loggerhead turtle habitat was predicted to extend farther north than Kemp’s ridley habitat and was generally found in deeper areas of the middle Bay. There is some evidence that these 2 species are partitioning habitat. Any conservation measures adopted to conserve marine turtles in the Chesapeake Bay should consider the habitat of both species holistically to avoid shifting impacts from one species to another.

Cruise event log from the R/V Atlantic Explorer AE0922 cruise in the North Atlantic, Bermuda BATS region during 2009 (TZEX project)