Disentangle the larval supply-side from the post-settlement side of shellfish and macroalgal recruitment ecology to improve sustainable use of wild and cultured resources.
Relate scallop and mussel gamete/larval and kelp spore densities to eDNA concentration as a function of local broodstock abundances, spawning timing, and oceanic conditions (1.2.1). Relate spatiotemporal variability in lobster larval fate, abundance, settlement, and diet to eDNA-inferred community structure (1.2.2).
Quantify and contextualize the trophic and ecosystem consequences of historical losses and recovery of an important restoration target linking coastal ecosystems (anadromous river herring) and in turn improve our spatiotemporal ecological reference points for restoration.
Reconstruct the historical and contemporary roles of river herring in the trophic cascades of multiple lake communities with different histories of fish extirpation and recovery (1.1.1). Quantify the specific coastal community interactions of river herring within a broader assemblage of ecologically similar secondary-consumers (1.1.2).
Identify the community and biogeochemical processes that underlay the emergence and outcomes of harmful marine and freshwater blooms.
Use eDNA approaches to characterize harmful blooms, the planktonic communities associated with their emergence (and decline), and their ecosystem consequences (2.1.1). Engage citizen scientists to expand eDNA observations and opportunities (2.1.2).
Identify species shifts now occurring in the greater GoM kelp forest ecosystem, elucidate the putative causes of these shifts, and develop predictive models to forecast geographic range shifts among native and non-native taxa. This goal will be achieved by (a) comparatively studying kelp forests that span from the warm leading edge to the core of forest distribution in the region, (b) employing controlled field experiments, and (c) using computational approaches including statistical models, network analyses, and machine learning.
Map species range shifts now unfolding across the greater GoM kelp forest ecosystem, an area defined by a steep oceanographic thermal gradient and rapid regional warming (2.2.1). Identify putative drivers of species range shifts, successful or otherwise (2.2.2). Forecast ongoing and near-future range shifts in the greater GoM kelp forest system (2.2.3).
Advance next generation eDNA-based ecological inference for cross ecosystem community structure comparison by integrating and analyzing a shared Big Data resource of eDNA data and associated spatiotemporal and environmental data amassed by all research teams.
Integrate program wide sampling from a set of interconnected coastal habitats into an eDNA Big Data resource (3.1.1). Conduct comprehensive comparative studies of spatiotemporal dynamics across the set of interconnected coastal habitats (3.1.2).
To understand coastal microbial ecosystem structure, and determine its efficacy as a biosensor for changes in the environment.
Establish capacity to quantify coastal disturbances and their biogeochemical consequences (3.2.1). Advance eDNA methods for the study of estuarine microbial communities as biosensors of environmental disturbance events (3.2.2).
Understand specific communication factors that shape inter- and transdisciplinary research in eDNA-based ecological inferences in the context of team science.
Establish communication and team science (CaTS) research program for knowledge co-production and transdisciplinary integration (3.3.1). Develop processes for effective and equitable team science processes (3.3.2). Advance eDNA macrosystem research for decision making and transdisciplinary integration (3.3.3).
Polinski, J. M., Bucci, J. P., Gasser, M., & Bodnar, A. G. (2019). Metabarcoding assessment of prokaryotic and eukaryotic taxa in sediments from Stellwagen Bank National Marine Sanctuary. Scientific Reports, 9(1), 14820. https://doi.org/10.1038/s41598-019-51341-3
The most recent SBNMS Management Plan and Environmental Assessment estimated a species richness of over 575 species, but only included three genera of microorganisms25. This plan cites challenges with accurately evaluating the scale and consequences of changes in the sanctuary’s resource state due to a lack of baseline data for comparisons. Although several studies have since focused on pelagic microbes in the Gulf of Maine26,27, no studies have utilized 16S rRNA, 18S rRNA, or ITS rDNA gene metabarcoding to assess sediment microbial diversity within the boundaries of SBNMS. This study presents the first NGS metabarcoding assessment of diversity of microorganisms within SBNMS and provides much needed baseline data for future assessments and comparisons.