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Fish biodiversity is an important indicator of ecosystem health and a priority for the National Park Service in Drakes Estero, a shallow estuary within Point Reyes National Seashore, Marin County, California. However, fish diversity has yet to be described following the removal of oyster aquaculture infrastructure within Drakes Estero from 2016 to 2017. We used environmental DNA (eDNA) to characterize fish biodiversity within Drakes Estero. We amplified fish eDNA with MiFish primers and classified sequences with a 12S rRNA reference database. We identified 110 unique operational taxonomic units (OTUs, at 97% similarity) within the estuary from 40 samples across 4 sites. From these 110 OTUs, we identified 9 species and 13 taxonomic groups at the genus, family, order, or class level within the estuary. Species‐level assignments are limited by a lack of representative sequences targeted by the MiFish primers for 42% of eelgrass fishes in our region that we identified from a literature review in the Northeast Pacific (NEP) from Elkhorn Slough to Humboldt Bay. Despite this limitation, we identified some common Drakes Estero fishes with our eDNA surveys, including the three‐spined stickleback (Gasterosteus aculeatus), Pacific staghorn sculpin (Leptocottus armatus), surfperches (Embiotocidae), gobies (Gobiidae), and a hound shark (Triakidae). We also compared fish biodiversity within the estuary with that from nearby Limantour Beach, a coastal site. Limantour beach differed in community composition from Drakes Estero and was characterized by high relative abundances of anchovy (Engraulissp.) and herring (Clupeasp.). Thus, we can distinguish estuarine and non‐estuarine sites (<10 km away) with eDNA surveys. Further, eDNA surveys accounted for greater fish diversity than seine surveys conducted at one site within the estuary. Environmental DNA surveys will likely be a useful tool to monitor fish biodiversity across eelgrass estuaries in the Northeast Pacific, especially as reference databases become better populated with regional species.

 

Plant microbiomes depend on environmental conditions, stochasticity, host species, and genotype identity. Eelgrass (Zostera marina)is a unique system for plant–microbe interactions as a marine angiosperm growing in a physiologically-challenging environment with anoxic sediment, periodic exposure to air at low tide, and fluctuations in water clarity and flow. We tested the influence of host origin versus environment on eelgrass microbiome composition by transplanting 768 plants among four sites within Bodega Harbor, CA. Over three months following transplantation, we sampled microbial communities monthly on leaves and roots and sequenced the V4–V5 region of the 16S rRNA gene to assess community composition. The main driver of leaf and root microbiome composition was destination site; more modest effects of host origin site did not last longer than one month. Community phylogenetic analyses suggested that environmental filtering structures these communities, but the strength and nature of this filtering varies among sites and over time and roots and leaves show opposing gradients in clustering along a temperature gradient. We demonstrate that local environmental differences create rapid shifts in associated microbial community composition with potential functional implications for rapid host acclimation under shifting environmental conditions.

 

ABSTRACT Predicting outcomes of marine disease outbreaks presents a challenge in the face of both global and local stressors. Host-associated microbiomes may play important roles in disease dynamics but remain understudied in marine ecosystems. Host–pathogen–microbiome interactions can vary across host ranges, gradients of disease, and temperature; studying these relationships may aid our ability to forecast disease dynamics. Eelgrass, Zostera marina , is impacted by outbreaks of wasting disease caused by the opportunistic pathogen Labyrinthula zosterae . We investigated how Z. marina phyllosphere microbial communities vary with rising wasting disease lesion prevalence and severity relative to plant and meadow characteristics like shoot density, longest leaf length, and temperature across 23° latitude in the Northeastern Pacific. We detected effects of geography (11%) and smaller, but distinct, effects of temperature (30-day max sea surface temperature, 4%) and disease (lesion prevalence, 3%) on microbiome composition. Declines in alpha diversity on asymptomatic tissue occurred with rising wasting disease prevalence within meadows. However, no change in microbiome variability (dispersion) was detected between asymptomatic and symptomatic tissues. Further, we identified members of Cellvibrionaceae, Colwelliaceae, and Granulosicoccaceae on asymptomatic tissue that are predictive of wasting disease prevalence across the geographic range (3,100 kilometers). Functional roles of Colwelliaceae and Granulosicoccaceae are not known. Cellvibrionaceae, degraders of plant cellulose, were also enriched in lesions and adjacent green tissue relative to nonlesioned leaves. Cellvibrionaceae may play important roles in disease progression by degrading host tissues or overwhelming plant immune responses. Thus, inclusion of microbiomes in wasting disease studies may improve our ability to understand variable rates of infection, disease progression, and plant survival. IMPORTANCE The roles of marine microbiomes in disease remain poorly understood due, in part, to the challenging nature of sampling at appropriate spatiotemporal scales and across natural gradients of disease throughout host ranges. This is especially true for marine vascular plants like eelgrass ( Zostera marina ) that are vital for ecosystem function and biodiversity but are susceptible to rapid decline and die-off from pathogens like eukaryotic slime-mold Labyrinthula zosterae (wasting disease). We link bacterial members of phyllosphere tissues to the prevalence of wasting disease across the broadest geographic range to date for a marine plant microbiome-disease study (3,100 km). We identify Cellvibrionaceae, plant cell wall degraders, enriched (up to 61% relative abundance) within lesion tissue, which suggests this group may be playing important roles in disease progression. These findings suggest inclusion of microbiomes in marine disease studies will improve our ability to predict ecological outcomes of infection across variable landscapes spanning thousands of kilometers. 

While considerable evidence exists of biogeographic patterns in the intensity of species interactions, the influence of these patterns on variation in community structure is less clear. Studying how the distributions of traits in communities vary along global gradients can inform how variation in interactions and other factors contribute to the process of community assembly. Using a model selection approach on measures of trait dispersion in crustaceans associated with eelgrass ( Zostera marina ) spanning 30° of latitude in two oceans, we found that dispersion strongly increased with increasing predation and decreasing latitude. Ocean and epiphyte load appeared as secondary predictors; Pacific communities were more overdispersed while Atlantic communities were more clustered, and increasing epiphytes were associated with increased clustering. By examining how species interactions and environmental filters influence community structure across biogeographic regions, we demonstrate how both latitudinal variation in species interactions and historical contingency shape these responses. Community trait distributions have implications for ecosystem stability and functioning, and integrating large-scale observations of environmental filters, species interactions and traits can help us predict how communities may respond to environmental change. 

ABSTRACT Coupling remote sensing with microbial omics-based approaches provides a promising new frontier for scientists to scale microbial interactions across space and time. These data-rich, interdisciplinary methods allow us to better understand interactions between microbial communities and their environments and, in turn, their impact on ecosystem structure and function. Here, we highlight current and novel examples of applying remote sensing, machine learning, spatial statistics, and omics data approaches to marine, aquatic, and terrestrial systems. We emphasize the importance of integrating biochemical and spatiotemporal environmental data to move toward a predictive framework of microbiome interactions and their ecosystem-level effects. Finally, we emphasize lessons learned from our collaborative research with recommendations to foster productive and interdisciplinary teamwork.