Search for: All records

These data describe the estimated dispersal duration of spores of giant kelp, Macrocystis pyrifera, among connectivity cells in a high-resolution, three-dimensional, spatiotemporally-explicit ocean circulation model (Regional Oceanic Modeling System, ROMS) in southern California, USA, for an 11-year period from the beginning of 1996 to the end of 2006. Asymmetrical and dynamic estimates of giant kelp spore dispersal durations connecting source and destination ROMS cells were estimated on monthly and annual timescales using minimum mean transit times. 

Disentangling the roles of the external environment and internal biotic drivers of plant population dynamics is challenging due to the absence of relevant physiological and abundance information over appropriate space and time scales. Remote observations of giant kelp biomass and photosynthetic pigment concentrations are used to show that spatiotemporal patterns of physiological condition, and thus growth and production, are regulated by different processes depending on the scale of observation. Nutrient supply was linked to regional scale (>1 km) physiological condition dynamics, and kelp forest stands were more persistent where nutrient levels were consistently high. However, on local scales (<1 km), internal senescence processes related to canopy age demographics determined patterns of biomass loss across individual kelp forests despite uniform nutrient conditions. Repeat measurements of physiology over continuous spatial fields can provide insights into complex dynamics that are unexplained by the environmental drivers thought to regulate abundance. Emerging remote sensing technologies that provide simultaneous estimates of abundance and physiology can quantify the roles of environmental change and demographics governing plant population dynamics for a wide range of aquatic and terrestrial ecosystems. 

The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting. 

Quantifying phytoplankton composition is critical to predicting marine ecosystem structure and function. DNA meta‐barcoding and high‐performance liquid chromatography (HPLC) pigment analysis are two widely used methods for assessing phytoplankton composition; however, comparing their performance has been done only rarely. Here, we integrate DNA meta‐barcoding and HPLC pigment observations to determine eukaryotic phytoplankton composition in the Santa Barbara Channel, California. We find that both methods identify the same four dominant eukaryotic phytoplankton taxa (diatoms, dinoflagellates, chlorophytes, and prymnesiophytes), but inter‐ and intra‐lineage variability in biomarker pigmentation (associated with both a lack of taxonomic specificity of biomarker pigments and intrinsic differences in accessory pigmentation) drives substantial disagreement between the methods. Covariation network analysis circumvents this disagreement and reveals that diverse assemblages of phytoplankton and other protists covary with distinct suites of biomarker pigments. Our results highlight the strengths and weaknesses of each method in characterizing phytoplankton composition and reveal novel insights into phytoplankton physiology that could only be gained by integrating the two methods. Finally, we suggest a path to monitor eukaryotic plankton communities on unprecedented spatiotemporal scales based on the covariation of unique phytoplankton and protistan assemblages with remotely sensible phytoplankton pigment concentrations.