Andrew D. Barton’s research while affiliated with University of California, San Diego and other places

Many microbes disperse through the air, yet the phenotypic traits that enhance or constrain aerial dispersal or allow successful colonization of new habitats are poorly understood. We used a metabarcoding bacterial and eukaryotic data set to explore the trait structures of the aquatic, terrestrial, and airborne microbial communities near the Salton Sea, California, as well as those colonizing a series of experimental aquatic mesocosms. We assigned taxonomic identities to amplicon sequence variants (ASVs) and matched them to functional trait values through published papers and databases that infer phenotypic and/or metabolic traits information from taxonomy. We asked what traits distinguish successful microbial dispersers and/or colonizers from terrestrial and aquatic source communities. Our study found broad differences in taxonomic and trait composition between dispersers and colonizers compared to the source soil and water communities. Dispersers were characterized by larger cell diameters, colony formation, and fermentation abilities, while colonizers tended to be phototrophs that form mucilage and have siliceous coverings. Shorter population doubling times, spore-, and/or cyst-forming organisms were more abundant among the dispersers and colonizers than the sources. These results show that the capacity for aerial dispersal and colonization varies among microbial functional groups and taxa and is related to traits that affect other functions like resource acquisition, predator avoidance, and reproduction. The ability to disperse and colonize new habitats may therefore distinguish microbial guilds based on tradeoffs among alternate ecological strategies. IMPORTANCE Microbes have long been thought to disperse rapidly across biogeographic barriers; however, whether dispersal or colonization vary among taxa or groups or is related to cellular traits remains unknown. We use a novel approach to understand how microorganisms disperse and establish themselves in different environments by looking at their traits (physiology, morphology, life history, and behavior characteristics). By collecting samples from habitats including water, soil, and the air and colonizing experimental tanks, we found dispersal and invasion vary among microorganisms. Some taxa and functional groups are found more often in the air or colonizing aquatic environments, while others that are commonly found in the soil or water rarely disperse or invade new habitat. Interestingly, the traits that help microorganisms survive and thrive also play a role in their ability to disperse and colonize. These findings have significant implications for understanding microorganisms’ success and adaptation to new environments.

Anchovies and sardines are some of the most economically and ecologically important and well-studied fishes on Earth, but there is still uncertainty regarding how distributions and abundances change through time and space. We bring together larval abundance data for northern anchovy (Engraulis mordax) and Pacific sardine (Sardinops sagax) collected by United States and Mexican scientists over 50 years (1963–2015) to test the Basin and Asynchrony hypotheses. The Basin hypothesis states that a species’ geographic range and spawning area (R) increase with overall abundance (A) according to a power law, R = aAb , where the exponent (b) is less than ∼0.5 when the rate of increasing area occupied saturates as population size increases. The Asynchrony hypothesis postulates that anchovy and sardine abundances are negatively correlated through time. We found that the Basin hypothesis was supported for both species but the Asynchrony hypothesis was not during this 53-year period. Due to collaboration between US and Mexican scientists, we were able to better understand how two important fishes utilize their environment.

The Arctic Ocean experiences significant seasonal to interannual environmental changes, including in temperature, light, sea ice, and surface nutrient concentrations, that influence the dynamics of marine plankton populations. Here, we use a hindcast simulation (1948–2009) of size-structured Arctic Ocean plankton communities, ocean circulation, and biogeochemical cycles in order to better understand how seasonal to interannual changes in the environment influence phytoplankton physiology, plankton community structure, trophic dynamics, and fish production in the Arctic Ocean. The growth of model phytoplankton was primarily limited in winter, spring, and fall by light, but in summer, the growth of smaller and larger phytoplankton was mostly limited by temperature and nutrient availability, respectively. The dominant trophic pathway in summer was from phytoplankton to herbivorous zooplankton such that the average trophic position of model zooplankton was lower in the summer growing season compared to the rest of the year. On interannual timescales, changes in plankton community composition were strongly tied to interannual changes in bottom-up forcing by the environment. In the summer, in years with less ice and warmer temperatures, the biomass of phytoplankton and zooplankton was higher, the size–abundance relationship slopes were more negative (indicative of a phytoplankton community enriched in smaller phytoplankton), zooplankton had higher mean trophic position (indicative of greater carnivory), and potential fishery production was greater, fueled by increased mesozooplankton biomass and flux of organic matter to the benthos. The summertime shift toward greater carnivory in warmer and low-ice years was due primarily to changes in phenology, with phytoplankton and microzooplankton blooms occurring approximately 1 month earlier in these conditions and carnivorous zooplankton increasing in abundance during summer. The model provides a spatially and temporally complete overview of simulated changes in plankton communities in the Arctic Ocean occurring on seasonal to interannual timescales, and it provides insights into the mechanisms underlying these changes as well as their broader biogeochemical and ecosystem significance.

Observations of phytoplankton abundances and community structure are critical towards understanding marine ecosystems. Common approaches to determine group-specific abundances include measuring phytoplankton pigments via high-performance liquid chromatography and DNA-based metabarcoding. Increasingly, mRNA abundances via metatranscriptomics are also employed. As phytoplankton pigments are used to develop and validate remote sensing algorithms, further comparisons between pigments and other metrics are needed to validate the extent to which these measurements agree for group-specific abundances; however, most previous comparisons have been hindered by metabarcoding and metatranscriptomics solely producing relative abundance data. By employing quantitative approaches that express both 18S rDNA and total mRNA as concentrations, we show that these measurements are related for several eukaryotic phytoplankton groups. We further propose that integration of these can be used to examine ecological patterns more deeply. For example, productivity-diversity relationships of both the whole community and individual groups show a dinoflagellate-driven negative trend rather than the commonly-found unimodal pattern. Pigments are also shown to relate to certain harmful algal bloom-forming taxa as well as the expression of sets of genes. Altogether, these results suggest that potential models of pigment concentrations via hyperspectral remote sensing may enable improved assessments of global phytoplankton community structure, including the detection of harmful algal blooms, and support the development of ecosystem models.

The relative prevalence of endemic and cosmopolitan biogeographic ranges in marine microbes, and the factors that shape these patterns, are not well known. Using prokaryotic and eukaryotic amplicon sequence data spanning 445 near-surface samples in the Southern California Current region from 2014 to 2020, we quantified the proportion of taxa exhibiting endemic, cosmopolitan, and generalist distributions in this region. Using in-situ data on temperature, salinity, and nitrogen, we categorized oceanic habitats that were internally consistent but whose location varied over time. In this context, we defined cosmopolitan taxa as those that appeared in all regional habitats and endemics as taxa that only appeared in one habitat. Generalists were defined as taxa occupying more than one but not all habitats. We also quantified each taxon’s habitat affinity, defined as habitats where taxa were significantly more abundant than expected. Approximately 20% of taxa exhibited endemic ranges, while around 30% exhibited cosmopolitan ranges. Most microbial taxa (50.3%) were generalists. Many of these taxa had no habitat affinity (> 70%) and were relatively rare. Our results for this region show that, like terrestrial systems and for metazoans, cosmopolitan and endemic biogeographies are common, but with the addition of a large number of taxa that are rare and randomly distributed.

The Arctic Ocean experiences significant seasonal to interannual environmental changes, including in temperature, light, sea ice, and surface nutrient concentrations, that influence the dynamics of marine plankton populations. Here, we use a hindcast simulation (1948–2009) of size-structured Arctic Ocean plankton communities, ocean circulation, and biogeochemical cycles in order to better understand how seasonal to interannual changes in the environment influence phytoplankton physiology, plankton community structure, trophic dynamics, and fish production in the Arctic Ocean. The growth of model phytoplankton was primarily limited in winter, spring, and fall by light, but in summer, the growth of smaller and larger phytoplankton was mostly limited by temperature and nutrient availability, respectively. The dominant trophic pathway in summer was from phytoplankton to herbivorous zooplankton, such that the average trophic position of model zooplankton was lower in the summer growing season compared with the rest of the year. On interannual timescales, changes in plankton community composition were strongly tied to interannual changes in bottom-up forcing by the environment. In the summer, in years with lower ice and warmer temperatures, the biomass of phytoplankton and zooplankton was higher, the size abundance relationship slopes were more negative (indicative of a phytoplankton community enriched in smaller phytoplankton), zooplankton had higher mean trophic position (indicative of greater carnivory), and potential fisheries production was greater, fueled by increased mesozooplankton biomass and flux of organic matter to the benthos. The summertime shift toward greater carnivory in warmer and low-ice years was due primarily to changes in phenology, with phytoplankton and microzoopankton blooms occurring approximately one month earlier in these conditions, and carnivorous zooplankton increasing in abundance during summer. The model provides a spatially and temporally complete overview of changes in plankton communities in the Arctic Ocean occurring on seasonal to interannual timescales, and provides insights on the mechanisms underlying these changes as well as their broader biogeochemical and ecosystems significance.

Plankton community modeling is a critical tool for understanding the processes that shape marine ecosystems and their impacts on global biogeochemical cycles. These models can be of variable ecological, physiological, and physical complexity. Many published models are either not publicly available or implemented in static and inflexible code, thus hampering adoption, collaboration, and reproducibility of results. Here we present Phydra, an open-source library for plankton community modeling, and Xarray-simlab-ODE (XSO), a modular framework for efficient, flexible, and reproducible model development based on ordinary differential equations. Both tools are written in Python. Phydra provides pre-built models and model components that can be modified and assembled to develop plankton community models of various levels of ecological complexity. The components can be created, adapted, and modified using standard variable types provided by the XSO framework. XSO is embedded in the Python scientific ecosystem and is integrated with tools for data analysis and visualization. To demonstrate the range of applicability and how Phydra and XSO can be used to develop and execute models, we present three applications: (1) a highly simplified nutrient–phytoplankton (NP) model in a chemostat setting, (2) a nutrient–phytoplankton–zooplankton–detritus (NPZD) model in a zero-dimensional pelagic ocean setting, and (3) a size-structured plankton community model that resolves 50 phytoplankton and 50 zooplankton size classes with functional traits determined by allometric relationships. The applications presented here are available as interactive Jupyter notebooks and can be used by the scientific community to build, modify, and run plankton community models based on differential equations for a diverse range of scientific pursuits.

Phytoplankton species exhibit fundamental temperature niches that drive observed species distributions linked to realized temperature niches. A recent analysis of field observations of Prochlorococcus showed that for all ecotypes, the realized niche was, on average, colder and wider than the fundamental niche. Using a simple trait‐based metacommunity model that resolves fundamental temperature niches for a range of competing phytoplankton, we ask how dispersal and local temperature variability influence species distributions and diversity, and whether these processes help explain the observed discrepancies between fundamental and realized niches for Prochlorococcus . We find that, independently, both dispersal and temperature variability increase realized temperature niche widths and local diversity. The combined effects result in high diversity and realized temperature niches that are consistently wider than fundamental temperature niches. These results have broad implications for understanding the drivers of phytoplankton biogeography as well as for refining species distribution models used to project how climate change impacts phytoplankton distributions.

Phytoplankton exhibit diverse physiological responses to temperature which influence their fitness in the environment and consequently alter their community structure. Here, we explored the sensitivity of phytoplankton community structure to thermal response parameterization in a modelled marine phytoplankton community. Using published empirical data, we evaluated the maximum thermal growth rates (μmax) and temperature coefficients (Q10; the rate at which growth scales with temperature) of six key Phytoplankton Functional Types (PFTs): coccolithophores, cyanobacteria, diatoms, diazotrophs, dinoflagellates, and green algae. Following three well‐documented methods, PFTs were either assumed to have (1) the same μmax and the same Q10 (as in to Eppley, 1972), (2) a unique μmax but the same Q10 (similar to Kremer et al., 2017), or (3) a unique μmax and a unique Q10 (following Anderson et al., 2021). These trait values were then implemented within the Massachusetts Institute of Technology biogeochemistry and ecosystem model (called Darwin) for each PFT under a control and climate change scenario. Our results suggest that applying a μmax and Q10 universally across PFTs (as in Eppley, 1972) leads to unrealistic phytoplankton communities, which lack diatoms globally. Additionally, we find that accounting for differences in the Q10 between PFTs can significantly impact each PFT's competitive ability, especially at high latitudes, leading to altered modeled phytoplankton community structures in our control and climate change simulations. This then impacts estimates of biogeochemical processes, with, for example, estimates of export production varying by ~10% in the Southern Ocean depending on the parameterization. Our results indicate that the diversity of thermal response traits in phytoplankton not only shape community composition in the historical and future, warmer ocean, but that these traits have significant feedbacks on global biogeochemical cycles.

Harmful algal blooms (HABs) are increasing globally, causing economic, human health, and ecosystem harm. In spite of the frequent occurrence of HABs, the mechanisms responsible for their exceptionally high biomass remain imperfectly understood. A 50-y-old hypothesis posits that some dense blooms derive from dinoflagellate motility: organisms swim upward during the day to photosynthesize and downward at night to access deep nutrients. This allows dinoflagellates to outgrow their nonmotile competitors. We tested this hypothesis with in situ data from an autonomous, ocean-wave-powered vertical profiling system. We showed that the dinoflagellate Lingulodinium polyedra's vertical migration led to depletion of deep nitrate during a 2020 red tide HAB event. Downward migration began at dusk, with the maximum migration depth determined by local nitrate concentrations. Losses of nitrate at depth were balanced by proportional increases in phytoplankton chlorophyll concentrations and suspended particle load, conclusively linking vertical migration to the access and assimilation of deep nitrate in the ocean environment. Vertical migration during the red tide created anomalous biogeochemical conditions compared to 70 y of climatological data, demonstrating the capacity of these events to temporarily reshape the coastal ocean's ecosystem and biogeochemistry. Advances in the understanding of the physiological, behavioral, and metabolic dynamics of HAB-forming organisms from cutting-edge observational techniques will improve our ability to forecast HABs and mitigate their consequences in the future.