Prospective undergraduate students, graduate students, and postdoctoral fellows:
We are seeking motivated scientists to join our lab. Please contact Nicolas Cassar for available positions.
A list of funding support (Duke and external) for graduate and postdoctoral studies can be found here. See also the database provided by the Institute for Broadening Participation (IBP).
Droughts and climate-change-driven warming are leading to more frequent and intense wildfires. We use satellite and autonomous biogeochemical Argo float data to evaluate the effect of 2019–2020 Australian wildfire aerosol deposition on phytoplankton productivity. We find anomalously widespread phytoplankton blooms from December 2019 to March 2020 in the Southern Ocean downwind of Australia. Aerosol samples originating from the Australian wildfires contained a high iron content and atmospheric trajectories show that these aerosols were likely to be transported to the bloom regions, suggesting that the blooms resulted from the fertilization of the iron-limited waters of the Southern Ocean. Climate models project more frequent and severe wildfires in many regions1,2,3. A greater appreciation of the links between wildfires, pyrogenic aerosols13, nutrient cycling and marine photosynthesis could improve our understanding of the contemporary and glacial–interglacial cycling of atmospheric CO2 and the global climate system.
Since the middle of the past century, the Western Antarctic Peninsula has warmed rapidly with a significant loss of sea ice but the impacts on plankton biodiversity and carbon cycling remain an open question. Here, using a 5-year dataset of eukaryotic plankton DNA metabarcoding, we assess changes in biodiversity and net community production in this region. Our results show that sea-ice extent is a dominant factor influencing eukaryotic plankton community composition, biodiversity, and net community production. Species richness and evenness decline with an increase in sea surface temperature (SST). In regions with low SST and shallow mixed layers, the community was dominated by a diverse assemblage of diatoms and dinoflagellates. Conversely, less diverse plankton assemblages were observed in waters with higher SST and/or deep mixed layers when sea ice extent was lower. A genetic programming machine-learning model explained up to 80% of the net community production variability at the Western Antarctic Peninsula. Among the biological explanatory variables, the sea-ice environment associated plankton assemblage is the best predictor of net community production. We conclude that eukaryotic plankton diversity and carbon cycling at the Western Antarctic Peninsula are strongly linked to sea-ice conditions.
The significance of the water-side gas transfer velocity for air–sea CO2 gas exchange (k) and its non-linear dependence on wind speed (U) is well accepted. What remains a subject of inquiry are biases associated with the form of the non-linear relation linking k to U (hereafter labeled as f(U), where f(.) stands for an arbitrary function of U), the distributional properties of U (treated as a random variable) along with other external factors influencing k, and the time-averaging period used to determine k from U. To address the latter issue, a Taylor series expansion is applied to separate f(U) into a term derived from time-averaging wind speed (labeled as ⟨U⟩, where ⟨.⟩ indicates averaging over a monthly time scale) as currently employed in climate models and additive bias corrections that vary with the statistics of U. The method was explored for nine widely used f(U) parameterizations based on remotely-sensed 6-hourly global wind products at 10 m above the sea-surface. The bias in k of monthly estimates compared to the reference 6-hourly product was shown to be mainly associated with wind variability captured by the standard deviation σσU around ⟨U⟩ or, more preferably, a dimensionless coefficient of variation Iu= σσU/⟨U⟩. The proposed correction outperforms previous methodologies that adjusted k when using ⟨U⟩ only. An unexpected outcome was that upon setting I2u = 0.15 to correct biases when using monthly wind speed averages, the new model produced superior results at the global and regional scale compared to prior correction methodologies. Finally, an equation relating I2u to the time-averaging interval (spanning from 6 h to a month) is presented to enable other sub-monthly averaging periods to be used. While the focus here is on CO2, the theoretical tactic employed can be applied to other slightly soluble gases. As monthly and climatological wind data are often used in climate models for gas transfer estimates, the proposed approach provides a robust scheme that can be readily implemented in current climate models
New insights into the distributions of nitrogen fixation and diazotrophs revealed by high-resolution sensing and sampling methods
N2 fixation rates and molecular sampling sites
Nitrogen availability limits marine productivity across large ocean regions. Diazotrophs can supply new nitrogen to the marine environment via nitrogen (N2) fixation, relieving nitrogen limitation. The distributions of diazotrophs and N2 fixation have been hypothesized to be generally controlled by temperature, phosphorus and iron availability in the global ocean. However, even in the North Atlantic where most research on diazotrophs and N2 fixation has taken place, environmental controls remain contentious. Here we measure diazotroph composition, abundance and activity at high resolution using newly developed underway sampling and sensing techniques. We capture a diazotrophic community shift from Trichodesmium to UCYN-A between the oligotrophic, warm (25-29°C) Sargasso Sea and relatively nutrient-enriched, cold (13-24°C) subpolar and eastern American coastal waters. Meanwhile, N2 fixation rates measured in this study are among the highest ever recorded globally and show significant increase with phosphorus availability across the transition from the Gulf Stream into subpolar and coastal waters despite colder temperatures and higher nitrate concentrations. Transcriptional patterns in both Trichodesmium and UCYN-A indicate phosphorus stress in the subtropical gyre. Over this iron-replete transect spanning the western North Atlantic, our results suggest that temperature is the major factor controlling the diazotrophic community structure while phosphorous drives N2 fixation rates. Overall, the occurrence of record-high UCYN-A abundance and peak N2 fixation rates in the cold coastal region where nitrate concentrations are highest (~200 nM) challenges current paradigms on what drives the distribution of diazotrophs and N2 fixation. See publication.
The global distribution of marine euphotic-depth integrated Gross Primary Production (GPP) by machine-learning (Random Forest) upscaling of field observations of GPP derived from the triple isotopes of dissolved oxygen
Approximately half of global primary production occurs in the ocean. While the large-scale variability in net primary production (NPP) has been extensively studied, ocean gross primary production (GPP) has thus far received less attention. In this study, we derived two satellite-based GPP models by training machine-learning algorithms (Random Forest) withlight-dark bottle incubations (GPPLD) and the triple isotopes of dissolved oxygen (GPP17Δ).The two algorithms predict global GPPs of 9.2 ± 1.3 * 1015 and 15.1 ± 1.05 * 1015 mol O2 yr-1 for GPPLD and GPP17Δ, respectively. The projected GPP distributions agree with our understanding of the mechanisms regulating primary production. Global GPP17Δwas higher than GPPLD by an average factor of 1.6 which varied meridionally. The discrepancy between GPP17Δ and GPPLD simulations can be partly explained by the known biases of each methodology. After accounting for some of these biases, the GPP17Δ and GPPLD converge to 9.5~12.6 *1015 mol O2 yr-1, equivalent to 103~150 Pg C yr-1. Our results suggest that global oceanic GPP is 1.5-2.2 fold larger than oceanic NPP and comparable to GPP on land.
Machine-learning (random forest) estimate of depth-integrated N2 fixation and nifH gene distribution (Tang et al. 2019, Tang & Cassar 2019)
Marine nitrogen (N2) fixation supplies “new” nitrogen to the global ocean, supporting uptake and sequestration of carbon. Despite its central role, marine N2 fixation and its controlling factors remain elusive. In this study, we compile over 1,100 published observations to identify the dominant predictors of marine N2 fixation and derive global estimates based on the machine learning algorithms of random forest (RF) and support vector regression (SVR). We find that no single environmental property predicts N2 fixation at global scales. Our RF and SVR algorithms, trained with sampling coordinates and month, solar radiation, wind speed, sea surface temperature, sea surface salinity, surface nitrate, surface phosphate, surface excess phosphorus, minimum oxygen in upper 500 m, photosynthetically available radiation (PAR), mixed layer depth, averaged PAR in the mixed layer, and chlorophyll-a concentration, estimate global marine N2 fixation ranging from 68 to 90 Tg N yr-1. Comparison of our machine learning estimates and 11 other model outputs currently available in literature shows substantial discrepancies in the global magnitude and spatial distribution of marine N2 fixation, especially in the tropics and in high latitudes. The large uncertainties in marine N2 fixation highlighted in our study argue for increased and more coordinated efforts using geochemical tracers, modeling, and observations over broad ocean regions.
The Presidential Early Career Award for Scientists and Engineers (PECASE) is the highest honor bestowed by the United States government on outstanding scientists and engineers in the early stages of their independent research careers.
NSF announcement: Cassar receives PECASE award “for developing a revolutionary method to measure marine nitrogen fixation on regional and global scales and robustly quantify nitrogen cycling between the ocean and the atmosphere, and for building partnerships with community science educators to foster climate change literacy”. See Duke announcement.
See Nicholas School Press Release and original manuscript Lorrain et al. (2019): Considerable uncertainty remains over how increasing atmospheric CO2 and anthropogenic climate changes are affecting open‐ocean marine ecosystems from phytoplankton to top predators. Biological time series data are thus urgently needed for the world’s oceans. Here, we use the carbon stable isotope composition of tuna to provide a first insight into the existence of global trends in complex ecosystem dynamics and changes in the oceanic carbon cycle. From 2000 to 2015, considerable declines in δ13C values of 0.8‰–2.5‰ were observed across three tuna species sampled globally, with more substantial changes in the Pacific Ocean compared to the Atlantic and Indian Oceans. Tuna recorded not only the Suess effect, that is, fossil fuel‐derived and isotopically light carbon being incorporated into marine ecosystems, but also recorded profound changes at the base of marine food webs. We suggest a global shift in phytoplankton community structure, for example, a reduction in 13C‐rich phytoplankton such as diatoms, and/or a change in phytoplankton physiology during this period, although this does not rule out other concomitant changes at higher levels in the food webs. Our study establishes tuna δ13C values as a candidate essential ocean variable to assess complex ecosystem responses to climate change at regional to global scales and over decadal timescales. Finally, this time series will be invaluable in calibrating and validating global earth system models to project changes in marine biota.