Bailey David A.

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Bailey
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David A.
ORCID
0000-0002-6584-0663

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  • Article
    Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice mean state
    (American Geophysical Union, 2022-09-29) Smith, Madison M. ; Holland, Marika M. ; Petty, Alek A. ; Light, Bonnie ; Bailey, David A.
    Many modern sea ice models used in global climate models represent the subgrid‐scale heterogeneity in sea ice thickness with an ice thickness distribution (ITD), which improves model realism by representing the significant impact of the high spatial heterogeneity of sea ice thickness on thermodynamic and dynamic processes. Most models default to five thickness categories. However, little has been done to explore the effects of the resolution of this distribution (number of categories) on sea‐ice feedbacks in a coupled model framework and resulting representation of the sea ice mean state. Here, we explore this using sensitivity experiments in CESM2 with the standard 5 ice thickness categories and 15 ice thickness categories. Increasing the resolution of the ITD in a run with preindustrial climate forcing results in substantially thicker Arctic sea ice year‐round. Analyses show that this is a result of the ITD influence on ice strength. With 15 ITD categories, weaker ice occurs for the same average thickness, resulting in a higher fraction of ridged sea ice. In contrast, the higher resolution of thin ice categories results in enhanced heat conduction and bottom growth and leads to only somewhat increased winter Antarctic sea ice volume. The spatial resolution of the ICESat‐2 satellite mission provides a new opportunity to compare model outputs with observations of seasonal evolution of the ITD in the Arctic (ICESat‐2; 2018–2021). Comparisons highlight significant differences from the ITD modeled with both runs over this period, likely pointing to underlying issues contributing to the representation of average thickness.
  • Article
    Estimating production cost for large-scale seaweed farms
    (Taylor and Francis, 2022-11-11) Kite-Powell, Hauke L. ; Ask, Erick ; Augyte, Simona ; Bailey, David ; Decker, Julie ; Goudey, Clifford A. ; Grebe, Gretchen ; Li, Yaoguang ; Lindell, Scott ; Manganelli, Domenic ; Marty-Rivera, Michael ; Ng, Crystal ; Roberson, Loretta ; Stekoll, Michael ; Umanzor, Schery ; Yarish, Charles
    Seaweed farming has the potential to produce feedstocks for many applications, including food, feeds, fertilizers, biostimulants, and biofuels. Seaweeds have advantages over land-based biomass in that they require no freshwater inputs and no allocation of arable land. To date, seaweed farming has not been practiced at scales relevant to meaningful biofuel production. Here we describe a techno-economic model of large-scale seaweed farms and its application to the cultivation of the cool temperate species Saccharina latissima (sugar kelp) and the tropical seaweed Eucheumatopsis isiformis. At farm scales of 1000 ha or more, our model suggests that farm gate production costs in waters up to 200 km from the onshore support base are likely to range between $200 and $300 per dry tonne. The model also suggests that production costs below $100 per dry tonne may be achievable in some settings, which would make these seaweeds economically competitive with land-based biofuel feedstocks. While encouraging, these model results and some assumptions on which they are based require further field validation.
  • Article
    Genomic selection in algae with biphasic lifecycles: a Saccharina latissima (sugar kelp) case study
    (Frontiers Media, 2023-02-22) Huang, Mao ; Robbins, Kelly R. ; Li, Yaoguang ; Umanzor, Schery ; Marty-Rivera, Michael ; Bailey, David ; Aydlett, Margaret ; Schmutz, Jeremy ; Grimwood, Jane ; Yarish, Charles ; Lindell, Scott ; Jannink, Jean-Luc
    Introduction Sugar kelp ( Saccharina latissima ) has a biphasic life cycle, allowing selection on both thediploid sporophytes (SPs) and haploid gametophytes (GPs). Methods We trained a genomic selection (GS) model from farm-tested SP phenotypic data and used a mixed-ploidy additive relationship matrix to predict GP breeding values. Topranked GPs were used to make crosses for further farm evaluation. The relationship matrix included 866 individuals: a) founder SPs sampled from the wild; b) progeny GPs from founders; c) Farm-tested SPs crossed from b); and d) progeny GPs from farm-tested SPs. The complete pedigree-based relationship matrix was estimated for all individuals. A subset of founder SPs ( n = 58) and GPs ( n = 276) were genotyped with Diversity Array Technology and whole genome sequencing, respectively. We evaluated GS prediction accuracy via cross validation for SPs tested on farm in 2019 and 2020 using a basic GBLUP model. We also estimated the general combining ability (GCA) and specific combining ability (SCA) variances of parental GPs. A total of 11 yield-related and morphology traits were evaluated. Results The cross validation accuracies for dry weight per meter ( r ranged from 0.16 to 0.35) and wet weight per meter ( r ranged 0.19 to 0.35) were comparable to GS accuracy for yield traits in terrestrial crops. For morphology traits, cross validation accuracy exceeded 0.18 in all scenarios except for blade thickness in the second year. Accuracy in a third validation year (2021) was 0.31 for dry weight per meter over a confirmation set of 87 individuals. Discussion Our findings indicate that progress can be made in sugar kelp breeding by using genomic selection.