Estimating Carbon Biomass Using DNA: Phytoplankton as a Case Study
<b>Background/Objectives:</b> Estimating carbon content for cells is often necessary but difficult. In many biological, oceanographic, and marine biogeochemical studies, information on phytoplankton species composition and their biomass contribution to the community is essential. However...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | DNA |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-8856/5/1/13 |
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| Summary: | <b>Background/Objectives:</b> Estimating carbon content for cells is often necessary but difficult. In many biological, oceanographic, and marine biogeochemical studies, information on phytoplankton species composition and their biomass contribution to the community is essential. However, it is technically challenging to estimate the biomass of individual species in a natural assemblage. DNA analysis has the potential to profile species composition and estimate species-specific carbon biomass simultaneously. However, this requires an established relationship between carbon biomass and DNA content with species resolution using a measurable DNA index such as rDNA. <b>Methods:</b> In this study, DNA, rDNA, and carbon contents were measured for species from major phytoplankton phyla grown in different growth stages and under different nutrient and temperature conditions. Correlations between these parameters were examined. <b>Results:</b> Our data resulted in significant log-log regression equations: Log C = 0.8165 × Log DNA + 2.407 (R<sup>2</sup> = 0.9577, <i>p</i> < 0.0001), Log rDNA = 0.7472 × Log DNA − 0.0289 (R<sup>2</sup> = 0.9456, <i>p</i> < 0.0001), and Log C = 1.09 × Log rDNA + 2.41 (R<sup>2</sup> = 0.9199, <i>p</i> < 0.0001). Furthermore, similar strong regression functions were found when incorporating previously published data on a wide range of organisms including bacteria, plants, and animals. <b>Conclusions:</b> Carbon biomass is significantly correlated with DNA and rDNA abundances in phytoplankton and other organisms. The regression equations we developed offer a tool for estimating phytoplankton carbon biomass using DNA or rDNA and serve as a foundation for establishing similar models for other organisms. |
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| ISSN: | 2673-8856 |