CORTEXA
← Browse
openalexFrontiers in Marine Science2026-07-23Cited by 0

Effects of ultraviolet-B radiation on the reproduction, germling growth, and physio-biochemical characteristics of green alga Ulva pertusa (Chlorophyta)

Jie Zhang, Hengjiang Cai, Siqi Hu, Wenjing Xie, Weiyao Zhao, Wenfei Gu, Zhu J, Zheng Li

Introduction Enhanced ultraviolet-B (UV-B, 280-320nm) radiation caused by ozone depletion is a global environmental issue. Ulva pertusa Kjellman (Chlorophyta) is often found in shallow intertidal environments exposed to ambient solar radiation. Therefore, the effects of UV-B radiation on the reproduction, germling growth, and physiological and biochemical characteristics of Ulva pertusa were investigated in this study. Methods Four daily UV-B doses (0.00, 0.31, 0.62 and 1.24 kJ/m 2 ·d) were applied to U. pertusa samples for 30 days. Results UV-B radiation reduced the density of adherent propagules (zoospores and gametes), in addition to the germination rate. It also reduced the length and leaf surface area of Ulva pertusa germlings, and this effect increased with UV-B radiation treatment dose. Additionally, UV-B radiation increased the mortality rate of germlings; the process of cell death in germlings involved a reduction in green pigment and cell contraction. Under high-dose UV-B radiation (1.24 kJ m⁻² d⁻¹), the chlorophyll a content of germlings decreased significantly, while the soluble protein content increased. UV-B-exposed germling cells produced excessive superoxide radical (•O₂⁻) and hydrogen peroxide (H₂O₂), leading to increased malondialdehyde content. However, superoxide dismutase and catalase activities in germlings were elevated for the removal of excessive reactive oxygen species. Discussion These findings demonstrate that UV-B radiation impairs the reproduction and early growth of Ulva pertusa through oxidative stress, and that the antioxidant defense system is insufficient to fully prevent UV-B-induced damage.

View free PDFSource page

Related papers

openalexFrontiers in Marine Science2026-07-24

Data-driven modelling of coastal water quality dynamics

Muhammad Uzair Mahmood, Man Sing Wong, Majid Nazeer, Sawaid Abbas, Alessandro Stocchino, Ken Tse Man Wong

Long-term coastal monitoring networks provide the opportunity to evaluate how water-quality predictability varies across contrasting coastal bays. However, most machine learning studies focused on individual stations, optically active variables or short validation periods. We ana…

View free PDFSource page
openalexFrontiers in Marine Science2026-07-24

The future of our oceans: negotiating marine fisheries, aquaculture, and living resources with unbiased science

Stephen J. Newman

Marine fisheries, aquaculture, and living marine resources occupy a broad field of marine science that encompasses biological information, harvest data, climate science, assessment modelling, economics, social science, and policy. At its core, frank, fearless, and unbiased scienc…

View free PDFSource page
openalexFrontiers in Marine Science2026-07-24

Cold seeps: climate relevance and anthropogenic impacts

Daniel M. Labbé, Heidi Gartner, Anya Dunham, Sophia Johannessen, Cherisse Du Preez

Cold seeps are deep-sea benthic features which emit hydrocarbon-rich fluids (primarily methane), representing notable conduits for carbon from long-term geologic stores to the hydrosphere. Methane is a potent greenhouse gas with substantial implications for earth’s climate. Due t…

View free PDFSource page
openalexFrontiers in Marine Science2026-07-24

Corridor-scale sea-ice navigability and its interannual volatility: a multi-model assessment of the Arctic Europe–Pacific route

Rong Wang, Jiabiao Li, Ni Feng, Yuyang Liang, Xin Zhou, Jian Ren, et al.

Climate warming has brought encouraging prospects for Arctic shipping, with many studies projecting longer navigable seasons and expanded route availability as sea ice declines. Yet most assessments still focus on the lengthening of open-water duration, that is, mean accessibilit…

View free PDFSource page
openalexFrontiers in Marine Science2026-07-23

Machine learning predictions for microbial eukaryotic plankton: implications from unevenly structured data

Christian Marchese, María Laura Zoffoli, Pierre Ramond, Timotej Turk Dermastia, Tinkara Tinta, Ramiro Logares, et al.

Machine learning models provide a scalable approach for predicting the diversity of eukaryotic microbial plankton from environmental predictors. However, the extent to which these models generalize to data outside the training set remains poorly quantified. In this study, XGBoost…

View free PDFSource page