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Summary Microalgae adapted to near‐zero temperatures and high light levels live on snowfields and glaciers worldwide. Snow algae have red‐colored pigments that darken snow surfaces, lowering its albedo and accelerating snowmelt. Despite their importance to the cryosphere, we know little about controls on snow algal productivity and biomass.Here, we characterize photophysiology from diverse natural field‐collected populations of alpine snow algae from the North Cascades of Washington, USA, where the major red‐bloom producing generaChlainomonas,Sanguina, andRosettawere present. We tested short‐term physiological responses of snow algae to light (0–3000 μmol m−2 s−1) and CO2levels (0–1600 ppm), allowing us to determine the saturating light and CO2levels for snow algal community net photosynthesis.All snow algal communities surveyed were adapted to extremely high light levels (3000 μmol m−2 s−1). In addition, photosynthesis rates of all the snow algal communities responded strongly to increasing CO2levels. At current atmospheric CO2levels (420 ppm), snow algal net photosynthesis rates were onlyc.50% saturated.Together, these results suggest the primary productivity of important bloom‐forming snow algal communities in alpine ecosystems will likely rise as atmospheric CO2concentrations increase, regardless of potential changes in available light levels.
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Probabilistic prediction of algal blooms from basic water quality parameters by Bayesian scale-mixture of skew-normal model
Abstract The timeliness of monitoring is essential to algal bloom management. However, acquiring algal bio-indicators can be time-consuming and laborious, and bloom biomass data often contain a large proportion of extreme values limiting the predictive models. Therefore, to predict algal blooms from readily water quality parameters (i.e. dissolved oxygen, pH, etc), and to provide a novel solution to the modeling challenges raised by the extremely distributed biomass data, a Bayesian scale-mixture of skew-normal (SMSN) model was proposed. In this study, our SMSN model accurately predicted over-dispersed biomass variations with skewed distributions in both rivers and lakes (in-sample and out-of-sample predictionR2ranged from 0.533 to 0.706 and 0.412 to 0.742, respectively). Moreover, we successfully achieve a probabilistic assessment of algal blooms with the Bayesian framework (accuracy >0.77 and macro-F1score >0.72), which robustly decreased the classic point-prediction-based inaccuracy by up to 34%. This work presented a promising Bayesian SMSN modeling technique, allowing for real-time prediction of algal biomass variations andin-situprobabilistic assessment of algal bloom.
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- Award ID(s):
- 2025982
- PAR ID:
- 10492674
- Publisher / Repository:
- Environmental Research Letters
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 18
- Issue:
- 1
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- 014034
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1748-9326/acaf11
