Enhancement of diatom growth and phytoplankton productivity with reduced O2 availability is moderated by rising CO2

AbstractMany marine organisms are exposed to decreasing O2 levels due to warming-induced expansion of hypoxic zones and ocean deoxygenation (DeO2). Nevertheless, effects of DeO2 on phytoplankton have been neglected due to technical bottlenecks on examining O2 effects on O2-producing organisms. Here we show that lowered O2 levels increased primary productivity of a coastal phytoplankton assemblage, and enhanced photosynthesis and growth in the coastal diatom Thalassiosira weissflogii. Mechanistically, reduced O2 suppressed mitochondrial respiration and photorespiration of T. weissflogii, but increased the efficiency of their CO2 concentrating mechanisms (CCMs), effective quantum yield and improved light use efficiency, which was apparent under both ambient and elevated CO2 concentrations leading to ocean acidification (OA). While the elevated CO2 treatment partially counteracted the effect of low O2 in terms of CCMs activity, reduced levels of O2 still strongly enhanced phytoplankton primary productivity. This implies that decreased availability of O2 with progressive DeO2 could boost re-oxygenation by diatom-dominated phytoplankton communities, especially in hypoxic areas, with potentially profound consequences for marine ecosystem services in coastal and pelagic oceans.

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Simulating Spatiotemporal Dynamics of Sichuan Grassland Net Primary Productivity Using the CASA Model and In Situ Observations

The Scientific World JOURNAL ◽

10.1155/2014/956963 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Chuanjiang Tang ◽

Xinyu Fu ◽

Dong Jiang ◽

Jingying Fu ◽

Xinyue Zhang ◽

...

Keyword(s):

Primary Productivity ◽

Net Primary Productivity ◽

Alpine Meadow ◽

Growth Period ◽

Temporal Variations ◽

Light Use Efficiency ◽

Important Indicator ◽

Casa Model ◽

Use Efficiency

Net primary productivity (NPP) is an important indicator for grassland resource management and sustainable development. In this paper, the NPP of Sichuan grasslands was estimated by the Carnegie-Ames-Stanford Approach (CASA) model. The results were validated with in situ data. The overall precision reached 70%; alpine meadow had the highest precision at greater than 75%, among the three types of grasslands validated. The spatial and temporal variations of Sichuan grasslands were analyzed. The absorbed photosynthetic active radiation (APAR), light use efficiency (ε), and NPP of Sichuan grasslands peaked in August, which was a vigorous growth period during 2011. High values of APAR existed in the southwest regions in altitudes from 2000 m to 4000 m. Light use efficiency (ε) varied in the different types of grasslands. The Sichuan grassland NPP was mainly distributed in the region of 3000–5000 m altitude. The NPP of alpine meadow accounted for 50% of the total NPP of Sichuan grasslands.

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Sources of uncertainty in gross primary productivity simulated by light use efficiency models: Model structure, parameters, input data, and spatial resolution

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2018.08.003 ◽

2018 ◽

Vol 263 ◽

pp. 242-257 ◽

Cited By ~ 25

Author(s):

Yi Zheng ◽

Li Zhang ◽

Jingfeng Xiao ◽

Wenping Yuan ◽

Min Yan ◽

...

Keyword(s):

Spatial Resolution ◽

Primary Productivity ◽

Input Data ◽

Gross Primary Productivity ◽

Structure Parameters ◽

Model Structure ◽

Light Use Efficiency ◽

Sources Of Uncertainty ◽

Use Efficiency

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A MODIS-Derived Primary Productivity Dataset for North America Based on Topographically-Aware Weather Data and Light-Use Efficiency

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518831 ◽

2018 ◽

Author(s):

Sergio Bernardes

Keyword(s):

North America ◽

Primary Productivity ◽

Weather Data ◽

Light Use Efficiency ◽

Use Efficiency

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Tree–grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna

Biogeosciences ◽

10.5194/bg-14-111-2017 ◽

2017 ◽

Vol 14 (1) ◽

pp. 111-129 ◽

Cited By ~ 13

Author(s):

Caitlin E. Moore ◽

Jason Beringer ◽

Bradley Evans ◽

Lindsay B. Hutley ◽

Nigel J. Tapper

Keyword(s):

Primary Productivity ◽

Vegetation Index ◽

Temporal Dynamics ◽

Seasonal Pattern ◽

Gross Primary Productivity ◽

Perennial Grasses ◽

Light Use Efficiency ◽

Tropical Savanna ◽

Flux Tower ◽

Use Efficiency

Abstract. The coexistence of trees and grasses in savanna ecosystems results in marked phenological dynamics that vary spatially and temporally with climate. Australian savannas comprise a complex variety of life forms and phenologies, from evergreen trees to annual/perennial grasses, producing a boom–bust seasonal pattern of productivity that follows the wet–dry seasonal rainfall cycle. As the climate changes into the 21st century, modification to rainfall and temperature regimes in savannas is highly likely. There is a need to link phenology cycles of different species with productivity to understand how the tree–grass relationship may shift in response to climate change. This study investigated the relationship between productivity and phenology for trees and grasses in an Australian tropical savanna. Productivity, estimated from overstory (tree) and understory (grass) eddy covariance flux tower estimates of gross primary productivity (GPP), was compared against 2 years of repeat time-lapse digital photography (phenocams). We explored the phenology–productivity relationship at the ecosystem scale using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and flux tower GPP. These data were obtained from the Howard Springs OzFlux/Fluxnet site (AU-How) in northern Australia. Two greenness indices were calculated from the phenocam images: the green chromatic coordinate (GCC) and excess green index (ExG). These indices captured the temporal dynamics of the understory (grass) and overstory (trees) phenology and were correlated well with tower GPP for understory (r2 =  0.65 to 0.72) but less so for the overstory (r2 =  0.14 to 0.23). The MODIS enhanced vegetation index (EVI) correlated well with GPP at the ecosystem scale (r2 =  0.70). Lastly, we used GCC and EVI to parameterise a light use efficiency (LUE) model and found it to improve the estimates of GPP for the overstory, understory and ecosystem. We conclude that phenology is an important parameter to consider in estimating GPP from LUE models in savannas and that phenocams can provide important insights into the phenological variability of trees and grasses.

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Measured and modeled interactive effects of potassium deficiency and water deficit on gross primary productivity and light‐use efficiency in E ucalyptus grandis plantations

Global Change Biology ◽

10.1111/gcb.12817 ◽

2015 ◽

Vol 21 (5) ◽

pp. 2022-2039 ◽

Cited By ~ 31

Author(s):

Mathias Christina ◽

Guerric Le Maire ◽

Patricia Battie‐Laclau ◽

Yann Nouvellon ◽

Jean‐Pierre Bouillet ◽

...

Keyword(s):

Water Deficit ◽

Primary Productivity ◽

Potassium Deficiency ◽

Gross Primary Productivity ◽

Interactive Effects ◽

Light Use Efficiency ◽

Use Efficiency

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LINKING PHOTOSYNTHETIC LIGHT USE EFFICIENCY AND OPTICAL VEGETATION ACTIVE INDICATORS: IMPLICATIONS FOR GROSS PRIMARY PRODUCTION ESTIMATION BY REMOTE SENSING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2020-571-2020 ◽

2020 ◽

Vol V-3-2020 ◽

pp. 571-578

Author(s):

S. Wang ◽

Z. Li ◽

Y. Zhang ◽

D. Yang ◽

C. Ni

Keyword(s):

Remote Sensing ◽

Primary Productivity ◽

Seasonal Changes ◽

Gross Primary Production ◽

Vegetation Indices ◽

Canopy Structure ◽

Remote Sensing Data ◽

Growing Season ◽

Light Use Efficiency ◽

Use Efficiency

Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.

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