A general method for parameter estimation in light-response models

Chen, Lei; Li, Zhongbin; Hui, Cang; Cheng, Xiaofei; Li, Bai-Lian; Shi, Peijian

doi:10.1038/srep27905

Cited by 4 publications

(8 citation statements)

References 34 publications

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“…Three model parameters were estimated for the selected two periods estimated using the Levenberg-Marquardt method, implemented in the minpack.lm package [51] in R [47]. The Levenberg-Marquardt method is a widely used algorithm for analyzing non-linear light-response curves [52,53]. NEP sat was calculated with the obtained three parameters and by fixing PAR at 1500 µmol photon m −2 s −1 .…”

Section: Data Anlaysis and Statistical Modelmentioning

confidence: 99%

Temperature Control of Spring CO2 Fluxes at a Coniferous Forest and a Peat Bog in Central Siberia

et al. 2021

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Climate change impacts the characteristics of the vegetation carbon-uptake process in the northern Eurasian terrestrial ecosystem. However, the currently available direct CO2 flux measurement datasets, particularly for central Siberia, are insufficient for understanding the current condition in the northern Eurasian carbon cycle. Here, we report daily and seasonal interannual variations in CO2 fluxes and associated abiotic factors measured using eddy covariance in a coniferous forest and a bog near Zotino, Krasnoyarsk Krai, Russia, for April to early June, 2013–2017. Despite the snow not being completely melted, both ecosystems became weak net CO2 sinks if the air temperature was warm enough for photosynthesis. The forest became a net CO2 sink 7–16 days earlier than the bog. After the surface soil temperature exceeded ~1 °C, the ecosystems became persistent net CO2 sinks. To change into the full spring photosynthesis recovery, the forest is likely to need a minimum accumulated air temperature of ~80 to 137 °C, and the bog requires 141 to 211 °C. During these periods, soil temperature in the forest still remained nearly 0 °C, suggesting that it is likely that forests appear more sensitive to the rise of air temperature than bogs. Net ecosystem productivity was highest in 2015 for both ecosystems because of the anomalously high air temperature in May compared with other years. Our findings demonstrate that long-term monitoring of flux measurements at the site level, particularly during winter and its transition to spring, is essential for understanding the responses of the northern Eurasian ecosystem to spring warming.

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Section: Data Anlaysis and Statistical Modelmentioning

confidence: 99%

Temperature Control of Spring CO2 Fluxes at a Coniferous Forest and a Peat Bog in Central Siberia

et al. 2021

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“…Following the recommendations of Chen et al. (2016), the lower and upper limits of the following parameters in the model: θ (curvature factor), ϕ (quantum yield), A max (light‐saturated rate of gross CO 2 assimilation) and R d (dark respiration) were set at 0, 0, 0, 0 and 1, 1, 100, 100, respectively, during the optimisation process.…”

Section: Methodsmentioning

confidence: 99%

“…Curve fitting was optimised using differential evolution via the 'DEoptim' 2.4-4 (Ardia et al, 2010;Mullen et al, 2011) package in r 3.5.3 (R Development Core Team, 2019). Following the recommendations of Chen et al (2016), the lower and upper limits of the following parameters in the model: θ (curvature factor), ϕ (quantum yield), A max (light-saturated rate of gross CO 2 assimilation) and R d (dark respiration) were set at 0, 0, 0, 0 and 1, 1, 100, 100, respectively, during the optimisation process.…”

Section: Experiments 1: Photosynthetic Capacity and Responsiveness To...mentioning

confidence: 99%

The role of shade in maintaining alternative stable states between open‐ and closed‐canopy vegetation

et al. 2021

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Fire is commonly identified as strong driver of alternative stable states such as adjacent open‐ versus closed‐canopy vegetation types. The absence of open‐canopy species from closed‐canopy understoreys, where light availability is low and dynamic, however, suggests shade tolerance is an integral determinant of such vegetation boundaries. While the importance of light dynamics between alternative stable states has been acknowledged, the physiological mechanisms behind sun versus shade tolerance are unclear. Here we investigated the differences in light interception and carbon assimilation of open‐ and closed‐canopy species, and whether this contributes to the maintenance of alternative stable states. Evergreen forest and fynbos species were grown under three light availability treatments (24%, 54% and 100%) in a glasshouse with their photosynthetic response to continuous and fluctuating light, dark respiration, root respiration, biomass and leaf traits quantified. Fynbos species displayed smaller, thicker and tightly aggregated leaves compared to forest species. Under low light, fynbos species experienced some mortality, and showed lower light‐use efficiency with fluctuating light and lower in situ photosynthetic rates under the 54% light treatment compared to forest species (0.8 vs. 3.4 μmol CO2 m−2 s−1). Synthesis. Contrasting light availability in forest and fynbos selects for dissimilar traits, where fynbos species are unable to efficiently intercept light and maintain a positive carbon balance under low, dynamic light compared to forest species. This shade intolerance is exacerbated by divergent nutrient and fire regimes in forest and fynbos environments, requiring incompatible trait specialisations that promote the emergence and maintenance of alternative stable states.

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“…It has been shown that the Nonrectangular Hyperbola Model offers greater flexibility compared to the Rectangular Hyperbola Model, attributed to its inclusion of an additional curvature parameter. This enhanced flexibility is particularly advantageous when fitting photosynthetic light response curves for specific species of plants [21] . Additionally, the Modified Rectangular Hyperbola Model for higher plants can reproduce the irradiance response trends of photosynthesis well and for phytoplankton species can obtain close values to the measured data, but the fitted curves exhibited some slight deviations under low intensity of irradiance [22] .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is a need to test which photosynthetic light response model describes the relationship between light intensity and the rate of photosynthesis in plants in the most accurate manner. Prior researches in model comparison have predominantly concentrated on assessing the goodness of fit (e.g., coefficient of determination) or exploring the trade-off between goodness of fit and model structural complexity (e.g., the Akaike information criterion) [21,22] . However, there remains a comparative dearth in quantifying and comparing the nonlinearity of photosynthetic light response models, despite its potential to provide valuable insights into photosynthetic mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Comparison of four light-response models using relative curvature measures of nonlinearity

Wang,

He,

Ratkowsky

et al. 2024

Preprint

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Photosynthetic light response curves serve as powerful mathematical tools for quantitatively describing the rate of photosynthesis of plants in response to changes in irradiance. However, in practical applications, the daunting task of selecting an appropriate nonlinear model to accurately fit these curves persists as a significant challenge. Thus, there arises a need for a method to systematically evaluate the efficacy of such models. In the present study, four distinct nonlinear models, namely Exponential Model (EM), Rectangular Hyperbola Model (RHM), Nonrectangular Hyperbola Model (NHM), and Modified Rectangular Hyperbola Model (MRHM), were used to fit the relationship between light intensity and the rate of photosynthesis across 42 empirical datasets. The goodness of fit for each model was assessed using the root-mean-square error, and relative curvature measures of nonlinearity were employed to assess the nonlinear behavior of the models. In terms of goodness of fit, pairwise difference tests of the root-mean-square error revealed that there was little to choose among the four models, although RHM gave a marginally poorer fit. However, in terms of nonlinear behavior, EM not only provided the most favorable linear approximation performance at the global level, but also exhibited the best close-to-linear behavior at the individual parameter level among the four models across the 42 datasets. Consequently, the results strongly advocate for EM as the most suitable mathematical framework for fitting photosynthetic light response curves. These findings provide insights into the model assessment for nonlinear regression in describing the relationship between the photosynthetic rate and light intensity.

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A general method for parameter estimation in light-response models

Cited by 4 publications

References 34 publications

Temperature Control of Spring CO2 Fluxes at a Coniferous Forest and a Peat Bog in Central Siberia

Temperature Control of Spring CO2 Fluxes at a Coniferous Forest and a Peat Bog in Central Siberia

The role of shade in maintaining alternative stable states between open‐ and closed‐canopy vegetation

Comparison of four light-response models using relative curvature measures of nonlinearity

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