Impacts of climate change on biological rotation of Larix olgensis plantations for timber production and carbon storage in northeast China using the 3-PGmix model

Xie, Yifei; Lei, Xiangdong; Shi, Jingning

doi:10.1016/j.ecolmodel.2020.109267

Cited by 21 publications

(11 citation statements)

References 85 publications

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“…While the amount of CO 2 emitted as a result of shell formation has been shown to vary with temperature, salinity, and local carbonate chemistry (Morris & Humphreys, 2019), it is generally accepted that ~0.6 mol CO 2 is produced for every mol of precipitated CaCO 3 (Macreadie et al, 2017; Saderne et al, 2019). Here, we credit carbon temporarily stored in mussel shell over an average individual's lifetime, or roughly 30 years (a timescale comparable to the rotation of timber stands recognized as carbon stores in the terrestrial realm; Brockerhoff et al, 2008; Zhang et al, 2012; Xie et al, 2020). To quantify carbon stored in shell, we used the following equation (Equation 2):

C_{s} goodbreak= W * C_{f}

Where C s is the amount of carbon stored (g m −2 year −1 ), W is the annual change in dry shell weight, and C f is the carbon fraction of mussel shell's dry weight.…”

Section: Methodsmentioning

confidence: 99%

The influence of mussel restoration on coastal carbon cycling

Sea

Hillman

Thrush

2022

Global Change Biology

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Increasing responsiveness to anthropogenic climate change and the loss of global shellfish ecosystems has heightened interest in the carbon storage and sequestration potential of bivalve‐dominated systems. While coastal ecosystems are dynamic zones of carbon transformation and change, current uncertainties and notable heterogeneity in the benthic environment make it difficult to ascertain the climate change mitigation capacity of ongoing coastal restoration projects aimed at revitalizing benthic bivalve populations. In this study we sought to distinguish between direct and indirect effects of subtidal green‐lipped mussels (Perna canaliculus) on carbon cycling, and combined published literature with in‐situ experiments from restored beds to create a carbon budget for New Zealand's shellfish restoration efforts. A direct summation of biogenic calcification, community respiration, and sediment processes suggests a moderate carbon efflux (+100.1 to 179.6 g C m−2 year−1) occurs as a result of recent restoration efforts, largely reflective of the heterotrophic nature of bivalves. However, an examination of indirect effects of restoration on benthic community metabolism and sediment dynamics suggests that beds achieve greater carbon fixation rates and support enhanced carbon burial compared to nearby sediments devoid of mussels. We discuss limitations to our first‐order approximation and postulate how the significance of mussel restoration to carbon‐related outcomes likely increases over longer timescales. Coastal restoration is often conducted to support the provisioning of many ecosystem services, and we propose here that shellfish restoration not be used as a single measure to offset carbon dioxide emissions, but rather used in tandem with other initiatives to recover a bundle of valued ecosystem services.

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C_{s} goodbreak= W * C_{f}

Where C s is the amount of carbon stored (g m −2 year −1 ), W is the annual change in dry shell weight, and C f is the carbon fraction of mussel shell's dry weight.…”

Section: Methodsmentioning

confidence: 99%

The influence of mussel restoration on coastal carbon cycling

Sea

Hillman

Thrush

2022

Global Change Biology

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“…Northeast China is covered by rich vegetation types (forest, grass, cropland) and is one of the regions that are most sensitive to climate change [ 15 ], which is expected to affect forest growth, productivity, mortality, distribution, and biodiversity [ 16 ]. Specifically, the rise in CO 2 concentration may have impacts on the vegetation NPP through modifying photosynthesis [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Decade’s Change in Vegetation Productivity and Its Response to Climate Change over Northeast China

Yan

Xue

Zhang

et al. 2021

Plants

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In this study, we simulated vegetation net primary productivity (NPP) using the boreal ecosystem productivity simulator (BEPS) between 2003 and 2012 over Northeast China, a region that is significantly affected by climate change. The NPP was then validated against the measurements that were calculated from tree ring data, with a determination coefficient (R2) = 0.84 and the root mean square error (RMSE) = 42.73 gC/m2·a. Overall, the NPP showed an increasing trend over Northeast China, with the average rate being 4.48 gC/m2·a. Subsequently, partial correlation and lag analysis were conducted between the NPP and climatic factors. The partial correlation analysis suggested that temperature was the predominant factor that accounted for changes in the forest NPP. Solar radiation was the main factor that affected the forest NPP, and the grass NPP was the most closely associated with precipitation. The relative humidity substantially affected the annual variability of the shrub and crop NPPs. The lag time of the NPP related to precipitation increased with the vegetation growth, and it was found that the lag period of the forest was longer than that of grass and crops, whereas the cumulative lag month of the forest was shorter. This comprehensive analysis of the response of the vegetation NPP to climate change can provide scientific references for the managing departments that oversee relevant resources.

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“…We consider a sampling area of at least 140 × 140 m to reach the requirements for estimates of biomass productivity in this region (Figure 4 and Figure ), as the turning point in R 2 and rRMSE indicates the minimal scale for effective sampling is the above scale, sampling at a median scale may be more cost effective ( R 2 > 0.7, rRMSE <20%). Using 3‐PGmix model, Xie, Lei, and Shi (2020); Xie, Wang, and Lei (2020) explored the impacts of climate change on the biological rotation of Larix olgensis plantations for timber production and carbon storage in 492 sample plots of 0.0667 ha each in northeast China. Based on the results of this study, the results of these previous research may have some problems.…”

Section: Discussionmentioning

confidence: 99%

Assessing scale‐dependent effects on Forest biomass productivity based on machine learning

Fan

Geng

et al. 2022

Ecology and Evolution

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Estimating forest above‐ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30‐ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand density variable as input across a series of area scales using the Random Forest (RF) algorithm. As the grain size increased from 10 to 200 m, we found that the relative importance of explanatory variables that drove the variation of biomass productivity varied a lot, and the model accuracy was gradually improved. The minimum sampling area for biomass productivity modeling in this region was 140 × 140 m. Our study shows that the relationship of topography, species diversity, stand structure, and stand density variables with biomass productivity modeled using the RF algorithm changes when moving from scales typical of forest surveys (10 m) to larger scales (200 m) within a controlled methodology. These results should be of considerable interest to scientists concerned with forest assessment.

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Impacts of climate change on biological rotation of Larix olgensis plantations for timber production and carbon storage in northeast China using the 3-PGmix model

Cited by 21 publications

References 85 publications

The influence of mussel restoration on coastal carbon cycling

The influence of mussel restoration on coastal carbon cycling

A Decade’s Change in Vegetation Productivity and Its Response to Climate Change over Northeast China

Assessing scale‐dependent effects on Forest biomass productivity based on machine learning

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