Small-scale variation in ecosystem CO2 fluxes in an alpine meadow depends on plant biomass and species richness

Hirota, Minoru; Zhang, Pengcheng; Gu, Song; Shen, Haihua; Kuriyama, Takeo; Li, Yingnian; Tang, Yanhong

doi:10.1007/s10265-010-0315-8

Cited by 36 publications

(37 citation statements)

References 47 publications

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“…Peichl et al (2014)). As regards productivity indices, a number of studies compare GPP and biomass or LAI, some of them reporting correlations similar to ours (Hirota et al 2010) and some reporting a stronger relationship (e.g. Flanagan et al (2002) and Xu and Baldocchi (2004)).…”

Section: Relationship Between Indices and Gross Primary Productionsupporting

confidence: 70%

Five years of phenological monitoring in a mountain grassland: inter-annual patterns and evaluation of the sampling protocol

et al. 2015

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The increasingly important effect of climate change and extremes on alpine phenology highlights the need to establish accurate monitoring methods to track inter-annual variation (IAV) and longterm trends in plant phenology. We evaluated four different indices of phenological development (two for plant productivity, i.e., green biomass and leaf area index; two for plant greenness, i.e., greenness from visual inspection and from digital images) from a 5-year monitoring of ecosystem phenology, here defined as the seasonal development of the grassland canopy, in a subalpine grassland site (NW Alps). Our aim was to establish an effective observation strategy that enables the detection of shifts in grassland phenology in response to climate trends and meteorological extremes. The seasonal development of the vegetation at this site appears strongly controlled by snowmelt mostly in its first stages and to a lesser extent in the overall development trajectory. All indices were able to detect an anomalous beginning of the growing season in 2011 due to an exceptionally early snowmelt, whereas only some of them revealed a later beginning of the growing season in 2013 due to a late snowmelt. A method is developed to derive the number of samples that maximise the trade-off between sampling effort and accuracy in IAV detection in the context of long-term phenology monitoring programmes. Results show that spring phenology requires a smaller number of samples than autumn phenology to track a given target of IAV. Additionally, productivity indices (leaf area index and green biomass) have a higher sampling requirement than greenness derived from visual estimation and from the analysis of digital images. Of the latter two, the analysis of digital images stands out as the more effective, rapid and objective method to detect IAV in vegetation development.

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Section: Relationship Between Indices and Gross Primary Productionsupporting

confidence: 70%

Five years of phenological monitoring in a mountain grassland: inter-annual patterns and evaluation of the sampling protocol

et al. 2015

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“…Our results demonstrated that spatial variations in AGB and PSN were the best predictors of the variability of photosynthetic uptake and respiratory release of CO 2 (Table 3). Other studies have reported similar spatial relationships between GPP and AGB (Risch and Frank, 2006;Petrone et al, 2008), between P max and PSN (Hirota et al, 2010), and between R eco and AGB (Risch and Frank, 2006;Petrone et al, 2008). As for temporal variability, many studies have shown a strong relationship between GPP and the amount of growing plant tissue, represented by indices such as the leaf area index (LAI) or AGB (Frank and Dugas, 2001;Flanagan et al, 2002;Byrne et al, 2005;Aires et al, 2008;Nakano et al, 2008).…”

Section: Discussionmentioning

confidence: 88%

“…This relationship reflects the fact that photosynthetic productivity has a strong influence on the activity of roots and associated organisms (Moyano et al, 2008) and the belowground supply of labile carbon (Sampson et al, 2007). Hirota et al (2010) reported that PSN correlated positively with GPP, but not with R eco and explained that the relationship might be attributable to interspecies differences in photosynthetic light use efficiencies of leaves. In the present study, we found a significant correlation between PSN and both GPP and R eco , and between PSN and AGB ( Table 3).…”

Section: Discussionmentioning

confidence: 98%

“…Petrone et al (2008) measured NEE, GPP, and R eco at three grassdominated sites within a small catchment (500 m × 500 m) in Ontario, Canada, and reported CV values for GPP and R eco of 18 % and 20 %, respectively. Hirota et al (2010) also measured NEE, GPP, and R eco at 14 points in a small area (30 m × 40 m) in an alpine meadow on the Qinghai-Tibetan Plateau, China, and determined P max , α, R 20 , and Q 10 . They reported that CV values for P max and R 20 were 29.4 % and 27.3 %, respectively.…”

Section: Discussionmentioning

confidence: 99%

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Spatial variability of photosynthetic production and ecosystem respiration on a hundred-kilometer scale within a Mongolian semiarid grassland

Nakano¹,

Shinoda²

2014

J. Agric. Meteorol.

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Grassland ecosystems cover a large area of the land surface and play an important role in the global carbon cycle. The objectives of this study were to examine the spatial variability of gross primary production (GPP) and ecosystem respiration (R eco ) within a semiarid grassland ecosystem and to evaluate the environmental factors that control the spatial patterns of carbon exchanges. We determined GPP and R eco using transparent and opaque closed chambers at nine grassland sites in central Mongolia during the summers from 2009 through 2011. To remove effects of temporal changes in radiation and temperature, GPP and R eco were fitted to light-and temperature-response curves. The fitting parameters (light-saturated GPP [P max ], apparent quantum yield [α], standardized R eco at 20 ℃ [R 20 ], and the temperature sensitivity [Q 10 ] of R eco ) were compared among the sites. The GPP and R eco parameters differed significantly among the sites. Spatial patterns of P max and R 20 were highly correlated with plant aboveground green biomass (AGB) and number of plant species. The Q 10 of R eco was significantly related to soil moisture. The light-saturated GPP normalized to the amount of AGB did not differ significantly among the sites, whereas the R 20 residuals of the linear biomass model were correlated with soil water content and carbon/nitrogen ratios. The results suggest that, within this semiarid grassland, spatial variations of GPP are strongly controlled by AGB and that variations in R eco are associated mainly with AGB and secondarily with soil water content and soil nutrient condition.

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“…R s in all chambers in each area was measured using an AOCC system. The AOCC system had a similar concept to that used by Hirota et al [29]. Without electricity at the study site, this system was connected to two 12-volt DC car batteries as its electric power for continuous measurement around 2-3 days.…”

Section: Measurement Of the Soil Co2 Effluxmentioning

confidence: 99%

Spatial Upscaling of Soil Respiration under a Complex Canopy Structure in an Old‐Growth Deciduous Forest, Central Japan

Suchewaboripont

Ando

Yoshitake

et al. 2017

Forests

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Abstract:The structural complexity, especially canopy and gap structure, of old-growth forests affects the spatial variation of soil respiration (R s ). Without considering this variation, the upscaling of R s from field measurements to the forest site will be biased. The present study examined responses of R s to soil temperature (T s ) and water content (W) in canopy and gap areas, developed the best fit model of R s and used the unique spatial patterns of R s and crown closure to upscale chamber measurements to the site scale in an old-growth beech-oak forest. R s increased with an increase in T s in both gap and canopy areas, but the effect of W on R s was different between the two areas. The generalized linear model (GLM) analysis identified that an empirical model of R s with the coupling of T s and W was better than an exponential model of R s with only T s . Moreover, because of different responses of R s to W between canopy and gap areas, it was necessary to estimate R s in these areas separately. Consequently, combining the spatial patterns of R s and the crown closure could allow upscaling of R s from chamber-based measurements to the whole site in the present study.

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Small-scale variation in ecosystem CO2 fluxes in an alpine meadow depends on plant biomass and species richness

Cited by 36 publications

References 47 publications

Five years of phenological monitoring in a mountain grassland: inter-annual patterns and evaluation of the sampling protocol

Five years of phenological monitoring in a mountain grassland: inter-annual patterns and evaluation of the sampling protocol

Spatial variability of photosynthetic production and ecosystem respiration on a hundred-kilometer scale within a Mongolian semiarid grassland

Spatial Upscaling of Soil Respiration under a Complex Canopy Structure in an Old‐Growth Deciduous Forest, Central Japan

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