Hibiki Noda

Morphological and Physiological Acclimation Responses to Contrasting Light and Water Regimes in Primula sieboldii

The effects of the availability of light (high, medium and low) and soil water (wet and dry) on morphological and physiological traits responsible for whole plant carbon gain and ramet biomass accumulation were examined in a splitter-type clonal herbaceous species Primula sieboldii, a spring plant inhabiting broad range of light environments including open grassland and oak forest understory. Growth experiments were conducted for three genets originated from natural microhabitats differing in light and soil water availability. Ramets of a genet from high light and wet microhabitat, which were grown in low light (relative photon flux density: R-PPFD of 5%) showed 41% less light-saturated photosynthetic rate, 50% less dark respiration rate and earlier defoliation than the ramets in high light (R-PPFD of 61%). The estimation of daily photosynthesis revealed that the light acclimation response in leaf gas exchange contributes to efficient carbon gain of whole plants, irrespective of experimental light conditions. Water stress increased root weight ratio, decreased ramet leaf area, petiole length and photosynthetic capacity. These morphological effects of water stress were larger in high and medium light regimes than in low light regime. The consequence of the above responses was recognized in the relative growth rate of the ramets. The relative growth rate of the ramets in high light with wet regime was four-fold of that in low light plus wet regime, and was 1.5-fold of that in high light plus dry regime. However, even in low light and/or dry regimes, ramets kept positive relative growth rates and produced gemma successfully. We could not detect significant variation in growth responses among genets. The high photosynthetic plasticity revealed in the present study should enable Primula sieboldii to inhabit in a broad range of light and soil water availability.

Examination of the extinction coefficient in the Beer–Lambert law for an accurate estimation of the forest canopy leaf area index

Leaf area index (LAI) is a crucial ecological parameter that represents canopy structure and controls many ecosystem functions and processes, but direct measurement and long-term monitoring of LAI are difficult, especially in forests. An indirect method to estimate the seasonal pattern of LAI in a given forest is to measure the attenuation of photosynthetically active radiation (PAR) by the canopy and then calculate LAI by the Beer–Lambert law. Use of this method requires an estimate of the PAR extinction coefficient (k), a parameter needed to calculate PAR attenuation. However, the determination of k itself requires direct measurement of LAI over seasons. Our goals were to determine (1) the best way to model k values that may vary seasonally in a forest, and (2) the sensitivity of estimates of canopy ecosystem functions to the errors in estimated LAI. We first analyzed the seasonal pattern of the “true” k (k p) under cloudy and sunny conditions in a Japanese deciduous broadleaved forest by using the inverted form of the Beer–Lambert law with the true LAI and PAR. We next calculated the errors of PAR-based LAIs estimated with an assumed constant k (LAIpred) and determined under what conditions we should expect k to be approximately constant during the growing period. Finally, we examined the effect of errors in LAIpred on estimates of gross primary production (GPP), net ecosystem production (NEP), and latent heat flux (LE) calculated with a land-surface model using LAIpred as an input parameter. During the growing period, cloudy k p varied from 0.47 to 1.12 and sunny k p from 0.45 to 1.59. Results suggest that the value of LAIpred was adequately estimated with the k p obtained under cloudy conditions during the fully-leaved period (0.53–0.57). However, LAIpred was overestimated by up to 0.6 m2 m–2 in May and November. The errors in LAIpred propagated to errors in modeled carbon and latent heat fluxes of –0.21 to 0.32 g C m–2 day–1 in GPP, –0.09 to 0.19 g C m–2 day–1 in NEP, and –3.2 to 3.9 W m–2 in LE, which is close to the measurement errors recognized in the tower flux measurement. LAIpred estimated with an assumed constant k can be useful for some ecosystem studies as a second-best alternative if k is equated to the value of k p measured under cloudy conditions especially during the fully-leaved period.

A Vegetation Index to Estimate Terrestrial Gross Primary Production Capacity for the Global Change Observation Mission-Climate (GCOM-C)/Second-Generation Global Imager (SGLI) Satellite Sensor

To estimate global gross primary production (GPP), which is an important parameter for studies of vegetation productivity and the carbon cycle, satellite data are useful. In 2014, the Japan Aerospace Exploration Agency (JAXA) plans to launch the Global Change Observation Mission-Climate (GCOM-C) satellite carrying the second-generation global imager (SGLI). The data obtained will be used to estimate global GPP. The rate of photosynthesis depends on photosynthesis reduction and photosynthetic capacity, which is the maximum photosynthetic velocity at light saturation under adequate environmental conditions. Photosynthesis reduction is influenced by weather conditions, and photosynthetic capacity is influenced by chlorophyll and RuBisCo content. To develop the GPP estimation algorithm, we focus on photosynthetic capacity because chlorophyll content can be detected by optical sensors. We hypothesized that the maximum rate of low-stress GPP (called “GPP capacity”) is mainly dependent on the chlorophyll content that can be detected by a vegetation index (VI). The objective of this study was to select an appropriate VI with which to estimate global GPP capacity with the GCOM-C/SGLI. We analyzed reflectance data to select the VI that has the best linear correlation with chlorophyll content at the leaf scale and with GPP capacity at canopy and satellite scales. At the satellite scale, flux data of seven dominant plant functional types and reflectance data obtained by the Moderate-resolution Imaging Spectroradiometer (MODIS) were used because SGLI data were not available. The results indicated that the green chlorophyll index, CIgreen(ρNIR/ρgreen-1), had a strong linear correlation with chlorophyll content at the leaf scale (R2 = 0.87, p < 0.001) and with GPP capacity at the canopy (R2 = 0.78, p < 0.001) and satellite scales (R2 = 0.72, p < 0.01). Therefore, CIgreen is a robust and suitable vegetation index for estimating global GPP capacity.

Linking Remote Sensing and In Situ Ecosystem/Biodiversity Observations by “Satellite Ecology”

Climate change and human activity (land use change and management) are the major drivers of changes in biodiversity, which ranges from the genetic composition of a given population to the structure and functions in an ecosystem and to the ecosystems in a landscape. The structural and functional diversity of an ecosystem on a landscape or regional scale could have a serious impact on the regional to global environmental sustainability and ecosystem services. Also, those ecosystem properties could have feedback effects on the population, individual, and genetic levels (e.g., Schulze and Mooney 1994). These cross-hierarchy consequences strongly suggest the need for understanding the relations between ecosystem properties and their internal and external drivers (Noss 1990; Scholes et al. 2008).

Accurate measurement of optical properties of narrow leaves and conifer needles with a typical integrating sphere and spectroradiometer.

Accurate information on the optical properties (reflectance and transmittance spectra) of single leaves is important for an ecophysiological understanding of light use by leaves, radiative transfer models and remote sensing of terrestrial ecosystems. In general, leaf optical properties are measured with an integrating sphere and a spectroradiometer. However, this method is usually difficult to use with grass leaves and conifer needles because they are too narrow to cover the sample port of a typical integrating sphere. Although ways to measure the optical properties of narrow leaves have been suggested, they have problems. We propose a new measurement protocol and calculation algorithms. The protocol does not damage sample leaves and is valid for various types of leaves, including green and senescent. We tested our technique with leaves of Aucuba japonica, an evergreen broadleaved shrub, and compared the spectral data of whole leaves and narrow strips of the leaves. The reflectance and transmittance of the strips matched those of the whole leaves, indicating that our technique can accurately estimate the optical properties of narrow leaves. Tests of conifer needles confirmed the applicability.

High-resolution prediction of leaf onset date in Japan in the 21st century under the IPCC A1B scenario

Reports indicate that leaf onset (leaf flush) of deciduous trees in cool-temperate ecosystems is occurring earlier in the spring in response to global warming. In this study, we created two types of phenology models, one driven only by warmth (spring warming [SW] model) and another driven by both warmth and winter chilling (parallel chill [PC] model), to predict such phenomena in the Japanese Islands at high spatial resolution (500 m). We calibrated these models using leaf onset dates derived from satellite data (Terra/MODIS) and in situ temperature data derived from a dense network of ground stations Automated Meteorological Data Acquisition System. We ran the model using future climate predictions created by the Japanese Meteorological Agencys MRI-AGCM3.1S model. In comparison to the first decade of the 2000s, our results predict that the date of leaf onset in the 2030s will advance by an average of 12 days under the SW model and 7 days under the PC model throughout the study area. The date of onset in the 2090s will advance by 26 days under the SW model and by 15 days under the PC model. The greatest impact will occur on Hokkaido (the northernmost island) and in the central mountains.

Interpreting Temporal Changes of Atmospheric CO 2 Over Fire Affected Regions Based on GOSAT Observations

The carbon dioxide (CO₂) emissions released from biomass burning significantly affect the temporal variations of atmospheric CO₂ concentrations. Based on a longterm (July 2009-June 2015) retrieved data sets by the greenhouse gases observing satellite (GOSAT), the seasonal cycle and interannual variations of column-averaged volume mixing ratios of atmospheric carbon dioxide (XCO₂) in four fire affected continental regions were analyzed. The results showed that Northern Africa (NA) had the largest seasonal variations after removing its regional trend of XCO₂ with peak-to-peak amplitude of 6.2 ppm within the year, higher than central South America (CSA) (2.4 ppm), Southern Africa (SA) (3.8 ppm), and Australia (1.7 ppm). The detrended regional XCO₂ (ΔXCO₂) was found to be positively correlated with the fire CO₂ emissions during the fire activity period but with different seasonal variabilities. NA recorded the largest change of seasonal variations of ΔXCO₂ with a total of 12.8 ppm during fire seasons, higher than CSA, SA, and Australia with 5.4, 6.7, and 2.2 ppm, respectively. During the fire episode, the positive ΔXCO₂ was noticed during June-November in CSA, December to next June in NA, and May-November in SA. The Pearson correlation coefficients between the variations of ΔXCO₂ and fire CO₂ emissions achieved the best correlations in SA (R = 0.77 and p <; 0.05). This letter revealed that fire CO₂ emissions and GOSAT XCO₂ presented consistent seasonal variations.