Bamboo, which has dense culms and root rhizome systems, can alter soil properties when it invades adjacent forests. Therefore, this study investigated whether bamboo invasions can cause changes in soil organic matter (SOM) composition and soil humification. We combined solid-state 13C NMR spectroscopy and chemical analysis to examine the SOM in a Japanese cedar (Cryptomeria japonica) and adjacent bamboo (Phyllostachys edulis) plantation. Bamboo reduced soil organic C (SOC) content, compared to the cedar plantation. The value of ∆logK (ratio of absorbance of humic acids at 400 and 600 nm) was cedar > transition zone > bamboo soils. Our results indicated that bamboo increased SOM humification, which could be due to the fast decomposition of bamboo litter with the high labile C. Furthermore, intensive management in the bamboo plantation could enhance the humification as well. Overall, litter type can control an ecosystem’s SOC nature, as reflected by the finding that higher labile C in bamboo litter contributed the higher ratios of labile C to SOC and lower ratios of recalcitrant C to SOC in bamboo soils compared with cedar soils. The invasion of bamboo into the Japanese cedar plantation accelerated the degradation of SOM.

The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 to 2017, including 3533 real estate transaction price records. The hedonic price method, spatial lag model and spatial error model were used to conduct global spatial self-correlation tests to explore the performance of house price variables and space price aggregation. We compare the three models by R² and Akaike Information Criterion (AIC) to determine the spatial self-correlation ability performance, and explore the spatial distribution of prices and the changes of price regions from the regional local indicators of spatial association spatial distribution map. Actual analysis results show an improvement in the ability to interpret real estate prices through the feature price mode from the R² value assessment, the spatial delay model and the spatial error model. Performance from the AIC values show that the difference of the spatial delay model is smaller than that of the feature price model and the spatial model, demonstrating a better performance from the space delay model and the spatial error model compared to the feature price model; improving upon the estimation bias caused by spatial self-correlation. For variables affecting house pricing, research results show that Moran’s I is more than 0 in real estate price analysis over the years, all of which show spatial positive correlation. From the LISA analysis of the spatial aggregation phenomenon, we see real estate prices rise in spaces surrounded by high-priced real estate contrast with the scope of space surrounded by low-cost real estate shifting in boundary over the years due to changes in the location and attributes of real estate trading transactions. Through the analysis of space price aggregation characteristics, we are able to observe the trajectory of urban development.

Studying the influence of climatic and/or site-specific factors on soil organic matter (SOM) along an elevation gradient is important for understanding the response of SOM to global warming. We evaluated the composition of SOM and structure of humic acids along an altitudinal gradient from 600 to 1400 m in moso bamboo (Phyllostachys edulis) plantations in central Taiwan using NMR spectroscopy and photometric analysis. Total organic C and total nitrogen (N) content increased with increasing elevation. Aromaticity decreased and ΔlogK (the logarithm of the absorbance ratio of humic acids at 400 and 600 nm) increased with increasing elevation, which suggests that SOM humification decreased with increasing elevation. High temperature at low elevations seemed to enhance the decomposition (less accumulation of total organic C and N) and humification (high aromaticity and low ΔlogK). The alkyl-C/O-alkyl-C (A/O-A) ratio of humic acids increased with increasing elevation, which suggests that SOM humification increased with increasing elevation; this finding was contrary to the trend observed for ΔlogK and aromaticity. Such a discrepancy might be due to the relatively greater remaining of SOM derived from high alkyl-C broadleaf litter of previous forest at high elevations. The ratio of recalcitrant C to total organic C was low at low elevations, possibly because of enhanced decomposition of recalcitrant SOM from the previous broadleaf forest during long-term intensive cultivation and high temperature. Overall, the change in SOM pools and in the rate of humification with elevation was primarily affected by changes in climatic conditions along the elevation gradient in these bamboo plantations. However, when the composition of SOM, as assessed by NMR spectroscopy and photometric analysis was considered, site-specific factors such as residual SOM from previous forest and intensive cultivation history could also have an important effect on the humic acid composition and humification of SOM.

Badland soils—which have high silt and clay contents, bulk density, and soil electric conductivity— cover a large area of Southern Taiwan. This study evaluated the amelioration of these poor soils by thorny bamboo, one of the few plant species that grows in badland soils. Soil physiochemical and biological parameters were measured from three thorny bamboo plantations and nearby bare lands. Results show that bamboo increased microbial C and N, soil acid-hydrolysable C, recalcitrant C, and soluble organic C of badland soils. High microbial biomass C to total organic C ratio indicates that soil organic matter was used more efficiently by microbes colonizing bamboo plantations than in bare land soils. High microbial respiration to biomass C ratio in bare land soils confirmed environmentally induced stress. Soil microbes in bare land soils also faced soil organic matter with the high ratio of recalcitrant C to total organic C. The high soil acid-hydrolysable C to total organic C ratio at bamboo plantations supported the hypothesis that decomposition of bamboo litter increased soil C in labile fractions. Overall, thorny bamboo improved soil quality, thus, this study demonstrates that planting thorny bamboo is a successful practice for the amelioration of badland soils.

Vegetation phenology reflects the response of a terrestrial ecosystem to climate change. In this study, we attempt to evaluate the El Niño/La Niña-Southern Oscillation (ENSO)-associated temporal dynamics of the vegetation onset and its influence on the net primary productivity (NPP) in a subtropical island (Taiwan) of Pacific Asia. We utilized a decade-long (2001-2010) time series of photosynthetically active vegetation cover (PV) data, which were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data, to delineate the vegetation phenology. These data served as inputs for the phenological analysis toolbox TIMESAT. The results indicated that the delayed vegetation onset time was directly influenced by a dry spring (February and March) in which less than 40 mm of rainfall was received. This seasonal drought impeded vegetation growth in the subsequent growing season, most likely due to delayed impacts of moisture stress related to the preceding ENSO events. The significant correlations obtained between the annual MODIS NPP and both the vegetation onset time and the length of the growing season may imply that the accumulated rainfall in the spring season governs the annual NPP. The model simulations revealed that the frequency and intensity of the ENSO-related spring droughts might increase, which would result in cascading effects on the ecosystem metabolism.

Litterfall is important for returning nutrients and carbon to the forest floor, and microbes decompose the litterfall to release CO 2 into the atmosphere. Litterfall is a pivotal component in the forest biogeochemical cycle, which is sensitive to climate variability and plant physiology. In this study, we combined field litterfall estimates and time series (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) climate (the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and Tropical Rainfall Measuring Mission (TRMM) precipitations) and green vegetation (MODIS photosynthetically active vegetation cover (PV)) variables to estimate regional annual litterfall in tropical/subtropical forests in Taiwan. We found that time series MODIS LST-and PV-derived metrics, the annual accumulated MODIS LST, and coefficient of variation of PV, respectively, but not the TRMM precipitation variables were salient factors for the estimation (r 2 = 0.548 and p < 0.001). The mean (±standard deviation) annual litterfall was 5.1 ± 1.2 Mg ha À1 yr À1 during the observation period. The temporal dynamics of the litterfall revealed that typhoons and consecutive drought events might affect the litterfall temporal variation. Overall, the annual litterfall decreased along the elevation gradient, which may reflect a change in the vegetation type. The northeast and northwest facing slopes yielded the highest amount of annual litterfall (≥5.9 Mg ha À1 yr À1 ), which was in contrast with the southern aspect (5.1 Mg ha À1 yr À1 ). This variation may be associated with the dryness of the microclimate influenced by solar radiation. This study demonstrates the feasibility of utilizing time series MODIS LST and PV data to predict large-scale field litterfall, which may facilitate large-scale monitoring of biogeochemical cycles in forest ecosystems.

No abstract

Tropical and subtropical ecosystems, the largest terrestrial carbon pools, are very susceptible to the variability of seasonal precipitation. However, the assessment of drought conditions in these regions is often overlooked due to the preconceived notion of the presence of high humidity. Drought indices derived from remotely sensed imagery have been commonly used for large-scale monitoring, but feasibility of drought assessment may vary across regions due to climate regimes and local biophysical conditions. Therefore, this study aims to evaluate the feasibility of 11 commonly used MODIS-derived vegetation/drought index in the forest regions of Taiwan through comparison with the station-based standardized precipitation index with a 3-month time scale (SPI3). The drought indices were further transformed (standardized anomaly, SA) to make them better delineate the spatiotemporal variations of drought conditions. The results showed that the Normalized Difference Infrared Index utilizing the near-infrared and shortwave infrared bands (NDII6) may be more superior to other indices in delineating drought patterns. Overall, the NDII6 SA-SPI3 pair yielded the highest correlation (mean r ± standard deviation = 0.31 ± 0.13) and was most significant in central and south Taiwan (r = 0.50-0.90) during the cold, dry season (January and April). This study illustrated that the NDII6 is suitable to delineate drought conditions in a relatively humid region. The results suggested the better performance of the NDII6 SA-SPI3 across the high climate gradient, especially in the regions with dramatic interannual amplifications of rainfall. This synthesis was conducted across a wide bioclimatic gradient, and the findings could be further generalized to a much broader geographical extent.