Long-term spatiotemporal mapping of landslides is crucial for understanding the dynamics of landslide, their impact on forest carbon stocks, and their interactions with environmental factors, climate variability, and disturbances. This study analyzed 33 years (1990-2022) of Landsat imagery and topography using machine learning (Random Forest) to map landslide dynamics in a 24,386-ha subtropical montane forest in Northeast Taiwan. We also quantified forest aboveground biomass (AGB) losses from landslides using temporally corresponding Landsat and lidar data. We observed pronounced interannual variability, with total landslide coverage ranging from 0.68% to 3.19%, and forest-to-landslide transitions driving annual AGB losses of 2 to 85 Gg yr⁻¹. Temporal analysis revealed exponential declines in landslide frequency (median = 2 events), persistence (one year), and reoccurrence (two times), indicating most landslides were short-lived. However, nearly half of affected sites reoccurred multiple times, indicating spatially persistent susceptibility. Topographic attributes, including elevation, aspect, slope, and local relief, exhibited greater sensitivity to extreme events. Crucially, typhoon-driven extreme rainfall, particularly daily maximum precipitation (r = 0.559, p = 0.004), showed a stronger relationship with newly formed landslides than daily maximum precipitation during the rainy season (r = 0.399, p = 0.026), emphasizing typhoon’s dominant triggering role. AGB losses from typhoon-triggered landslides were roughly 14-fold greater than in quiet years, profoundly impacting the regional forest carbon budget. Post-landslide vegetation recovery exhibited a highly variable trajectory and plateaued at ~63% of pre-disturbance biomass within 25 years, based on a non-linear asymptotic model. As climate change is projected to intensify typhoon activity and extreme rainfall, landslide risks and associated forest carbon losses will increase, particularly in vulnerable, typhoon-prone regions like Asia. These findings highlight typhoons are not only a principal driver of landslide activity but also a major disruptor of forest carbon budgets, underscoring their critical inclusion in carbon accounting frameworks for vulnerable montane ecosystems.

Forested watersheds provide clean water and stabilize hydrological services. Associations between vegetation growth and climatic variation significantly influence hydrological regimes, which are region-dependent. However, the climate-phenology-hydrology nexus has rarely been investigated in subtropical forested watersheds, which remain underexplored due to their year-round vegetation activity complicating the phenological shift detection, and highly variable seasonal rainfall introducing uncertainty in hydrological modeling. Understanding these dynamics provides insights into subtropical forests' buffering capacities against climatic fluctuations and their water regulation. This study examined monthly temperature and precipitation impacts on vegetation growth using monthly photosynthetic active vegetation cover fraction (PV) from satellite imagery, assessing the effects of spring and summer rainfall (2001-2020) on vegetation phenology and streamflow in subtropical Fushan (annual precipitation >4000 mm) and tropical Leinhuachi (annual precipitation >2300 mm) Experimental Forests. PV and temperature exhibited linear correlations without time-lag effect (R-2 = 0.51-0.57, p < 0.001). However, PV and precipitation had no time-lag in Fushan, but demonstrated a log-linear relationship with two-month lag in Leinhuachi (R-2 = 0.15-0.59, p < 0.001), highlighting rainfall accumulation during the relatively dry season (winter-spring) as critical for vegetation growth. Structural equation modeling (SEM) revealed that an earlier start of growing season (SOS), driven by high spring rainfall (February-March), led to an extended growing season and higher P-Q deficit (precipitation minus runoff) during Leinhuachi's growing season (Goodness of fit index = 0.988, chi(2) = 0.825, df = 2, p = 0.662). Surprisingly, abundant growing season precipitation had no significant impact on season end, length, or P-Q deficit. These patterns were absent in Fushan (Goodness of fit index = 0.997, chi(2) = 0.137, df = 2, p = 0.934). Integrating seasonal precipitation variability into watershed management is critical for water security, particularly in drought-prone subtropical regions. Furthermore, incorporating these climate-phenology-hydrology patterns into climate change models will enhance predictions of ecosystem responses, especially where seasonal precipitation affects vegetation productivity, water budgets, and carbon cycling. Understanding subtropical forest regulation of water and carbon cycles is essential to improve climate projections and conservation policies.

The data that support the findings of this study.

Long-term spatiotemporal mapping of landslides is crucial for understanding land surface dynamics and their impact on forest carbon fluxes. In a warming climate, clarifying how landslides interact with changing rainfall and typhoon extremes is critical for hazard assessment and regional forest carbon budgets. This study analyzed 33 years (1990-2022) of Landsat imagery and topography using machine learning (Random Forest) to map landslide dynamics in a 24,386-ha subtropical montane forest in Northeast Taiwan. We also quantified forest aboveground biomass (AGB) losses from landslides using temporally corresponding Landsat and lidar data. We observed pronounced interannual variability, with total landslide coverage ranging from 0.68 % to 3.19 %, and forest-to-landslide transitions driving annual AGB losses of 2-85 Gg yr(-)(1). Although landslide frequency, persistence, and reoccurrence declined exponentially over time, nearly half of affected sites failed repeatedly, indicating persistent spatial susceptibility. Elevation, slope, and aspect emerged as key topographic controls on landslide susceptibility. Extreme rainfall during typhoons, particularly daily maxima (r = 0.559, p = 0.004), was the most dominant driver, underscoring typhoons as primary drivers of disturbance and biomass loss, with AGB losses approximately 14-fold higher in extreme typhoon years than in quiet years. Post-landslide vegetation recovery exhibited a highly variable trajectory and plateaued at similar to 63 % of pre-disturbance biomass within 25 years, based on a non-linear asymptotic model. Our analysis highlights that while vegetation recovery contributes to carbon uptake, its effectiveness is constrained by recurrent landslides driven by slope instability and frequent extreme rainfall. As climate change increases typhoon intensity and extreme rainfall frequency, landslide risks and associated carbon losses are expected to rise, while repeated landslides may further disrupt recovery and amplify uncertainty in future carbon dynamics. These findings underscore the need to integrate spatiotemporal disturbance-recovery interactions into global carbon cycle assessments, particularly in vulnerable, typhoon-prone mountain regions like East Asia.

An integrated, remotely sensed approach to assess land-use and land-cover change (LULCC) dynamics plays an important role in environmental monitoring, management, and policy development. In this study, we utilized the advantage of land-cover seasonality, canopy height, and spectral characteristics to develop a phenology-based classification model (PCM) for mapping the annual LULCC in our study areas. Monthly analysis of normalized difference vegetation index (NDVI) and near-infrared (NIR) values derived from SPOT images enabled the detection of temporal characteristics of each land type, serving as crucial indices for land type classification. The integration of normalized difference built-up index (NDBI) derived from Landsat images and airborne LiDAR canopy height into the PCM resulted in an overall performance of 0.85, slightly surpassing that of random forest analysis or principal component analysis. The development of PCM can reduce the time and effort required for manual classification and capture annual LULCC changes among five major land types: forests, built-up land, inland water, agriculture land, and grassland/shrubs. The gross change LULCC analysis for the Taoyuan Tableland demonstrated fluctuations in land types over the study period (2013 to 2022). A negative correlation ( r = − 0.79) in area changes between grassland/shrubs and agricultural land and a positive correlation ( r = 0.47) between irrigation ponds and agricultural land were found. Event-based LULCC analysis for Taipei City demonstrated a balance between urbanization and urban greening, with the number of urbanization events becoming comparable to urban greening events when the spatial extent of LULCC events exceeds 1000 m 2 . Besides, small-scale urban greening events are frequently discovered and distributed throughout the metropolitan area of Taipei City, emphasizing the localized nature of urban greening events.

Vegetation growth is sensitive to climatic variations which has a critical implication for hydrological regimes. However, the intertwined associations of climate-phenology-hydrology have rarely been explored in tropical/subtropical regions particularly. In this study, we synthesize hydroclimate records in forested watershed, central Taiwan for last five decades (1975-2020), and the results indicate that the incidences of meteorological and hydrological droughts are becoming prominent after 2001. We further examine the influences of temperature and precipitation on vegetation growth of watershed scale using EVI (enhanced vegetation index) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) at monthly scale, and explore the effects of seasonal precipitation on the variations of landscape phenology and following watershed streamflow between 2001 and 2020. The EVI and temperature shows a linear relationship (R2 = 0.50, p < 0.001) without time-lag effect, whereas EVI and precipitation exhibits a log-linear relationship with two months lag (R2 = 0.40, p < 0.001), showing the accumulative rainfall during relatively dry period (winter-spring) is crucial for vegetation growth. Structural equation modeling reveals that earlier start of growing season (SOS) caused by relatively high spring rainfall (February-March) leads to longer growing season (LOS) and higher P-Q deficit (precipitation minus runoff) during the growing season. Nevertheless, the large amount of precipitation during growing season has no effect on the end of growing season (EOS), LOS and P-Q deficit. Realizing the vegetation growth responding to climatic variations is necessary for current and future hydrologic regime, especially under changing climate.

Montane cloud forests (MCFs) are ecosystems frequently immersed in fog and are vital for the terrestrial hydrological cycle and biodiversity hotspots. However, the potential impacts of climate change, particularly intensified droughts and typhoons, on the persistence of ecosystems remain unclear. Our study conducted cross‐scale assessments using 6‐year (2016–2021) ground litterfall and 21‐year (2001–2021) satellite greenness data (the Enhanced Vegetation Index [EVI] and the EVI anomaly change [ΔEVI % ]), gross primary productivity anomaly change (ΔGPP % ), and meteorological variables (the standardized precipitation index [SPI] and wind speed). We found a positive correlation between summer EVI and ΔGPP % with the SPI‐3 (3‐month time scale), while winter litterfall showed a negative correlation. Maximum typhoon daily wind speed was negatively correlated with summer and the monthly ΔEVI % and ΔGPP % . These findings suggest vegetation damage and productivity loss were related to drought and typhoon intensities. Furthermore, our analysis highlighted that chronic seasonal droughts had more pronounced impacts on MCFs than severe typhoons, implying that high precipitation and frequent fog immersion do not necessarily mitigate the ramifications of water deficit on MCFs but might render MCFs more sensitive and vulnerable to drought. A significant negative correlation between the summer and winter ΔEVI % and ΔGPP % of the same year, suggesting disturbance severity during summer may facilitate vegetation regrowth and carbon accumulation in the subsequent winter. This finding may be attributed to the ecological resilience of MCFs, which enables them to recover from the previous summer. In the long‐term, our results indicated an increase in vegetation resilience over two decades in MCFs, likely driven by rising temperatures and elevated carbon dioxide levels. However, the enhancement of resilience might be overshadowed by the potential intensified droughts and typhoons in the future, potentially causing severe damage and insufficient recovery times for MCFs, thus raising concerns about uncertainties regarding their sustained resilience.

Upstream forested watersheds supply critical ecosystem services through providing clean freshwater and maintaining stable hydrological conditions. Responses of vegetation phenology to climatic variations have vital implications for hydrological regimes that are region-specific, but the associations of climate-phenology-hydrology have rarely been examined especially in tropical/subtropical regions. In this study, we utilized 46-year (1975–2020) hydroclimate records in forested watershed at central Taiwan, and showed that precipitation and streamflow anomalies and incidences of meteorological and hydrological droughts are becoming prominent after 2001. We further investigated the effects of monthly temperature and precipitation on vegetation growth using monthly EVI (enhanced vegetation index) of a watershed derived from MODIS (Moderate Resolution Imaging Spectroradiometer), and explored the effects of seasonal precipitation on the variations of vegetation phenological and subsequent watershed streamflow between 2001 and 2020. The EVI and temperature showed a linear relationship without time-lag effect (R2 = 0.50, p < 0.001), whereas EVI and precipitation exhibited a log-linear relationship with 2-month lag (R2 = 0.40, p < 0.001), indicating the accumulation of rainfall during relatively dry period was crucial for vegetation growth. Structural equation modeling revealed that earlier start of growing season (SOS) caused by relatively high spring rainfall (February-March) led to longer growing season (LOS) and higher P-Q deficit (precipitation minus runoff) during the growing season. Nevertheless, the large amount of precipitation during growing season has no effect on the end of growing season (EOS), LOS and P-Q deficit. Neither EOS has influence on LOS and P-Q deficit. Understanding the vegetation responses to climatic variations is required for future hydrologic regime projections, especially under changing climate.

As a temperature-sensitive transition zone, the subtropical alpine region responds quickly to global warming. However, little is known about soil microbial communities at the treeline, where changes in vegetation in response to global warming are anticipated. Barcoded pyrosequencing of the 16S rRNA gene was used to investigate the bacterial communities of coniferous forest and grassland soils at the treeline of four different peaks above 3,000 m a.s.l. Although the forest soils were more acidic than the grassland soils, the other soil properties were highly variable with no consistent pattern in C and N contents and microbial biomass between two vegetation types. The Acidobacteria and alpha-Proteobacteria were the most abundant phylogenetic groups, although their relative abundances differed among the forest and grassland soil communities and between sites. The composition of bacterial communities or beta-diversity, varied significantly between the sites and vegetation types. In contrast, alpha-diversity only differed significantly between sites. Two of the grassland sites and one forest had converted from forests and grassland, respectively within the last 60 years. The abundances of some genera, such as Acidobacteria Gp2 in the grasslands converted from forests, more closely resembled those of the other forest sites than the historically grassland sites, while the abundance of Acidobacteria Gp1 in the forest converted from grassland was more similar to the historically grassland sites. These results suggest a legacy effect for the transition of forest to grassland. Bacterial community structure also correlated significantly with soil pH, organic C and C/N. These results suggest that the present vegetation at the treeline influences soil bacterial community structure, although there is also a significant legacy effect on the abundance of certain bacterial groups.

Vegetation phenology is an integrative indicator of environmental change, and remotely–sensed data provide a powerful way to monitor land surface vegetation responses to climatic fluctuations across various spatiotemporal scales. In this study, we synthesize the local climate, mainly temperature and precipitation, and large-scale atmospheric anomalies, El Niño-Southern Oscillation (ENSO)-connected dynamics, on a vegetative surface in a subtropical mountainous island, the northwest Pacific of Taiwan. We used two decadal photosynthetically active vegetation cover (PV) data (2001–2020) from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data to portray vegetation dynamics at monthly, seasonal, and annual scales. Results show that PV is positively related to both temperature and precipitation at a monthly timescale across various land cover types, and the log-linear with one-month lagged of precipitation reveals the accumulation of seasonal rainfall having a significant effect on vegetation growth. Using TIMESAT, three annual phenological metrics, SOS (start of growing season), EOS (end of growing season), and LOS (length of growing season), have been derived from PV time series and been related to seasonal rainfall. The delayed SOS was manifestly influenced by a spring drought, <40 mm during February–March. The later SOS led to a ramification on following late EOS, shorter LOS, and reduction of annual NPP. Nevertheless, the summer rainfall (August–October) and EOS had no significant effects on vegetation growth owing to abundant rainfall. Therefore, the SOS associated with spring rainfall, instead of EOS, played an advantageous role in regulating vegetation development in this subtropical island. The PCA (principal component analysis) was applied for PV time series and explored the spatiotemporal patterns connected to local climate and climatic fluctuations for entire Taiwan, North Taiwan, and South Taiwan. The first two components, PC1 and PC2, explained most of data variance (94–95%) linked to temporal dynamics of land cover (r > 0.90) which was also regulated by local climate. While the subtle signals of PC3 and PC4 explained 0.1–0.4% of the data variance, related to regional drought (r = 0.35–0.40) especially in central and southwest Taiwan and ENSO-associated rainfall variation (r = −0.40–−0.37). Through synthesizing the relationships between vegetation dynamics and climate based on multiple timescales, there will be a comprehensive picture of vegetation growth and its cascading effects on ecosystem productivity.

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.

Reforestation can alter the chemical composition of soil organic matter (SOM) and humification; however, information on how specific plant types impact SOM lability and humification is not well documented. In this study, we used solid-state C-13 nuclear magnetic resonance spectroscopy, photometric analysis, and chemical fractionation to examine carbon (C) components and lability of SOM in a Japanese cedar (Cryptomeria japonica) forest and bamboo (Phyllostachys edulis) plantation that reforested a cutover primary broadleaf forest. The increment logK value of soil humic acids, the inverse index of SOM humification, was lowest in the bamboo plantation, suggesting a higher SOM humification stage in the bamboo plantation. The soil labile C/Total C ratio was highest in the bamboo plantation, and this can be attributed to low aromaticity and alkyl-C/O-alkyl-C ratio (A/O-A) in the bamboo litter. Intensive cultivation of the bamboo plantation accelerated litter breakdown in the strongly acidic soil, resulting in the depletion of SOM. Cedar coniferous leaves, with their high recalcitrant substances and slow decomposition, only slightly lowered SOM humification due to the substantial broadleaf understory. Our results suggest that the type of plants involved in reforestation and understory reestablishment is critical to how SOM humification and lability change during the reforestation through the control of litter C components. Further research into the interaction between microclimate change and forest type in forest conversion will be useful for increasing understanding on the impact of forest conversion on SOM lability and humification in subtropical high mountain forest ecosystems.

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.

Typhoon is the most frequent natural disturbance in northwest Pacific Ocean, and it is an important factor to affect the structure and function of forest ecosystem in East Asia [1, 2]. Recent observations revealed that climate change may alter the intensity or frequency of typhoons in the past decade [3, 4]. Assessing the potential impacts of extreme typhoon events on ecosystem structure and carbon cycle is critical, especially in frequently perturbed regions such as Taiwan (3.7 typhoons/year) [1].

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.

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.

Litterfall is important for returning nutrients and carbon to the forest floor, and microbes decompose the litterfall to release CO2 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 (2001-2011) 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 5.11.2Mgha(-1)yr(-1) 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 (5.9Mgha(-1)yr(-1)), which was in contrast with the southern aspect (5.1Mgha(-1)yr(-1)). 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.

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.

There are knowledge gaps in our understanding of vegetation responses to multi-scale climate-related variables in tropical/subtropical mountainous islands in the Asia-Pacific region. Therefore, this study investigated inter-annual vegetation dynamics and regular/irregular climate patterns in Taiwan. We applied principal component analysis (PCA) on 11 years (2001∼2011) of high-dimensional monthly photosynthetically active vegetation cover (PV) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and investigated the relationships between spatiotemporal patterns of the eigenvectors and loadings of each component through time and multi-scale climaterelated variations. Results showed that the first five components contributed to 96.4% of the total variance. The first component (PC1, explaining 94.5% of variance) loadings, as expected, were significantly correlated with the temporal dynamics of the PV (r = 0.94), which was mainly governed by regional climate. The temporal loadings of PC2 and PC3 (0.8% and 0.6% of variance, respectively) were significantly correlated with the temporal dynamics of the PV of forests (r = 0.72) and the farmlands (r = 0.80), respectively. The low-order components (PC4 and PC5, 0.3% and 0.2% of variance, respectively) were closely related to the occurrence of drought (r = 0.49) and to irregular ENSO associated climate anomalies (r = −0.54), respectively. Pronounced correlations were also observed between PC5 and the Southern Oscillation Index (SOI) with one to three months of time lags (r = −0.35 ∼ −0.43, respectively), revealing biophysical memory effects on the time-series pattern of the vegetation through ENSO-related rainfall patterns. Our findings reveal that the sensitivity of the ecosystems in this tropical/subtropical mountainous island may not only be regulated by regional climate and human activities but also be susceptible to large-scale climate anomalies which are crucial and comparable to previous large scale analyses. This study demonstrates that PCA can be an effective tool for analyzing seasonal and inter-annual variability of vegetation dynamics across this tropical/subtropical mountainous islandin the Pacific Ocean, which provides an opportunity to forecast the responses and feedbacks of terrestrial environments to future climate scenarios.