Rebirth after death: forest succession dynamics in response to climate change on Gongga Mountain,Southwest China

Global climate change is having long-term impacts on the geographic distribution of forest species. However, the response of vertical belts of mountain forests to climate change is still little known. The vertical distribution of forest vegetation(vertical vegetation belt) on Gongga Mountain in Southwest China has been monitored for 30 years. The forest alternation of the vertical vegetation belt under different climate conditions was simulated by using a mathematical model GFSM(the Gongga Forest Succession Model). Three possible Intergovernmental Panel on Climate Change(IPCC) climate scenarios(increase of air temperature and precipitation by 1.8℃/5%, 2.8℃/10% and 3.4℃/15% for B_1, A_1B and A_2 scenarios, respectively) were chosen to reflect lower, medium and higher changes of global climate. The vertical belts of mountainous vegetation will shift upward by approximately 300 m, 500 m and 600 m in the B_1, A_1B and A_2 scenarios, respectively, according to the simulated results. Thus, the alpine tree-line will move to a higher altitude. The simulation also demonstrated that, in a changing climate, the shift in the vegetation community will be a slow and extended process characterized by two main phases. During the initial phase, trees of the forest community degrade or die, owing to an inability to adapt to a warmer climate. This results in modest environment for the introduction of opportunistic species, consequently, the vegetation with new dominant tree species becomes predominant in the space vacated by the dead trees at the expense of previously dominated original trees as the succession succeed and climate change advance. Hence, the global climate change would dramatically change forest communities and tree species in mountainous regions because that the new forest community can grow only through the death of the original tree. Results indicated that climate change will cause the change of distribution and composition of forest communities on Gongga Mountain, and this change may enhance as the intensity of climate change increases. As a result, the alternation of death and rebirth would finally result in intensive landscape changes, and may strongly affect the eco-environment of mountainous regions.     

Application of different clustering approaches to hydroclimatological catchment regionalization in mountainous regions,a case study in Utah State

With respect to the different hydrological responses of catchments, even the adjacent ones, in mountainous regions, there are a great number of motivations for classifying them into homogeneous clusters. These motivations include prediction in ungauged basins (PUB), model parameterization, understanding the potential impact of environmental changes, transferring information from gauged catchments to the ungauged ones. The present study investigated the similarity of catchments through the hydro-climatological pure time-series of a 14-year period from 2001 to 2015. Data sets encompass more than 13,000 month-station streamflow, rainfall, and temperature data obtained from 27 catchments in Utah State as one of the eight mountainous states of the USA. The identification, analysis, and interpretation of homogeneous catchments were investigated by applying the four approaches of clustering, K-means, Ward, and SOM (Self-Organized Map) and a newly proposed Wavelet-Entropy-based (WE-SOM) clustering method. By using two clustering evaluation criteria, 3, 5, and 6 clusters were determined as the best numbers of clusters, depending on the method employed, where each cluster represents different hydro-climatological behaviors. Despite the absence of geographic characteristics in input data matrix, the results indicated a regionalization in agreement with topographic characteristics. Considering the dependency of the hydrological behavior of catchments on the physiographic field aspects and characteristics, WE-SOM method demonstrated a more acceptable performance, compared to the other three conventional clustering methods, by providing more clusters. WE-SOM appears to be a promising approach in catchment clustering. It preserves the topological structure of data which can, as a result, be proofed in a greater number of clusters by dividing data into higher numbers of distinct clusters with similar altitudes of catchments in each cluster. The results showed the aptitude of wavelets to quantify the time-based variability of temperature, rainfall and streamflow, in the way contributing to the regionalization of diverse catchments.     

Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains,China

Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R~2=0.55 vs.R~2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.     

Surface energy balance of Bayi Ice Cap in the middle of Qilian Mountains,China

Energy balance at the glacier surface is important for understanding the impacts of climate change on glaciers. Here, we analyzed the characteristics of the glacier surface energy fluxes along with their contributions to glacier melt on Bayi Ice Cap in Qilian Mountains by using a point-scale energy balance model. The half-hourly meteorological data from an automatic weather station (AWS) located on the glacier was used to drive the energy balance model. The model simulated results could accurately represent the mass-balance observations from the stake near the weather station during summer 2016. Our results showed the net radiation (86%) played an important role in the surface energy balance, and the contribution of the turbulent heat fluxes (14%) to the energy budget was relatively less important. A distinct behavior of energy balance, as compared to other continental glaciers in China (e.g., two adjacent glaciers Laohugou No. 12 Glacier and Qiyi Glacier), is the fact that a sustained period of positive turbulent latent flux exists on Bayi Ice Cap during August, causing faster melt rate in the month of August. Our study also presented the effect of frequent summer snowfall in slowing down surface melt by changing the surface albedo during the beginning of the melting season.     

Comparison of the spatio-temporal dynamics of vegetation between the Changbai Mountains of eastern Eurasia and the Appalachian Mountains of eastern North America

The Changbai Mountains and the Appalachian Mountains have similar spatial contexts. The elevation, latitude, and moisture gradients of both mountain ranges offer regional insight for investigating the vegetation dynamics in eastern Eurasia and eastern North America. We determined and compared the spatial patterns and temporal trends in the normalized difference vegetation index (NDVI) in the Changbai Mountains and the Appalachian Mountains using time series data from the Global Inventory Modeling and Mapping Studies 3rd generation dataset from 1982 to 2013. The spatial pattern of NDVI in the Changbai Mountains exhibited fragmentation, whereas NDVI in the Appalachian Mountains decreased from south to north. The vegetation dynamics in the Changbai Mountains had an insignificant trend at the regional scale, whereas the dynamics in the Appalachian Mountains had a significant increasing trend. NDVI increased in 55% of the area of the Changbai Mountains and in 95% of the area of the Appalachian Mountains. The peak NDVI occurred one month later in the Changbai Mountains than in the Appalachian Mountains. The results revealed a significant increase in NDVI in autumn in both mountain ranges. The climatic trend in the Changbai Mountains included warming and decreased precipitation, and whereas that in the Appalachian Mountains included significant warming and increased precipitation. Positive and negative correlations existed between NDVI and temperature and precipitation, respectively, in both mountain ranges. Particularly, the spring temperature and NDVI exhibited a significant positive correlation in both mountain ranges. The results of this study suggest that human actives caused the differences in the spatial patterns of NDVI and that various characteristics of climate change and intensity of human actives dominated the differences in the NDVI trends between the Changbai Mountains and the Appalachian Mountains. Additionally, the vegetation dynamics of both mountain ranges were not identical to those in previous broader-scale studies.     

Estimating aboveground biomass using Pléiades satellite image in a karst watershed of Guizhou Province,Southwestern China

Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants.Satellite observation, especially at high spatial resolution, is an important surrogate for the quantification of the biomass of karst forests and shrublands. In this study, an artificial neural network(ANN) model was built using Pléiades satellite imagery and field biomass measurements to estimate the aboveground biomass(AGB) in the Houzhai River Watershed, which is a typical plateau karst basin in Central Guizhou Province, Southwestern China. A back-propagation ANN model was also developed.Seven vegetation indices, two spectral bands of Pléiades imagery, one geomorphological parameter,and land use/land cover were selected as model inputs. AGB was chosen as an output. The AGB estimated by the allometric functions in 78 quadrats was utilized as training data(54 quadrats, 70%),validation data(12 quadrats, 15%), and testing data(12 quadrats, 15%). Data-model comparison showed that the ANN model performed well with an absolute root mean square error of 11.85 t/ha, which was 9.88%of the average AGB. Based on the newly developed ANN model, an AGB map of the Houzhai River Watershed was produced. The average predicted AGB of the secondary evergreen and deciduous broadleaved mixed forest, which is the dominant forest type in the watershed, was 120.57 t/ha. The average AGBs of the large distributed shrubland,tussock, and farmland were 38.27, 9.76, and 11.69 t/ha, respectively. The spatial distribution pattern ofthe AGB estimated by the new ANN model in the karst basin was consistent with that of the field investigation. The model can be used to estimate the regional AGB of karst landscapes that are distributed widely over the Yun-Gui Plateau.     

Stabilization versus decomposition in alpine ecosystems of the Northwestern Caucasus: The results of a tea bag burial experiment

Mountainous areas exhibit highly variable decomposition rates as a result of strong local differences in climate and vegetation type. This paper describes the effect of these factors on two major determinants of the local carbon cycle: litter decomposition and carbon stabilization. In order to adequately reflect local heterogeneity, we have sampled 12 typical plant communities of the Russian Caucasus. In order to minimize confounding effects and encourage comparative studies, we have adapted the widely used tea bag index (TBI) that is typically used in areas with low decomposition. By incubating standardized tea litter for a year, we investigated whether (1) initial litter decomposition rate (k) is negatively correlated with litter stabilization (S) and (2) whether k or S exhibit correlations with altitude and other environmental conditions. Our results show that S and k are not correlated. Altitude, pH, and water content significantly influenced the stabilization factor S, while soil-freezing had no influence. In contrast, none of these factors predicted the decomposition rate k. Based on our data, we argue that collection of decomposition rates alone, as is now common practice, is not sufficient to understand carbon input to soils and can potentially lead to misleading results. Our data on community-specific decomposition and stabilization rates further constrain estimates of litter accumulation in subalpine communities and the potential effects of climate change.     

Spatio-temporal Variation of Soil Respiration and Its Driving Factors in Semi-arid Regions of North China

Soil respiration (SR) is the second-largest flux in ecosystem carbon cycling. Due to the large spatio-temporal variability of environmental factors, SR varied among different vegetation types, thereby impeding accurate estimation of CO₂ emissions via SR. However, studies on spatio-temporal variation of SR are still scarce for semi-arid regions of North China. In this study, we conducted 12-month SR measurements in six land-use types, including two secondary forests (Populus tomentosa (PT) and Robinia pseudoacacia (RP)), three artificial plantations (Armeniaca sibirica (AS), Punica granatum (PG) and Ziziphus jujuba (ZJ)) and one natural grassland (GR), to quantify spatio-temporal variation of SR and distinguish its controlling factors. Results indicated that SR exhibited distinct seasonal patterns for the six sites. Soil respiration peaked in August 2012 and bottomed in April 2013. The temporal coefficient of variation (CV) of SR for the six sites ranged from 76.98% to 94.08%, while the spatial CV of SR ranged from 20.28% to 72.97% across the 12-month measurement. Soil temperature and soil moisture were the major controlling factors of temporal variation of SR in the six sites, while spatial variation in SR was mainly caused by the differences in soil total nitrogen (STN), soil organic carbon (SOC), net photosynthesis rate, and fine root biomass. Our results show that the annual average SR and Q₁₀ (temperature sensitivity of soil respiration) values tended to decrease from secondary forests and grassland to plantations, indicating that the conversion of natural ecosystems to man-made ecosystems may reduce CO₂ emissions and SR temperature sensitivity. Due to the high spatio-temporal variation of SR in our study area, care should be taken when converting secondary forests and grassland to plantations from the point view of accurately quantifying CO₂ emissions via SR at regional scales.     

Evolution Characteristics of Government-Industry-University-Research Cooperative Innovation Network for China’s Agriculture and Influencing Factors: Illustrated According to Agricultural Patent Case

Under the special background of China, the cooperative innovation between different government-industry-university-research institutes plays an increasingly important role in the agricultural field. However, the existing literature has paid little attention to it. Considering the cooperation patents, published in the agriculture field stemming from the Full-text Database of China Patents as the study object, the spatial and institutional attribute of the authors as the data source, and by combining the social network and spatial econometrics analysis, this paper analyzes the structure evolution characteristics of cooperative innovation networks of agricultural government-industry-university-research institute in the city level of China in 1985–2014, based on the triple helix theory, with the influence factors discussed. This shows that, 1) since 1985, China's agricultural innovation level has been substantially increased, but the development degree of the cooperative innovation network is low, and the patent cooperation mainly relies on authors in the same unit; 2) enterprises play a leading role in the agricultural cooperative innovation. The effect of the government and hybrid organizations driven by the government is not obvious; 3) the cooperative innovation in the province and city dominates, and a multi-pole pattern has been formed. The cooperative innovation network structure evolves from a single helix empty core and double helix multi core to a double helix hierarchical network; 4) the city's science, education funding and personnel investment are key factors determining the agricultural cooperative innovation, while the agricultural development of the city presents slight negative impacts on it. The spatial mismatch of supply and demand is present in the technical cooperative innovation of China's agriculture. Therefore, the science enhancement and education investment to big agricultural provinces should be promptly implemented.     

Iron Regulation of Wetland Vegetation Performance Through Synchronous Effects on Phosphorus Acquisition Efficiency

Iron-rich groundwater flowing into wetlands is a worldwide environmental pollution phenomenon that is closely associated with the stability of wetland ecosystems. Combined with high phosphorus(P) loading from agricultural runoff, the prediction of the evolution of wetland vegetation affected by compound contamination is particularly urgent. We tested the effects of anaerobic iron-rich groundwater discharge in a freshwater marsh by simulating the effect of three levels of eutrophic water on native plants(Glyceria spiculosa(Fr. Schmidt.) Rosh.). The management of wetland vegetation with 1–20 mg/L Fe input is an efficient method to promote the growth of plants, which showed an optimum response under a 0.10 mg/L P surface water environment. Iron-rich groundwater strongly affects the changes in ecological niches of some wetland plant species and the dominant species. In addition, when the P concentration in a natural body of water is too high, the governance effect of eutrophication might not be as expected. Under iron-rich groundwater conditions, the δ~(13)C values of organs were more depleted, which can partially explain the differences in δ~(13)C in the soil profile. Conversely, the carbon isotope composition of soil organic carbon is indicative of past changes in vegetation. The results of our experiments confirm that iron-rich groundwater discharge has the potential to affect vegetation composition through toxicity modification in eutrophic environments.