Annual Vegetation Research Articles

Characteristics of soil enzyme stoichiometry along an altitude gradient on Qinghai-Tibet Pla-teau alpine meadow, China

Soil enzymes play critical roles in material cycle and energy flow of ecosystems. Understanding soil enzyme activities is of great significance for exploring ecosystem functions. In this study, we investigated soil enzyme activities, stoichiometry and their driving factors at six different altitudes (4300-5100 m) on Qinghai-Tibet Plateau alpine meadow using Biolog microplate analysis. The results showed that β-1,4-glucosidase (βG) closely related to C cycle, β-1,4-N-acetylglucosaminidase (NAG) and L-leucine aminopeptidase (LAP) closely related to N cycle and the activity of acid phosphatase (AP), which was closely related to P cycle, all exhibited unimodal trends with increasing altitude, with the order of 4800 m＞4950 m＞4400 m＞4650 m＞5100 m＞4300 m. Soil N:P enzyme activity ratio showed the same trend as soil enzyme activity, and reached the highest value at 4950 m, however, soil C:N and C:P enzyme activities ratios increased along the altitude. Pearson correlation analysis showed that SOC, TN and soil water content were significantly positively correlated with the activities of four types of enzymes. Mean annual precipitation was significantly negatively associated with the activities of NAG and AP. Mean annual precipitation, mean annual temperature, Shannon diversity, vegetation richness, vegetation coverage and TN affected ratios of soil C:P and N:P enzymes. Soil C:N activity ratio correlated with mean annual temperature, mean annual precipitation, vegetation richness, vegetation coverage, SOC and TN. In summary, soil enzyme activities and stoichiometry had remarkable difference along the altitude gradient on Qinghai-Tibet Plateau alpine meadow, with certain N limitation in high altitude areas. Soil water content, TN, SOC, mean annual precipitation and mean annual temperature were key factors driving such differences.

Structural characteristics of river networks and their relations to basin factors in the Yangtze and Yellow River basins

The integration of rivers and basins highly implies the possible existence of certain relationships between hierarchical characteristics of river networks and primary basin factors. Here we investigated river networks in two large basins, the Yangtze River and the Yellow River, characterized with basic factors such as annual precipitation, slope, soil erodibility and vegetation. Hierarchical analysis demonstrated a fair self-similarity of river networks at the stream-order 1–5 in both rivers, described by the structural parameters including bifurcation ratio, side-branching ratio, drainage density, and length of headwater-river. Besides precipitation, basin slope was essential in shaping river networks in both basins, showing a significant positive correlation ( R 2=0.39–0.85) to bifurcation ratio, side-branching ratio, and drainage density. Given the same basin slope (5°–15°), thehigher soil erodibility and sparse vegetation would promote greater side-branching ratio and drainage density in the Yellow River, which were estimated 11.97 % and 63.70 % larger, respectively than those in the Yangtze River. This study highlights the importance to formulate basin-specific strategies for water and soil conservation in terms of different structures of river networks.

Phytosociology and biodiversity patterns of annual wetland communities of Pyriatynskyi National Nature Park (Poltava Oblast, Ukraine)

Abstract The results of a comparative structural analysis of the annual wetland herb vegetation syntaxa (class Isoëto-Nano-Juncetea) of Pyriatynskyi National Nature Park (Poltava Oblast, Ukraine) are presented. The systematic, biomorphological, ecological and geographical structures of syntaxa were studied using cluster, discriminant and factor analysis. The principal conformity of the floristic similarity dendrogram and the previously developed classification scheme were analysed. It was revealed that the leading factor of coenosis development is the soil moisture, while most parameters of these ecotopes are constant. The critical differentiation of Nano-Cyperion and Eleocharition ovatae alliances and the legitimacy of the recognition of the Radiolion linoidis alliance and Polygono recti-Juncetum juzepczukii association as separate syntaxa of the main ranks are emphasized.

Knowledge Change and Donation Intentions of Field Day Attendees

Participation in field days increases adoption of new techniques and fosters learning. Since 1977, the Iowa State University (ISU) Department of Horticulture has hosted several Home Demonstration Garden (HDG) field days at ISU research farms each year to educate consumers on best practices and cultivars for growing annual flowers and vegetables. Gardens are planted at the farms and feature a specific topic or theme. In 2016 and 2017, 12 HDG field days were hosted in July or August. The objective of these field days was to showcase cultivars of vegetables that are in demand at food pantries and that home gardeners could grow easily for donation to these pantries. In addition to showcasing crops, presentations were delivered that focused on food insecurity implications in Iowa and how community members could impact food security locally. Of more than 400 field-day attendees in 2016, 151 (38%) participated in an optional survey at the end of the day. Similarly, in 2017, 140 of 350 (40%) attendees participated in the survey. Participants reflected on their food security knowledge and intentions to donate fresh produce before and after participation in the field day. Slightly more than a third (39.53% and 37.12%, respectively) of attendees reported some increase in food-security knowledge after participation. In addition, 85% and 72.5% of respondents reported that they will or would consider donating fresh produce to a local pantry after participation in this field day in 2016 and 2017, respectively. This was a change of more than 40% from previous donation patterns in both years. Results from this study are being used to focus future programming of the HDG field days and content of the field day surveys.

Spatiotemporal variation of vegetation coverage and its associated influence factor analysis in the Yangtze River Delta, eastern China.

Vegetation is a natural tie that connects the atmosphere, hydrosphere, biosphere, and pedosphere. Quantitatively evaluating the variability of vegetation coverage and exploring its associated influence factors are essential for ecological security and sustainable economic development. In this paper, the spatiotemporal variation of vegetation coverage and its response to climatic factors and land use change were investigated in the Yangtze River Delta (YRD) from 2001 to 2015, based on normalized difference vegetation index (NDVI) data, vegetation type data, climate data, and land use/cover change (LUCC) data. The results indicated that the annual mean vegetation coverage revealed a nonsignificant decreasing trend over the whole YRD. Areas characterized by significant decreasing (P < 0.05) trends were mainly concentrated on the central and northern part of the YRD, and significant increasing (P < 0.05) trends were mainly located in the southern part of the study area. Except for grassland and cultivated crops, vegetation coverage of the other types of vegetation was all exhibiting increasing trends. Temperature has a more pronounced impact on vegetation growth than precipitation at both the annual and monthly scales. Furthermore, vegetation growth exhibited a time lag effect for 1~2 months in response to precipitation, while there was no such phenomenon with temperature. Land use change caused by urbanization is an important driving factor for the decrease of vegetation coverage in the YRD, and the effect of land use change on the vegetation dynamic should not be overlook.

Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016

Since the implementation of China’s afforestation and conservation projects during recent decades, an increasing number of studies have reported greening trends in the karst regions of southwest China using coarse-resolution satellite imagery, but small-scale changes in the heterogenous landscapes remain largely unknown. Focusing on two typical karst regions in the Nandong and Xiaojiang watersheds in Yunnan province, we processed 2,497 Landsat scenes from 1988 to 2016 using the Google Earth Engine cloud platform and analyzed vegetation trends and associated drivers. We found that both watersheds experienced significant increasing trends in annual fractional vegetation cover, at a rate of 0.0027 year−1 and 0.0020 year−1, respectively. Notably, the greening trends have been intensifying during the conservation period (2001–2016) even under unfavorable climate conditions. Human-induced ecological engineering was the primary factor for the increased greenness. Moreover, vegetation change responded differently to variations in topographic gradients and lithological types. Relatively more vegetation recovery was found in regions with moderate slopes and elevation, and pure limestone, limestone and dolomite interbedded layer as well as impure carbonate rocks than non-karst rocks. Partial correlation analysis of vegetation trends and temperature and precipitation trends suggested that climate change played a minor role in vegetation recovery. Our findings contribute to an improved understanding of the mechanisms behind vegetation changes in karst areas and may provide scientific supports for local afforestation and conservation policies.

Litter quality and site characteristics interact to affect the response of priming effect to temperature in subtropical forests

Abstract Forest litter inputs to soil can stimulate the decomposition of older soil organic matter (SOM) via a priming effect (PE). The magnitude and underlying mechanisms driving PE are poorly understood, with especially little know about how litter quality and site conditions affect PE in situ. Further, very few studies have examined PE in tropical and subtropical soils. Here, we established low and high elevation sites (600 vs. 1,400 m a.s.l.) in the subtropical Wuyishan National Park, China, that differed with respect to mean annual temperature (MAT; ∆MAT = 4.2°C), vegetation, soil texture and soil moisture. We conducted a 1‐year field incubation study at these two sites to compare PE induced by adding low‐ and high‐quality 13C‐labelled leaf litter to soils. At the low elevation site, additions of high‐quality (low C/N) litter caused a PE that was 140% greater than the PE observed following additions of low‐quality (high C/N) litter. In contrast, we saw no significant differences in PE between litter types at the high elevation site, perhaps because PE was not limited by substrate quality at this cooler, finer textured and higher soil moisture coniferous site. In addition, we found a negative relationship between home‐field advantage (HFA) for litter decomposition and PE, indicating that specialized litter decomposer community driving HFA may not accelerate SOM decomposition via PE in the same way. In line with our observed strong relationship between PE and the efficiency of priming (PE size per unit of mineralized litter C), PEs induced by the high‐ and low‐quality litters were directed to microbial phosphorus (P) mining rather than nitrogen (N) mining. This interpretation aligns with observed increases in the activity of P acquiring extracellular enzymes, often described as phosphatases (P‐tases), as well as the positive relationship between the PE, P‐tase activity and the activity of C acquiring extracellular enzymes. Overall, this PE study across two contrasting sites highlights the important role of site characteristics and litter quality in regulating PE size. Further, we suggest that MAT may be a dominant driver of soil priming, through both the direct effects of litter quantity on labile substrate supply and the indirect effects of litter quality changes on downstream decomposer communities. A free Plain Language Summary can be found within the Supporting Information of this article.

Predicting the distribution and abundance of invasive plant species in a sub-tropical woodland-grassland ecosystem in northeastern India

Invasive plant species have become increasingly problematic in tropical and sub-tropical ecosystems, with the potential to decrease native plant diversity, increase fire occurrence, and cause ecosystem degradation. Numerous factors including disturbance due to fire, grazing, roads, human activities, reduction of native diversity, and soil fertility are known to influence invasibility of a habitat and/or promote the spread of invasive species. We studied invasive species distribution and abundance in a 519 km2 wildlife reserve that has sub-tropical woodland and grasslands. We sampled 134 plots of size 30 × 30 m2 and found that Mikania micrantha (a climber) and Chromolaena odorata (a shrub) were the most prominent invasive plants. We then tested the influence of eleven environmental variables that are either direct measures or proxies of resource availability, vegetation density, disturbance, and moisture stress. Using these predictors, we performed a decision-tree-based regression and prediction to test the influence of these variables on invasive species abundance and to generate distribution maps. The model had significant predictive power in the case of Mikania (R2 = 0.469) but was poor for Chromolaena (R2 = 0.056). Annual precipitation, soil phosphorus, and vegetation attributes had a significant influence in Mikania, and fire frequency had the strongest influence on Chromolaena. We could not quantify direct disturbance such as cattle grazing and resource extraction, which could add to the predictive power for these species. Given that invasive species continue to expand in range and abundance, more directed ecological monitoring and analyses are needed to manage ecosystems under the threat of invasions.

A Combined AHP- and TOPSIS-Based Approach in the Assessment of Desertification Disaster Risk

In this study, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP) methods were used to determine, prioritize, and assess the most effective desertification indices. A list of 32 influential indices was identified using existing literature in Iran and around the globe through questionnaires. Criteria for evaluating indices were weighted using Shannon entropy, and then TOPSIS ranking method was applied to determine the most effective desertification assessment to deal with this issue in Kashan region, Isfahan province. Indices were weighed and prioritized by analytics hierarchy process. Finally, the Mediterranean Desertification and Land Use (MEDALUS) model was used to assess the intensity and develop a desertification risk map. The results asserted that sensitivity to minor changes with a weight of 0.341 has the highest importance, while time and cost with a weight of 0.11 has the least importance for the evaluation of indices. Among the 32 ranked indices, 20 of which were identified as the most effectual indices including annual precipitation, salinity, and vegetation, to name a few. The results also showed that the land use change index with a weight of 0.217 and annual rainfall with a weight of 0.166 have the highest priority among human and natural indices that should be prioritized in managing and controlling desertification. The average weight of quantitative desertification intensity for the whole area was 1.391 placed in critical (1) class of desertification. Among the six considered desertification criteria, the climate and the destruction of water resources were the most important criteria.

California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach.

California’s almond growers face challenges with nitrogen management as new legislatively mandated nitrogen management strategies for almond have been implemented. These regulations require that growers apply nitrogen to meet, but not exceed, the annual N demand for crop and tree growth and nut production. To accurately predict seasonal nitrogen demand, therefore, growers need to estimate block-level almond yield early in the growing season so that timely N management decisions can be made. However, methods to predict almond yield are not currently available. To fill this gap, we have developed statistical models using the Stochastic Gradient Boosting, a machine learning approach, for early season yield projection and mid-season yield update over individual orchard blocks. We collected yield records of 185 orchards, dating back to 2005, from the major almond growers in the Central Valley of California. A large set of variables were extracted as predictors, including weather and orchard characteristics from remote sensing imagery. Our results showed that the predicted orchard-level yield agreed well with the independent yield records. For both the early season (March) and mid-season (June) predictions, a coefficient of determination (R2) of 0.71, and a ratio of performance to interquartile distance (RPIQ) of 2.6 were found on average. We also identified several key determinants of yield based on the modeling results. Almond yield increased dramatically with the orchard age until about 7 years old in general, and the higher long-term mean maximum temperature during April–June enhanced the yield in the southern orchards, while a larger amount of precipitation in March reduced the yield, especially in northern orchards. Remote sensing metrics such as annual maximum vegetation indices were also dominant variables for predicting the yield potential. While these results are promising, further refinement is needed; the availability of larger data sets and incorporation of additional variables and methodologies will be required for the model to be used as a fertilization decision support tool for growers. Our study has demonstrated the potential of automatic almond yield prediction to assist growers to manage N adaptively, comply with mandated requirements, and ensure industry sustainability.

GIS-based ecological climate suitability regionalization for Cordyceps sinensis in Shiqu County, Sichuan Province, China.

Based on the biological characteristics of Cordyceps sinensis, combined with the spatial and temporal distribution characteristics of local agro-climatic resources and the investigation data of C. sinensis resources, we investigated the ecological climate suitability regionalization and the spatial distribution of C. sinensis in Shiqu County using mathematical statistics analysis, optimization method and GIS spatial analysis. We used altitude, mean annual temperature, mean annual precipitation, vegetation, and soil as the leading indicators and topographic gradient as the auxiliary indicators, as the main basis for the suitability zoning of C. sinensis resources. The results showed that C. sinensis grew in most of the townships in Shiqu County, with their distribution areas being fragmented and scattered, showing sporadic patches and blocks. They were mainly distributed in east and west parts of the county and in the Zhaqu River basin in the central part. The suitable distribution area for C. sinensis in Shiqu was 4000-4700 m above sea level, with mean annual temperature of -2.5-3 ℃ and mean annual precipitation of 550-850 mm. The growth environment was generally alpine mea-dow and subalpine meadow with good hydrophobicity and slope of 15°-50°. The suitable growth environment and meteorological conditions were beneficial to the growth and development of feeding plants and bat moths. The unsuitable area was in the high mountain area above the river wide valley area, pastoral area, wetland, or snowline.

A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States

Abstract. Medium-resolution satellite observations show great potential for characterizing seasonal and annual dynamics of vegetation phenology in urban domains from local to regional and global scales. However, most previous studies were conducted using coarse-resolution data, which are inadequate for characterizing the spatiotemporal dynamics of vegetation phenology in urban domains. In this study, we produced an annual vegetation phenology dataset in urban ecosystems for the conterminous United States (US), using all available Landsat images on the Google Earth Engine (GEE) platform. First, we characterized the long-term mean seasonal pattern of phenology indicators of the start of season (SOS) and the end of season (EOS), using a double logistic model. Then, we identified the annual variability of these two phenology indicators by measuring the difference of dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived phenology indicators agree well with in situ observations from the PhenoCam network and Harvard Forest. Comparing with results derived from the moderate-resolution imaging spectroradiometer (MODIS) data, our Landsat-derived phenology indicators can provide more spatial details. Also, we found the temporal trends of phenology indicators (e.g., SOS) derived from Landsat and MODIS are consistent overall, but the Landsat-derived results from 1985 offer a longer temporal span compared to MODIS from 2001 to present. In general, there is a spatially explicit pattern of phenology indicators from the north to the south in cities in the conterminous US, with an overall advanced SOS in the past 3 decades. The derived phenology product in the US urban domains at the national level is of great use for urban ecology studies for its medium spatial resolution (30 m) and long temporal span (30 years). The data are available at https://doi.org/10.6084/m9.figshare.7685645.v5.

A review on underexploited nutrient rich perennial vegetables

Vegetables are rich in essential nutrients, vitamins, amino acids and minerals. They are huge suppliers of antioxidants too. Hence, they are termed as 'protective food'. Some vegetables like asparagus, artichokes, chekurmanis, drumstick, pointed gourd and rhubarb are perennial in growth habit and have a high nutritional profile. Globe and Jerusalem artichokes have 10.5 and 17.4 g 100 g(-1) carbohydrates, respectively, in their tubers. The fruit of breadfruit (15.8 g 100 g(‑1)), chekurmanis leaves (11.6 g 100 g(‑1)), and drumstick leaves (12.5 g 100 g(‑1)) also contain a fairly good amount of carbohydrates compared to any other annual leafy vegetable. The energy supply by chekurmnis (103 kcal 100 g(‑1)) and drumstick leaves (92 kcal 100 g(‑1)) is comparable to the energy supplies by potato (97 kcal 100 g(‑1)). High dietary fibre content was observed in sweet gourd (5.9 mg 100 g(‑1)) followed by globe artichoke (5.4 g 100 g(‑1)), drumstick (3.2 g 100 g(‑1)) and pointed gourd (3.0 g 100 g(‑1)). Chekurmanis followed by drumstick leaves are rich in minerals such as calcium (570 and 400 mg 100 g(‑1)), phosphorus (200 and 70 mg 100 g(‑1)) and iron (28 and 7 mg 100 g(‑1)), respectively. The calcium content in chekurmanis and drumstick leaves is higher than any other commonly grown leafy vegetable in India such as 330 mg 100 g(‑1) in amaranthus, 380 mg 100 g(‑1) in palak and 310 mg 100 g(‑1) in radish leaves. Basella (12276 IU 100 g(‑1)), drumstick leaves (11187 IU 100 g(‑1)) and chekurmanis (9510 IU 100 g(‑1)) contain high levels of vitamin A with other annual vegetables such as amaranthus (9108 IU 100 g‑1), spinach (8100 IU 100 g(‑1)) and carrot (1000 IU 100 g(‑1)). Chekurmanis stands first in the thiamin content among all the vegetables with a value of 0.48 mg 100 g(‑1) (Pandey, 2008) followed by drumstick (0.5 mg 100 g(‑1)), pea (0.35 mg 100 g(‑1)), palak (0.26 mg 100 g(‑1)) and lima bean (0.24 mg 100 g(‑1)). They can steadily supply vegetables year after year and they provide more income per unit area. Their cultivation helps in socio economic development and uplifting the rural societies. They improve soil health; prevent soil erosion and also help in carbon sequestration.

Optimal use of areas in perennial vegetable crops during first year of growing

Abstract. This paper tries to illustrate how intercropping increases the overall output of a cropping system and achieving much larger yields than sole crops by using environmental resources more fully over time or more efficiently in space. The research was conducted to establish the opportunity for optimal use of the growing area in perennial vegetables crops (Asparagus officinalis L., Rheum rhabarbarum L., Cynara cardunculus var. Scolymus, Levisticum officinale) by an intercropping system with annual vegetables (Brassica rapa subsp. Chinensis, Eruca sativa) in open field. The experiment was carried out in 2018 year in Vegetable Crops Research Institute (VCRI) Maritsa, Plovdiv, Bulgaria. Crops were grown under schemes: asparagus – 200+60/50 cm; artichoke – 200+60/90 cm; rhubarb – 200+60/90 cm; lovage – 200+60/50 cm; pak choi – 55+45+45+55/20 cm and 55+30+30+30+55/20 cm; rocket – 55+45+45+55/15 cm and 55+30+30+30+55/15 cm. As a result it was determined that the use of annual crops increases efficiency of the areas in perennial vegetables during the first year of growing. Annual vegetables pak choi and rocket grown under scheme 55 + 30 + 30 + 30 + 55/20cm and 55 + 30 + 30 + 30 + 55/15cm, respectively, are suitable for an intercropping system.

Modeling the carbon dynamics of alpine grassland in the Qinghai-Tibetan Plateau under scenarios of 1.5 and 2 °C global warming

Alpine grassland occupies two-thirds of the Qinghai-Tibetan Plateau (QTP). It is vital to project changes of this vulnerable ecosystem under different climate change scenarios before taking any mitigation or adaptation measures. In this study, we used a process-based ecosystem model, driven with output from global circulation models under different Representative Concentration Pathways (RCPs), to project the carbon dynamics of alpine grassland. The results showed the following: 1) Vegetation carbon (C) on the QTP increased by 22–38 gC m−2 during periods of 1.5 and 2 °C warming under different RCPs when compared to the baseline period (1981–2006), while soil C increased by 85–122 gC m−2. 2) The increases of vegetation C and soil C at the period of 1.5 °C warming were about 15 gC m−2 and 40 gC m−2 smaller than those at the period of 2 °C warming, respectively; increase of C was greater for alpine meadow than for alpine steppe. 3) Precipitation, radiation, and permafrost changed significantly and showed heterogeneous spatial patterns, and caused heterogeneous response of C dynamics. For alpine meadow in regions transformed from permafrost to seasonally frozen soil with medium annual precipitation (200–400 mm), vegetation C and net primary production decreased by 18.7 gC m−2 and 3.1 gC m−2 per year during 2 °C warming under RCP 4.5, respectively. This decrease can be attributed to the disappearing impermeable permafrost. Different from previous studies that indicated an unfavorable response of alpine grassland to climate warming, this study showed a relatively favorable response, which is mainly attributed to CO2 fertilization.

Soil phosphorus fractions vary with harvest intensity and vegetation control at two contrasting Douglas-fir sites in the Pacific northwest

Effects of intensive forest management on soil phosphorus (P) are unclear and may impact long-term site productivity. We assessed changes in P availability over 10 years associated with harvest intensity (bole-only vs. whole-tree harvest) and vegetation control treatments (initial vegetation control (IVC) vs. five years of annual vegetation control (AVC)) using a P fractionation procedure. Fractions were characterized at 0–15, 15–30, and 30–60 cm soil depths in two coast Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesii) plantations with strongly contrasting soil properties near Matlock, WA (young soils formed in glacial outwash) and Molalla, OR (relatively old soils formed in igneous residuum and exhibiting andic properties). Al and Fe concentrations associated with short-range order minerals were greater at Molalla than Matlock and generally decreased with depth at both sites. We observed decreases in most total-P and P-fraction concentrations across the three soil depths at the Molalla site. Effects were less pronounced and generally inconsistent at the Matlock site. Decreases in total P and P fraction concentrations were greatest in the AVC treatments at Matlock, but opposite trends were observed at Molalla where decreases were greatest with IVC. There was no difference between harvest treatments on the change in P fractions in most instances, with the exception of the 30–60 cm depth at Matlock where concentrations of some P fractions were maintained or increased with bole-only harvesting. Ten-year responses indicate harvest intensity has limited effects on long-term productivity associated with soil P because of the large size of the soil P pools and the relatively small changes in soil P that occurred with treatment. Decreases in P concentrations with AVC at Matlock and IVC at Molalla were larger than the other treatments and highlight the important role of vegetation in P dynamics following harvesting at these sites.

Dynamics of gross primary productivity with VPM model in Changbai Mountain Natural Reserve, Northeast China.

Precise estimation of gross primary productivity (GPP), the key parameter in carbon cycle analysis, plays an important role in the research of carbon cycle and global climate change. Vegetation GPP was simulated by VPM model based on MOD09A1 and climate data in Changbai Mountain Natural Reserve from 2000 to 2015. The results showed that mean GPP was 1203 g C·m-2·a-1. The annual vegetation GPP significantly increased from 2000 to 2015. There was no significant difference in the temporal trends of forest GPP at different vertical vegetation zones. However, GPP of the alpine tundra decreased remarkably. The correlation between GPP and precipitation was not significant. The positive correlation of GPP and temperature was mainly distributed in broad-leaved Korean pine forests and alpine tundra. Spring temperature had the strongest influence on GPP, with 80% pixels had a positive correlation with temperature. The GPP had a stronger correlation with temperature compared with precipitation.

Differing Sensitivities to Fire Disturbance Result in Large Differences Among Remotely Sensed Products of Vegetation Disturbance

Recent advances in high-performance computing (HPC) have promoted the creation of standardized remotely sensed products that map annual vegetation disturbance through two primary methods: (1) conventional approaches that integrate remote sensing-derived vegetation indices with field data and other data on disturbance events reported by public agencies on a year-to-year basis, and (2) “big” data approaches using HPC to automate algorithms and workflows across an entire time series. Given the recent proliferation of these annual products and their potential utility for understanding vegetation dynamics, it is important for product end users (that is, practitioners and researchers in domains other than remote sensing) to understand the differences in their representations of disturbance and the conditions under which they report it. We use fire in California as a case study to compare reported disturbance across three widely used vegetation disturbance products—LANDFIRE (representing the conventional approach), Hansen Global Forest Change (GFC), and North America Forest Dynamics (NAFD), the latter two created from automated approaches. Using Google’s Earth Engine, we compared their total and annual amounts of fire and non-fire disturbance for 2001–2010 and examined the products’ reported disturbance across different environmental and burn conditions. We found that GFC and NAFD reported similar amounts of disturbance that were consistently much lower than LANDFIRE’s reported disturbance across all years, regions, and habitats. We also found that despite the differences in amounts of reported disturbance, the products identified disturbance in similar ranges of bioclimatic conditions and habitat types, and thus, differing environmental conditions in areas reported as disturbed were not the drivers of the difference. Rather, we found that lower sensitivity to fire disturbance for GFC and NAFD, as compared to LANDFIRE, was a key driver of the overall differences in the amounts and locations of reported disturbance; both GFC and NAFD reported much lower amounts of fire disturbance than LANDFIRE across all burn conditions. Furthermore, the difference in reported disturbance between LANDFIRE and GFC/NAFD was greater for fire disturbance than for non-fire disturbance; LANDFIRE reported more than double the total amounts of fire disturbance of GFC and NAFD in the study period. Based on our results, we encourage end users to choose the appropriate disturbance product based not only on spatial extent and habitat but also on the disturbance type of interest (that is, fire and non-fire). Overall, rather than focusing on accuracy, our study quantifies the extent to which the products exhibited differences in the amounts and locations of reported disturbance to provide insight into these products’ representations of disturbance and help end users evaluate and choose the most appropriate product for their needs.

Ecological analysis of Kashin-Beck osteoarthropathy risk factors in Tibet\u2019s Qamdo City, China

We studied Tibet’s Qamdo City, which currently hosts the most serious prevalence of Kashin-Beck osteoarthropathy (KB) in China. This study utilizes the geographical detector (GeoDetector) algorithm to measure the individual and interactive influences of risk factors on KB and to quantify the highest potential risk subzones of each principal factor. With a comprehensive consideration of 13 possible related factors, namely, the tectonic division, stratum, moisture index, gross domestic product, mean annual precipitation, soil type, groundwater type, elevation, mean annual temperature, vegetation type, geomorphic type, slope degree and slope aspect, our results indicate that the main exposure factors for KB in Qamdo City are geological factors (tectonic division and stratum), wetting factors (moisture index and mean annual precipitation), and an economic factor (gross domestic product). In contrast, other factors have little effect on the prevalence of KB in Qamdo City. All 13 factors either nonlinearly or bivariately enhance each other, and the interactions between these factors can increase the prevalence of KB. Consequently, it can be inferred that KB in Qamdo City is caused primarily by a set of multiple and interrelated disease risk factors.

An inexpensive and open‐source method to study large terrestrial animal diet and behaviour using time‐lapse video and GPS

Abstract The behaviour of free‐ranging animals is difficult to study, especially on the large spatial and temporal scales relevant to long‐lived large species. Animal‐borne video and environmental data collection systems (AVEDs) record behaviour and other data in real time as animals conduct daily activities. However, few studies have combined systematically collected, long‐term AVED foraging data with environmental and movement data to test hypotheses on animal foraging. Additionally, AVEDs are often either prohibitively expensive, or require extensive fabrication and programming knowledge. The video and coordinate animal‐mounted system (VACAMS) is an animal‐mounted data collection system based on a modified GoPro® action camera platform that records short, first ‘person’ perspective videos of animal behaviour on an automated time‐lapse schedule. As most videos are georeferenced, researchers can return to the locations of specific behaviours and collect accurate, fine‐grained data on non‐woody vegetation and other habitat characteristics that may influence animal behaviour. Moreover, VACAMS are inexpensive and easy to use. This study describes VACAMS preliminary data on cattle foraging and a hypothesis exploring free‐ranging cattle browsing habits throughout the rainy season in the tropical dry forest of Sonora, Mexico. I generated a database of vegetation types consumed by cows each month (annual, woody and leaf litter) and compared actual vegetation type frequencies to a priori assumptions based on seasonal patterns of forage availability. During the monsoons, when palatable vegetation was abundant, frequencies of annual and woody perennial vegetation in cattle diets did not differ from month to month. When the rains ceased and palatable vegetation became scarce, cows switched to leaf litter, dead annual vegetation, twigs and dried leguminous fruits. Open‐source software and commercially available hardware make VACAMS financially attainable for many researchers, land managers, students and other user groups. VACAMS could be used on a range of domestic and semi‐domestic free‐ranging animals, particularly in dense forests where conventional observations are impossible. With improvements to GPS battery life and durability, the weakest points of the system, VACAMS could also potentially apply to studies of other large terrestrial animals.