Non-growing Season Research Articles

Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery

Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research.

Differences in respiration components and their dominant regulating factors across three alpine grasslands on the Qinghai−Tibet Plateau

Greenhouse gases (GHGs) emissions from high-cold terrestrial ecosystems underlain by permafrost on the Qinghai–Tibet Plateau (QTP) have received widespread attention. However, the dominant factors regulating ecosystem respiration (Re) and its components (soil respiration Rs and heterotrophic respiration Rh) and how the differences in carbon emissions from different ecotypes and seasons remain are still unclear. We conducted a 2-year field investigation (August 2018 to October 2020) and applied the structural equation model (SEM) to clarify the changes in the factors controlling the respiration components during different seasons. The results indicate that the Re and its controlling factors in three alpine grassland ecosystems (alpine steppe, alpine meadow, and swamp meadow) vary with seasons. Furthermore, autotrophic respiration (Ra) contributes the most to the seasonal changes in Re. The Re gradually increases in the early growing season and rapidly decreases in the late growing season. Rh remains relatively stable during the year. Under these seasonal variations in the respiration components, the dominant factors controlling Re in the nongrowing season are the temperature of the atmosphere–soil interface (heat flux, atmospheric temperature, and soil temperature at 5 cm depth) and microbial activity (microbial carbon and pH) with the variable importance projections >1.5. During the growing season, the dominant factors regulating Re, Rs, and Rh are the soil temperature with a standardized direct effect (SDE) of 0.424, soil nutrient conditions (total nitrogen and pH) with SDEs of 0.570–0.614, and microbial activity (microbial carbon) with a SDE of 0.591, respectively. In addition, meteorological conditions have an important impact on the respiration components during the growing season. Specifically, the atmospheric vapor pressure is the dominant factor regulating the three respiration components (standardized total effects = 0.44−0.53, p < 0.001). The optimal soil water contents during the growing season (water content at which Re reaches the maximum) are 10% in the alpine steppe, 13%–15% in the alpine meadow, and 40%–43% in the swamp meadow, respectively. The effect of the soil water content on Re is more important in arid ecosystems (alpine steppe and alpine meadow) than in wet ecosystem (swamp meadow). The alleviation of water limitations in arid ecosystems may potentially increase Re.

Research Progress and Development Trends of Greenhouse Gas Emissions from Cereal–Legume Intercropping Systems

High yields and low carbon emissions are new challenges for modern crop production. Balancing the crop yield and reducing greenhouse gas (GHG) emissions has become a new field of agronomic technology innovation. Cereal–legume intercropping is a typical diversification planting system, which has been expected to achieve the dual goals of high production and low GHG emissions. However, the synergistic effect of integrating various technologies in an intercropping system on GHG emissions and whether it will achieve the high yield and low emissions goal remains to be determined. Therefore, bibliometric analysis has investigated the worldwide development trend of cereal–legume intercropping designs. The literature on the GHG emissions of the cereal–legume intercropping system was summarized. Additionally, the effects and mechanisms of different agricultural management methods regarding soil nitrous oxide and carbon dioxide emissions in the cereal–legume intercropping system were summarized. The research on GHG emissions of cereal–legume intercropping systems in non-growing seasons must be revised. In situ observations of GHG emissions from intercropping systems in different regions should be strengthened. This work is valuable in supporting and evaluating the potential of GHG reduction in a cereal–legume intercropping system in various farming areas.

Soil salinity simulation based on electromagnetic induction and deep learning

In pursuit of fine modeling and forecasting of water-salt transport in arid zones, this study uses the rapid electromagnetic induction survey as the main tool to establish an empirical relationship between soil volumetric water content, salinity and electrical conductivity based on on-site measurement data. By using deep learning to set up a surrogate model for electromagnetic induction inverse problem, the vertical distribution of soil salinity during salt leaching at non-growth season is revealed. HYDRUS-1D was used to perform inversions of soil hydraulic parameters to obtain accurate numerical solutions. The results show that the surrogate of electromagnetic induction inversion using a deep convolutional encoding-decoding network (NET) with a learning rate of 0.0006 and 1500 sets of training samples has high accuracy, with an R2 of 0.9930 and RMSE of 0.2585 mg/cm3. The HYDRUS inverse modeling was performed using synthetic ECa data, water content and soil salinity observation data, and the combination of ECa and shallow ground (10 cm deep) water content respectively. It was shown that although the accuracy of the water-salt transport model obtained by using ECa data is slightly lower than that of the inversion model using traditional observation data, it can still accurately simulate the water-salt distribution and has the incomparable advantages of being fast, convenient and low-cost, and the accuracy of the model is improved to some extent by incorporating shallow ground water content data into the inversion based on ECa data. Therefore, the water-salt transport simulation framework incorporating soil apparent electrical conductivity proposed in this study can accurately simulate salt transport and can provide strategies and methods for the simulation and prediction of soil water-salt transport using electromagnetic induction technology.

Reclamation intensifies the positive effects of warming on N2O emission in an alpine meadow.

Climatic warming can alter grassland nitrous oxide (N2O) emissions due to soil property alterations. However, how the reclamation affect grassland N2O flux under warming conditions remains unclear in alpine meadow ecosystems. We conducted a long-term manipulative warming experiment in a natural alpine meadow and a cultivated grassland on the Qinghai-Tibetan Plateau to explore the separate and interactive effects of warming and reclamation on the soil N2O emission flux. N2O fluxes were measured under four treatments including control (CK), warming (W), reclamation (R) and warming under reclamation (WR) from August 2018 to July 2019. We measured the content of soil C, N nutrients and 5 enzymatic activities in 2018 and 2019. Correlation analysis and structural equation modeling were used to clarify how soil N availability and soil enzyme activities affect N2O emission. Our results indicated that compared to the ambient conditions for the growing and non-growing seasons, soil N2O flux was significantly increased 59.1% and 152.0% by warming and 28.4% and 142.4% by reclamation, respectively. Compared with W, WR significantly increased N2O flux by 18.9% and 81.1% during the growing and non-growing seasons, respectively. Soil moisture was negatively correlated to enzymatic activity and N2O flux. Both warming and reclamation promoted soil nitrification by increasing related enzymatic activities that acted to increase the N2O flux. Reclamation resulted in a greater sensitivity of the activity of ammonia monooxygenase and hydroxylamine oxidoreductase to warming, thus enhancing the effects of warming on increasing the N2O flux. Our research indicated that reclamation can additionally increase the effects of warming on N2O emissions for alpine meadows. Therefore, excessive expansion of arable land should be avoided, and new reclamation sites should be planned scientifically, as warming is expected to intensify in the future.

Spatial Water Consumption Test and Analysis of Various Typical Vegetation in the Sanjiangyuan Region

Vegetation water consumption in the Sanjiangyuan Region is of direct significance to the utilization of local water resources. To measure the actual evapotranspiration of various typical vegetation with different vegetation types in the Sanjiangyuan Region, a Lysimeter was used between November 2019 and October 2020. Additionally, the Penman–Monteith equation was used to estimate the condensation water of different vegetation types. Based on the measured data, this paper analyzes the spatial distribution of annual water consumption and annual runoff of various vegetation types. Furthermore, the spatial and temporal distribution of monthly water consumption of vegetation types on different underlying surfaces are discussed. To establish the relationship between the precipitation and runoff of various vegetation types, an artificial rainfall test was conducted. This study’s results reveal several key findings: (1) Condensation water is widespread and can be observed throughout the year. The annual condensation water volume ranges between 28.47 and 56.88 mm, which is particularly significant for the growth of alpine desert steppe and alpine steppe vegetation. (2) The annual water consumption in the Sanjiangyuan Region was higher in the south than in the north. Shrub water consumption was found to be 58.1–73.3 mm higher than that of grasses. Water consumption primarily occurred during the growing season, spanning from May to October. (3) The total water consumption in the growing season of the alpine meadow was less affected by precipitation compared to the non-growing season (from November to the next April). (4) The runoff yield can be ignored in the non-growing season when calculating water balance. However, during the growing season, the calculation of runoff cannot be ignored due to its significant impact on vegetation water consumption.

Probabilistic assessment of drought stress vulnerability in grasslands of Xinjiang, China.

In the process of climate warming, drought has increased the vulnerability of ecosystems. Due to the extreme sensitivity of grasslands to drought, grassland drought stress vulnerability assessment has become a current issue to be addressed. First, correlation analysis was used to determine the characteristics of the normalized precipitation evapotranspiration index (SPEI) response of the grassland normalized difference vegetation index (NDVI) to multiscale drought stress (SPEI-1 ~ SPEI-24) in the study area. Then, the response of grassland vegetation to drought stress at different growth periods was modeled using conjugate function analysis. Conditional probabilities were used to explore the probability of NDVI decline to the lower percentile in grasslands under different levels of drought stress (moderate, severe and extreme drought) and to further analyze the differences in drought vulnerability across climate zones and grassland types. Finally, the main influencing factors of drought stress in grassland at different periods were identified. The results of the study showed that the spatial pattern of drought response time of grassland in Xinjiang had obvious seasonality, with an increasing trend from January to March and November to December in the nongrowing season and a decreasing trend from June to October in the growing season. August was the most vulnerable period for grassland drought stress, with the highest probability of grassland loss. When the grasslands experience a certain degree of loss, they develop strategies to mitigate the effects of drought stress, thereby decreasing the probability of falling into the lower percentile. Among them, the highest probability of drought vulnerability was found in semiarid grasslands, as well as in plains grasslands and alpine subalpine grasslands. In addition, the primary drivers of April and August were temperature, whereas for September, the most significant influencing factor was evapotranspiration. The results of the study will not only deepen our understanding of the dynamics of drought stress in grasslands under climate change but also provide a scientific basis for the management of grassland ecosystems in response to drought and the allocation of water in the future.

Comparison of GEDI LiDAR Data Capability for Forest Canopy Height Estimation over Broadleaf and Needleleaf Forests

The GEDI LiDAR system was specifically designed to detect vegetation structure and has proven to be a suitable tool for estimating forest biophysical parameters, especially canopy height, at a global scale. This study compares the GEDI relative height metric (RH100) over different forest types, especially deciduous broadleaf and evergreen coniferous located in Thuringia, Germany, to understand how the forest structural differences affect the GEDI height estimation. A canopy height model that was produced using digital terrain and surface models (DTM and DSM) derived from airborne laser scanning data is used as the reference data. Based on the result, GEDI canopy height over needleleaf forest is slightly more accurate (RMSE = 6.61 m) than that over broadleaf (RMSE = 8.30 m) and mixed (RMSE = 7.94 m) forest. Evaluation of the GEDI acquisition parameters shows that differences in beam type, sensitivity, and acquisition time do not significantly affect the accuracy of canopy heights, especially over needleleaf forests. Considering foliage condition impacts on canopy height estimation, the contrasting result is observed in the broadleaf and needleleaf forests. The GEDI dataset acquired during the winter when deciduous species shed their leaves (the so-called leaf-off dataset), outperforms the leaf-on dataset in the broadleaf forest but shows less accurate results for the needleleaf forest. Considering the effect of the plant area index (PAI) on the accuracy of the GEDI canopy height, the GEDI dataset is divided into two sets with low and high PAI values with a threshold of median PAI = 2. The results show that the low PAI dataset (median PAI &lt; 2) corresponds to the non-growing season (autumn and winter) in the broadleaf forest. The slightly better performance of GEDI using the non-growing dataset (RMSE = 7.40 m) compared to the growing dataset (RMSE = 8.44 m) in the deciduous broadleaf forest and vice versa, the slightly better result using the growing dataset (RMSE = 6.38 m) compared to the non-growing dataset (RMSE = 7.24 m) in the evergreen needleleaf forest is in line with the results using the leaf-off/leaf-on season dataset. Although a slight improvement in canopy height estimation was observed using either the leaf-off or non-growing season dataset for broadleaf forest, and either the leaf-on or growing season dataset for needleleaf forest, the approach of filtering GEDI data based on such seasonal acquisition time is recommended when retrieving canopy height over pure stands of broadleaf or needleleaf species, and the sufficient dataset is available.

Mapping the spatial distribution of alpine shrubs on the Qinghai–Tibet Plateau using Sentinel-2 images

ABSTRACT Shrubs are typical vegetation on the Qinghai – Tibet Plateau, and their spatial distribution pattern is crucial for understanding the regional ecological environment. Existing global land cover products cannot represent shrubs well in this area, and the relationship between shrubs and the corresponding environmental factors is still unclear. In this study, using the Dogxung Zangbo River Basin as an example, Sentinel-2 images were applied to estimate the regional shrub fraction image, using the random forest regression model trained with samples generated from fine-resolution Jilin-1 images. The estimated shrub fraction image was further analysed using an elevation dataset to explore the impact of topography on the spatial distribution of shrubs. The results showed that using Sentinel-2 images acquired in both plant growing and non-growing seasons can better distinguish shrubs from grass than using only one season of data, and the coefficient of determination and root-mean-square error derived from this model were 0.821 and 0.087, respectively. The shrub distribution was strongly correlated with topography. In the study area, areas with a southeast or south orientation and large slope are more suitable for shrub growth.

Growing and non-growing season nitrous oxide emissions from a manured semiarid cropland soil under irrigation

Dairy manure is used in semiarid southern Idaho to improve soil fertility, but campaigns to measure resulting nitrous oxide (N2O) emissions over the complete year have not been conducted to date. The objective of this study was to measure N2O fluxes throughout the growing (April to Sept) and non-growing (Oct to Mar) seasons in 2020 (sugarbeet) and 2021 (silage corn and triticale) in a field that received inorganic N fertilizer or was previously treated with dairy manure solids on an annual and biennial basis for 8 years. Gas fluxes were measured daily using automated chambers that were connected to a gas chromatograph for in situ analysis of N2O. The N2O emissions were found to be highly episodic and major pulses were associated with irrigation during the growing season, warming events in the winter, and soil disturbance at harvest. Emissions were greatest from soil that had received manure at the highest annual application rate of 52 Mg ha−1 (dry wt.), with cumulative totals of 3.6 and 3.0 kg N2O-N ha−1 in 2020 and 2021, respectively. These cumulative totals were about 3-fold greater than emissions from plots treated with inorganic fertilizer or manure at 17 Mg ha−1 annually or 35 Mg ha−1 biennially. This outcome can be attributed to high concentrations of nitrate produced through mineralization of organic N in manure. Emission factors indicated that up to 1.2% of the total N applied was lost as N2O-N, with the greatest loss from inorganic fertilizer treated soil. When breaking down the emissions by season, anywhere from 49%–63% (2020) and 37–58% (2021) of the N2O-N emissions occurred during the non-growing season. Growing and non-growing season N2O emissions were found to be statistically equivalent for each of the respective fertilizer or manure treatments. This finding stresses the need to also measure N2O emissions during the non-growing season as a way to improve the accuracy of annual emission estimates.

Temporal Response of Mesocarnivores to Human Activity and Infrastructure in Taihang Mountains, Central North China: Shifts in Activity Patterns and Their Overlap

Mesocarnivores play essential roles in terrestrial ecosystems, but anthropocentric disturbances have profoundly transformed their intraguild interactions worldwide. In this study, we explored how a guild of four mesocarnivores (red fox Vulpes vulpes, leopard cat Prionailurus bengalensis, Asian badger Meles leucurus, and hog badger Arctonyx collaris) partition their temporal niche in the temperate montane forests in North China under different human influences. We conducted a systemic camera-trapping survey on the study species in the central Taihang Mountains from 2016 to 2020. With an extensive survey effort of 111,063 camera-days from 187 camera stations, we obtained 10,035 independent detections of the four mesocarnivores and examined the activity patterns of each species under different levels of human disturbance and their overlaps. The results showed that, while the leopard cat and the badgers shifted their activity towards nocturnality, the red fox showed no significant change. The leopard cat's degree of nocturnality varied between growing and non-growing seasons, likely a response to avoid humans and other competitors. However, the activity overlaps between species pairs demonstrated no statistically significant difference, indicating a long-developed coexistence mechanism that is homogenous across the landscape. Demonstrating how mesocarnivores shift activity patterns in response to human risks while partitioning resources, this study enhances our understanding of mesocarnivore behavioral changes and interspecific interactions at human-nature interfaces.

Interannual variability in the ecosystem CO2fluxes at a paludified spruce forest and ombrotrophic bog in the southern taiga

Abstract. Climate warming in high latitudes impacts CO2 sequestration of the northern peatlands through the changes in production and decomposition processes. The response of the net CO2 fluxes between ecosystems and the atmosphere to climate change and weather anomalies can vary across forest and non-forest peatlands. To better understand the differences in CO2 dynamics at forest and non-forest boreal peatlands induced by changes in environmental conditions, the estimates of interannual variability in the net ecosystem exchange (NEE), total ecosystem respiration (TER), and gross primary production (GPP) was obtained at two widespread peatland ecosystems – paludified spruce forest and the adjacent ombrotrophic bog in the southern taiga of west Russia using 6 years of paired eddy covariance flux measurements. Both positive and negative annual and growing season air temperature and precipitation anomalies were observed in the period of measurement (2015–2020). Flux measurements showed that, in spite of the lower growing season TER (332±17 … 339±15 gC m−2) and GPP (442±13 … 464± 11 gC m−2) rates, the bog had a higher CO2 uptake rates (NEE was -132±11 … -108±6) than the forest, except for the warmest and the wettest year of the period (2020), and was an atmospheric CO2 sink in the selected years, while the forest was a CO2 sink or source, depending on the environmental conditions. Growing season NEE at the forest site was between -142±48 and 28±40 gC m−2, TER between 1135±64 and 1366±58 gC m−2, and GPP between 1207±66 and 1462±107 gC m−2. Annual NEE at the forest was between -62±49 and 145±41 gC m−2, TER between 1429±87 and 1652±44 C m−2, and GPP between 1345±89 and 1566±41 gC m−2, respectively. Under the anomalously warm winter conditions with sparse and thin snow cover (2019/2020), the increased daily GPP, TER, and net CO2 uptake at the forest was observed, while at the bog, the changes in CO2 fluxes between the warm and cold winters were not significant. This study suggests that the warming in winter can increase the CO2 uptake of the paludified spruce forests of the southern taiga in non-growing seasons.

Scale-specific controls of soil water storage along a transect in a semiarid catchment

Spatiotemporal variability in soil water storage (SWS) is controlled by different factors operating at various intensities and scales. The traditional Pearson’s correlation analysis can be used to identify the linear correlations at the measurement scale only. In this study, wavelet coherency analysis was used to investigate scale-specific relationships between SWS and selected controlling factors. SWS of 135 sampling locations were calculated along a 1,340-m long sampling transect established in a typical catchment on the Loess Plateau, China. The selected controlling factors were soil saturated hydraulic conductivity (Ks); clay, silt, sand, and soil organic carbon (SOC) contents; elevation; and aboveground biomass (AGB). The spatial pattern of SWS measured in growing and nongrowing seasons and at different soil depths was similar. At all the sampling points, except for some locations in the depression area, SWS in the growing season was relatively greater than that in the nongrowing season. The influence of factors on SWS varied with scale, with soil Ks, clay and sand contents significantly correlated with SWS at large scales. Season and soil depth had no significant effect on scale-specific relationships between SWS and the controlling factors. The wavelet coherency analysis identified the type of correlation at different scales and locations. The outcomes of this study offer meaningful insights into the hydrological processes that can be observed in the Loess Plateau region and could have significant implications for future hydrological systems and water resources management.

Aboveground litter input is not important for soil microbes during the non-growing season

Aboveground litter input is not important for soil microbes during the non-growing season

Different responses of agroecosystem greenhouse gas emissions to tillage practices in a Chinese wheat–maize cropping system

To mitigate greenhouse gas (GHG) emissions of intensified agriculture, conservation practices are gradually being implemented in Chinese wheat–maize cropping systems. However, the effects of different tillage practices on agricultural field GHG emissions and subsequent global warming potential (GWP) are poorly documented. In this study, a three-year field experiment was conducted from 2019 to 2021 to assess the effects of tillage on the emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and eventually GWP. Compared to conventional tillage (CT), no-tillage (NT) significantly decreased CO2, CH4, and N2O emissions by 35.43%, 67.33%, 339.07%, respectively, which resulted in a decrease of 37.25% in GWP during three annual cycles. Based on the results of this study, soil could potentially act as a net source of CO2 and CH4 under both CT and NT, and a net sink of N2O under NT. Annually, non-growing season contributed 16.9%, 15.6%, and 13.8% soil CO2, CH4, and N2O fluxes, and 16.6% GWP under CT and 17.3%, 16.4%, 21.6%, and 17.3% under NT, respectively. Compared to CT, NT improved the aboveground biomass and grain yields of wheat by 21.3% and 13.3% from averaged results, respectively; no significant differences were found for maize yields. Although principal component analysis showed that soil temperature had higher correlations with CO2 emissions and GWP as compared to soil moisture, soil moisture affected GHG emissions more than soil temperature as demonstrated by the structural equation model. The modeling analysis found that NT increased soil moisture, pH, and bulk density, thus increasing soil organic carbon and decreasing total nitrogen content, eventually inhibiting GHG emissions. This research demonstrated that NT had the potential to mitigate GHG emissions, yet stability needed further investigation on long-term scales.∙Graphical

Physiological Characteristics and Cold Resistance of Five Woody Plants in Treeline Ecotone of Sygera Mountains

Investigating the distribution of internal physiological indicators and the cold resistance of woody plants in the alpine treeline ecotone is of great ecological importance to explain the mechanism of alpine treeline formation. Less research has been conducted on the cold resistance mechanisms of alpine treeline woody plants than on commercial crops. In this paper, five different tree species in the alpine treeline ecotone of the Sygera Mountains were used as the research objects and the leaves, branches, and roots of 19 woody plants were collected in the non-growing season (November) of 2019. Their non-structural carbohydrate content (soluble sugar and starch), malondialdehyde, hydrogen peroxide (H2O2), proline, superoxide dismutase, and peroxidase levels were measured. The contents of C, N, P, and K elements were analyzed, along with the distribution characteristics of physiological indices and organs of various woody plants and their relationship to plant nutrients. Results showed that the MDA (5.46 ± 1.95 μg·g−1) and H2O2 (4.11 ± 0.76 mmol·g−1) of tree root organs and the MDA (3.03 ± 2.05 μg·g−1) and H2O2 (4.25 ± 1.03 mmol·g−1) of shrub leaf organs were higher than those of other organs, indicating that under the stress of low temperatures, the root organ of arbor species and the leaf organ of shrub species experienced the most damage. Osmotic substances, particularly soluble sugars, play a crucial role in the response of the woody plants in Sygera Mountains to low-temperature stress. Plant nutrients could enhance plant stress resistance by further activating the activity of the antioxidant system and increasing the synthesis of osmotic substances. This study hypothesized that the stress on the root organs of the arbor species in the treeline ecotone may not be repaired in time, which may be a key mechanism for the formation of the alpine treeline in the Sygera Mountains.

Temporal Stability of Salmonella enterica and Listeria monocytogenes in Surface Waters Used for Irrigation in the Mid-Atlantic United States

Enteric bacterial pathogen levels can influence the suitability of irrigation water sources for fruits and vegetables. We hypothesize that stable spatial patterns of Salmonella enterica and Listeria monocytogenes levels may exist across surface water sources in the Mid-Atlantic U.S. Water samples were collected at four streams and two pond sites in the mid-Atlantic U.S. over 2 years, biweekly during the fruit and vegetable growing seasons, and once a month during nongrowing seasons. Two stream sites and one pond site had significantly different mean concentrations in growing and nongrowing seasons. Stable spatial patterns were determined for relative differences between the site concentrations and average concentration of both pathogens across the study area. Mean relative differences were significantly different from zero at four of the six sites for S. enterica and three of six sites for L. monocytogenes. There was a similarity between the mean relative difference distribution between sites over growing season, nongrowing season, and the entire observation period. Mean relative differences were determined for temperature, oxidation-reduction potential, specific electrical conductance, pH, dissolved oxygen, turbidity, and cumulative rainfall. A moderate-to-strong Spearman correlation (rs > 0.657) was found between spatial patterns of S. enterica and 7-day rainfall, and between relative difference patterns of L. monocytogenes and temperature (rs = 0.885) and dissolved oxygen (rs = -0.885). Persistence in ranking sampling sites by the concentrations of the two pathogens was also observed. Finding spatially stable patterns in pathogen concentrations highlights spatiotemporal dynamics of these microorganisms across the study area can facilitate the design of an effective microbial water quality monitoring program for surface irrigation water.

Divergent seasonal responses of above- and below-ground to environmental factors in alpine grassland.

Under current global warming, the relationship between season changes of plants and environmental factors is focused on high-elevation and latitude regions. Due to the desynchronized growth of above- and below-ground and the buffering of soil, the driving factors in leaf and root show seasonal dynamics. We measured above- and below-ground intensity in the alpine steppe over the non-growing season (October-April) and growing season (May-September). Air temperature, precipitation, soil moisture, and soil temperature were used to analyze the correlation based on the growth rhythm. Results showed that an earlier growth in spring and a delayed dormancy in autumn of root than leaf was observed. Our results strongly suggest that soil moisture plays a more important role in leaf unfolding while temperature is consistent with the withering of the shoots. Soil moisture comes from soil melt driving the spring phenology of roots, which derived from the storage of the subsoil layer in the last autumn. Climate change will affect the strong seasonal patterns that characterized these precipitation-limited systems, especially in the spring and fall shoulder seasons. As seasonality changes in the alpine steppe, divergent responses of leaf and fine root would be explored.

Extreme temperature events reduced carbon uptake of a boreal forest ecosystem in Northeast China: Evidence from an 11-year eddy covariance observation.

Boreal forests, the second continental biome on Earth, are known for their massive carbon storage capacity and important role in the global carbon cycle. Comprehending the temporal dynamics and controlling factors of net ecosystem CO2 exchange (NEE) is critical for predicting how the carbon exchange in boreal forests will change in response to climate change. Therefore, based on long-term eddy covariance observations from 2008 to 2018, we evaluated the diurnal, seasonal, and interannual variations in the boreal forest ecosystem NEE in Northeast China and explored its environmental regulation. It was found that the boreal forest was a minor CO2 sink with an annual average NEE of -64.01 (± 24.23) g CO2 m-2 yr-1. The diurnal variation in the NEE of boreal forest during the growing season was considerably larger than that during the non-growing season, and carbon uptake peaked between 8:30 and 9:30 in the morning. The seasonal variation in NEE demonstrated a "U" shaped curve, and the carbon uptake peaked in July. On a half-hourly scale, photosynthetically active radiation and vapor pressure deficit had larger impacts on daytime NEE during the growing season. However, temperature had major control on NEE during the growing season at night and during the non-growing season. On a daily scale, temperature was the dominant factor controlling seasonal variation in NEE. Occurrence of extreme temperature days, especially extreme temperature events, would reduce boreal forest carbon uptake; interannual variation in NEE was substantially associated with the maximum CO2 uptake rate during the growing season. This study deepens our understanding of environmental controls on NEE at multiple timescales and provides a data basis for evaluating the global carbon budget.

Methane dynamics from a mixed plantation of north China: Observation using closed-path eddy covariance method.

Although an important greenhouse gas, methane flux in hilly forest ecosystems remains unclear. By using closed-path eddy covariance systems, methane flux was measured continuously from 2017 to 2019 in a mixed plantation in the Xiaolangdi area of the Yellow River in North China. The methane flux footprint and its diurnal and monthly variations were analysed, and its characteristics on rainy days are discussed. The results showed that: (a) the observation data were reliable with good spatial representation (b) The methane flux in the mixed plantation ecosystem had obvious diurnal and seasonal variations: the monthly average diurnal variation of the methane flux had a single-peak; the methane flux value was source in the daytime and sink at night. The daily mean maximum value of methane flux in growing season was lower than that in non-growing season with the maximum value appearing in March, and the minimum value in October. (c) The forest is an atmospheric CH4 source with the annual emission in 2017 of (3.31g C·m-2·year -1) >2019 (2.94g C·m-2·year-1) >2018 (2.81g C·m-2·year -1), and the main influencing factor was precipitation. Rainfall affected CH4 flux with a lag period of approximately three days. Rainfall also changed the balance of CH4 flux between sink or source according to precipitation intensity and frequency.