Minimum Temperature In January Research Articles

Prediction of Severe Epidemics of Chickpea Ascochyta Blight Using Weather Variables

ABSTRACTChickpea production is threatened by severe epidemics of Ascochyta blight occurring in main chickpea growing lands under appropriate weather conditions worldwide. In this 4‐year research, occurrence of Ascochyta blight was monitored across nine main chickpea growing areas of Kermanshah province, western part of Iran. Each year, commercial chickpea fields were studied on a weekly basis from March to June. Disease data were collected as disease incidence (percentage of infected plants) and severity (percentage of infected tissues) and occurrence of epidemics. Weather data were collected as air temperature, rainfall, and relative humidity (RH) on a daily basis. According to a factor analysis, which explained 83% of data variance, 13 weather predictors were selected to estimate disease epidemics developed across different areas. Before modeling, a principal component analysis determined predictive values for these selected weather variables. Then, eight predictors of rainy days in March and April, mean RH in February, mean minimum temperature in January–March–April, and rainfalls in May and June were involved in model based on their predictive values. Current findings advanced our knowledge on the best weather predictors of severe epidemics of Ascochyta blight in chickpea crops at large scale.

Three Gorges Dam: Potential differential drivers and trend in the spatio-temporal evolution of the change in snail density based on a Bayesian spatial–temporal model and 5-year longitudinal study

BackgroundSnail abundance varies spatially and temporally. Few studies have elucidated the different effects of the determinants affecting snail density between upstream and downstream areas of the Three Gorges Dam (TGD). We therefore investigated the differential drivers of changes in snail density in these areas, as well as the spatial–temporal effects of these changes.MethodsA snail survey was conducted at 200 sites over a 5-year period to monitor dynamic changes in snail abundance within the Yangtze River basin. Data on corresponding variables that might affect snail abundance, such as meteorology, vegetation, terrain and economy, were collected from multiple data sources. A Bayesian spatial–temporal modeling framework was constructed to explore the differential determinants driving the change in snail density and the spatial–temporal effects of the change.ResultsVolatility in snail density was unambiguously detected in the downstream area of the TGD, while a small increment in volatility was detected in the upstream area. Regarding the downstream area of the TGD, snail density was positively associated with the average minimum temperature in January of the same year, the annual Normalized Difference Vegetation Index (NDVI) of the previous year and the second, third and fourth quartile, respectively, of average annual relative humidity of the previous year. Snail density was negatively associated with the average maximum temperature in July of the previous year and annual nighttime light of the previous year. An approximately inverted “U” curve of relative risk was detected among sites with a greater average annual ground surface temperature in the previous year. Regarding the upstream area, snail density was positively associated with NDVI and with the second, third and fourth quartile, respectively, of total precipitation of the previous year. Snail density was negatively associated with slope.ConclusionsThis study demonstrated a rebound in snail density between 2015 and 2019. In particular, temperature, humidity, vegetation and human activity were the main drivers affecting snail abundance in the downstream area of the TGD, while precipitation, slope and vegetation were the main drivers affecting snail abundance in the upstream area. These findings can assist authorities to develop and perform more precise strategies for surveys and control of snail populations.Graphical

Dendroclimatic response of Pinus tabuliformis Carr. along an altitudinal gradient in the warm temperate region of China.

Global climate change can affect the sensitivity of tree radial growth to climate factors, but the specific responses of tree radial growth to microclimate along the altitudinal gradient in the long term are still unclear. In this study, the tree-ring width chronologies of Pinus tabuliformis Carr. in Shanxi Province of China were studied at three altitude gradients (1200-1300m (low altitude), 1300-1400m (medium altitude) and 1400-1500m (high altitude)) during 1958-2017. The results showed that (1) the climate background could be divided into two periods based on the Mann-Kendall test analysis: 1958-1996 was a stable period (mean annual temperature (MAT)=10.25°C, mean annual precipitation (MAP)=614.39 mm), and 1997-2017 was a rapid change period (MAT=10.91°C, MAP=564.70 mm), indicating a warming and drying trend in the study region. (2) The radial growth of P. tabuliformis at different altitudes showed inconsistent variation patterns. The tree radial growth at low and medium altitudes (CV=27.01% for low altitude and CV=24.69% for medium altitude) showed larger variation amplitudes during the rapid change period than that in the stable period (CV=12.40% for low altitude and CV=18.42% for medium altitude). In contrast to the increasing trend, the tree radial growth rates at the high altitude showed a decreasing trend across years. (3) In the stable period, the radial growth of P. tabuliformis at the low altitude showed a significantly negative response to temperature and a positive response to precipitation in May and June. The tree radial growth at the medium altitude was positively related to precipitation in June and minimum temperature in February. The tree growth at the high altitude was mainly positively correlated with the temperature in May and August. In the rapid change period, the radial growth of P. tabuliformis at the low altitude was affected by more meteorological factors than that in the stable period. Medium-altitude trees were positively influenced by precipitation in June and minimum temperature in January, whereas high-altitude trees responded positively to wind speed in February. (4) Along altitudinal gradients, tree radial growth was more related to temperature than precipitation in the stable period. The tree radial growth at the high altitude during the rapid change period was only affected by wind speed in February, whereas the tree radial growth at low and medium altitudes was mainly affected by temperature to a similar extent during the two periods. The study indicated that tree growth-climate response models could help deeply understand the impact of climate change on tree growth adaptation and would be beneficial for developing sustainable management policies for forest ecosystems in the transition zone from warm-temperate to subtropical climates.

Моніторинг погодних умов осінньо-зимового періоду та їх вплив на морозостійкість рослин ожини звичайної (Rubus fruticosus L.)

Goal. To determine the resistance of blackberry varieties to low temperatures in field and laboratory conditions; to select the most resistant varieties and to investigate the influence of weather conditions on the state of their overwintering. Methods. Laboratory, field, analytical. Results. Weather conditions were monitored in the autumn–winter period of 2019/20 and 2020/21. It was established that the winter months of these years differ significantly in terms of temperature, amount, and type of precipitation. It was noted that the Cacanska Bestrna variety (12.2 points) was characterized by the highest level of frost resistance in field conditions. The average level of frost resistance was fixed for Heaven Can Wait (17.0 points), Loch Tay (14.5 points), and Triple Crown (19.2 points) varieties. The results of the experiment on freezing under artificial conditions showed that the bud and part of the stem on the section through the bud were the most vulnerable to low temperatures, and the middle part of the shoot was the least vulnerable. The buds of the Triple Crown and Loch Tay varieties experienced the least freezing during artificial freezing to–25°C, which indicated their potential frost resistance and more stable fruiting. Conclusions. Coming from the weather conditions in the winter, the period of 2020/21 was a good period for conducting field research on the frost resistance of blackberry varieties, which was characterized by significant temperature changes (minimum temperature in January –20°C, February — –18°C) and sharp thaws (up to +4.5°C). It was determined that the weather conditions of the winter of 2019/20 did not negatively affect the plants in field conditions. However, we assume that the autumn weather conditions of 2019 contributed to a longer period of growth of shoots, which caused a decrease in the resistance of plants to low temperatures. The potential level of frost resistance of the researched varieties under laboratory conditions of freezing to temperatures of–25 and–30°C was established.

Prediction of the Potential Distributions of Prunus salicina Lindl., Monilinia fructicola, and Their Overlap in China Using MaxEnt

Prunus salicina Lindl. (P. salicina) is an essential cash crop in China, and brown rot (BR) is one of its most important diseases. In this study, we collected geographic location information on P. salicina and Monilinia fructicola (G. Winter) Honey (M. fructicola), one of the BR pathogenic species, and applied the MaxEnt model to simulate its potential suitable distribution in China. There have been discussions about the dominant environmental variables restricting its geographical distribution and their overlap. The results showed that the mean temperature of the coldest quarter, precipitation of the warmest quarter, precipitation in July, and minimum temperatures in January and November were the main climatic variables affecting the potential distribution of P. salicina, while the coldest quarter, precipitation of the driest month, precipitation of March, precipitation of October, maximum temperatures of February, October, and November, and minimum temperature of January were related to the location of M. fructicola. Southern China had suitable conditions for both P. salicina and M. fructicola. Notably, the overlap area of P. salicina and M. fructicola was primarily located southeast of 91°48' E 27°38' N to 126°47' E 41°45' N. The potential overlap area predicted by our research provided theoretical evidence for the prevention of BR during plum planting.

Spatial Homogenization Adjustment and Application of Weather Station Networks in Xinjiang, China

In this study, we define the S0 value (buffer zone area centred on a meteorological station) and two inhomogeneity measurement parameters, the station domain area and station network density, for 89 weather stations in the Xinjiang region, and we construct the weight coefficient of the station network according to the station domain area. Applying the weight coefficient, we calculate the mean temperature, maximum temperature, and minimum temperature in January, April, July, October, and annually in the Xinjiang region from 1961 to 2021. The results show that the S0 value of 200,000 km2 is suitable for determining the weight coefficient of the station network in the Xinjiang region. The two measurement parameters can quantitatively reflect the inhomogeneity of the distribution of 89 weather stations in the Xinjiang region. The spatial distribution density of the station network is positively proportional to the station network density and inversely proportional to the station domain area and weight coefficient of the stations. The equal-weighted average is lower than the spatially homogenized revised average, which underestimates the mean temperature in the Xinjiang region, and the spatially homogenized revised average better reflects the real temperature in the Xinjiang region. The annual and monthly mean temperatures, maximum and minimum temperatures calculated by the spatially homogenized revised average, and the equal-weighted average have the same upwards trend, and the mean temperature warming trend calculated by the two methods have differences, but the differences are not significant. The annual, January, April, July, and October minimum temperature warming trends according to the spatial homogenization revised average are greater than the maximum temperature warming trend and the mean temperature warming trend, and the annual minimum temperature warming trend is 3.3 times the annual maximum warming trend and two times the annual mean temperature.

A geographical traceability method for Lanmaoa asiatica mushrooms from 20 township-level geographical origins by near infrared spectroscopy and ResNet image analysis techniques

Food authenticity and traceability and climate change are key scientific issues that must be addressed in response to the food crisis in 2050. Lanmaoa asiatica mushroom is an expensive and nutritious fungi-based diets resource, it is necessary to identify its geographical origin and explore the impact of the climate on it. Thus, the purpose of this study is to establish a fast and accurate geographical traceability model based on L. asiatica mushrooms chemical information collected by near-infrared spectroscopy (NIRS) technology, and screen out key climate variables by competitive adaptive reweighted sampling (CARS) algorithm. Based on the NIRS information of L. asiatica mushrooms, two-dimensional correlation spectroscopy (2D-COS) images were generated and a residual neural network (ResNet) image recognition model was established to identify the geographical origin of L. asiatica mushrooms. The accuracy of training set and test set of ResNet model is 100%, and the loss value is 0.052, which indicates that the model is effective. In addition, the CARS algorithm was used to select the feature variables from 105 climate variables. Four important variables (February, March, and April precipitation and January minimum temperature) related to NIRS difference of L. asiatica mushroom were obtained by CARS algorithm. The results can provide a fast and accurate method for food authenticity and traceability research, and provide an innovative idea for screening key climate factors.

Modeling and Forecasting Selected Meteorological Parameters for the Environmental Awareness in Sub-Sahel West Africa Stations

The monthly air temperature, rainfall, air pressure, and wind speed direction for the environmental time series recorded between January 1, 1980 and December 31, 2020 in six African stations from different climatic zones were modeled and forecasted. In the forecasting, Augmented Dickey Fuller test, ARIMA models, Auto correlation Function (ACF) and Partial Autocorrelation Function (PACF) were used. Result showed that in most of the fitted models, the Moving Average terms both seasonal and non- seasonal were also significant (p<0.05) indicating that the previous day value of the stochastic term also had a significant effect on the present value of meteorological parameters in the environment. It was observed that in all the fitted models except for wind direction in Conakry and rainfall in Abidjan have all their Autoregressive term of order 1 significant (p<0.05) which implies that previous day value of these meteorological parameter had a significant effect on the present day value of the parameters. Therefore, the forecast model indicates that maximum temperature are expected in February, March, April, and June while minimum temperatures in January, August, December. Although, the selected models cannot forecast the precise air temperature, this can also provide information that can be of help to create tactics for appropriate preparation of farming which can be used as tools for effective envi­ronmental preparation and policymaking.

Survey of overwintering trait in Chinese rice cultivars (Oryza sativa L)

Overwintering rice can survive through the natural cold-winter field environment, sprout from rice tillering node in the following spring, tiller, flower, seed, and being harvested in the following autumn, which is a type of an extreme case of cold tolerance of rice. The successful utilization of cold tolerance rice is the most economical strategy for the cold tolerance rice cultivar breeding project. This work aims to identify the OW rice for the future development of cold tolerance cultivars. Altogether 1034 Chinese existing rice cultivars including 735 (71.08%) conventional Japonica rice cultivars and 299 (28.92%) conventional Indica rice cultivars were collected and evaluated for their responses to low temperatures under the natural field cold-winter environment. Among them, altogether 262 (25.34%) conventional Japonica rice cultivars could withstand cold tolerance to 4 °C of the daily minimum temperatures in December 2019 throughout the cold-winter season and distributed in 13 provinces of China, survive through the natural cold-winter field environment, and sprout from rice tillering node in March 2020. Only 24 (2.32%) conventional japonica rice cultivars could withstand cold tolerance to 0 °C of the daily minimum temperatures in January 2021 throughout the cold-winter season, which could also sprout from rice tillering node in March 2021 and distributed in seven provinces of China. The present cold tolerance rice cultivars will provide beneficial breeding germplasm for the future cold tolerance rice breeding project and new strategies involved in elucidating the molecular mechanism of the cold tolerance of rice.

Evaluation of WRF Cumulus Parameterization Schemes for the Hot Climate of Sudan Emphasizing Crop Growing Seasons

High spatiotemporal resolution climate data are essential for climate-related impact studies. The Weather Research and Forecasting (WRF) model is widely used to downscale climate data for different regions with regional-specific physics configurations. This study aimed to identify robust configurations of the WRF model, especially cumulus parameterization schemes, for different climatic zones of Sudan. We focused on wet season (June–September) rainfall and dry season (November–February) temperature, which are determinants of summer crop and irrigated wheat yields, respectively. Downscaling experiments were carried out to compare the following schemes: Betts–Miller–Janjic (BMJ), improved Kain–Fritch (KFT), modified Tiedtke (TDK), and Grell–Freitas (GF). Results revealed that the BMJ performed better for wet season rainfall in the hyper-arid and arid zones; KFT performed better for rainfall in July and August in the semi-arid zone where most summer crops are cultivated. For dry season temperature, the BMJ and TDK outperformed the other schemes in all three zones, except that the GF performed best for the minimum temperature in December and January in the arid zone, where irrigated wheat is produced, and in the semi-arid zone. Specific parameterization schemes therefore need to be selected for specific seasons and climatic zones of Sudan.

Development of agro-climatic grape yield model with future prospective

Agriculture sector is most vulnerable to climate change. To predict the crop yield in accordance with the changing climate is a need of hour than choice. To know the climate in advance is crucial for grape growing farmers and grape export agencies for its better planning and security of grape industries from climate change perspective. In the present study, the Agro-Climatic Grape Yield (ACGY) model is developed on monthly scale climatic parameters using correlation, significance and multi-regression analysis approach. The developed model is statistically tested for its predictive ability. The discrepancy ratio, the standard deviation of discrepancy ratio, mean percentage error and standard deviation of mean percentage error for the developed model is obtained as 1.03, 0.19, 0.03% and 0.19 respectively. Sensitivity analysis is carried out for the developed ACGY model using the parametric sensitivity method. In order to know the grape yield for future using developed ACGY model, climate scenarios are generated under Canadian Earth System Model (CanESM2) for three emissions Representative Concentration Pathways (RCP) as RCP2.6, RCP4.5, and RCP8.5. Model response variability is carried out to understand the variation of grape yield. It is observed that grape yield is showing adverse variation with the increase in minimum temperature in January and November months, and precipitation in August and November months. Whereas, minimum temperature in April and sum of monthly mean evapotranspiration showing accordance effect on the grape yield. It is recommended the use of ACGY model for grape yield estimations applicable for the present and future climate of the study area based on the predictive capability of developed model.

Predicting Changes in the Suitable Agro-climate Zone of Italian Ryegrass Cultivars with RCP 8.5 Climate Change Scenario

We aimed to predict the Italian ryegrass (IRG) productivity change of introduced and domestic varieties based on climate factors and identify suitable areas for IRG cultivation using the RCP 8.5 scenario. The minimum mean air temperature in January showed the highest correlation with productivity. The ratio of possible and low productivity areas was high in Gangwondo, and the ratio of suitable and best suitable areas was relatively high in the central and southern regions in the past 30 years. The change in the IRG cultivation area was found to be 26.9% in the best suitable area between 1981–2010 but increased significantly to 88.9% between 2090s. In the IRG suitability comparison classes between domestic and introduced cultivars, the ratio of suitable and best suitable areas was relatively high in the domestic varieties during the past 30 years. However, there is almost no difference between the IRG domestic and introduced varieties in the IRG suitability classes after the 2050s. These results can predict changes in the IRG suitability classes between domestic and introduced cultivars according to the climate change scenario, but there are limitations in accurately predicting the productivity of IRG because the results may vary depending on other environmental factors.

Grey correlation analysis and path analysis between isoflavones content in Astragali Radix and climate factors

In this study, HPLC was used to determine the content of the four isoflavones of Astragalus membranceus var. mongholicus from different regions(calycosin-7-glucoside, ononin, calycosin and formononetin), and gray correlation analysis and path analysis were used to explore the influence of climate factors on the content of isoflavone components in A. membranceus var. mongholicus. The results showed that there were significant differences in the content of the four isoflavones in different areas(P<0.05); grey correlation analysis showed that the highest temperature in July, the lowest temperature in January and the daily average temperature had a greater impact on the content of flavonoid glycosides, meanwhile precipitation and relative humidity were the more important factors for the accumulation of flavonoid aglycones. According to the general analysis, the direct positive effects of the lowest temperature in January and altitude on the contents of four isoflavones in A. membranceus var. mongholicus were significant. High altitude and extreme temperature conditions might be more adverse to the formation and accumulation of isoflavone components. Therefore, the religions of A. membranceus var. mongholicus with high contents of isoflavones should be chosen the low altitude region with higher minimum temperature in January. This study provides a reference basis for the quality evaluation of A. membranceus var. mongholicus, and basic data for the selection of suitable habitat, construction of planting standards and directional cultivation of medicinal materials in A. membranceus var. mongholicus.

Colonisation dynamic and diversity patterns of Holocene forest snail fauna across temperate Europe: The imprint of palaeoclimate changes

The development of biotic communities since the last glaciation has been shaped by both dramatic climate changes and pathways of species colonisation from glacial refugia. Although the growing body of literature has emerged recently on possible scenarios of postglacial colonisation, less is known about the effect of climate. We analysed the dynamics of Holocene mollusc succession with an undetected human impact using three well-dated sequences from spring tufa deposits across temperate Europe. For the first time, the detailed Holocene mollusc successions can be compared with climate parameters in the corresponding time windows. High-resolution palaeoclimate data accompanied the species data, and the data derived from stable isotope analyses. The number of closed-canopy forest species that colonised the sites until 5000 cal BP and the maximum number of species per sample systematically increased towards the interior of the continent. We also observed earlier colonisation of forest snail species in the Western Carpathians. While the aridity index was the best predictor of local species richness in Normandy and Luxembourg, minimum January temperature drove the variation in snail data in Slovakia. The short period of an abrupt cooling and drying around 8500 cal BP was found to stop the colonisation, sharply reducing the number of local species across the continent. Our results document the importance of climate for the colonisation and development of forest biota during the first half of the Holocene, both at continental and local scales. They also elucidate processes shaping the current distribution of forest snail fauna across the European temperate zone.

Risk factors and assessments for bacterial black node of barley

Bacterial black node (BBN) due to Pseudomonas syringae pv. syringae (PSS) is the most serious bacterial disease of barley in Japan. Here we identified the risk factors that affect barley at harvest using statistical models and graphic models updating the chance of BBN incidence after each event occurred. Three models, based on separate data from Okayama and Kagawa Prefectures or combined data from both prefectures from 2002 through 2018, were developed using a generalized linear mixed model (GLMM). The Okayama model included the number of days that the minimum temperature in January was − 4 °C or below (AIC value = 39.7), and the Kagawa model included that parameter and the number of days that the mean temperature in January was ≥ 5 °C, and the number of rainy days (≥ 5 mm/day) in March (AIC value = 230.4). The combined data model included the number of days in January ≤ − 4 °C and number of rainy days in March (AIC value = 330.2). Thus, three predictive models were obtained by GLMM. In results of risk analyses constructed by Bayesian inference, when risk factors occurred, disease incidence was 2.1 to 2.5 times higher than when risk factors were not present. This is the first report to demonstrate risk factors for BBN incidence using GLMM and to assess the contribution of each risk factor to increased BBN based on 17 years of historical data.

Predicting potential current distribution of Lycorma delicatula (Hemiptera: Fulgoridae) using MaxEnt model in South Korea

Abstract Lycorma delicatula (Hemiptera: Fulgoridae) is an invasive insect in Korea which causes plant damages by sucking and sooty molds. Lycorma delicatula was first detected in South Korea in 2004, where its introduction and spreading possibly were affected by human activity-related factors. Here, we used MaxEnt to describe current distribution of L. delicatula in Korea and tried to find out the impact of human influences on distribution. We used 143 sites of occurrence data, 19 bioclimatic variables, duration of temperature below −11 °C, average daily minimum temperature in January, cumulative thermal unit variable, the distribution of grape orchard variable and human footprint to create models. These models were estimated by two sets of 24 candidates with feature combinations and regularization multipliers. In addition, these two sets were created as models with and without footprint to assess human influence on distribution. Model selection for optimal model was performed by selecting a model with a lowest sum of each rank in small sample-size corrected Akaike’s information criterion and difference between training and test AUC. Model of LQ10 parameter combinations was selected as optimal models for both model sets. Consequently, both of distribution maps from these models showed similar patterns of presence probability for L. delicatula. Both models expected that low altitude regions were relatively more suitable for L. delicatula than mountain areas in Korea. Footprint might be limited for the distribution and L. delicatula might already occupy most of available habitats. Human-related factors might contribute to spread of L. delicatula to uninfected areas.

Components and Predictors of Biological Soil Crusts Vary at the Regional vs. Plant Community Scales

Although biological soil crusts (biocrusts) occur globally in arid and semi-arid environments, most of our knowledge of biocrust cover and ecology is from a relatively small number of locations worldwide. Some plant communities are known to have high cover of biocrusts, but the abundance of biocrusts is largely undocumented in most plant communities. Using a data driven approach, we identified 16 plant communities based on plant cover from the Assessment, Inventory, and Monitoring Strategy data from the Bureau of Land Management (AIM, 5200 plots). We found that abundance of lichens and mosses varies among communities, but that both components of biocrusts are present in all plant communities. Biocrusts are indicators of two of these communities: one that is defined by high cover of mosses and basin big sagebrush and one that is defined by high cover of lichens and shadscale saltbush. Using non-parametric multiplicative regression, we evaluated a suite of abiotic and disturbance variables to assess the degree to which climate and soils are associated with the abundance of lichens and mosses at the regional scale. At the regional scale, soil depth and maximum vapor pressure deficit were found to be strongly associated with the abundance of lichens and January minimum temperature dictated the abundance of mosses. At the scale of plant communities, community specific metrics of soil and climate were better able to explain the abundance of biocrusts. Given our demonstration of the presence of biocrusts across the western US, we hope to call attention to the need to include biocrusts in studies on ecosystem function given their arguably stronger association with climatic variation, direct association with soils, and their contribution to ecosystem functions that are not solely maintained by vascular plants.

Laboratory and Field Assessment of the Frost Resistance of Sosnowsky’s Hogweed

The frost resistance of Sosnowsky’s hogweed plants (Heracleum sosnowsky Manden.) has been evaluated under laboratory and field conditions. The death of seedlings and adult plants observed within a temperature range from –6 to –12°C indicates low frost tolerance of the species. A snow cover provides a stable soil temperature (no less than –3°С) at the depth of renewal bud location and, therefore, provides the sustainability of meristematic potential in cenopopulations of H. sosnowsky. A shifting of freezing temperature for a H. sosnowsky meristem from –12°С (autumn) to –5 to –7°С (spring) is probably caused by the lack of a true dormancy stage and by changes in the content of cryoprotectors. Plant seeds also demonstrate reduced frost resistance after stratification (overwintering) and increased tissue water content. Field studies were carried out with assistance of volunteers within the framework of the “Moroz” (“Frost”) project arranged within the borders of the invasion habitat of the species in European Russia and based on the principles of citizen science. The results of the study show that the destruction of Sosnowsky’s hogweed plants after the snow cover removal depends only on weather conditions. Thus, elimination of H. sosnovsky stands by freezing can be recommended only for regions where average long-term minimum temperatures in January and February do not exceed –25°C; this method can be relevant for territories where the use of chemical herbicides is limited or prohibited.

伊春地区红松和红皮云杉径向生长对气候变化的响应

PDF HTML阅读 XML下载 导出引用 引用提醒 伊春地区红松和红皮云杉径向生长对气候变化的响应 DOI: 10.5846/stxb201812292845 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41877426，31770490）；中央高校基本科研业务费高层次人才发展专项（2572017DG02）；中国气象局东北地区生态气象创新开放实验室基金（stqx2018zd02） Radial growth of Pinus koraiensis and Picea koraiensis response to climate change in Yichun City,Heilongjiang Province Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:树木生长-气候关系对准确评估气候变化对森林生态系统影响、预测森林生产力与植被动态及揭示树木对气候变化的响适应策略至关重要。在全球变暖背景下，升温可能会对树木的生长产生影响，从而改变区域森林生态系统的生产力或碳储量。本研究利用生长-气候响应函数、滑动相关分析等树木年轮学方法，探讨伊春地区阔叶红松林内红松和红皮云杉径向生长的主要限制因子及两者径向生长对快速升温（1980年后）响应的异同。结果表明：1980年前红松径向生长有明显加速的趋势，红皮云杉上升趋势较弱；而1980年后红松径向生长趋势显著下降，红皮云杉则下降不明显。红皮云杉径向生长与上一年9月及当年6月平均气温显著负相关，而红松径向生长与上一年12月及当年1月、4月和6月最低气温显著正相关。1980年快速升温后，高温对两树种生长的抑制作用增强，尤其是红松。生长季末（9月）降水对红松和红皮云杉的限制作用由升温前的负相关转变为升温后的显著正相关。温度是限制红松和红皮云杉径向生长的主要气候因子，降水影响相对较弱；其中红松径向生长对气候变化的响应比红皮云杉更敏感。快速升温后，红松和红皮云杉生长-气候关系的变化可能与升温导致的暖干旱化有关。若气候变暖持续或加剧，二者径向生长的气候限制因子也将由温度转变为水分；红松和红皮云杉会出现生长衰退，尤其是红松。 Abstract:Tree growth-climate relationship is critical for accurately assessing the impact of climate change on forest ecosystems, predicting forest productivity and vegetation dynamics, and revealing tree adaptation strategies to climate change. In the context of global warming, rising temperatures may have an impact on tree growth, thereby changing the productivity or carbon storage in regional forest ecosystems. We investigated the main climatic limiting factors of radial growth of Korean pine (Pinus koraiensis) and Korean spruce (Picea koraiensis) in broad-leaved Korean pine forests in Yichun city, China. We also discussed the similarities and differences of their response to rapid warming (after 1980) by using the growth-climate response function analysis, moving correlation analysis, and other dendroclimatology methods. The results showed that the radial growth of Korean pine was accelerated before 1980, and the upward trend of Korean spruce was weaker than that of Korean pine. However, after 1980, the radial growth trend of Korean pine was significantly decreased, while that of Korean spruce was not. The radial growth of Korean spruce was significantly negatively correlated with the mean temperature in previous September and current June, while the radial growth of Korean pine was significantly positively correlated with the minimum temperatures in previous December and January, April and June of the current year. After rapid warming in 1980, the high temperature significantly increased the growth inhibition of the two species, especially for the Korean pine. The limiting effect of precipitation at the end of growing season (September) on Korean pine and Korean spruce changed from the negative correlation before warming to significant positive correlation after warming. In general, temperature is the main climate factor limiting the radial growth of Korean spruce and Korean, followed by precipitation.The radial growth of Korean pine was more sensitive to climate change than that of Korean spruce. After rapid warming, the change of growth-climate relationship of Korean pine and Korean spruce may be related to the 'Warm-dry Phenomenon' caused by the warming. If climate warming continues or intensifies, the climatic limiting factor of the radial growth of the two species will change from temperature to moisture, and their radial growth will decline, especially for Korean pine. 参考文献 相似文献 引证文献

Occurrence Prediction of the Citrus Flatid Planthopper (Metcalfa pruinosa (Say, 1830)) in South Korea Using a Random Forest Model

Invasive species cause a severe impact on existing ecosystems. The citrus flatid planthopper (CFP; Metcalfa pruinosa (Say, 1830)) is an invasive species in many countries. Predicting potential occurrence areas of the species related to environmental conditions is important for effective forest ecosystem management. In this study, we evaluated the occurrence patterns of the CFP and predicted its potential occurrence areas in South Korea using a random forest model for a hazard rating of forests considering meteorological and landscape variables. We obtained the occurrence data of the CFP in South Korea from literature and government documents and extracted seven environmental variables (altitude, slope, distance to road (geographical), annual mean temperature, minimum temperature in January, maximum temperature in July, and annual precipitation (meteorological)) and the proportion of land cover types across seven categories (urban, agriculture, forest, grassland, wetland, barren, and water) at each occurrence site from digital maps using a Geographic Information System. The CFP occurrence areas were mostly located at low altitudes, near roads and urbanized areas. Our prediction model also supported these results. The CFP has a high potential to be distributed over the whole of South Korea, excluding high mountainous areas. Finally, factors related to human activities, such as roads and urbanization, strongly influence the occurrence and dispersal of the CFP. Therefore, we propose that these factors should be considered carefully in monitoring and surveillance programs for the CFP and other invasive species.