Assessing the potential distribution of insect pests: case studies on large pine weevil (Hylobius abietis L) and horse‐chestnut leaf miner (Cameraria ohridella) under present and future climate conditions in European forests

Forest insect pests represent a serious threat to European forests and their negative effects could be exacerbated by climate change. This paper illustrates how species distribution modelling integrated with host tree species distribution data can be used to assess forest vulnerability to this threat. Two case studies are used: large pine weevil (Hylobius abietis L) and horse‐chestnut leaf miner (Cameraria ohridella Deschka & Dimi?) both at pan‐European level. The proposed approach integrates information from different sources. Occurrence data of insect pests were collected from the Global Biodiversity Information Facility (GBIF), climatic variables for present climate and future scenarios were sourced, respectively, from WorldClim and from the Research Program on Climate Change, Agriculture and Food Security (CCAFS), and distributional data of host tree species were obtained from the European Forest Data Centre (EFDAC), within the Forest Information System for Europe (FISE). The potential habitat of the target pests was calculated using the machine learning algorithm of Maxent model. On the one hand, the results highlight the potential of species distribution modelling as a valuable tool for decision makers. On the other hand, they stress how this approach can be limited by poor pest data availability, emphasizing the need to establish a harmonised open European database of geo‐referenced insect pest distribution data.     

Allometric models for estimating shrub biomass in desert grassland in northern China

The development of shrub allometric models is crucial for accurate biomass assessment, as well as for scientific studies of carbon storage and carbon cycling of desert ecosystems. The aim of the present study was to construct allometric models to predict biomass using easily measured variables for xerophytic shrubs. The 12 most widespread shrub species of northern China were selected and a total of 385 individuals were harvested to obtain the weight of its components (leaves, twigs, branches, and roots), the crown area (CA) and plant height (H). Based on a high coefficient of determination (R2), a low standard error of estimate (SEE), and low Akaike information criterion (AIC) values, 72 species-specific and 24 multispecies models with CA and H as independent variables were developed. The function lnW (biomass of different components)?=?a?+?b?×?lnX (predictor variable) was selected as optimal model. CA was revealed as the best independent variable for the biomass of leaves and twigs, and V (CA?×?H) was the best predictor variable for branches, aboveground, belowground, and total biomass. In conclusion, for the first time species-specific and multispecies models were constructed with a high goodness of fit of leaves, twigs, branches, aboveground, belowground, and total biomass for 12 shrub species in northern China. Compared to multispecies models, species-specific models had improved accuracy. Since biomass quantification is the basis of carbon stocks estimation, the models presented here can be considered as alternative tool for assessing carbon storage and carbon cycling of desert ecosystems.     

Canopy cover or remotely sensed vegetation index,explanatory variables of above-ground biomass in an arid rangeland,Iran

Estimation of above-ground biomass is vital for understanding ecological processes. Since direct measurement of above-ground biomass is destructive, time consuming and labor intensive, canopy cover can be considered as a predictor if a significant correlation between the two variables exists. In this study, relationship between canopy cover and above-ground biomass was investigated by a general linear regression model. To do so, canopy cover and above-ground biomass were measured at 5 sub-life forms(defined as life forms grouped in the same height classes) using 380 quadrats, which is systematic-randomly laid out along a 10-km transect, during four sampling periods(May, June, August, and September) in an arid rangeland of Marjan, Iran. To reveal whether obtained canopy cover and above-ground biomass of different sampling periods can be lumped together or not, we applied a general linear model(GLM). In this model, above-ground biomass was considered as a dependent or response variable, canopy cover as an independent covariate or predictor factor and sub-life forms as well as sampling periods as fixed factors. Moreover, we compared the estimated above-ground biomass derived from remotely sensed images of Landsat-8 using NDVI(normalized difference vegetation index), after finding the best regression line between predictor(measured canopy cover in the field) and response variable(above-ground biomass) to test the robustness of the induced model. Results show that above-ground biomass(response variable) of all vegetative forms and periods can be accurately predicted by canopy cover(predictor), although sub-life forms and sampling periods significantly affect the results. The best regression fit was found for short forbs in September and shrubs in May, June and August with R~2 values of 0.96, 0.93 and 0.91, respectively, whilst the least significant was found for short grasses in June, tall grasses in August and tall forbs in June with R~2 values of 0.71, 0.73 and 0.75, respectively. Even though the estimated above-ground biomass by NDVI is also convincing(R~2=0.57), the canopy cover is a more reliable predictor of above-ground biomass due to the higher R~2 values(from 0.75 to 0.96). We conclude that canopy cover can be regarded as a reliable predictor of above-ground biomass if sub-life forms and sampling periods(during growing season) are taken into account. Since,(1) plant canopy cover is not distinguishable by remotely sensed images at the sub-life form level, especially in sparse vegetation of arid and semi-arid regions, and(2) remotely sensed-based prediction of above-ground biomass shows a less significant relationship(R~2=0.57) than that of canopy cover(R~2 ranging from 0.75 to 0.96), which suggests estimating of plant biomass by canopy cover instead of cut and weighting method is highly recommended. Furthermore, this fast, nondestructive and robust method that does not endanger rare species, gives a trustworthy prediction of above-ground biomass in arid rangelands.     

Rainfall,evapotranspiration, and soil moisture as herbage production predictors for Wyoming rangelands

Rangelands are an important ecosystem in the western US, and herbage and livestock production are important issues throughout the western states. Making stocking rate decisions early in the growing season is difficult because of high variation in annual herbage production. In this study, regression analysis was used to relate herbage biomass to monthly and growing season predicator variables (rainfall, actual evapotranspiration, and soil moisture) using data collected from fifteen Wyoming rangeland sites. Both predictor and response variables were scaled before regression to correct for different physical and environmental conditions between sites. Growing season precipitation was the strongest predictor of herbage biomass production (r² = 0.79), followed by growing season actual evapotranspiration (r² = 0.69), and growing season profile-average soil water content (r² = 0.59). April profile-average (0–90 cm) and April surface (0–30 cm) soil moisture also predicted herbage biomass (r² = 0.53–0.54), indicating that early growing season soil moisture can be used to inform stocking rate and grazing management decisions as it provides information at the onset of the growing season.     

Environmental factors influencing snowfall and snowfall prediction in the Tianshan Mountains,Northwest China

Snowfall is one of the dominant water resources in the mountainous regions and is closely related to the development of the local ecosystem and economy. Snowfall predication plays a critical role in understanding hydrological processes and forecasting natural disasters in the Tianshan Mountains, where meteorological stations are limited. Based on climatic, geographical and topographic variables at 27 meteorological stations during the cold season(October to April) from 1980 to 2015 in the Tianshan Mountains located in Xinjiang of Northwest China, we explored the potential influence of these variables on snowfall and predicted snowfall using two methods: multiple linear regression(MLR) model(a conventional measuring method) and random forest(RF) model(a non-parametric and non-linear machine learning algorithm). We identified the primary influencing factors of snowfall by ranking the importance of eight selected predictor variables based on the relative contribution of each variable in the two models. Model simulations were compared using different performance indices and the results showed that the RF model performed better than the MLR model, with a much higher R~2 value(R~2=0.74; R~2, coefficient of determination) and a lower bias error(RSR=0.51; RSR, the ratio of root mean square error to standard deviation of observed dataset). This indicates that the non-linear trend is more applicable for explaining the relationship between the selected predictor variables and snowfall. Relative humidity, temperature and longitude were identified as three of the most important variables influencing snowfall and snowfall prediction in both models, while elevation, aspect and latitude were of secondary importance, followed by slope and wind speed. These results will be beneficial to understand hydrological modeling and improve management and prediction of water resources in the Tianshan Mountains.     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

Combining permanent aerobiological networks and molecular analyses for large-scale surveillance of forest fungal pathogens: A proof-of-concept

Forest disease management relies principally on a preventive approach in which epidemiological surveillance plays a crucial role. However, efficient and cost-effective surveillance methods are not currently available for large spatial scales. Nevertheless, aerobiological networks have been set up for several decades in many countries to monitor pollen dispersal and provide real-time assessments of allergenic risk. Here, we suggest that the same approach could be used for the surveillance of forest pathogens. Using molecular methods, we analysed samples from 12 sites of the French aerobiological network, at different dates. Both metabarcoding by high-throughput sequencing (using two markers and two different bioinformatics approaches) and real-time PCR targeting eight important forest pathogens were conducted. To validate the approach, temporal and spatial trends of spore detection were compared with field disease data. The metabarcoding approach demonstrated that many fungal plant pathogens could be found in aerobiological samples. Moreover, five of the eight targeted forest pathogens were detected by real-time PCR, with temporal and spatial trends of spore capture consistent with field data. In particular, Hymenoscyphus fraxineus was detected at high frequency in aerobiological samples in the areas where ash dieback has been present for the longest period of time, and at lower frequency in areas with more recent invasion. Spore detection of seasonal pathogens showed a temporal pattern similar to that of disease reports. Overall, our study provides a proof of concept that permanent aerobiological networks combined with molecular methods may provide a useful tool for large-scale surveillance of forest pathogens.     

天山云杉林碳储量研究

以南山林场为例,对天山云杉林碳储量进行研究。结果表明:南山林场的总碳储量为332664.4305t,碳密度为59.5492 tC/hm2,高于全国的平均水平;针叶林碳储量占全林场碳储量的96.16%,而天山云杉的碳储量就占全林场的95.67%;成龄林的碳储量占全林场的95.09%;由此说明天山云杉是该林区碳储量的主要来源,但该林区正处于碳积累速率下降的成熟阶段,要加大对成熟林中幼树更新以及幼龄林的人工抚育的力度。以确保在森林碳储量急剧下降时,幼龄林已成长起来,使该林区的碳储量处于较稳定的状态。     

空间竞争指数在华北土石山区天然次生林的应用

为了揭示森林群落内林木竞争关系,对比分析不同空间竞争指数的特点与关系,采用大小比数和开敞度对木兰围场国有林场管理局域内天然次生林林木竞争强度进行定量分析。研究结果显示:研究区天然次生林林分内各树种大小分化较为明显,平均大小比数为0.476;林分内不同种群和林木个体生长空间分配并不均匀,差异较大,全林分开敞度为0.282,处于不足状态;树种大小比数与开敞度呈负相关关系,但其相关性显著程度存在差异。大小比数与开敞度计算结果存在差异,且这一差异主要受控于两种指数对参照木与相邻木距离的不同处理方法。大小比数全面考虑了参照木与相邻木之间的大小关系,但忽略了参照木与相邻木之间的干扰程度随二者距离增大而降低的特性;开敞度虽然考虑了参照木与相邻木之间的距离,却又将参照木与相邻木抽象为没有大小的点与线,忽略了二者之间的大小关系。     

科尔沁沙地东南缘不同草地恢复方式下的物种多样性与生物量

对科尔沁沙地4种典型退化草地恢复方式下的生物多样性特征及其生物量进行了定量研究。结果表明：4种退化草地恢复方式下,物种多样性排序为防护林围纯草地〉樟子松疏林草地〉榆树疏林草地〉撂荒草地;4种恢复方式下的群落总生物量与物种多样性的变化趋势一致;从物种多样性与生物量的综合分析表明,与未采取任何人为措施的撂荒草地相比,防护林...     

三江源区森林植被对气候变化响应的研究分析

基于植物生态学原理,在设置样地、建立样方的基础上,分别采用角规法对三江源区10个典型乔木林样地蓄积量、每木检尺法对三江源区10个典型灌木林样地生物量、收获法对三江源区10个典型草本样地生物量进行年度连续监测,在同期、同空间、同尺度条件下发现:2006年度乔木林蓄积量相比2005年稍有增长,2006年度灌木林生物量相比2005年有增有减,2006年度草本生物量相比2005年均有减少。分析其原因主要是由于气候变化造成的,2006年较2005年平均气温升高0.2-1.0℃、蒸发量增加54-334mm、年降水量减少60.7-164.5mm。     

内蒙古大兴安岭林区森林碳储量及其动态变化研究

基于内蒙古大兴安岭林区第四次至第六次复查期(1994-2008年)的森林资源数据,利用不同森林类型生物量和蓄积量之间的回归方程,估算大兴安岭林区森林生物量和碳储量,分析大兴安岭林区森林碳储量和碳密度变化规律。结果表明:在近15年间,大兴安岭林区森林面积呈持续增长的趋势,此期间森林碳储量也呈逐渐增加的趋势,其值变化规律为350.052 TgC(第六次复查)>330.468 TgC(第五次复查)>285.431 TgC(第四次复查)。不同复查期的森林碳密度变化规律为第六次复查>第四次复查>第五复查。从林龄方面来看,三次复查期内,森林碳储量变化规律为中龄林>成熟林>近熟林>过熟林>幼龄林,碳密度的变化规律为过熟林>成熟林>近熟林>中龄林>幼龄林。落叶松林和桦木林是大兴安岭林区的主要森林类型,在15年复查期内,落叶松林和桦木林面积、生物量和碳储量均呈逐渐增加的趋势,其中落叶松林的贡献率最大。     

西天山雪岭云杉林分密度对森林生物量分配格局和异速生长的影响

雪岭云杉林是新疆天山北坡山地森林中广泛分布的优势种,探讨林分密度对天山雪岭云杉林器官生物量分配格局和树高-胸径异速生长的影响,对于阐明雪岭云杉林生物量在不同环境中的适应具有重要意义.通过分析在不同林分密度(≤300株·hm-2、300～450株·hm-2、450～600株·hm-2、>600株·hm-2)下雪岭云杉林(...     

黄土高原马栏林区森林群落生态梯度分析

应用TW INSPAN,CCA和partialDCA方法,对黄土高原马栏林区45个森林群落样地进行多元分析,揭示该区不同森林群落类型之间在环境梯度上的生态关系。结果表明:①45个森林群落可划分为8组群落类型;②CCA排序表明,枯枝落叶层厚度、坡向、海拔和有机质含量是影响该区森林群落类型分布的重要实测环境因素;而partialDCA排序表明,干扰是影响该区森林群落类型分布最重要的潜在环境因素;③各森林群落类型在排序图中有规律的分布是群落中物种适应环境的结果,且人工油松林有向天然油松林恢复的趋势;④CCA与partial DCA相结合是研究植物群落生态梯度的一种非常有效的方法。     

Biomass evaluation of concealed insect pests at parasitism based on allometric scaling laws

In general, it is difficult to evaluate host resource utilization of parasitoids by examining the biomass of concealed wood borers at parasitism, because the larval tissues of parasitized hosts observed in field surveys are often almost completely consumed. This work explored a precise and convenient method for evaluating the biomass of concealed wood-boring insect pests after they are parasitized. Allometric scaling laws were used to determine mathematical relationships between larval weight and other size variables in two host insects: the emerald ash borer [EAB], Agrilus planipennis Fairmaire, and the oak long-horned beetle [OLB], Massicus raddei (Blessig). These insects are hosts of the parasitoid Sclerodermus pupariae Yang et Yao (Hymenoptera: Bethylidae). Our results showed that body weight among different instars was significantly different for both host species, with greater overlap between neighboring instars. Mean larval weight had a highly significant exponential relationship with instar number. EAB larval weight showed a highly significant power relationship to body length and width, width of the prothoracic plate, peristoma width, urogomphus length, and anteriormost width of the urogomphus. Significant power relationships were also observed between OLB larval weight and body size parameters (including body length, length of the mesothoracic spiracle, width of the prothoracic plate, distance between the main ocelli, and mandible length). These findings indicated that host biomass could be easily calculated if any one size variable could be measured at the time of parasitism. Allometric methods provide a precise means of evaluating time-specific biomass of concealed wood borers.     

中国北方天然草地的生物量分配及其对气候的响应

通过收集我国近20年来草地生物量的有关文献,估算了我国北方天然草地根冠比(R/S)及地下生产力占总生产力比例(fBNPP)的大小及其对气候变化的响应。结果表明:不同草地类型R/S比及fBNPP变异较大,根冠比为1.66~15.21,fBNPP为0.29~0.98;荒漠草原的R/S比及fBNPP较大,但变幅较小;森林草原R/S比和fBNPP较小,但变幅较大;R/S比及fBNPP随年降水的增加而显著降低,随年平均气温增加而降低的趋势不明显。采用生长季最大生物量估算的方法可能高估了R/S比及fBNPP。长期、高质量的生物量观测数据,尤其是地下生物量的数据以及开展不同研究方法对生产力估算结果的影响,对于准确评价草地在区域及全球碳循环中的作用是十分必要的。     

Assessing land transformation and associated degradation of the west part of Ganga River Basin using forest cover land use mapping and residual trend analysis

The west part of Ganga River Basin (WGRB) has experienced continuous land transformation since the Indus Valley Civilisation shifted from the Indus basin to the Ganga basin. Particularly in the last few decades the land transformation has increased many-folds due to the changing climate and rapid increase in population. In this paper, we assessed land transformation and associated degradation in the WGRB based on the forest cover land use (FCLU) mapping and residual trend analysis (RTA). The FCLU maps for 1975 and 2010 were generated using 216 Landsat satellite images and validated using 1509 ground points. We mapped 29 forest and 18 non-forest types and estimated a total loss of 5571 km² forest cover and expansion in settlement areas (5396 km²). Other major changes mapped include a decrease in wetlands and water bodies, while an increase in agriculture and barren lands with an overall mapping accuracy of 85.3% (kappa, 0.82) and 88.43% (kappa, 0.84) for 1975 and 2010, respectively. We also performed the RTA analysis using GIMMS-NDVI3g to identify areas of significant negative vegetative photosynthetic change as an indicator for land degradation. All the RTA models showed monotonic nature of the residual trends and resulted as moderately positive but highly significant (P<0.001). Land degradation in the form of barren land accompanied by a decline in vegetation quality and coverage was found prominent in the basin with a possibility of an accelerated rate of land degradation in future due to the rapid loss of permanent forest cover.     

林隙对黄土高原马栏林区优势种更新的影响

林隙对森林的结构和动态具有重要的影响。黄土高原马栏林区主要群落类型中,对优势种辽东栎(Quercus liaotungensis)、油松(Pinus tabulaeformis)不同大小林隙内的幼苗和幼树进行统计计算,并与林冠下进行对比分析。结果表明:两个优势种存在不同的更新策略,即辽东栎主要通过高萌发量来维持其种群的更新,而油松则是通过降低幼苗到幼树过程中的死亡率来维持其种群的更新;林隙面积在20~40m²时油松的自然更新情况最好,而辽东栎在不同大小林隙中的自然更新情况较为复杂;适当的林隙干扰总体上促进了该地区优势种的自然更新,林隙对其更新的促进机制各异;在林隙干扰下,辽东栎和油松在各自占优势群落(辽东栎林和油松林)中的优势地位均没有被对方取代的趋势,所以辽东栎林和油松林会作为该地区的顶级群落和亚顶级群落而长期存在。     

水分和温度对紫茎泽兰幼苗生长的影响

比较2个水分和3个温度梯度条件下,中国和墨西哥两种群紫茎泽兰（Eupatorium adenophorum Spreng）的形态、生物量分配和光合色素的可塑性反应,探讨两种群在幼苗阶段生长特点及入侵潜力。结果表明：（1）各温度水分处理下墨西哥种群株高、叶片数、总叶面积、总生物量、叶生物量比、叶根比、总叶绿素较高,中国种群根长、根生物量、根冠比较高。表明墨西哥种群在适宜的环境条件下比中国种群有更高的资源捕获能力,而中国种群对干旱和低温条件有更好的适应能力。（2）两种群紫茎泽兰对水分响应显著,100%水分下总叶面积、平均叶面积、总生物量、叶生物量比、叶面积比、叶根比、总叶绿素均高于50%水分条件。对温度响应显著,尤其20℃时,中国种群根长,墨西哥种群总叶面积,100%水分下平均叶面积、总生物量、叶生物量比、叶根比5,0%水分下根生物量比,均达最高值。两种群喜湿润的环境,温度20℃时生长较好。     

半干旱地带森林草原旅游地竞争力综合性评价——以呼和浩特市周边旅游地为例

介绍了旅游地竞争力综合性评价的方法,然后根据半干旱地带的森林草原旅游地的特征,构建了其竞争力综合性评价的指标体系。并以呼和浩特市哈达门国家森林公园、乌素图国家森林公园及小井沟生态园等三个旅游地为例,进行竞争力的综合性评价。最后对评价结果作了比较与分析。结果表明:哈达门国家森林公园旅游综合竞争力在三者中最强;同时还表明:呼和浩特周边森林草原旅游地旅游的开发优势是旅游景观资源,劣势是社会经济条件及区位特征条件。