Accuracy of visual cover assessments in predieting standing crop and environmental correlation in deciduous forests

Five plots with a uniform vegetation were selected subjectively in two riverine forests in Flanders and ground vegetation was sampled on seven occasions. The percentage cover of all species was visually estimated at each occasion using a decimal cover scale. Afterwards standing crop of the moss and herb layer was clipped, sorted, oven-dried and weighed. Linear regressions of standing crop on cover and Spearman rank correlations between the data from the two measures and some environmental variables were used to compare both methods of recording species performance. For the total data matrix the variation explained by the regression equations ranged from 52% for the herb layer to 71% for the moss layer; coefficients of determination for the individual species varied from 30% forUrtica dioica to 90% forRanunculus ficaria. The coincidence in correlation with the environment of biomass and cover data is highly significant (r=0.87). The difference in correlation is usually <5%. Seasonal changes in shoot and leaf development and interspecific differences in growth form strongly affected the accuracy of predicting biomass from cover. In general, predictions are best for low growing species (e.g.Eurhynchium praelongum andLamium galeobdolon). Differences in correlation varied around 0.1 and decreased with increasing number of observations. For general purposes, it is suggested that the precision of a harvest method (e.g. standing crop determination) does not compensate for the time profit and the repeatability of the visual cover assessment technique, despite the inconsistencies reported for the latter method.     

Expansion of bamboo forests caused by reduced bamboo-shoot harvest under different natural and artificial conditions

To elucidate the expansion of bamboo (Phyllostachys pubescens Mazel ex Houzeau de Lehaie) forests, we used multiple linear regression analysis and determined whether there were site differences for data obtained in Hirasawa, Otaki-machi, Chiba Prefecture, and Kofuki, Takehara City, Hiroshima Prefecture, Japan. Vegetation maps from 1984 and 2001 for Hirasawa, and from 1986, 1996, 2000, and 2006 for Kofuki, were compared, and the annual expansion rate of each P. pubescens forest was calculated. We evaluated nine indices, including original bamboo forest area, neighbouring vegetation, slope inclination and aspect, distance from roads, and shipment of bamboo shoots. Shipment of bamboo shoots was a positive factor for P. pubescens forest expansion, whereas the proportion of adjacent short vegetation, northness (the cosine of slope aspect), and area negatively affected the expansion in Hirasawa (R ² = 0.683). On the other hand, distance from roads and eastness (the sine of slope aspect) positively affected expansion, while slope inclination was a negative factor in Kofuki (R ² = 0.942). We calculated that P. pubescens forests expanded at about 2% per year in regions of reduced shipments of bamboo-shoot harvest. We showed that not only natural conditions but also management factors affect P. pubescens forest expansion. Regional differences in natural and anthropogenic effects on expansion were also found.     

A large‐scale field assessment of carbon stocks in human‐modified tropical forests

Tropical rainforests store enormous amounts of carbon, the protection of which represents a vital component of efforts to mitigate global climate change. Currently, tropical forest conservation, science, policies, and climate mitigation actions focus predominantly on reducing carbon emissions from deforestation alone. However, every year vast areas of the humid tropics are disturbed by selective logging, understory fires, and habitat fragmentation. There is an urgent need to understand the effect of such disturbances on carbon stocks, and how stocks in disturbed forests compare to those found in undisturbed primary forests as well as in regenerating secondary forests. Here, we present the results of the largest field study to date on the impacts of human disturbances on above and belowground carbon stocks in tropical forests. Live vegetation, the largest carbon pool, was extremely sensitive to disturbance: forests that experienced both selective logging and understory fires stored, on average, 40% less aboveground carbon than undisturbed forests and were structurally similar to secondary forests. Edge effects also played an important role in explaining variability in aboveground carbon stocks of disturbed forests. Results indicate a potential rapid recovery of the dead wood and litter carbon pools, while soil stocks (0–30 cm) appeared to be resistant to the effects of logging and fire. Carbon loss and subsequent emissions due to human disturbances remain largely unaccounted for in greenhouse gas inventories, but by comparing our estimates of depleted carbon stocks in disturbed forests with Brazilian government assessments of the total forest area annually disturbed in the Amazon, we show that these emissions could represent up to 40% of the carbon loss from deforestation in the region. We conclude that conservation programs aiming to ensure the long‐term permanence of forest carbon stocks, such as REDD+, will remain limited in their success unless they effectively avoid degradation as well as deforestation.     

基于年轮分析的不同恢复途径下森林乔木层生物量和蓄积量的动态变化

基于树木年轮学与标准地调查法, 研究了川西亚高山林区3种恢复森林类型生物量、蓄积量及生产力动态变化特征, 旨在尝试年轮学在森林生长过程反演中的运用, 并探索不同恢复模式下森林生物量和蓄积量的动态变化。结果表明, 不同恢复类型发育至20年以后, 均进入生长加速期, 平均胸径间差异逐渐显著, 人工云杉(Picea asperata)林胸径增长最快, 明显高于天然恢复的次生桦木(Betula spp.)林和次生针阔混交林。在恢复过程中, 次生针阔混交林一直保持最高的林分平均地上生物量与林分蓄积量, 其地上平均生物量一直显著高于人工云杉林(p < 0.05), 在20年以后显著高于次生桦木林(p < 0.05)。与人工云杉林相比, 次生桦木林在25年前具有相对较高的生物量, 而在25年之后则低于人工云杉林。在0-20年桦木林林分蓄积量略高于云杉林, 而20年以后, 云杉林蓄积量则超过桦木林。不同恢复类型的生产力大小对比显示, 30年之前, 次生针阔混交林>次生桦木林>人工云杉林, 30年之后, 针阔混交林生产力仍然最高, 而人工云杉林则超过次生桦木林。川西林区次生针阔混交林恢复模式在生物量和蓄积量积累方面均具有显著优势。     

天山雪岭云杉林生物量估测及空间格局分析

雪岭云杉林是新疆天山山脉重要的水源涵养林,精确估算雪岭云杉林生物量及准确表征空间格局特征对其生态系统的生物生产力和生态服务功能的评估具有重要作用。结合Landsat 8 OLI遥感数据和66块天山雪岭云杉林样地调查数据,选择包括各波段灰度值、不同波段灰度值之间的线性和非线性组合(包括5种植被指数)以及环境因子在内的42个自变量,分别采用多元逐步回归分析法、偏最小二乘法和主成分分析法建立天山雪岭云杉林生物量估测模型。结果表明:多元逐步回归法采用3个自变量所建模型平均拟合精度为69.07%,绝对误差为64.50 t/hm²,平均相对误差为10.89%,样地生物量实测值与预测值相关系数为0.465;偏最小二乘回归法采用11个自变量所建模型平均拟合精度为74.36%,绝对误差为144.94 t/hm²,平均相对误差为28.78%,相关系数为0.717;主成分分析方法提取3个主成分,所建模型平均拟合精度为71.22%,相关系数为0.730;因此偏最小二乘法要优于主成分分析法和多元逐步回归法。天山雪岭云杉林生物量随经纬度的增加而降低,整体呈现西部高,中东...     

Detecting change in an Australian flowering record: Comparisons of linear regression and cumulative sum analysis change point analysis

Linear regression and cumulative sum analysis (CUSUM) change point analyses were used to determine whether there had been a significant change in the first flowering date between 1983 and 2006 for 65 species. Both methods agreed that the first flowering date of 47 species did not change and that eight species had a significant change (P < 0.05) in their flowering. Three species shifted to later flowering and five species to earlier. Over the observation period, each method found that the average shift to later flowering was greater (37.4 days or approx. 1.56 days per year for CUSUM change point analysis and 51.4 days or 2.14 days per year for linear regression) than that to earlier flowering (28.4 days or approx. 1.20 days per year for change point analysis and 46.5 days or 1.97 days per year for linear regression). For the remaining 10 species the results of linear regression and change point methods differed. Each method found five species (three earlier flowering and two later) to have a significantly changed first flowering date over their observation period, where the other method did not. Some of these differences can be attributed to the fact that the CUSUM method can detect multiple change points whereas linear regression can not. Significant change points in first flowering date were identified for 13 species between the years 1987 to 1998. The most frequent year identified as a change point year was 1995. The two methods, although not interchangeable, had strong agreement (84.6%) in detecting shifts. This gives greater confidence that a change in flowering has occurred for eight species and equally importantly, that no change in first flowering date has occurred for 47 species.     

西双版纳山黄麻林鸟类群落结构及功能分析

山黄麻 (Tremaorientalis)林是西双版纳热带森林次生演替的初级阶段 ,鸟类与山黄麻林的相互关系对森林更新和生物多样性保护有重要意义。我们采用样带观测法 ,在山黄麻林内设 10条观测带 (各带 10 0m长 ,10m宽 ,合计面积 1hm2 )观测记录鸟类 ,网捕鸟类了解摄食情况。观测到的鸟类隶属 7个营养生态位集团 ,涉及 11科、2 4属、4 5种 ;这些鸟类除 5种主要以虫为食 ,4种主要吃草籽外 ,绝大多数都摄食山黄麻果实 ,因而山黄麻果实大量成熟期鸟类较多。山黄麻林鸟类群落的季节动态与山黄麻物候关系密切 ,随山黄麻林的花果量而变动 ,鸟类集中在果熟的山黄麻树上摄食 ,在山黄麻果熟的高峰期 ,鸟类群落呈现出种群数量丰富 ,多样性高而均匀度低的分布格局。 36种食果鸟类的种群数量直接与山黄麻果实丰盛度有关 ,食虫、食草籽的鸟类与山黄麻林结构和食用山黄麻花果的昆虫数量均相关 ,山黄麻林维持了相应的鸟类群落 ,同时鸟类也控制了林内害虫 ,帮助山黄麻传授花粉、散播种子。在植被演替的过程中 ,山黄麻林与鸟类群落相互作用 ,协同发展 ,鸟类多样性联系着森林更新     

哀牢山亚热带常绿阔叶林乔木碳储量及固碳增量

为了解哀牢山亚热带常绿阔叶林的乔木碳储量及其固碳增量,利用2005和2008年的植被调查数据,对哀牢山3种主要常绿阔叶林的乔木碳储量及其固碳增量进行了分析。结果表明:原生的中山湿性常绿阔叶林、滇山杨次生林和旱冬瓜次生林的乔木碳储量分别为257.90、222.95和105.39tC·hm-2;中山湿性常绿阔叶林乔木碳储量主要存储在DBH≥91cm的乔木中(34.68%);而次生林的乔木碳储量主要分布在径级21cm≤DBH41cm的乔木中(滇山杨林77.29%;旱冬瓜林69.28%)。由此可见,哀牢山地区原生的中山湿性常绿阔叶林乔木层在碳蓄积方面占主导优势。哀牢山亚热带常绿阔叶林的3个森林类型乔木层均具有固碳增量,即使是原生的中山湿性常绿阔叶林,其乔木层年平均固碳增量也达2.47tC·hm-2·a-1;次生林乔木层的年平均固碳增量约为原生林的2倍,显示了哀牢山亚热带常绿阔叶林乔木层具有较强的碳汇增量。初步估算,哀牢山亚热带常绿阔叶林林区内每年乔木固碳增量为8.52×104tC·a-1。     

基于遥感和空间分析的上海城市森林生物量分布特征

城市森林发挥着改善和维护城市生态环境质量的作用, 研究城市森林生物量和分布特点对其生态系统服务评价和林分经营均具有重要意义。该文根据上海城市森林的种植分布和经营状况利用2011年6月-2012年6月样地实测森林生物量数据和同期Landsat ETM+遥感图像, 在基于逐步回归分析建立森林生物量反演模型的基础上, 引入回归残差及空间分析, 研究了城市森林及其主要优势树种樟(Cinnamomum camphora)林分的生物量分布特征, 探讨了区域尺度森林生物量的遥感估测方法。结果表明: (1)上海城市森林生物量密度总体呈现中心城区(静安区、黄浦区等)较高, 生物量密度集中在35-70 t·hm^-2之间, 郊区(嘉定区、青浦区等)空间分布状况相对较低, 生物量密度介于15-50 t·hm^-2之间的变化特征。上海优势树种樟林分生物量密度范围为20-110 t·hm^-2; 空间上呈现出东北部较高、西南部较低的变化特征。(2)上海城市森林及樟林分的生物量总量分别为3.57 Tg和1.33 Tg。林地面积小, 具有较高森林生物量密度的上海中心城区, 其森林生物量占总量的6.1%, 其中林地面积最小的静安区生物量最低, 仅占总量的0.11%。在所有区县中, 林地面积最大的崇明县、浦东新区具有较高的森林生物量, 分别占总量的20.08%和19.18%。(3)所建立的基于回归反距离插值的城市森林生物量估测模型, 其标准误差、平均绝对误差、平均相对误差分别为8.39、6.86、24.22%, 较回归模型分别降低了57.69%、55.43%、64.00%, 较空间插值的方法分别降低了62.21%、58.50%、65.40%。残差的引入减少了由于空间变异引发的城市森林生物量遥感估测的不确定性。相比基于实测数据通过空间插值的估测, 遥感为快速便捷、客观高效的森林生物量监测提供了可能, 更加完善的结果和模型的优化有待引入其他信息源如高分高光谱信息或改善残差空间分析方法获得。     

Seasonal rhythms of seed rain and seedling emergence in two tropical rain forests in southern Brazil

Seasonal tropical forests show rhythms in reproductive activities due to water stress during dry seasons. If both seed dispersal and seed germination occur in the best environmental conditions, mortality will be minimised and forest regeneration will occur. To evaluate whether non-seasonal forests also show rhythms, for 2 years we studied the seed rain and seedling emergence in two sandy coastal forests (flooded and unflooded) in southern Brazil. In each forest, one 100 x 30-m grid was marked and inside it 30 stations comprising two seed traps (0.5 x 0.5 m each) and one plot (2 x 2 m) were established for monthly monitoring of seed rain and a seedling emergence study, respectively. Despite differences in soil moisture and incident light on the understorey, flooded and unflooded forests had similar dispersal and germination patterns. Seed rain was seasonal and bimodal (peaks at the end of the wetter season and in the less wet season) and seedling emergence was seasonal and unimodal (peaking in the wetter season). Approximately 57% of the total species number had seedling emergence 4 or more months after dispersal. Therefore, both seed dormancy and the timing of seed dispersal drive the rhythm of seedling emergence in these forests. The peak in germination occurs in the wetter season, when soil fertility is higher and other phenological events also occur. The strong seasonality in these plant communities, even in this weakly seasonal climate, suggests that factors such as daylength, plant sensitivity to small changes in the environment (e.g. water and nutrient availability) or phylogenetic constraints cause seasonal rhythms in the plants.     

不同林龄落叶松人工林土壤微生物生物量碳氮的季节变化

为从土壤微生物生物量角度分析不同林龄落叶松人工林的土壤肥力状况,对辽宁东部山区两种林龄(9年生,幼龄林;43年生,成熟林)落叶松人工林不同土层(腐殖质层和矿化层)微生物生物量碳、氮季节变化进行了监测,并分析了微生物生物量碳氮的季节变化与土壤养分及水分的关系.结果表明:两种林龄落叶松腐殖质层微生物生物量碳、氮含量均高于矿化层;在腐殖质层,幼龄林微生物生物量碳、氮含量高于成熟林.方差分析表明,在春、秋季节,同一土层两林龄土壤微生物生物量碳、氮含量之间差异达到显著水平(P<0.01).在观测的3个季节内,幼龄林腐殖质层的微生物生物量碳基本无变化,而成熟林的微生物生物量碳在秋季达到最高;两种林龄落叶松微生物生物量氮均在夏季达到最高.在矿化层,两种林龄落叶松微生物生物量碳、氮均在秋季达到最大.相关分析发现,微生物生物量碳、氮之间以及土壤微生物生物量碳、氮与土壤有机碳、全氮呈显著正相关,而与土壤水分无相关性;另外,落叶松人工林内的灌木种类和数量以及季节性温度变化对土壤微生物生物量碳氮也有影响.上述结果表明,研究区域土壤微生物生物量碳、氮的季节波动与土壤养分状况密切相关,幼龄林土壤养分状况优于成熟林.     

A comparative analysis of the relationship between phytoplankton standing crops and environmental parameters in four eutrophic prairie reservoirs

An analysis was performed with multivariate statistical methods of the relationship between chlorophyll a concentrations and eighteen physico-chemical parameters measured over a six year period in four eutrophic Nebraska reservoirs. In the reservoirs with relatively clear water early in the growing season, physical factors (Secchi depth, turbidity, temperature) and non-nutrient chemical factors (alkalinity, hardness, C. O. D.) were significantly related to chlorophyll a concentrations, but macronutrients (nitrogen and phosphorus) were not. In the reservoir with persistent abiogenic turbidity, chemical factors including nitrogen and phosphorus were significant but physical factors were not. Six models based upon intercorrelations between measured parameters and chlorophyll a are evaluated for their usefulness in accounting for chlorophyll a variance. The best model accounts for 67–70 percent of the total variation in chlorophyll a in the four reservoirs.     

Estimating switchgrass productivity in the Great Plains using satellite vegetation index and site environmental variables

Switchgrass is being evaluated as a potential feedstock source for cellulosic biofuels and is being cultivated in several regions of the United States. The recent availability of switchgrass land cover maps derived from the National Agricultural Statistics Service cropland data layer for the conterminous United States provides an opportunity to assess the environmental conditions of switchgrass over large areas and across different geographic locations. The main goal of this study is to develop a data-driven multiple regression switchgrass productivity model and identify the optimal climate and environment conditions for the highly productive switchgrass in the Great Plains (GP). Environmental and climate variables used in the study include elevation, soil organic carbon, available water capacity, climate, and seasonal weather. Satellite-derived growing season averaged Normalized Difference Vegetation Index (GSN) was used as a proxy for switchgrass productivity. Multiple regression analyses indicate that there are strong correlations between site environmental variables and switchgrass productivity (r = 0.95). Sufficient precipitation and suitable temperature during the growing season (i.e., not too hot or too cold) are favorable for switchgrass growth. Elevation and soil characteristics (e.g., soil available water capacity) are also an important factor impacting switchgrass productivity. An anticipated switchgrass biomass productivity map for the entire GP based on site environmental and climate conditions and switchgrass productivity model was generated. Highly productive switchgrass areas are mainly located in the eastern part of the GP. Results from this study can help land managers and biofuel plant investors better understand the general environmental and climate conditions influencing switchgrass growth and make optimal land use decisions regarding switchgrass development in the GP.     

Variations of root and heterotrophic respiration along environmental gradients in China's forests

Aims Root and heterotrophic respiration may respond differently to environmental variability, but little evidence is available from large-scale observations. Here we aimed to examine variations of root and heterotrophic respiration across broad geographic, climatic, soil and biotic gradients.Methods We conducted a synthesis of 59 field measurements on root and heterotrophic respiration across China's forests.Important findings Root and heterotrophic respiration varied differently with forest types, of which evergreen broadleaf forest was significantly different from those in other forest types on heterotrophic respiration but without statistically significant differences on root respiration. The results also indicated that root and heterotrophic respiration exhibited similar trends along gradients of precipitation, soil organic carbon and satellite-indicated vegetation growth. However, they exhibited different relationships with temperature: root respiration exhibited bimodal patterns along the temperature gradient, while heterotrophic respiration increased monotonically with temperature. Moreover, they showed different relationships with MOD17 GPP, with increasing trend observed for root respiration whereas insignificant change for heterotrophic respiration. In addition, root and heterotrophic respiration exhibited different changes along the age sequence, with insignificant change for root respiration and decreasing trend for heterotrophic respiration. Overall, these results suggest that root and heterotrophic respiration may respond differently to environmental variability. Our findings could advance our understanding on the different environmental controls of root and heterotrophic respiration and also improve our ability to predict soil CO₂ flux under a changing environment.     

Seeing the wood despite the trees: Exploring human disturbance impact on plant diversity,community structure,and standing biomass in fragmented high Andean forests

High Andean forests harbor a remarkably high biodiversity and play a key role in providing vital ecosystem services for neighboring cities and settlements. However, they are among the most fragmented and threatened ecosystems in the neotropics. To preserve their unique biodiversity, a deeper understanding of the effects of anthropogenic perturbations on them is urgently needed. Here, we characterized the plant communities of high Andean forest remnants in the hinterland of Bogotá in 32 0.04 ha plots. We assessed the woody vegetation and sampled the understory and epiphytic cover. We gathered data on compositional and structural parameters and compiled a broad array of variables related to anthropogenic disturbance, ranging from local to landscape‐wide metrics. We also assessed phylogenetic diversity and functional diversity. We employed nonmetric multidimensional scaling (NMDS) to select meaningful variables in a first step of the analysis. Then, we performed partial redundancy analysis (pRDA) and generalized linear models (GLMs) in order to test how selected environmental and anthropogenic variables are affecting the composition, diversity, and aboveground biomass of these forests. Identified woody vegetation and understory layer communities were characterized by differences in elevation, temperature, and relative humidity, but were also related to different levels of human influence. We found that the increase of human‐related disturbance resulted in less phylogenetic diversity and in the phylogenetic clustering of the woody vegetation and in lower aboveground biomass (AGB) values. As to the understory, disturbance was associated with a higher diversity, jointly with a higher phylogenetic dispersion. The most relevant disturbance predictors identified here were as follows: edge effect, proximity of cattle, minimum fragment age, and median patch size. Interestingly, AGB was efficiently predicted by the proportion of late successional species. We therefore recommend the use of AGB and abundance of late successional species as indicators of human disturbance on high Andean forests.     

Research on characteristics of biomass distribution in urban forests of Shanghai metropolis based on remote sensing and spatial analysis

Aims
Monitoring and quantifying the biomass and its distribution in urban trees and forests are crucial to understanding the role of vegetation in an urban environment. In this paper, an estimation method for biomass of urban forests was developed for the Shanghai metropolis, China, based on spatial analysis and a wide variety of data from field inventory and remote sensing.
Methods
An optimal regression model between forest biomass and auxiliary variables was established by stepwise regression analysis. The residual value of regression model was computed for each of the sites sampled and interpolated by Inverse-distance weighting (IDW) to predict residual errors of other sites not subjected to sampling. Forest biomass in the study area was estimated by combining the regression model based on remote sensing image data and residual errors of spatial distribution map. According to the distribution of plantations and management practices, a total of 93 sample plots were established between June 2011 and June 2012 in the Shanghai metropolis. To determine a suitable model, several spectral vegetation indices relating to forest biomass and structure such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and new images synthesized through band combinations such as the sum of TM2, TM3 and TM4 (denoted Band 234), and the sum of TM3, TM4 and TM5 (denoted Band 345) were used as alternative auxiliary parameters .
Important findings
The biomass density in urban forests of the Shanghai metropolis varied from 15 to 120 t·hm^-2. The higher densities of forest biomass concentrated mostly in the urban areas, e.g. in districts of Jing’an and Huangpu, mostly ranging from 35 to 70 t·hm^-2. Suburban localities such as the districts of Jiading and Qingpu had lower biomass densities at around 15 to 50 t·hm^-2. The biomass density of Cinnamomum camphora trees across the Shanghai metropolis varied between 20 and 110 t·hm^-2. The spatial biomass distribution of urban forests displayed a tendency of higher densities in northeastern areas and lower densities in southwestern areas. The total biomass was 3.57 million tons (Tg) for urban forests and 1.33 Tg for C. camphora trees. The overall forest biomass was also found to be distributed mostly in the suburban areas with a fraction of 93.9%, whereas the urban areas shared a fraction of only 6.1%. In terms of the areas, the suburban and urban forests accounted for 95.44% and 4.56%, respectively, of the total areas in the Shanghai metropolis. Among all the administrative districts, the Chongming county and the new district of Pudong had the highest and the second highest biomass, accounting for 20.1% and 19.18% of the total forest biomass, respectively. In contrast, the Jing’an district accounted for only 0.11% of the total forest biomass. The root-mean-square error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of the model for estimating urban forest biomass in this study were 8.39, 6.86 and 24.22%, respectively, decreasing by 57.69%, 55.43% and 64.00% compared to the original simple regression model and by 62.21%, 58.50%, 65.40% compared to the spatial analysis method. Our results indicated that a more efficient way to estimate urban forest biomass in the Shanghai metropolis might be achieved by combining spatial analysis with regression analysis. In fact, the estimated results based on the proposed model are also more comparable to the up-scaled forest inventory data at a city scale than the results obtained using regression analysis or spatial analysis alone.     

基于环境卫星数据的黄河湿地植被生物量反演研究

回归模型拟合植被指数与生物量的定量关系是植被生物量反演的重要研究方法之一.研究在此基础上,基于环境卫星遥感数据和同步野外实地采样数据,以郑州黄河湿地自然保护区为试验区,比较MLRM(多元线性回归模型)与SCRM(一元曲线回归模型)反演植被生物量的能力,并估算研究区植被生物量,生成研究区生物量分布图.结果表明,文中所建立的MLRM在研究区具有较好的反演精度和预测能力.其模型显著性检验为极显著,相关系数为0.9791,模型拟合精度达到29.8 g/m2,其模型预测结果系统误差为49.9g/m2,均方根误差为67.2 g/m2,预测决定系数为0.8742,比传统的一元回归模型具有更高的精度和可靠性.估算研究区域2010年8月湿生植被生物量约为6.849199 t/hm2,相对误差为4.73％.     

Prediction of macrofungal occurrence in Swedish beech forests from soil and litter variable models

The spatial distribution of 49 macrofungal species in Swedish beech forests was related to the statistical variation in 31 edaphic variables. In order to reduce the multicollinearity problem, the variables were transformed into eight principal components, PCs, which are used in two-group discriminant analysis (on absence/presence patterns) and multiple regression analysis (on number of fruit-bodies). The results suggested that base saturation and organic matter content are of outstanding importance. However, significant relationships were also found with other variables, i.e. Cd or Zn in soil and litter, soil nitrogen mineralization rate, and Na or S in litter. One interesting interpretation of the results is that fungi do not only respond to the main variables of a gradient (soil pH, organic mater, base saturation) but also to other variables. Attempts were made to interpret the PCs to characterize fungal occurrence from the models they formed.     

不同植被防护措施对三峡库区土质道路边坡侵蚀的影响

为了探明沙湖浮游动物密度、生物量、分布与水环境因子之间的关系,运用多元逐步回归分析、通径分析和典范对应分析方法对2009年4月、7月、10月、2010年1月测定的沙湖浮游动物密度、生物量数据与水环境因子进行多元分析。结果表明:沙湖浮游动物密度与叶绿素a含量、总氮、总磷之间呈显著线性相关,影响沙湖浮游动物密度的主要水环境因子依次为叶绿素a含量、总氮、总磷;浮游动物生物量与叶绿素a含量、总氮、透明度、化学需氧量(CODMn)之间呈显著线性相关,影响浮游动物生物量的主要水环境因子依次为叶绿素a含量、总氮、透明度、化学需氧量;叶绿素a含量和总氮对浮游动物的直接影响作用最强,其他水环境因子主要通过影响叶绿素a含量间接影响浮游动物密度及生物量。浮游动物与水环境因子的CCA排序结果将分别适应不同水环境的16种浮游动物分为3组,叶绿素a含量、化学需氧量、水温和总磷是影响沙湖浮游动物群落特征及时空分布的主要水环境因子。