利用聚类算法监测森林乔木物种多样性

该研究基于机载激光雷达(LiDAR)和高光谱数据, 从森林物种叶片的生理化学源头探寻生化特征与光谱特征的内在关联, 探讨生化多样性、光谱多样性与物种多样性之间的响应机制, 选择最优植被指数并结合最优结构参数, 通过聚类方法构建森林物种多样性遥感估算模型, 在古田山自然保护区开展森林乔木物种多样性监测。研究结果表明: (1)从16种叶片生化组分中, 筛选出叶绿素a、叶绿素b、类胡萝卜素、叶片含水量、比叶面积、纤维素、木质素、氮、磷和碳可通过偏最小二乘法用叶片光谱有效模拟(R² = 0.60-0.79, p < 0.01), 并选择有效的植被指数: 转换型吸收反射指数/优化型土壤调整指数(TCARI/OSAVI)、类胡萝卜素反射指数(CRI)、水波段指数(WBI)、比值植被指数(RVI)、生理反射指数(PRI)和冠层叶绿素浓度指数(CCCI)表征相应的最优生化组分; (2)基于机载LiDAR数据利用结合形态学冠层控制的分水岭算法获得高精度单木分离结果(R ² = 0.77, RMSE = 16.48), 同时采用逐步回归方法从常用的森林结构参数中选取树高和偏度作为最优结构参数(R ² = 0.32, p < 0.01); (3)基于6个最优植被指数和2个最优结构参数, 以20 m × 20 m为窗口通过自适应模糊C均值方法进行聚类, 实现了研究区森林乔木物种丰富度(Richness, R ²= 0.56, RMSE = 1.81)和多样性指数Shannon-Wiener (R ² = 0.83, RMSE = 0.22)与Simpson (R ² = 0.85, RMSE = 0.09)的成图。该研究在冠层尺度上获取了与物种多样性相关的生化、光谱和结构参数, 将单木个体作为最小单元, 利用聚类算法直接估算物种类别差异, 无需判定具体的树种属性, 是利用遥感数据进行区域尺度森林物种多样性监测与成图的实践, 可为亚热带地区常绿阔叶林的物种多样性监测提供借鉴。     

Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

Aims　Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR (light detection and ranging) data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accurately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency (e.g. labor-intensive, time-consuming) of conventional ground survey, and provide foundations to forest inventory.Methods　In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM+ data. Firstly, we calculated the canopy closure from ALS (Airborne Laser Scanning) high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM+ data. Three approaches, multi-variable stepwise regression (MSR), random forest (RF) and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively.Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models (determination coefficient (R²) = 0.722, root mean square error (RMSE) = 0.126, relative root mean square error (rRMSE) = 0.209, estimation accuracy (EA) = 79.883%). The combination of LiDAR data and LANDSAT ETM+ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.     

Remote sensing estimation of gross primary productivity and its response to climate change in the upstream of Heihe River Basin

AimsQuantifying the gross primary productivity (GPP) of vegetation is of primary interest in studies of global carbon cycle. This study aims to optimize the MODIS GPP model for specific environments of a fragile waterhead ecosystem, by performing simulations of long-term (from 2001 to 2012) GPP with optimized MOD_17 model, and to analyze the response of GPP to the local climatic variations.Methods The original MODIS GPP products that underestimate GPP were validated against two years (2010-2011) of eddy covariance (EC) data at two sites (i.e. an alpine pasture site and a forest site, respectively) in the upstream of Heihe River Basin. Three comparative experiments were then conducted to analyze the effects of input parameters derived from three sources (i.e. meteorological, biome-specific, and fraction of absorbed photosynthetically active radiation (fPAR) parameters) on the model behavior. After refining the model-driven parameters, long-term GPPs of the study area were estimated using the optimized MOD_17 model, and the Least Absolute Deviation method was applied to analyze the partial correlations between interannual GPPs and climatic variables (temperature, precipitation and vapor pressure deficit (VPD)). Important findings The uncertainties in the original MODIS GPP products are attributable to biome-specific parameters, input data (e.g. meteorological and radiometry data) and vegetation maps. At the pasture site, the light use efficiency had the strongest impact on the GPP simulations. The refined fPAR calculated from the leaf area index (LAI) products of Global Land Surface Satellite (GLASS) greatly improved the GPP estimates, especially at the forest site. The GPPs from the optimized MOD_17 model well matched the EC data (R² = 0.90, root mean squared error (RMSE) = 1.114 g C·m^-2·d^-1 at the alpine pasture site; R² = 0.91, RMSE = 0.649 g C·m^-2·d^-1 at the forest site). The time series of GPPs displayed an up trend at an average rate of 9.58 g C·m^-2·a^-1 from 2001 to 2012. Examination of the partial correlations between interannual GPPs and climatic variables showed that the annual mean temperature and VPD generally had significant positive impacts on GPP, and the annual precipitation had a negative impact on GPP.     

Application and comparison of remote sensing GPP models with multi-site data in China

Aims Estimation of gross primary productivity (GPP) of vegetation at the global and regional scales is important for understanding the carbon cycle of terrestrial ecosystems. Due to the heterogeneous nature of land surface, measurements at the site level cannot be directly up-scaled to the regional scale. Remote sensing has been widely used as a tool for up-saling GPP by integrating the land surface observations with spatial vegetation patterns. Although there have been many models based on light use efficiency and remote sensing data for simulating terrestrial ecosystem GPP, those models depend much on meteorological data; use of different sources of meteorological datasets often results in divergent outputs, leading to uncertainties in the simulation results. In this study, we examines the feasibility of using two GPP models driven by remote sensing data for estimating regional GPP across different vegetation types. Methods Two GPP models were tested in this study, including the Temperature and Greenness Model (TG) and the Vegetation Index Model (VI), based on remote sensing data and flux data from the China flux network (ChinaFLUX) for different vegatation types for the period 2003-2005. The study sites consist of eight ecological stations located in Xilingol (grassland), Changbaishan (mixed broadleaf-conifer forest), Haibei (shrubland), Yucheng (cropland), Damxung (alpine meadow), Qianyanzhou (evergreen needle-leaved forest), Dinghushan (evergreen broad-leaved forest), and Xishuangbanna (evergreen broad-leaved forest), respectively. Important findings All the remote sensing parameters employed by the TG and VI models had good relationships with the observed GPP, with the values of coefficient of determination, R², exceeding 0.67 for majority of the study sites. However, the root mean square errors (RMSEs) varied greatly among the study sites: the RMSE of TG ranged from 0.29 to 6.40 g·m^-2·d^-1, and that of VI ranged from 0.31 to 7.09 g·m^-2·d^-1, respectively. The photosynthetic conversion coefficients m and a can be up-scaled to a regional scale based on their relationships with the annual average nighttime land surface temperature (LST), with 79% variations in m and 58% of variations in a being explainable in the up-scaling. The correlations between the simulated outputs of both TG and VI and the measured values were mostly high, with the values of correlation coefficient, r, ranging from 0.06 in the TG model and 0.13 in the VI model at the Xishuangbanna site, to 0.94 in the TG model and 0.89 in the VI model at the Haibei site. In general, the TG model performed better than the VI model, especially at sites with high elevation and that are mainly limited by temperature. Both models had potential to be applied at a regional scale in China.     

普洱季风常绿阔叶林乔木物种多样性高分辨率遥感估测

季风常绿阔叶林是我国南亚热带典型的地带性植被,也是云南省普洱地区重要森林类型。季风常绿阔叶林乔木物种多样性遥感估测对研究区域尺度生物多样性格局及其规律具有重要作用。根据光谱异质性假说和环境异质性假说,首先使用1m空间分辨率的机载高光谱数据和激光雷达数据提取了光谱多样性特征和垂直结构特征。然后利用基于随机森林算法的递归特征消除方法选择对研究区森林乔木物种多样性指数具有较好解释能力的遥感特征,并对Shannon-Winner物种多样性指数进行建模、制图。研究结果表明：(1)基于机载LiDAR数据提取的垂直结构特征和机载高光谱数据提取的光谱多样性特征均对研究区森林乔木物种多样性具有较好的解释能力,随机森林模型估测结果分别为R²=0.48,RMSE=0.46和R²=0.5,RMSE=0.45;两种数据源融合可以进一步提高遥感数据的森林乔木物种多样性估测精度,随机森林估测模型R²和RMSE分别为0.69和0.37。(2)机载激光雷达数据对研究区针阔混交林乔木物种多样性的估测能力优于机载高光谱数据。(3)机器学习方法有助于从高维遥感...     

Classification of Pinus massoniana and secondary deciduous tree species in northern subtropical region based on high resolution and hyperspectral remotely sensed data

Aims Using remote sensing data for tree species classification plays a key role in forestry resource monitoring, sustainable forest management and biodiversity research.Methods This study used integrated sensor LiCHy (LiDAR, CCD and Hyperspectral) to obtain both the high resolution imagery and the hyperspectral data at the same time for the natural secondary forest in south Jiangsu hilly region. The data were used to identify the crown and to classify tree species at multiple levels. Firstly, tree crowns were selected by segmenting high-resolution imagery at multiple scales based on edge detection; secondly, characteristic variables of hyperspectral images were extracted, then optimization variables were selected based on the theory of information entropy. Tree species and forest types were classified using either all characteristic variables or optimization variables only. Finally, tree species and forest types were reclassified along with the tree crowns information, and the accuracy of classification was discussed. Important findings Based on all available characteristic variables, the overall accuracy for four typical tree species classification was 64.6%, and the Kappa coefficient was 0.493. The overall accuracy for forest types classification was 81.1%, and the Kappa coefficient was 0.584. Based on optimization variables only, the overall accuracy for four typical tree species classification dropped to 62.9%, and the Kappa coefficient was 0.459. The overall accuracy for forest types classification was 77.7%, and the Kappa coefficient was 0.525. Obtaining both high resolution image and hyperspectral data at the same time by integrated sensor can increase overall accuracy in classifying forest types and tree species in northern subtropical forest.     

Relationship between ecosystem multifuntionality and species diversity in grassland ecosystems under land-use types of clipping,enclosure and grazing

Aims Over the past twenty years, most biodiversity and ecosystem functioning (BEF) research has focused on the effects of species diversity on single or just a few ecosystem functions. However, ecosystems are primarily valued for their ability to maintain multiple functions and services simultaneously (i.e. multifunctionality here- after). This paper first introduced the constantly perfected concept of “multifunctionality”, and then tried to make some modifications to the current mainstream quantitative method in order to evaluate the multifunctionality of grassland communities with the management of clipping, enclosure and grazing in Inner Mongolia, investigating the relationship between the multifunctionality and species diversity. Methods In free grazing grassland, four sites were set and each site was divided into two parts to conduct enclosure and clipping management respectively. After seven years, 15 quadrats (1 m × 1 m) were established for each type of management in each site (total 60 quadrats for each type) using the regular arrangement method; as a control, we also established 20 quadrats (two sites) in grazing grassland. For each quadrat, we carried out plants census and collected soil mixture sample, measuring 16 soil variables, and then calculated the biodiversity indices and multifunctionality index (M-index) by means of factor analysis. Important findings The results showed that M-indexes by the two evaluation methods were strongly correlated at both quadrat and site scale, suggesting that our modified method was reliable. Over-grazed communities had the lowest biodiversity indices and their most soil indicators were also low, showing obvious degradation features. Enclosure and clipping communities (seven years) had higher biodiversity and better soil indicators. The rank of M-indexes was clipping community (0.2178) > enclosure community (0.0704) > grazing community (-0.8031). The vegetation was distributed mainly along the gradients of water and fertility. Among the biodiversity indices, evenness (Pielou) index and richness (Margelf) index were most strongly correlated with multifunctionality, and their explanatory power (R²) for M-index were higher at site scale (R² = 0.5921, p = 0.0093; R² = 0.7499, p = 0.0007) than at quadrat scale (R² = 0.1871, p < 0.0001; R² = 0.1601, p < 0.0001), indicating study scale played an important role in the determinants of multifunctionality. At both quadrat and site scales, M-indexes is a linear positive function with species evenness and a hump-shaped function of species richness. Therefore, in contrast to enclosure, clipping was more conducive to maintain the ecosystem multifunctionality in this region, and the ecosystem with moderate specie richness, where these species are evenly distributed might have better multifunctionality.     

模拟氮沉降对北京东灵山辽东栎林树木生长的影响

传统的元素限制模型认为氮是温带森林生长的限制元素, 不过该结论更多是从地上生物量以及群落水平进行阐述, 忽视了不同物种以及不同径级树木对外源氮的响应差异。辽东栎(Quercus wutaishanica)林是华北地区常见的森林类型, 该研究以北京东灵山辽东栎林为研究对象, 通过设置3个氮添加水平的实验, 即对照CK (0 kg·hm ^-2·a ^-1), N50 (50 kg·hm ^-2·a ^-1)和N100 (100 kg·hm ^-2·a ^-1), 模拟氮沉降对群落和物种水平以及不同径级树木生长的影响。经过7年氮添加, 实验结果显示: 物种水平上, 氮添加明显促进了优势树种辽东栎的生长; 群落水平上, 树木生长随氮浓度增加有不断上升趋势, 但统计学差异不显著; 氮添加显著抑制了辽东栎以及群落内小径级(3-10 cm)树木生长, 中(10-20 cm)、大径级(>20 cm)树木生长随氮沉降水平增加呈上升趋势, 但统计学差异不显著。表明氮是辽东栎以及温带森林树木生长的限制元素; 不同径级的辽东栎和群落内其他植物对氮添加响应不一致, 氮添加抑制了小径级树木生长, 中、大径级树木生长对氮添加响应不明显。     

亚热带不同树种凋落叶分解对氮添加的响应

为探究不同质量凋落物对氮(N)沉降的响应, 该研究采用尼龙网袋分解法, 在亚热带福建三明格氏栲(Castanopsis kawakamii)自然保护区的米槠(Castanopsis carlesii)天然林, 选取4种本区常见的具有不同初始化学性质的树种凋落叶进行模拟N沉降(N添加)分解实验(施N水平为对照0和50 kg·hm ^-2·a ^-1)。研究结果表明: 在2年的分解期内, 对照处理的各树种凋落叶的分解速率依次为观光木(Michelia odora, 0.557 a ^-1)、米槠(0.440 a ^-1)、台湾相思(Acacia confusa, 0.357 a ^-1)、杉木(Cunninghamia lanceolata, 0.354 a ^-1); N添加处理凋落叶分解速率依次为观光木(0.447 a ^-1)、米槠(0.354 a ^-1)、杉木(0.291 a ^-1)、台湾相思(0.230 a ^-1), 除杉木凋落叶外, N添加显著降低了其他3种凋落叶分解速率。N添加不仅使4种树木凋落叶分解过程中的N释放减慢, 同时还抑制凋落叶化学组成中木质素和纤维素的降解; N添加在凋落叶分解过程中总体上提高β-葡萄糖苷酶(βG)和酸性磷酸酶活性, 对纤维素水解酶的活性影响不一致, 而降低β-N-乙酰氨基葡萄糖苷酶活性和酚氧化酶活性。凋落叶分解速率与凋落叶中的碳获取酶(βG)活性以及其化学组分中的可萃取物含量极显著正相关, 与初始碳浓度、纤维素和木质素含量极显著负相关, 与初始N含量没有显著相关性。凋落物类型和N添加的交互作用虽未影响干质量损失速率, 但对木质素和纤维素的降解具有显著效应。综上所述, 化学组分比初始N含量能更好地预测凋落叶分解速率, 而N添加主要通过抑制分解木质素的氧化酶(如PHO)来降低凋落叶分解速率。     

山东蒙山6种造林树种40余年成林效果评价

植被恢复一直是恢复生态学研究的核心问题和首要解决目标。该文拟评估山东蒙山森林植被恢复与重建的现状和程度, 评价不同造林树种对植物多样性的影响, 筛选一批造林工具种, 为今后沂蒙山区森林植被恢复与重建的调控和预测提供依据。采用样地法和样方法, 选择林龄超过40年的6种主要人工林为样地, 采用典型取样法进行林内调查。根据乔木层、灌木层和草本层的物种丰富度、Shannon-Wiener多样性指数、Simpson多样性指数和Pielou均匀度指数评价物种多样性, 分析乔木径级分布, 判断植物发展类型。结果表明: 乔木层、灌木层和草本层的4种多样性指数较为一致地显示赤松(Pinus densiflora)、栓皮栎(Quercus variabilis)、黑松(Pinus thunbergii)、日本落叶松(Larix kaempferi)和刺槐(Robinia pseudoacacia)的成林效果较好, 油松(Pinus tabuliformis)相对较差。6种人工林群落灌木层和草本层的4种多样性指数基本无显著差异, 而乔木层中, 油松人工林群落的4种多样性指数与黑松人工林、刺槐人工林、栓皮栎人工林和日本落叶松人工林存在部分显著差异(p < 0.05)。从整体上看, 人工林群落灌木层发育最好, 4种多样性指数均为灌木层>乔木层>草本层, 且差异大多极显著(p < 0.01)。研究发现: 乔木扩展种29种、隐退种3种、稳定侵入种9种和随机侵入种11种, 表明人工林群落正处于向森林演替的过程中。数据显示: 3种隐退种赤松、油松和日本落叶松均为群落局部优势种, 赤松和油松种群更新困难, 日本落叶松完全没有更新幼苗。作者认为林下发达的灌草层可能是阻碍针叶林天然更新的关键因素。评估筛选出15种造林工具种: 早期开荒工具种(A类) 7种和后期顶级工具种(B类) 8种。其中A1类(建群种) 2种: 赤松和油松, A2类(伴生种) 5种: 黄檀(Dalbergia hupeana)、花曲柳(Fraxinus rhynchophylla)、君迁子(Cerasus serrulata)、山合欢(Albizia kalkora)和鹅耳枥(Carpinus turczaninowii); B1类(建群种) 2种: 栓皮栎和麻栎(Quercus acutissima), B2类(伴生种) 6种: 水榆花楸(Sorbus alnifolia)、槲树(Quercus dentata)、山樱花(Diospyros lotus)、小叶朴(Celtis bungeana)、大叶朴(Celtis koraiensis)和三桠乌药(Lindera obtusiloba)。     

黑河上游植被总初级生产力遥感估算及其对气候变化的响应

定量描述植被总初级生产力(GPP)对于全球碳循环和全球气候变化研究具有重要意义。针对MODIS MOD_17 GPP (MOD_17)产品在通量站点低估的现象, 通过3个实验依次改进了模型输入参数(气象数据和吸收的光合有效辐射吸收比例(fPAR))和模型本身的参数(最大光能利用率), 分析了各个参数对模拟结果的不确定性影响, 结果表明各参数对模拟结果都有不同程度的影响。在阿柔草地站, 最大光能利用率的重新标定对结果影响最大, GPP估算结果的提高最为明显; 在关滩森林站利用广义神经网络算法得到的GLASS fPAR代替原始MODIS fPAR产品, 比其他参数的改进效果更明显, GPP的值更接近涡动通量观测值。利用改进的MOD_17模型重新估算了黑河上游2001-2012年间植被GPP, 通过趋势分析得出该研究时段内GPP以9.58 g C·m^-2·a^-1的平均速率呈上升趋势。同时计算了气候因子(温度、降水和饱和水汽压差(VPD))与时间序列GPP的偏相关性, 分析了植被GPP对气候变化的响应情况, 2001-2012年平均温度和VPD与年GPP大部分区域呈正相关, 体现了温度和VPD对植被生长的促进作用; 2001-2012年的降水量与年GPP无明显相关, 且大部分区域呈负相关。     

Current stocks and potential of carbon sequestration of the forest tree layer in Qinghai Province,China

为阐明青海省森林生态系统乔木层植被碳储量现状及其分布特征, 该研究利用240个标准样地实测的乔木数据, 估算出青海省森林生态系统不同林型处于不同龄级阶段的平均碳密度, 并结合青海省森林资源清查资料所提供的不同龄级的各林型面积, 估算了青海省森林生态系统乔木层的固碳现状、速率和潜力。结果表明: 1) 2011年青海省森林乔木层平均碳密度为76.54 Mg·hm ^-2, 总碳储量为27.38 Tg。云杉(Picea spp.)林、柏木(Cupressus funebris)林、桦木(Betula spp.)林、杨树(Populus spp.)林是青海地区的主要林型, 占青海省森林面积的96.23%, 占青海省乔木层碳储量的86.67%, 其中云杉林的碳储量(14.78 Tg)和碳密度(106.93 Mg·hm ^-2)最高。按龄级划分, 乔木层碳储量表现为过熟林>中龄林>成熟林>近熟林>幼龄林。2)青海省乔木层总碳储量从2003年的23.30 Tg增加到2011年的27.38 Tg, 年平均碳增量为0.51 Tg·a ^-1。乔木层固碳速率为1.06 Mg·hm ^-2·a ^-1, 其中柏木林的固碳速率最大(0.44 Mg·hm ^-2·a ^-1); 桦木林的固碳速率为负值(-1.06 Mg·hm ^-2·a ^-1)。3)青海省乔木层植被固碳潜力为8.50 Tg, 其中云杉林固碳潜力最高(3.40 Tg)。该研究结果表明青海省乔木层具有较大的固碳潜力, 若对现有森林资源进行合理管理和利用, 将会增加青海省森林的碳固存能力。     

海南尖峰岭热带山地雨林林冠层树种功能多样性特征

以海南尖峰岭热带山地雨林3块1 hm²样地为研究对象,利用11个林冠功能性状结合样地地形及林冠乔木树种样地清查数据,分别基于单维性状和多维性状比较物种多度加权对群落功能离散度指数——平均成对距离(MPD)和平均最近类群距离(MNTD)的影响;同时分析林冠层功能丰富度(FRic)与物种丰富度之间的关系,最后利用零模型探讨不同生境类型下标准化效应MPD和MNTD(经过物种多度加权且剔除群落物种丰富度差异影响)的变化,进而评价林冠层群落水平功能多样性格局及其对局域生境异质性的响应.结果表明: 功能性状维度和物种多度对MPD的影响强烈,不同维度功能性状多度加权前后MPD相关性较弱(R=0.359～0.628);但对MNTD的影响相对较弱,不同维度功能性状多度加权前后MNTD相关性较强(R=0.746～0.820);未经物种多度加权的MPD和MNTD均普遍高估了林冠层的功能离散度.林冠层功能丰富度与物种丰富度基本呈指数相关关系(F=128.20;R²=0.632;AIC=97.72;P＜0.001),且功能丰富度很有可能存在一定的物种丰富度阈值.基于不同维度功能性状的林冠层功能多样性格局及其生境响应存在一定的差异性.在生物竞争激烈的低沟生境中,林冠层功能多样性倾向于比预期零模型随机产生的功能多样性高,林冠树种功能性状表现出离散分布;而在其他生境类型中,林冠层功能多样性倾向于接近或低于随机产生的功能多样性,林冠树种功能性状随机或聚集分布.     

海南热带雨林国家公园不同植被类型的大型真菌多样性

为揭示海南热带雨林国家公园大型真菌多样性及不同植被类型对真菌群落的影响, 本研究于2020年和2021年湿季对海南热带雨林国家公园内7个管理局辖区开展了大型真菌多样性调查, 比较了不同植被类型(山地雨林、低地雨林、低地雨林次生林、人工林)的大型真菌生活型(共生型、腐生型)组成差异。从设置的58条1 km长的样带内采集到1,869份子实体标本, 根据子实体形态与ITS rDNA序列分析, 从中鉴定出562种真菌, 涉及17目64科174属, 其中80%以上的物种由伞菌目、牛肝菌目、红菇目、多孔菌目、鸡油菌目、锈革孔菌目和炭角菌目构成。大型真菌的营养型以腐生型(占48.2%物种)和共生型(44.8%)为主。每条样带的平均物种丰富度和多度以中海拔的山地雨林最高, 分别为28 ± 5种和33 ± 6个, 而人工林最低, 分别为11 ± 1种和11 ± 2个。植被类型主要影响共生型大型真菌物种丰富度(P = 0.026)和子实体多度(P = 0.019)及Shannon-Wiener多样性(P = 0.028), 但对腐生型大型真菌的影响并不显著。多响应置换过程(multiple response permutation procedure, MRPP)检验结果表明, 不同植被类型对共生型与腐生型大型真菌群落物种组成均有显著影响(腐生型: P = 0.004, 共生型: P = 0.041)。冗余分析(redundancy analysis, RDA)的结果表明, 植被类型对腐生型和共生型真菌群落物种组成差异的解释度均较低(共生型: R² = 0.068, P = 0.004; 腐生型: R² = 0.067, P = 0.004)。海拔仅对腐生型真菌群落物种组成产生微弱影响(R² = 0.029, P = 0.001), 而对共生型真菌影响不显著(R² = 0.024, P = 0.072)。在不同保护地之间, 共生型(R² = 0.148, P = 0.001)与腐生型(R² = 0.123, P = 0.002)真菌物种组成均具显著差异; 基于样带‒真菌矩阵的网络图显示, 海南热带雨林国家公园内尖峰岭、霸王岭、五指山等国家级自然保护区的山地雨林是共生型大型真菌多样性较高区域, 应作为共生型真菌与宿主的优先保护区域。     

Plant diversity and seasonal dynamics in forest gaps of varying sizes in Pinus massoniana plantations

人工林目前存在结构单一、土壤退化、生物多样性降低等人类普遍关注的生态问题。马尾松(Pinus massoniana)是长江上游低山丘陵区退耕还林的主要人工林树种。研究采伐林窗对植物物种组成和更新的影响, 对马尾松低效人工林的改造, 提升其生态服务功能具有重要的意义。该文以采伐39年生的马尾松人工林形成的7种不同大小的林窗为研究对象, 分析了不同季节林窗内的植物生活型组成及多样性变化。结果表明: 1)马尾松人工林林下植物以高位芽植物居多, 其次是地面、地下芽植物, 一年生植物较少而缺少地上芽植物。在林窗形成初期, 林窗的高位芽植物比例明显低于林下, 大林窗的高位芽植物比例稍高于小林窗, 地下芽和一年生植物的比例低于小林窗。2)林下的物种丰富度和物种多样性指数显著低于大林窗。不同林窗下植物的丰富度指数、优势度指数、多样性指数也存在显著差异。3)夏季林窗下植物多样性最高, 其次是秋季, 春季多样性最低。1225-1600 m²的大林窗能够促进马尾松人工林植物多样性恢复和植被更新。     

树冠结构对典型阔叶红松林生产力的影响

阔叶红松(Pinus koraiensis)林是东北东部山区的地带性森林植被, 阐明其生产力的影响因素, 对于理解温带森林生产力维持机制具有重要意义。该研究依托小兴安岭典型阔叶红松林9 hm²动态监测样地, 基于2005和2015年的30 m × 30 m样方内所有胸径>6.5 cm的木本植物的调查数据, 计算各样方的树冠结构复杂性、物种多样性和林分胸高断面积, 结合各样方的地形和土壤理化性质数据, 拟合结构方程模型, 定量分析影响典型阔叶红松林生产力的直接和间接因素。研究结果显示: 树冠结构复杂性和物种多样性与生产力显著正相关, 且树冠结构复杂性对生产力的影响显著高于物种多样性; 树冠结构复杂性对生产力的作用分为树冠垂直分层和树冠可塑性, 其中树冠垂直分层是树冠结构复杂性影响阔叶红松林生产力的主要因素, 而树冠可塑性无显著影响; 林分胸高断面积与生产力显著正相关, 其解释权重仅次于树冠结构复杂性, 树冠结构复杂性与物种多样性均通过影响林分胸高断面积对阔叶红松林生产力产生间接影响; 考虑不同树冠结构复杂性时, 坡度和土壤全磷含量代表的环境因素在调节生产力上发挥的作用存在差异, 移除树冠垂直分层的作用后两者与生产力呈显著的负相关关系。综上可知, 在典型阔叶红松林中, 树冠结构复杂性比物种多样性更有效地解释了生产力的变化, 同时不可忽视其他生物和非生物因素对生产力的作用。     

福建三都澳游泳动物种类组成及群落结构稳定性

利用2012-2013年三都澳渔业资源的定置张网调查资料, 应用物种多样性指数、数量生物量比较曲线(ABC曲线)及鱼类分类多样性指数等方法分析三都澳游泳动物种类组成特征和群落结构的稳定性。调查中共出现游泳动物195种, 隶属于17目64科125属, 其中鱼类143种, 甲壳类47种, 头足类5种。大黄鱼(Larimichthys crocea)在三都澳4个航次的调查中都是最主要的优势种类, 其他优势种类还包括叫姑鱼(Johnius belengerii)、白姑鱼(Argyrosomus argentatus)、虾虎鱼类和一些甲壳类。大黄鱼多为养殖群体, 其他优势种类的共同特征是个体小, 繁殖周期短, 生物量季节或年际间波动剧烈。物种多样性分析表明, 三都澳游泳动物群落平均Shannon-Wiener多样性指数为2.61, 9、10月高, 1、5月低。ABC曲线分析表明, 4次调查中群落结构存在明显的变化, 繁殖群体的补充、个体生长、捕捞、伏季休渔等是影响群落结构稳定性的因素。本次研究表明, 大黄鱼生物量的比例与Shannon-Wiener多样性指数呈极显著负相关(P < 0.01, R = -0.890), 与种类数呈显著负相关(P < 0.05, R = -0.563)。结合近年来的调查数据, 统计得到三都澳的现存鱼类约224种, 其平均分类差异指数(△⁺)为59.5, 分类差异变异指数(∧⁺)为260.8。相对于中国沿海其他海域, 三都澳鱼类群落分类学范围较小, 且群落间的分类地位关系极不均匀, 群落抗干扰的能力较差。     

Edge effects of forest gap in Pinus massoniana plantations on the ecological stoichiometry of Cinnamomum longepaniculatum

Aims Pinus massoniana is one of the major plantation tree species in the low hilly lands along the upper reaches of the Yangtze River Valley in China’s “Grain for Green” project. The objective of this study was to explore the edge effects of forest gap on the ecological stoichiometry of dominant tree species in a P. massoniana plantation forest.Methods We collected Cinnamomum longepaniculatum leaves in a 39-year-old P. massoniana plantation forest with seven forest gap sizes (G1: 100 m²; G2: 225 m²; G3: 400 m²; G4: 625 m²; G5: 900 m²; G6: 1 225 m²; G7: 1 600 m², and the control: closed canopy) located in Gao County, south Sichuan Province during different seasons. The contents of C, N and P in leaves were measured, and the effects of edges, seasons and their interaction on leaf C, N and P contents and C:N:P stoichiometry were evaluated.Important findings The leaf C content, C:N and C:P of C. longepaniculatum at the edge of forest gaps in different seasons were all significantly higher than those of understory plants in P. massoniana plantation. With increasing size of forest gaps, leaf C content and C:N ratio, C:P and N:P of C. longepaniculatum increased initially and then decreased with the maximum at medium size (400-900 m²). From spring to winter, leaf N and P contents of C. longepaniculatum increased after an obvious decrease; and the C:N and C:P increased first but then decreased. However, the inflection point all appeared in the summer. The nutrient utilization of C. longepaniculatum at the edge of forest gaps was more efficient in summer and autumn than in spring and winter, indicating significant edge effects. The results of principal component analysis (PCA) suggested that gap size, relative light intensity and monthly average air temperature were the main environmental factors affecting the stoichiometry of C. longepaniculatum at the different edge of forest gaps in the P. massoniana plantation. These results indicated that forest gap with size 625 m² had the highest organic matter storage and nutrient utilization efficiency in the edge areas in all seasons, and therefore had the most significant edge effect on leaf element stoichiometry.     

Effects of grazing intensity and grazing exclusion on litter decomposition in the temperate steppe of Nei Mongol,China

Aims Grazing intensity and grazing exclusion affect ecosystem carbon cycling by changing the plant community and soil micro-environment in grassland ecosystems. The aims of this study were: 1) to determine the effects of grazing intensity and grazing exclusion on litter decomposition in the temperate grasslands of Nei Mongol; 2) to compare the difference between above-ground and below-ground litter decomposition; 3) to identify the effects of precipitation on litter production and decomposition. Methods We measured litter production, quality, decomposition rates and soil nutrient contents during the growing season in 2011 and 2012 in four plots, i.e. light grazing, heavy grazing, light grazing exclusion and heavy grazing exclusion. Quadrate surveys and litter bags were used to measure litter production and decomposition rates. All data were analyzed with ANOVA and Pearson’s correlation procedures in SPSS. Important findings Litter production and decomposition rates differed greatly among four plots. During the two years of our study, above-ground litter production and decomposition in heavy-grazing plots were faster than those in light-grazing plots. In the dry year, below-ground litter production and decomposition in light-grazing plots were faster than those in heavy-grazing plots, which is opposite to the findings in the wet year. Short-term grazing exclusion could promote litter production, and the exclusion of light-grazing could increase litter decomposition and nutrient cycling. In contrast, heavy-grazing exclusion decreased litter decomposition. Thus, grazing exclusion is beneficial to the restoration of the light-grazing grasslands, and more human management measures are needed during the restoration of heavy-grazing grasslands. Precipitation increased litter production and decomposition, and below-ground litter was more vulnerable to the inter-annual change of precipitation than above-ground litter. Compared to the light-grazing grasslands, heavy-grazing grasslands had higher sensitivity to precipitation. The above-ground litter decomposition was strongly positively correlated with the litter N content (R² = 0.489, p < 0.01) and strongly negatively correlated with the soil total N content (R² = 0.450, p < 0.01), but it was not significantly correlated with C:N and lignin:N. Below-ground litter decomposition was negatively correlated with the litter C (R² = 0.263, p < 0.01), C:N (R² = 0.349, p < 0.01) and cellulose content (R² = 0.460, p < 0.01). Our results will provide a theoretical basis for ecosystem restoration and the research of carbon cycling.     

Sap flow characteristics and its responses to precipitation in Robinia pseudoacacia and Platycladus orientalis plantations

Aim In the loess hilly region, drought stress frequently occurs during the late spring and early summer as a result of insufficient water supply and asynchronous changes between temperature and precipitation. Our objective was to quantify the characteristics of water-consumption through transpirations and their responses to precipitation in the dominant plantations in this region. Methods Thermal dissipation probe (TDP) was used to measure the sap flow density (F_d) of Robinia pseudoacacia and Platycladus orientalis from April through October in 2009 in Ansai National Ecological Experimental Station. Environmental variables, including meteorological factors and soil water content, were simultaneously measured. Important findings The diurnal variation of F_d exhibited a single-peak curve during the growing season of R. pseudoacacia and P. orientalis. The maximum F_d was three times greater in R. pseudoacacia (0.12068 m³·m^-2·h^-1) than that in P. orientalis (0.03737 m³·m^-2·h^-1). Except in the rapid-growth season (July to August), the F_d of these two species during the post-precipitation period were significantly higher than that during the pre-precipitation period. The F_d of P. orientalis and R. pseudoacacia was well fitted with transpiration (VT), an integrated index calculated from both vapor pressure deficit (VPD) and solar radiation (R_s), using an exponential saturation function. Generally, F_d increased in response to rising VT, while these values tended to be stable when VT reached about 50 kPa (W·m^-2)^1/2. Furthermore, R. pseudoacacia showed more sensitive to precipitation (p < 0.001) than P. orientalis, according to different hydraulic conductance model coefficients (fitting parameter b) between pre- and post-precipitation periods. Therefore, R. pseudoacacia could be considered as a precipitation-sensitive species, while P. orientalisasa precipitation-insensitive species. Through analyzing the different responses of plantation species to precipitation in the loess hilly region, this study provides a scientific basis for the local plantation management from the aspect of tree water use during ecological restoration.