包含哑变量的黑龙江省落叶松人工林碳储量预测模型系统

利用2005—2010年两期黑龙江省落叶松人工林固定监测样地数据,分析黑龙江省落叶松人工林各林分变量因子之间的关系,建立地位级指数曲线模型和林分密度指数模型,采用两步最小二乘的方法建立预测包含林分平均断面积和林分碳储量的联立方程组,将以上所构建的模型统称为黑龙江省落叶松人工林碳储量预测模型系统.同时将龄组和区域作为哑变量加入到预测模型中.结果表明: 模型系统中除地位级指数曲线模型之外,剩余模型的确定系数(R²)均大于0.98,均方根误差均小于4,而加入哑变量的模型R²有所增加,均方根误差均小于3,说明模型稳定性较好,预估参数较为精确.各模型的平均相对误差均小于2%,大部分模型平均相对误差绝对值小于15%,模型精度均在95%以上,研究结果可以对黑龙江省不同区域和龄组的落叶松人工林林分平均树高进行精确拟合.根据地位级指数曲线模型和联立方程组的拟合参数进行分析,当调查样地在同一区域时,林分年龄越大,林分平均树高、林分平均断面积和林分碳储量越大,符合实际生长规律;而在林分年龄相同、区域不同时,不同区域林分平均树高由大到小的排列顺序为: 平原地区、小兴安岭南坡地区、张广才岭东坡地区、完达山地区、张广才岭西坡地区、小兴安岭北坡地区.不同区域林分断面积和林分碳储量由多到少的排列顺序为: 张广才岭东坡地区、小兴安岭北坡地区、张广才岭西坡地区、小兴安岭南坡地区、完达山地区、平原地区.     

长白落叶松林生物量的模拟估测

利用样木收获法收集了34个样地中长白落叶松林分地上部分生物量信息,选取其中29个样地生物量信息分别与样地林分因子信息和TM遥感影像信息拟合建立生物量模型,利用其余5个样地的生物量信息进行模型精度检验和误差分析.结果表明：长白落叶松地上部分生物量均可用林分因子和遥感因子进行线性拟合;林分因子线性模型对长白落叶松中幼林地上生物量的估测精度较高（林分P=94.33%,遥感P=92.32%）,且检验误差较小（林分MRE=6%,遥感MRE=31%）,模型模拟效果较好;若只考虑长白落叶松中龄林,这2种模型的估测效果相当（林分模型和遥感模型的误差分别为329.9和313.6 t）.整体而言,林分因子模型估测长白落叶松树皮、干材和总生物量的效果优于遥感因子模型,对于中龄林来说,遥感模型估测叶花果、树枝和树冠生物量的效果较好.     

基于枝条木材密度分级的鼎湖山南亚热带常绿阔叶林树高曲线模型

如何便捷准确地测量树高一直是林学及群落生态学所关心的问题。由于木材密度与树木生长密切相关, 因此基于木材密度建立树高曲线模型能够为测量树高提供新的方法。本文以鼎湖山南亚热带常绿阔叶林1.44 ha塔吊样地内119个物种的4,032个个体为研究对象, 利用树高、胸径和木材密度数据来探究基于枝条木材密度分级的树高曲线模型。首先, 对个体进行随机抽样, 将其划分为建模样本(占总样本量的70%)和检验样本(占总样本量的30%), 并通过聚类分析将所有个体的木材密度划分为4级。其次, 基于建模样本利用常见的5种理论生长方程(Richards、Korf、Logistic、Gompertz和Weibull方程)对不同分级建立树高-胸径模型; 基于检验样本检验模型精度, 并确定各分级的最适模型。最后, 构建基于物种分类的树高曲线模型, 并比较其与木材密度分级模型的差异。结果表明: 基于木材密度分级的模型, 各分级小组检验样本的平均绝对误差(MAE)和均方根误差(RMSE)最小值所对应的模型类型与建模样本结果一致, 确定Gompertz模型和Weibull模型为鼎湖山南亚热带常绿阔叶林最适树高模型类型。比较基于木材密度分级的模型与基于物种分类的模型, 发现二者的MAE、RMSE指数差异小。综上, 基于木材密度分级的树高曲线模型对树高估测精度高, 使用方便, 为树高预测提供了新方法, 可以更好服务森林调查等生产实践。     

长白山主要针叶树种胸径和树高变异系数与竞争因子的关系

选取长白山地区8个皆伐标准地的1139株标准木,利用变异系数(CV)分析了云杉、冷杉和红松3个主要针叶树种的胸径和树高随龄阶、树高随径阶的变化.结果表明：在研究地区,3个主要针叶树种树高随径阶的变异相对较小,胸径和树高随龄阶的变异较大,胸径随龄阶的变异系数最大.在天然林中,传统的以径阶为自变量的树高曲线能较好地反映实际生长,以龄阶为自变量的胸径和树高生长模型对实际生长程度的反映较差.竞争是引起树高和胸径变动的主要因子.若将竞争因子添加到传统的生长方程和树高曲线中,可以大幅度提高模型的精度.     

基于混合效应的兴安落叶松树高与胸径关系模拟

以黑龙江省带岭林业局大青川林场和永翠林场的兴安落叶松人工林为研究对象,基于41块样地调查数据和Richards模型,构建了含有林分变量的树高与胸径关系模型。利用混合效应模型方法拟合常规Richards模型yij=(β1+bi1)(1-e-(β2+bi2)xij)(β3+bi3)+εij和含有林分变量的模型yij=(β1+bi1)(Dq)(β2+bi2)(1-e-(β3+bi3)(N(β4+bi4))xij)+εij。结果表明:当对Richards混合效应模型拟合时,引入随机参数b1、b2时模型拟合最好;当对含有林分变量的Richards混合效应模型拟合时,引入随机参数b2、b4时模型拟合最好。模型检验表明:当随机抽取独立样本时,混合模型误差小于固定效应模型。如果随机抽取4个样本校正时,混合模型的误差和均方根误差降低71.8%和42.1%。     

灰色模型应用于估测落叶松林分生长量的研究

本文用灰色GM（1,1）模型对林分平均生长过程进行模拟,给出落叶松人工林林分在树高、胸径、材积3个指标上平均总生长量的灰色模型,经过检验,估测效果好．     

落叶松人工林树干形状模型和可变参数

对以往树木干形的一系列可变参数削度方程进行比较,根据模型拟合统计量(残差平方和及相关指数),选出其中对落叶松干形拟合效果较好(残差平方和较小、相关指数较高)的模型,并根据模型中可变参数的意义提出了5种描述干形的指数.结果表明:Lee等提出的削度方程的拟合效果较好,可以用来描述落叶松人工林的树干形状;5种描述干形的指数分别为根部梢头削度率、影响点、圆柱体和抛物线体范围值、最小可变参数、最小可变参数所在的相对高度,这些指数可以作为比较干形的方法和工具.较大密度(870株.hm-2)和较小密度林分(275株.hm-2)的林木干形质量都较差,只有适中密度林分(487株.hm-2)的落叶松干形质量较好.     

马尾松人工林Sloboda多形地位指数模型的研究

将德国生物统计学家Sloboda B的树高生长模型应用于马尾松人工林优势高生长模型模拟中。结果表明,用Sloboda树高生长方程拟合马尾松人工林多形地位指数曲线能获得良好效果,且优于Richards多形地位指数曲线。     

人工长白落叶松树干边材、心材和树皮密度预测模型

基于黑龙江省林口林业局、东京城林业局和东北林业大学帽儿山实验林场的35株人工长白落叶松的解析样木数据,构建长白落叶松的边材、心材和树皮密度的Beta回归模型,采用赤池信息准则、决定系数、平均绝对偏差、均方根误差和似然比检验对模型的拟合优度进行比较评价,进而选取边材、心材和树皮密度的最优模型,最后采用刀切法对选择出的最优模型进行检验,评价模型预测能力。结果表明: 边材、心材和树皮密度的最优模型的自变量不完全相同,其中,边材密度与树木年龄、树高、相对高度和相对高度的平方关系较好,而心材密度最优模型的自变量为年生长量、相对高度和相对高度的平方,树皮密度最优模型的自变量为树木年龄、年生长量、相对高度和相对高度的平方。对最优模型分析可知,从树干基部到树梢,边材密度逐渐减小,心材密度先减小后增加,树皮密度先增加后减小。本研究所建立的Beta回归模型可以预估该研究区域的人工林内长白落叶松的边材、心材和树皮任意位置的木材密度,为树干平均密度和生物量的研究奠定基础。     

基于混合效应的大别山地区杉木树高-胸径模型比较

基于安徽省大别山区马鬃岭林场杉木人工林30块样地1087组数据,选用7个常用树高-胸径(H-D)模型(线性模型、Chapman-Richards模型、Logistic模型等),采用最小二乘法拟合并选出最优基础模型(式11,只含D变量的Chapman-Richards模型),然后基于该模型构建含林分变量优势木平均高度、密度的H-D模型(式12),同时考虑样地水平的随机效应,分别基于式11、12构建混合模型(式13、14),并用幂函数、指数函数消除误差异方差,利用决定系数(R²)、均方根误差(RMSE)、平均绝对误差(MAE)和平均相对误差绝对值(MAPE)等指标来评价模型的拟合与预测能力,最终获取最优树高预测模型.结果表明:含林分变量的模型的拟合精度(式12,R²=0.863、RMSE=1.381、MAE=0.971)优于基础模型(式11,R²=0.827、RMSE=1.554、MAE=0.101).对于误差方差,幂函数、指数函数均能较好地消除异方差,但幂函数相对最好.混合模型的拟合与预测能力均优于式11、12,但混合模型(式13、14)之间的拟合与预测精度相差不大.基于混合效应的H-D模型(式13)能够较好地描述不同林分间H-D关系的差异,实际运用中可选用该模型来预测杉木树高,具有较高的预测精度.     

Relative need for conservation assessments of vascular plant species among ecoregions

Aim (1) To determine the relative need for conservation assessments of vascular plant species among the world’s ecoregions given under‐assessed species distributions; (2) to evaluate the challenge posed by the lack of financial resources on species assessment efforts; and (3) to demonstrate the utility of nonlinear mixed‐effects models with both homoscedastic and heteroscedastic error structures in the identification of species‐rich ecoregions. Location Global. Methods We identified the world’s ecoregions that contain the highest vascular plant species richness after controlling for area using species–area relationship (SAR) models built within a mixed‐effects multi‐model framework. Using quantitative thresholds, ecoregions with the highest plant species richness, historical habitat loss and projected increase in human population density were deemed to be most in need of conservation assessments of plant species. We used generalized linear models to test if countries that overlap with highly important ecoregions are poorer compared with others. Results We classed ecoregions into nine categories based on the relative need for conservation assessments of vascular plant species. Ecoregions of highest relative need are found mostly in the tropics, particularly Southeast Asia, Central America, Tropical Andes and the Cerrado of South America, and the East African montane region and its surrounding areas. Countries overlapping with ecoregions deemed important for conservation assessments are poorer as measured by their capita gross national income than the other countries. The nonlinear mixed modelling framework was effective in reducing residual spatial autocorrelation compared with nonlinear models comprised of only fixed effects. In contrasting multiple SAR models to identify species‐rich ecoregions, there was not one SAR model that fitted best across all biomes. Not all SAR models displayed homoscedastic errors; therefore it is important to consider models with both homoscedastic and heteroscedastic error structures. Main conclusions We propose that conservation assessments should be conducted first in ecoregions with the greatest predicted species richness, historical habitat loss and future human population increase. As ecoregions deemed to be important for conservation assessments are located in the poorest countries, we urge international aid agencies and botanic gardens to cooperate with both local and international scientists to fund and implement conservation assessment programmes there.     

Incorporating natural variability in the bioassessment of stream condition in the Atlantic Forest biome,Brazil

Most bioassessment programs in Brazil face difficulties when scaling up from small spatial scales because larger scales usually encompass great environmental variability. Covariance of anthropogenic pressures with natural environmental gradients can be a confounding factor in the evaluation of biologic responses to anthropogenic pressures. The objective of this study was to develop a multimetric index (MMI) with macroinvertebrates for two stream types and two ecoregions in the Atlantic Forest biome in Rio de Janeiro state, Brazil. We hypothesized that by using two approaches – (1) testing and adjusting metrics to landscape parameters, and (2) selecting metrics using a cluster analysis to avoid metrics redundancy – the final MMI would perform better than the traditional approach (unadjusted metrics, one metric representing each category). Four MMIs were thus developed: MMI-1 – adjusted MMI with metrics selected after cluster analysis); MMI-2 – adjusted MMI with one metric from each category; MMI-3 – unadjusted MMI with metrics selected after cluster analysis; MMI-4 – unadjusted MMI with one metric from each category. We used three decision criteria to assess MMI’s performance: precision, responsiveness and sensitivity. In addition, we tested the MMI’s by using an independent set of sites to validate the results. Although all MMIs performed well in the three criteria, adjusting metrics to natural variation increased MMI response and sensitivity to impairment. In addition, the selected MMI-2 was able to classify sites of two stream types and two ecoregions. The use of cluster analysis, however, did not avoid high redundancy between metrics of different branches. The MMI-4 had the poorest performance among all tested MMIs and it was not able to distinguish adequately reference and impaired sites from both ecoregions. We present some considerations on the use of metrics and on the development of MMI’s in Brazil and elsewhere.     

Ant diversity partitioning across spatial scales: Ecological processes and implications for conserving Tropical Dry Forests

Several ecological and evolutionary processes can drive changes in diversity at different spatial scales. To determine the scale at which these processes are most influential, we hypothesized that (i) broad‐scale differences between ecoregions had greater influence on ant species richness and species turnover than local differences among fragments within ecoregions; and (ii) the degree of dissimilarity in ant species composition is larger between Tropical Dry Forest fragments and the surrounding vegetations than among Tropical Dry Forests located in different ecoregions, indicating that extant Tropical Dry Forests are relicts of a broader distribution of this vegetation. To examine ant diversity patterns, we built a nested hierarchical design on three spatial scales, ranging from fragments (local scale), Tropical Dry Forest + surroundings vegetation (landscape scale) and Brazilian ecoregions (regional scale). We used 450 sampling units (45 sampling units × two fragments × five ecoregions = 450). A null model based on the sample was used to identify variations in the random distribution across spatial scales. Spatial partitioning of ant diversity showed that observed β₁ diversity (between fragments) and β₂ diversity (among ecoregions) were higher than expected by chance. When the partitioning was analysed separately for each region, the observed β₁ diversity (Tropical Dry Forest and surrounding vegetation) was higher than expected by the null hypothesis in all ecoregions of Brazil. Based on species composition and diversity patterns, we stress the importance of creating more protected areas throughout the coverage area of Tropical Dry Forests, favouring a more efficient conservation process.     

基于Sentinel-1和Sentinel-2A的西小山林场平均树高估测

森林资源调查对于我国森林生态系统可持续发展具有重要意义,森林平均高度是森林资源调查的主要结构参数,也是获取难度最大的关键参数之一。为探究联合主被动遥感技术在估测森林平均高度方面的潜力,本研究以吉林省临江市西小山林场为研究区,利用Sentinel-1 SAR和Sentinel-2A数据,通过提取Sentinel-1的2个后向散射系数、8个纹理信息,以及Sentinel-2A的10个光谱波段及其纹理信息和11个植被指数,采用多元线性回归方法分别建立基于上述变量以及融合4类变量的5组平均树高估算模型,并评估各变量对反演精度的影响。结果表明: 单一数据源变量中,基于Sentinel-2A光谱波段提取的纹理信息建模效果较好,能够作为估算森林平均高度的有效数据;融合4类变量的森林平均高度估算模型最优,R²达0.56、留一交叉验证均方根误差为2.92 m、相对留一交叉验证均方根误差为21.5%。基于Sentinel-1与Sentinel-2A特征变量的平均树高模型能够提高森林高度的估算精度,可用于区域森林平均高度估测和制图。     

人工神经网络与相容性生物量模型预测单木地上生物量的比较

森林生物量是林业生产经营和森林资源监测的重要指标,为探索高效低偏的单木生物量估测方法,引入人工神经网络.本研究采用黑龙江省东折棱河林场的101株长白落叶松地上生物量数据,基于不同变量(胸径、树高、冠幅)组合建立了4个聚合模型体系(AMS),采用加权回归消除模型的异方差.然后,基于最优的变量组合建立人工神经网络(ANN)...     

Land Cover Change in Colombia: Surprising Forest Recovery Trends between 2001 and 2010

Background

Monitoring land change at multiple spatial scales is essential for identifying hotspots of change, and for developing and implementing policies for conserving biodiversity and habitats. In the high diversity country of Colombia, these types of analyses are difficult because there is no consistent wall-to-wall, multi-temporal dataset for land-use and land-cover change.

Methodology/Principal Findings

To address this problem, we mapped annual land-use and land-cover from 2001 to 2010 in Colombia using MODIS (250 m) products coupled with reference data from high spatial resolution imagery (QuickBird) in Google Earth. We used QuickBird imagery to visually interpret percent cover of eight land cover classes used for classifier training and accuracy assessment. Based on these maps we evaluated land cover change at four spatial scales country, biome, ecoregion, and municipality. Of the 1,117 municipalities, 820 had a net gain in woody vegetation (28,092 km²) while 264 had a net loss (11,129 km²), which resulted in a net gain of 16,963 km² in woody vegetation at the national scale. Woody regrowth mainly occurred in areas previously classified as mixed woody/plantation rather than agriculture/herbaceous. The majority of this gain occurred in the Moist Forest biome, within the montane forest ecoregions, while the greatest loss of woody vegetation occurred in the Llanos and Apure-Villavicencio ecoregions.

Conclusions

The unexpected forest recovery trend, particularly in the Andes, provides an opportunity to expand current protected areas and to promote habitat connectivity. Furthermore, ecoregions with intense land conversion (e.g. Northern Andean Páramo) and ecoregions under-represented in the protected area network (e.g. Llanos, Apure-Villavicencio Dry forest, and Magdalena-Urabá Moist forest ecoregions) should be considered for new protected areas.     

Biogenic VOC emissions from forested Amazonian landscapes

A tethered balloon‐sampling platform was used to study biogenic volatile organic compounds (BVOCs) in the atmospheric boundary layer in three distinct moist tropical forest ecoregions, as well as an extensive pasture area, in Amazonia. Approximately 24–40 soundings, including as many as four VOC samples collected simultaneously at various altitudes, were made at each site. Concentrations in the mixed layer increased during morning hours and were relatively constant midday through afternoon. Since most important meteorological and chemical parameters were very similar among the sites during the measurement periods, a BVOC canopy emission model was used with a model of the chemistry of the boundary layer to reproduce the atmospheric concentrations observed. The simulations indicated significantly different midday landscape isoprene and α‐pinene emission rates for the three forest ecoregions (2200, 5300, 9800 μg m^?2 h^?1 isoprene and 90, 120, and 180 μg m^?2 h^?1α‐pinene for the three moist forest ecoregions studied, respectively). The differences in emissions among the ecoregions may be attributed to the species composition, which were markedly different and in which the percentage of isoprene and terpene emitting species also differed significantly.     

Comparative dynamics of avian communities across edges and interiors of North American ecoregions

Aim Based on a priori hypotheses, we developed predictions about how avian communities might differ at the edges vs. interiors of ecoregions. Specifically, we predicted lower species richness and greater local turnover and extinction probabilities for regional edges. We tested these predictions using North American Breeding Bird Survey (BBS) data across nine ecoregions over a 20‐year time period. Location Data from 2238 BBS routes within nine ecoregions of the United States were used. Methods The estimation methods used accounted for species detection probabilities < 1. Parameter estimates for species richness, local turnover and extinction probabilities were obtained using the program COMDYN. We examined the difference in community‐level parameters estimated from within exterior edges (the habitat interface between ecoregions), interior edges (the habitat interface between two bird conservation regions within the same ecoregion) and interior (habitat excluding interfaces). General linear models were constructed to examine sources of variation in community parameters for five ecoregions (containing all three habitat types) and all nine ecoregions (containing two habitat types). Results Analyses provided evidence that interior habitats and interior edges had on average higher bird species richness than exterior edges, providing some evidence of reduced species richness near habitat edges. Lower average extinction probabilities and turnover rates in interior habitats (five‐region analysis) provided some support for our predictions about these quantities. However, analyses directed at all three response variables, i.e. species richness, local turnover, and local extinction probability, provided evidence of an interaction between habitat and region, indicating that the relationships did not hold in all regions. Main conclusions The overall predictions of lower species richness, higher local turnover and extinction probabilities in regional edge habitats, as opposed to interior habitats, were generally supported. However, these predicted tendencies did not hold in all regions.     

基于经验模型和机理模型的杉木人工林生物量估测对比研究

为了揭示间伐干扰下杉木人工林生物量的变化规律,研究利用江西省吉水县石阳林场的36块杉木人工林样地的实测数据和研究区气候数据,通过基于经验的引入地位指数(SI)的生物量生长方程组和基于机理的3-PG模型,模拟并预估林分生物量,分析在间伐和非间伐的情况下,不同立地的林分其生物量0-50a的变化。结果表明:(1)构建了生物量生长方程组,并在参数a、b、c中引入地位指数SI,发现改进后的模型对于基础模型拟合精度更高,且对数似然比检验表明,改进效果显著(P<0.05)。(2)通过对3-PG模型预测精度验证发现,预估值和实测值之间有很高的一致性,各因子的决定系数(R²)在0.65-0.96之间,其中,胸径和树高的R²均高于0.92;各因子的平均相对误差(MRE)不超过26%。(3)通过比较经验模型和机理模型的生物量预测发现,经验模型的预测误差MRE为16.50%,机理模型为23.52%,经验模型预估精度更高。进一步对未来预测对比分析表明,机理模型预估值高于经验模型。(4)两个模型模拟的杉木人工林生物量规律一致,即随着林龄的增加,杉木人工林林分总生物量均表现出先快速增加,后逐渐平稳的趋势;并且间伐不会改变这种趋势,但间伐林分在间伐后的生物量生长速率高于无间伐林分。此外,由于SI对经验模型影响显著,改进模型拟合效果更好,更具有生态学意义。参数化后的3-PG模型模预估精度较高,能够为江西杉木人工林生长规律研究提供依据。虽然经验模型和机理模型在对研究区杉木人工林生物量的预估上均具有较好的表现,但各具特点和局限性。经验模型参数较易获得,且经验模型预测生物量、林分胸高断面积和林分平均树高的R²、MRE均优于机理模型;但模型对于建模数据内的评价效果较好,对于建模数据外的应用具有局限性,即经验模型更适合模拟生长期间的某一阶段的林分生物量。机理模型虽然需要的参数较多,但是考虑了生态学原理,弥补了经验模型的不足,可较好解释和模拟环境因子对树木生长的影响,对校正数据之外生长阶段的林分生物量预测更有优势。