Models for prediction of mortality in even‐aged forest

Models for predicting mortality in even‐aged stands were developed. The models rely on data from the Norwegian National Forest Inventory, and were designed for use in large‐scale forestry scenario models. A two‐step modelling strategy was applied: (1) logistic regression models predicting the probability of complete survival occurring,” and (2) multiplicative regression models for stem number reduction and diameter calibration. A joint model for all species predicting the probability of survival occurring on a plot was developed. Separate models for forests dominated by spruce, pine and broadleaved trees were developed for stem number reduction, while no appropriate models for diameter calibration were found. The phenomenon mortality is a stochastic, rare and irregular event, and this was reflected as low R ² in the models. However, the model performance appeared logical and the results of validations based on independent data were reasonably good, i.e. the presented models may be applied to large‐scale forestry scenario analyses. With new rotations of permanent sample plot measurements, the models should be evaluated and, if necessary, revised.     

兼性互利关系影响地表蚂蚁群落多样性的特点

[目的]为探讨兼性互利关系在生物多样性保护方面的作用,[方法]于2015年5月和10月,采用陷阱法对云南墨江县雅邑镇4种类型样地地表蚂蚁群落进行调查。[结果]显示:(1)物种组成及多度:纯砂仁地(样地I)采集蚂蚁400头,23种;从未放养紫胶虫的紫胶-砂仁混农林样地(样地II)采集蚂蚁382头,15种;曾经放养过紫胶虫的紫胶-砂仁混农林样地(样地III)采集蚂蚁406头,18种;放养紫胶虫的紫胶-砂仁混农林样地(样地IV)采集蚂蚁655头,23种。(2)多样性:4种类型样地地表蚂蚁群落物种丰富度和相对多度存在显著差异,兼性互利关系可以提高地表蚂蚁的物种丰富度和相对多度,物种丰富度大小为IVIIIIII,相对多度排序为IIIIVIII。(3)群落结构相似性:样地III、IV和样地II、I不相似。(4)指示物种:样地I指示物种为毛发铺道蚁,样地II指示物种为卡泼林大头蚁和茸毛铺道蚁,样地III、IV中指示物种为巴瑞弓背蚁。[结论]互利关系对蚂蚁群落的物种丰富度、相对多度和群落结构具有明显的积极影响,并且这种兼性互利关系对生物多样性保护有一定的时空效应。     

Assessment of bias due to random measurement errors in stem volume growth estimation by the Swedish National Forest Inventory

Abstract

We evaluated the performance of two methods for estimating stem volume increment at individual tree level with respect to bias due to random measurement errors. Here, growth is either predicted as the difference between two consecutive volume estimates where single-tree volume functions are applied to data from repeated measurements or by a regression model that is applied to data from a single survey and includes radial increment. In national forest inventories (NFIs), the first method is typically used for permanent plots, the second for temporary plots. The Swedish NFI combines estimates from both plot types to assess growth at national and regional scales and it is, therefore, important that the two methods provide similar results. The accuracy of these estimates is affected by random measurement errors in the independent variables, which may lead to systematic errors in predicted variables due to model non-linearity. Using Taylor series expansion and empirical data from the Swedish NFI we compared the expected bias in stem volume growth estimates for different diameter classes of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). Our results indicate that both methods are fairly insensitive to random measurement errors of the size that occur in the Swedish NFI. The empirical comparison between the two methods showed greater differences for large diameter trees of both pine and spruce. A likely explanation is that the regressions are uncertain because few large trees were available for developing the models.     

Determination of number of stems in coniferous forest stands by means of aerial photo‐interpretation

Two models for determination of the number of stems per hectare in forest stands (N) from attributes derived by aerial photo‐interpretation were developed. The models relied on the assumption that N could be determined by dividing the total stand volume per hectare with the volume of the “average tree”; defined by stand mean height and the diameter corresponding to mean basal area of a stand. Input variables of the models were stand mean height, crown closure and site quality. Additionally, model II required input of average stand volume per hectare and average mean diameter derived from stratified field sample plot inventories. Material for 143 coniferous stands was used for the testing of the models. The stands were recorded by intensive field measurements. Aerial photographs at the approximate scale of 1:15 000 were used for photo‐interpretation. The N value was underestimated in model I by 5.4–47.0%. The standard deviation for the differences was 15.2–26.2% for mature stands and 41.4–44.2% for young thinning phase stands. For model II, the mean difference between the predicted and observed N value was in the range ‐16.1% to 12.2%.     

Differences in nitrogen economy of temperate trees

Twenty-four temperate tree species were classified into three groups based on cluster analysis of relative growth rate, nitrogen concentration, nitrogen-production efficiency, nitrogen-distribution ratio and nitrogen-use efficiency as follows: Group I (Asteridae and Rosidae), Group II (Dilleniidae and Hamamelidae) and Group III (Coniferopsidae). Relative growth rate (RGR) was high in Group II, moderate in Group I and low in Group III. The regression coefficient for the relationship between RGR and leaf nitrogen concentration was higher in Group II than in Group I, and no relationship was observed in Group III. Parameter analysis of RGR indicated that RGR per unit leaf nitrogen was important for all three groups, but that the allocation of nitrogen to leaves was particularly important in Groups I and II. The ratio of dark respiratory rate (R) to net photosynthetic rate (A) was higher in Group I than in Group II. Neither A nor R was measured in the Group III species. A linear relationship was observed between leaf nitrogen concentration and A in Group II, but this relationship was not evident in Group I.     

限制性内切酶在PCR缓冲液中酶切活性的研究(英文)

选取6 种常用的限制性内切酶(Acc I、Ban II、EcoR I、Hind III、Sac I、Sca I)用于检测其直接在PCR 缓冲液中完全酶切DNA 的活性。结果表明:在PCR 缓冲液中,需要加入MgCl2作为激活因子的前提条件下,除了EcoR I 表现出星状活性外,其他酶均能完全酶切无甲基化λDNA,其结果与在酶生产厂商推荐的反应缓冲液中进行的酶切反应结果一致;在完成PCR 扩增的PCR 缓冲液中,所有酶(包括RcoR I)均能功能正常地对PCR 产物完全酶切,表明可在PCR 扩增完毕后的反应体系中直接加入限制性内切酶进行酶切反应,无需对PCR 产物进行预先纯化;镁离子浓度在2.5 mmol·L-1至10 mmol·L-1范围内对PCR 缓冲液中的限制性内切酶的活性无显著影响。上述方法可用于限制性片段长度多态(RFLP)以及单链构像多态(SSCP)研究中,但能否用于分子克隆有待进一步实验。图3 表1 参7。     

Using near-infrared hyperspectral images on subalpine fir board. Part 1: Moisture content estimation

Abstract

In this study, moisture content (MC) images of subalpine fir (abies lasiocarpa Hook) boards were derived from near-infrared hyperspectral images in the 947–1637 nm range. One hundred and seven cubic samples with the size of 4 cm were prepared from 14 boards. All samples were dried to various MCs during several steps until being completely dried. Hyperspectral images and weight measurements were acquired over each sample at each drying step. The samples have MC ranging from 1% to 137% (dry basis). The images were first calibrated into reflectance. Then, bad pixels were found and replaced by a corrected value using a median filter. A modified version of the boxplot method was used to find abnormal spectra that were then removed. The remaining spectra were converted into absorbance spectra. They were then split into a calibration and a validation data-set according to the boards they were extracted from to build and validate a partial least squares (PLS) regression model between the near-infrared absorbance spectra and the measured MCs. The PLS model was applied first to the sample images, then to the whole board images in order to produce 2D images of MC.     

A comparative study of the use of laser scanner data and field measurements in the prediction of crown height in boreal forests

Abstract

This study compares the results of the prediction of crown height characteristics using airborne laser scanner (ALS) data and intensive field measurements in boreal forests. The data consisted of 31 sample plots located in Kalkkinen, southern Finland. Crown height models were constructed at both the tree and plot level. Scots pine, Norway spruce and birches were used. The models included independent variables of tree levels, such as tree height, crown area and independent plot-level variables, i.e. canopy height and density quantiles and proportion of vegetation hits. Field measurement-based models used tree height and diameter at breast height as the independent tree-level variables, whereas basal area, mean diameter and height were used as the plot-level variables. The results indicated that the ALS-based crown height models were more accurate than the field measurement-based models when plot-level information was used as independent variables. However, the field measurement-based tree-level models for Scots pine and Norway spruce were more accurate than the ALS-based models. Even so, the accuracy of the different models was very similar and the study data set was quite small. The results of this study can be used for different tree growth studies and for the assessment of tree stock quality in boreal forests.     

Effect of cluster configuration and auxiliary variables on the efficiency of local pivotal method for national forest inventory

ABSTRACT

Planning a forest inventory comprises making decisions related to the sampling strategy: cluster configuration, sample size and sample allocation within the survey area. Cluster configuration includes deciding on the number of sample plots within the cluster and distances between them. Available resources set the limit for field work in terms of man-days. If the time consumption for measurements is known, the sample size can be determined under the constraint. In this study, we simulated the second phase of inventory sampling with fixed time resources by replicating sample selection with a spatially balanced sampling utilizing local pivotal method (LPM) for different cluster configurations to find the most efficient. As a result, the temporary cluster configuration was changed from 9 to 5-sample plot configuration in a pilot inventory. Further, the sample selection was performed with LPM having total growing stock volume and broadleaf volume proportion as auxiliary information. The pilot results were aligned with the time series in respect to forest area and total growing stock volume, but in tree species groups deviations were observed in growing stock volume. A more comprehensive optimization should include the travelling routes, the plot-to-plot distances and the plot design. In any case, the result is region specific.     

Estimation of taper curve using stand variables and sample tree measurements

The aim of the study was to develop methods for estimating the taper curves for trees tallied in a forest inventory. The average stem form in a stand was described by the principal components of the stand effects in the stem dimensions measured in the polar coordinate system. Measurements of diameter at breast height, diameter at a height of 6 meters, and height taken from trees on the sample tree plots were used for determining the first four principal components. Regression models were derived to predict the principal components from the site and growing stock variables. These models were used to estimate the taper curves of the tallied trees. Use of the principal components estimated by the regression models gave less reliable results than use of the principal component estimates based on measurement of the height of one randomly chosen tree on the sample plot. The best result was found with combined use of the principal component estimates and one height measurement per sample plot.     

Regional mixed-effects height–diameter models for loblolly pine (<Emphasis Type="Italic">Pinus taeda</Emphasis> L.) plantations

A height–diameter mixed-effects model was developed for loblolly pine (Pinus taeda L.) plantations in the southeastern US. Data were obtained from a region-wide thinning study established by the Loblolly Pine Growth and Yield Research Cooperative at Virginia Tech. The height–diameter model was based on an allometric function, which was linearized to include both fixed- and random-effects parameters. A test of regional-specific fixed-effects parameters indicated that separate equations were needed to estimate total tree heights in the Piedmont and Coastal Plain physiographic regions. The effect of sample size on the ability to estimate random-effects parameters in a new plot was analyzed. For both regions, an increase in the number of sample trees decreased the bias when the equation was applied to independent data. This investigation showed that the use of a calibrated response using one sample tree per plot makes the inclusion of additional predictor variables (e.g., stand density) unnecessary. A numerical example demonstrates the methodology used to predict random effects parameters, and thus, to estimate plot specific height–diameter relationships.     

Simultaneous Estimations of Forest Parameters using Aerial Photograph Interpreted Data and the k Nearest Neighbour Method

Information about the state of the forest is of vital importance in forest management planning. To enable high-precision modelling, many forest planning systems demand input data at the single-tree level. The conventional strategy for collecting such data is a plot-wise field inventory. This is expensive and, thus, cost-efficient alternatives are of interest. During recent years, the focus has been on remote sensing techniques. The k nearest neighbour (kNN) estimation method is a way to assign plot-wise data to all stands in a forest area, using remotely sensed data in connection with a sparse sample of field reference plots. Plot-wise aerial photograph interpretations combined with information from a stand register were used in this study. Nearness to a reference plot was decided upon using a regression transform distance. Standing stem volume was estimated with a relative root mean square error (RMSE) equal to 20% at the stand level, while age could be estimated with a RMSE equal to 15%. A cost-efficient data-capturing strategy could be to assign plot data with the presented kNN method to some types of forest, while using traditional field inventories in other, more valuable, stands.     

Genetic Correlations Among Wood,Growth Capacity and Stem Traits in Pinus sylvestris

Wood density and tracheid length are two traits that significantly affect wood products. Genetic correlations were estimated to evaluate the effect on these traits from a selection for traits included in the Swedish Pinus sylvestris L. breeding programme. Measurements from a non-contiguous single-tree plot progeny trial with controlled matings between 30 parent trees was used. Heritabilities were high for the wood traits, intermediate for the growth capacity traits and low for the stem traits, with the exception of branch angle. Wood density showed no or non-significant negative genetic correlations with the growth capacity traits and a positive correlation with relative branch diameter. Tracheid length showed positive genetic correlations with height and a positive correlation with relative branch diameter. A selection that increased height growth at the age of 13 yrs by 10% was expected to decrease mean wood density at 33 yrs by 1%. The expected correlated response to tracheid length from the same selection was a 3% increase in the juvenile and mature wood.     

寡核苷酸管芯片技术检测和鉴别我国不同组植原体

[目的]不同组植原体检测和鉴别的特异性探针已有报道,为了筛选出适合于我国不同组植原体检测和鉴别的特异性探针,建立管芯片检测和鉴别植原体技术,并对我国发生的疑似植原体病害进行鉴别。[方法]通过PCR扩增结合管芯片杂交技术,对收集到的15种植原体侵染的植物样品及其健康对照进行检测和鉴别。[结果]建立了管芯片检测和鉴别植原体技术体系。15种病害样品中,13种获得显著的阳性杂交信号,并且所有的健康对照都呈现为阴性。13种植原体病害依16Sr DNA直接测序可分为16SrⅠ、Ⅱ、Ⅴ、XIX四组植原体。在所有探针中,植原体的通用探针(Pp-502)可以检测到所有确定的植原体样品。16SrⅠ组特异性探针(PpⅠ-465)可以确定16SrⅠ组的泡桐丛枝、苦楝丛枝、桑树萎缩和莴苣黄化4种植原体样品。16Sr II组特异性探针(PpⅡ-629)仅可以确定16Sr II组的花生丛枝、甘薯丛枝和臭矢菜丛枝3种植原体样品。但16Sr V组的枣疯病、樱桃致死黄化和重阳木丛枝及16Sr XIX组的板栗黄化皱缩植原体与其他组专化性探针皆有明显的交叉杂交信号。相比于PCR扩增的凝胶电泳检测,管芯片检测的灵敏度提高了1 000倍。对疑似植原体病害的诊断结果显示河南濮阳的红花槐丛枝的病原应为16Sr V组植原体,福建福州的长春花黄化丛枝应为16SrⅠ组植原体;而北京戒台寺牡丹黄化皱叶和内蒙古包头柳树丛枝未出现任何植原体专化的杂交信号。[结论]管芯片杂交技术作为一种检测和鉴别植原体的方法,可应用于我国植原体病害调查和诊断,并为植原体的鉴别和分类提供可靠的依据。     

Estimating Stem Volume and Basal Area in Forest Compartments by Combining Satellite Image Data with Field Data

The weighted k - nearest neighbour (kNN) method was used for estimating stem volume (m3 ha?¹) and basal area (m2 ha?¹) on a compartment level (average 19 ha) by combining satellite image data with measurements from Swedish National Forest (NFI) inventory plots. In the kNN method each estimation location (target plot) is assigned a value that is an average, which is weighted, of the attribute data from the k closest reference plots (NFI plots). The distance between target and reference plot was measured on different scales, which were transforms of spectral values and/or ancillary data. The standard error (assuming bias with no trend) of stem volume estimates in the compartments was 36% using only spectral data. This estimation accuracy improved to 17% if site index, age of the forest and mean tree height (ancillary data) were known for the compartments. Low volumes were overestimated and high volumes underestimated. This bias was reduced if ancillary data were added but was also dependent on the transform of the original scales.     

A generalized nonlinear mixed-effects height–diameter model for Eucalyptus globulus L. in northwestern Spain

A generalized height–diameter model was developed for Eucalyptus globulus Labill. stands in Galicia (northwestern Spain). The study involved a variety of pure stands ranging from even-aged to uneven-aged. Data were obtained from permanent circular sample plots in which trees were sampled within different radii according to their diameter at breast height. A combination of weighted regression, to take into account the unequal selection probabilities of such an inventory design, and mixed model techniques, to accommodate local random fluctuations in the height–diameter relationship, were applied to estimate fixed and random parameters for several models reported in the relevant literature. The models that provided the best results included dominant height and dominant diameter as fixed effects. These models explained more than 83% of the observed variability, with mean errors of less than 2.5 m. Random parameters for particular plots were estimated with different tree selection options. Height–diameter relationships tailored to individual plots can be obtained by calibration of the height measurements of the three smallest trees in a plot. An independent dataset was used to test the performance of the model with data not used in the fitting process, and to demonstrate the advantages of calibrating the mixed-effects model.     

Using near-infrared hyperspectral images on subalpine fir board. Part 2: Density and basic specific gravity estimation

Abstract

Wood density (ρ_MC) and basic specific gravity (BSG) are important properties in several forest products manufacturing processes. In this study, near-infrared hyperspectral images were tested to produce two-dimensional (2D) ρ_MC and BSG images of subalpine fir (Abies lasiocarpa Hook) board. A total of 107 cubic samples with the size of 4 cm were prepared from 14 boards. All samples were dried to various moisture contents (MCs) during several steps until being completely dried. The resulting MCs ranged from 1% to 137% (dry basis). After the last drying step, the samples were soaked in water to determine BSG. Hyperspectral images and weight measurements were acquired over each sample at each drying step. ρ_MC was also estimated at each MC level. Partial least squares (PLS) models were developed for estimating both ρ_MC and BSG from the near-infrared hyperspectral imaging (NIR-HSI) system absorbance spectra acquired over all the samples during each drying step. The ρ_MC model provides a reasonable accuracy with the validation data-set (R² = 0.81, RMSE = 39 kg/m³, and RPD = 2.3). For BSG, only models built with samples having MC of less than 12% are significant. The calibration data-set provides similar accuracy as the ρ_MC model (RMSE = 0.004, R² = 0.82, and RPD = 2.28), but the accuracy is lower with the validation data-set (RMSE = 0.007, R² = 0.53, and RPD = 1.39). Our data-set has BSG values varying only from 0.326 to 0.374, and further work is needed to apply these methods to a data-set that includes a more extended range of BSG variations for improving estimation accuracy.     

基于空间结构的杉木树冠生长可视化模拟

[目的]定量研究杉木林分中不同空间结构对林木冠形生长变化的影响,实现基于空间结构的杉木树冠生长可视化模拟。[方法]以湖南省攸县黄丰桥国有林场为试验区,在林分中选择并划分空间结构单元,进行冠形和空间结构数据调查,将林木东南西北4个方向的活枝下高、冠高、冠幅作为林木冠形描述因子,将周围木影响距离、相对树高定义为水平空间结构参数Ph和垂直空间结构参数Pv,使用多元逐步回归的分析方法,分析冠形描述因子与年龄、水平、垂直空间结构参数的关系,建立不同空间结构下杉木冠形的生长变化模型。基于B样条曲线模拟杉木冠形,构建不同生长阶段的三维杉木模型,结合三维动态渲染技术,实现杉木树冠生长的可视化模拟。[结果]通过对年龄和水平、垂直空间结构参数的逐步回归分析,结果表明:林木活枝下高、冠高和年龄、垂直空间结构参数呈现显著相关关系,模型决定系数R~2分别为0.754和0.813;林木各向冠幅和年龄、水平空间结构参数呈现极显著相关关系,模型决定系数R~2为0.623。基于杉木树冠生长模型和三维动态渲染技术,实现了树冠在东南西北不同方向的生长可视化。[结论]通过划分空间结构单元的方法选择研究目标并进行数据调查,使用逐步回归的方法,分析杉木冠形数据和年龄与空间结构数据的关系,建立树冠各方向活枝下高、冠高、冠幅的生长模型,结合三维动态渲染技术,使用MOGRE三维渲染引擎作为工具,实现了基于空间结构的杉木树冠生长可视化模拟。