森林种群径阶转移模型中转移概率的估算方法

基于统计分析理论和微分方程理论,给出了森林种群径阶转移模型中估算转移概率的方法：第一种是在有两次样地观测数据,不考虑林分环境因子等因素的条件下估算转移概率;第二种是在已知林分环境因子条件下,不需要对样地有两次观测数据来估算转移概率.实例验证结果表明,两种估算转移概率的方法具有计算简单和实用性强的特点,对森林经营与管理有一定的理论指导和实际应用价值.     

森林种群径阶转移模型中转移概率的估算方法

基于统计分析理论和微分方程理论,给出了森林种群径阶转移模型中估算转移概率的方法：第一种是在有两次样地观测数据,不考虑林分环境因子等因素的条件下估算转移概率;第二种是在已知林分环境因子条件下,不需要对样地有两次观测数据来估算转移概率．实例验证结果表明,两种估算转移概率的方法具有计算简单和实用性强的特点,对森林经营与管理有一定的理论指导和实际应用价值．     

Minimizing Bias in Biomass Allometry: Model Selection and Log‐Transformation of Data

Nonlinear regression is increasingly used to develop allometric equations for forest biomass estimation (i.e., as opposed to the traditional approach of log‐transformation followed by linear regression). Most statistical software packages, however, assume additive errors by default, violating a key assumption of allometric theory and possibly producing spurious models. Here, we show that such models may bias stand‐level biomass estimates by up to 100 percent in young forests, and we present an alternative nonlinear fitting approach that conforms with allometric theory.     

Multiple sensitive estimation and optimal sample size allocation in the item sum technique

For surveys of sensitive issues in life sciences, statistical procedures can be used to reduce nonresponse and social desirability response bias. Both of these phenomena provoke nonsampling errors that are difficult to deal with and can seriously flaw the validity of the analyses. The item sum technique (IST) is a very recent indirect questioning method derived from the item count technique that seeks to procure more reliable responses on quantitative items than direct questioning while preserving respondents' anonymity. This article addresses two important questions concerning the IST: (i) its implementation when two or more sensitive variables are investigated and efficient estimates of their unknown population means are required; (ii) the determination of the optimal sample size to achieve minimum variance estimates. These aspects are of great relevance for survey practitioners engaged in sensitive research and, to the best of our knowledge, were not studied so far. In this article, theoretical results for multiple estimation and optimal allocation are obtained under a generic sampling design and then particularized to simple random sampling and stratified sampling designs. Theoretical considerations are integrated with a number of simulation studies based on data from two real surveys and conducted to ascertain the efficiency gain derived from optimal allocation in different situations. One of the surveys concerns cannabis consumption among university students. Our findings highlight some methodological advances that can be obtained in life sciences IST surveys when optimal allocation is achieved.     

Variance component estimation techniques compared for two mating designs with forest genetic architecture through computer simulation

Computer simulation was used to compare minimum variance quadratic estimation (MIVQUE), minimum norm quadratic unbiased estimation (MINQUE), restricted maximum likelihood (REML), maximum likelihood (ML), and Henderson's Method 3 (HM3) on the basis of variance among estimates, mean square error (MSE), bias and probability of nearness for estimation of both individual variance components and three ratios of variance components. The investigation also compared three procedures for dealing with negative estimates and included the use of both individual observations and plot means as the experimental unit of the analysis. The structure of data simulated (field design, mating designs, genetic architecture and imbalance) represented typical analysis problems in quantitative forest genetics. Results of comparing the estimation techniques demonstrated that: estimates of probability of nearness did not discriminate among techniques; bias was discriminatory among procedures for dealing with negative estimates but not among estimation techniques (except ML); sampling variance among estimates was discriminatory among procedures for dealing with negative estimates, estimation techniques and unit of observation; and MSE provided no additional information to variance of the estimates. HM3 and REML were the closest competitors under these criteria; however, REML demonstrated greater robustness to imbalance. Of the three negative estimate procedures, two are of practical significance and guidelines for their application are presented. Estimates from individual observations were always preferable to those from plot means over the experimental levels of this study.This is Journal Series NO. R-03768 of the Institute of Food and Agricultural Sciences     

Integrating remote sensing and demography for more efficient and effective assessment of changing mountain forest distribution

Species range shifts have been well studied in light of rising global temperatures and the role climate plays in restricting species distribution. In mountain regions, global trends show upward elevational shifts of altitudinal treelines. However, there is significant variation in response between geographic locations driven by climatic and habitat heterogeneity and biotic interactions. Accurate estimation of treeline shifts requires fine-scale patterns of forest structure to be discriminated across mountain ranges. Satellite remote sensing allows detailed information on forest structure to be extrapolated across mountain ranges, however, variation in methodology combined with a lack of information on accuracy and repeatability has led to high uncertainty in the utility of remotely sensed data in studies of mountain treelines. We unite three themes; suitability of remote sensing products, ecological relevance of classifications and effectiveness of the training and validation process in relation to the study of mountain treeline ecotones. We identify needs for further research comparing the utility of different remotely sensed data sets, better characterisation of treeline structure and incorporation of accuracy assessment. Collectively, the improvements we describe will significantly improve the utility of remote sensing by facilitating a more consistent approach to defining geographic variation in treeline structure, improving our ability to link processes from stand to regional scale and the accuracy of range shift assessments. Ultimately, this advance will enable better monitoring of mountain treeline shifts and estimation of the associated to biodiversity and ecosystem function.     

Double-observer approach to estimating egg mass abundance of pool-breeding amphibians

Interest in seasonally flooded pools, and the status of associated amphibian populations, has initiated programs in the northeastern United States to document and monitor these habitats. Counting egg masses is an effective way to determine the population size of pool-breeding amphibians, such as wood frogs (Rana sylvatica) and spotted salamanders (Ambystoma maculatum). However, bias is associated with counts if egg masses are missed. Counts unadjusted for the proportion missed (i.e., without adjustment for detection probability) could lead to false assessments of population trends. We used a dependent double-observer method in 2002–2003 to estimate numbers of wood frog and spotted salamander egg masses at seasonal forest pools in 13 National Wildlife Refuges, 1 National Park, 1 National Seashore, and 1 State Park in the northeastern United States. We calculated detection probabilities for egg masses and examined whether detection probabilities varied by species, observers, pools, and in relation to pool characteristics (pool area, pool maximum depth, within-pool vegetation). For the 2 years, model selection indicated that no consistent set of variables explained the variation in data sets from individual Refuges and Parks. Because our results indicated that egg mass detection probabilities vary spatially and temporally, we conclude that it is essential to use estimation procedures, such as double-observer methods with egg mass surveys, to determine population sizes and trends of these species.     

Development and Evaluation of Models for the Relationship between Tree Height and Diameter at Breast Height for Chinese-Fir Plantations in Subtropical China

Tree diameter at breast height (dbh) and height are the most important variables used in forest inventory and management as well as forest carbon-stock estimation. In order to identify the key stand variables that influence the tree height-dbh relationship and to develop and validate a suit of models for predicting tree height, data from 5961 tree samples aged from 6 years to 53 years and collected from 80 Chinese-fir plantation plots were used to fit 39 models, including 33 nonlinear models and 6 linear models, were developed and evaluated into two groups. The results showed that composite models performed better in height estimate than one-independent-variable models. Nonlinear composite Model 34 and linear composite Model 6 were recommended for predicting tree height in Chinese fir plantations with a dbh range between 4 cm and 40 cm when the dbh data for each tree and the quadratic mean dbh of the stand (Dq) and mean height of the stand (Hm) were available. Moreover, Hm could be estimated by using the formula Hm=11.707×ln(Dq)-18.032. Clearly, Dq was the primary stand variable that influenced the height-dbh relationship. The parameters of the models varied according to stand age and site. The inappropriate application of provincial or regional height-dbh models for predicting small tree height at local scale may result in larger uncertainties. The method and the recommended models developed in this study were statistically reliable for applications in growth and yield estimation for even-aged Chinese-fir plantation in Huitong and Changsha. The models could be extended to other regions and to other tree species only after verification in subtropical China.     

Forest and Landscape Structure as Predictors of Capercaillie Occurrence

ABSTRACT Capercaillie (Tetrao urogallus) is a large, endangered forest grouse species with narrow habitat preferences and large spatial requirements that make it susceptible to habitat changes at different spatial scales. Our aim was to evaluate the relative power of variables relating to forest versus landscape structure in predicting capercaillie occurrence at different spatial scales. We investigated capercaillie-habitat relationships at the scales of forest stand and forest-stand mosaic in 2 Swiss regions. We assessed forest structure from aerial photographs in 52 study plots each 5 km². We classified plots into one of 3 categories denoting the observed local population trend (stable, declining, extinct), and we compared forest structure between categories. At the stand scale, we used presence-absence data for grid cells within the plots to build predictive habitat models based on logistic regression. At this scale, habitat models that included only variables relating to forest structure explained the occurrence of capercaillie only in part, whereas variables selected by the models differed between regions. Including variables relating to landscape features improved the models significantly. At the scale of stand mosaic, variables describing forest structure (e.g., mean canopy cover, proportion of open forest, and proportion of multistoried forest) differed between plot categories. We conclude that small-scale forest structure has limited power to predict capercaillie occurrence at the stand scale, but that it explains well at the scale of the stand mosaic. Including variables for landscape structure improves predictions at the forest-stand scale. Habitat models built with data from one region cannot be expected to predict the species occurrence in other regions well. Thus, multiscale approaches are necessary to better understand species-habitat relationships. Our results can help regional authorities and forest-management planners to identify areas where suitable habitat for capercaillie is not available in the required proportion and, thus, where management actions are needed to improve habitat suitability.     

Revisiting a universal airborne light detection and ranging approach for tropical forest carbon mapping: scaling-up from tree to stand to landscape

Airborne laser scanning provides continuous coverage mapping of forest canopy height and thereby is a powerful tool to scale-up above-ground biomass (AGB) estimates from stand to landscape. A critical first step is the selection of the plot variables which can be related to light detection and ranging (LiDAR) statistics. A universal approach was previously proposed which combines local and regional estimates of basal area (BA) and wood density with LiDAR-derived canopy height to map carbon at a regional scale (Asner et al. in Oecologia 168:1147–1160, 2012). Here we explore the contribution of stem diameter distribution, specific wood density and height-diameter (H–D) allometry to forest stand AGB and propose an alternative model. By applying the new model to a large tropical forest data set we show that an appropriate choice of input variables is essential to minimize prediction error of stand AGB which will propagate at larger scale. Stem number (N) and average stem cross-sectional area should be used instead of BA when scaling from tree to plot. Stand quadratic mean diameter above the census threshold diameter size should be preferred over stand mean diameter as it reduces the prediction error of stand AGB by a factor of ten. Wood density should be weighted by stem volume per species instead of BA. LiDAR-derived statistics should prove useful for estimating local H-D allometries as well as mapping N and the mean quadratic diameter above 10 cm at the landscape level. Prior stratification into forest types is likely to improve both estimation procedures significantly and is considered the foremost current challenge.     

森林动态模型概论

讨论了模拟森林林分动态变化的模型,并把模型分为森林生长模型和演替模型。森林生长模型包括：全林分模型、林分级模型和单木模型;演替模型包括马尔可夫类模型和林窗模型。文中给出了演替模型的基本原理和适用性,在比较早期和最新发展的林窗模型后,叙述了林窗模型的新进展。生长和演替模型的结构和数据要求不同决定了它们的在时间和空间上的适应性,最后指出模型将向综合总体方向发展     

森林动态模型概论

讨论了模拟森林林分动态变化的模型,并把模型分为森林生长模型和演替模型。森林生长模型包括：全林分模型、林分级模型和单木模型;演替模型包括马尔可夫类模型和林窗模型。文中给出了演替模型的基本原理和适用性,在比较早期和最新发展的林窗模型后,叙述了林窗模型的新进展。生长和演替模型的结构和数据要求不同决定了它们的在时间和空间上的适应性,最后指出模型将向综合总体方向发展。     

利用混合模型分析地域对国内马尾松生物量的影响

开展全国森林生物量监测和评估,建立适合较大区域范围的通用性立木生物量模型是一项重要的基础工作,而分析森林生物量受不同地域的影响并保证不同尺度范围森林生物量估计值的可靠性,是必须面临的问题。以南方马尾松(Pinus massoniana)地上生物量数据为例,介绍了如何利用混合模型理论来分析地域对马尾松地上生物量的影响以及利用混合模型构建全国通用性立木生物量模型,为得到不同区域尺度范围内可靠的森林生物量评价和估计提供了有效途径。结果表明,混合模型不仅提高了模型的精度和通用性,并且模型中每个参数都有特定的数学含义,通过这些参数很容易分析出随机因子对生物量的影响程度。因此混合模型方法具有较大的灵活性和适应性,可推广到其它通用性模型(如材积方程)的建立。     

卫星遥感监测产品在中国森林生态系统的验证和不确定性分析——基于海量无人机激光雷达数据

准确获取森林结构参数对森林生态系统研究及其保护有着重要意义。卫星遥感数据作为获取大尺度森林结构参数的重要数据源, 已被制作成各种植被监测产品并被应用于森林质量状况变化评估、森林生物量估算以及森林干扰和生物多样性监测等研究。然而, 这些卫星遥感植被监测产品针对中国复杂多样的森林区域缺乏有效验证, 在不同林况和地形条件下的不确定性也不明确。激光雷达具备高精度三维信息采集的优势, 在国内外已被广泛用于森林生态系统监测和卫星遥感产品验证。为此, 该研究利用在中国114个样地收集的153 km²的无人机激光雷达数据, 构建了我国森林结构参数验证数据集, 并以此为基础对3套全球遥感监测产品(全球叶面积指数(GLASS LAI)、全球冠层覆盖度(GLCF TCC)、全球冠层高度(GFCH))进行了像元尺度的验证, 并分析了其在不同坡度、覆盖度和林型条件下的不确定性。研究结果表明: 与无人机激光雷达获取的叶面积指数、覆盖度以及冠层高度相比, GLASS LAI、GLCF TCC、GFCH在中国森林区域均存在一定的不确定性, 且受林况和地形因素影响的程度不一致。对GLASS LAI和GLCF TCC影响的最大因素分别为林型和覆盖度; 而GFCH则更易受地形坡度和覆盖度的影响。     

New methods and technologies for regional-scale abundance estimation of land-breeding marine animals: application to Adélie penguin populations in East Antarctica

Land-breeding marine animals such as penguins, flying seabirds and pinnipeds are important components of marine ecosystems, and their abundance has been used extensively as an indication of ecosystem status and change. Until recently, many efforts to measure and monitor abundance of these species’ groups have focussed on smaller populations and spatial scales, and efforts to account for perception bias and availability bias have been variable and often ad hoc. We describe a suite of new methods, technologies and estimation procedures for cost-effective, large-scale abundance estimation within a general estimation framework and illustrate their application on large Adélie penguin populations in two regions of East Antarctica. The methods include photographic sample counts, automated cameras for collecting availability data, and bootstrap estimation to adjust counts for the sampling fraction, perception bias, and availability bias, and are applicable for a range of land-breeding marine species. The methods will improve our ability to obtain population data over large spatial and population scales within tight logistic, environmental and time constraints. This first application of the methods has given new insights into the biases and uncertainties in abundance estimation for penguins and other land-breeding marine species. We provide guidelines for applying the methods in future surveys.     

Hurricane Disturbance Alters Secondary Forest Recovery in Puerto Rico

Land-use history and large-scale disturbances interact to shape secondary forest structure and composition. How introduced species respond to disturbances such as hurricanes in post-agriculture forest recovery is of particular interest. To examine the effects of hurricane disturbance and previous land use on forest dynamics and composition, we revisited 37 secondary forest stands in former cattle pastures across Puerto Rico representing a range of exposure to the winds of Hurricane Georges in 1998. Stands ranged from 21 to>80 yr since agricultural abandonment and were measured 9 yr posthurricane. Stem density decreased as stands aged, while basal area and species richness tended to increase. Hurricane disturbance exerted contrasting effects on stand structure, contingent on stand age. In older stands, the basal area of large trees fell, shifting to a stand structure characteristic of younger stands, while the basal area of large trees tended to rise in younger stands with increasing hurricane disturbance. These results demonstrate that large-scale natural disturbances can alter the successional trajectory of secondary forest stands recovering from human land use, but stand age, precipitation and soil series were better predictors of changes in stand structure across all study sites. Species composition changed substantially between census intervals, but neither age nor hurricane disturbance consistently predicted species composition change. However, exposure to hurricane winds tended to decrease the abundance of the introduced tree Spathodea campanulata, particularly in smaller size classes. In all sites the abundance of the introduced tree Syzygium jambos showed a declining trend, again most strongly in smaller size classes, suggesting natural thinning through succession.     

Estimating the intensity of a spatial point process from locations coarsened by incomplete geocoding

Summary .   The estimation of spatial intensity is an important inference problem in spatial epidemiologic studies. A standard data assimilation component of these studies is the assignment of a geocode, that is, point-level spatial coordinates, to the address of each subject in the study population. Unfortunately, when geocoding is performed by the standard automated method of street-segment matching to a georeferenced road file and subsequent interpolation, it is rarely completely successful. Typically, 10–30% of the addresses in the study population, and even higher percentages in particular subgroups, fail to geocode, potentially leading to a selection bias, called geographic bias, and an inefficient analysis. Missing-data methods could be considered for analyzing such data; however, because there is almost always some geographic information coarser than a point (e.g., a Zip code) observed for the addresses that fail to geocode, a coarsened-data analysis is more appropriate. This article develops methodology for estimating spatial intensity from coarsened geocoded data. Both nonparametric (kernel smoothing) and likelihood-based estimation procedures are considered. Substantial improvements in the estimation quality of coarsened-data analyses relative to analyses of only the observations that geocode are demonstrated via simulation and an example from a rural health study in Iowa.     

凉水自然保护区阔叶红松林林分空间结构特征及其与影响因子关系简

根据凉水自然保护区28块典型阔叶红松林样地的5个林分空间结构参数和18个影响因子数据,采用典范对应分析（CCA）方法,对凉水自然保护区阔叶红松林林分空间结构与影响因子间关系进行分析。研究结果表明：（1）研究区域阔叶红松林整体具有较好的林分空间结构,其水平分布格局主要表现为随机分布,树木生长整体处于中庸状态,林木的整体混交程度较高;（2）林分空间结构的CCA排序较好的揭示了该区林分空间结构与影响因子的关系;CCA第一排序轴反映了林龄、坡度、阔叶比和坡向的变化,第二排序轴反映了坡向、土壤有机质和平均胸径的变化,上述6因子的组合是决定林分空间结构特征的主要影响因子;（3）影响林分空间结构的变量中,地形、土壤和林分因子共解释了林分空间结构变化的59.20%,其中纯地形因子占30.68%,纯林分因子占19.01%,纯土壤因子占8.21%,未能解释部分为40.80%。     

A fast and reliable computational method for estimating population genetic parameters

The estimation of ancestral and current effective population sizes in expanding populations is a fundamental problem in population genetics. Recently it has become possible to scan entire genomes of several individuals within a population. These genomic data sets can be used to estimate basic population parameters such as the effective population size and population growth rate. Full-data-likelihood methods potentially offer a powerful statistical framework for inferring population genetic parameters. However, for large data sets, computationally intensive methods based upon full-likelihood estimates may encounter difficulties. First, the computational method may be prohibitively slow or difficult to implement for large data. Second, estimation bias may markedly affect the accuracy and reliability of parameter estimates, as suggested from past work on coalescent methods. To address these problems, a fast and computationally efficient least-squares method for estimating population parameters from genomic data is presented here. Instead of modeling genomic data using a full likelihood, this new approach uses an analogous function, in which the full data are replaced with a vector of summary statistics. Furthermore, these least-squares estimators may show significantly less estimation bias for growth rate and genetic diversity than a corresponding maximum-likelihood estimator for the same coalescent process. The least-squares statistics also scale up to genome-sized data sets with many nucleotides and loci. These results demonstrate that least-squares statistics will likely prove useful for nonlinear parameter estimation when the underlying population genomic processes have complex evolutionary dynamics involving interactions between mutation, selection, demography, and recombination.     

林分因子对神木垒不同森林群落林下植物多样性的影响

为探究神木垒不同森林群落林下植物多样性的差异,本研究采用典型样地法,以夹金山神木垒的5种主要森林群落：云杉林、丽江云杉林、红杉林、针阔混交林、阔叶林为研究对象,对不同森林群落林下植物物种组成和物种多样性进行比较,并对林分因子和林下植物多样性进行冗余分析,确定影响林下植物多样性的主要林分因子,为当地森林经营管理提供理论依据。结果表明：（1）在研究区内共记录林下植物147种,隶属于61科,108属;云杉林群落林下植物的科属种组成最丰富。（2）各类型群落的H值、H"值、D值、JSW值均为：草本层＞灌木层;灌木层多样性最高的群落为云杉林群落,草本层多样性最高的群落为丽江云杉林群落,针阔混交林群落、阔叶林群落林下植物多样性较差。（3）平均枝下高与林分密度是影响灌木层物种多样性的主要林分因子（P<0.01）,平均枝下高与灌木层的D值、H值、H"值呈负相关关系,林分密度与灌木层4个多样性指数均呈正相关关系;平均枝下高是影响草本层物种多样性的主要林分因子（P<0.01）,平均枝下高与草本层H值、H"值、JSW值呈正相关关系。本研究认为,云杉林群落与丽江云杉林群落的林下植物多样性水平较高,平均枝下高与林分密度是影响神木垒不同森林群落林下植物多样性的主要林分因子。