Forest gap dynamics and the Ising model

The vegetation height in forest ecosystems is spatially heterogeneous. Canopy gaps (sites with low vegetation) are formed by treefalls, and they recover to canopy sites (with high vegetation) either by growth of small trees or by branch extension of surrounding trees. The dynamics of canopy gaps have been studied using a spatial Markov chain with nearest neighbor interaction. (1) If the canopy recovery rate is constant and if the gap formation rate for a site increases exponentially with the number of neighboring gap sites, the equilibrium distribution is the same as the one generated by the Ising model in statistical mechanics. Here, we extend the equivalence to the situation in which both the gap formation and canopy recovery depend on the neighborhood, as shown in recent forest data. (2) We develop a statistical test of whether a given spatial pattern is a random sample from the Ising model. The test is based on the conditional probability of configurations on a partial lattice. We apply the method to vegetation height data from the Ogawa forest reserve, Japan, measured on a 5x5 m grid in 1976, 1981, 1986, and 1991. The spatial pattern of the original forest data deviates significantly from the Ising model. We examine whether a larger sampling distance or the removal of the effects of the topography can reduce this deviation.     

Effects of heterogeneity of pre-fire forests and vegetation burn severity on short-term post-fire vegetation density and regeneration in Samcheok,Korea

This study investigated the combined effects of heterogeneity of pre-fire forest cover and vegetation burn severity on post-fire vegetation density and regeneration at an early stage in Samcheok, Korea. To measure the spatial heterogeneity of pre-fire forests, spatial pattern metrics at a landscape level and class level were adopted, and a regression tree analysis for post-fire vegetation density and regeneration was used to avoid spatial autocorrelation. Two regression tree models were estimated for post-fire vegetation density and post-fire vegetation regeneration with the same independent variable sets, including heterogeneity of pre-fire forest cover and vegetation burn severity. The estimated model suggested that the percentage of Japanese red pine and burn severity were the most significant variables for post-fire vegetation density and regeneration, respectively. The compositional and spatial heterogeneity of pre-fire forest and burn severity, as well as the degree of burn severity, was found to have significant impacts on post-fire vegetation density and regeneration. Overall, more rapid vegetation regeneration can be expected in more severely burned areas. However, this rapid vegetation regeneration at an early stage is due mostly to perennials and shrubs, not to the sprouting or regrowth of trees. The study results strongly indicated that a susceptible forest cover type and its spatial patterns directly influence the heterogeneity of burn severity and early vegetation density and regeneration. Hence, the management of susceptible forest cover types is particularly critical for establishing more fire-resilient forests and for post-fire forest restoration.     

黄龙山森林植被地上碳储量时空变化及驱动力

快速准确地估计植被地上碳储量及其动态变化,对评估森林固碳能力具有重要意义。以陕西省黄龙林区森林为研究对象,基于样地实测和卫星遥感数据,建立黄龙山林区植被地上碳储量模型,实现研究区2000-2021年长时间序列的森林植被地上碳储量的反演及时空分异研究。结果表明：（1）黄龙山森林植被地上碳储量平均值总体呈波动上升趋势,碳储量高于全省平均水平。（2）研究区东部、南部、中部及西北部是植被地上碳储量高值分布地区,且呈增加趋势;而东北部、西部和西南部植被地上碳储量较低,且呈减少趋势,研究区22年间森林植被地上部分固碳量增加,生产力提升。（3）年均温、年蒸散发量、年降水量和海拔是2000-2021年影响研究区森林植被地上碳储量空间分异主要因素;任意两个因子间的交互作用对黄龙山森林地上碳储量影响都大于单个因子,表明黄龙山森林植被地上碳储量在不同时间的空间分布受多种因素共同作用。年降水量对其空间分布影响逐渐减小,森林稳定性提高。研究在信息有限的基础上提出了快速估算地区植被地上碳储量的方法,了解了地区森林植被地上碳储量时空分异情况及其驱动因素,为掌握地区植被地上碳储量信息、评估森林固碳能力提供了重要依据。     

近地遥感在森林冠层物候动态监测中的应用

近地遥感技术是原位观测森林冠层物候的重要手段,具有高时间分辨率的优点,而且空间尺度适中,是实现物候尺度推绎的有力工具.本研究首先评述了利用3种光学传感器(辐射表、光谱仪和数码相机)监测森林物候的近地遥感方法;结合帽儿山通量观测站的实测数据分析识别物候期的不确定性来源,发现最重要的误差来自物候提取方法;剖析近地遥感与其他物候观测方法的衔接以及该技术自身存在的问题.最后提出该领域的重点研究方向: 加强冠层光学(或冠层结构)物候与功能(生理、生态过程)物候的联系;整合各区域冠层物候观测网络,实现冠层尺度的全球物候联网观测与数据共享;充分发挥近地遥感的优势,整合多源多尺度物候数据;发展近地遥感物候模型,改进动态全球植被模型中物候模拟.     

我国林火发生预测模型研究进展

通过文献回顾,总结了国内林火发生预测模型的研究现状,并从林火发生驱动因子、林火发生概率预测模型、林火发生频次预测模型和模型检验方法等方面进行归纳分析。得出以下结论: 1)气象、地形、植被、可燃物、人类活动等因素是影响林火发生及模型预测精度的主要驱动因子;2)林火发生概率模型中,地理加权逻辑斯蒂回归模型考虑了变量之间的空间相关性,Gompit回归模型适宜非对称结构的林火数据,随机森林模型不需要多重共线性检验,在避免过度拟合的同时提高了预测精度,是林火发生概率预测模型的优选方法之一;3)林火发生频次模型中,负二项回归模型更适合对过度离散数据进行模拟,零膨胀模型和栅栏模型可以处理林火数据中包含大量零值的问题;4)ROC检验、AIC检验、似然比检验和Wald检验方法是林火概率和频次模型的常用检验方法。林火发生预测模型研究仍是我国当前林火管理工作的重点,预测模型的选择需要依据不同地区林火数据特点。此外,构建林火预测模型时需要考虑更多的影响因素,以提高模型预测精度;未来,需要进一步探索其他数学模型在林火发生预测中的应用,不断提高林火发生预测模型的准确度。     

基于无人机多源遥感数据的亚热带森林树种分类

树种多样性是生态学研究的重要内容,树木的种类和空间分布信息可有效服务于可持续森林管理。但在复杂林分条件下,获取高精度分类结果的难度大。而无人机遥感可获取局域超精细数据,为树种分类精度的提高提供了可能。基于可见光、高光谱、激光雷达等多源无人机遥感数据,探究其在亚热带林分条件下的树种分类潜力。研究发现:(1)随机森林分类器总体精度和各树种的F1分数最高,适合亚热带多树种的分类制图,其区分13种类别(8乔木,4草本)的总体精度为95.63%,Kappa系数为0.948;(2)多源数据的使用可以显著提高分类精度,全特征模型精度最高,且高光谱和激光雷达数据显著影响全特征模型分类精度,可见光纹理数据作用较小;(3)分类特征重要性从大到小排序为结构信息,植被指数,纹理信息,最小噪声变换分量。     

川西亚高山森林恢复的空间格局分析

以米亚罗林区为例,利用森林样地调查和遥感影像解译方法,通过森林植被图与数字地形的叠加,分析了川西亚高山森林大规模采伐和更新后,主要森林植被类型外貌与起源之间的联系,以及各类型分布的地形分异规律和空间格局.结果表明,大规模采伐和更新后,森林植被类型的外貌与起源相关,老龄针叶林为保留下来的原始林,中幼龄针叶林为人工林,落叶阔叶林为天然次生林,而针阔混交林中既有天然次生的成分,也有人工、天然更新共同作用的成分.海拔2 800~3 600 m是米亚罗的主要伐区,森林恢复表现出坡向分异:人工更新的中幼龄针叶林主要分布于阳坡、半阳坡;落叶阔叶林和针阔混交林受天然更新的影响,主要分布于阴坡、半阴坡.老龄针叶林主要保留在海拔3 600 m以上.恢复过程中各种森林植被类型镶嵌分布,景观破碎化严重.     

川西亚高山森林恢复的空间格局分析

以米亚罗林区为例,利用森林样地调查和遥感影像解译方法,通过森林植被图与数字地形的叠加,分析了川西亚高山森林大规模采伐和更新后,主要森林植被类型外貌与起源之间的联系,以及各类型分布的地形分异规律和空间格局.结果表明,大规模采伐和更新后,森林植被类型的外貌与起源相关,老龄针叶林为保留下来的原始林,中幼龄针叶林为人工林,落叶阔叶林为天然次生林,而针阔混交林中既有天然次生的成分,也有人工、天然更新共同作用的成分.海拔2 800～3 600 m是米亚罗的主要伐区,森林恢复表现出坡向分异:人工更新的中幼龄针叶林主要分布于阳坡、半阳坡;落叶阔叶林和针阔混交林受天然更新的影响,主要分布于阴坡、半阴坡.老龄针叶林主要保留在海拔3 600 m以上.恢复过程中各种森林植被类型镶嵌分布,景观破碎化严重.     

Driving factors of a vegetation shift from Scots pine to pubescent oak in dry Alpine forests

An increasing number of studies have reported on forest declines and vegetation shifts triggered by drought. In the Swiss Rhone valley (Valais), one of the driest inner‐Alpine regions, the species composition in low elevation forests is changing: The sub‐boreal Scots pine (Pinus sylvestris L.) dominating the dry forests is showing high mortality rates. Concurrently the sub‐Mediterranean pubescent oak (Quercus pubescens Willd.) has locally increased in abundance. However, it remains unclear whether this local change in species composition is part of a larger‐scale vegetation shift. To study variability in mortality and regeneration in these dry forests we analysed data from the Swiss national forest inventory (NFI) on a regular grid between 1983 and 2003, and combined it with annual mortality data from a monitoring site. Pine mortality was found to be highest at low elevation (below 1000 m a.s.l.). Annual variation in pine mortality was correlated with a drought index computed for the summer months prior to observed tree death. A generalized linear mixed‐effects model indicated for the NFI data increased pine mortality on dryer sites with high stand competition, particularly for small‐diameter trees. Pine regeneration was low in comparison to its occurrence in the overstorey, whereas oak regeneration was comparably abundant. Although both species regenerated well at dry sites, pine regeneration was favoured at cooler sites at higher altitude and oak regeneration was more frequent at warmer sites, indicating a higher adaptation potential of oaks under future warming. Our results thus suggest that an extended shift in species composition is actually occurring in the pine forests in the Valais. The main driving factors are found to be climatic variability, particularly drought, and variability in stand structure and topography. Thus, pine forests at low elevations are developing into oak forests with unknown consequences for these ecosystems and their goods and services.     

气候变化对森林演替的影响

森林演替是森林生态动力源驱动下森林再生的生态学过程,自20世纪初建立群落演替理论以来,演替研究成为生态学研究中的热点．客观准确地认识森林演替规律,研究森林演替动力学机理及其模型,是科学管理森林生态系统的需要;对于天然林保护工程与森林植被的恢复重建,具有重要的理论与实际意义．干扰是森林循环的驱动力,导致森林生态系统时空异质性,是更新格局和生态学过程的主要影响因素．它可改变资源的有效性,干扰导致的林隙是森林循环的起点．回顾了目前演替研究的几种方法,即马尔科夫模型、林窗模型(GAP)、陆地生物圈模型(BIOME)和非线性演替模式．介绍了气候变化对森林演替的影响;并在已有成果的基础上,提出了目前研究存在的问题及未来的发展方向．     

Application of the Ancient Forest Concept to Potential Natural Vegetation Mapping in Flanders, A Strongly Altered Landscape in Northern Belgium

Construction of potential natural vegetation (PNV) poses particular challenges in landscapes heavily altered by human activity and must be based on transparent, repeatable methods. We integrated the concept of ancient forest (AF) and ancient forest species (AFS) into a four-step procedure of PNV mapping: 1) classification of forest vegetation relevés; 2) selection of those vegetation types that can serve as PNV units, based on AF and AFS; 3) merging of selected vegetation types into five PNV units that can be predicted from a digital morphogenetic soil map; 4) mapping of three additional PNV units based on additional environmental data. The second step, concerning the selection of reference forest vegetation, is of particular interest for PNV construction in Flanders (northern Belgium), where forest cover has been subject to temporal disruption and spatial fragmentation. Among the variety of extant forest recovery states, we chose as PNV units those vegetation types for which a high proportion of relevés had been located in AF and that contained many AFS. As the frequency of AFS depends on site conditions, we only compared and selected vegetation types that are found on similar sites according to average Ellenberg indicator values. While succession is irrelevant for the definition of PNV, colonization rates of AFS can be used to estimate the time required for PNV to be restored in a site.     

High resolution land cover data improve understanding of mechanistic linkages with stream integrity

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Vegetation patterns and trajectories in disturbed landscapes, Great?Barrier?Island, northern New?Zealand

Fire has been a major driver of forest loss in New?Zealand. A conceptual model has been proposed in which positive feedbacks between vegetation, fire and soils can arrest regeneration of recurrently burned wet forest landscapes. We used vegetation data collected across three topographically similar landscapes ? Awana, Glenfern and Windy Hill ? on Great Barrier Island to (1) describe current vegetation composition and structure and predict future change in composition and (2) assess evidence for interactions between fire and soils slowing regeneration in these landscapes. Compositional data were analysed via classification and ordination, and we used transition matrix models to explore how vegetation composition may change in the future. The vegetation in the three landscapes spans repeatedly burned scrubland dominated by m?nuka (Leptospermum scoparium) and exotic fire-dependent woody species such as Hakea sericea, to intact mature forest. Scrubland vegetation tends to be found on north-facing upper slopes and ridges ? drier sites where fire has been more frequent and rendered soil conditions (e.g. organic matter and moisture) poor for plant growth. There is a slow reinvasion of forest species into the Leptospermum and Kunzea scrubland from gullies and other remnant patches, with wind-dispersed species preceding fleshy-fruited bird-dispersed ones. In the absence of fire in the next few decades the landscapes will continue to move back towards forest. More fires, however, will further degrade these landscapes by removing remaining fertile topsoils from ridges and slopes and by favouring exotic species adapted to recruit from seed and/or resprout vegetatively after fire.     

Estimating local interaction from spatiotemporal forest data, and Monte Carlo bias correction

We point out a general problem in fitting continuous time spatially explicit models to a temporal sequence of spatial data observed at discrete times. To illustrate the problem, we examined the continuous time Markov model for forest gap dynamics. A forest is assumed to be apportioned into discrete cells (or sites) arranged in a regular square lattice. Each site is characterized as either a gap or a non-gap site according to the vegetation height of trees. The model incorporates the influence of neighboring sites on transition rate: transition rate from a non-gap to a gap site increases linearly with the number of neighbors that are currently in the gap state, and vice versa. We fitted the model to the spatiotemporal data of canopy height observed at the permanent plot in Barro Colorado Island (BCI). When we used the approximate maximum likelihood method to estimate the parameters of the model, the estimated transition rates included a large bias-in particular, the strength of interaction between nearby sites was underestimated. This bias originated from the assumption that each transition between two observation times is independent. The interaction between sites at local scale creates a long chain of transitions within a single census interval, which violates the independence of each transition. We show that a computer-intensive method, called Monte Carlo bias correction (MCBC), is very effective in removing the bias included in the estimate. The global and local gap densities measuring spatial aggregation of gap sites were computed from simulated and real gap dynamics to assess the model. When the approximate likelihood estimates were applied to the model, the predicted local gap density was clearly lower than the observed one. The use of MCBC estimates, suggesting a strong interaction between sites, improved this discrepancy.     

Using an artificial neural network to characterize the relative suitability of environments for forest types in a complex tropical vegetation mosaic

A predictive understanding of the environmental controls on forest distributions is essential for the conservation of biodiversity and management of landscapes in the tropics. This is particularly true now because of potentially rapid climate change. The floristic complexity of tropical forests and the lack or absence of data severely limits the applicability of modelling methods based on the ecology or distribution of individual species. Here we present an artificial neural network (ANN) model using the information available in the humid tropics of North Queensland: a structural classification of forest types, maps of the forest mosaic, and estimates of spatial environmental variables. The ANN model characterizes the relative suitabilities of environments for 15 forest classes defined by their physiognomy and canopy structure. Inputs include seven climate variables, nine soil parent-material classes, and seven terrain variables. The data used to train the model consisted of a stratified random sample of 75000 points. Output of the model is used to measure the dissimilarity between the environment at each location and the environment that would be most suitable for the forest type that is mapped there. The model is highly successful at distinguishing the relative suitability of environments for the forest classes with 75% of the region's forest mosaic accurately predicted by the model at a one hectare resolution. In contrast, a comparable maximum likelihood classification has an accuracy of only 38%. In the remaining 25% of the region the environments are quite dissimilar to what would be expected for the forest types present there. This is especially the case at boundaries between forest classes and for a transitional forest class. Areas mapped as this disturbed, transitional class are generally classified by the model as having environments suitable to the forest type they are most likely to become. The approach has high potential for the analysis of climate change impacts as well as inferring vegetation patterns in the past and should be applicable wherever vegetation maps and spatial estimates of climate variables are available.     

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.     

祁连山区青海云杉林冠层叶面积指数的反演方法

叶面积指数(Leaf area index, LAI)是陆地生态系统的一个十分重要的结构参数。随着空间精细化模型的发展和基于过程的分布式模拟技术的应用, 对LAI的区域估算显得越来越重要, 但目前尚缺乏有效的估算手段。该项研究以青海云杉(Picea crassifolia)林为研究对象, 利用LAI-2000冠层分析仪、鱼眼镜头法和经验公式法对林冠层LAI进行了测定, 观测值分别为1.03~3.70、0.48~2.26和2.27~8.20, 显然, 仪器测定值偏低。针对针叶的集聚效应导致仪器测定值偏低的现象, 利用跟踪辐射与冠层结构测量仪(TRAC)测定的青海云杉林聚集系数计算调整系数, 对鱼眼镜头法获取的LAI值进行订正。根据高分辨率的遥感数据反演青海云杉林的植被指数与LAI的关系, 最后获得了较合理的该地区林冠层LAI的空间分布图。     

Landscape scenario modelling of vegetation condition

Summary   Landscape scenario modelling is a useful aid to planning for biodiversity conservation. Vegetation condition modelling is increasingly being integrated into such analysis. Model complexity and model uncertainty are critical factors that must be addressed when tailoring vegetation condition modelling to individual applications. We describe three approaches that we have used to compare the effects of different landscape scenarios on vegetation condition. The first is a simple land-use–condition approach where vegetation condition is determined solely by land use. The second is a land-use–regeneration approach that introduces transition functions to model vegetation condition dynamics associated with land use change. The third is a threat–regeneration approach, which models vegetation condition dynamics based on the interaction between regeneration and a range of mapped threats. The three approaches represent a progression towards increased refinement and realism, but also increased complexity and data requirements. We examine the relative usefulness of the three approaches and conclude that there is no single 'silver bullet' solution but recommend judicious matching of approaches to applications within a collaborative and adaptive setting.     

凉水自然保护区松鼠（Sciurus vulgaris）贮食生境选择

2003年10月~2004年4月,在黑龙江省小兴安岭凉水国家级自然保护区,应用跟踪观察法、样线调查法、样方调查法等研究方法,采用Vanderploeg和scavia选择系数Wi和选择指数Ei作为衡量指标,对松鼠贮食红松种子生境选择进行了研究。研究表明,松鼠贮食对生境具有选择性:以主要林型为划分标准,松鼠偏爱贮食生境依次为:原始红松林,云杉林,人工云杉林,针叶混交林,人工落叶松林,针阔混交林,阔叶混交林,次生白桦林和人工红松林。在原始红松林内,松鼠对微生境的利用存在选择性差异:松鼠对郁闭良好(郁闭度大于0.6)、灌丛密度中等、倒木和枯立木密度中等(1~5个)、盗食动物活动较弱(大林姬鼠Apodemus peninsulae为主要盗食动物)的原始红松林生境表现出较强的选择性(Ei>0.2);而对坡度小于5°、基质为霉菌土、倒木和枯立木密度较高(大于5个)、草本盖度较高、盗食动物活动中等(花鼠Eutamias sibiricus为主要盗食动物)的原始红松林生境表现出较强的负选择性(Ei<-0.2),对阴坡表现出强烈的回避性(Ei=-0.5368)。     

The Influence of Vegetation Height Heterogeneity on Forest and Woodland Bird Species Richness across the United States

Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity. Traditionally, studies have relied primarily on two-dimensional habitat structure to model broad scale species richness. Vegetation vertical structure is increasingly used at local scales. However, the spatial arrangement of vegetation height has never been taken into consideration. Our goal was to examine the efficacies of three-dimensional forest structure, particularly the spatial heterogeneity of vegetation height in improving avian richness models across forested ecoregions in the U.S. We developed novel habitat metrics to characterize the spatial arrangement of vegetation height using the National Biomass and Carbon Dataset for the year 2000 (NBCD). The height-structured metrics were compared with other habitat metrics for statistical association with richness of three forest breeding bird guilds across Breeding Bird Survey (BBS) routes: a broadly grouped woodland guild, and two forest breeding guilds with preferences for forest edge and for interior forest. Parametric and non-parametric models were built to examine the improvement of predictability. Height-structured metrics had the strongest associations with species richness, yielding improved predictive ability for the woodland guild richness models (r² = ∼0.53 for the parametric models, 0.63 the non-parametric models) and the forest edge guild models (r² = ∼0.34 for the parametric models, 0.47 the non-parametric models). All but one of the linear models incorporating height-structured metrics showed significantly higher adjusted-r² values than their counterparts without additional metrics. The interior forest guild richness showed a consistent low association with height-structured metrics. Our results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of forest bird species. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness.