长白山自然保护区阔叶红松林质量快速评价

根据林地的自然性、林地质量、林分生长状况、群落完整性和稳定性五方面特征,选择17 个指标构建评价模型,对长白山自然保护区阔叶红松林质量进行了评价。结果表明：长白山自然保护区阔叶红松林“一级”小班9 个,占2.190%,“二级”小班个数和面积分别占97.080%和97.627%,“三级”小班3 个,没有“四级”小班。自然保护区阔叶红松林总体质量得分0.858,等级为“二级”,表明长白山自然保护区对阔叶红松林的总体质量较好。类准则层质量评价中,群落完整性质量等级为“三级”,群落稳定性“三级”的小班个数和面积占20%以上,表明人类长期的林下干扰对阔叶红松林质量造成一定负面影响。     

利用GIS和RS确定长白山自然保护区森林景观分布的环境范围

在对遥感数据进行景观分类和对环境因子进行空间表达基础上,在地理信息系统的支持下,确定长白山自然保护区森林景观分布的环境(包括年均温、年降水量、坡度和坡向)范围．结果表明。从苔原、岳桦、云冷杉到阔叶红松林,最适海拔高度范围依次为1780-2212、1705-1956、1042-1625、823-1184m;最适温度范围分别为-4．75～-2．40℃、-3．42～-2．07℃、-1．49-1．39℃、0．71-2．37℃;最适降水范围分别为1034～1110、1014-1060、883-1017、824-925mm;长白山自然保护区的森林景观主要分布在平、缓坡地上,并与坡向关系密切。苔原在各个坡向上均有分布。且在各个坡向上分布面积的变化不大;岳样、云冷杉林、阔叶红松林、山杨白样林主要呈现北、西北向分布,其次为东北、西向分布;落叶松林主要为东北向分布。其次为东和北向分布;疏林主要为西向分布,其次为西南、西北和南向分布;风倒区主要为西、西南、西北向分布。     

东北阔叶红松林群落类型划分及物种多样性

运用TWINSPAN分类方法,对我国东北阔叶红松林群落类型进行划分,对乔木层树种进行聚类,并对东北地区长白山、大秃顶子山、平顶山和丰林保护区4个样点的物种多样性进行对比研究.结果表明:24个样地中共记录到维管束植物264种,隶属于64科147属.经过聚类,将阔叶红松林划分为3个群落类型组和7个群落类型;同时,将33个乔木树种间的关联性划分为8组.阔叶红松林群落的物种丰富度和多样性为草本层>灌木层>乔木层.在4个样点中,长白山的样地平均物种丰富度最高,为63.长白山和大秃顶子山乔木层和灌木层的物种多样性略高于平顶山和丰林保护区;丰林保护区草本层的物种多样性为2.83,高于其它3个样点.平顶山灌木层和长白山草本层的均匀度最低,分别为0.71和0.80.     

长白山四种森林类型凋落物分解动态

2003年5月—2004年9月在长白山自然保护区北坡4个森林类型阔叶红松林、红松云冷杉林、岳桦云冷杉林和岳桦林内,利用凋落物原位减少法对4种森林类型的凋落物分解动态进行了研究。结果表明,凋落物现存量最大的为红松云冷杉林,依次为阔叶红松林、岳桦云冷杉林、岳桦林;凋落物分解速率与时间均呈指数关系,凋落物年分解常数为0.25～0.47,分解95%所需时间为18～39年,其中阔叶红松林凋落物年分解常数最大,依次为岳桦林、红松云冷杉林、岳桦云冷杉林。同一类型森林中,不同植物组分的年分解系数不同,一般是阔叶最大,针叶最小。     

长白山区阔叶红松林残留片段木本植物物种组成与群落结构

阔叶红松林是我国东北东部山区的地带性植被,长白山区是阔叶红松林的核心分布区.由于人类活动的干扰,目前天然阔叶红松林只在长白山和小兴安岭地区残留了一些面积不等的片段.为比较分析阔叶红松林在不同分布区的异同,以广义长白山区阔叶红松林残留片段为研究对象,依照巴拿马Barro Colorado Island(BCI)50 hm2热带雨林样地的技术规范,于2012年在吉林长白山自然保护区和辽宁东部山区阔叶红松林残留片段分别建立了3个固定样地,对6个样地内胸径≥1 cm的木本植物进行定位调查.结果表明:6个样地共记录到木本植物69种,隶属于24科42属,其中槭树科为6个样地物种最丰富的科,区系组成均以北温带成分为主,同时混有亚热带成分;从物种多度、平均胸径、胸高断面积和重要值看,各样地优势种明显;6个样地所有独立个体的径级结构均呈倒"J"型,但各样地不同径级个体比例有很大差异,表明6个样地群落处于不同的演替阶段.各样地中重要值位于前3位物种的径级结构可分为倒"J"型、"L"型、单峰型和偏峰型4种类型;各样地主要树种随物种、径级的变化呈现出不同的空间分布格局,同一物种在不同样地的分布格局也表现出一定的差异.     

松果采摘对长白山阔叶红松林生态系统健康的影响

红松 (Ｐｉｎｕｓｋｏｒａｉｅｎｓｉｓ)是国家宝贵的自然资源 ,在我国仅产于东北三省 ,对我国东北地区的生态环境和经济建设发挥着重要的作用。长期以来 ,由于过度采伐利用 ,造成资源急剧减少 ,红松生存受到严重威胁。森林面积 1 9× 10 5ｈａ的吉林长白山是国家级自然保护区 ,是一个以保护森林和野生动物类型为主的自然保护区 ,是珍贵的“物种基因库” ,是我国最早加入联合国教科文组织“人与生物圈”计划的保护区之一。红松籽是长白山的重要资源。保护区内有阔叶红松林近 6× 10 4 ｈａ ,受经济利益驱动 ,2 0世纪 90年代以来 ,每到松籽丰…     

长白山阔叶红松林马氏链模型

我们曾经对长白山自然保护区阔叶红松林做过分类、格局和结构(树种组成和年龄结构)的研究。在此基础上,本文成功地应用马氏链模型研究该森林生态系统的演替趋势。正如Horn(1975)第一次应用马氏链模型一样,其成功的基础在于长期的世代更替过程中,森林生态系统必然将自身所含有的足够的信息传递给下一世代(我们所研究的林分,其优势种的年龄约在400—500年间)。然而,成功的关键则在于转移概率的确定。对于森林生态系统的演替,我们必须恰当地考虑幼树和小径级林木生长进入主林层的概率和时间。本文采用二种新的确定转移概率的方法,其结果相当吻合。而且与过去国内林学家采用传统的演绎方法的一般认识是一致的。     

四川唐家河自然保护区西北至东南方向景观格局的梯度变化

通过设置西北至东南方向样带的方式,对唐家河自然保护区的遥感解译图像从景观和类型两种水平上分析了该区植被景观格局的梯度变化.结果表明:1)该区域内森林植被状况良好,林地分布面积达到91.65%.其中,常绿落叶阔叶混交林的面积最大,是区域的景观基质,而亚高山灌丛草甸以及农耕地等在整个区域内分布面积较小,边界较简单,景观地位弱.2)沿样带方向,景观水平上的指数具有上升型、下降型和无明显趋势3种变化,表现为边界密度、邻近度、形状指数、斑块分形指数的递增,平均斑块面积和最近距离的递减,以及香农多样性和均匀性、最大斑块面积和散布与并列指数的稳定波动.3)该区景观类型以次生落叶阔叶林、常绿落叶阔叶混交林、针阔叶混交林和针叶林为主,其景观指数波动变化显著、具明显的峰值.     

老铁山自然保护区景观格局与生境质量时空变化

由于人口数量的增长和日益频繁的人类活动促进了城市扩张,自然生态系统面积逐渐缩小,一些自然保护区内部生态环境和物种栖息地随之受到影响。因此以辽宁蛇岛老铁山国家级自然保护区中的老铁山自然保护区为研究区,以1997年、2007年和2017年该地区的遥感影像为数据源,解译出15种景观类型,分析景观格局变化趋势。再选取3个景观指数作为衡量人类干扰强度的指标,生成人类干扰强度空间分布图。InVEST模型由美国自然资本项目组开发,能够定量分析生态系统服务功能,因此采用InVEST模型计算生境质量,并探讨该地区20年来人类干扰强度与生境质量的时空变化,并分析二者的相关性。结果表明:(1)1997—2017年,老铁山自然保护区建筑用地、园地面积增加最多,林地、草地略有减少;耕地向园地的转移面积最大。(2)1997—2017年,研究区内试验区和缓冲区人类干扰强度逐年增强,而核心区部分呈现先减弱再增强的趋势,保护区整体呈现出干扰逐渐增强的趋势。(3)1997—2017年该地区的生境质量整体呈现逐渐下降的趋势,生境质量时空变化的热点集中在研究区核心区的周边地带。(4)生境质量与代表人类干扰强度的景观指数呈现出较强的负相关。     

基于遥感和地面数据的景观尺度生态系统生产力的模拟

描述了一个反映系统碳循环和水循环的景观尺度生态系统生产力过程模型(EPPML)．该模型以遥感图像作为数据源,从中获取影响植被生产力的重要变量——叶面积指数(LAI);主要对景观尺度生态系统的净初级生产力(NPP)和蒸散量的空间分布格局和时间动态进行模拟;用地理信息系统(GIS)手段对空间数据进行处理、分析和显示,从而实现将植物生理生态研究的结果从小尺度向中尺度进行拓展和转换．本研究用EPPML对1995年长白山自然保护区的植被生产力进行了模拟,结果表明,EPPML可以比较准确地模拟该保护区主要植被的NPP．NPP的模拟值年均为0．680kgC·m^-2,变幅为0．105—1．241kgC·m^-2(82.1％),其中阔叶红松林的年NPP最高(1．084kgC·m^-2)．NPP年总量为1．332×10^6tC,以阔叶红松林和云冷杉林最高,分别为0．540×10^6tC和0．428×10^6tC．NPP的季节变化呈明显的单峰型,7月最大(6．13gC·m^2·d^-1)．NPP在夏季积累最多(0．465kgC·m^-2),春季次之,冬季最少。     

An indicator of forest dynamics using a shifting landscape mosaic

The composition of a landscape is a fundamental indicator in land-cover pattern assessments. The objective of this paper was to evaluate a landscape composition indicator called ‘landscape mosaic’ as a framework for interpreting land-cover dynamics over a 9-year period in a 360,000 km² study area in the southern United States. The indicator classified a land parcel into one of 19 possible landscape mosaic classes according to the proportions of natural, developed, and agriculture land-cover types in a surrounding 4.41-ha neighborhood. Using land-cover maps from remote sensing, the landscape mosaics were calculated for each 0.09-ha pixel in the study area in 1996 and 2005. Mosaic transition matrices estimated from the pixel change data were then used to develop two Markov chain models. A “landscape mosaic” model was a temporal model of the shifting landscape mosaic, based on the probability of landscape mosaic change for all pixels. A “forest security” model was the same, except that the Markov states were defined by both the landscape mosaic and the land-cover of each pixel, which allowed interpreting forest land-cover dynamics in the context of a shifting landscape mosaic. In the forest security model, the overall percentage of forest decreased from 33% in 2005 to 17% at steady-state, and there was little change in the relative distribution of existing forest area among landscape mosaic classes. In contrast, the landscape mosaic steady-state was reached later, and indicated that a maximum of 10% of total area was available for forest. The implication was that forest security depended ultimately on the dynamics of the landscape mosaics that contained forest, not on forest dynamics within those landscape mosaics.     

Towards a unified framework for connectivity that disentangles movement and mortality in space and time

Predicting connectivity, or how landscapes alter movement, is essential for understanding the scope for species persistence with environmental change. Although it is well known that movement is risky, connectivity modelling often conflates behavioural responses to the matrix through which animals disperse with mortality risk. We derive new connectivity models using random walk theory, based on the concept of spatial absorbing Markov chains. These models decompose the role of matrix on movement behaviour and mortality risk, can incorporate species distribution to predict the amount of flow, and provide both short‐ and long‐term analytical solutions for multiple connectivity metrics. We validate the framework using data on movement of an insect herbivore in 15 experimental landscapes. Our results demonstrate that disentangling the roles of movement behaviour and mortality risk is fundamental to accurately interpreting landscape connectivity, and that spatial absorbing Markov chains provide a generalisable and powerful framework with which to do so.     

Probabilistic logic methods and some applications to biology and medicine

For the computational analysis of biological problems-analyzing data, inferring networks and complex models, and estimating model parameters-it is common to use a range of methods based on probabilistic logic constructions, sometimes collectively called machine learning methods. Probabilistic modeling methods such as Bayesian Networks (BN) fall into this class, as do Hierarchical Bayesian Networks (HBN), Probabilistic Boolean Networks (PBN), Hidden Markov Models (HMM), and Markov Logic Networks (MLN). In this review, we describe the most general of these (MLN), and show how the above-mentioned methods are related to MLN and one another by the imposition of constraints and restrictions. This approach allows us to illustrate a broad landscape of constructions and methods, and describe some of the attendant strengths, weaknesses, and constraints of many of these methods. We then provide some examples of their applications to problems in biology and medicine, with an emphasis on genetics. The key concepts needed to picture this landscape of methods are the ideas of probabilistic graphical models, the structures of the graphs, and the scope of the logical language repertoire used (from First-Order Logic [FOL] to Boolean logic.) These concepts are interlinked and together define the nature of each of the probabilistic logic methods. Finally, we discuss the initial applications of MLN to genetics, show the relationship to less general methods like BN, and then mention several examples where such methods could be effective in new applications to specific biological and medical problems.     

samc: an R package for connectivity modeling with spatial absorbing Markov chains

Quantifying landscape connectivity is fundamental to better understand and predict how populations respond to environmental change. Currently, popular methods to quantify landscape connectivity emphasize how landscape features provide resistance to movement. While many tools are available to quantify landscape resistance, these do not discern between two fundamentally different sources of resistance: movement behavior and mortality. To address this issue, we developed the samc R package that quantifies landscape connectivity using absorbing Markov chain theory. Within this mathematical framework, movements are represented as transient states in the Markov chain, while mortality is represented by transitions to absorbing states. Not only does this framework explicitly account for these different issues, it provides a probabilistic approach that can incorporate both short-term and long-term dynamics, as well as species distribution and abundance. The package includes functions to quantify life expectancy, long-term visitation rates, and various spatially and temporally explicit measures of mortality and movement at the local and landscape scales. These functions in samc have been optimized to find computationally practical solutions in landscapes comprised of > 2 × 10⁶ cells. Here, we illustrate the workflow of the samc package with publicly available movement and mortality data on the endangered Florida panther Puma concolor coryi. This analysis showed that movement and mortality are generally correlated except for locations near roads (areas of high mortality risk) that are within the dispersal range of source locations. This pattern would have been undetectable with current methods that quantify movement resistance. Overall, the samc package provides a means for implementing spatial absorbing Markov chains that can distinguish between movement behavior and mortality resulting in more reliable landscape connectivity measures.     

Bayesian hierarchical model assessment of nest site and landscape effects on nest survival of aplomado falcons

We investigated potential effects of nest site and landscape scale factors, including anthropogenic disturbance and habitat patchiness, on the nesting success of a reintroduced population of northern aplomado falcons (Falco femoralis septentrionalis) in southern Texas. We monitored 62 nesting attempts during 2002–2004 in the Lower Rio Grande Valley. We developed hierarchical models describing daily nest survival rates (DSR) and compared the models using a Bayesian approach in R and WinBUGS. We considered possible effects of nest age, temporal trends, nest site variables, landscape structure, territory (a random effect), and 3 measures of anthropogenic disturbance: distance to paved road, proximity to power pole, and nocturnal light intensity. Whether evaluated by Deviance Information Criterion (DIC) scores or the models' overall posterior probabilities as estimated with a reversible jump Markov Chain Monte Carlo algorithm, none of our landscape or disturbance measures affected DSR. Rather, variation in DSR was best described by nest height, overhead cover, and nest source (artificial or natural). These nest site level factors may be manipulated by managers through provision of artificial nests. We recommend that artificial nests continue to be provided, as such nests are highly successful when located on moderately tall substrates, and they permit researchers to access nest contents for population monitoring. © 2011 The Wildlife Society.     

A theoretical framework of ecological phase transitions for characterizing tree-grass dynamics

This paper describes a theoretical framework of ecological phase transitions for modeling tree-grass dynamics and analyzing the shifts or phase transitions from one vegetation structure to another in the southern Texas landscape. This framework implements the integration of percolation theory, fractal geometry and phase transition theory as a method for modeling the spatial patterns of tree-grass dynamics, and nonlinear Markov non-equilibrium thermodynamic stability theory as a method for characterizing temporal tree-grass dynamics and phase transition. An historical sequence of aerial photographs at a Prosopis - thornscrub savanna parkland site in southern Texas was used to determine the parameters of the models. The preliminary analytical result accords well with current understanding and field survey of vegetation dynamics in the southern Texas landscape. The potential of such approaches and other relevant theories such as self-organized criticality and synergetics to vegetation dynamics is also discussed.     

Markov state modeling of membrane transport proteins

The flux of ions and molecules in and out of the cell is vital for maintaining the basis of various biological processes. The permeation of substrates across the cellular membrane is mediated through the function of specialized integral membrane proteins commonly known as membrane transporters. These proteins undergo a series of structural rearrangements that allow a primary substrate binding site to be accessed from either side of the membrane at a given time. Structural insights provided by experimentally resolved structures of membrane transporters have aided in the biophysical characterization of these important molecular drug targets. However, characterizing the transitions between conformational states remains challenging to achieve both experimentally and computationally. Though molecular dynamics simulations are a powerful approach to provide atomistic resolution of protein dynamics, a recurring challenge is its ability to efficiently obtain relevant timescales of large conformational transitions as exhibited in transporters. One approach to overcome this difficulty is to adaptively guide the simulation to favor exploration of the conformational landscape, otherwise known as adaptive sampling. Furthermore, such sampling is greatly benefited by the statistical analysis of Markov state models. Historically, the use of Markov state models has been effective in quantifying slow dynamics or long timescale behaviors such as protein folding. Here, we review recent implementations of adaptive sampling and Markov state models to not only address current limitations of molecular dynamics simulations, but to also highlight how Markov state modeling can be applied to investigate the structure–function mechanisms of large, complex membrane transporters.     

城郊景观动态模型研究：以沈阳市东陵区为例

基于渗透理论、马尔柯夫过程理论,采用中性模型方法,建立了３个不同的城郊景观动态模型,模型中分别介入不同自然因子和决策因子．利用模型对研究区景观进行了动态变化模拟．对模拟结果进行了评价,评价方法与指标包括：１）多分辨率拟合分析;２）最近邻概率;３）斑块大小和数目．结果发现,综合介入决策因素和自然因子的模型具有最好的效果．     

Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference

MOTIVATION: Bayesian estimation of phylogeny is based on the posterior probability distribution of trees. Currently, the only numerical method that can effectively approximate posterior probabilities of trees is Markov chain Monte Carlo (MCMC). Standard implementations of MCMC can be prone to entrapment in local optima. Metropolis coupled MCMC [(MC)(3)], a variant of MCMC, allows multiple peaks in the landscape of trees to be more readily explored, but at the cost of increased execution time. RESULTS: This paper presents a parallel algorithm for (MC)(3). The proposed parallel algorithm retains the ability to explore multiple peaks in the posterior distribution of trees while maintaining a fast execution time. The algorithm has been implemented using two popular parallel programming models: message passing and shared memory. Performance results indicate nearly linear speed improvement in both programming models for small and large data sets.     

Hidden Markov Models and Animal Behaviour

This paper proposes the use of hidden Markov time series models for the analysis of the behaviour sequences of one or more animals under observation. These models have advantages over the Markov chain models commonly used for behaviour sequences, as they can allow for time-trend or expansion to several subjects without sacrificing parsimony. Furthermore, they provide an alternative to higher-order Markov chain models if a first-order Markov chain is unsatisfactory as a model. To illustrate the use of such models, we fit multivariate and univariate hidden Markov models allowing for time-trend to data from an experiment investigating the effects of feeding on the locomotory behaviour of locusts (Locusta migratoria).