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2016 , Vol. 35 >Issue 1: 35 - 46

DOI: https://doi.org/10.18306/dlkxjz.2016.01.005

森林景观模型研究新进展及其应用

  • 奚为民 , 1, 2 ,
  • 戴尔阜 3, 4 ,
  • 贺红士 5, 6
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  • 1. Department of Biological and Health Sciences, Texas A&M University, Kingsville, TX 78363, USA
  • 2. 中国科学院沈阳应用生态研究所,沈阳 110016
  • 3. 中国科学院地理科学与资源研究所,北京 100101
  • 4. 中国科学院陆地表层格局与模拟重点实验室,北京 100101
  • 5. 东北师范大学,长春 130024
  • 6. School of Natural Resources, University of Missouri, Columbia MO 65211, USA

作者简介：奚为民(1963-),男,博士,研究员,主要从事森林生态系统、景观过程模拟、全球变化效应、生态系统管理和可持续发展研究,E-mail: 。

网络出版日期: 2016-01-31

基金资助

国家自然科学基金项目(31370483,31300507,41371199,31300404)

中国科学院王宽诚教育基金项目

National Natural Science Foundation of China, No.31370483,No.31300507,No.41371199,No.31300404.K.C.Wong Education Foundation

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Advances in forest landscape modeling: Current research and applications

  • XI Weimin , 1, 2 ,
  • DAI Erfu 3 ,
  • HE Hongshi 4, 5
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  • 1. Department of Biological and Health Sciences, Texas A&M University, Kingsville TX 78363, USA
  • 2. Institute of Applied Ecology, CAS, Shenyang 110016, China
  • 3. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 4. Northeast Normal University, Changchun 130024, China
  • 5. School of Natural Resources, University of Missouri, Columbia MO 65211, USA

Online published: 2016-01-31

Copyright

《地理科学进展》杂志 版权所有
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摘要

森林景观模型(forest landscape models)是基于森林动态机制和干扰作用在景观尺度上模拟和预测森林时空变化特征的计算机模型。该类模型越来越多地用于森林规划、经营管理、生态资源保护与恢复及全球气候变化研究。本文通过对大量文献资料的整理,对森林景观模型的概念、尺度、类型、方法、应用和最新研究进展进行了综述。随着计算机、地理信息系统、遥感等技术的迅猛发展,森林景观模型将会越来越多地与地理信息系统、规划经营管理决策等紧密结合,未来将向服务性决策模型方向发展。

关键词： 森林景观变化; 景观模型; 时空尺度; 森林经营管理; 资源保护与恢复; 决策性模型

本文引用格式

奚为民 , 戴尔阜 , 贺红士 . 森林景观模型研究新进展及其应用[J]. 地理科学进展, 2016 , 35(1) : 35 -46 . DOI: 10.18306/dlkxjz.2016.01.005

Abstract

Forest landscape models simulate temporal change of forests using spatially referenced data across a broad spatial scale (landscape scale) generally larger than a single forest stand. Spatial interactions between forest stands are a key component of such models. These models can incorporate other spatiotemporal processes such as natural disturbances (e.g., wildfires, hurricanes, outbreaks of native and exotic invasive pests and diseases) and human influences (e.g., harvesting and commercial thinning, planting, fire suppression). The models are increasingly used as tools for studying forest management, ecological assessment, restoration planning, and examining the impact of climate change. In this article, we define forest landscape models and discuss their development, components, and types. We also review commonly used methods and approaches in modeling, their applications, and the strengths and limitations of different forest landscape models. New developments in computer sciences, geographic information systems (GIS), remote sensing technologies, decision-support systems, and geo-spatial statistics have provided opportunities for developing new generations of forest landscape models that are more valuable in ecological research, restoration planning, and resource management.

Key words： forest landscape change; landscape models; temporal and spatial scales; forest management and planning; resource conservation and restoration; decision-making models

1 引言

景观模型(landscape models)是近30年来发展起来的在景观尺度上模拟生态系统时空变化的数学模型,并在计算机上加以体现和试验的技术;为现代景观生态学研究热点之一,也是支撑景观生态学定量化发展的重要技术手段(Baker, 1989; 邵国凡, 1989; 奚为民等, 1992; He et al, 2002; Garman, 2004; Mladenoff, 2004; He, 2008; Xi et al, 2009)。景观尺度是景观生态学研究的主要尺度,基本对应中尺度空间范围(从几十平方公里到几百平方公里)和时间跨度(从几十年到几百年)。作为一种跨学科的综合性技术手段,景观模型是景观生态学、数量生态学、现代计算机技术、遥感技术和地理信息系统(Geographic Information Systems, GIS)技术相结合的产物。与其他尺度的生态模型相比,景观模型具有其自身的优势。例如,与样地尺度上的植物生长模型相比,景观模型可以模拟区域生态系统的动态变化并更能反映区域整体特征;而与全球尺度的动态植被模型相比,景观模型不仅同样考虑了与大气系统的交互作用,更能关注干扰因子对区域生态系统的影响。景观模型经历了不同发展阶段,早期的模型侧重于对自然生态系统变化和自然干扰因子(野火、风灾和虫灾等)相互作用的模拟,但随着景观生态学研究领域的拓宽和景观模拟技术的发展,景观模拟研究逐渐包括了人文经济因素和社会变化对自然生态系统的影响,以及自然生态系统反馈和社会发展效应等。景观模拟对提高生态系统长期变化规律性的认识,特别是对全球气候变化大格局下景观尺度上保护生物多样性和调控人类活动至关重要(Turner, 1987; Shugart et al, 1992; 邵国凡等, 1996; Dale, 2003; He et al, 2005; Scheller et al, 2005; He, 2008; Xi et al, 2009)。
森林景观模拟(forest landscape modeling)是景观模拟的重要组成部分之一。森林生态系统是全球生态系统的重要组成部分,作为复杂的异质等级系统,森林生态系统的组成、结构和功能处于不断的变化之中。其变化规律和影响因子又因观测的时空尺度的不同而异。森林景观模拟以森林生态系统为模拟的核心主体,在生态系统的非平衡理论(non-equilibrium theory)和等级结构理论(hierarchical structure theory)的指导下,以森林生态系统变化的主要过程和格局量化成果为基础,根据生态系统演替(succession)和干扰(disturbance)学说,运用先进的计算机技术拟合手段,定量化地揭示森林景观和相邻生态系统中树种生物学特性、种内种间竞争关系及与生态环境干扰因子的综合作用效应(synergistic effects)。由于森林景观模型能在景观尺度上对森林树种的组成、结构(包括多度和生物量等)和功能(包括碳氮循环和生态服务等)的变化趋势予以定量描述和预测,还可揭示主要树木种群的空间地理区范围和分布边界的变化与其自然和人为的影响因素关系,因此广泛地应用于森林动态、景观和土地变化研究以及资源管理和生态环境评价等诸多领域(Mladenoff et al, 1999; He, 2008; Xi et al, 2008)。
随着计算机技术和新兴软件工程技术的快速发展和广泛应用,森林景观模拟技术也相应地迅速发展。在此基础上开发出来的森林景观模型也不断完善,其应用范围也从早期的基础研究拓宽到林业应用。有关森林景观模拟理论研究、技术开发和模型应用的出版物也不断涌现。在国际上目前已有几本英文专著和杂志专刊出版,还有若干篇英文综述文章阐述森林景观模拟技术的发展过程、森林景观模型的主要类型、研究现状及其在森林生态学研究和林业管理中的应用,并对目前国际上主要的森林景观模型的特点和适用领域进行对比和介绍(Scheller et al, 2005; He, 2008; Xi et al, 2009; Dai et al, 2015);在国内,郭晋平等探讨了森林景观模型进展(郭晋平等, 2001)。中国科学院应用生态研究所和东北林业大学等单位开展了森林景观模型开发和应用性研究,取得了有一定影响的成果(He et al, 2005; Wang et al, 2014)。但总的看,国内有关森林景观模型的文献较少,基础理论研究和实践应用还不够深入。本文以多年来在中国和北美地区开展的森林景观模拟工作为基础,综合国际上森林景观模拟研究现状,对近10年来森林景观模拟工作和研究开发应用的新进展加以综述, 提出开展森林景观模拟研究需要注意的问题和发展前景,以期促进森林景观模拟研究的发展和有效的应用。

2 森林景观模型概述

2.1 森林景观模型的概念

明确森林景观模型的概念和范畴对文献综述十分必要。不同学者对森林景观模型的理解和界定不尽相同。例如,Mladenoff等定义森林景观模型为在较宏观的时空尺度(spatio-temporal scale)上模拟森林景观变化的各种计算机模型(Mladenoff et al, 1999);Scheller等则定义森林景观模型为反映景观变化的计算机程序集或软件包(Scheller, Mladenoff et al, 2007)。He提出的较严格的森林景观模型定义为：可以预测模拟对象(simulation entity)的空间特征(如分布、形状、丰度等)变化的模型;同时提出森林景观模型应至少能够以空间交互的方式(spatially interactive manner)模拟周期性发生的空间过程的时空特征(He, 2008)。Xi等认为,森林景观模型是基于森林动态机制和干扰作用,在景观尺度上模拟和预测森林时空变化趋势的计算机模型,其研究对象包含森林景观中多种生态过程与空间格局的交互作用(interactive interaction)和干扰(如生长与演替过程,碳氮等矿物质循环、水循环、森林火灾、虫害与病害发展机制、气候变化及其影响机制等)对动植物生境的影响(Xi et al, 2009)。

2.2 森林景观模型的基础理论和建模方法

不断发展中的演替、干扰与生态系统的非平衡假说构成了生态模型开发的基础。生态学理论经历了从自然界是平衡的、生态演替具有顶级群落的观念向生态系统是一个动态的空间异质体的思想转变(Wu et al, 1995; Perry et al, 2002)。这种观念上的变化是森林景观模型建模的生态学基础(Perry et al, 2006)。森林景观模型的发展是过去二三十年中生物地理学者和生态学家对森林景观演替、多因子干扰和生态系统非平衡现象不断抽象并量化的过程。
早期的森林动态空间模型将元胞自动机(cellular automata)方法和森林动态原理相结合而发展起来的(Jeltsch et al, 2002)。该方法已经日益复杂化,并广泛用于生态学问题的研究 (Hargrove et al, 2000; Perry et al, 2002; Li et al, 2008)。等级分类理论(hierarchy theory)是关于复杂系统结构、功能和动态的理论,是建立多尺度复杂系统模型的理论基础(Costanza et al, 2004)。该理论以系统论、信息论、现代哲学和数学等相关理论为基础,为跨尺度的推绎研究提供了一种系统的科学思路。空间动态集成、尺度推绎等方法论的发展使建模过程更为客观有序,促进了森林景观模型的发展。尺度推绎是指利用某一尺度上所获得的信息和知识来推测其他尺度上的现象。当从一个尺度推绎到另外一个尺度,不同种类信息的保存与丢失也是跨尺度信息传递研究的关键。尺度推绎有助于空间现象在不同尺度上的综合(Costanza et al, 2004)。对空间推绎景观模型而言,尺度推绎也是融合和处理空间数据和社会数据的关键。

2.3 森林景观模型的尺度

尺度(scale)是景观生态学的重要特征,也是森林景观建模理论研究和模型应用的重要内容。森林景观模型的模拟对象是各种森林景观(forest landscape)。空间型森林景观模型通常用于模拟几十年甚至几百年的景观变迁趋势。一般来说,森林景观模型所模拟的时间尺度(temporal scale)从几十年到几百年(一般约50~500 a);空间尺度(spatial scale)大致为100~10000 km2。森林景观模型的尺度是指模型所模拟研究的适宜时空幅度。同时也包括模型的空间粒度大小(spatial grain size)、时间步长(time step)和复杂度(degree of complexity) (Costanza et al, 2004)。时间解析度与模型的重复次数有关,是指模拟时的最小时间步长。由于每个景观模型通常包含多个模拟目标过程,而每个过程又有其自身的时空尺度,因而景观模型可以同时具有多个时空解析度。值得注意的是,当重复次数一定时,单一的时空解析度无法同时反映各个模拟目标。
选择适宜的模型尺度是森林景观模型开发和应用时所面临的关键问题(Xi et al, 2008)。由于空间格局的复杂性,目前没有一种单一模型可以清晰地表达所有尺度的全部特征或信息。一方面,景观生态学家需要在适当的尺度上理解具体的生态过程;另一方面,林地拥有者和管理人员又必须在较大尺度上制定宏观管理政策(Rastetter et al, 2003)。森林景观模型的开发者必须综合考虑以上两种因素,并针对具体的景观模拟对象和已有的计算机软硬件条件做出相应的折中选择,其中,LANDIS PRO就将立地尺度与景观尺度有机结合,并成功地应用于区域森林景观的研究中(Wang et al, 2014)。综上,在模型模拟中如何更有针对性地确定“最佳”或比较合理的森林景观模型的尺度,以及与此相关的重要理论问题,如景观尺度推绎(scaling)和跨尺度景观模拟(across-scale landscape modeling),仍有待进一步探讨。

2.4 森林景观模型的类型

与一般生态学模型不同,森林景观模型中增加了空间维度,即同时包含了时间和空间两个方面的动态变化和交互作用。森林景观模型一般可分为随机景观模型和过程景观模型。随机模型是由马尔可夫过程理论发展而来,它是基于转移概率将空间信息与概率分布相结合的理论方法。当景观变化的机理和多因子相互作用的联合效应尚不清楚时,景观生态学家常用随机模型来模拟自然因子和人类活动对景观结构的影响。过程景观模型通过建立尽可能真实的计算机模型来模拟景观的时空交互过程。由于这种模型比较深入地研究组成景观和生态系统的空间结构,所以又被称作真实结构模型。随着人工智能的理论和方法在生态学中的应用,基于规则的景观模型将会得到进一步的完善和应用,成为解决复杂区域性资源与景观生态系统管理问题的有效工具。
纵观森林景观模型发展,不同学者对模型的分类标准有着不同的理解。Horn等将模拟景观变化的生态模型分为两类：分析模型和模拟模型(Horn et al, 1989)。分析模型是基于森林动态机制的分析模型而建立的数学分析方程组,侧重长期综合生态系统动态分析(Perry et al, 2006),因而又称作战略模型(Verboom et al, 2005)或通用模型(Bolliger et al, 2005),用于长期景观规划(Scheller, Mladenoff et al, 2007)。模拟模型通常运用更多的物理原理和非线性直观方程组,更注重所模拟系统的细节(Horn et al, 1989)。目前多数模拟模型都引入反复运行模型结合统计分析方法,称作战术模型(Verboom et al, 2005),用于短期景观规划(Scheller, Mladenoff et al, 2007)。Perry等根据模型是否模拟森林植被的动态机制,又将模拟模型细分为空间解译景观模型和林窗动态模型(Perry et al, 2006)。空间解译景观模型(Spatial explicit models)是一种模拟和研究景观动态机制的工具,并“体现了景观生态方法的精髓”(Baker, 1989)。这类模型假定景观空间组成结构随着时间而变化,并且这些动态变化规律可通过特定的数学关系来表示,一般适用于较大的时空尺度景观问题。Baker将景观模型分为3类：整体景观模型(whole landscape models)、分布景观模型(distributional landscape models)和空间景观模型(spatial landscape models) (Baker, 1989)。Perry等(2008)根据模型的用途将空间模型分为：预测模型(predictive models)和探究式模型(exploratory models)。前者包括实验—统计模型(empirical-statistical models)、转换矩阵模型(transition models)和林窗模型,主要用于预测系统的未来变化;后者主要用于探究模拟对象的时空变化过程和变因,又称启发式模型(Perry et al, 2008)。Scheller等从生态功能的角度将森林景观模型划分为8个类型,主要基于以下3个生态过程：空间交互作用(spatial reciprocal interaction)、群落动态(tree species community dynamics)和生态系统过程(ecosystem process)。该分类强调模型组内在的关系,在很大程度上反映了北美景观生态学者注重景观空间过程和生态系统研究的传统(Scheller, Mladenoff et al, 2007)。He在此基础上进一步提出了森林景观模型分类的定量标准,首先根据森林景观模型是否模拟空间过程区分大类;再使用模型的时间解析度和模拟森林演替方法等标准划分更具体的模型类型(He, 2008);该分类标准强调了模型开发人员在设计模型时对模型的时间解析度、空间过程的数量和森林演替方法的选择。

2.5 森林景观模型的发展

作为景观模型(landscape models)的一个重要组成部分,森林景观模型的发展反映了森林生态学和景观生态学的发展和融合的过程(Mladenoff et al, 1999)。早期,北美景观生态学家和林业经营者在寻求“最佳”经营和生态双重效益的思想指导下,具有悠久的开发和应用森林模型的历史(Mladenoff et al, 1999)。早期的非空间森林景观模型主要包括马尔可夫转换模型(Markov chain model)和生命–属性模型(vital-attribute model)(Mladenoff et al, 1999);此后,基于树木个体生长的林窗模型(forest gap models)和林分生长与收获模型(forest growth and yield model)得到了较大的发展。Botkin等开发了第一个林窗模型(JABOWA模型) (Botkin et al, 1972)。Shugart等在JABOWA的基础上开发了FORET模型(Shugart, 1984)。林窗模型对于理解类似森林生态系统中的森林林分动态机制有很重要的贡献(Shugart, 1984; Urban et al, 1992)。
20世纪80年代,随着卫星遥感影像(如Landsat卫星的30 m分辨率的TM数据)和GIS软件的应用,进行大幅度的空间分析成为可能。在计算速度和存储能力取得巨大突破的同时,各类编程语言和软件的涌现使得模型的开发、输入输出数据更为便捷,大大提高了模型多尺度模拟运算的能力。同时数据源也趋于多样化,如土地覆盖的遥感影像、大尺度的土壤类型空间数据库、植被样地(如美国森林清查和分析项目,Forest Inventory and Analysis National Program)和土地利用调查数据以及历史资料等数据源不断充实。在这样的发展环境驱动下,森林学家越来越重视大尺度的景观分析和景观管理问题,使得空间型森林景观模型的研究得到了迅速发展。这一时期的森林景观模型开发逐渐形成了两个基本特点：第一,森林景观模型具有空间特性(Scheller, Mladenoff et al, 2007)。对景观变化进行模拟必须对所有的景观要素进行地理编码,具有初始形状、空间定位,或运用GIS进行数据的输入、存储和显示等;第二,森林景观模型强调大尺度驱动因子(driving factors), 即各种环境干扰等(Baker, 1989; Mladenoff et al,1999)。目前森林景观模拟对干扰的模拟不仅限于在较大尺度上模拟自然干扰过程及其综合效应,而且模拟人类活动(如林业采伐、土地利用等)对景观变化造成的影响已成为森林景观模拟的重要方面(Gustafson et al, 2000)。20世纪90年代以后,森林景观模型的参数设计、初始数据和验证数据都较以前有了较大改善。这个时期的森林景观模型在参数设计方面普遍采用了两种方法：物理方法和经验方法。物理方法是利用数学方程建立物理变量和最终的预测结果的关系。经验方法则是使用从物理变量中归纳出的组合参数来模拟相关的变化过程。同期,模拟多尺度、多过程的森林景观模型得到了较快的发展,出现了诸多的森林景观模型,如：FORMOSAIC(Liu et al, 1998),DELTA(Mladenoff et al, 1999),LANDSIM (Mladenoff et al, 1999)和LANDIS(He et al, 2004; Shang et al, 2004, 2007)等。He认为这个时期的森林景观模型具有了生态系统过程模型的特点：它不仅追踪个体林木的空间变化,而且运用综合的物理方法模拟控制关键生态过程的物流和能流(He, 2008)。森林景观模型的应用也不再限于单片林区,而更多地用于多个林区。
进入21世纪以来,随着遥感探测(Remote Sensing, RS)技术的进一步发展,景观生态学家们能迅速地获取具有时间序列的遥感图像。遥感影像和相关的GIS数据可直接用作森林景观模型的初始数据,对整个目标区域进行逐像元模拟,大区域和高效计算机的出现及计算机图形技术的发展使大规模图象处理及复杂运算更为快捷。因此,利用遥感图像和空间分析软件进行景观变化的模拟和预测,已成为国内外研究者争相采用的方法(Xi et al, 2009; He et al, 2011)。

3 森林景观模型应用的新进展

森林景观模型的应用是指模型对实际问题的综合和对具体模型结果的解释,包括与同行和应用领域的专家交流并进一步完善模型。尽管森林景观模型目前仍主要是研究性工具,但它作为一种方法探讨,为森林景观空间分布与环境因子变化的关系以及森林景观对气候变化的反应,提供了一种新思路和有效方法。近10年来,森林景观模型越来越多地用于研究森林规划、经营管理、资源保护、生态恢复、全球气候变化研究,并逐渐成为辅助长期林业景观规划和资源管理决策的一种有效工具(Perry et al, 2008; Xi et al, 2008)。
森林景观模型已越来越多地用于研究和解决实际林业问题(Mladenoff, 2004)。He 将景观模型的应用归纳为3个方面：模型目标的时空模拟、模型目标对于输入参数的敏感性和经营预想方案的分析(He, 2008)。目前,森林景观模型已应用于林业经营管理(Costanza et al, 2004)、流域规划和管理、灾后森林景观的恢复(Xi et al, 2007, 2008; Orsi et al, 2011)、林地利用发展规划等(Aghnoum et al, 2014; Könnyű et al, 2014),其应用领域还在逐步扩大(表1)。
Tab.1 A list of forest landscape models: features, key research questions, and applications

表1 目前主要森林景观模型的方法和特点、关键问题和应用范围

参考文献 模型名称 方法和特点 关键问题与应用范围 空间幅度和解析度 空间交互 动态模型
Andrews (1986) BEHAVE 火行为预测和森林可燃物模拟的耦合模型 估算森林可燃物与野火扩散方式,用于预测林火的扩散行为并提供有效的林火管理决策 不详 否 否
Baker (1992) DISPATCH 运用GIS管理空间数据 探讨不同干扰作用和气候变化对美国明尼苏达州景观结构的效应 4000 km2;
200 m栅格		 是 否
Bugmann (1996) ForClim 模块化模型结构;包括较少的生态假设,具体的土壤过程 应用整合环境(ForClim-E),植物(ForClim-P)与土壤(ForClim-S)等模块模拟欧洲阿尔卑斯山区森林结构的长期(约1200年)变化 不详 否 是
Li et al (1997) ONFIRE 侧重林火特征(如林火风险和林火发生机率等) 模拟加拿大安大略省北部林区不同林火干扰情形对森林景观结构的长期影响 100 km2;
0.01 km 栅格		 是 否
Baskent (1997) LANDMAN 基于GIS的空间数据和景观管理模型 探讨不同的初始景观结构和采伐模式导致的加拿大New Brunswick地区未来森林景观变化 43 km2;
解析度不详		 是 是
Liu et al (1998) FORMOSAIC 整合森林管理策略,有机与无机环境因子的森林生长,建立与死亡动态模型 探讨小尺度热带森林景观空间变化过程与相邻区域生态条件的交互关系 5 km2;
10 m栅格		 是 是
Mladenoff et al (1999) LANDIS 基于JABOWA-FORET林窗与LANDSIM整合的栅格模型 引入概率和空间交互方法模拟森林景观空间变化过程(如演替、森林衰退等),探讨森林景观与干扰(如林火)的交互作用 10~10000 km2 是 是
Sessions et al (1999) SAFE FORESTS 基于非线性回归和栅格模型 研究火灾动态以及采伐对于内华达山脉森林景观变迁的影响,并应用于对林火、次生林和林木采伐的管理 120 km2;
10~25 km栅格		 是 否
Dale et al (1999) DELTA 基于土地利用GIS的空间数据和生态系动态过程的整合模型 应用概率与空间动态模型探讨人为土地利用政策对改变巴西亚马逊地区森林景观的影响,并估算森林破坏的速率 296 km2;
0.53 km 栅格		 是 是
Roberts et al (1999) LANDISIM 采用种类属性/模糊系统模拟方法;空间解译性模型 模拟美国犹他州国家森林内树种分布与树龄结构于空间与时间上的变化过程 142.5 km2;
解析度不详		 是 是
Wimberly et al (2000) LADS 基于树龄级统计特征的景观模拟模型 模拟美国俄勒冈州沿岸地区森林的历史变化和林火对森林结构和树种组成的长期(约3000年)影响 400~22500 km2;1 km栅格 是 是
Klenner et al (2000) VDDT/TELSA 空间解译模型;强调森林景观变迁与干扰与森林管理策略的关系 研究加拿大英属哥伦比亚地区森林管理策略与自然干扰作用对森林内生物栖息地发展的影响 62966 km2 是 否
Li et al (2000) SEM-LAND 空间解译模型;侧重于模拟林火前后森林植被与景观变化 模拟不同林火特征(林火面积,发生频率,周期等)对加拿大中西部森林景观结构的效应 74.32 km2;
0.01 km栅格		 是 否
Hargrove et al (2000) EMBYR 基于GIS空间数据模型 运用概率统计模型模拟大尺度林火,并探讨林火对不同景观结构的影响 625 km2;
50 m栅格		 是 是
Gustafson et al (2000) HARVEST LANDIS模型的采伐模块 模拟美国东南部美国密苏里州森林在不同采伐方式下森林景观的变化 8.36 km2;
30 m栅格		 是 否
Yemshanov et al (2002) BFOLDS 建立在时变马尔可夫链方法上的矩阵转换模型 研究与预测加拿大北部森林林种长期动态变化趋势,并探讨干扰在其中所起的作用 3.7×104 km2;
0.01 km栅格		 是 是
Keane et al (2002) LANDSUM 空间解译模拟模型 模拟美国西北部不同景观尺度上的植被分布随时间尺度变化 25~5160 km2;
解析度不详		 是 是
Pennanen et al (2004) Q-LAND LANDIS扩展模型;模型林分尺度和景观尺度过程 整合种子传播方式与林分树木体积,模拟加拿大魁北克地区北方针阔混交林长期(约1500年)演替与景观结构变化 约1 km2;0.01~0.1 km栅格 是 是
Pausas (2006) FATELAND 整合景观特性,干扰作用与植物分布动态变化的栅格模型 研究林火和景观模式对于群落结构的影响 1 km2;
10 m栅格		 是 是
Scheller et al (2007a) LANDIS-II LANDIS扩展和升级模型;包括生物量模块 模拟森林演替与干扰交互作用的关系与过程 104 km2;
50 m栅格		 是 是
Seidl et al (2012) iLand 以立地为基础的森林景观干扰模型 模拟景观尺度上森林生态系统的动态变化 100 m栅格 是 是
Wang et al (2014) LANDIS PRO 新一代LANDIS模型 预测美国中部阔叶森林组成与结构变化,整合了立地与景观过程 90 m栅格 是 是
在北美地区,森林景观模型主要用于森林生态系统的动态变化研究。如Berland等利用LANDIS-II模型模拟美国明尼苏达州森林长期的动态变化,利用模拟实验来评估影响该区域变化的重要生态因子(Berland et al, 2011)。Seidl等开发的iLand模型用于模拟美国俄勒冈州景观尺度上的森林动态性,突出环境驱动因子在生态系统动态过程中的作用,结果检验了模型处理复杂过程下模拟景观尺度森林生态系统动态的能力(Seidl et al, 2012)。森林资源的监测与规划是北美森林景观模型应用的又一主要领域。如Feng等将CA模型(ALFRESCO)和植物毒理响应模型(TDFRM)相结合,模拟监测植物毒性和营养级串联效应对美国阿拉斯加中部森林火烧及演替格局的影响(Feng et al, 2012)。Staus等应用生态系统管理决策支持模型(EMDs) 对研究区生态现状进行评价,为俄勒冈州西部森林提供森林管理与规划的政策支持(Staus et al, 2010)。而在欧洲地区,森林景观模型主要应用于探究气候变化对森林景观结构与功能的影响。如Henne等利用LandClim模型模拟了全新世阿尔卑斯山植被动态,并评估了浅层土壤对景观尺度上植被动态的影响,从而探究植被对气候变暖的响应变化(Henne, 2011)。Gustafson等利用LANDIS-II模型模拟了全球变化对西伯利亚中南部森林景观的影响,对比分析了森林砍伐、昆虫爆发以及气候变化的影响差异,他们认为该地区森林对全球的变化响应强烈,全球变化将显著地改变该地区森林景观组成及生物量(Gustafson et al, 2010)。
森林景观模型的其它应用还包括林火管理和林业经营管理(Scheller, Domingo et al, 2007; Shang et al, 2007)。LANDIS系列模型已广泛地应用于减缓林火的可燃物管理模拟,为森林火灾管理提供了有效的科学支持(Scheller et al, 2011; Loudermilk et al, 2014)。如He等(2004)和Yang等(2004, 2007)利用LANDIS研究了人类活动导致的林火干扰和不同森林采伐方式的效应。同时在这些研究的基础上,利用LANDIS PRO模型对美国中部阔叶林的森林组成和结构进行了研究,通过采用立地过程与景观过程相结合的模拟方式,成功地预测了该区域森林演替轨迹和森林发展格局(Wang et al, 2014)。Scheller等利用LANDIS-II模型研究了在气候变化条件下美国北部阔叶林与林火干扰、森林采伐方式和风灾的空间关系以及天然林火对森林景观的长期影响(Scheller et al, 2004, 2011; Scheller, Domingo et al, 2007)。此外,其他的森林景观模型也在林火管理及林业经营中发挥重要的作用。如FARSITE火灾扩散模型利用地形、可燃的枯枝以及天气与风的空间信息,将地表火(surface fire)、树冠火(crown fire)、星散火(spotting fire)及火灾扩展速率(fire acceleration)等模型集成到一个二维火灾模型中。该模型用于预测林火发生时的可能传播模式,还用于预测可燃枯枝空间变化。模型在近几年应用广泛,对林火管理者大有帮助(Wehmeyer, 2012; Jahdi et al, 2014)。SAFE FORESTS模型(Sessions et al, 1999)着重于火灾动态以及林木采伐,已用于对内华达山脉林火、次生林和林木采伐的联合管理。LINKNZ模型探索史前森林景观,是LINKAGES模型的一个扩展模型,已用于新西兰的森林管理(Hall et al, 2000, 2006)。
中国在森林演替动态模型,特别是森林景观模型的开发和应用研究起步相对较晚。但自1990年以来,在森林建模方面也有了长足的发展。森林景观模型的引进和应用研究已经起步,并在近几年以较快的速度发展。比如中国生态学者利用LANDIS模型在东北林区进行了较深入的森林动态和林业管理方面的应用性研究 (He et al, 2002)。胡远满等应用LANDIS模型研究了采伐和无采伐预案下大兴安岭呼中林区森林景观的长期变化,定量评价采伐对森林景观变化的影响(胡远满等, 2004),为森林经营管理者提供决策依据。He等应用LANDIS模型对长白山国家自然保护区的森林景观进行了长期预测(He et al, 2005)。目前中国森林景观模型的应用研究主要在东北林区,其重点主要结合中国国情,模拟森林景观生态系统的演替规律、植物群落的干扰机制和森林资源的动态与管理。

4 森林景观模型面临的挑战

4.1 森林景观模型的局限性

对于生态学家和森林管理者而言,越来越多的可用森林景观模型为空间模拟提供了机遇。但是,友好的交互界面并不能克服每个模型自身固有的局限性,如森林景观模型的局限性首先是由于人类对森林生态过程和格局认识的不完整性。因为模拟景观变化不仅要了解现状景观演化到未来景观的过程,更重要的是要搞清景观发生变化的原因(Schumacher et al, 2004)。其次,森林景观模型在结果验证上仍存在循环论证问题(He, 2008)。这是由于森林景观模型验证所需的时间和空间上相对独立的序列性数据一般无法获得。根据传统的方法,结果验证需要特定时间和空间数据来验证模型的预测结果,如果阶段性结果验证有效,则一般就认为后续的预测结果有效。但是森林景观模型不可能用传统的方法对所有时间序列的数据进行验证,因为如果整个时间序列都可验证,那么森林景观模型也就失去意义了。再次,当研究人员讨论导致模拟结果的生物的或非生物因素时,这些因素事实上正是建模时采用并希望得到的模拟结果。用于数据分析和结果验证的方法和工具是森林景观模型未来需要提升的一个重要方面。LANDISVIEW软件是这方面的一个有益尝试 (Xi et al, 2008; Birt et al, 2009)。

4.2 森林景观模型面临的挑战

目前森林景观模型研究和应用所面临的挑战主要包括：①如何确定模型的代表性,如何把握好模型尺度的选择,如何进行尺度推绎;②如何处理好林业管理和人为活动的关系,在森林景观模型中较好地模拟社会经济因素的作用;③如何更客观地检验和分析模型的有效性和不确定性;④如何避免模型的错用和滥用。特别值得强调的是,未来的空间推绎景观模型需要更多地考虑受人为因素以及社会经济因素的影响(如土地利用、土地覆盖变化、全球气候变化等)(Mladenoff et al, 1999; Xi et al, 2008; He et al, 2011)。这将有助于更好地将森林景观模型用于对现实社会的环境和资源问题的规划和管理。
在对景观管理的设计和评估方面,Perry等认为空间解译景观模型在未来的发展中将面临模型应用、数据采集和方便移植等多重挑战(Perry et al, 2006)。一是模型的易用性和模型结果的可视化(visualization)影响着景观模型的发展和应用。若要提高模型应用的效率,模型开发者和使用者需要相互协作,以使研究目标明确、框架合理;二是使用空间解译景观模型时也面临空间数据方面的限制。空间解译景观模型往往十分依赖空间数据,确定模型的参数(parameterization)的过程亦费时费力,上述因素都制约了模型的广泛应用(Jeltsch et al, 2002);三是空间解译景观模型及其核心程序的再利用(model reuse)仍是一个尚未解决好的技术问题。开发新的空间解译景观模型是一个既耗时又十分困难的过程,尽可能利用已有的程序资源将大大缩短新模型的开发周期。目前应用于开发景观模型的模块和程序移植方法已处于研发阶段。
在森林监测和规划方面,空间解译景观模型的开发还需探索在不同时空尺度下提取数据的最佳方法。比如Urban运用跨尺度数据对水分平衡条件下森林景观对气候条件变化的敏感性进行了研究,他使用较详细的空间数据和设计良好的森林动态模型,改进了提取景观要素中对气候变化较为敏感的环境因子的方法(Urban, 2000)。

5 对森林景观模型研究的前景展望

在现代陆地森林生态系统研究中,景观模型已成为一种必不可少的手段。开发和完善森林景观模型是现代生态学家十分关注的研究领域。从目前森林景观模型的发展来看,未来森林景观模型主要发展趋势有以下几点：
(1) 不同类型模型耦合。每个模型都有自身适用的领域,同时也有一定的局限性。Perry等就认为空间推绎景观模型与林窗模型体现了研究景观变化的两种不同方法,但这两种模型各有利弊(Perry et al, 2006)。使用林窗模型模拟景观级别的过程变化(如对干扰因子的变化反应)时会遇到问题,因为林窗模型并不包括发生在较大空间尺度的干扰生态过程,而空间推绎景观模型却能更好地反映这类大尺度变化。尽管景观模型对研究干扰和景观变化之间的相互作用很有效,但这类模型往往不包括管理森林生态系统所需要的树种年龄级和树木个体空间分布的准确信息。正如He等所强调的,用单一的模型模拟跨越较大时空尺度的复杂景观生态系统是不现实的(He et al, 1999)。将景观模型与全球气候模型以及其他过程模型联合起来,将会实现各个模型之间取长补短。
(2) 与地理信息系统技术的结合。现代计算机和空间图象处理技术,特别是GIS、GPS (Global Positioning System)和RS的发展和完善,大大加速了森林景观模型开发的进程。目前可利用卫星光谱图象和数字高程模型(digital elevation model, DEM)对大范围的植被进行识别分析和分类制图,并对综合动态模型的结果进行快速验证。GIS能够便捷地处理空间推绎景观模型所需的空间数据, 可把植被的动态与瞬时的气候条件结合起来以研究全球气候变化对植被的响应以及植被对气候的反馈作用。森林景观模型常采用松散耦合的方法与GIS结合使用,其优势是简便灵活。空间推绎景观模型与GIS技术在不同层次的结合,使得景观模型在处理空间信息和研究空间过程方面的能力大大增强(Fedra, 1993; Nyerges, 1993)。建立空间信息系统,使用GIS处理和分析遥感数据,将GIS、GPS和RS等技术融合到森林景观模型中,是当今景观模型发展的热点和未来的重要方向(Pennanen et al, 2002)。
(3) 向服务性决策模型发展。Walters认为要提供决策支持,景观模型需要考虑以下3个方面：①景观模型应能较好地甄别敏感性强的因素;②考虑选用适用的模型;③开发和应用模型的投入(Walters, 1993)。He(2008)提出,为了满足森林管理和规划的实际需要,森林景观模型应摆脱现有的理论探究模式,而应以战略性预测模型作为未来的发展方向。
目前,森林景观模型已不仅仅是一种研究工具,正逐渐在较大时间和空间尺度上成为一种辅助林业管理的工具(Perry et al, 2006)。从管理规划部门的角度考虑,定量地分析预测结果及模型的准确度,将空间过程的模拟与传统的统计分析方法结合起来十分必要(Landsberg, 2003)。今后森林景观模型的发展会更多地对空间属性的变化过程进行综合模拟。模拟研究的重点在于参数化过程中“最佳”空间变量的判别和诸多空间过程的交互作用。
针对上述发展趋势,我们认为今后森林景观模型的研究应加强以下六方面：
一是建模理论和方法的研究。Urban认为关于现有的建模基本理论通常过于简化,与真实景观内的物种复杂性和自然界演化的多样性不太符合。需要根据自然界演化的历史和复杂的多尺度空间过程进一步完善建模理论(Urban, 2005)。
二是开放式设计和公开程序问题。模型需要合理地对用户和研究者开放。开放式的模型应允许用户使用它的模型模块,而这类模型应在投入使用前经过严格的测试和评估。新模型应可直接使用以往模型的某些模块和数据(Syphard et al, 2004; Scheller, Domingo, 2007)。应逐渐建立标准化模型程序模块库,使之能较快捷方便地应用于其他模型(He et al, 2005)。
三是模型程序的再利用、互利用及模型标准化。实现这一设想首先需要确定利用目标,建立利用规范,使之可以操作。标准化的基本方法是对现有的模型(程序)进行分类集成,建立程序库,逐步实现建模的标准化。
四是加强计算机和地理信息技术等在森林景观模型中的应用,提高预测的准确度和效率。在景观模型中适当地运用GIS模块,提高嵌有GIS模块的模型的可运行性(Mladenoff et al, 1999),并使用多个计算机处理器实现生态过程的同步运算(He, 2008),解决好同步模拟生态过程的技术问题。未来的森林景观模型需要更快捷的数学算法,特别要解决由于采用矢量方法而带来的计算量加大的 问题。
五是模拟结果的三维可视化。三维数字高程模型是森林景观立体特征分析的基础。将遥感图像与DEM重叠起来,用户可自由设置观察路线和角度,模拟空中飞行,对研究区进行多方位观察。与已往的二维可视化不同,三维模拟能在不同地理环境下模拟森林景观动态,表达更为丰富的信息。
六是模型的有效性检验和应用。目前的景观模型一般利用假设检验方法来评估模型的有效性。这一传统意义上验证模型的方法已不能满足开放性复杂随机模型的要求(Rykiel, 1996; He et al, 1999)。Rykiel认为制定详细的规范模型验证标准十分必要。他提出模型的验证可分为操作验证、概念验证和数据验证(Rykiel, 1996)。Perry等认为面向格局法对评估空间推绎景观模型十分有效(Perry et al, 2002)。其主导思想是评价景观模型时要更多地发掘不同空间过程和格局之间的联系,进而通过建立有效的模型结构框架以评估和比较模型的可靠性和适用性(Grimm et al, 2013)。
景观模型模拟景观要素的时空过程,揭示景观变化规律,不仅能储存过去和现存的植被信息、干扰和管理的状态,而且能预测景观要素的变化趋势,有助于更有效地研究森林对各种干扰的反应和对森林景观的管理。由于它较好地模拟了空间格局的复杂过程,较清晰地表达时空特征的数量信息,因而在理论研究和实际应用方面都具有良好的发展前景。可以预见,未来几十年中,生态学家对森林景观的格局和过程的认识会进一步深化,森林景观模型的技术开发方法会更综合化,森林景观模型的类型会根据研究问题的不同而更趋于多样化。

The authors have declared that no competing interests exist.

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Dai E F, Wu Z, Wang X F, et al.2015. Progress and prospect of research on forest landscape model[J]. Journal of Geographical Sciences, 25(1): 113-128.lt;p>The Forest Landscape Model (FLM) is an efficiency tool of quantified expression of forest ecosystem's structure and function. This paper, on the basis of identifying FLM, according to the stage of development, summarizes the development characteristics of the model, which includes the theoretical foundation of mathematical model, FLM of stand-scale, primary development of spatial landscape model, rapid development of ecosystem process model as the priority, and developing period of structure and process driven by multi-factor. According to the characteristics of different FLMs, this paper classifies the existing FLM in terms of mechanism, property and application, and elaborates the identifications, advantages and disadvantages of different types of models. It summarizes and evaluates the main application fields of existing models from two aspects which are the changes of spatial pattern and ecological process. Eventually, this paper presents FLM's challenges and directions of development in the future, including: (1) more prominent service on the practical strategy of forest management's objectives; (2) construction of multi-modules and multi-plugin to satisfy landscape research demand in various conditions; (3) adoption of high resolution's spatial-temporal data; (4) structural construction of multi-version module; (5) improving the spatial suitability of model application.</p>

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[18]
Dale V H.2003. Ecological modeling for resource management[M]. New York: Springer.

[19]
Dale V H, Pearson S M.1999. Modeling the driving factors and ecological consequences of deforestation in the Brazilian Amazon[M]//Mladenoff D J, Baker W L. Spatial modeling of forest landscape change: Approaches and applications. Cambridge, UK: Cambridge University Press: 256-276.

[20]
Fedra K.1993. GIS and environmental modeling[M]//Goodchild M F, Parks B O, Steyaert L T. Environmental modeling with GIS. New York: Oxford University Press: 33-51.

[21]
Feng Z L, Alfaro-Murillo J A, DeAngelis D L, et al.2012. Plant toxins and trophic cascades alter fire regime and succession on a boreal forest landscape[J]. Ecological Modelling, 244: 79-92.Two models were integrated in order to study the effect of plant toxicity and a trophic cascade on forest succession and fire patterns across a boreal landscape in central Alaska. One of the models, ALFRESCO, is a cellular automata model that stochastically simulates transitions from spruce dominated 102km 2 spatial cells to deciduous woody vegetation based on stochastic fires, and from deciduous woody vegetation to spruce based on age of the cell with some stochastic variation. The other model, the ‘toxin-dependent functional response’ model (TDFRM) simulates woody vegetation types with different levels of toxicity, an herbivore browser (moose) that can forage selectively on these types, and a carnivore (wolf) that preys on the herbivore. Here we replace the simple succession rules in each ALFRESCO cell by plant–herbivore–carnivore dynamics from TDFRM. The central hypothesis tested in the integrated model is that the herbivore, by feeding selectively on low-toxicity deciduous woody vegetation, speeds succession towards high-toxicity evergreens, like spruce. Wolves, by keeping moose populations down, can help slow the succession. Our results confirmed this hypothesis for the model calibrated to the Tanana floodplain of Alaska. We used the model to estimate the effects of different levels of wolf control. Simulations indicated that management reductions in wolf densities could reduce the mean time to transition from deciduous to spruce by more than 15 years, thereby increasing landscape flammability. The integrated model can be useful in estimating ecosystem impacts of wolf control and moose harvesting in central Alaska.

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[22]
Garman S L.2004. Design and evaluation of a forest landscape change model for western Oregon[J]. Ecological Modelling, 175(4): 319-337.This article describes the design and evaluation of a forest landscape model, called LandMod, developed by scaling a forest gap model to operate at a coarser resolution. LandMod is a spatially explicit, stochastic model designed to simulate forest dynamics in the west-central Oregon Cascades over long time frames (500+ years) and large spatial extents (greater than or equal to18,000 ha) at a relatively fine grain (0.04-1 ha). LandMod tracks diameter growth, death, and regeneration of individual tree species in 5-cm size classes at a 5-year time step. Demographics are modeled using simplified procedures from the PNWGap gap model and statistical abstractions of gap-model behavior. LandMod was parameterized for the three predominant forest types of the western Oregon Cascades. Performance of the underlying equations of LandMod was assessed by comparison of predictions with those of the PNWGap model over an elevation and thinning gradient, and with field observations. Landscape-scale performance was assessed by comparing LandMod predictions of potential natural vegetation with empirically based estimates for an 18,000-ha watershed. Results of performance assessments indicated reasonable predictions with LandMod. Compared to PNWGap predictions and observed stands, percent critical errors (alpha = 0.05) of predictions for dominant tree species and stand-level measures with LandMod ranged from 1.4 to 29% with the majority of critical errors less than 15%. LandMod predictions of potential natural vegetation closely matched empirical estimates, with an average overall fit of 94% (S.E. = 0.01). Reasons for prediction error included under-prediction of canopy-stem size in old-growth stands and of mean size of sub-dominant species. Also, simplified light calculations in LandMod resulted in the under-prediction of stem growth under canopy structures induced by certain thinning strategies. Enhancements are recommended to improve model predictions. Intended applications with LandMod include ecological assessments of land-use strategies and research assessments of landscape pattern-process interactions that require explicit consideration of forest structure. (C) 2003 Elsevier B.V. All rights reserved.

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[23]
Grimm V, Railsback S F.2013. Individual-based modeling and ecology[M]. New Jersey: Princeton University Press.

[24]
Gustafson E J, Shifley S R, Mladenoff D J, et al.2000. Spatial simulation of forest succession and timber harvesting using LANDIS[J]. Canadian Journal of Forest Research, 30(1): 32-43.The LANDIS model simulates ecological dynamics, including forest succession, disturbance, seed dispersal and establishment, fire and wind disturbance, and their interactions. We describe the addition to LANDIS of capabilities to simulate forest vegetation management, including harvest. Stands (groups of cells) are prioritized for harvest using one of four ranking algorithms that use criteria re...

DOI

[25]
Gustafson E J, Shvidenko A Z, Sturtevant B R, et al.2010. Predicting global change effects on forest biomass and composition in south-central Siberia[J]. Ecological Applications, 20(3): 700-715.Multiple global changes such as timber harvesting in areas not previously disturbed by cutting and climate change will undoubtedly affect the composition and spatial distribution of boreal forests, which will, in turn, affect the ability of these forests to retain carbon and maintain biodiversity. To predict future states of the boreal forest reliably, it is necessary to understand the complex interactions among forest regenerative processes (succession), natural disturbances (e.g., fire, wind, and insects), and anthropogenic disturbances (e.g., timber harvest). We used a landscape succession and disturbance model (LANDIS-II) to study the relative effects of climate change, timber harvesting, and insect outbreaks on forest composition, biomass (carbon), and landscape pattern in south-central Siberia. We found that most response variables were more strongly influenced by timber harvest and insect outbreaks than by the direct effects of climate change. Direct climate effects generally increased tree productivity and modified probability of establishment, but indirect effects on the fire regime generally counteracted the direct effects of climate on forest composition. Harvest and insects significantly changed forest composition, reduced living aboveground biomass, and increased forest fragmentation. We concluded that: (1) Global change is likely to significantly change forest composition of south-central Siberian landscapes, with some changes taking ecosystems outside the historic range of variability. (2) The direct effects of climate change in the study area are not as significant as the exploitation of virgin forest by timber harvest and the potential increased outbreaks of the Siberian silk moth. (3) Novel disturbance by timber harvest and insect outbreaks may greatly reduce the aboveground living biomass of Siberian forests and may significantly alter ecosystem dynamics and wildlife populations by increasing forest fragmentation.

DOI PMID

[26]
Hall G M J, Hollinger D Y.2000. Simulating New Zealand forest dynamics with a generalized temperate forest gap model[J]. Ecological Applications, 10(1): 115-130.A generalized computer model of forest growth and nutrient dynamics (LINKAGES) was adapted for the temperate evergreen forests of New Zealand. Systematic differences in species characteristics between eastern North American species and their New Zealand counterparts prevented the initial version of the model from running acceptably with New Zealand species. Several equations were identified as ...

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[27]
Hall G M J, McGlone M S.2006. Potential forest cover of New Zealand as determined by an ecosystem process model[J]. New Zealand Journal of Botany, 44(2): 211-232.across some regions of the south‐eastern South Island where it was nearly absent before settlement suggested that the ecological knowledge of some competing conifer species was incomplete.

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[28]
Hargrove W W, Gardner R H, Turner M G, et al.2000. Simulating fire patterns in heterogeneous landscapes[J]. Ecological Modelling, 125(2-3): 243-263.A broad-scale probabilistic model of forest fires, EMBYR, was developed to simulate the effects of large fires burning through heterogeneous landscapes. Fire ignition and spread are simulated on a gridded landscape by (1) examining each burning site at each time step, (2) independently evaluating the probability of spread to eight neighbours based on fuel type, fuel moisture, wind speed and dir...

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[29]
He H S.2008. Forest landscape models: Definitions, characterization, and classification[J]. Forest Ecology and Management, 254(3): 484-498.Previous model classification efforts have led to a broad group of models from site-scale (non-spatial) gap models to continental-scale biogeographical models due to a lack of definition of landscape models. Such classifications become inefficient to compare approaches and techniques that are specifically associated with forest landscape modeling. This paper provides definitions of key terminologies commonly used in forest landscape modeling to classify forest landscape models. It presents a set of qualitative criteria for model classification. These criteria represent model definitions and key model implementation decisions, including the temporal resolution, number of spatial processes simulated, and approaches to simulate site-level succession. Four approaches of simulating site level succession are summarized: (1) no site-level succession (spatial processes as surrogates), (2) successional pathway, (3) vital attribute, and (4) model coupling. Computational load for the first three approaches is calculated using the Big O Notation, a standard method. Classification criteria are organized in a hierarchical order that creates a dichotomous tree with each end node representing a group of models with similar traits. The classified models fall into various groups ranging from theoretical and empirical to strategic and tactical. The paper summarizes the applications of forest landscape models into three categories: (1) spatiotemporal patterns of model objects, (2) sensitivities of model object to input parameters, and (3) scenario analyses. Finally, the paper discusses two dilemmas related to the use of forest landscape models: result validation and circular reasoning.

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[30]
He H S, Hao Z Q, Mladenoff D J, et al.2005. Simulating forest ecosystem response to climate warming incorporating spatial effects in north-eastern China[J]. Journal of Biogeography, 32(12): 2043-2056.Aim Predictions of ecosystem responses to climate warming are often made using gap models, which are among the most effective tools for assessing the effects of climate change on forest composition and structure. Gap models do not generally account for broad-scale effects such as the spatial configuration of the simulated forest ecosystems, disturbance, and seed dispersal, which extend beyond the simulation plots and are important under changing climates. In this study we incorporate the broad-scale spatial effects (spatial configurations of the simulated forest ecosystems, seed dispersal and fire disturbance) in simulating forest responses to climate warming. We chose the Changbai Natural Reserve in China as our study area. Our aim is to reveal the spatial effects in simulating forest responses to climate warming and make new predictions by incorporating these effects in the Changbai Natural Reserve.

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[31]
He H S, Larsen D R, Mladenoff D J.2002. Exploring component-based approaches in forest landscape modeling[J]. Environmental Modelling & Software, 17(6): 519-529.Forest management issues are increasingly required to be addressed in a spatial context, which has led to the development of spatially explicit forest landscape models. The numerous processes, complex spatial interactions, and diverse applications in spatial modeling make the development of forest landscape models difficult for any single research group. New developments in component-based modeling approaches provide a viable solution. Component-based modeling breaks a monolithic model into small, interchangeable, and binary components. They have these advantages compared to the traditional modeling work: 1) developing a component is a much smaller task than developing a whole model, 2) a component can be developed using most programming languages, since the interface format is binary, and 3) new components can replace the existing ones under the same model framework; this reduces the duplication and allows the modeling community to focus resources on the common products, and to compare results. In this paper, we explore the design of a spatially explicit forest landscape model in a component-based modeling framework, based on our work on object-oriented forest landscape modeling. We examine the representation of the major components and the interactions between them. Our goal is to facilitate the use of the component-based modeling approach at the early stage of spatially explicit landscape modeling. (C) 2002 Elsevier Science Ltd. All rights reserved.

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[32]
He H S, Mladenoff D J.1999. Spatially explicit and stochastic simulation of forest-landscape fire disturbance and succession[J]. Ecology, 80(1): 81-99.

[33]
He H S, Shang B Z, Crow T R, et al.2004. Simulating forest fuel and fire risk dynamics across landscapes- LANDIS fuel module design[J]. Ecological Modelling, 180(1): 135-151.Understanding fuel dynamics over large spatial (10 3 鈥10 6 ha) and temporal scales (10 1 鈥10 3 years) is important in comprehensive wildfire management. We present a modeling approach to simulate fuel and fire risk dynamics as well as impacts of alternative fuel treatments. The approach is implemented using the fuel module of an existing spatially explicit forest landscape model, LANDIS. The LANDIS fuel module tracks fine fuel, coarse fuel and live fuel for each cell on a landscape. Fine fuel is derived from vegetation types (species composition) and species age, and coarse fuel is derived from stand age (the oldest age cohorts) in combination with disturbance history. Live fuels, also called canopy fuels, are live trees that may be ignited in high intensity fire situations (such as crown fires). The amount of coarse fuel at a given time is the result of accumulation and decomposition processes, which have rates defined by ecological land types. Potential fire intensity is determined by the combination of fine fuel and coarse fuel. Potential fire risk is determined by the potential fire intensity and fire probability, which are derived from fire cycle (fire return interval) and the time since last fire. The LANDIS fuel module simulates common fuel management practices including prescribed burning, coarse fuel load reduction (mechanical thinning), or both. To test the design of the module, we applied it to a large landscape in the Missouri Ozarks. We demonstrated two simulation scenarios: fire suppression with and without fuel treatment for 200 years. At each decade of a simulation, we analyzed fine fuel, coarse fuel, and fire risk maps. The results show that the fuel module correctly implements the assumptions made to create it, and is able to simulate basic cause鈥揺ffect relationships between fuel treatment and fire risk. The design of the fuel module makes it amendable to calibration and verification for other regions.

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[34]
He H S, Yang J, Shifley S R, et al.2011. Challenges of forest landscape modeling-Simulating large landscapes and validating results[J]. Landscape and Urban Planning, 100(4): 400-402.Over the last 20 years, we have seen a rapid development in the field of forest landscape modeling, fueled by both technological and theoretical advances. Two fundamental challenges have persisted since the inception of FLMs: (1) balancing realistic simulation of ecological processes at broad spatial and temporal scales with computing capacity, and (2) validating modeled results using independent, spatially explicit time series data. The paper discusses the current status and future directions regarding these two challenges.

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[35]
Henne P D, Elkin C M, Reineking B, et al.2011. Did soil development limit spruce (Picea abies) expansion in the Central Alps during the Holocene: Testing a palaeobotanical hypothesis with a dynamic landscape model[J]. Journal of Biogeography, 38(5): 933-949.

[36]
Horn H S, Shugart H H, Urban D L.1989. Simulators as models of forest dynamics[M]//Roughgarden J, May R M, Levin S A. Perspectives in ecological theory. New Jersey: Princeton University Press: 256-267.

[37]
Jahdi R, Salis M, Darvishsefat A A, et al.2014. Calibration of FARSITE fire area simulator in Iranian northern forests[J]. Natural Hazards and Earth System Sciences Discussions, 2(9): 6201-6240.Wildfire simulators based on empirical or physical models need to be locally calibrated and validated when used under conditions that differ from those where the simulators were originally developed. This study aims to calibrate FARSITE fire spread model considering a set of recent wildfires occurred in Northern Iran forests. Site specific fuel models in the study areas were selected by sampling the main natural vegetation type complexes and assigning standard fuel models. Overall, simulated fires presented reliable outputs that accurately replicated the observed fire perimeters and behavior. Standard fuel models of Scott and Burgan (2005) afforded better accuracy in the simulated fire perimeters than the standard fuel models of Anderson (1982). The best match between observed and modeled burned areas was observed on herbaceous type fuel models. Fire modeling showed a high potential for estimating spatial variability in fire spread and behavior in the study areas. This work represents a first step in the application of fire spread modeling on Northern Iran for wildfire risk monitoring and management.

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[38]
Jeltsch F, Moloney K A.2002. Spatially explicit vegetation models: What have we learned[M]//Esser K, Lüttge U, Beyschlag W, et al. Progress in botany: Genetics, physiology, ecology. Berlin, Heidelberg: Springer, 63: 326-343.

[39]
Keane R E, Parsons R A, Hessburg P F.2002. Estimating historical range and variation of landscape patch dynamics: Limitations of the simulation approach[J]. Ecological Modelling, 151(1): 29-49.Landscape patterns in the northwestern United States are mostly shaped by the interaction of fire and succession, and conversely, vegetation patterns influence fire dynamics and plant colonization processes. Historical landscape pattern dynamics can be used by resource managers to assess current landscape conditions and develop target spatial characteristics for management activities. The historical range and variability (HRV) of landscape pattern can be quantified from simulated chronosequences of landscape vegetation maps and can be used to (1) describe temporal variation in patch statistics, (2) develop limits of acceptable change, and (3) design landscape treatment guidelines for ecosystem management. Although this simulation approach has many advantages, the limitations of this method have not been explored in detail. To demonstrate the advantages and disadvantages of this approach, we performed several simulation experiments using the spatially explicit, multiple pathway model a LANDscape Succession Model (LANDSUM) to quantify the range and variability in six class and landscape pattern metrics for four landscapes in the northwestern United States. First, we applied the model to spatially nested landscapes to evaluate the effect of landscape size on the HRV pattern metrics. Next, we averaged the HRV pattern metrics across maps generated from simulation time spans of 100, 500, and 1000 years and intervals 5, 10, 25 and 50 years to assess optimal output generation parameters. We then altered the elevation data layer to evaluate effect of topography on pattern metrics, and cut various shapes (circle, rectangle, square) from a landscape to examine landscape shape and orientation influences. Then, we altered the input vegetation maps to assess the influence of initial conditions on landscape metrics output. Finally, a sensitivity analysis of input fire probabilities and transition times was performed. Results indicate landscapes should be quite large to realistically simulation fire pattern. Landscape shape, and orientation are critically important to quantifying patch metrics. Simulation output need only be stored every 20鈥50 years but landscapes should be simulated for long time periods (鈮1000 years). All landscapes are unique so conclusions generated here may not be entirely applicable to all western US landscapes.

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[40]
Klenner W, Kurz W, Beukema S.2000. Habitat patterns in forested landscapes: Management practices and the uncertainty associated with natural disturbances[J]. Computers and Electronics in Agriculture, 27(1-3): 243-262.We present the results of a study to examine the effects of management actions and natural disturbances in influencing the evolution of habitat patterns on forested lands. TELSA, a spatially explicit vegetation succession model with the ability to apply user-defined management actions and stochastic wildfires calibrated to local conditions, was used to evaluate changes in several indicators of habitat condition. We assessed seral stage and patch size changes over multiple 200-year simulations under a constant rate of harvest within each of these analyses. In the absence of natural disturbances, old growth habitat and large patches of forest of similar age and tree species composition decreased unless special management practices were applied. Old-growth management area reserves and periodic 鈥榓ggregated cutblock鈥 harvesting entries helped maintain forest seral stage distribution at the target level, and patch size characteristics similar to the patterns that would have occurred under historic natural disturbances. Adding wildfire to the management scenarios substantially reduced the amount of old-growth habitat in designated old-growth management area reserves, compromising the ability to maintain old-growth at target levels. A 50% increase in the area designated as old-growth management area reserves would be required to offset the loss of old growth due to wildfire. Although the amount of old-growth habitat was diminished by wildfire, the availability of large habitat patches greater than 250 ha increased. We discuss the need to consider the role of management and natural disturbances in landscape planning, and suggest that redundancy is essential to maintain those features vulnerable to stochastic disturbances. Landscape scenario modeling can facilitate the development of risk averse plans, and encourage the development of innovative approaches to achieving timber and non-timber objectives.

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[41]
Könnyű N, Tóth S F, McDill M E, et al.2014. Temporal connectivity of mature patches in forest planning models[J]. Forest Science, 60(6): 1089-1099.We present a deterministic forest harvest scheduling model that ensures the temporal connectivity of mature forest habitat patches over time in a landscape managed for timber production. Past models have addressed the spatial aspects of habitat connectivity by requiring a certain amount of mature forest habitat to be retained throughout the planning horizon in contiguous patches of minimum size and age. These models do not recognize, however, that the dynamic patches of a managed forest ecosystem might not provide escape routes from “old” to “new” patches for certain wildlife unless there is temporal overlap among the patches. Biologists have suggested that the lifespan of patches is often more important than their size and contiguity for species survival. A mixed-integer programming formulation is proposed that guarantees overlap among patches of mature forest habitat that arise and disappear over time as the forest ages and is harvested. Four real forests are used to illustrate the mechanics of the approach and to show that the model is computationally tractable and in some cases even makes harvest scheduling models with minimum patch size constraints easier to solve.

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[42]
Landsberg J.2003. Modelling forest ecosystems: State of the art, challenges, and future directions[J]. Canadian Journal of Forest Research, 33(3): 385-397.

[43]
Li C, Flannigan M D, Corns I G W.2000. Influence of potential climate change on forest landscape dynamics of west-central Alberta[J]. Canadian Journal of Forest Research, 30(12): 1905-1912.Discusses how changes in climatic conditions may influence both forest biomass accumulation rates and natural disturbance regimes. Use of yield equations to describe changes in biomass accumulation of forests under various climatic conditions; Speculation about the impacts of global warming; Findings that the simulations showed decreases in landscape fragmentation and landscape diversity.

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[44]
Li C, Hans H, Barclay H, et al.2008. Comparison of spatially explicit forest landscape fire disturbance models[J]. Forest Ecology and Management, 254(3): 499-510.Comparisons of model behaviors are an efficient way of understanding the differences amongst various models and thus providing guidance to model users for selecting suitable models for their own purposes. This study focuses on a comparison of the most commonly used fire spread algorithms used in scenario fire regime models. This paper provides an overview of fire regime modeling and describes a simulation model, Ecological Disturbance Model, as a simulation shell for such a comparison using the Fort A La Corne forest area in central Saskatchewan, Canada, as the study area. Simulation results suggested that for a fire scenario modeling approach, various fire spread algorithms such as DISPATCH, percolation, and cellular automata (CA) may not result in significant differences between user-defined and simulated fire frequencies; however, significant differences in simulated forest dynamics could result when using different fire spread algorithms. The simulation results from DISPATCH and CA are more similar than those from the percolation algorithm; however, the latter appeared to be a better representative of observed fire spread processes due to its underlying assumption of fire spread mechanisms. Simulation results also suggested that the fire spread algorithms that replicate four or eight direction fire spread in percolation and CA will not make a significant difference in simulated fire regimes or forest dynamics. It is thus recommend that using simulation shells as a tool to take alternative assumptions or models into account to narrow down the uncertainty parameters and avoid the paradoxes in the modeling of natural resource management.

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[45]
Li C, Perera A H.1997. ON-FIRE: A landscape model for simulating the fire regime of northwest Ontario[M]//Chen X, Dai X, Hu T. Ecological research and sustainable development. Beijing, China: China Environmental Science Press: 369-392.

[46]
Liu J G, Ashton P S.1998. FORMOSAIC: An individual-based spatially explicit model for simulating forest dynamics in landscape mosaics[J]. Ecological Modelling, 106(2-3): 177-200.A forest is embedded in heterogeneous landscape mosaics and interacts with the surrounding environment through processes such as seed dispersal. Previous forest models, however, have either ignored such interactions or made unrealistic assumptions. We developed a landscape model (FORMOSAIC) that explicitly considers not only the dynamics of a focal forest but also ecological impacts of adjacent areas on the focal forest. FORMOSAIC is hierarchically structured, spatially explicit, multi-scale, stochastic, and individual-based. It integrates information of tree position, regeneration, growth, death, spatial interaction, and environmental factors. Data for parameterizing FORMOSAIC were mainly from a 50 ha permanent study plot in the Pasoh forest reserve (Malaysia), which contained over 800 tree species and more than 330鈥000 trees with diameter at breast height (dbh) 鈮1.0 cm. Model simulation results agreed well with independent field census data in terms of species richness, species composition, tree abundance, and basal area at two spatial scales. Sensitivity analysis indicated that minimum harvest size was the most sensitive parameter. Species richness was particularly sensitive to the duration of seed immigration from species-rich surrounding forests. For tree abundance and basal area, the second most sensitive parameters varied at two spatial scales. Through uncertainty analysis we found that many parameters had scale-dependent and non-linear relationships with species richness, tree abundance, and basal area. There also existed significant interactive effects between parameters. The model could be a useful tool for addressing important issues such as fragmentation and deforestation in forest management for species diversity and timber production from a landscape perspective.

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[47]
Loudermilk E L, Stanton A, Scheller R M, et al.2014. Effectiveness of fuel treatments for mitigating wildfire risk and sequestering forest carbon: A case study in the Lake Tahoe Basin[J]. Forest Ecology and Management, 323: 114-125.Fuel-reduction treatments are used extensively to reduce wildfire risk and restore forest diversity and function. In the near future, increasing regulation of carbon (C) emissions may force forest managers to balance the use of fuel treatments for reducing wildfire risk against an alternative goal of C sequestration. The objective of this study was to evaluate how long-term fuel treatments mitigate wildfires and affect forest C. For the Lake Tahoe Basin in the central Sierra Nevada, USA, fuel treatment efficiency was explored with a landscape-scale simulation model, LANDIS-II, using five fuel treatment scenarios and two (contemporary and potential future) fire regimes. Treatment scenarios included applying a combination of light (hand) and moderate (mechanical) forest thinning continuously through time and transitioning from these prescriptions to a more mid-seral thinning prescription, both on a 15 and 30year rotation interval. In the last scenario, fuel treatments were isolated to around the lake shore (nearby urban settlement) to simulate a low investment alternative were future resources may be limited. Results indicated that the forest will remain a C sink regardless of treatment or fire regime simulated, due to the landscape legacy of historic logging. Achievement of a net C gain required decades with intensive treatment and depended on wildfire activity: Fuel treatments were more effective in a more active fire environment, where the interface between wildfires and treatment areas increased and caused net C gain earlier than as compared to our scenarios with less wildfire activity. Fuel treatments were most effective when continuously applied and strategically placed in high ignition areas. Treatment type and re-application interval were less influential at the landscape scale, but had notable effects on species dynamics within management units. Treatments created more diverse forest conditions by shifting dominance patterns to a more mixed conifer system, with a higher proportion of fire-tolerant species. We demonstrated that a small amount of wildfire on the landscape resulted in significant changes in the C pool, and that strategically placed fuel treatments substantially reduced wildfire risk, increased fire resiliency of the forest, and is beneficial for long-term C management. Implications for landscape management included consideration for prioritization of treatment areas and creating ideal re-entry schedules that meet logistic, safety, and conservation goals. In forests with a concentrated wildland urban interface, fuel treatments may be vital for ensuring human welfare and enhancing forest integrity in a fire-prone future.

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[48]
Mladenoff D J.2004. LANDIS and forest landscape models[J]. Ecological Modelling, 180(1): 7-19.This paper provides contextual documentation of the LANDIS model development to provide a framework for the other papers in this special issue. The LANDIS model of forest landscape disturbance and succession was developed since the early 1990s as a research and management tool that optimizes the possible landscape extent (100聽s聽ha to 1000聽s聽km 2 ), while providing mechanistic detail adequate for a broad range of potential problems. LANDIS is a raster model, and operates on landscapes mapped as cells, containing tree species age classes. Spatial processes, such as seed dispersal, and disturbances such as fire, wind, and harvesting can occur. LANDIS development benefited from the modelling and research progress of the 1960s to the1980s, including the growth of landscape ecology during the 1980s. In the past decade the model has been used by colleagues across North America, as well as in Europe and China. This has been useful to those not able to undertake the cost and effort of developing their own model, and it has provided a growing diverse set of test landscapes for the model. These areas include temperate, southern, and boreal forests of eastern North America, to montane and boreal western forests, coastal California forest and shrub systems, boreal Finnish forests, and montane forests in Switzerland and northeastern China. The LANDIS model continues to be refined and developed. Papers in this special issue document recent work. Future goals include integration within a larger land use change model, and applications to landscape and regional global change projection based on newly incorporated biomass and carbon dynamics.

DOI

[49]
Mladenoff D J, Baker W B.1999. Spatial modeling of forest landscape change: Approaches and application[M]. Cambridge, UK: Cambridge University Press.

[50]
Nyerges T L.1993. Understanding the scope of GIS: Its relationship to environmental modeling[M]//Goodchild M F, Parks B O, Steyaert L T. Environmental modeling with GIS. New York: Oxford University Press: 75-93.

[51]
Orsi F, Church R L, Geneletti D.2011. Restoring forest landscapes for biodiversity conservation and rural livelihoods: A spatial optimisation model[J]. Environmental Modelling & Software, 26(12): 1622-1638.Conserving nature in the presence of humans is especially challenging in areas where livelihoods are largely based on locally available natural resources. The restoration of forests in such contexts calls for the identification of sites and actions that improve biodiversity protection, and ensure the provision of and accessibility to other forest-related ecosystem services. This paper introduces an integer-linear programming (ILP) approach to identify reforestation priorities that achieve such goals. Applications of ILP to nature conservation are many, but only a few of them deal with the problem of restoration, and none of the available models considers the basic needs of the local population. Given constraints on a restoration budget, the potential conversion of productive lands and the travel time to reach harvestable forest, the model maximises the amount of reforestation area (weighted by priority values) and minimises the harvesting of existing forest, while ensuring the conservation of landscape diversity, the satisfaction of timber demands and the stabilisation of erosion-prone land. As an input, suitability maps, generated through a combination of ecological criteria, are used to prioritise the selection of reforestation sites. An application to a 430km 2 area in Central Chiapas (Mexico) resulted in compact patches and thus a manageable reforestation plan. Acceptable trade-offs were found between the amount of soil stabilisation possible and the prioritisation goals, while uncertainty in the prioritisation scores did not significantly affect the results. We show that restoration actions can be spatially designed to benefit both nature and people with minimal losses on both sides.

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[52]
Pausas J G.2006. Simulating Mediterranean landscape pattern and vegetation dynamics under different fire regimes[J]. Plant Ecology, 187(2): 249-259.In the Mediterranean Basin, landscape patterns are strongly human-modified. In recent decades, because of industrialisation and rural exodus, many fields have been abandoned, generating changes in the landscape pattern. In this framework, I aim to study the effect of landscape pattern on landscape dynamic processes in the Mediterranean Basin using simulation models and considering that fire may interact with landscape pattern. First I generate a gradient of five artificial random landscapes. In each landscape I include four species types growing in the Mediterranean Basin, each type with different plant traits (Quercus, Pinus, Erica and Cistus types). In each landscape scenario, each species covers 30% of the landscape but with a different spatial distribution, from the coarsest-grained (L1) to the finest-grained (L5). Then, the dynamics of each of these five landscapes were simulated for 100聽years using the F ATELAND simulation model. Simulations were run with six fire regime scenarios in each landscape scenario (no fire, mean fire interval of 80, 40, 20, 10 and 5聽years). Landscape attributes were computed for the initial and the final landscapes. As expected, the results suggest that, as expected, some species increase and others decrease depending on the fire regime. However, the results also show that different landscape structures produce different dynamics and thus that there is a clear interaction between landscape pattern and fire regime. For instance, coarse-grained spatial patterns generate slower dynamics than fine-grained patterns, and fire-sensitive species are maintained longer under coarse-grained patterns (i.e., fragmentation accelerates extinction of fire-sensitive species).

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[53]
Pennanen J, Greene D F, Fortin M J, et al.2004. Spatially explicit simulation of long-term boreal forest landscape dynamics: Incorporating quantitative stand attributes[J]. Ecological Modelling, 180(1): 195-209.Spatial simulation models of long-term dynamics of forest landscapes are needed for investigating how different actual or potential disturbance regimes determine the structure and dynamics of forest landscapes. We propose a new approach to bridge the forest stand and landscape processes. Hence, while interested in the boreal forest dynamics at the landscape level, we develop a submodel of stand-level forest dynamics that responds to the landscape-level processes in a spatially explicit landscape model. Compared to the LANDIS model that we used as a starting point, our approach incorporates, in a spatially explicit and quantitative manner: (1) stand-level prediction of basal area and tree volume, and (2) seed dispersal, and sexual and asexual regeneration. Stand development is partly based on growth tables given as model input which means that stand submodel behavior is constrained within a reasonable range. We tested the approach in simulating the development of mixed boreal forests of Quebec, Canada. The simulations demonstrate that stand dynamics can be calibrated to

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[54]
Pennanen J, Kuuluvainen T.2002. A spatial simulation approach to natural forest landscape dynamics in boreal Fennoscandia[J]. Forest Ecology and Management, 164(1-3): 157-175.Spatially explicit simulation models are needed for predicting the landscape-level effects of historical or potential disturbance regimes. We present a stochastic, grid-based model incorporating a mechanistic model of fire spread, tailored for simulating the fire-driven forest landscapes of boreal Fennoscandia, based on the North American LANDIS model. The approach is semi-quantitative, and suited to qualitative predictions on the fire鈥搇andscape interaction. We tested the extent to which the model is able to reproduce the current forest composition of the Ulvinsalo nature reserve (2516ha) in eastern Finland, based on a GIS map of ecological site types and an empirically-derived fire history of the area. The model satisfactorily predicted the forest composition based on the dominant tree species and the relative occurrence of tree species, as well as the qualitative pattern of the tree age distribution. The major discrepancies were related to the occurrence of sub-dominant broadleaved species and the spatial pattern of fire frequency. We conclude that while the modeling approach is suitable for qualitative exploration, quantitatively accurate predictions require better empirical data on fire behavior, tree autecology, and their interaction, as well as more rigorous methods of model parameterization. The simulated scenario illustrates the change in forest composition from the uneven-aged old-growth pine forests maintained by the historical fire regime to the old-growth spruce forests dominating in the absence of fires.

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[55]
Perry G L W, Enright N J.2002. Spatial modelling of landscape composition and pattern in a maquis-forest complex, Mont Do, New Caledonia[J]. Ecological Modelling, 152(2-3): 279-302.The endemic conifer species Araucaria laubenfelsii forms a characteristic component of the various forest and non-forest vegetation assemblages present on Mont Do, New Caledonia. It is the only tree species in the landscape to be found in both maquis (shrubland) and forest patches. Adult density ranges from zero in some parts of the maquis to extremely high in others, forming an Araucaria woodland. The current landscape structure on Mont Do appears to be the result of recurrent disturbance, especially by fire. However, it is uncertain whether this structure is the result of recent changes in the fire regime (as a result of Melanesian and European phases of human colonisation of New Caledonia) and is therefore transient, or represents a more stable formation. A spatially explicit grid-based model was developed to explore some of these issues. The model is primarily concerned with understanding how alterations to the disturbance regime (past and future) may influence the landscape structure and, in particular, whether the current landscape pattern is a result of increased fire frequencies over the last century and whether certain unusual vegetation assemblages in the landscape, such as maquis with emergent Araucaria , are likely to persist in the long-term. The ecological context of the spatial landscape model is described as is a simple sensitivity analysis of the model and an example of its use under 鈥榖aseline鈥 conditions.

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[56]
Perry G L W, Enright N J.2006. Spatial modelling of vegetation change in dynamic landscapes: A review of methods and applications[J]. Progress in Physical Geography, 30(1): 47-72.ABSTRACT

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[57]
Perry G L W, Millington J D A.2008. Spatial modelling of succession-disturbance dynamics in forest ecosystems: Concepts and examples[J]. Perspectives in Plant Ecology, Evolution and Systematics, 9(3-4): 191-210.Over the last few decades it has become increasingly obvious that disturbance, whether natural or anthropogenic in origin, is ubiquitous in ecosystems. Disturbance-related processes are now considered to be important determinants of the composition, structure and function of ecological systems. However, because disturbance and succession processes occur across a wide range of spatio-temporal scales their empirical investigation is difficult. To counter these difficulties much use has been made of spatial modelling to explore the response of ecological systems to disturbance(s) occurring at spatial scales from the individual to the landscape and above, and temporal scales from minutes to centuries. Here we consider such models by contrasting two alternative motivations for their development and use: prediction and exploration, with a focus on forested ecosystems. We consider the two approaches to be complementary rather than competing. Predictive modelling aims to combine knowledge (understanding and data) with the goal of predicting system dynamics; conversely, exploratory models focus on developing understanding in systems where uncertainty is high. Examples of exploratory modelling include model-based explorations of generic issues of criticality in ecological systems, whereas predictive models tend to be more heavily data-driven (e.g. species distribution models). By considering predictive and exploratory modelling alongside each other, we aim to illustrate the range of methods used to model succession and disturbance dynamics and the challenges involved in the model-building and evaluation processes in this arena.

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[58]
Rastetter E B, Aber J D, Peters D P C, et al.2003. Using mechanistic models to scale ecological processes across space and time[J]. BioScience, 53(1): 68-76.Human activities affect the natural environment at local to global scales. To understand these effects, knowledge derived from short-term studies on small plots needs to be projected to much broader spatial and temporal scales. One way to project short-term, plot-scale knowledge to broader scales is to embed that knowledge in a mechanistic model of the ecosystem. The National Science Foundation's Long Term Ecological Research (LTER) Network makes two vital contributions to this type of modeling effort: (1) a commitment to multidisciplinary research at individual sites, which results in a broad range of mutually consistent data, and (2) long-term data sets essential for estimating rate constants for slow ecosystem processes that dominate long-term ecosystem dynamics. In this article, we present four examples of how a mechanistic approach to modeling ecological processes can be used to make projections to broader scales. The models are all applied to sites in the LTER Network. [References: 48]

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[59]
Roberts D W, Betz D W.1999. Simulating landscape vegetation dynamics of Bryce Canyon National Park with the vital attributes/fuzzy systems model VAFS/LANDSIM[M]//Mladenoff D J, Baker W L. Spatial modeling of forest landscape change: Approaches and applications. Cambridge, UK: Cambridge University Press: 99-123.

[60]
Rykiel E J Jr.1996. Testing ecological models: The meaning of validation[J]. Ecological Modelling, 90(3): 229-244.

[61]
Scheller R M, Domingo J B, Sturtevant B R, et al.2007. Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution[J]. Ecological Modelling, 201(3-4): 409-419.We introduce LANDIS-II, a landscape model designed to simulate forest succession and disturbances. LANDIS-II builds upon and preserves the functionality of previous LANDIS forest landscape simulation models. LANDIS-II is distinguished by the inclusion of variable time steps for different ecological processes; our use of a rigorous development and testing process used by software engineers; and an emphasis on collaborative features including a flexible, open architecture. We detail the variable time step logic and provide an overview of the system architecture. Finally, we demonstrate model behavior and sensitivity to variable time steps through application to a large boreal forest landscape. We simulated pre-industrial forest fire regimes in order to establish base-line conditions for future management. Differing model time steps substantially altered our estimates of pre-industrial forest conditions. Where disturbance frequency is relatively high or successional processes long, the variable time steps may be a critical element for successful forest landscape modeling.

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[62]
Scheller R M, Mladenoff D J.2004. A forest growth and biomass module for a landscape simulation model, LANDIS: Design, validation, and application[J]. Ecological Modelling, 180(1): 211-229.Predicting the long-term dynamics of forest systems depends on understanding multiple processes that often operate at vastly different scales. Disturbance and seed dispersal are landscape-scale phenomena and are spatially linked across the landscape. Ecosystem processes (e.g., growth and decomposition) have high annual and inter-specific variation and are generally quantified at the scale of a forest stand. To link these widely scaled processes, we used biomass (living and dead) as an integrating variable that provides feedbacks between disturbance and ecosystem processes and feedbacks among multiple disturbances. We integrated a simple model of biomass growth, mortality, and decay into LANDIS, a spatially dynamic landscape simulation model. The new biomass module was statically linked to PnET-II, a generalized ecosystem process model. The combined model simulates disturbances (fire, wind, harvesting), dispersal, forest biomass growth and mortality, and inter- and intra-specific competition. We used the model to quantify how fire and windthrow alter forest succession, living biomass and dead biomass across an artificial landscape representative of northern Wisconsin, USA. In addition, model validation and a sensitivity analysis were conducted.

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[63]
Scheller R M, Mladenoff D J.2005. A spatially interactive simulation of climate change, harvesting, wind, and tree species migration and projected changes to forest composition and biomass in northern Wisconsin, USA[J]. Global Change Biology, 11(2): 307-321.In the coming century, forecast climate changes caused by increasing greenhouse gases may produce dramatic shifts in tree species distributions and the rates at which individual tree species sequester carbon or release carbon back to the atmosphere. The species composition and carbon storage capacity of northern Wisconsin (USA) forests are expected to change significantly as a result. Projected temperature changes are relatively large (up to a 5.8掳C increase in mean annual temperature) and these forests encompass a broad ecotone that may be particularly sensitive to climate change. Our objective was to estimate the combined effects of climate change, common disturbances, and species migrations on regional forests using spatially interactive simulations. Multiple scenarios were simulated for 200 years to estimate aboveground live biomass and tree species composition. We used a spatially interactive forest landscape model (LANDIS-II) that includes individual tree species, biomass accumulation and decomposition, windthrow, harvesting, and seed dispersal. We used data from two global circulation models, the Hadley Climate Centre (version 2) and the Canadian Climate Center (version 1) to generate transient growth and decomposition parameters for 23 species. The two climate change scenarios were compared with a control scenario of continuing current climate conditions. The results demonstrate how important spatially interactive processes will affect the aboveground live biomass and species composition of northern Wisconsin forests. Forest composition, including species richness, is strongly affected by harvesting, windthrow, and climate change, although five northern species ( Abies balsamea , Betula papyrifera , Picea glauca , Pinus banksiana , P. resinosa ) are lost in both climate scenarios regardless of disturbance scenario. Changes in aboveground live biomass over time are nonlinear and vary among ecoregions. Aboveground live biomass will be significantly reduced because of species dispersal and migration limitations. The expected shift towards southern oaks and hickory is delayed because of seed dispersal limitations.

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[64]
Scheller R M, Mladenoff D J.2007. An ecological classification of forest landscape simulation models: Tools and strategies for understanding broad-scale forested ecosystems[J]. Landscape Ecology, 22(4): 491-505.<a name="Abs1"></a>Computer models are increasingly being used by forest ecologists and managers to simulate long-term forest landscape change. We review models of forest landscape change from an ecological rather than methodological perspective. We developed a classification based on the representation of three ecological criteria: spatial interactions, tree species community dynamics, and ecosystem processes. Spatial interactions are processes that spread across a landscape and depend upon spatial context and landscape configuration. Communities of tree species may change over time or can be defined a priori. Ecosystem process representation may range from no representation to a highly mechanistic, detailed representation. Our classification highlights the implicit assumptions of each model group and helps define the problem set for which each model group is most appropriate. We also provide a brief history of forest landscape simulation models, summarize the current trends in methods, and consider how forest landscape models may evolve and continue to contribute to forest ecology and management. Our classification and review can provide novice modelers with the ecological context for understanding or choosing an appropriate model for their specific hypotheses. In addition, our review clarifies the challenges and opportunities that confront practicing model users and model developers.

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[65]
Scheller R M, Spencer W D, Rustigian-Romsos H, et al.2011. Using stochastic simulation to evaluate competing risks of wildfires and fuels management on an isolated forest carnivore[J]. Landscape Ecology, 26(10): 1491-1504.Natural resource managers are often challenged with balancing requirements to maintain wildlife populations and to reduce risks of catastrophic or dangerous wildfires. This challenge is exemplified in the Sierra Nevada of California, where proposals to thin vegetation to reduce wildfire risks have been highly controversial, in part because vegetation treatments could adversely affect an imperiled population of the fisher ( Martes pennanti ) located in the southern Sierra Nevada. The fisher is an uncommon forest carnivore associated with the types of dense, structurally complex forests often targeted for fuel reduction treatments. Vegetation thinning and removal of dead-wood structures would reduce fisher habitat value and remove essential habitat elements used by fishers for resting and denning. However, crown-replacing wildfires also threaten the population鈥檚 habitat, potentially over much broader areas than the treatments intended to reduce wildfire risks. To investigate the potential relative risks of wildfires and fuels treatments on this isolated fisher population, we coupled three spatial models to simulate the stochastic and interacting effects of wildfires and fuels management on fisher habitat and population size: a spatially dynamic forest succession and disturbance model, a fisher habitat model, and a fisher metapopulation model, which assumed that fisher fecundity and survivorship correlate with habitat quality. We systematically varied fuel treatment rate, treatment intensity, and fire regime, and assessed their relative effects on the modeled fisher population over 60years. After estimating the number of adult female fishers remaining at the end of each simulation scenario, we compared the immediate negative effects of fuel treatments to the longer-term positive effect of fuel treatment (via reduction of fire hazard) using structural equation modeling. Our simulations suggest that the direct, negative effects of fuel treatments on fisher population size are generally smaller than the indirect, positive effects of fuel treatments, because fuels treatments reduced the probability of large wildfires that can damage and fragment habitat over larger areas. The benefits of fuel treatments varied by elevation and treatment location with the highest net benefits to fisher found at higher elevations and within higher quality fisher habitat. Simulated fire regime also had a large effect with the largest net benefit of fuel treatments occurring when a more severe fire regime was simulated. However, there was large uncertainty in our projections due to stochastic spatial and temporal wildfires dynamic and fisher population dynamics. Our results demonstrate the difficulty of projecting future populations in systems characterized by large, infrequent, stochastic disturbances. Nevertheless, these coupled models offer a useful decision-support system for evaluating the relative effects of alternative management scenarios; and uncertainties can be reduced as additional data accumulate to refine and validate the models.

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[66]
Schumacher S, Bugmann H, Mladenoff D J.2004. Improving the formulation of tree growth and succession in a spatially explicit landscape model[J]. Ecological Modelling, 180(1): 175-194.To demonstrate the utility of the added detail, we applied the model in scenario mode under a range of changes in climatic and disturbance parameters, assuming a continuation of the current management regime. The simulations showed that the various driving forces have quite different effects on different species, and that their combined effect differs from one scenario to the next. Notably, there are few models that integrate forest growth and succession with disturbance dynamics in a semi-mechanistic manner. Our version of LANDIS achieves this integration based on simple concepts and methods that do not require many parameter estimates. We conclude that the new model has the potential to provide an integrated picture of the impacts of both direct and indirect effects of climate change on forest landscape dynamics.

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[67]
Seidl R, Rammer W, Scheller R M, et al.2012. An individual-based process model to simulate landscape-scale forest ecosystem dynamics[J]. Ecological Modelling, 231(1): 87-100.Forest ecosystem dynamics emerges from nonlinear interactions between adaptive biotic agents (i.e., individual trees) and their relationship with a spatially and temporally heterogeneous abiotic environment. Understanding and predicting the dynamics resulting from these complex interactions is crucial for the sustainable stewardship of ecosystems, particularly in the context of rapidly changing environmental conditions. Here we present iLand (the individual-based forest landscape and disturbance model), a novel approach to simulating forest dynamics as an emergent property of environmental drivers, ecosystem processes and dynamic interactions across scales. Our specific objectives were (i) to describe the model, in particular its novel approach to simulate spatially explicit individual-tree competition for resources over large scales within a process-based framework of physiological resource use, and (ii) to present a suite of evaluation experiments assessing iLands ability to simulate tree growth and mortality for a wide range of forest ecosystems. Adopting an approach rooted in ecological field theory, iLand calculates a continuous field of light availability over the landscape, with every tree represented by a mechanistically derived, size- and species-dependent pattern of light interference. Within a hierarchical multi-scale framework productivity is derived at stand-level by means of a light-use efficiency approach, and downscaled to individuals via local light availability. Allocation (based on allometric ratios) and mortality (resulting from carbon starvation) are modeled at the individual-tree level, accounting for adaptive behavior of trees in response to their environment. To evaluate the model we conducted simulations over the extended environmental gradient of a longitudinal transect in Oregon, USA, and successfully compared results against independently observed productivity estimates (63.4% of variation explained) and mortality patterns in even-aged stands. This transect experiment was furthermore replicated for a different set of species and ecosystems in the Austrian Alps, documenting the robustness and generality of our approach. Model performance was also successfully evaluated for structurally and compositionally complex old-growth forests in the western Cascades of Oregon. Finally, the ability of our approach to address forest ecosystem dynamics at landscape scales was demonstrated by a computational scaling experiment. In simulating the emergence of ecosystem patterns and dynamics as a result of complex process interactions across scales our approach has the potential to contribute crucial capacities to understanding and fostering forest ecosystem resilience under changing climatic conditions.

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[68]
Sessions J, Johnson K N, Franklin J F, et al.1999. Achieving sustainable forest structures on fire-prone landscapes while pursuing multiple goals[M]//Mladenoff D J, Baker W L. Spatial modeling of forest landscape change: Approaches and applications. Cambridge, UK: Cambridge University Press: 210-253.

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Shang Z B, He S H, Crow R T, et al.2004. Fuel load reductions and fire risk in central hardwood forests of the United States: A spatial simulation study[J]. Ecological Modelling, 180(1): 89-102.

[70]
Shang Z B, He H S, Lytle D E, et al.2007. Modeling the long-term effects of fire suppression on central hardwood forests in Missouri Ozarks, using LANDIS[J]. Forest Ecology and Management, 242(2-3): 776-790.ABSTRACT Fire suppression has been found to dramatically change fire regimes, lead to accumulation of fuels, and alter forest composition and species abundance in the Central Hardwood Forests in the Missouri Ozarks, United States. After a half century of fire suppression, fire hazards have increased to a high level and high intensity fires are more likely to occur. We used LANDIS, a spatially explicit landscape dynamics model, to simulate the long-term effects of fire suppression on forests in Missouri Ozarks. Specifically, we examined to what extent fire suppression would affect fuel loads and fire hazards, and how fire suppression would affect forest tree species abundance. Using a spatial modeling approach, we conducted 200-year simulations of two management scenarios: (1) a fire suppression scenario circa 1990s and (2) a historic fire regime scenario prior to fire suppression, with a mean fire-return interval of 14 years. Under the fire suppression scenario, the simulation showed that both fine and coarse fuels were at a medium-high level after a few more decades of fire suppression. Fire hazard also rapidly increased to a medium-high level within a few decades. After one century of fire suppression, simulated fire intensity increased to a dangerous level, with more than 3/4 of the fires at a medium-high intensity level. Fire suppression also led to distinct changes in species abundance; the pine and oak&ndash;pine forests which used to dominate the study area prior to fire suppression were replaced by mixed-oak forests. This study suggests that it may be desirable to re-introduce frequent fire. By greatly increasing the use of fire over current management levels, our simulation suggests less accumulation of dangerous fuels, reduced fire hazard, and decreased occurrence of high intensity fires. Results imply that frequent fire would greatly increase the abundance of fire-resistant species (e.g., shortleaf pine) and decrease the abundance of more fire-sensitive species such as red oaks. Such a compositional shift should also decrease the recent phenomenon of widespread oak decline events.

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[71]
Shugart H H.1984. A theory of forest dynamics: The ecological implications of forest succession models[M]. New York: Springer.

[72]
Shugart H H, Smith T M, Post W M.1992. The potential for application of individual-based simulation models for assessing the effects of global change[J]. Annual Review of Ecology and Systematics, 23(1): 15-38.A total of 328 daily assessments of standing height were made on one boy between the ages of 12.83 and 13.95 years; 292 of these were replicates to establish reliability of measurement. On 300 days, measurements were taken in the morning within 1/2 h of rising (which varied between 0700 and 1100 h) and repeated before bed on the same day, between 2100 and 2300 h. The standard error of measurement from 292 duplicate measurements was 0.12 cm. A mean of 0.98 +/- 0.2 cm decrease in stature occurred during the course of the day. A similar decrease was found on three occasions after 2-3 h naps.

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[73]
Staus N L, Strittholt J R, DellaSala D A.2010. Evaluating areas of high conservation value in Western Oregon with a decision-support model[J]. Conservation Biology, 24(3): 711-720.The Northwest Forest Plan was implemented in 1994 to protect habitat for species associated with old-growth forests, including Northern Spotted Owls (Strix occidentailis caurina) in Washington, Oregon, and northern California (U.S.A.). Nevertheless, 10-year monitoring data indicate mixed success in meeting the ecological goals of the plan. We used the ecosystem management decision-support model to evaluate terrestrial and aquatic habitats across the landscape on the basis of ecological objectives of the Northwest Forest Plan, which included maintenance of late-successional and old-growth forest, recovery, and maintenance of Pacific salmon (Oncorhynchus spp.), and viability of Northern Spotted Owls. Areas of the landscape that contained habitat characteristics that supported these objectives were considered of high conservation value. We used the model to evaluate ecological condition of each of the 36, 180 township and range sections of the study area. Eighteen percent of the study area was identified as habitat of high conservation value. These areas were mostly on public lands. Many of the sections that contained habitat of exceptional conservation value were on Bureau of Land Management land that has been considered for management-plan revisions to increase timber harvests. The results of our model can be used to guide future land management in the Northwest Forest Plan area, and illustrate how decision-support models can help land managers develop strategies to better meet their goals.

DOI PMID

[74]
Syphard A D, Franklin J.2004. Spatial aggregation effects on the simulation of landscape pattern and ecological processes in southern California plant communities[J]. Ecological Modelling, 180(1): 21-40.Accurate representation of the processes and components of natural systems is necessary for reliable ecological models, yet data generalization is often needed to reduce unneeded detail and to increase model efficiency. A spatially explicit, raster-based simulation model of disturbance and succession (LANDIS) was used to examine the effects of spatial aggregation on modeled pattern (species composition) and process (fire disturbance). At systematically increased levels of data aggregation, the model was tested on two landscapes, one based on species patterns that were initially random and one based on more realistic distributions, over 500-year time periods for a southern California (Mediterranean-climate) landscape. In both landscapes, spatial aggregation resulted in less frequent, more unpredictable, yet higher-severity fires, and plant species cover became more variable over time in response to infrequent, high-severity fire. The systematic effects of aggregation on pattern, process, and species response suggest that modelers can detect ranges of resolutions for which parameters hold, helping to identify appropriate levels of spatial generalization for their research.

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[75]
Turner M G.1987. Spatial simulation of landscape changes in Georgia: A comparison of 3 transition models[J]. Landscape Ecology, 1(1): 29-36.Spatial simulation models were developed to predict temporal changes in land use patterns in a piedmont county in Georgia (USA). Five land use categories were included: urban, cropland, abandoned cropland, pasture, and forest. Land use data were obtained from historical aerial photography and digitized into a matrix based on a 1 ha grid cell format. Three different types of spatial simulation were compared: (1) random simulations based solely on transition probabilities; (2) spatial simulations in which the four nearest neighbors (adjacent cells only) influence transitions; and (3) spatial simulations in which the eight nearest neighbors (adjacent and diagonal cells) influence transitions. Models and data were compared using the mean number and size of patches, fractal dimension of patches, and amount of edge between land uses. The random model simulated a highly fragmented landscape having numerous, small patches with relatively complex shapes. The two versions of the spatial model simulated cropland well, but simulated patches of forest and abandoned cropland were fewer, larger, and more simple than those in the real landscape. Several possible modifications of model structure are proposed. The modeling approach presented here is a potentially general one for simulating human-influenced landscapes.

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[76]
Urban D L.2000. Using model analysis to design monitoring programs for landscape management and impact assessment[J]. Ecological Applications, 10(6): 1820-1832.While ecologists have long recognized the key role of monitoring programs in natural-resource management, we have only recently come to appreciate the logistical difficulties of designing powerful yet efficient schemes for monitoring large, heterogeneous landscapes. Such designs are especially challenging if the signal to be monitored is uncertain, such as in the case of ecosystem response to c...

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[77]
Urban D L.2005. Modeling ecological processes across scales[J]. Ecology, 86(8): 1996-2006.The issue of scaling impinges on every aspect of landscape ecology and much of ecology in general. Consequently, the topic has invited a vast commentary. One result of scaling research is so-called scaling laws that describe how observations scale (e.g., as power laws). Importantly, such scaling rules seldom derive from a process-based understanding of why they emerge. Alternatively, the task of scaling is often addressed via simulation models. This is a scaling operation about which we are somewhat less confident, although recent advances in computing power and computational statistics provide for some promising new solutions. Here, I focus on methods for scaling simulations developed at fine grain and small extent, to their implications over much larger extent. The intent in scaling is to simplify the model while retaining those details essential for larger-scale applications. This approach should lead to scaling rules that are well founded in fine-scale ecological process and yet useful for making predictions at the larger scales of management and environmental policy.

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[78]
Urban D L, Shugart H H.1992. Individual-based models of forest succession[M]//Glennlewin D C, Peet R K, Veblen T T. Plant succession: Theory and prediction. London: Chapman and Hall.

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Verboom J, Wamelink W.2005. Spatial modeling in landscape ecology[M]//Wiens J A, Moss M R. Issues and perspectives in landscape ecology. Cambridge, UK: Cambridge University Press: 79-89.

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Walters C J.1993. Dynamic models and large scale field experiments in environmental impact assessment and management[J]. Australian Journal of Ecology, 18(1): 53-61.

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Wang W J, He H S, Fraser J S, et al.2014. LANDIS PRO: A landscape model that predicts forest composition and structure changes at regional scales[J]. Ecography, 37(3): 225-229.LANDIS PRO predicts forest composition and structure changes incorporating species-, stand-, and landscape-scales processes at regional scales. Species-scale processes include tree growth, establishment, and mortality. Stand-scale processes contain density- and size-related resource competition that regulates self-thinning and seedling establishment. Landscape-scale processes include seed dispe...

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Wehmeyer G.2012. Simulations and analysis of a 2012 Kansas Wildland fire using FARSITE[D]. Texas: The University of Texas at Austin.

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Wimberly M C, Spies T A, Long C J, et al.2000. Simulating historical variability in the amount of old forests in the Oregon Coast Range[J]. Conservation Biology, 14(1): 167-180.Abstract: We developed the landscape age-class demographics simulator (LADS) to model historical variability in the amount of old-growth and late-successional forest in the Oregon Coast Range over the past 3,000 years. The model simulated temporal and spatial patterns of forest fires along with the resulting fluctuations in the distribution of forest age classes across the landscape. Parameters describing historical fire regimes were derived from data from a number of existing dendroecological and paleoecological studies. Our results indicated that the historical age-class distribution was highly variable and that variability increased with decreasing landscape size. Simulated old-growth percentages were generally between 25% and 75% at the province scale (2,250,000 ha) and never fell below 5%. In comparison, old-growth percentages varied from 0 to 100% at the late-successional reserve scale (40,000 ha). Province-scale estimates of current old-growth (5%) and late-successional forest (11%) in the Oregon Coast Range were lower than expected under the simulated historical fire regime, even when potential errors in our parameter estimates were considered. These uncertainties do, however, limit our ability to precisely define ranges of historical variability. Our results suggest that in areas where historical disturbance regimes were characterized by large, infrequent fires, management of forest age classes based on a range of historical variability may be feasible only at relatively large spatial scales. Comprehensive landscape management strategies will need to consider other factors besides the percentage of old forests on the landscape, including the spatial pattern of stands and the rates and pathways of landscape change. Resumen: Desarrollamos un simulador de demograf&iacute;as de clases de edades en paisajes (LADS) para modelos la variabilidad hist&oacute;rica de la cantidad de bosque maduro y bosque sucesional tard&iacute;o en la cordillera costera de Oregon, USA para los &uacute;ltimos 3,000 a&ntilde;os. El modelo simul&oacute; patrones espaciales y temporales de incendios junto con fluctuaciones en la distribuci&oacute;n de clases de edades del bosque utilizando datos de un n&uacute;mero de estudios dendroec&oacute;logicos y paleoecol&oacute;gicos existentes. Nuestros resultados indican que la distribuci&oacute;n hist&oacute;rica de las clases de edades fue altamente variable y que la variabilidad incrementaba con una disminuci&oacute;n en el tama&ntilde;o del paisaje. Procentajes de bosque maduro simulados fueron generalmente entre 25% y 75% a escala de provincia (2,250,000 ha) y nunca fue menor a 5%. En comparaci&oacute;n, los porcentajes de bosque maduro variaron de 0% a 100% a escala de reserva sucesional tard&iacute;a (40,000 ha). Las estimaciones actuales a escala de provincia para bosque maduro (5%) y bosque sucesional tard&iacute;o (11%) en la cordillera costera de Oregon fueron m&aacute;s bajas que las esperadas bajo el r&eacute;gimen de incendios hist&oacute;ricos simulado, a&uacute;n cuando se consider&oacute; un error potencial en nuestros parametros estimados. Estas incertidumbres limitan, sin embargo, nuestra habilidad para definir con precisi&oacute;n los rangos hist&oacute;ricos de variabilidad. Nuestros resultados sugieren que en &aacute;reas donde los reg&iacute;menes de perturbaci&oacute;n hist&oacute;rica estuvieron caracterizados por incendios grandes poco frecuentes, el manejo de clases de edades del bosque basado en un rango de variabilidad hist&oacute;rica puede ser posible solo a escalas espaciales relativamente grandes. Estrategias de manejo de paisaje comprensivas necesitar&aacute;n considerar otros factores adem&aacute;s del porcentaje de bosque maduro en el paisaje, incluyendo los patrones espaciales de bosque y las tasas y v&iacute;as de cambio del paisaje.

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[84]
Wu J G, Loucks O L.1995. From balance of nature to hierarchical patch dynamics: A paradigm shift in ecology[J]. The Quarterly Review of Biology, 70(4): 439-466.ABSTRACT

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[85]
Xi W M, Coulson R N, Birt A G, et al.2009. Review of forest landscape models: Types, methods, development and applications[J]. Acta Ecologica Sinica, 29(1): 69-78.Forest landscape models simulate forest change through time using spatially referenced data across a broad spatial scale (i.e. landscape scale) generally larger than a single forest stand. Spatial interactions between forest stands are a key component of such models. These models can incorporate other spatio-temporal processes such as natural disturbances (e.g. wildfires, hurricanes, outbreaks of native and exotic invasive pests and diseases) and human influences (e.g. harvesting and commercial thinning, planting, fire suppression). The models are increasingly used as tools for studying forest management, ecological assessment, restoration planning, and climate change. In this paper, we define forest landscape models and discuss development, components, and types of the models. We also review commonly used methods and approaches of modeling forest landscapes, their application, and their strengths and weaknesses. New developments in computer sciences, geographic information systems (GIS), remote sensing technologies, decision-support systems, and geo-spatial statistics have provided opportunities for developing a new generation of forest landscape models that are increasingly valuable for ecological research, restoration planning and resource management.

DOI

[86]
Xi W M, Coulson R N, Waldron J D, et al.2008. Landscape modeling for forest restoration planning and assessment: Lessons from the southern Appalachian Mountains[J]. Journal of Forestry, 106(4): 191-197.Restoration planning, evaluation, and implementation are important in areas where abiotic disturbances (e.g., wildfires, hurricanes, and ice storms), biotic disturbances (e.g., outbreaks of native and exotic invasive pests and diseases), and anthropogenic disturbances (e.g., harvesting, planting, and fire exclusion) have altered forest landscapes. However, the effects of restoration practices are difficult to measure, and restoration goals often are unclear. Landscape modeling provides a tool for evaluating outcomes of various management scenarios and restoration strategies. In this article, we provide a framework for using landscape models for forest restoration. Specifically, we present a case study using LANDIS, a landscape simulation model of forest disturbance and succession, to explore the effects of restoration strategies for forests damaged by southern pine beetle in the southern Appalachians. Our research suggests that landscape models are valuable tools in the forest restoration decisionmaking process. Future work on landscape models for forest restoration and other related issues is discussed.

DOI

[87]
Xi W M, Waldron J D, Coulson R N, et al.2007. Landscape modeling for forest restoration: Concepts and applications[C]//Stanturf J. Proceedings of the IUFRO conference on forest landscape restoration. Seoul, Korea: Korea Forest Research Institute: 268.

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Yang J, He H S, Gustafson E J.2004. A hierarchical fire frequency model to simulate temporal patterns of fire regimes in LANDIS[J]. Ecological Modelling, 180(1): 119-133.Fire disturbance has important ecological effects in many forest landscapes. Existing statistically based approaches can be used to examine the effects of a fire regime on forest landscape dynamics. Most examples of statistically based fire models divide a fire occurrence into two stages鈥攆ire ignition and fire initiation. However, the exponential and Weibull fire-interval distributions, which model a fire occurrence as a single event, are often inappropriately applied to these two-stage models. We propose a hierarchical fire frequency model in which the joint distribution of fire frequency is factorized into a series of conditional distributions. The model is consistent with the framework of statistically based approaches because it accounts for the separation of fire ignition from fire occurrence. The exponential and Weibull models are actually special cases of our hierarchical model. In addition, more complicated non-stationary temporal patterns of fire occurrence also can be simulated with the same approach. We implemented this approach as an improved fire module in LANDIS and conducted experiments within forest landscapes of northern Wisconsin and southern Missouri. The results of our experiments demonstrate this new fire module can simulate a wide range of fire regimes across heterogeneous landscapes with a few parameters and a moderate amount of input data. The model possesses great flexibility for simulating temporal variations in fire frequency for various forest ecosystems and can serve as a theoretical framework for future statistical modeling of fire regimes.

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[89]
Yang J, He H S, Shifley S R, et al.2007. Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands[J]. Forest Science, 53(1): 1-15.The spatial pattern of forest fire locations is important in the study of the dynamics of fire disturbance. In this article we used a spatial point process modeling approach to quantitatively study the effects of land cover, topography, roads, municipalities, ownership, and population density on fire occurrence reported between 1970 and 2002 in the Missouri Ozark Highland forests, where more than 90% of fires are human-caused. We used the AIC (Akaike information criterion) method to select an appropriate inhomogeneous Poisson process model to best fit to the data. The fitted model was diagnosed using residual analysis as well. Our results showed that fire locations were spatially clustered, and high fire occurrence probability was found in areas that (1) were public land, (2) within 6 km to 17 km of municipalities, and (3) 270 m) elevations, reflecting the effects of natural factors on fire occurrence. The results serve as a provisional hypothesis for expanding fire risk estimation to surrounding areas. The spatial scale of analysis (approximately 1 ha) provides new information to guide planning and risk reduction efforts.

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[90]
Yemshanov D, Perera A H.2002. A spatially explicit stochastic model to simulate boreal forest cover transitions: General structure and properties[J]. Ecological Modelling, 150(1): 189-209.

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