空间直观景观模型LANDIS在大兴安岭呼中林区的应用

应用空间直观景观模型(LANDIS),研究有采伐和无采伐预案下大兴安岭呼中林区的森林景观的长期变化。用APACK计算每一个物种及各年龄级的分布面积。为了研究物种分布格局的变化,计算了物种分布的聚集度指数。研究结果如下：(1)在无采伐预案下。火干扰模式为低频率大面积高强度火烧;在有采伐预下,火干扰模式为高频率小面积低强度火烧;(2)在无采伐预案下,火会造成各种群分布面积的强烈波动,但是对种群的年龄结构没有很大影响;在有采伐预案下,火对种群分布面积和年龄结构都没有很大的影响;(3)采伐能完全改变各种群的年龄结构。降低种群分布的聚集度,但是对各种群的分布面积并没有很大影响;(4)在有采伐预案下,各种群为增长型种群,增长量通过采伐取走,群落处于演替的干扰顶极状态;在无采伐预案下,各种群为稳定型种群(樟子松和偃松除外),大面积高强度火烧使群落产生较大的波动。结果表明,在呼中林业局,在没有人为干扰情况下,火干扰是森林景观变化的主导因素。自从有了人为干扰,采伐开始逐渐取代火干扰成为影响森林景观变化的主导因素。空间直观景观模型的一个挑战是模型的验证。由于缺乏详细的空间数据及模型模拟中的随机性,很难通过模型模拟结果与实地调查或遥感数据的比较进行验证。通过对火模拟、物种分布和物种组成的生态或生物学实现对模型进行验证。     

空间直观景观模型LANDIS Ⅰ．运行机制

空间直观景观模型是指在异质景观中模拟景观尺度上生态过程的空间直观模型．LANDIS是一个用于模拟森林景观干扰、演替和管理的空间直观景观模型．通过在样地尺度上跟踪以10年为间隔的物种年龄级,半定量化地描述火和风倒,及使用位数组表示物种年龄结构,LANDIS能同时在物种、样地和景观尺度上模拟各种生态过程及其相互关系．详细论述了LANDIS模型对种子传播、火、风倒和砍伐等生态过程的模拟,并讨论了模型中存在的一些不足．     

景观空间动态模型研究现状和应重点解决的问题

分析了景观空间动态模型研究的现状及今后的重点发展方向。论文重点介绍了随机景观模型、邻域规则模型和景观过程模型(包括渗透模型、个体行为模型和空间生态系统模型)3类景观空间模型的发展现状、存在的主要问题以及对这些模型途径进行完善的有关工作,并从确证性分析、有效性分析与敏感性分析等3个方面阐述了模型检验技术的发展现状。最后,总结了未来景观空间动态模型发展中应重点解决的主要问题,即模型算法的优化、尺度转换、模型的复杂化与简化、模型检验与评价,通用模型的建立以及传统模型与社会经济因素衔接问题。     

岷江上游杂谷脑流域景观可视化初探

在空间数据(包括数字高程模型和景观类型图)的支持下,采用景观可视化软件VisualNatureStudio(VNS)2.0对岷江上游杂谷脑流域的景观进行了可视化研究,研究结果比较直观地再现了该流域主要的景观类型,包括林地、灌木、果园、草地和耕地,为流域的管理提供了一种全新的管理工具;并阐明了景观可视化研究未来的发展趋势,对我国开展这方面的研究具有一定的指导意义。     

南方丘陵区耕地景观生态安全影响因素及其空间差异

构建耕地景观生态安全格局是抵御区域耕地生态风险,保障耕地生态质量的重要举措。研究以南方丘陵区典型县-奉新县为研究区,选取部分景观格局指数构建南方丘陵区耕地景观生态安全评价模型,分析了县域尺度耕地景观生态安全状况及其空间聚集特征,并尝试应用地理加权回归模型探究自然和社会经济因素对区域耕地景观生态安全影响作用的空间地域差异。结果表明：耕地景观生态安全处于"危险"状态的行政村占研究区总数的12.92%,区域耕地景观生态安全问题存在明显的空间聚集性;地理加权回归模型较传统全局回归模型有较好的拟合优度,可以较好地揭示影响因素作用的地域差异;自然,社会经济因素对耕地景观生态安全的影响具有显著的空间非平稳性,依据影响因素作用强度的地域差异可以将研究区划分为距居民点的距离、地形位指数、年平均降雨量负向影响区和人口密度正向影响区。研究为确定耕地景观生态安全干扰来源及探究耕地保护对策如何落实到空间地域上提供了参考依据。     

兴安落叶松老头林对大兴安岭森林景观变化的影响研究

采用空间直观景观模型LANDIS ,以大兴安岭呼中林业局为研究区 ,研究对老头林进行采伐和不进行采伐预案下的森林景观变化动态。结果表明 ,在对老头林不进行采伐预案下 ,各物种的过熟林分布面积要高于进行采伐预案下的分布面积。老头林的存在能使森林景观组分和格局具有更高的稳定性 ,对采伐等干扰具有更大的抗性。因此 ,保护老头林对于大兴安岭森林的可持续发展具有重要的生态学意义。     

异质种群动态模型:破碎化景观动态模拟的新途径

景观破碎化导致物种以异质种群方式存活,使得基于异质种群动态模拟破碎化景观动态成为可能。异质种群动态模型的发展为景观动态模拟奠定了良好基础。根据空间处理方式的不同,异质种群模型可分为三大类,可不同程度地用于描述破碎化景观动态。(1)空间不确定异质种群模型,假定所有局域种群间均等互联,模型中不包含空间信息,仅能用于景观斑块动态描述;(2)空间确定异质种群模型,假设局域种群在二维空间上以规则格子形式排列,是一种准现实的空间处理方式,可用于景观动态的简单描述;(3)空间现实异质种群模型,包含了破碎化景观中局域种群的几何特征,可直接用于真实景观动态的模拟研究。空间现实的和基于个体的异质种群模型不但是未来异质种群模型发展的主流,也将成为未来破碎化景观动态研究的重要工具。为了更加准确完整地描述破碎化景观动态,不但应该综合运用已有的各种异质种群模型方法,更要引进新模型来刎画多物种、多变量、高维度、复杂连接的破碎化景观格局与过程。     

SWAT模型对景观格局变化的敏感性分析——以丹江口库区老灌河流域为例

景观的空间配置与类型组成能够对流域的产流、产沙及非点源污染产生影响。在以往SWAT模型研究中,往往默认水文模型考虑了该影响。为分析SWAT模型对不同景观格局变化的敏感性,根据老灌河流域2000年土地利用在各子流域的组成,模拟研究区更为破碎、复杂的景观空间配置,通过设置多套试验参数,利用SWAT模型生成基于不同景观格局的模拟结果。结果表明,SWAT模型不能反映除坡度和面积变化之外的景观水平下各斑块之间因景观空间格局改变对流域产流、产沙以及非点源污染的影响;模型通过其他参数的调整,弥补了模型分析数据的不足,使实测数据与模型部分结果高度吻合。这表明,一个能够反映流域部分水文特征的SWAT模型,未必是对研究区真实情形的模拟,而是各个参数间平衡的结果。因此,在利用SWAT模型分析模拟景观变化时,不应默认模型能够模拟景观空间格局改变对流域水文过程的影响,同时研究者可以通过划分坡度带,提高模型对不同坡度土地利用的敏感性。     

中性景观模型在景观生态学中的应用和发展

中性景观模型可以模拟产生大量具有相似统计特征而不包含特定生态过程或自然地理过程的格局,为研究真实景观提供一个参照系统,在大范围景观格局变化研究等方面提供便利.目前,中性景观模型成为景观生态学研究的一种重要方法和手段,已成功应用于检验现有景观指数并发展新的景观指数、景观格局对种群动态影响和干扰过程研究等多个领域.本文介绍了中性景观模型的产生、内涵和类型,阐述了中性景观模型在景观生态学中的应用.同时,展望了中型景观模型研究的发展方向:模型类型趋于多样化,结构趋于复杂化,着重模型模拟结果的验证,以及随着景观生态学理论和实践的发展、结合中性景观模型和景观生态学研究方法,深入地应用于格局和过程两者之间关系的研究.     

基于模型的景观格局与生态过程研究

景观格局与生态过程关系的研究是景观生态学的主要特色和理论核心之一。模型可以充分利用实验和观测数据并综合不同时间和空间尺度上的信息提炼规律或揭示内在机制,模拟景观格局与生态过程的动态与相互关系,成为景观生态学研究的有力工具。结合研究实例,总结了基于模型的景观格局研究、生态过程研究和格局-过程关系研究的发展现状和薄弱环节,同时探讨了通过构建耦合模型研究格局-过程相互关系的途径。总结了景观模型研究亟待发展的领域与发展趋势。     

Long-term primary succession: a comparison of non-spatial and spatially explicit inferential techniques

Trajectories of plant primary succession are commonly inferred from temporal changes in non-spatially explicit metrics that characterise the whole sampling area with a single statistic (e.g. community diversity). However, the derivation of these metrics is affected by the presence of spatial structure (patchiness) in vegetation. The emergence of spatial patchiness during succession is therefore likely to have an impact on attempts to infer the rate and direction of vegetation development. This study examines the impact of patchiness on inferred developmental trajectories by comparing a non-spatial analysis of long-term primary succession with a spatially explicit analysis of the same data. The data used in the analysis were collected from an 850-year-old chronosequence of 7 lava flows in southern Iceland. The non-spatial analysis captured broad developmental trends, including an overall increase in community diversity with time, and a split between early pioneer communities (sites <150-year-old) dominated by cryptogams and later assemblages (sites older than ≈150 years) where vascular plants were more important. However, the non-spatial analysis missed key community processes apparent in the spatially explicit analysis, including divergence in vegetation development related to metre-scale topographic differences. The results of this study emphasise the need for spatially explicit, multi-scale studies of vegetation development, both in the inference of past vegetation dynamics, and in modelling the response of spatially patchy vegetation to future environmental change.     

A spatial open-population capture-recapture model

A spatial open-population capture-recapture model is described that extends both the non-spatial open-population model of Schwarz and Arnason and the spatially explicit closed-population model of Borchers and Efford. The superpopulation of animals available for detection at some time during a study is conceived as a two-dimensional Poisson point process. Individual probabilities of birth and death follow the conventional open-population model. Movement between sampling times may be modeled with a dispersal kernel using a recursive Markovian algorithm. Observations arise from distance-dependent sampling at an array of detectors. As in the closed-population spatial model, the observed data likelihood relies on integration over the unknown animal locations; maximization of this likelihood yields estimates of the birth, death, movement, and detection parameters. The models were fitted to data from a live-trapping study of brushtail possums (Trichosurus vulpecula) in New Zealand. Simulations confirmed that spatial modeling can greatly reduce the bias of capture-recapture survival estimates and that there is a degree of robustness to misspecification of the dispersal kernel. An R package is available that includes various extensions.     

Influence of habitat fragmentation on population structure of red deer in Croatia

The genetic structure of red deer populations is under strong influence of human activities such as game management and habitat fragmentation. Using multilocus genotypes from 193 geo-referenced individuals, we evaluated the population genetic structure of three red deer populations in Croatia. The effect of habitat fragmentation on genetic structure was tested using Bayesian non-spatial and spatial clustering methods. Our results indicate levels of genetic diversity similar to the ones previously reported by other authors for stable and appropriately managed populations within all populations analyzed. The spatial clustering model was able to detect the effect of habitat fragmentation on population differentiation, supporting the use of spatially explicit methods in landscape genetics, and giving important guidelines for future road planning.     

Moving beyond abundance distributions: neutral theory and spatial patterns in a tropical forest

Assessing the relative importance of different processes that determine the spatial distribution of species and the dynamics in highly diverse plant communities remains a challenging question in ecology. Previous modelling approaches often focused on single aggregated forest diversity patterns that convey limited information on the underlying dynamic processes. Here, we use recent advances in inference for stochastic simulation models to evaluate the ability of a spatially explicit and spatially continuous neutral model to quantitatively predict six spatial and non-spatial patterns observed at the 50 ha tropical forest plot on Barro Colorado Island, Panama. The patterns capture different aspects of forest dynamics and biodiversity structure, such as annual mortality rate, species richness, species abundance distribution, beta-diversity and the species–area relationship (SAR). The model correctly predicted each pattern independently and up to five patterns simultaneously. However, the model was unable to match the SAR and beta-diversity simultaneously. Our study moves previous theory towards a dynamic spatial theory of biodiversity and demonstrates the value of spatial data to identify ecological processes. This opens up new avenues to evaluate the consequences of additional process for community assembly and dynamics.     

Intraguild predation with spatially structured interactions

Consumer–resource interactions with intraguild predation (IGP) were studied in a spatial setting (i.e., predators catch prey and individuals reproduce within local neighborhoods only). Pair approximation (a method for deriving ordinary differential equations that approximate the dynamics of a community that interacts in a lattice environment) was used to study the effect of spatially structured species interactions. An individual-based computer simulation was used to extend the study to a case with spatially variable resource densities. The qualitative results of the pair approximation model were similar to those of the corresponding non-spatial model. However, the spatial model predicted coex((istence over a wider range of parameters than the non-spatial model when intraguild prey are nutritionally valuable to intraguild predators. Spatially heterogeneous resource distributions and spatially structured interaction could overturn the qualitative predictions of non-spatial models.     

Introducing a new method for assessing spatially explicit processes of landscape fragmentation

Landscape fragmentation is commonly defined as having five distinct phases (perforation, dissection, subdivision, shrinkage, and attrition). Previous studies focus on using landscape pattern metrics to interpret the phase of the landscape. A critical but underinvestigated aspect is that these five phases are also spatially explicit processes. This study proposes a new method to map and measure the different spatially explicit processes of landscape fragmentation. Unlike previous studies that are based on landscape pattern metrics, this current study measures landscape change directly. The new method is applied to Bao’an District in Shenzhen, China, to investigate forest land fragmentation under rapid urbanization. Landsat data were used to map the land use distributions from 1978 to 2009. Results show that different spatial processes occur simultaneously throughout the study area as well as in different study periods of forest fragmentation. Shrinkage was the dominant spatial process in the early period. In the later period, subdivision played a more important role, followed by attrition. Interestingly, urban land was not the leading land use that directly encroached on forest land, as many other studies have shown. Instead, forest land was first converted to orchard land, and orchard land was converted to urban land. The study contributes to Forman's general model of spatially explicit processes of landscape fragmentation and holds implications for urban planning policy and practice. The study concludes that the new method is effective in understanding and assessing forest land fragmentation, especially in the context of rapid urbanization.     

The trouble with isolation by distance

The genetic population structure of many species is characterised by a pattern of isolation by distance (IBD): due to limited dispersal, individuals that are geographically close tend to be genetically more similar than individuals that are far apart. Despite the ubiquity of IBD in nature, many commonly used statistical tests are based on a null model that is completely non-spatial, the Island model. Here, I argue that patterns of spatial autocorrelation deriving from IBD present a problem for such tests as it can severely bias their outcome. I use simulated data to illustrate this problem for two widely used types of tests: tests of hierarchical population structure and the detection of loci under selection. My results show that for both types of tests the presence of IBD can indeed lead to a large number of false positives. I therefore argue that all analyses in a study should take the spatial dependence in the data into account, unless it can be shown that there is no spatial autocorrelation in the allele frequency distribution that is under investigation. Thus, it is urgent to develop additional statistical approaches that are based on a spatially explicit null model instead of the non-spatial Island model.     

Abundance of rare and elusive species: Empirical investigation of closed versus spatially explicit capture–recapture models with lynx as a case study

Effective conservation and management require reliable monitoring methods and estimates of abundance to prioritize human and financial investments. Camera trapping is a non-invasive sampling method allowing the use of capture–recapture (CR) models to estimate abundance while accounting for the difficulty of detecting individuals in the wild. We investigated the relative performance of standard closed CR models and spatially explicit CR models (SECR) that incorporate spatial information in the data. Using simulations, we considered 4 scenarios comparing low versus high detection probability and small versus large populations and contrasted abundance estimates obtained from both approaches. Standard CR and SECR models both provided minimally biased abundance estimates, but precision was improved when using SECR models. The associated confidence intervals also provided better coverage than their non-spatial counterpart. We concluded SECR models exhibit better statistical performance than standard closed CR models and allow for sound management strategies based on density maps of activity centers. To illustrate the comparison, we considered the Eurasian lynx (Lynx lynx) as a case study that provided the first abundance estimates of a local population in France. © 2012 The Wildlife Society.