Positive feedbacks promote power-law clustering of Kalahari vegetation

The concept of local-scale interactions driving large-scale pattern formation has been supported by numerical simulations, which have demonstrated that simple rules of interaction are capable of reproducing patterns observed in nature. These models of self-organization suggest that characteristic patterns should exist across a broad range of environmental conditions provided that local interactions do indeed dominate the development of community structure. Readily available observations that could be used to support these theoretical expectations, however, have lacked sufficient spatial extent or the necessary diversity of environmental conditions to confirm the model predictions. We use high-resolution satellite imagery to document the prevalence of self-organized vegetation patterns across a regional rainfall gradient in southern Africa, where percent tree cover ranges from 65% to 4%. Through the application of a cellular automata model, we find that the observed power-law distributions of tree canopy cluster sizes can arise from the interacting effects of global-scale resource constraints (that is, water availability) and local-scale facilitation. Positive local feedbacks result in power-law distributions without entailing threshold behaviour commonly associated with criticality. Our observations provide a framework for integrating a diverse suite of previous studies that have addressed either mean wet season rainfall or landscape-scale soil moisture variability as controls on the structural dynamics of arid and semi-arid ecosystems.     

贝叶斯概率与元胞自动机的非线性转换规则

利用朴素贝叶斯分类器(NBC)、高斯径向基函数和离散模型获取城市CA模型的非线性转换规则,提出NBC—CA模型,并将该模型应用于深圳市1988-2010年城市演变的动态模拟中。研究结果表明,所提出的CA模型能反映复杂城市系统的非线性、不确定性特点,模型模拟的结果要比传统MCE方法模拟精度高。NBC—CA模型中的径向基参数可解释不同空间变量的作用下,城市发展的集中区域以及空间变量对城市发展的影响范围,能够反映城市发展的模式和特征。     

GPU-CA model for large-scale land-use change simulation

Land-use change simulation for large-scale regions(i.e.provincial regions or countries) is very useful for many global studies.Such simulation,however,is affected by computational capability of general computers.This paper proposes a method to implement cellular automata(CA) for land use change simulation based on graphics processing units(GPUs).This method can be applied to large-scale land-use change simulations by combining the latest GPU high-performance computing technology and CA.We carried out the experiments by simulating land-use change processes at a provincial scale.This involves a lot of sophisticated techniques,such as model mapping,and computational procedure of GPU-CA model.This proposed model has been validated by land-use change simulation in Guangdong Province,China.The comparison indicates that the GPU-CA model is faster than traditional CA by 30 times.Such improvement is crucial for land-use change simulations in provincial regions and countries.The outputs of the simulation can be further used to provide information to other global change models.     

复杂空间接触模式下传染病蔓延模拟建模

针对现代社会越来越复杂的交互行为,提出了一种离散形式的空间分区模型预测传染病蔓延。该模型考虑了现代社会个体之间复杂的空间接触模式,以及真实情况下普遍存在的人群异质性和分布异质性。基于该模型在地理信息系统环境中建立了传染病蔓延的可视化模拟程序。模拟结果表明,该模型能较真实地模拟现实环境下传染病的蔓延过程,可为公共卫生应急中传染病疫情事件的预测预警和应急处置提供决策支持。     

Balance between facilitation and competition determines spatial patterns in a plant population

A considerable challenge in plant ecology is to understand how interactions,such as competition or facilitation,shape the spatial distribution of plants.The‘‘stress gradient hypothesis’’predicts that facilitation and competition will vary inversely across gradients of abiotic stress or consumer pressure.Surprisingly,few previous studies have explored how the balance between facilitation and competition affects spatial patterns along gradients of stress in a plant population based on field experiments.In order to investigate the effects of consumer pressure,facilitation,and competition on the spatial pattern of plant populations,we conducted a restoration succession series field experiment in the Inner Mongolian steppe in which sample sites of graded consumer pressure,specifically grazing stress,were established.We chose to examine the spatial patterns of Leymus chinensis,a dominant species in our experimental site.In order to test the‘‘stress gradient hypothesis,’’we applied the univariate O-ring statistic to analyze local neighborhood density at different spatial scales.We used the pair-correlation function to detect the characteristics of point patterns using complete spatial randomness,the Poisson cluster process,and the nested double-cluster process.We found that the local densities of L.chinensis were higher under high stress than lower stress environments.This demonstrated the‘‘stress gradient hypothesis’’in that facilitation and competition varied inversely across gradients of consumer pressure.However,we found nodifferences in the spatial patterns of L.chinensis based on complete spatial randomness when interactions shifted from facilitation to competition along gradients of consumer pressure.Furthermore,we detected the characteristics of point patterns using the Poisson cluster and nested doublecluster processes.The results showed the spatial patterns of L.chinensis to fit well with the nested double-cluster model under highly stressful conditions,while in lower stress environments they were best approximated by the Thomas process.Our results illustrate that a shift in interactions from facilitation to competition along gradients of consumer pressure can shape spatial patterns and that a combination of the Poisson cluster process and nested doublecluster process can detect spatial pattern characteristics which cannot be detected by complete spatial randomness.     

Analysis of correlations between protein complex and protein-protein interaction and mRNA expression

Protein-protein interaction is a physical interaction of two proteins in living cells. In budding yeast Saccharomyces cerevisiae, large-seale protein-protein interaction data have been obtained through high-throughput yeast two-hybrid systems (Y2H) and protein complex purification techniques based on mass-spectrometry. Here, we collect 11855 interactions between total 2617 proteins. Through seriate genome-wide mRNA expression data, similarity between two genes could be measured. Protein complex data can also be obtained publicly and can be translated to pair relationship that any two proteins can only exist in the same complex or not. Analysis of protein complex data, protein-protein interaction data and mRNA expression data can elucidate correlations between them. The results show that proteins that have interactions or similar expression patterns have a higher possibility to be in the same protein complex than randomized selected proteins, and proteins which have interactions and similar expression patterns are even more possible to exist in the same protein complex. The work indirates that comprehensive integration and analysis of public large-seale bioinformatical data, such as protein complex data, protein-protein interaction data and mRNA expression data, may help to uncover their relationships and common biological information underlying these data. The strategies described here may help to integrate and analyze other functional genomic and proteomic data, such as gene expression profiling, protein-localization mapping and large-scale phenotypic data, both in yeast and in other organisms.     

Variance in ecological consumer-resource interactions

Food-web models use the effect size of trophic interactions to predict consumer-resource dynamics. These models anticipate that strong effects of consumers increase spatial and temporal variability in abundance of species, whereas weak effects dampen fluctuations. Empirical evidence indicates that opposite patterns may occur in natural assemblages. Here I show that spatial variance in the distribution of resource populations is sensitive to changes in the variance of the trophic interaction, in addition to the mean effect of consumers, relative to other causes of spatial variability. Simulations indicate that both strong and weak direct effects of consumers can promote spatial variability in abundance of resources, but only trophic interactions with a large mean effect size can reduce variation. Predictions of the model agree with the results of repeated field experiments and are consistent with data from published consumer-resource interactions, proving to be robust across widely varying environmental conditions and species' life histories. Thus, food-web models that embody variance in trophic interactions may have increased capacity to explain the wide range of effects of consumers documented in empirical studies.     

规则计算——探索复杂性的有力模式

自然界中存在着许许多多的复杂系统，这些系统的每一部分的结构可以非常简单，但由于各部分之间存在一定的耦合，最终表现出系统的整体性态极其复杂．基于规则计算的元胞自动机为模拟自然现象和生命现象提供了新的思路和方法，成为探索复杂系统的一种有力模式论文介绍了规则计算的产生和发展，着重阐述了规则计算的本质，并对自下而上的基于规则的建模方法中存在的一些问题进行了总结与展望．     

基于注意的图像识别模型及其在人像检测中的应用

提出了一种基于注意机制的图像识别模型。其基本想法是：在进行复杂的场景分析或目标识别时,首先通过注视控制机制,获得视景中的关键特征区域,并将注视点按照一定的顺序对这些关键特征区域进行串行扫描。在扫描到每一个关键特征区时,将对该区域附近的局部模式进行记忆或匹配。对一个复杂目标的记忆将包括2部分,一部分是对局部模式的记忆,即组成该目标的各“部件”的模式;另一部分是对各局部模式之间的空间关系的记忆,即组成该目标的各“部件”之间的结构关系。与记忆过程对应,识别过程也包括2部分,一部分是对局部模式的匹配,另一部分是对各局部模式之间结构关系的匹配。当匹配上的局部模式足够多且其空间关系正确时,目标即得到识别。用该模型对复杂背景中的人像进行了检测。实验结果表明,模型较好地解决了不变性识别的问题,即识别结果与目标物的平移、旋转和尺度变化无关,并且具有良好的鲁棒性和速度,是一种具有认知意义并且可以实用化的模型。     

人类行为、复杂网络及信息挖掘的统计物理研究

通过实证统计与理论模型分析相结合对复杂系统进行研究是一种全新的认识和探索.建议从人类行为的统计特性、复杂网络同步与复杂神经网络、信息挖掘与复杂网络链路预测3个方面,基于大量的实证统计和分析,结合有效的动力学模型,针对人类自身行为的规律特性、社会个体之间的相互作用、神经系统的动力学演化、信息的有效推荐和网络演化的有效预测等重要问题,运用统计物理理论进行全方位的探索,深入挖掘各种决定复杂系统演化过程的基本机制与规律.     

基于原子模型的非局域连续模型

在传统的连续模型中,本构关系,例如胡克定律、热传导的傅立叶定律是宏观现象的表征.在原子尺度下,传统连续模型具有很大的局限性,其中一个原因就是传统连续模型忽略了原子间相互作用的非局域性.本文在不采用任何经验的本构假设和局部均匀变形假设(如Cauchy-Born假设)的情况下,严格基于原子模型,构造了一个连续模型,将原子间相互作用力的非局域性和非线性性自然地嵌入到模型中.此模型可以有效表现原子尺度的非均匀变形.     

The large-scale organization of metabolic networks

In a cell or microorganism, the processes that generate mass, energy, information transfer and cell-fate specification are seamlessly integrated through a complex network of cellular constituents and reactions. However, despite the key role of these networks in sustaining cellular functions, their large-scale structure is essentially unknown. Here we present a systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variation in their individual constituents and pathways, these metabolic networks have the same topological scaling properties and show striking similarities to the inherent organization of complex non-biological systems. This may indicate that metabolic organization is not only identical for all living organisms, but also complies with the design principles of robust and error-tolerant scale-free networks, and may represent a common blueprint for the large-scale organization of interactions among all cellular constituents.     

Speciation along environmental gradients

Traditional discussions of speciation are based on geographical patterns of species ranges. In allopatric speciation, long-term geographical isolation generates reproductively isolated and spatially segregated descendant species. In the absence of geographical barriers, diversification is hindered by gene flow. Yet a growing body of phylogenetic and experimental data suggests that closely related species often occur in sympatry or have adjacent ranges in regions over which environmental changes are gradual and do not prevent gene flow. Theory has identified a variety of evolutionary processes that can result in speciation under sympatric conditions, with some recent advances concentrating on the phenomenon of evolutionary branching. Here we establish a link between geographical patterns and ecological processes of speciation by studying evolutionary branching in spatially structured populations. We show that along an environmental gradient, evolutionary branching can occur much more easily than in non-spatial models. This facilitation is most pronounced for gradients of intermediate slope. Moreover, spatial evolutionary branching readily generates patterns of spatial segregation and abutment between the emerging species. Our results highlight the importance of local processes of adaptive divergence for geographical patterns of speciation, and caution against pitfalls of inferring past speciation processes from present biogeographical patterns.     

引入地形特征的田块尺度玉米遥感估产与空间格局分析

田块尺度作物估产引入地形特征提升精度与产量空间格局分析对规模经营具有重要意义。以黑龙江省规模化种植的春玉米为研究对象,测定无人机(unmanned aerial vehicle, UAV)高精度地形与变量测产数据,基于多时期SPOT-6影像提取7种植被指数;采用最小二乘法,构建不同时期植被指数与春玉米实测产量的经验统计模型,确定遥感估产最佳时期和最优植被指数;提取6种地形因子,使用多元逐步回归评价引入地形因子的遥感估产模型,应用空间统计分析探索产量空间分布格局。结果表明:春玉米灌浆期是遥感估产的最佳时期,决定系数R~2达到0.6以上的植被指数共6种,比值植被指数(ratio vegetation index, RVI)为最优植被指数,其余依次为修正比值植被指数(modified simple ratio, MSR)、无蓝色波段增强型植被指数(enhanced vegetation index without a blue band, EVI2)、归一化植被指数(normalized difference vegetation index, NDVI)、次生修正土壤调节植被指数(modified secondary soil adjusted vegetation index, MSAVI2)、绿度归一化植被指数(green normalized difference vegetation index, GNDVI);最佳估产模型引入地形辅助信息后R~2提升5.6%,达到0.79,均方根误差(root mean squared error, RMSE)为347.03 kg/hm~2;高海拔与高坡度区域产量均值最低为7 502.64 kg/hm~2,中海拔与低坡度区域产量均值最高为9 157.63 kg/hm~2。优化后的遥感估产模型可以快速评估作物产量,确定春玉米最佳生长区域,为规模化农业精细管理、土地整治与作物种植结构调整提供科学依据。     

基于移动大数据的城市空间结构感知

准确描绘城市结构、深刻理解城市特性对于智慧城市建设、提升居民生活体验至关重要。遍布于城市的基础设施,如公交车站、银行网点、移动网络等,提供了大量具有时空标记、描述个人行为的数据,使得基于人类行为的城市时空特性分析成为可能,且逐渐成为城市研究的热点。针对这些移动大数据,提出一种方法,将人类移动行为投影到城市空间实体之间的联系中,从而可以方便地从整体和局部研究城市空间实体间联系。基于该方法,利用中国西南某地级市手机上网数据进行实证研究,发现城市空间实体间联系的变化具备周期性,且城市空间结构存在与行政区划相一致的区域中心,这些区域中心的时空变化,反映了人类作息习惯与移动特性。研究结果揭示了城市空间联系的动态特性,为数据治理城市、基础设施的合理配置和资源分配提供了有效的依据。所提出的方法提供一个新的视角,将人类运动整合到空间分布式系统的研究中。     

生态信息表达的尺度问题

尺度是生态学中的核心问题,时间和空间尺度包含于任何生态过程中,揭示和把握复杂的生态学规律依赖于研究所采纳的尺度。于分文揭示了生态信息科学中尺度的内涵,综述了生态信息表达的四种最优尺度选择方法,即基于局部方差的方法、基于变异函数的方法、基于离散度的方法和基于信息熵的方法,分别阐述了四种方法的基本思路、优点及不足。目前基于信息熵的方法是考虑最全面的最优尺度选择方法,但仍需改进。论述了生态信息表达中2种上推转换方法、4种下推转换方法和1种综合尺度上推和尺度下推两种范式的方法的特点,认为每一种尺度转换的方法都存在一定的优势和不足,需不断完善目前已有的转换方法或引入和应用新的转换方法,充实和完善尺度转换方法体系。     

Entrainment-mixing parameterization in shallow cumuli and effects of secondary mixing events

Parameterization of entrainment-mixing pro- cesses in cumulus clouds is critical to improve cloud parameterization in models, but is still at its infancy. For this purpose, we have lately developed a formulation to represent a microphysical measure defined as homoge- neous mixing degree in terms of a dynamical measure defined as transition scale numbers, and demonstrated the formulation with measurements from stratocumulus clouds. Here, we extend the previous work by examining data from observed cumulus clouds and find positive cor- relations between the homogeneous mixing degree and transition scale numbers. These results are similar to those in the stratocumulus clouds, but proved valid for the first time in observed cumulus clouds. The empiricalrelationships can be used to parameterize entrainment- mixing processes in two-moment microphysical schemes. Further examined are the effects of secondary mixing events on the relationships between homogeneous mixing degree and transition scale numbers with the explicit mixing parcel model. The secondary mixing events are found to be at least partially responsible for the larger scatter in the above positive correlations based on obser- vations than that in the previous results based on numerical simulations without considering secondary mixing events.     

Neutral metacommunity models predict fish diversity patterns in Mississippi-Missouri basin

River networks, seen as ecological corridors featuring connected and hierarchical dendritic landscapes for animals and plants, present unique challenges and opportunities for testing biogeographical theories and macroecological laws. Although local and basin-scale differences in riverine fish diversity have been analysed as functions of energy availability and habitat heterogeneity, scale-dependent environmental conditions and river discharge, a model that predicts a comprehensive set of system-wide diversity patterns has been hard to find. Here we show that fish diversity patterns throughout the Mississippi-Missouri River System are well described by a neutral metacommunity model coupled with an appropriate habitat capacity distribution and dispersal kernel. River network structure acts as an effective template for characterizing spatial attributes of fish biodiversity. We show that estimates of average dispersal behaviour and habitat capacities, objectively calculated from average runoff production, yield reliable predictions of large-scale spatial biodiversity patterns in riverine systems. The success of the neutral theory in two-dimensional forest ecosystems and here in dendritic riverine ecosystems suggests the possible application of neutral metacommunity models in a diverse suite of ecosystems. This framework offers direct linkage from large-scale forcing, such as global climate change, to biodiversity patterns.     

多空间尺度融合的出行轨迹规律分析

多源时空轨迹数据隐含丰富的城市出行信息,通过对其进行挖掘、处理和分析,可以找到个体与群体之间的交互关系。针对轨迹数据挖掘研究范围单一,缺少多空间尺度研究的问题,提出一种融合多空间尺度特征的出行轨迹数据挖掘分析方法。以广东为例,结合社交媒体腾讯用户密度(Tencent user density, TUD)数据集,通过具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noise, DBSCAN)聚类算法与局部密度峰值计算法提取时空相似性轨迹区域,进而簇类分成一系列热点区域,获得不同时间粒度、不同空间尺度下的出行轨迹规律特征。这能够实现在不同空间尺度融合下展示同一地区的热点区域,进一步探讨出行轨迹的规律变化。可见所提出的方法为利用时空大数据进行城市空间结构研究提供科学参考。     

Aggregation and vesiculation of membrane proteins by curvature-mediated interactions

Membrane remodelling plays an important role in cellular tasks such as endocytosis, vesiculation and protein sorting, and in the biogenesis of organelles such as the endoplasmic reticulum or the Golgi apparatus. It is well established that the remodelling process is aided by specialized proteins that can sense as well as create membrane curvature, and trigger tubulation when added to synthetic liposomes. Because the energy needed for such large-scale changes in membrane geometry significantly exceeds the binding energy between individual proteins and between protein and membrane, cooperative action is essential. It has recently been suggested that curvature-mediated attractive interactions could aid cooperation and complement the effects of specific binding events on membrane remodelling. But it is difficult to experimentally isolate curvature-mediated interactions from direct attractions between proteins. Moreover, approximate theories predict repulsion between isotropically curving proteins. Here we use coarse-grained membrane simulations to show that curvature-inducing model proteins adsorbed on lipid bilayer membranes can experience attractive interactions that arise purely as a result of membrane curvature. We find that once a minimal local bending is realized, the effect robustly drives protein cluster formation and subsequent transformation into vesicles with radii that correlate with the local curvature imprint. Owing to its universal nature, curvature-mediated attraction can operate even between proteins lacking any specific interactions, such as newly synthesized and still immature membrane proteins in the endoplasmic reticulum.