On Limits to the Use of Linear Markov Strategies in Common Property Natural Resource Games

We derive conditions that must be satisfied by the primitives of the problem in order for an equilibrium in linear Markov strategies to exist in some common property natural resource differential games. These conditions impose restrictions on the admissible form of the natural growth function, given a benefit function, or on the admissible form of the benefit function, given a natural growth function.

基于马尔可夫随机场的人群密度监测技术

针对目前人群密度估计和人群统计方法的不足,提出基于马尔可夫随机场的人群密度监测技术,将这一方法归纳为两个主要步骤,并详细阐述了其计算原理;讨论存在严重遮挡情况下,通过给定域值,如何将其实际应用于公共场所人群聚集风险管理中的方法.     

长江中游城市群城市创新差异的时空格局演变

以专利申请总量为主要指标,采用地理集中指数和马尔可夫链方法,从时间、空间和地理临近性视角来分析2000~2014年长江中游城市群城市创新差异的时空格局演变。研究结果表明:(1)城市群城市创新差异呈现由高度集中向渐进式分散,由核心城市向外围城市缓慢扩散的演变趋势,出现高水平和低水平趋同俱乐部强化锁定,中低水平趋同俱乐部局部变动的趋势。(2)城市群城市创新类型发生空间转移与城市创新发展水平紧密相关,城市创新发展水平差距越小,发生转移的概率越高;反之,发生转移的概率越低;(3)邻域创新环境影响城市群城市创新差异的演变,地理临近性在低水平、中低水平城市创新类型表现不明显,而在中高水平城市创新类型显著增加。     

2000~2020年辽宁省生态系统服务评估与多情景预测

生态系统服务评估和预测在可持续地区发展和资源管理中扮演着关键角色. 辽宁省作为中国东北地区的一个典型代表,在面临着城市化、工业化和农业现代化等快速发展的挑战同时,也迫切需要更深入地了解其生态系统的演化趋势以及对生态系统服务的影响. 以辽宁省2000~2020年土地利用变化情况为基础,利用InVEST-Markov-PLUS模型对辽宁省过去-未来的生态系统服务评估及多情景预测等开展了模拟研究,为更好地促进生态系统服务,推动辽宁省高质量均衡发展提供科学可靠的建议. 结果发现：①2000~2020 年,辽宁省碳储量和土壤保持量总体呈升高趋势,产水量呈现先降后升再降的波动降低态势. ②辽宁省碳储量和土壤保持量呈现出东部山区和西部丘陵高,中部较低的趋势,产水量呈现出从东到西递减的趋势. ③生态系统服务价值从5479.44亿元上升到5655.26亿元,总计上升了175.82亿元,且4种服务在研究时期内均有上升,文化服务变化最快. ④2030年,辽宁省碳储量和土壤保持总量除生态保护情景外均有所下降,产水量除耕地保护情景有所上升,其他3种情景下均有所下降,研究区生态系统服务价值除经济优先情景外均有所上升.     

A pioneer validation of a state-space model of vessel trajectories (VMS) with observers’ data

In the context of the expansion of animal tracking and bio-logging, state-space models have been developed with the objective to characterise animals’ trajectories and to understand the factors controlling their behaviour. In the fisheries community, the electronic tagging of vessels commonly designated by Vessel Monitoring Systems (VMS) is developing and provides a new insight for the understanding, the analysis and the modelling of the trajectories of vessels and their prospecting behaviour. VMS data are thus a clue for the proper definition of fishing effort which remains a fundamental parameter of tuna stock assessments. In this context, we used the VMS (recording of hourly positions) of the French tropical tuna purse-seiners operating in the Indian Ocean to characterise three types of movement (states) on the VMS trajectories (stillness, tracking, and cruising). Based on empirical evidences, and on the regular frequency of VMS acquisition, this was achieved by the development of a Bayesian Hidden Markov model for the speeds and turning angles derived from the hourly steps of the trajectories. In a second phase, states were related to activities disentangling stillness into fishing or stop at sea. Finally the quality of the model performances was rigorously quantified thanks to observers’ data. Confronting model prediction and true activities allowed estimating that 10% of the hourly steps were misclassified. The assumptions and model’ choices are discussed, highlighting the fact that VMS data and observers’ data having different time resolutions, the effective use of validating data was troublesome. However, without validation, these analyses remain speculative. The validation part of this work represents an important step for the operational use of state-space models in ecology in the broad sense (predators’ tracking data, e.g. birds or mammals trajectories).     

Robust optimal observation of a metapopulation

We consider a two-parameter continuous-time Markovian model for patch occupancy in metapopulation, and address the question of when to observe the population in order to obtain the most accurate and precise estimator of the parameters. Using the likelihood obtained from a diffusion approximation we derive a robust procedure for determining the optimal observation schedule, which is considerably simpler than would otherwise be possible. We investigate the performance of two optimality criteria, ED-optimality and a particular form of maximin-optimality. Both allow one to incorporate prior belief about the parameter values before any data is collected. Kernel density estimates of the maximum likelihood estimator are compared under the various combinations of optimality criteria and prior distributions. Our methods are illustrated with reference to a model for the spread of crown of thorns starfish (Acanthaster planci) among the 55 islands comprising the Ryukyu group in Japan.     

基于随机Petri网的跨组织应急协同模型构建

突发事件应急处置需要公安、消防、医疗、交通等多个组织进行跨组织协同合作,以此实现跨部门的统一指挥、快速反应的应急响应行动,为城市突发事件应对、城市公共安全提供强有力的保障。利用随机Petri网的构建跨组织应急协同流程,根据确定可能出现的状态集,同构其相应的马尔科夫链;以汶川地震为例,根据可能出现状态的稳态概率及相关数学方法分析其均衡状态和变动规律,对应急响应的协同效果进行分析,根据评估结果提出事前应急协同控制与改进的措施,加强事前应急演练与培训,为提高突发事件应急响应协调能力提供理论支持。     

Spatiotemporal trends of illegal activities from ranger‐collected data in a Ugandan national park

Within protected areas, biodiversity loss is often a consequence of illegal resource use. Understanding the patterns and extent of illegal activities is therefore essential for effective law enforcement and prevention of biodiversity declines. We used extensive data, commonly collected by ranger patrols in many protected areas, and Bayesian hierarchical models to identify drivers, trends, and distribution of multiple illegal activities within the Queen Elizabeth Conservation Area (QECA), Uganda. Encroachment (e.g., by pastoralists with cattle) and poaching of noncommercial animals (e.g., snaring bushmeat) were the most prevalent illegal activities within the QECA. Illegal activities occurred in different areas of the QECA. Poaching of noncommercial animals was most widely distributed within the national park. Overall, ecological covariates, although significant, were not useful predictors for occurrence of illegal activities. Instead, the location of illegal activities in previous years was more important. There were significant increases in encroachment and noncommercial plant harvesting (nontimber products) during the study period (1999–2012). We also found significant spatiotemporal variation in the occurrence of all activities. Our results show the need to explicitly model ranger patrol effort to reduce biases from existing uncorrected or capture per unit effort analyses. Prioritization of ranger patrol strategies is needed to target illegal activities; these strategies are determined by protected area managers, and therefore changes at a site‐level can be implemented quickly. These strategies should also be informed by the location of past occurrences of illegal activity: the most useful predictor of future events. However, because spatial and temporal changes in illegal activities occurred, regular patrols throughout the protected area, even in areas of low occurrence, are also required.     

Hierarchical Bayesian space-time models

Space-time data are ubiquitous in the environmental sciences. Often, as is the case with atmo- spheric and oceanographic processes, these data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations. These factors can limit the effectiveness of traditional space- time statistical models and methods. In this article, we propose the use of hierarchical space-time models to achieve more flexible models and methods for the analysis of environmental data distributed in space and time. The first stage of the hierarchical model specifies a measurement- error process for the observational data in terms of some 'state' process. The second stage allows for site-specific time series models for this state variable. This stage includes large-scale (e.g. seasonal) variability plus a space-time dynamic process for the anomalies'. Much of our interest is with this anomaly proc ess. In the third stage, the parameters of these time series models, which are distributed in space, are themselves given a joint distribution with spatial dependence (Markov random fields). The Bayesian formulation is completed in the last two stages by speci- fying priors on parameters. We implement the model in a Markov chain Monte Carlo framework and apply it to an atmospheric data set of monthly maximum temperature.     

Modelling the distribution of plant species using the autologistic regression model

For modeling the distribution of plant species in terms of climate covariates, we consider an autologistic regression model for spatial binary data on a regularly spaced lattice. This model belongs to the class of autologistic models introduced by Besag (1974). Three estimation methods, the coding method, maximum pseudolikelihood method and Markov chain Monte Carlo method are studied and comparedvia simulation and real data examples. As examples, we use the proposed methodology to model the distributions of two plant species in the state of Florida.