On the use of correlated beta random variables with animal population modelling

We give reasons why demographic parameters such as survival and reproduction rates are often modelled well in stochastic population simulation using beta distributions. In practice, it is frequently expected that these parameters will be correlated, for example with survival rates for all age classes tending to be high or low in the same year. We therefore discuss a method for producing correlated beta random variables by transforming correlated normal random variables, and show how it can be applied in practice by means of a simple example. We also note how the same approach can be used to produce correlated uniform, triangular, and exponential random variables.     

Deriving state-and-transition models from an image series of grassland pattern dynamics

We present how state-and-transition models (STMs) may be derived from image data, providing a graphical means of understanding how ecological dynamics are driven by complex interactions among ecosystem events. A temporal sequence of imagery of fine scale vegetation patterning was acquired from close range photogrammetry (CRP) of 1 m quadrats, in a long term monitoring project of Themeda triandra (Forsskal) grasslands in north western Australia. A principal components scaling of image metrics calculated on the imagery defined the state space of the STM, and thereby characterised the different patterns found in the imagery. Using the state space, we were able to relate key events (i.e. fire and rainfall) to both the image data and aboveground biomass, and identified distinct ecological ‘phases’ and ‘transitions’ of the system. The methodology objectively constructs a STM from imagery and, in principle, may be applied to any temporal sequence of imagery captured in any event-driven system. Our approach, by integrating image data, addresses the labour constraint limiting the extensive use of STMs in managing vegetation change in arid and semiarid rangelands.     

Modeling the population dynamics of cotton bollworm Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) over a wide area in northern China

The cotton bollworm Helicoverpa armigera (Lepidoptera: Noctuidae) is one of the most serious crop pests in northern China, calling for accurate prediction of pest outbreaks and strategies for pest control. A computer model is developed to simulate the population dynamics of H. armigera over a wide area in northern China. The area considered covers 12 provinces where serious outbreaks of H. armigera have been observed. In this model, pest development is driven by local ambient temperature, and adults migrate long distances between regions and select preferred hosts for oviposition within a region. Six types of host including cotton, wheat, corn, peanut, soybean and a single category composed of all other minor hosts are considered in this model. Survival rates of eggs and larvae are based on life-table data, and simulated as a function of host type, host phenology and temperature. The incidence of diapause depends on temperature and photoperiod experienced during the larval stage. Survival rate of non-diapause pupae is a nonlinear function of rainfall, and overwinter survival rate is a nonlinear function of temperature. Insecticide is applied when population density exceeds the economic threshold on a host crop within a region. Comparisons of model output with light-trap data indicate that our model reflects the pest population dynamics over a wide area, and could potentially be used for testing novel pest control strategies in northern China.     

Analysis of the global warming dynamics from temperature time series

Global warming is the observed increase of the average temperature of the Earth. The primary cause of this phenomenon is the release of the greenhouse gases by burning of fossil fuels, land cleaning, agriculture, among others, leading to the increase of the so-called greenhouse effect. An approach to deal with this important problem is the time series analysis. In this regard, different techniques can be applied to evaluate the global warming dynamics. This kind of analysis allows one to make better predictions increasing our comprehension of the phenomenon. This article applies nonlinear tools to analyze temperature time series establishing state space reconstruction and prediction. Since noise contamination is unavoidable in data acquisition, it is important to employ robust techniques. The method of delay coordinates is employed for state space reconstruction and delay parameters are evaluated using the method of average mutual information and the method of false nearest neighbors. Afterwards, the simple nonlinear prediction method is employed to estimate temperatures of the future. Temperature time series from different places of the planet are used. Initially, the approach is verified considering known parts of the time series and afterwards, results are extrapolated for future values estimating temperature until 2028. Results show that these techniques are interesting to estimate temperature time history, presenting coherent estimations.     

Influence of low and decreasing food levels on Daphnia-algal interactions: Numerical experiments with a new dynamic energy budget model

Based on numerical experiments with a new physiologically structured population model we demonstrate that predator physiology under low food and under starving conditions can have substantial implications for population dynamics in predator-prey interactions. We focused on Daphnia-algae interactions as model system and developed a new dynamic energy budget (DEB) model for individual daphnids. This model integrates the κ-rule approach common to net assimilation models into a net-production model, but uses a fixed allocation of net-productive energy in juveniles. The new DEB-model agrees well with the results of life history experiments with Daphnia. Compared to a pure κ-rule model the new allocation scheme leads to significant earlier maturation at low food levels and thus is in better agreement with the data. Incorporation of the new DEB-model into a physiologically structured population model using a box-car elevator technique revealed that the dynamics of Daphnia-algae interactions are highly sensitive to the assumptions on the energy allocation of juveniles under low food conditions. Additionally we show that also other energy allocation rules of our DEB-model concerning decreasing food levels and starving conditions at the individual level have strong implications for Daphnia-algae interactions at the population level. With increasing carrying capacity of algae a stable equilibrium with coexistence of Daphnia occurs and algae shifts to limit cycles. The amplitudes of the limit cycles increase with increasing percentage of sustainable weight loss. If a κ-rule energy allocation is applied to juveniles, the stable equilibrium occurs for a much narrower range of algal carrying capacities, the algal concentration at equilibrium is about 2 times larger, and the range of algae carrying capacities at which daphnids become extinct extends to higher carrying capacities than in the new DEB-model. Because predator-prey dynamics are very sensitive to predator physiology under low food and starving conditions, empirical constraints of predator physiology under these conditions are essential when comparing model results with observations in laboratory experiments or in the field.     

粤北2座饮用水源地水库的富营养化与浮游植物群落动态

粤北地区的水库以山地型水库为主,其中有不少担负着饮用水源的功能。为了解粤北地区水源地水库的富营养化状态与浮游植物种群的动态变化,于2011年的枯水期（2―3月）和丰水期（6―7月）对花山和白水礤2座中型水库进行了采样调查,对水库的营养盐和浮游植物种群进行了分析。结果表明：2座水库均为贫营养型;浮游植物在枯水期和丰水期的种类变化不大,共鉴定出的浮游植物6门37种（属）,以硅藻为主要优势种群,优势种为小环藻（Cyclotella sp.）和颗粒直链藻（Melosira granulata）。同时,枯水期和丰水期2座水库浮游植物的丰度和生物量都比较低,其值分别为0.65×106～1.95×106cells.L-1、0.11 mg.L-1和0.73×106～8.9×106cells.L-1、0.05～0.50 mg.L-1。在浮游植物种群动态中,2座水库浮游植物丰度和生物量的季节变化主要表现为硅藻丰度和生物量的变化,低浓度的氮、磷营养盐限制是影响这2座贫营养水库浮游植物动态变化主要因素。     

园林绿化树种香樟叶片的含硫量动态分析

植物对一定浓度范围内的大气污染物,具有一定程度的抵抗及吸收净化作用。为了解南京市主要园林绿化树种香樟（Cinnamomum camphora）叶片吸收净化SO2的能力,选择在5个不同污染靶区,以3个不同胸径级的香樟叶片为研究对象,采用硫酸钡比浊法测定了不同季节香樟叶片的含硫量。结果表明：香樟叶片对SO2具有一定的吸收净化能力,其叶片含硫量平均为0.2160%,且其含量随分布区、生长季节及个体胸径不同差异显著;并与异域大气中SO2污染指数成一定的正相关;与个体胸径大小成显著负相关;季节间呈现出＂先降后升再降＂的动态趋势,于春秋季较高,而夏冬季较低。     

Threshold management in a coupled economic–ecological system

Economic analysis of optimal ecosystem management in the presence of a threshold has typically ignored the potential for induced behavioral responses. This paper contributes to the literature on non-convex ecosystem management by considering the implications of a particular behavioral response in a regional economy – that of amenity-led growth – to changes in ecosystem services generated by a lake ecosystem subject to a eutrophication threshold. The essential policy challenge is to achieve optimal levels of lake nutrients and urbanization given that improvements to water quality will induce additional migration and urbanization in the region with attendant ecological impacts. We show that policies that ignore the recursive relationship between urbanization and water quality unintentionally exacerbate boom-bust cycles of regional growth and decline and risk pushing the system towards long-run economic decline. In contrast, the optimal policy accounts for the behavioral feedbacks to improved ecosystem services, and balances regional growth and ecological degradation.     

基于系统动力学的食品企业安全生产监管博弈研究

为了研究食品安全生产及监管的内在机理、影响因素及监管模式,采用演化博弈论方法建立了政府监管部门与食品企业之间的博弈模型,运用复制动态方程和系统动力学方法深入分析了食品安全生产中相关主体的决策选择。结果表明:食品企业在追求利益最大化的前提下须主动重视食品安全问题,食品企业的策略选择受安全生产投入成本、处罚成本和声誉收益的影响;政府监管部门的监管行为受监管成本、外部负效益及社会成本等多方因素的影响。因此,增大监管部门惩处力度、降低监管成本、完善社会公众监督机制、强化声誉效应是食品安全问题得以解决的有力保障。     

面向实时人群动力学分析的深度基本图网络

当前行人疏散实验中基本图计算方法通常是通过对每个行人进行跟踪实现的.但这种跟踪方法难以实现实时人群动力学分析.针对这一问题,提出了深度基本图网络.实验提出的网络框架由两个模块组成,即多尺度递归卷积神经网络(MSR-Net)和光流模块,分别对行人密度和行人速度进行估计.具体来讲,MSR-Net学习了输入图像与行人密度图之...