基于广义可加模型的昆虫种群动态非线性分析及R语言实现

昆虫种群受到气候、天敌和土壤等多种生态因子的综合作用,其动态具有复杂性、不确定性和非线性等特征。广义加性模型(generalized additive models,GAM)就是适用于响应变量与解释变量之间的关系是非线性或非单调的数据分析。本文以1973—1990年稻纵卷叶螟种群数量与降雨持续天数和降雨量的相关性分析为例,介绍了广义可加模型的应用及其R语言实现步骤,为研究昆虫种群动态及其驱动因子提供了有效的分析工具。     

昆虫种群动态模拟方法的一点改进──三化螟种群动态模拟模型的研究

以Ｒｕｅｓｉｎｋ（１９７６）的模型为基础,根据昆虫个体一般不同步地进入下一发育阶段的状况,当昆虫各虫态发育到完成该虫态发育所需要的最低年龄级数后,假定各年龄级的昆虫种群均以一定的概率分布函数值进入下一个发育阶段,同时根据有效积温向前推进。据此,对昆虫种群动态模拟方法作了一点改进。该方法综合了已有的种群模型的优点,因而较Ｒｕｅｓｉｎｋ（１９７６）和ＣｈｉＨｓｉｎ等（１９８５）提出的方法更真实地反映了昆虫种群动态的变化规律。根据三化螟自然种群生命表的资料,分析和确定逐日存活率、逐日发育率和逐日生殖率,对三化螟种群进行逐日动态模拟和预测,同时引入环境因素对种群的控制作用,研究不同环境条件下的种群动态,经验证,模型基本能够反映田间三化螟的发生规律。     

《昆虫种群生态学——基础与前沿》

该书由科学出版社2005年出版，徐汝梅、成新跃编。介绍了昆虫生态学的基础与前沿，包括昆虫种群数量的时、空动态规律、调节机制及有关的研究方法。同时，还特别强调了空间生态学在昆虫种群生态学研究中的应用;并介绍了当前昆虫种群生态学的研究热点，如种群变动的遗传机制、昆虫与植物的协同进化等；结合重大的生态学问题，对昆虫暴发的一般理论、昆虫濒危与生物多样性保育、种群扩散与生物入侵、全球变化与昆虫种群动态等进行了论述。     

昆虫种群的一类时空动态模型研究

昆虫种群的时空动态包括种群的数量变化和空间分布变化.根据密度制约性原理, F推导出描述昆虫种群时空动态的非线性偏微分方程模型.该模型由扩散、迁移、出生及死亡等成分组成.建立了模型的差分解法,也给出模型参数的拟合方法.模型的初始分布确定为二项分布,Poisson分布,以及负二项分布.给出产生3种空间分布的计算方法.给定初始分布类型及参数,由各算法组装的计算机模型可得到初始分布,田间各点各时刻的昆虫数量 ,以及该时刻的空间分布类型和聚集性.     

昆虫种群动态模拟模型

昆虫是动物界中最大的类群,与人类有着密切的利害关系。对昆虫的数量预测与符合经济和生态规律的管理,一直都被国内外列入重点研究课题。种群动态模拟是害虫管理中重要的基础工作。近十年来,关于昆虫种群动态模型的理论和实验研究进展迅速。现分别从单种种群和多种种群两个方面对国内外近些年来昆虫种群动态模拟模型的研究进展进行了概括和总结。单种种群从两个方面阐述:一是最基本的种群动态模拟模型Log istic方程的研究成果,包括方程的修正、参数的拟合与最优捕获策略等;另一个方面是对种群动态模拟常用的矩阵模型的概述,主要介绍不等期年龄组、矩阵维数的变化、矩阵维数与历期的关系、个体之间的发育差异以及发育速率差异等等对昆虫种群动态模型的影响。多种群主要从建模和模型应用两个部分对国内外研究成果进行综述。最后,对种群动态模拟模型研究的发展方向做了深入地讨论,即在原有的数据采集工作的基础上,使用面向对象程序设计语言,把各种要素包括各种物种及各种环境条件抽象成类,用消息传递来表示昆虫种群内个体与个体、昆虫种群与环境之间的相互作用,再结合先进的数学算法,建立一个直观的、操作简单的昆虫种群动态模型库,使模型结构与现实世界有最大的相似性。这样就可以实现昆虫种群动态的可视化、立体化、实时化和精确化的监测及预测。     

蚕豆蚜种群动态与蚕豆生理变化的关系

本文以蚕豆和蚕豆蚜构成的人工种间关系系统为对象,研究了蚜虫种群动态和植物生理变化的关系。发现蚕豆生理应激过程能影响蚕豆蚜种群的生殖率、存活率等种群特征,从而调节其种群的动态。蚕豆还能传导放大昆虫种群自主调节的信息。蚜虫种群的适应过程,包括减小种群数量(低生殖率和迁移),降低对植物的胁迫,从而维持种间关系系统的持续发展。本文还初步将植物的生理应激过程与昆虫种群的动态过程相耦联,建立动态模型,对种间关系的发展趋势进行了分析和讨论。     

非线性自食种群动力系统的稳定性

本文讨论了一类主要由昆虫自食引起的非线性种群动态模型的稳定性．首先给出确定性模型,并着重讨论其一个特殊情形．通过在参数空间中辨识稳定域的边界,可以相对直观地分析种群动态．对于随机模型,是利用在确定性模型中加入对数尺度下的正态随机项形成的,该模型具有较好的统计性质,便于将现实的非线性时间序列数据引进系统中来．     

昆虫种群动态的空间分析方法探讨

近年来,随着地理信息系统、卫星遥感、航空数字化摄像。全球定位系统以及虚拟可视化等空间技术的迅速发展,昆虫种群的空间动态也已经引起了国内外学者和生产应用部门的高度重视［１］。然而,在考虑生态系统的空间问题时,区别于传统的研究时间尺度的经验和方法,某些涉及空间分析的概念、方法和技术也必须受到足够的关注。本文将就分析昆虫空间动态问题时,必须考虑的一些因素、方法和可能存在的问题作一探讨,以供同行商榷。１空间异质性研究昆虫种群的空间动态,首先需要考虑的就是空间异质性问题。昆虫的发生与其所生存的外部环境密切…     

具有吸收状态的昆虫种群矩阵模型的研究

在Vandermeer的昆虫矩阵模型的基础上,提出了具有吸收状态的昆虫种群矩阵模型。并且导出了k步吸收状态与转移状态的矩阵表达式,将有助于昆虫种群动态的研究．     

三化螟种群系统的最优管理决策

以三化螟Tryporyza invertulas(Walker)种群动态模型和水稻产量损失预测模型为基础,根据水稻插植期、品种抗性,保护利用自然天敌和杀虫剂多次使用等控制措施以及它们的各种不同组合对该虫种群动态、水稻产量损失串和净收益的影响,以净收益最大为目标函数,研究三化螟种群的最优管理决策。其中,对昆虫种群动态模拟方法作了一点改进,它综合了前人所提出的种群动态模型的优点。建立的系统模型能够提供包括农业防治、生物防治和化学防治措施在内的、对三化螟种群实施有效管理的最优决策方案。     

Population dynamics of rice rats (a Hantavirus reservoir) in southern Chile: feedback structure and non-linear effects of climatic oscillations

We studied a fluctuating population of the long-tail rice rat ( Oligoryzomys longicaudatus ), the main Hantavirus vector in southern Chile, and spanning 19 years of monitoring. We determined that a first-order feedback structure and non-linear effects of Antarctic Oscillation Index (AAOI) and Southern Oscillation Index (SOI) explain 96% of the variation in annual per capita population growth rates. One important result of this study is that first-order feedback structure captures the essential features of population dynamics of long-tailed rice rats. This regulatory structure suggests that rice rats are limited by food, space or predators and regulated by intra-specific competition. The first-order dynamics observed in long-tailed rice rats strongly suggests that Hantavirus have no harmful effects on survival or reproductive processes. Besides the non-linear climatic signature in population dynamics, the periodic event of bamboo-flowering and mast seeding strongly influence rice rats population growth rates. Because of this, bamboo flowering may be used as a signal for forecasting long-tail rice rats outbreaks and for implementing information and health policies to avoid human-rodent contacts in specific areas. The observed effects of the two large-scale climatic indexes that influence climatic variability along southern Pacific Ocean, the AAOI and the SOI, emphasizes the role of considering non-linear feedback structures and climatic forces for understanding small rodent population dynamics. Because long-tailed rice rats represent the major Hantavirus reservoir in southern Chile and Argentina, we need to gain an in-depth understanding of the structure and functioning of these small rodent populations in face of the potential consequences of global change and climatic fluctuations.     

Non-Linear Analysis Indicates Chaotic Dynamics and Reduced Resilience in Model-Based Daphnia Populations Exposed to Environmental Stress

In this study we present evidence that anthropogenic stressors can reduce the resilience of age-structured populations. Enhancement of disturbance in a model-based Daphnia population lead to a repression of chaotic population dynamics at the same time increasing the degree of synchrony between the population''s age classes. Based on the theory of chaos-mediated survival an increased risk of extinction was revealed for this population exposed to high concentrations of a chemical stressor. The Lyapunov coefficient was supposed to be a useful indicator to detect disturbance thresholds leading to alterations in population dynamics. One possible explanation could be a discrete change in attractor orientation due to external disturbance. The statistical analysis of Lyapunov coefficient distribution is proposed as a methodology to test for significant non-linear effects of general disturbance on populations. Although many new questions arose, this study forms a theoretical basis for a dynamical definition of population recovery.     

Modeling the dynamics of natural rotifer populations: Phase-parametric analysis

A model of the dynamics of natural rotifer populations is described as a discrete non-linear map depending on three parameters, which reflect characteristics of the population and environment. Model dynamics and their change by variation of these parameters were investigated by methods of bifurcation theory. A phase-parametric portrait of the model was constructed and domains of population persistence (stable equilibrium, periodic and a-periodic oscillations of population size) as well as population extinction were identified and investigated. The criteria for population persistence and approaches to determining critical parameter values are described. The results identify parameter values that lead to population extinction under various environmental conditions. They further illustrate that the likelihood of extinction can be substantially increased by small changes in environmental quality, which shifts populations into new dynamical regimes.     

农业害虫发生动态的Fuzzy优选识别模式及其应用

本文对山东省曲阜市１９８２－１９９４年二代棉铃虫发生动态的虫情与相应年份的气象资料进行了数量分析,应用Ｆｕｚｚｙ优选识别原理,建立了二代棉铃虫发生动态的Ｆｕｚｚｙ优选识别模式·对历史资料进行回代验证,其历史拟合率为 １００％．书 １９９５,１９９６ 两年的观测数据资料作为独立样本进行试报,预测结果与实际一致．本研究为农业害虫发生动态的预测预报提供了一种新的研究方法．     

Life history perspectives on pest insects: What’s the use?

In his seminal 1954 paper on the ‘population consequences of life history phenomena’, Cole noted that ‘these computations may have practical value in dealing with valuable or noxious species’. In the present paper, the question is asked: ‘is research based on evolutionary perspectives in general, and life history theory specifically, really useful for dealing with insect pests?’ Perhaps such theory‐based research is rather a luxury: time and resources would be better spent on entirely applied aspects of the problem. The conclusion of the present discussion is that having an evolutionary perspective guiding research is actually a very cost‐effective way of dealing with applied problems, as it provides a clear basis for interpretations, generalizations and predictions. Life history theory is a very central and necessary part of both population ecology and general evolutionary theory, and its specific usefulness in pest forecasting and management are discussed. Nevertheless, our ability to predict insect population dynamics is still limited, and so is our ability to make use of an insect’s life history traits to predict its propensity to become a pest. I suggest that the former shortcoming is largely due to poor understanding of insect life history plasticity. This, in turn, may partly be due to a paucity of studies where reaction norms are investigated as putative adaptations. I suggest that the latter shortcoming is due to problems inherent with studying life history traits as adaptations, for example the lack of an independent fitness model and the fact that life histories tend to form syndromes of coadapted traits. These points are illustrated with examples from my own work on non‐pest butterflies and from insect–Eucalyptus systems.     

Non-linear feedback processes and a latitudinal gradient in the climatic effects determine green spruce aphid outbreaks in the UK

The role of climatic fluctuations in determining the dynamics of insect populations has been a classical problem in population ecology. Here, we use long-term annual data on green spruce aphid populations at nine localities in the UK for determining the importance of endogenous processes, local weather and large-scale climatic factors. We rely on diagnostic and modelling tools from population dynamic theory to analyse these long-term data and to determine the role of the North Atlantic Oscillation (NAO) and local weather as exogenous factors influencing aphid dynamics. Our modelling suggests that the key elements determining population fluctuations in green spruce aphid populations in the UK are the strong non-linear feedback structure, the high potential for population growth and the effects of winter and spring weather. The results indicate that the main effect of the NAO on green spruce aphid populations is operating through the effect of winter temperatures on the maximum per capita growth rate (R_m). In particular, we can predict quite accurately the occurrence of an outbreak by using a simple logistic model with weather as a perturbation effect. However, model predictions using different climatic variables showed a clear geographical signature. The NAO and winter temperature were best for predicting observed dynamics toward the southern localities, while spring temperature was a much better predictor of aphid dynamics at northern localities. Although aphid species are characterized by complex life-cycles, we emphasize the value of simple and general population dynamic models in predicting their dynamics.     

Non-linear biological responses to environmental noise affect population extinction risk

Non-linearities are commonly observed in the responses of organisms to environment. They potentially modify the qualitative and quantitative properties of population dynamics. We studied how non-linear responses to environment, or "noise filters", influence population variability and extinction risk by introducing coloured noise to the growth rate in the Hassell single-species model. The consequences of noise filtering were analysed by comparing the model dynamics with linearly filtered and non-linearly filtered noise that have the same mean. Three biologically plausible filters we used: saturating, unimodal optimum type, and sigmoid responses.
Filtering can either decrease or increase population variability when compared to linear noise response. The effect of noise filtering on variability is most pronounced with stable population dynamics and the outcome depends on the filter type, population growth rate, and noise colour.
Non-linear noise filtering predominantly increases extinction risks when population growth rate is low (R<5). As an exception, saturating filter has a window of decreased risk at very low growth rate and reddened environment. In the unstable range of the dynamics (15These results suggest that accounting for the non-linear responses to environment should be considered when estimating extinction risks and population variability. Moreover, the non-linear responses make noise colour a more important factor in these analyses.     

Fuzzy subset approach in coupled population dynamics of blowflies

This paper is a study on the population dynamics of blowflies employing a density-dependent, non-linear mathematical model and a coupled population formalism. In this study, we investigated the coupled population dynamics applying fuzzy subsets to model the population trajectory, analyzing demographic parameters such as fecundity, survival, and migration. The main results suggest different possibilities in terms of dynamic behavior produced by migration in coupled populations between distinct environments and the rescue effect generated by the connection between populations. It was possible to conclude that environmental heterogeneity can play an important role in blowfly metapopulation systems. The implications of these results for population dynamics of blowflies are discussed.     

Multi-Step Polynomial Regression Method to Model and Forecast Malaria Incidence

Malaria is one of the most severe problems faced by the world even today. Understanding the causative factors such as age, sex, social factors, environmental variability etc. as well as underlying transmission dynamics of the disease is important for epidemiological research on malaria and its eradication. Thus, development of suitable modeling approach and methodology, based on the available data on the incidence of the disease and other related factors is of utmost importance. In this study, we developed a simple non-linear regression methodology in modeling and forecasting malaria incidence in Chennai city, India, and predicted future disease incidence with high confidence level. We considered three types of data to develop the regression methodology: a longer time series data of Slide Positivity Rates (SPR) of malaria; a smaller time series data (deaths due to Plasmodium vivax) of one year; and spatial data (zonal distribution of P. vivax deaths) for the city along with the climatic factors, population and previous incidence of the disease. We performed variable selection by simple correlation study, identification of the initial relationship between variables through non-linear curve fitting and used multi-step methods for induction of variables in the non-linear regression analysis along with applied Gauss-Markov models, and ANOVA for testing the prediction, validity and constructing the confidence intervals. The results execute the applicability of our method for different types of data, the autoregressive nature of forecasting, and show high prediction power for both SPR and P. vivax deaths, where the one-lag SPR values plays an influential role and proves useful for better prediction. Different climatic factors are identified as playing crucial role on shaping the disease curve. Further, disease incidence at zonal level and the effect of causative factors on different zonal clusters indicate the pattern of malaria prevalence in the city. The study also demonstrates that with excellent models of climatic forecasts readily available, using this method one can predict the disease incidence at long forecasting horizons, with high degree of efficiency and based on such technique a useful early warning system can be developed region wise or nation wise for disease prevention and control activities.     

Timescales of population rarity and commonness in random environments

This is a mathematical study of the interactions between non-linear feedback (density dependence) and uncorrelated random noise in the dynamics of unstructured populations. The stochastic non-linear dynamics are generally complex, even when the deterministic skeleton possesses a stable equilibrium. There are three critical factors of the stochastic non-linear dynamics; whether the intrinsic population growth rate (lambda) is smaller than, equal to, or greater than 1; the pattern of density dependence at very low and very high densities; and whether the noise distribution has exponential moments or not. If lambda < 1, the population process is generally transient with escape towards extinction. When lambda > or = 1, our quantitative analysis of stochastic non-linear dynamics focuses on characterizing the time spent by the population at very low density (rarity), or at high abundance (commonness), or in extreme states (rarity or commonness). When lambda >1 and density dependence is strong at high density, the population process is recurrent: any range of density is reached (almost surely) in finite time. The law of time to escape from extremes has a heavy, polynomial tail that we compute precisely, which contrasts with the thin tail of the laws of rarity and commonness. Thus, even when lambda is close to one, the population will persistently experience wide fluctuations between states of rarity and commonness. When lambda = 1 and density dependence is weak at low density, rarity follows a universal power law with exponent -3/2. We provide some mathematical support for the numerical conjecture [Ferriere, R., Cazelles, B., 1999. Universal power laws govern intermittent rarity in communities of interacting species. Ecology 80, 1505-1521.] that the -3/2 power law generally approximates the law of rarity of 'weakly invading' species with lambda values close to one. Some preliminary results for the dynamics of multispecific systems are presented.