局域种群的Allee效应和集合种群的同步性

从包含Allee效应的局域种群出发,建立了耦合映像格子模型,即集合种群模型.通过分析和计算机模拟表明:(1)当局域种群受到Allee效应强度较大时,集合种群同步灭绝;(2)而当Allee效应强度相对较弱时,通过稳定局域种群动态(减少混沌)使得集合种群发生同步波动,而这种同步波动能够增加集合种群的灭绝风险;(3)斑块间的连接程度对集合种群同步波动的发生有很大的影响,适当的破碎化有利于集合种群的续存.全局迁移和Allee效应结合起来增加了集合种群同步波动的可能,从而增加集合种群的灭绝风险.这些结果对理解同步性的机理、利用同步机理来制定物种保护策略和害虫防治都有重要的意义.     

集合种群空间混沌的模拟研究以及Allee效应、拥挤效应与捕食效应对空间模式的影响

集合种群的空间模式研究是当今生态学的核心问题之一。本研究利用常微分动力系统以及基于网格模型的元胞自动机模型对Allee效应、拥挤效应以及捕食作用集合种群的空间分布模式做了全面的模拟研究。Allee效应描述当种群水平低于某一阈值时会发生由生殖成功几率下降造成的种群负增长率,而拥挤效应是指当种群密度过高时引起的个体性为异常从而达到调节种群增长率的作用。文章组建了3个空间确定性模型：局部作用模型(CIM)、距离敏感模型(DSM)和集合种群捕食模型(MMP)。局部作用模型显示在一维生境中空斑块形成金字塔状,二维模型显示出明显的动态拟周期性以及由空间混沌所形成的异质性。距离敏感模型可导致由迁移个体中密度制约强度决定的集合种群大小复杂动态与种群密度的双峰分布。这些结果说明动态行为的复杂性,不仅可用于表征研究物种的特性,而且可以表明该物种的续存能力与灭绝风险。集合种群捕食模型是概率转换空间模型,利用该模型得出了依赖于模型参数和生境尺度的白组织种群概率空间分布模式。模拟的结果表明,系统的内在机制和这种白组织模式导致捕食者形成集团型不明显的“捕食小组”或“杀手小组”,并具有较高扩散力．但却包括侵占率低、灭绝率高的特点。而使猎物种群形成高集团性、高侵占率、低灭绝率、低扩散力的种群集团。这种特点又使捕食者种群在生境中处于中心地带,而使猎物种群形成在捕食者和生境边缘间的环状分布。这些结果还说明了尺度对于生态学的研究是至关重要的,不同的尺度将产生不同的系统模式。     

马尾松毛虫种群动态的时间序列分析及复杂性动态研究

自从May(1974)指出即使是简单的种群模型也能揭示混沌动态以来,自然种群是否存在混沌一直具有争论,如何检测自然种群的混沌行为也成为种群动态研究的一个难点,通过时间序列分析和反应面模型建模的8方法分析了马尾松毛虫的复杂性动态,用自相关函数对马尾松毛虫发生的时间动态分析的结果认为动态是平衡的,其周期性不显著,而具有一定的复杂性,这种类型可以是减幅波动,有限周期或弱混沌,波动主要由系统内因引起,进一步采用反应面模型估计全局李雅普若夫指数和局域李雅普若夫指数结果均为负,显示马尾松毛虫种群动态不存在混沌现象,但是在增加一个小的噪音以后,局域李雅普若夫指数变为在0以上的波动,说明系统对噪音非常敏感,噪音对松毛虫种群动态具有很大的影响,可以将其从非混沌状态变为混沌,研究结果认为全局郴雅普若夫指数λ是一定时间内两个变动轨迹的总平均偏差,而随着种群动态的波动,指数也是波动的,所以对于检测自然种群的混沌来说不是一个好的指标,局域李雅普若夫指数λM能更好地表示自然种群混沌的存在和产生混沌的条件,对害虫管理来说对种群暴发初期的预测是尤其重要的,而此时又最难于预测,所以对种群动态的监测就尤为重要,由于马尾松毛虫的代间种群动态为第一级密度相关,前一代的虫口密度与下一代的虫口密度相关性最强,所以前一代预测下一代是最可靠的。     

昆虫种群动态非线性建模理论与应用

本文以非线性动力学为基础,对自然界中昆虫种群动态的复杂性、不确定性进行了建模方法的探讨,在讨论了昆虫种群动态的混沌与非线性时间序列预测方法的前提下,以山东省玉米螟等种群动态资料进行了实例分析。     

相互作用的集合种群研究动态

在集合种群水平上,两个或更多物种可以生活在同一个斑块网络中而没有相互作用.但在很多情况下,种间的相互作用会影响种群的迁移率、灭绝率和侵占率,从而调节相应物种的集合种群动态.这方面的研究主要有集合种群水平上物种之间的竞争、捕食以及在没有任何环境异质性的条件下物种在空间上聚集分布的产生和维持等.综述了近年来关于集合种群水平上的竞争,捕食者和猎物系统以及捕食与复杂空间动态的最新研究成果.     

集合种群的似Allee效应

从局域种群出发,建立了一个既包括局域种群动态,又包含集合种群侵占率的整合模型,并在这两个层次上进行了计算机模拟,结果表明：（1）同局域种群的Allee效应相类似,集合种群的斑块（适宜生境）侵占比例也存在一个临界值,即使有足够的适宜生境,当斑块的侵占比例低于这个临界值时,集合种群优将趋于灭绝。（2）这个临界值与局域种各的Allee效应密切相关,这将给自然保护,尤其稀有生物的保护以很大的启示。     

蚜虫种群时空分布动态模型

种群空间格局是昆虫种群的重要属性,是为害虫防治提供动态信息的重要前提。关于种群空间格局的时空动态,前人曾建立了富立叶模型和有阻尼自由震荡模型,但忽略了生境资源和空间资源的限制,不能很好地描述昆虫种群在自然界摆布状况的动态行为。因此,在前人研究的基础上,根据蚜虫在自然界的聚集扩散行为逐步建立了描述蚜虫种群聚集扩散规律的变幅、变周期时空分布动态模型,即:y=Ae-nt[sin(w0emtt+φ)+b]+c,并应用该模型对麦长管蚜(Sitobion avenae Fabricius)、麦二叉蚜(Schizaphis graminum Rondani)、禾缢管蚜(Rhopalosiphum padi Linnaeus)和玉米蚜(Rhopalosiphum maidis Fitch)的实验数据进行了拟合。结果表明,麦蚜种群和玉米蚜种群呈现出不同的规律,3种麦蚜均为减幅减周期的变化趋势,玉米蚜则表现为减幅增周期的变化趋势。此外,该模型的拟合效果较好(R20.942,SSE2.6)、生物意义明确,不仅可用于描述蚜虫以及蚜虫以外的其他昆虫和螨类种群的时空动态,还可准确描述不同年龄阶段和不同空间位置上种群的动态,具有普遍适用性。应用该模型考察不同种蚜虫在同一作物上的竞争情况和蚜虫与其天敌的空间分布动态,可为害虫的综合防治奠定基础;对不同小麦抗性品种上同一种蚜虫的聚集扩散行为进行刻画、分析,还可为小麦的抗性育种提供参考依据。     

集合种群动态对生境毁坏空间异质性的响应

首次将分形几何(Fractal geometry)与元胞自动机(Cellular automata)相结合,研究了破碎化生境中集合种群的空间分布格局动态,以及集合种群动态对生境毁坏空间异质性的响应。研究发现:(1)各个物种种群在生境中的分布具有很好的分形特征,物种的计盒维数(Box dimension)不仅可以很好地反映种群的空间分布结构,也能很好地反映种群动态。(2)如果将空间因素考虑进来的话,生境毁坏的灭绝债务(Time debt)将大于空间隐含模式所模拟的结果。(3)物种灭绝同时存在强物种灭绝和弱物种灭绝。并且只有在生境随机毁坏下,才与空间隐含的模拟结果比较接近,即强物种中将是最强物种率先灭绝。而在边缘毁坏这种比较集中成块的开发方式下,将是较强的物种灭绝。(4)边缘毁坏相对随机毁坏有利于物种,尤其是弱物种的长期续存。     

生境破碎化对生物多样性的影响

生境破碎化对生物多样性和生态系统功能的影响是当前国内外生态学家研究的热点问题之一。生境破碎化导致原生境的总面积减小,产生隔离的异质种群,从而影响个体行为特性、种群间基因交换、物种间相互作用及生态过程。生境破碎化的过程引起栖息地内部食物、繁殖场所、局部小气候、边缘效应等生物和非生物条件的变化,从而影响植物种群的大小和灭绝速率、扩散和迁入、遗传和变异以及存活力等,影响动物种群的异质种群动态、适宜生境比例、灭绝阈值、种间关系等。随着景观生态学与农业科学的融合,探索利用景观布局控制害虫发生将是人类利用生境破碎化为人类服务的一条新途径。     

Allee效应对具有生境恢复的集合种群的影响

Allee效应与种群的灭绝密切相关,其研究对生态保护和管理至关重要。Allee效应对物种续存是潜在的干扰因素,濒危物种更容易受其影响,可能会增加生存于生境破碎化斑块的濒危物种的死亡风险,因此研究Allee效应对种群的动态和续存的影响是必要的。从包含由生物有机体对环境的修复产生的Allee效应的集合种群模型出发,引入由其他机制形成的Allee效应,建立了常微分动力系统模型和基于网格模型的元胞自动机模型。通过理论分析和计算机模拟表明:(1)强Allee效应不利于具有生境恢复的集合种群的续存;(2)生境恢复有利于种群续存;(3)局部扩散影响了集合种群的空间结构、动态行为和稳定性,生境斑块之间的局部作用将会减缓或消除集合种群的Allee效应,有利于集合种群的续存。     

Biological relativity to water–energy dynamics

Climate has long been related to geographical differences in the distribution and diversity of life. What has eluded explanation is why this should be so. One emerging possibility is biological relativity to water–energy dynamics: the relative nature of biotic dynamics to changes in energy/matter conditions caused by changes in water (all states) while doing work, especially liquid water. The dynamic parameters involved – liquid water and optimal energy conditions – are independent of life, and have been shown to provide a simple, globally predictive explanation for co‐variation between climate and the species richness of woody plants. Here I elaborate on what I mean by ‘biological relativity to water–energy dynamics’ and how it should relate to the geography and evolution of life in general (terrestrial, subterranean, marine/aquatic biota). Working through a natural hierarchy of physical, geographical, ecological and biological first principles, I outline the hierarchical, abiotic → biotic conceptual framework within which this idea operates. The implications of this idea include the following. First, the biosphere is better conceptualized as a ‘subsphere’ of the liquid hydrosphere – a system within a system, wherein ‘life’ has all the unique physical properties of liquid water, plus unique emergent properties of its own. Second, the fundamental capacity for life to exist and be dynamic in all biotic systems is determined by the abiotic capacity for liquid water to exist and be dynamic, which is always relative to the capacity for water–energy dynamics in general. Third, liquid water–energy dynamics acts as a fundamental mechanism of evolution, while being a constant mechanism of natural selection. Fourth, over space and time, there should be first‐order predictable and/or systematic differences in the capacity for, operation and outcomes of, biotic dynamics globally (e.g. species richness), that necessarily dissolve into apparent chaos locally. Fifth, biological relativity to water–energy dynamics provides a fundamental and natural framework for operationalizing hierarchy theory and developing trans‐scalar explanations for the geography and evolution of life's diversity.     

Blowing-up of deterministic fixed points in stochastic population dynamics

We discuss the stochastic dynamics of biological (and other) populations presenting a limit behaviour for large environments (called deterministic limit) and its relation with the dynamics in the limit. The discussion is circumscribed to linearly stable fixed points of the deterministic dynamics, and it is shown that the cases of extinction and non-extinction equilibriums present different features. Mainly, non-extinction equilibria have associated a region of stochastic instability surrounded by a region of stochastic stability. The instability region does not exist in the case of extinction fixed points, and a linear Lyapunov function can be associated with them. Stochastically sustained oscillations of two subpopulations are also discussed in the case of complex eigenvalues of the stability matrix of the deterministic system.     

Perspective of practical biological control and population theories

We have not yet had sufficient theoretical explanation for successful biological control in which a key pest is controlled after an introduction of natural enemies. I compare here real features of successful biological control and theoretical host–parasitoid population models to reduce the gap between theory and practice. I first review the historical interaction between classical biological control projects and theoretical population models. Second, I consider the importance of host refuges in host–parasitoid population dynamics as concerns the mechanisms of low and stable host density. The importance of density–dependent parasitism through parasitoid reproduction in multivoltine host–parasitoid systems and supplemental generalist natural enemies are also discussed. Finally, I consider the difference in tactics for classical biological control and for augmentation of natural enemies in annual crop systems. Received: December 20, 1998 / Accepted: January 15, 1999     

Deterministic chaоs and the problem of predictability in population dynamics

The studies of the processes that can significantly influence the predictability in population dynamics are reviewed and the results of mathematical simulations of population dynamics are compared to the time series obtained in field observations. Considerable attention is given to the chaotic changes in population abundance. Some methods of numerical analysis of chaoticity and predictability of the time series are considered. The importance of comparing the results of mathematical simulation and observation data is tightly linked to problems in detecting chaos in the dynamics of natural populations and estimating the prevalence of chaotic regimes in nature. Insight into these problems can allow identification of the functional role of chaotic regimes in population dynamics.     

Distinguishing error from chaos in ecological time series

Over the years, there has been much discussion about the relative importance of environmental and biological factors in regulating natural populations. Often it is thought that environmental factors are associated with stochastic fluctuations in population density, and biological ones with deterministic regulation. We revisit these ideas in the light of recent work on chaos and nonlinear systems. We show that completely deterministic regulatory factors can lead to apparently random fluctuations in population density, and we then develop a new method (that can be applied to limited data sets) to make practical distinctions between apparently noisy dynamics produced by low-dimensional chaos and population variation that in fact derives from random (high-dimensional) noise, such as environmental stochasticity or sampling error. To show its practical use, the method is first applied to models where the dynamics are known. We then apply the method to several sets of real data, including newly analysed data on the incidence of measles in the United Kingdom. Here the additional problems of secular trends and spatial effects are explored. In particular, we find that on a city-by-city scale measles exhibits low-dimensional chaos (as has previously been found for measles in New York City), whereas on a larger, country-wide scale the dynamics appear as a noisy two-year cycle. In addition to shedding light on the basic dynamics of some nonlinear biological systems, this work dramatizes how the scale on which data is collected and analysed can affect the conclusions drawn.     

Rotifer Population Dynamics in Two Coupled Habitats: Invasion of Chaos

We study the role of interactions between habitats in rotifer dynamics. We use a simple discrete-time model to simulate the interactions between neighboring habitats with different intrinsic dynamics. Being uncoupled, one habitat shows periodical oscillations of the rotifer biomass while the other one demonstrates chaotic oscillations. As a result of the exchange of rotifer biomass, chaos replaces regular oscillations. As a result, the rotifer dynamics becomes chaotic in both habitats. We show that the invasion of chaos is followed by the synchronization of the chaotic regimes of both habitats, and this synchronization increases as coupling between the habitats is increased. We also demonstrate that the biological invasion of the rotifer species, which show chaotic dynamics, to a neighboring habitat with intrinsically regular plankton dynamics leads to the invasion of chaos and the synchronization of chaotic oscillations of the plankton biomass in both the habitats.     

茉莉酸类植物激素分析研究进展

以茉莉酸(iasmonic acid,JA)和茉莉酸甲酯(methyl jasmonate,MeJA)为代表的茉莉酸类物质(jasmonates,JAs)是一种新型天然植物生长调节剂,具有广谱的生理效应,存在于多种高等植物体内。内源茉莉酸类激素通常以多种手性异构体存在,且含量超微(约为ng/g鲜重),因此,对内源性茉莉酸类激素进行准确的定性、定量分析具有很大的难度,研究高效分离富集、高灵敏度检测以及分离与检测联用等分析方法,对加速植物激素分子作用机理研究具有重大意义。该文就JAs(含其衍生物及手性异构体)的分离与检测技术进行了综述,包括各种色谱、色谱与质谱联用技术、毛细管电泳技术、免疫分析等各种方法,并展望了JAs分析方法未来的发展趋势。     

Chaos and the (un)predictability of evolution in a changing environment

Among the factors that may reduce the predictability of evolution, chaos, characterized by a strong dependence on initial conditions, has received much less attention than randomness due to genetic drift or environmental stochasticity. It was recently shown that chaos in phenotypic evolution arises commonly under frequency‐dependent selection caused by competitive interactions mediated by many traits. This result has been used to argue that chaos should often make evolutionary dynamics unpredictable. However, populations also evolve largely in response to external changing environments, and such environmental forcing is likely to influence the outcome of evolution in systems prone to chaos. We investigate how a changing environment causing oscillations of an optimal phenotype interacts with the internal dynamics of an eco‐evolutionary system that would be chaotic in a constant environment. We show that strong environmental forcing can improve the predictability of evolution by reducing the probability of chaos arising, and by dampening the magnitude of chaotic oscillations. In contrast, weak forcing can increase the probability of chaos, but it also causes evolutionary trajectories to track the environment more closely. Overall, our results indicate that, although chaos may occur in evolution, it does not necessarily undermine its predictability.     

Birth, growth and death as structuring operators in bacterial population dynamics

A new model is presented that describes microbial population dynamics that emerge from complex interactions among birth, growth and death as oriented, discrete events. Specifically, birth and death act as structuring operators for individual organisms within the population, which become synchronised as age clusters (called cell generations that are structured in age classes) that are born at the same time and die in concert; a pattern very consistent with recent experimental data that show bacterial group death correlates with temporal population dynamics in chemostats operating at carrying capacity. Although the model only assumes “natural death” (i.e., no death from predation or antimicrobial exposure), it indicates that short-term non-linear dynamic behaviour can exist in a bacterial population growing under longer term pseudo-steady-state conditions (a confined dynamic equilibrium). After summarizing traditional assumptions about bacterial aging, simulations of batch, continuous-flow, and bioreactors with recycle are used to show how population dynamics vary as function of hydraulic retention time, microbial kinetics, substrate level, and other factors that cause differential changes in the distribution of living and dead cells within the system. In summary, we show that population structures induced by birth and death (as discrete and delayed events) intrinsically create a non-linear dynamic system, implying that a true steady state can never exist in growing bacterial populations. This conclusion is discussed within the context of process stability in biotechnology.     

Selection-delayed population dynamics in baleen whales and beyond

While it is known that population cycles are driven by delayed density-dependent feedbacks, the search for a common feedback mechanism in natural populations with cyclic dynamics has remained unresolved for almost a century. To identify the existence and cause of delayed feedbacks I apply six age- and sex-structured population dynamics models to seven species of baleen whales (suborder Mysticeti) that were heavily depleted by past commercial whaling. The six models include a predator–prey model with killer whale (Orcinus orca) as the predator, and five singe-species models based on (1) exponential growth, (2) density-regulated growth, (3) density-regulated growth with depensation, (4) delayed density-regulated growth and (5) selection-delayed dynamics. The latter model has a density-regulated growth rate that is accelerated and decelerated by the intra-specific natural selection that arises from the density-dependent competitive interactions between the individuals in the population. Essential parameters are estimated by a Bayesian statistical framework, and it is shown that baleen whales have a delayed recovery relative to density-regulated growth. The time-lag is not explained by depensation, or by interactions with prey or predators. It is instead resolved by a selection-delayed acceleration of the intrinsic growth rate. The results are discussed in relation to the literature on cyclic dynamics, and it is noted (1) that selection-delayed dynamics is both theoretically and empirically sufficient for cyclic population dynamics, (2) that it is widespread in natural populations owing to the widespread occurrence of otherwise unexplained phenotypic cycles in populations with cyclic dynamics, and (3) that there is a lack of empirical evidence showing that predator–prey interactions is a sufficient cause for the cyclic dynamics of natural populations. The conclusion stresses the importance of intra-specific delays in cyclic dynamics, and suggests that it is the acceleration of the growth rate, and not the growth rate itself, that is determined by the density-dependent environment.