自然种群中混沌的检测及其在种群动态研究中的意义

混沌现象广泛地存在于自然界,20世纪70年代以来,通过大量的生物模型模拟说明混沌也存在于生物系统中。几十年来生态学家一直在努力寻找混沌在自然生态系统存在的证据,但所获不多,这是源于自然的现实还是由于检测方法的不当和数据的局限?一直困扰着生态学家,自然界中对混沌的检测成为一个要点,也是一个难点。在概述混沌概念和性质的基础上,着重介绍目前在自然生态系统检测混沌的方法,对各种方法的应用条件和范围进行了概述。这些方法包括功率谱法、时间序列的自相关函数分析、模型参数估计、庞加莱截面法、全局和局域李雅普若夫特征指数的估计、吸引子关联维的确定、非线性预测。大量研究结果显示,虽然在自然界检测到的混沌的例子还不多,但其存在却是不容怀疑的。问题是什么样的系统在什么样的条件下会出现混沌?研究表明食物链的结构、种群的迁入和迁出、环境噪音都会对种群的复杂性动态特征产生影响。混沌动态可能对产生系统的多样性和适应性有利,它比随机系统对外界干扰的抵抗能力更强。自然界的变化和系统的维持是持续性和混沌相互矛盾统一的结果。害虫种群复杂性动态的研究为害虫的管理提供了更多的理论依据。混沌控制的理论和方法有可能为害虫管理提供新的思路和途径。在孤立的种群中,混沌会增加种群的灭绝概率,而在集合种群中,混沌动态降低了各局域种群的同步性和同时灭绝的倾向,所以混沌虽然能增加局域种群灭绝的概率,但却能减少整个集合种群灭绝的概率。系统结构及其时空动态与混沌及种群灭绝之间的关系,是保护生物学及生物多样性保护研究的一个重要方面。今后的研究应更多地从种群、群落、生态系统及景观不同层次上的时空动态入手,利用3S等信息技术和空间动态分析方法,研究复杂性动态产生的条件及其在系统调控中的作用机制。     

昆虫种群动态时空回归预测方法及应用研究

根据昆虫种群内个体空间相互作用的特点,提出分析昆虫种群时空相关的三维相关图方法及种群动态预测的时空混合回归模型,对马尾松毛虫幼虫密切分布的预测采用了时空自回归加空趋势面的形式,预测的马尾松毛虫幼虫的平均密度和实测的平均密度在时空变化的趋势上是完全一致的,而且预测结果给出了马尾松毛虫的幼虫密度分布的图形形式,给防治决策提供了方便。     

不同受害类型松林中马尾松毛虫自然种群生命表研究

通过组建自然种群生命表来研究比较受害松林与未受害松林间马尾松毛虫Dendrolimus punctatus Walker的种群动态.结果表明,在未受害松林中马尾松毛虫的存活率达15.33%,种群趋势指数为24.0489;在受害松林中马尾松毛虫的存活率为0.61%,种群趋势指数为0.4864.在受害松林中的总K值(3.2183)也要远远大于未受害松林中的总K值(0.8146).在未受害松林中易引起马尾松毛虫种群数量的剧增,而在受害的松林中马尾松毛虫的种群呈下降趋势.这一结果表明受害的松林本身对马尾松毛虫种群具有一定的自然调控作用.     

基于神经网络的灯诱法预测马尾松毛虫发生量的研究

【目的】为了探索灯诱马尾松毛虫Dendrolimus punctatus(Walker)成虫预测下一代幼虫发生量的办法,建立预测预报模型。【方法】2013-2017年连续5年采用灯诱法对马尾松毛虫每年的越冬代、第1代灯诱成虫数、雌虫数、雄虫数、雌性比等数据进行收集,实地调查第1代、第2代(越冬代)幼虫林间的发生量(虫口密度),采用Excel2016进行相关性分析,筛选出与下一代幼虫发生量(虫口密度)关系密切的灯诱成虫数、雌虫数、雌性比等关联因子,并应用神经网络Matlab2016a建立预测模型。【结果】所建立的灯诱成虫预测模型,拟合度0.92以上,预测精度0.90以上。【结论】采用灯诱马尾松毛虫成虫预测下一代幼虫发生量的神经网络Matlab模型,十分适用于短期精细化预报,方法简单、实用,值得在马尾松毛虫生产性防控中大力推广。     

不同松林内马尾松毛虫种群动态的特征

通过对1992-2001年5种不同混交林型的马尾松林内各代幼虫发生量,受害面积和有虫株率的系统调查,研究了各种不同林型的松林对马尾松毛虫种群动态的影响。结果表明,不同树种混交的马尾松林对马尾松毛虫的种群动态有较大的影响。马尾松毛虫在马尾松纯林中发生量多,为害最严重。暴发周期最短;在与杉木等混交的马尾松林中发生次之;而在与阔叶树混交的马尾松林中平均每株虫口数少,发生与危害率及有虫株率均较其它林型小,不易引起松毛虫暴发为害。     

马尾松毛虫种群消长与寄主受害程度的关系

研究不同受害程度松林与马尾松毛虫Dedrolimuspunctatus(Walker)种群发育阶段的关系。结果表明,松毛虫大面积发生时松林的受害程度可以很好地标示着松毛虫种群的发生阶段。松林点片受害显示松毛虫种群进入上升开始阶段;松林受害程度中等时,松毛虫仍处于上升种群阶段;当松林的针叶被吃光,表明松毛虫已处暴发阶段。松毛虫暴发种群在自然情况下,将出现典型的蛹期和下一代卵期的寄生率大幅度升高、雌性比、蛹重和产卵量等种群参数大幅度下降等特征;其后代会自然转入下降种群或进入低虫口期。但这些暴发种群的卵转入在保证充足饲料的可控条件下饲养时,其后代的生长发育、存活率、性比、蛹重等主要种群参数没有明显差异。即没显示出上升种群和暴发种群的特征差异,说明暴发种群的终止主要是受外界环境的限制而不是松毛虫本身的内因所致。     

马尾松毛虫自然种群生命表

<正> 马尾松毛虫Dendrolimus punctatus Walker是马尾松的主要害虫,大发生过后,林木如同火烧,严重影响马尾松的生长。笔者于1983年在华云山天池林场对马尾松毛虫自然种群生命表进行了研究,旨在为马尾松毛虫的预测预报提供资料。     

应用球孢白僵菌防治马尾松毛虫的策略及其生物多样性基础的研究

对３种不同方式应用球孢白僵菌防治马尾松毛虫的结果表明,３个林场的虫口在连续４年内均被抑制在低水平,但松毛虫种群动态不同：以淹没式施放白僵菌为主,但有时也淹没式施放化学杀虫剂的麻姑山林场平均虫口密度为３．１６±４．９４条·株－１,波动最大;长期接种式应用白僵菌的戴公山林场平均虫口密度只有０．０９±０．１４条·株－１,一直处于低密度状态;以化学农药处理小面积虫源地结合不频繁地淹没式施放白僵菌的金寺山林场平均虫口密度为１．４５±２．２９条·株－１,偶可达防治指标．对群落组成的调查结果表明,从麻姑山、金寺山到戴公山,动物和虫生真菌群落各物种的总个体数递减,而总物种数、总物种数与总个体数的比值,天敌与害虫种数和个体数的比值以及群落多样性指数皆递增,表明群落多样性渐趋丰富,食物链网渐趋复杂,群落稳定性和对害虫的自然控制能力逐渐增强．对群落动态的主成分分析进一步表明,从麻姑山、金寺山到戴公山,动物和虫生真菌群落的演化时序分明,趋于稳定,自我调节力强．     

浙北马尾松人工林鸟类捕食松毛虫幼虫的研究

对浙北马尾松人工林的鸟类区系及数量动态以及鸟类捕食松毛虫幼虫进行了实验观察。松毛虫可按季节分为三个世代阶段,各世代阶段的种群密度波动较大,而相应的鸟类平均密度却较稳定。利用防鸟围网采用样方对照法调查鸟类捕食松毛虫幼虫的作用,4至9月鸟类移走实验虫数的4.7—22.2％。但不同时期,不同地块鸟类的捕食作用不同。     

浙江沿海防护林马尾松毛虫的预测预报模型

运用Statistical Analysis System(SAS)软件的原理和方法,根据SAS软件的相关性分析及逐步回归分析,选取仙居县1982年4月～2006年3月与马尾松毛虫Dendrolimus punctatus Walker虫口密度关系密切的的气象因子作为变量、1983～2006年的虫口密度作为因变量,建立沿海防护林马尾松毛虫虫口密度与气象因子的预测预报模型。方程经拟和性检验,其历史符合率均在85%以上;用2007～2011年的相关数据进行测报检验,除受自然灾害影响较为严重的年份外,发生情况与预报结果的相对误差较小。     

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.     

Chaotic dynamics can select for long-term dormancy

Extended dormancy in a population is evolutionarily costly unless some variance in season-to-season fitness (usually driven by variance in environmental quality) makes bet hedging useful. Consequently, dormancy in a population is usually accepted as evidence of environmental variance. Using a Ricker-type model with heritable variation in dormancy, we show that this need not be so. Intrinsic population dynamics can generate chaotic fluctuations in the absence of environmental variance. Chaotic dynamics increase the frequency of a range of dormant strategists under natural selection, even when mortality during dormancy is relatively high. The buffering effect of dormant individuals then eliminates chaotic dynamics or generates periodic orbits of relatively low amplitude. These stabilized populations harbor a high frequency of dormant individuals that express a range of propensities to enter dormancy.     

Structural perturbations to population skeletons: transient dynamics, coexistence of attractors and the rarity of chaos

BACKGROUND: Simple models of insect populations with non-overlapping generations have been instrumental in understanding the mechanisms behind population cycles, including wild (chaotic) fluctuations. The presence of deterministic chaos in natural populations, however, has never been unequivocally accepted. Recently, it has been proposed that the application of chaos control theory can be useful in unravelling the complexity observed in real population data. This approach is based on structural perturbations to simple population models (population skeletons). The mechanism behind such perturbations to control chaotic dynamics thus far is model dependent and constant (in size and direction) through time. In addition, the outcome of such structurally perturbed models is [almost] always equilibrium type, which fails to commensurate with the patterns observed in population data. METHODOLOGY/PRINCIPAL FINDINGS: We present a proportional feedback mechanism that is independent of model formulation and capable of perturbing population skeletons in an evolutionary way, as opposed to requiring constant feedbacks. We observe the same repertoire of patterns, from equilibrium states to non-chaotic aperiodic oscillations to chaotic behaviour, across different population models, in agreement with observations in real population data. Model outputs also indicate the existence of multiple attractors in some parameter regimes and this coexistence is found to depend on initial population densities or the duration of transient dynamics. Our results suggest that such a feedback mechanism may enable a better understanding of the regulatory processes in natural populations.     

Nonlinear population dynamics: complication in the age structure influences transition-to-chaos scenarios

The work continues a series of studies on the evolution of a natural population of explicitly seasonal organisms. Model analyses have revealed relationships between the duration of ontogenesis and the pattern of temporal dynamics in size of an isolated population (i.e., the structure and dimensionality of the chaotic attractors). For nonlinear models of age-structured population dynamics (under long-lasting ontogenesis), increase in the reproductive potential is shown to result in the chaotic attractors whose structure and dimensionality changes in response to variations in the model parameters. When the ontogenesis becomes longer and more complicated, it does not, "on the average", augment the level of chaos in the attractors observed. There are wide enough regions in the space of the birth and death parameter values that provide for windows in the chaotic dynamics where the total or partial regularization occurs.     

Data estimation and the colour of time series.

There has been a long debate on the source of temporal fluctuations in natural population densities. The difficulty is that unpredictable irregularities might be attributed either to external environmental factors or to chaotic dynamics of populations, or even to the interaction of these two factors. Some years ago Cohen (1995) pointed out that real time series follow redshifted Fourier power spectra, while the simplest chaotic population dynamical models are mostly blueshifted. Since then, the controversy has focused on comparisons of Fourier spectra originating from different models and data. Here, we show experimentally that estimation process by human observers shifts power spectra to the red. This result implies that because of estimation distortion, real population data must be less redshifted than many recorded time series suggest.     

Red environmental noise and the appearance of delayed density dependence in age-structured populations

Previous work suggests that red environmental noise can lead to the spurious appearance of delayed density dependence (DDD) in unstructured populations regulated only by direct density dependence. We analysed the effect of noise reddening on the pattern of spurious DDD in several variants of the density-dependent age-structured population model. We found patterns of spurious DDD in structured populations with either density-dependent fertility or density-dependent survival of the first age class, inconsistent with predictions from unstructured population models. Moreover, we found that nonspurious negative DDD always emerges in populations with deterministic chaotic dynamics, regardless of population structure or the type of environmental noise. The effect of noise reddening in generating spurious DDD is often negligible in the chaotic region of population deterministic dynamics. Our findings suggest that differences in species' life histories may exhibit different patterns of spurious DDD (owing to noise reddening) than predicted by unstructured models.     

Incorporating spatial variation in density enhances the stability of simple population dynamics models

Simple discrete time models of population growth admit a wide variety of dynamic behaviors, including population cycles and chaos. Yet studies of natural and laboratory populations typically reveal their dynamics to be relatively stable. Many explanations for the apparent rarity of unstable or chaotic behavior in real populations have been developed, including the possible stabilizing roles of migration, refugia, abrupt density-dependence, and genetic variation in sensitivity to density. We develop a theoretical framework for incorporating random spatial variation in density into simple models of population growth, and apply this approach to two commonly used models in ecology: the Ricker and Hassell maps. We show that the incorporation of spatial density variation into both these models has a strong stabilizing influence on their dynamic behavior, and leads to their exhibiting stable point equilibria or stable limit cycles over a relatively much larger range of parameter values. We suggest that one reason why chaotic population dynamics are less common than the simple models indicate is, these models typically neglect the potentially stabilizing role of spatial variation in density.     

Travelling wave dynamics and self-organization in a spatio-temporally structured population

We analyse spatial population dynamics showing that periodic or period-like chaotic dynamics produce self-organization structures, such as travelling waves. We suggest that self-organized patterns are associated with spatial synchrony patterns that often depend on geographical distance between subpopulations. The population dynamics also show statistical spatial autocorrelation patterns. We contrast our theoretical simulations with empirical data on annual damages in young sapling stands caused by voles. We conclude, on the basis of the periodicity, synchrony, and spatial autocorrelation patterns, and our simulation results, that vole dynamics represent travelling waves in population dynamics. We suggest that because such synchrony patterns are frequently observed in natural populations, spatial self-organization may be more common in population dynamics than reported in the literature.     

The demographic basis of population regulation in columbian ground squirrels

Environmental factors influence the dynamics and regulation of biological populations through their influences on demographic variables, but demographic mechanisms of population regulation have received little attention. We investigated the demographic basis of regulation of Columbian ground squirrel (Spermophilus columbianus) populations under natural and experimentally food-supplemented conditions. Food supplementation caused substantial increases in population density, and population densities returned to pretreatment levels when the supplementation ended. Control (untreated) populations remained relatively stable throughout the study period (1981-1986). Because food resources regulated the size of the ground squirrel populations, we used life-table response experiment (LTRE) analyses to examine the demographic basis of changes in population growth rate and thus also demographic influences on population regulation. LTRE analyses of two food-manipulated populations revealed that changes in age at maturity and fertility rate of females generally made the largest contributions to observed changes in population growth rate. Thus, our results suggested that abundance of food resources regulated the size of our study populations through the effects of food resources on age at maturity and fertility rates. Our results also indicated that different demographic mechanisms can underlie population regulation under different environmental conditions, because lower juvenile survival substantially contributed to population decline, but in only one of the populations. Demographic analyses of experimental data, such as those presented here, offer a rigorous and unambiguous means to elucidate the demographic basis of population regulation and to help identify environmental factors that underlie dynamics and regulation of biological populations.