Predation, individual variability and vertebrate population dynamics

Both predation and individual variation in life history traits influence population dynamics. Recent results from laboratory predator–prey systems suggest that differences between individuals can also influence predator–prey dynamics when different genotypes experience different predation-associated mortalities. Despite the growing number of studies in this field, there is no synthesis identifying the overall importance of the interactions between predation and individual heterogeneity and their role in shaping the dynamics of free-ranging populations of vertebrates. We aim to fill this gap with a review that examines how individual variability in prey susceptibility, in predation costs, in predator selectivity, and in predatory performance, might influence prey population dynamics. Based on this review, it is clear that (1) predation risk and costs experienced by free-ranging prey are associated with their phenotypic attributes, (2) many generalist predator populations consist of individual specialists with part of the specialization associated with their phenotypes, and (3) a complete understanding of the population dynamic consequences of predation may require information on individual variability in prey selection and prey vulnerability. Altogether, this work (1) highlights the importance of maintaining long-term, detailed studies of individuals of both predators and prey in contrasting ecological conditions, and (2) advocates for a better use of available information to account for interactive effects between predators and their prey when modelling prey population dynamics.     

The role of prey taxis in biological control: a spatial theoretical model

We study a reaction-diffusion-advection model for the dynamics of populations under biological control. A control agent is assumed to be a predator species that has the ability to perceive the heterogeneity of pest distribution. The advection term represents the predator density movement according to a basic prey taxis assumption: acceleration of predators is proportional to the prey density gradient. The prey population reproduces logistically, and the local population interactions follow the Holling Type II trophic function. On the scale of the population, our spatially explicit approach subdivides the predation process into random movement represented by diffusion, directed movement described by prey taxis, local prey encounters, and consumption modeled by the trophic function. Thus, our model allows studying the effects of large-scale predator spatial activity on population dynamics. We show under which conditions spatial patterns are generated by prey taxis and how this affects the predator ability to maintain the pest population below some economic threshold. In particular, intermediate taxis activity can stabilize predator-pest populations at a very low level of pest density, ensuring successful biological control. However, very intensive prey taxis destroys the stability, leading to chaotic dynamics with pronounced outbreaks of pest density.     

Generalizations of the Moran effect explaining spatial synchrony in population fluctuations

The Moran effect for populations separated in space states that the autocorrelations in the population fluctuations equal the autocorrelation in environmental noise, assuming the same linear density regulation in all populations. Here we generalize the Moran effect to include also nonlinear density regulation with spatial heterogeneity in local population dynamics as well as in the effects of environmental covariates by deriving a simple expression for the correlation between the sizes of two populations, using diffusion approximation to the theta-logistic model. In general, spatial variation in parameters describing the dynamics reduces population synchrony. We also show that the contribution of a covariate to spatial synchrony depends strongly on spatial heterogeneity in the covariate or in its effect on local dynamics. These analyses show exactly how spatial environmental covariation can synchronize fluctuations of spatially segregated populations with no interchange of individuals even if the dynamics are nonlinear.     

烟蚜及其捕食性天敌草间小黑蛛种群空间结构分析

采用地统计学原理和方法,拟合了不同时期烟蚜种群及其捕食性天敌草间小黑蛛种群的空间结构模型,分析了其空间关系．结果表明,不同时期烟蚜种群的空间结构模型均为球型,空间格局呈聚集型分布,空间相关距离在2．0252～4．1495m之间,异质系数为10281．36～300216．30,空间依赖程度为12176．81～303433．70;不同时期草间小黑蛛种群的空间结构模型也均为球型,空间格局呈聚集型分布,空间相关距离在3．7328～4．8983m之间,异质系数为1．4482～4．4134,空间依赖程度为1．6941～5．8167．该结果和方法可用于监测烟田目标害虫的时空格局动态．     

Revealing the role of predator interference in a predator–prey system with disease in prey population

Predation on a species subjected to an infectious disease can affect both the infection level and the population dynamics. There is an ongoing debate about the act of managing disease in natural populations through predation. Recent theoretical and empirical evidence shows that predation on infected populations can have both positive and negative influences on disease in prey populations. Here, we present a predator–prey system where the prey population is subjected to an infectious disease to explore the impact of predator on disease dynamics. Specifically, we investigate how the interference among predators affects the dynamics and structure of the predator–prey community. We perform a detailed numerical bifurcation analysis and find an unusually large variety of complex dynamics, such as, bistability, torus and chaos, in the presence of predators. We show that, depending on the strength of interference among predators, predators enhance or control disease outbreaks and population persistence. Moreover, the presence of multistable regimes makes the system very sensitive to perturbations and facilitates a number of regime shifts. Since, the habitat structure and the choice of predators deeply influence the interference among predators, thus before applying predators to control disease in prey populations or applying predator control strategy for wildlife management, it is essential to carefully investigate how these predators interact with each other in that specific habitat; otherwise it may lead to ecological disaster.     

How the spatial scales of dispersal, competition, and environmental heterogeneity interact to affect coexistence

Spatial coexistence depends on a variety of biological and physical processes, and the relative scales of these processes may promote or suppress coexistence. We model plant competition in a spatially varying environment to show how shifting scales of dispersal, competition, and environmental heterogeneity affect coexistence. Spatial coexistence mechanisms are partitioned into three types: the storage effect, nonlinear competitive variance, and growth-density covariance. We first describe how the strength of each of these mechanisms depends on covariances between population densities and between population densities and the environment, and we then explain how changes in the scales of dispersal, competition, and environmental heterogeneity should affect these covariances. Our quantitative approach allows us to show how changes in the scales of biological and physical processes can shift the relative importance of different classes of spatial coexistence mechanisms and gives us a more complete understanding of how environmental heterogeneity can enable coexistence. For example, we show how environmental heterogeneity can promote coexistence even when competing species have identical responses to the environment.     

Local Explanations of Landscape Patterns: Can Analytical Approaches Approximate Simulation Models of Spatial Processes?

Research over the last 100 years has demonstrated the importance of space for ecological processes. Given this importance, it may seem natural to start investigations into broad-scale ecological processes with a comprehensive, broad-scale spatial map. Here we argue that it may sometimes be possible to answer important questions about spatial processes using crude spatial information obtained when a comprehensive map is not available. To present our argument, we first develop a simple simulation model for a perennial plant reproducing and dying on a landscape with different arrangements of suitable and unsuitable sites. We then develop a simple, analytical approximation to predict the fraction of suitable sites that are occupied by the simulated plants. The analytical approximation summarizes the spatial map by using a single parameter that gives the probability that a site adjacent to a suitable site is suitable. Comparing the predictions of both approaches highlights three points: (a) The role of the spatial environment in ecological processes may play out at the local scale. Therefore, studying the local-scale processes may provide insights into landscape patterns. (b) The predictions from the analytical approximation fail noticeably when suitable sites are rare and are distributed randomly (rather than clumped) on the map. In these situations, patches of interconnected suitable sites are very small, and populations within small patches may go extinct via demographic stochasticity. This illustrates how analytical approximations can be used to identify cases when local-scale spatial processes are not sufficient to understand the ecological consequences of space. (c) For many natural systems, constructing the appropriate environmental map needed to study ecological processes is difficult or impossible. However, summary characteristics such as those employed by the analytical approximation may be estimated directly in nature. Therefore, even in the absence of an explicitly spatial broad-scale map, it may be possible to study spatial processes by understanding which local-scale characteristics of space are important. Received 5 May 1997; accepted 31 July 1997.     

Implications of scale for patterns and processes in stream ecology

Abstract Although the scale-dependence of ecological patterns and processes is recognized by freshwater ecologists, current knowledge of scale effects is rudimentary and non-quantitative. We review issues of spatial and temporal scale in this paper to highlight conceptual problems relating to scale and some potential solutions. We present examples of how the spatial scale of a study influences observed patterns and their interpretation, and discuss how the size of an experimental arena influences the degree to which the dynamics of studied populations are influenced by exchange processes (immigration and emigration). The results of small-scale field experiments in streams will often be strongly influenced by the per capita exchange rates of organisms and differences in exchange rates may explain differences in the perceived effects of stream manipulations across scales. Spatial extent also influences the amount of spatial heterogeneity within a study site or arena, with important consequences for the outcome of predator-prey interactions. We suggest that changes in the availability of prey refuges may help explain why predator manipulations in streams appear to weaken as arena size increases. We also recommend that new techniques for decomposing and quantifying spatial heterogeneity be applied to characterize scale-dependent variation in freshwater systems. Lastly, we discuss the pitfalls of mismatching the temporal scale of experiments and models. Models incorporating spatial heterogeneity and the behaviour of organisms are needed to predict the short-term outcome of perturbations in streams, whereas models predicting long-term dynamics will need to integrate the impacts of episodic disturbance and all life history stages of organisms. In general, we recommend that freshwater ecologists undertake more multi-scale sampling and experimentation to examine patterns and processes at multiple scales, and make greater attempts to match the scales of their observations and experiments to the characteristic scales of the phenomena that they investigate.     

Handling time promotes the coevolution of aggregation in predator-prey systems

Predators often have type II functional responses and live in environments where their life history traits as well as those of their prey vary from patch to patch. To understand how spatial heterogeneity and predator handling times influence the coevolution of patch preferences and ecological stability, we perform an ecological and evolutionary analysis of a Nicholson-Bailey type model. We prove that coevolutionarily stable prey and searching predators prefer patches that in isolation support higher prey and searching predator densities, respectively. Using this fact, we determine how environmental variation and predator handling times influence the spatial patterns of patch preferences, population abundances and per-capita predation rates. In particular, long predator handling times are shown to result in the coevolution of predator and prey aggregation. An analytic expression characterizing ecological stability of the coevolved populations is derived. This expression implies that contrary to traditional theoretical expectations, predator handling time can stabilize predator-prey interactions through its coevolutionary influence on patch preferences. These results are shown to have important implications for classical biological control.     

The effect of dilution on eco‐evolutionary dynamics of experimental microbial communities

Changing environmental conditions can infer structural modifications of predator‐prey communities. New conditions often increase mortality which reduces population sizes. Following this, predation pressure may decrease until populations are dense again. Dilution may thus have substantial impact not only on ecological but also on evolutionary dynamics because it amends population densities. Experimental studies, in which microbial populations are maintained by a repeated dilution into fresh conditions after a certain period, are extensively used approaches allowing us to obtain mechanistic insights into fundamental processes. By design, dilution, which depends on transfer volume (modifying mortality) and transfer interval (determining the time of interaction), is an inherent feature of these experiments, but often receives little attention. We further explore previously published data from a live predator‐prey (bacteria and ciliates) system which investigated eco‐evolutionary principles and apply a mathematical model to predict how various transfer volumes and transfer intervals would affect such an experiment. We find not only the ecological dynamics to be modified by both factors but also the evolutionary rates to be affected. Our work predicts that the evolution of the anti‐predator defense in the bacteria, and the evolution of the predation efficiency in the ciliates, both slow down with lower transfer volume, but speed up with longer transfer intervals. Our results provide testable hypotheses for future studies of predator‐prey systems, and we hope this work will help improve our understanding of how ecological and evolutionary processes together shape composition of microbial communities.     

Spatially explicit analyses unveil density dependence

Density-dependent processes are fundamental in the understanding of species population dynamics. Whereas the benefits of considering the spatial dimension in population biology are widely acknowledged, the implications of doing so for the statistical detection of spatial density dependence have not been examined. The outcome of traditional tests may therefore differ from those that include ecologically relevant locational information on both the prey species and natural enemy. Here, we explicitly incorporate spatial information on individual counts when testing for density dependence between an insect herbivore and its parasitoids. The spatially explicit approach used identified significant density dependence more frequently and in different instances than traditional methods. The form of density dependence detected also differed between methods. These results demonstrate that the explicit consideration of patch location in density-dependence analyses is likely to significantly alter current understanding of the prevalence and form of spatial density dependence in natural populations.     

Metapopulation dynamics for spatially extended predator–prey interactions

Traditional metapopulation theory classifies a metapopulation as a spatially homogeneous population that persists on neighboring habitat patches. The fate of each population on a habitat patch is a function of a balance between births and deaths via establishment of new populations through migration to neighboring patches. In this study, we expand upon traditional metapopulation models by incorporating spatial heterogeneity into a previously studied two-patch nonlinear ordinary differential equation metapopulation model, in which the growth of a general prey species is logistic and growth of a general predator species displays a Holling type II functional response. The model described in this work assumes that migration by generalist predator and prey populations between habitat patches occurs via a migratory corridor. Thus, persistence of species is a function of local population dynamics and migration between spatially heterogeneous habitat patches. Numerical results generated by our model demonstrate that population densities exhibit periodic plane-wave phenomena, which appear to be functions of differences in migration rates between generalist predator and prey populations. We compare results generated from our model to results generated by similar, but less ecologically realistic work, and to observed population dynamics in natural metapopulations.     

Evolutionary ecology of movement by predators and prey

An essential key to explaining the mechanistic basis of ecological patterns lies in understanding the consequences of adaptive behavior for distributions and abundances of organisms. We developed a model that simultaneously incorporates (a) ecological dynamics across three trophic levels and (b) evolution of behaviors via the processes of mutation, selection, and drift in populations of variable, unique individuals. Using this model to study adaptive movements of predators and prey in a spatially explicit environment produced a number of unexpected results. First, even though predators and prey had limited information and sometimes moved in the “wrong” direction, evolved movement mechanisms allowed them to achieve average spatial distributions approximating optimal, ideal free distributions. Second, predators’ demographic parameters had marked, nonlinear effects on the evolution of movement mechanisms in the prey: As the predator mortality rate was increased past a critical point, prey abruptly shifted from making very frequent movements away from predators to making infrequent movements mainly in response to resources. Third, time series analyses revealed that adaptive, conditional movements coupled ecological dynamics across species and space. Our results provide general predictions, heretofore lacking, about how predators and prey should respond to one another on both ecological and evolutionary time scales.     

Using Moment Equations to Understand Stochastically Driven Spatial Pattern Formation in Ecological Systems

Spatial patterns in biological populations and the effect of spatial patterns on ecological interactions are central topics in mathematical ecology. Various approaches to modeling have been developed to enable us to understand spatial patterns ranging from plant distributions to plankton aggregation. We present a new approach to modeling spatial interactions by deriving approximations for the time evolution of the moments (mean and spatial covariance) of ensembles of distributions of organisms; the analysis is made possible by “moment closure,” neglecting higher-order spatial structure in the population. We use the growth and competition of plants in an explicitly spatial environment as a starting point for exploring the properties of second-order moment equations and comparing them to realizations of spatial stochastic models. We find that for a wide range of effective neighborhood sizes (each plant interacting with several to dozens of neighbors), the mean-covariance model provides a useful and analytically tractable approximation to the stochastic spatial model, and combines useful features of stochastic models and traditional reaction-diffusion-like models.     

Islands of water in a sea of dry land: hydrological regime predicts genetic diversity and dispersal in a widespread fish from Australia's arid zone, the golden perch (Macquaria ambigua)

Rivers provide an excellent system to study interactions between patterns of biodiversity structure and ecological processes. In these environments, gene flow is restricted by the spatial hierarchy and temporal variation of connectivity within the drainage network. In the Australian arid zone, this variability is high and rivers often exist as isolated waterholes connected during unpredictable floods. These conditions cause boom/bust cycles in the population dynamics of taxa, but their influence on spatial genetic diversity is largely unknown. We used a landscape genetics approach to assess the effect of hydrological variability on gene flow, spatial population structure and genetic diversity in an Australian freshwater fish, Macquaria ambigua. Our analysis is based on microsatellite data of 590 samples from 26 locations across the species range. Despite temporal isolation of populations, the species showed surprisingly high rates of dispersal, with population genetic structure only evident among major drainage basins. Within drainages, hydrological variability was a strong predictor of genetic diversity, being positively correlated with spring-time flow volume. We propose that increases in flow volume during spring stimulate recruitment booms and dispersal, boosting population size and genetic diversity. Although it is uncertain how the hydrological regime in arid Australia may change under future climate scenarios, management strategies for arid-zone fishes should mitigate barriers to dispersal and alterations to the natural flow regime to maintain connectivity and the species' evolutionary potential. This study contributes to our understanding of the influence of spatial and temporal heterogeneity on population and landscape processes.     

Demographic stochasticity and temporal autocorrelation in the dynamics of structured populations

Populations can show temporal autocorrelation in the dynamics arising from different mechanisms, including fluctuations in the demographic structure. This autocorrelation is often treated as a complicating factor in the analyses of stochastic population growth and extinction risk. However, it also reflects important information about the demographic structure. Here, we consider how temporal autocorrelation is related to demographic stochasticity in structured populations. Demographic stochasticity arises from inherent randomness in the demographic processes of individuals, like survival and reproduction, and the resulting impact on population growth is measured by the demographic variance. Earlier studies have shown that population structure have positive or negative effects on the demographic variance compared to a model where the structure is ignored. Here, we derive a new expression for the demographic variance of a structured population, using the temporal autocorrelation function of the population growth rate. We show that the relative difference in demographic variance when the structure is included or ignored (the effect of structure on demographic variance) is approximately twice the sum of the autocorrelations. We demonstrate the result for a simple hypothetical example, as well as a set of empirical examples using age‐structured models of 24 mammals from the demographic database COMADRE. In the empirical examples, the sum of the autocorrelation function was negative in all cases, indicating that age structure generally has a negative effect on the demographic variance (i.e. the demographic variance is lower compared to that of a model where the structure is ignored). Other kinds of structure, such as spatial heterogeneity affecting fecundity, can have positive effects on the demographic variance, and the sum of the autocorrelations will then be positive. These results yield new insights into the complex interplay between population structure, demographic variance, and temporal autocorrelation, that shapes the population dynamics and extinction risk of populations.     

A tale of two cycles – distinguishing quasi-cycles and limit cycles in finite predator–prey populations

Periodic predator – prey dynamics in constant environments are usually taken as indicative of deterministic limit cycles. It is known, however, that demographic stochasticity in finite populations can also give rise to regular population cycles, even when the corresponding deterministic models predict a stable equilibrium. Specifically, such quasi-cycles are expected in stochastic versions of deterministic models exhibiting equilibrium dynamics with weakly damped oscillations. The existence of quasi-cycles substantially expands the scope for natural patterns of periodic population oscillations caused by ecological interactions, thereby complicating the conclusive interpretation of such patterns. Here we show how to distinguish between quasi-cycles and noisy limit cycles based on observing changing population sizes in predator – prey populations. We start by confirming that both types of cycle can occur in the individual-based version of a widely used class of deterministic predator – prey model. We then show that it is feasible and straightforward to accurately distinguish between the two types of cycle through the combined analysis of autocorrelations and marginal distributions of population sizes. Finally, by confronting these results with real ecological time series, we demonstrate that by using our methods even short and imperfect time series allow quasi-cycles and limit cycles to be distinguished reliably.     

Mammal population regulation, keystone processes and ecosystem dynamics

The theory of regulation in animal populations is fundamental to understanding the dynamics of populations, the causes of mortality and how natural selection shapes the life history of species. In mammals, the great range in body size allows us to see how allometric relationships affect the mode of regulation. Resource limitation is the fundamental cause of regulation. Top-down limitation through predators is determined by four factors: (i). body size; (ii). the diversity of predators and prey in the system; (iii). whether prey are resident or migratory; and (iv). the presence of alternative prey for predators. Body size in mammals has two important consequences. First, mammals, particularly large species, can act as keystones that determine the diversity of an ecosystem. I show how keystone processes can, in principle, be measured using the example of the wildebeest in the Serengeti ecosystem. Second, mammals act as ecological landscapers by altering vegetation succession. Mammals alter physical structure, ecological function and species diversity in most terrestrial biomes. In general, there is a close interaction between allometry, population regulation, life history and ecosystem dynamics. These relationships are relevant to applied aspects of conservation and pest management.     

Do rabbits eat voles? Apparent competition,habitat heterogeneity and large‐scale coexistence under mink predation

Habitat heterogeneity is predicted to profoundly influence the dynamics of indirect interspecific interactions; however, despite potentially significant consequences for multi-species persistence, this remains almost completely unexplored in large-scale natural landscapes. Moreover, how spatial habitat heterogeneity affects the persistence of interacting invasive and native species is also poorly understood. Here we show how the persistence of a native prey (water vole, Arvicola terrestris ) is determined by the spatial distribution of an invasive prey (European rabbit, Oryctolagus cuniculus ) and directly infer how this is defined by the mobility of a shared invasive predator (American mink, Neovison vison ). This study uniquely demonstrates that variation in habitat connectivity in large-scale natural landscapes creates spatial asynchrony, enabling coexistence between apparent competitive native and invasive species. These findings highlight that unexpected interactions may be involved in species declines, and also that in such cases habitat heterogeneity should be considered in wildlife management decisions.     

Ungulate Carcasses Perforate Ecological Filters and Create Biogeochemical Hotspots in Forest Herbaceous Layers Allowing Trees a Competitive Advantage

Ecological filters and nutrient heterogeneity are important in the function of ecosystems. Herbaceous layers alter forest ecosystems by filtering tree species during early stages of tree reproduction and influencing nutrient cycling. Important aspects about how tree species successfully establish below and extend above this ecological filter are unanswered in forest ecology. We experimentally tested the effects of large ungulate carcasses on the filtering function of herbaceous layers. Even well-utilized carcasses created unexpected disturbances that reduced herbaceous cover, which effectively perforated the herbaceous layer filter that can differentially influence tree reproduction. Carcasses also created lasting biogeochemical “hotspots” in forest soils that may help maintain plant biodiversity by creating resource heterogeneity and shifting competitive relationships. Because the spatial distribution of carcasses is influenced by predators, these data establish an unrecognized link between large carnivores, prey carcasses, and ecosystem processes. This link supports a novel understanding of disturbance by large herbivores in forest ecosystems by demonstrating an important interaction between predator–prey functional traits and tree seedling dynamics on either side of a major ecological filter.