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Population ecologists track wild animals over their lifetimes using mark-recapture methods. Odonates are easily marked and remain near water bodies, allowing for high recapture rates. In recent years, the focus in mark-recapture models has switched from population size estimates to survival and recapture rate estimation, and from testing hypotheses to model selection and inference. This chapter presents a review of the literature on mark-recapture studies, with a suggestion of areas where more research is needed. These include the effect of marking on survival and recapture rates, differences in survival between sexes and female colour morphs, the relative importance of processes in the larval and the adult stage in driving population dynamics, and the contribution of local and regional processes in shaping metapopulation dynamics.

Spatial patterns of parasitism are thought to be strongly associated with local adaptation, relative rates of migration by hosts and parasites, and local levels of resource availability. This chapter reviews theoretical and empirical studies of local adaptation, and their bearings on spatial patterns of parasitism. It then reviews theoretical and empirical studies of dispersal and parasitism, and theoretical and empirical studies of the relationship between productivity and spatial patterns of levels of parasite defence and host offence. The effects of these processes on the distribution and spatial patterns of coexistence of hosts and parasites are investigated.

Populations are seldom uniform; they tend to be subdivided, with gene frequencies unevenly distributed across landscapes. In this chapter, determinants of population structure in parasites are first reviewed. Lice turn out to be excellent models for studies of population structure. Population genetic structure arises largely because of limitations to dispersal and gene flow. Population variation in parasites is influenced by the rate of parasite dispersal at three scales: dispersal among host individuals, among host populations, and among host species. If most dispersal consists of vertical transmission from parent hosts to their offspring, then parasites living on individual hosts and their progeny will accumulate genetic differences, contributing to population genetic structure. If horizontal transmission is common, however, it will tend to erode population genetic structure among host individuals. It is essential to have information regarding the dispersal ecology of parasites in order to interpret parasite population structure.

Population structure is a ubiquitous feature of natural populations that has an important influence on evolutionary change. In the real world, populations are not homogenous units; instead, they develop an internal structure, created by the physical properties of the environment and the biological characteristics of the species (such as dispersal ability). However, our basic ecological and population genetic models generally ignore population structure and focus on randomly mating (panmictic) populations. Such structure can profoundly change the evolution of a population. In fact, the myriad of influences that population structure exerts can only be hinted at in a single chapter. Since an exhaustive review is not possible, I will focus on presenting the conceptual issues linking mathematical models of population structure to empirical studies. To do this, it is useful to recognize two different kinds of population structure that both reflect and influence evolutionary change. The first is genetic structure. This is defined as the nonrandom distribution of genotypes in space and time. Thus, genetic structure reflects the genetic differences that develop among the different components of one or more populations. The second is what I will call proximity structure, defined by the size and composition of the group of neighbors that influence an individual’s fitness. Fitness is commonly influenced by local intraspecific interactions. Perhaps the most obvious example is competition. When individuals compete for some resource, they don’t usually compete equally with every other member of the population; in general, they compete only with a few of the most proximate individuals. These two forms of population structure, genetic structure and proximity structure, provide a foundation for understanding why we have shifted away from viewing populations as homogenous units. For good reason, this is a theme that is explored in many of the other chapters in this book. Genetic structure can develop within a population over a single generation, generally either as a result of local family associations or as a result of spatial variation in selection. For example, limited seed dispersal results in genetic correlations among neighbors even in the face of long-distance pollen movement, due to the clustering of maternal half sibs.

New volcanic islands may be colonized from neighboring islands rather than from distant continents. The source islands may subsequently erode and subside, and eventually form atolls or submerged seamounts. Island taxa persist more or less in situ as dynamic metapopulations on individually ephemeral islands. These metapopulations may evolve by vicariance during rifting and basin formation, as in continental groups. In the Pacific, phases of Cretaceous volcanism associated with the South Pacific superswell have produced large igneous plateaus. Some of these include fossil wood in sedimentary strata intercalated with the volcanics. This chapter examines clades that are widely distributed in the central Pacific and endemic there, and relates these to the complex geological history.

The southern pine beetle, Dendroctonus frontalis Zimmermann (Coleoptera: Scolytidae), is an economically important pest of pine forests in the southern United States (Price et al. 1992). This native bark beetle is able to attack and kill living trees, typically loblolly (Pinus taeda L.) or shortleaf (Pinus echinata Mill.) pine, through a process of mass attack coordinated by pheromones emitted by the beetle (Payne 1980). During the attack process, thousands of beetles bore through the outer bark of the tree and begin constructing galleries in the phloem layer. Trees can respond to beetle attack by exuding resin from a network of ducts, but the large number of simultaneous attacks usually overcomes this defense, literally draining the resin from the tree. Oviposition and brood development then occur in the girdled (and ultimately dead) tree. Once a tree is fully colonized the attack process shifts to adjacent trees, often resulting in a cluster of freshly attacked trees, trees containing developing brood, and dead and vacated trees (Coulson 1980). These infestations can range in size from a single tree to tens of thousands, although the latter only occur in areas where no control methods are applied. Approximately six generations can be completed in a year in the southern United States (Ungerer et al. 1999). Like many other forest insect pests, D. frontalis populations are characterized by a considerable degree of fluctuation. The longest time series available are Texas Forest Service records of infestations in southeast Texas since 1958 (figure 5.la). These data suggest that the fluctuations have at least some periodic component, with major outbreaks occurring at intervals of 7-9 years (1968, 1976, 1985, and 1992). A variety of different analyses, including standard time series analysis and response surface methodology (Turchin 1990, Turchin and Taylor 1992), suggest that D.frontalis dynamics are indeed cyclic and appear governed by some kind of delayed negative feedback acting on population growth (see chapter 1). This effect can be seen by plotting the realized per-capita rate of growth (R-values) over a year against population density in the previous year (figure 5.1b).

The study of metapopulation dynamics has had a profound impact on our understanding of how species relate to their habitats. A natural, if naïve, set of assumptions would be that species are to be found wherever there is suitable habitat that they can get to; that species will rarely, if ever, be found in unsuitable habitat; that they will be most abundant in their preferred habitat; that species can be preserved as long as a good-size chunk of suitable habitat is conserved for them; and that destruction of a species’ habitat is always detrimental for its abundance. We will see that none of these reasonable-sounding assumptions is necessarily true. Metapopulation biology is a vast field, so to focus this chapter I will be guided partly by questions relevant to conservation biology. There are two important kinds of metapopulation. The so-called Levins metapopulation idea (Levins, 1970) is illustrated in Figure 4.1. It is imagined that patches of habitat suitable for a species are distributed across a landscape. Over time, there is a dynamical process of colonization and extinction: the colonization of empty patches by occupied patches sending out colonizing propagules and the extinction of local populations on occupied patches. This extinction can occur for a number of reasons. Small populations are prone to extinction just by the chance vagaries of the environment, reproduction, and death—environmental and demographic stochasticity (May, 1974b; Lande et al., 2003). An example of a species for which this is important is the Glanville fritillary butterfly (Melitaea cinxia), which has been extensively studied by Hanski and colleagues (Hanski, 1999). This Scandinavian butterfly lives in dry meadows which are small and patchily distributed. Another reason for local population extinction is that the habitat patch itself may be ephemeral. For example, wood-rotting fungi will find that their patch ultimately rots completely away (Siitonen et al., 2005) and epiphytic mosses will ultimately find that their tree falls over (Snall et al., 2005). The second type of metapopulation consists of local populations connected by dispersal, but without the extinction of the local populations.

This chapter reviews the impact of dispersal on the distribution and abundance of unionoids. Dispersal serves two essential functions: firstly, dispersal allows a species to move into previously unoccupied areas and thereby expand its geographic range; and secondly, dispersal connects the subpopulations within the established range of the species and contributes to the maintenance of unionoid metapopulations. Metapopulation models are used to estimate the degree to which unionoid species might be affected by human-induced reductions in dispersal rates. This is a special case of “extinction debt” in which the effects of human actions on biodiversity are not fully realized until many years after those actions took place.

In this chapter three major concepts relating animal population dynamics to landscape change mediated by animal mobility are outlined: meta-population/community dynamics (affecting many habitat specialists), spillover (affecting e.g. ground-living predators), and landscape complementation (affecting e.g. central-place foragers). It is shown that all three concepts contribute to current understanding of animal population dynamics in production landscapes, and that animals differ fundamentally in the extent to which the concepts are applicable. Therefore, it is argued that general recipes such as ‘reduce fragmentation’, ‘increase connectivity’, or ‘increase ecological heterogeneity’ may not provide a universal solution for conserving animals in contemporary agricultural landscapes. In addition, although animal movement studies have contributed to the understanding of biodiversity conservation in farmland, current knowledge about animal mobility is still limited. Thus research based on emerging methods such as landscape genetics or novel methods of tracking small animals is essential for increasing basic understanding of animal mobility.

The distribution and dynamics of populations reflect the interplay between dispersal and demography with landscape structure. Understanding how landscape structure affects populations is essential to effective habitat management and species conservation, especially within landscapes undergoing habitat loss and fragmentation as a result of human land-use activities. This chapter thus begins with an overview of the effects of habitat loss and fragmentation on populations, followed by a discussion of species distribution modeling. Then, because population assessment figures so prominently in evaluating a species’ extinction risk to landscape change, the chapter considers the different classes of population models used to estimate population growth rates and population viability, including the use of metapopulation and spatially explicit simulation models.

Populations and species are distributed heterogeneously across the landscape and this has important consequences for their abundance, persistence, and interactions with other species. This chapter introduces the concept of a metapopulation, a “population of populations”, where populations occur in patches of suitable habitat surrounded by areas of unsuitable habitat (“matrix”), and where dispersal serves to connect patch dynamics. Metapopulation theory provides an important conceptual underpinning to the field of conservation biology, fostering the study of corridors and assisted migration as important conservation tools. There also are important parallels between metapopulation theory and epidemiology. The study of patchily distributed populations leads naturally to considerations of species interactions, where it is shown that an inferior competitor may coexist with a superior competitor if the inferior competitor is better a colonizing open patches—a “fugitive species”. This competition-colonization trade-off can be a strong stabilizing mechanism for maintaining biodiversity in a patchy environment.

This chapter traces the history of metapopulation studies from the introduction of the term by Levins in 1970 to its investigation in a variety of field, theoretical and laboratory systems. The prerequisites for group selection in a metapopulation and how genetic interactions affect selection within and among groups of individuals are discussed. The metaphor of the card games war and poker is introduced to illustrate how the meaning of gene effect is changed by interactions.

This chapter aims to quantify the temporal variation existing in the dispersal kernel by making the kernels comparable. Variation of dispersal kernels in time has received less attention, even if temporal change in dispersal rates among local populations has been repeatedly documented in the metapopulation literature. Changes in individual mobility that generate temporal shifts in dispersal kernels would obviously be context- and phenotypic-dependent. Both environmental conditions and conspecific density are thus expected to play a central role in temporal variation of dispersal kernels. This chapter uses standardized capture-mark-recapture (CMR) data from long-term monitoring of bog fritillary butterfly, Boloria eunomia, metapopulation dynamics in a single landscape to investigate the amount of temporal variability and the amount of this temporal variability that has been explained by climatic variables and conspecific density.

This chapter, which discusses problems on genetic erosion and offers a theoretical framework to improve understanding of genetic erosion, suggests two ecological models that might serve to generate more robust crop ecology. Modern niche theory and metapopulation analysis offer numerous insights and advantages to efforts to understand genetic erosion. A shared insight is that general population processes, such as genetic erosion, are affected by environmental heterogeneity. Modern niche theory and metapopulation analysis provide a middle ground between general theory and site specificity. The chapter discusses how the application of formal population models to crops presents daunting challenges—to define key variables and specify functional relationships. The need to include both biological and social variables and functional relationships is particularly difficult to satisfy.

Investigation of how spatial processes affect the maintenance of biodiversity and its geographic distribution has led to landmark contributions in community ecology. Theory has followed a logical complexification of the objects of study, with specific models at each step, from populations connected by dispersal to ecosystems connected by flows of energy and material. This large body of theory is not only diverse in the questions it addresses, and the scales and organization levels it encompasses, but also in the types of models used to represent spatial dynamics. Unfortunately, this makes it hard to establish clear, standard, quantitative predictions stemming from a coherent mathematical formalism. Here our objectives are : i) to propose a general metacommunity model that allows the investigation of spatial ecology from populations to entire food webs ; ii) use the model to review a set of principles driving coexistence in all types of metacommunities; iii) reveal how these principles constrain the spatial distribution of diversity, with a particular emphasis on species co-distribution. The model is based on the well-established representation of spatial dynamics through colonization and extinction processes. We generalize Levins’ metapopulation model to all types of ecological interactions, using a formalism akin to Lotka–Volterra equations for local community dynamics. Doing so, we revisit coexistence mechanisms proposed for competitive metacommunities, along with the assembly dynamics for spatial food webs and mutualistic interactions.

Ecological systems are prone to dramatic shifts between alternative stable states. In reality, these shifts are often caused by slow forces external to the system that eventually push it over a tipping point. Theory predicts that when ecological systems are brought close to a tipping point, the dynamical feedback intrinsic to the system interact with intrinsic noise and extrinsic perturbations in characteristic ways. The resulting phenomena thus serve as “early warning signals” for shifts such as population collapse. In this chapter, we review the basic (qualitative) theory of such systems. We then illustrate the main ideas with a series of models that both represent fundamental ecological ideas (e.g. density-dependence) and are amenable to mathematical analysis. These analyses provide theoretical predictions about the nature of measurable fluctuations in the vicinity of a tipping point. We conclude with a review of empirical evidence from laboratory microcosms, field manipulations, and observational studies.

This chapter considers populations structured in a different dimension: space. This begins by representing population dynamics with a spatial continuity equation (analogous to the M’Kendrick/von Foerster model for continuity in age or size). If organisms move at random, this motion can be approximated as diffusion. This proves useful for modeling spreading populations, such as the expansion of sea otter populations along the California coast. Adding directional advection represents a population in a flowing stream. Metapopulation models are then introduced using a simple model of the fraction of occupied patches; these are made more realistic by accounting for inter-patch distance using incidence function models. The next level of complexity is models with population dynamics in each patch. These are used to examine how metapopulations can persist as a network even if no patch would persist by itself. Finally, the consequences of synchrony (or lack thereof) among spatially separated populations is described.

This chapter introduces mathematical and statistical modelling approaches in population ecology. It starts with a conceptual section, continues with mathematical and statistical sections, and ends with a perspectives section. The conceptual section motivates the modelling approaches by providing the necessary background to population ecology. The mathematical sections start by constructing an individual-based model in homogeneous space, and then simplifies the model to derive the classical model of logistic population growth. The models are then expanded to heterogeneous space in two contrasting ways, resulting in models called the plant population model and the butterfly metapopulation model. Both types of models are used to analyse the consequences of habitat loss and fragmentation at the population level. To illustrate the interplay between models and data, the statistical section analyses data generated by the mathematical models, with emphasis on the analyses of time-series data, species distribution modelling, and metapopulation modelling.

Local populations are in most cases open and connected with other populations through dispersal. Dispersal, aside from its multiplicative nature, has a demographic additive effect for the spatiotemporal dynamics and extinction–colonization turnover of the donor and the receiver populations. Population dynamics are more sensitive to dispersal under perturbations, because dispersing is a resilience mechanism to avoid or reduce novel mortality risk. Furthermore, dispersing individuals carry information, a process that may create dynamic landscape information networks. In social species, the decision to stay or to disperse is made based on decisions made by others. When perturbations accumulate and jeopardize survival or fecundity, leading individuals may decide to disperse, and this decision is copied by others, generating a runaway dispersal to other patches. The decision trade-off between staying and dispersing depends on the dynamic spatiotemporal heterogeneity in patch quality. What matters for making a decision is not the difference in patch quality, but the ratio between the patch currently occupied and the rest of the patches. Decision-making in social animals for dispersing is explored under the frameworks of the prospect theory, the neoclassical economic theory, and the hypercycle theory. It is also shown how runaway dispersal may occur from a theoretical point of view due to a very simple mechanism of copying others in a density-dependent manner. This simple mechanism overruns a rational scenario when making decisions in social animals. This chapter ends by assessing the potential consequences of runaway dispersal for nonlinear responses in communities and entire ecosystems.