Population Dynamics

Alan Hastings , in Encyclopedia of Biodiversity (Second Edition), 2013

Conclusions

Population dynamics is one of the key areas of ecology, forming both the basis for the study of more complex communities and of many practical questions. Understanding population dynamics is the key to agreement the relative importance of contest for resources and predation in structuring ecological communities, which is a cardinal question in ecology.

Population dynamics plays a cardinal part in many approaches to preserving biodiversity, which until now take been primarily focused on a single species approach. The calculation of the intrinsic growth rate of a species from a life table is often the cardinal piece of conservation plans. Similarly, management of natural resources, such as fisheries, depends on population dynamics every bit a style to make up one's mind appropriate management actions.

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Population Dynamics

Timothy D. Schowalter , in Insect Ecology (Second Edition), 2006

D Computerized models

Computerized simulation models take been developed to projection abundances of insect populations affecting ingather and woods resources (e.g., Gutierrez 1996, Royama 1992, Rykiel et al. 1984 ). The models developed for several important woods and range insects are arguably the nearly sophisticated population dynamics models developed to appointment because they incorporate long time frames, effects of a variety of interacting factors (including climate, soils, host constitute variables, competition, and predation) on insect populations, and effects of population change on ecosystem structure and processes. Often, the population dynamics model is integrated with plant growth models; impact models that accost effects of population change on ecological, social, and economic variables; and management models that address effects of manipulated resource availability and insect mortality on the insect population ( Colbert and Campbell 1978, Leuschner 1980). As more than information becomes available on population responses to various factors, or furnishings on ecosystem processes, the model can be updated, increasing its representation of population dynamics and the accuracy of predictions.

Effects of various factors can be modeled as deterministic (fixed values), stochastic (values based on probability functions), or chaotic (random values) variables (e.g., Croft and Gutierrez 1991, Cushing et al. 2003, Hassell et al. 1991, Logan and Allen 1992). If natality, mortality, and survival are highly correlated with temperature, these rates would be modeled as a deterministic office of temperature. However, effects of found condition on these rates might be described best by probability functions and modeled stochastically (Fargo et al. 1982, Matis et al. 1994).

Advances in chaos theory are contributing to development of population models that more than accurately represent the erratic behavior of many insect populations (Cavalieri and Koçak 1994, 1995a, b, Constantino et al. 1997, Cushing et al. 2003, Hassell et al. 1991, Logan and Allen 1992). Chaos theory addresses the unpredictable ways in which initial conditions of a system can bear upon subsequent system behavior. In other words, population tendency at any instant is the result of the unique combination of population and environmental conditions at that instant. For example, changes in cistron frequencies and behavior of individuals over time touch the way in which populations respond to environmental conditions. Fourth dimension lags, nested cycles, and nonlinear interactions with other populations are characteristics of ecological structure that inherently destabilize mathematical models and introduce chaos (Cushing et al. 2003, Logan and Allen 1992).

Chaos has been hard to demonstrate in population models, and its importance to population dynamics is a topic of contend. Dennis et al. (2001) demonstrated that a deterministic skeleton model of flour beetle, Tribolium castaneum, population dynamics accounted for >92% of the variability in life stage abundances but was strongly influenced by chaotic beliefs at sure values for the coefficient of developed cannibalism of pupae (Fig. 6.8).

Fig. 6.8. Frequency of predicted deterministic attractors for modeled survival probabilities of pupae in the presence of cannibalistic adults (cpa) of Tribolium castaneum for 2000 bootstrap parameter estimates. For example, for cpa = 0.35, 83.5% of estimates produced cluttered attractors, 7.1% produced stable nineteen-cycles, and 9.iii% produced stable cycles of higher periods. From Dennis et al. (2001) with permission of the Ecological Society of America. Please run across extended permission list pg 570.

Several studies suggest that insect population dynamics can undergo recurring transition between stable and cluttered phases when certain variables have values that place the system near a transition point between social club and anarchy (Cavalieri and Koçak 1995a, b, Constantino et al. 1997) or when influenced by a generalist predator and specialist pathogen (Dwyer et al. 2004). Cavalieri and Koçak (1994, 1995b) found that modest changes in weather condition-related parameters (increased bloodshed of pathogen-infected individuals or decreased natality of uninfected individuals) in a European corn tapping, Ostrinia nubilalis, population dynamics model caused a regular population wheel to go erratic. When this chaotic state was reached, the population reached college abundances than it did during stable cycles, suggesting that pocket-size changes in population parameters resulting from biological control agents could be counterproductive. Although chaotic behavior fundamentally limits long-term prediction of insect population dynamics, improved modeling of transitions betwixt deterministic or stochastic phases and cluttered phases may facilitate prediction of short-term dynamics (Cavalieri and Koçak 1994, Cushing et al. 2003, Logan and Allen 1992).

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Terrestrial Arthropods

P.J. Lester , G.C. Burns , in Encyclopedia of Ecology, 2008

Local Extinction and Metapopulation Dynamics

Population dynamics are often visualized by bold that individuals live in a single locale isolated from other populations. However, this is rarely the instance and individuals of many arthropod species migrate between spatially isolated patches of suitable habitat. Populations inhabiting spatially segregated habitat patches can also go locally extinct, and these patches are subsequently colonized by futurity immigrants. This way of conceptualizing population dynamics is known as metapopulation dynamics. The key concept here is that different populations inside the metapopulation are continued past dispersal merely are undergoing dissimilar dynamics, but are connected by dispersal. Nevertheless, the density-independent and density-dependent dichotomy described before may still be important and play a major function in the fluctuations and persistence of the metapopulation. The full part of metapopulation dynamics is discussed in another commodity (see Metapopulation Models).

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Novelty and Synthesis in the Development of Population Dynamics

Peter W. Cost , Mark D. Hunter , in Population Dynamics, 1995

I. Introduction

Although population dynamics is a centerpiece in ecology, at that place is less accent in the field than should be expected. In a recent review, research papers in population ecology every bit a wide area outnumbered those on communities and ecosystems 5 to ane in some major journals during the years 1987–1991. Notwithstanding, the subcategory of population dynamics/regulation was represented by merely five% of all papers [51.5% of all papers were in population environmental, 5.five% in population regulation, nine.4% in community ecology, and 9.six% in ecosystems ecology ( Stiling, 1994)]. Areas favored by researchers in population ecology were competition (half-dozen.eight% of papers), predation (half-dozen.three%), institute–herbivore interactions (8.4%), habitat pick (6.8%), and life-history strategies (9.0%).

If population dynamics is at the core of the ecological sciences, why is it and then poorly represented in the current literature? The field necessitates an integration of virtually of the areas favored by ecologists mentioned here. In add-on, such integration is essential for an adequate understanding of community ecology (Strong et al., 1984; Colwell, 1984). There is a rich theoretical background on which to build in population dynamics, whereas in other areas, such as plant–plant eater interactions, theory appears to have been of pocket-size concern. The pressing needs to understand the dynamics of pest species in agriculture and forestry, vectors of disease, the pathogens themselves, and the biological science of mutual and rare species should all fuel an energetic discipline in population dynamics. Maybe the cause of the underrepresentation of papers in population dynamics lies in the maturing of the science into a multifaceted subject area. Synthesis of ecological, behavioral, and evolutionary aspects of population dynamics is developing rapidly, with two consequences for the literature. First, relevant literature is likely to appear exterior the main ecological journals. Second, integration and synthesis are perhaps more readily and usefully published in volumes such as this book.

Information technology may exist that synthesis in population dynamics has been slow to emerge considering population change is more than complicated than it start appears. Afterwards all, population modify is determined ultimately past just four factors: nascency, death, immigration, and emigration. This apparent simplicity is deceptive. It is easy to underestimate the complexity of biotic and abiotic interactions in the natural world that can influence these four population parameters. Indeed, we will fence in this chapter that the evolution of related fields such as plant-animal interactions, chemical ecology, and life-history development has proven to exist a prerequisite for a realistic synthesis in population dynamics. These related fields provide the mechanistic footing, and therefore the predictive ability, underlying the birth, death, and movement of organisms.

Nonetheless, synthesis in the field of population dynamics has deep historical roots. Of course, there has been a long tradition of empirical population study, such as by Howard (1897), which had obvious bear upon on the development of early theory by Lotka (1924). The development of life tables for field populations and their assay gave a tremendous boost to the field (e.g., Morris and Miller, 1954; Varley and Gradwell, 1960). Major reputations were developed during this time of the 1950s and 1960s (cf. Southwood, 1968; Watson, 1970; Tamarin, 1978). Simply equally the area of population dynamics prospered, the fledgling fields of evolutionary ecology (fostered past Robert MacArthur), coevolution, chemical ecology, life-history evolution, and constitute–herbivore interactions were gaining footing, as noted in Chapter 1 (e.g., Sondheimer and Simeone, 1970). They flourished in the 1970s (e.1000., Pianka, 1974; Gilbert and Raven, 1975; Rosenthal and Janzen, 1979; Collins, 1986). Our view is that these highly tractable fields eclipsed the core of population dynamics, which was bogging down: "the ecologist'south phlogiston theory" (Krebs, 1979, p. 351) was proving to exist intractable (McIntosh, 1985). "Because MacArthur'southward arroyo often began with the assumption that populations were at a steady state, the report of population dynamics was pushed into the background" (Kareiva, 1989, p. 71).

From these newer fields of ecology, the area of population dynamics has gained a new importance, and a new power. Its importance lies in the potential that population dynamics has to provide a primal conceptual footing for the fusion of these newer fields, which seem to exist growing autonomously rather than together. Fusion and synthesis is also inevitable when population dynamics encompasses behavior and phylogenetic relationships. By the same token, population dynamics is gaining enormous explanatory ability as the newer fields reveal fundamental mechanisms driving population change. "The ecologist's phlogiston theory" is existence replaced by breaths of fresh air (and oxygen), equally the newly constructed science develops.

The new synthesis in population dynamics may be every bit of import to ecology as a similar synthesis was to evolutionary theory (cf. Huxley, 1942; Mayr and Provine, 1980). Although the synthesis in population dynamics is incomplete, developments run parallel to the synthesis in development. Many disciplines in biology are becoming integrated under 1 umbrella. Scientists from many countries bring their ain special talents and contributions. Equally the spousal relationship proceeds, new debates are generated that accelerate the step of scientific discipline and discovery, and old debates are resolved.

In the rest of this chapter, we explore what we consider to be modern approaches to population dynamics. Showtime, we consider the various elements that make up a synthetic approach to the written report of population alter. Section II is the backbone of the affiliate and in it we offer a list of components that we regard as important to the study of population dynamics. Some of these elements are related fields of enquiry, such as microbial ecology, whereas others are conceptual approaches, such as international cooperation in field research along important ecological gradients. Second, we describe how population dynamics has changed since its emergence as a field. Finally, we draw three broad scales of approaches to questions of population dynamics, and some of the difficulties in integrating the various elements of population biology together.

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Population Ecology

In Insect Ecology (Second Edition), 2006

Population dynamics reflect the internet effects of differences amid individuals in their physiological and behavioral interactions with the environment. Changes in private success in finding and exploiting resources, mating and reproducing, and avoiding mortality agents decide numbers of individuals, their spatial distribution, and genetic limerick at whatsoever betoken in fourth dimension. Population structure is a component of the environment for the members of the population and provides information that affects individual physiology and behavior, and hence fitness (see Section I). For instance, population density affects competition for food and oviposition sites (too as other resources), propensity of individuals to disperse, and the proximity of potential mates.

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Population Dynamics of White Sharks in South Africa

CRAIG A. FERREIRA , THEO P. FERREIRA , in Great White Sharks, 1996

Summary

Population dynamics of white sharks C. carcharias were studied forth the S African declension from Oct 1991 through August 1994. Written report areas included Dyer Island and Struis, False, and Mossel bays. During the written report, 255 white sharks were recorded, 147 of which were tagged. Of the latter, 30 individuals were resighted 59 times; 1 shark (AGT) was resighted ten times. Resighting intervals of individual sharks ranged from one to 545 days. Although gender separation was non absolute, a notably high percent of females occurred at both Struis Bay and Dyer Island. Sharks observed ranged from 150 to 500 cm TL. No notable size segregation occurred, although a loftier percentage of sharks 260–300 cm TL was observed. White shark abundance overall showed no substantial change during the study. Abundance, still, fluctuated at sites over curt periods.

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Eco-Evolutionary Dynamics

Tom C. Cameron , ... Tim K. Benton , in Advances in Ecological Research, 2014

iii.2.three Population dynamic experiments

Population dynamic experiments involve monitoring free-running populations over multiple generations. Such experiments take been started in different ways depending on the purpose of the experiment. Where the purpose was to investigate the timescale of parental effects, populations were started with controlled numbers of eggs from parents of unlike environmental backgrounds or ages (Pinder, 2009; Plaistow et al., 2006, 2007). To investigate the interplay between population and phenotypic dynamics, populations were initiated with a mix of sexed adults (n  =   75–150/sex) and juveniles (north  =   500–1000), approximately at stable stage distribution to minimise transient dynamics. To investigate the links between ecological plasticity and life-history modify, populations were initiated with mites recently collected from the wild to maximise genetic diversity (n  =   150 adult/sex and m juveniles).

In the population experiments, nosotros take oft manipulated stochasticity by varying the timing and amount of food supplied, while trying to maintain other factors equally shut to abiding as possible. Our rationale for this is that many natural environmental factors volition either vary the absolute food supply (due east.g. the atmospheric condition), the requirement for food (e.g. temperature) or the availability of food (e.thousand. patchiness, territoriality, inter-specific competition). Each treatment supplied food at the aforementioned mean daily rate (equivalent to one or 2 assurance of yeast per twenty-four hour period), merely at a variable amount on different days. The algorithms we developed were to supply balls of yeast randomly, or periodically, within each window of fourth dimension, such that over repeating window lengths, the cultures received a constant number of balls of yeast. Other populations were maintained on constant nutrient regimes either to act as contrasts to those in the variable environments, or on their ain for some parental effect experiments. Effects of the different distributions of food supply on variation in population abundance are described elsewhere (Benton et al., 2002).

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Dispersal–Migration

A.P. Ramakrishnan , in Encyclopedia of Environmental (Second Edition), 2008

Abstract

Population dynamics are directly affected by dispersal through the immigration of individuals into populations and by the emigration of individuals out of populations. Much of what nosotros understand near dispersal patterns, their causes and effects comes from mathematical models. These models range in complexity from estimating the effect of simple improvidence processes on a population (i.e., simple reaction-diffusion models) to incorporating explicit information about multiple parameters into a detailed model (i.e., complex cellular automata models). Field measurements of dispersal can be hard, depending on the level of detail desired. Ideally, demographic studies are combined with measurements of dispersal taken from individuals tracked in detail throughout their lifetimes. However, it is common practice to focus on simply one or a few parameters of dispersal, depending on resource available to the researcher. Methods including mark–recapture, seed traps, and genetic estimates of dispersal tin be used to collect dispersal information. Each method has its strengths and weaknesses, which should be carefully evaluated by the researcher prior to utilization. As methods for modeling and detecting dispersal events improve, our ability to predict population responses to environmental perturbations will further benefit a wide spectrum of biological sciences.

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Giraffe Demography and Population Ecology

D.E. Lee , 1000.Thou.Fifty Strauss , in Reference Module in Earth Systems and Environmental Sciences, 2016

Utility of Population Models

Caswell (2001) explained the utility of population models to conservation via an analogy to medical practice. A patient (the population) is examined to assess its condition, the crusade of a problem is diagnosed, a treatment is prescribed to address the trouble, and continued monitoring determines the outcome of handling.

Population assessment is a matter of determining the population growth rate (λ). Cess may exist achieved by examining changes in population numbers or past marking–recapture studies. Mark–recapture provides data for estimation of demographic rates that can be used to construct a population model, and the model's dominant eigenvalue is equivalent to asymptotic λ. Assessment can exist made without knowledge of demographic rates, but they are the best means of assessment and necessary for diagnosis and treatment.

Diagnosis attempts to determine why the population is in problem. The best tool for diagnosis is life-table response experiments (LTREs; Caswell, 2001). Given the data from a population during time periods with dissimilar population trajectories or the data from multiple populations with variation in population trajectories, LTRE volition quantify the contributions of demographic rates to the change in λ.

If information for LTRE are lacking, the proper tool for evaluating potential management prescriptions is prospective perturbation analysis (Caswell, 2001). This technique uses a population model to investigate the theoretical furnishings of irresolute different demographic rates on overall population growth rate. Prospective perturbation analysis is by and large made by sensitivity or elasticity calculations. Sensitivity is the incremental change in λ due to an incremental alter in a demographic rate. Elasticity is the proportional modify in λ related to a proportional change in demographic rate.

Another diagnosis tool aims to predict a population's fate via population viability analyses (PVAs; Morris and Doak, 2002). PVA uses quantitative methods such as stochastic population models with varying demographic rates under various specific conditions to predict possible future population status and probability of population persistence, within a certain period (e.g., Lee, 2015; Marmontel et al., 1997). PVAs can appraise local extinction or extirpation run a risk, and PVAs guide management by offering predicted outcomes from hypothetical treatments. PVAs tin guide decisions on minimum reserve size, minimum number of individuals for translocation to establish a viable new population, setting sustainable harvest or taking limits of exploited populations, and minimum number of populations necessary to protect against global extinction (Morris and Doak, 2002).

Treatments are perturbation action(due south) enacted by direction with the purpose of changing specific demographic rates in an attempt to alter λ. Treatments should exist informed by results from LTRE, perturbation analyses, PVA, or a combination of all three diagnosis tools and should be implemented inside an experimental or adaptive direction and monitoring framework. Monitoring treatment implementation and event should be done in a manner that permits robust evaluation of the effectiveness of the direction action(southward). Nosotros recommend a earlier–afterwards control–bear on study design for monitoring prescription implementation (Underwood, 1992).

Using Population Models for Conservation Planning and Evaluation

Population dynamic and metapopulation models serve an important role in planning and evaluating conservation programs (Akçakaya and Sjögren-Gulve, 2000; Coulson et al., 2005; Heppell, 1998; Letcher et al., 1998). Evaluation of direction deportment should include quantifying the possible benefits of active and passive conservation efforts and measuring the costs of not implementing conservation efforts. Population modeling and PVAs allow different conservation scenarios to be evaluated using a common method. Population models also provide a framework for exposing data gaps where nosotros have bereft information and allow us to evaluate the significance of these uncertainties. Ii population models have been developed for giraffes, a stage-structured model (Strauss et al., 2015) and an historic period-structured model (Lee, 2015). Both provide important and useful starting points for evaluating issues of conservation biological science for giraffes. Conservation of a species or population ultimately entails understanding why a population is growing or shrinking and enacting management activities that touch on population growth in the desired style. Quantitative population environmental, as we have described here, provides the best framework for developing and evaluating conservation and management efforts for giraffes or other large herbivores with similar life histories.

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Primer on Wild fauna Population Dynamics

John R. Skalski , ... Joshua J. Millspaugh , in Wildlife Demography, 2005

ii.1 Introduction

Population dynamics is concerned with changes in affluence, besides as the factors that influence those changes ( Gotelli 2001). Components of population assessment include an evaluation of condition and vitality. Population status refers to the current state of the population and considers factors such as abundance, age and sex structure, and health (i.due east., nutritional and physiological status). In contrast, population vitality, which is commonly expressed as the relative change in population size from i year to the next, refers to the demographic health of the population and the ability of the population to be cocky-sustaining.

A basic agreement of the theories governing animal population dynamics is warranted before application of the sex- and historic period-ratio techniques discussed in this book. Considering our primary interest is to assess population status and vitality from sex- and age- ratio data, we brainstorm our discussion with basic principles of population growth. Equally the chapter proceeds, nosotros build in complication by relaxing assumptions about how populations abound. Eventually, we consider population growth of age- and stage-structured populations and how noesis of historic period- or stage-specific rates can help guide direction activities. Because we employ harvest data for many applications, we conclude the affiliate with an overview of population harvest theory, including a discussion of the concepts of the almanac surplus model, sustained yield harvesting, and additive and compensatory mortality. This chapter is non meant to be exhaustive; rather, it is intended to provide context for using sex, age, and count data to evaluate the status and trends of animal populations discussed afterward in this book. Johnson (1994), Caughley (1977), Caswell (2001), Donovan and Welden (2001), and Gotelli (2001) offer more than comprehensive discussions of these topics.

At the almost fundamental level, the number of animals once step in the future (Nt +1) is afflicted by the current population size (Nt ), the number of additions to the population (i.e., number of births, B, and number of immigrants, I), and the number of reductions in the population (i.e., number of deaths, D, and number of emigrants, E). That is,

(two.1) North t + 1 = N t + ( B + I ) - ( D + Eastward ) .

To simplify our assessment of local populations, nosotros ofttimes assume a population closed to movement (i.eastward., no clearing or emigration). Thus, we drop I and E from Eq. (two.1).

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