Which Four Variables Govern Changes In Population Size?

Population Dynamics

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

Conclusions

Population dynamics is i of the fundamental areas of ecology, forming both the basis for the study of more circuitous communities and of many applied questions. Understanding population dynamics is the key to understanding the relative importance of competition for resource and predation in structuring ecological communities, which is a central question in ecology.

Population dynamics plays a fundamental role in many approaches to preserving biodiversity, which until now take been primarily focused on a single species approach. The adding of the intrinsic growth rate of a species from a life table is frequently the central piece of conservation plans. Similarly, direction of natural resources, such every bit fisheries, depends on population dynamics as a way to decide advisable management actions.

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Population Dynamics

Timothy D. Schowalter , in Insect Ecology (2d Edition), 2006

D Computerized models

Computerized simulation models have been developed to project 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 about sophisticated population dynamics models adult to date because they contain long time frames, furnishings of a variety of interacting factors (including climate, soils, host plant variables, competition, and predation) on insect populations, and furnishings of population change on ecosystem structure and processes. Often, the population dynamics model is integrated with plant growth models; touch on models that accost furnishings of population change on ecological, social, and economical variables; and management models that accost furnishings of manipulated resource availability and insect mortality on the insect population ( Colbert and Campbell 1978, Leuschner 1980). As more information becomes available on population responses to various factors, or effects on ecosystem processes, the model can be updated, increasing its representation of population dynamics and the accurateness of predictions.

Effects of various factors can be modeled equally deterministic (fixed values), stochastic (values based on probability functions), or chaotic (random values) variables (e.k., Croft and Gutierrez 1991, Cushing et al. 2003, Hassell et al. 1991, Logan and Allen 1992). If natality, bloodshed, and survival are highly correlated with temperature, these rates would be modeled every bit a deterministic office of temperature. However, effects of institute condition on these rates might exist described best past 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 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 arrangement can affect subsequent system beliefs. In other words, population trend at any instant is the consequence of the unique combination of population and environmental weather condition at that instant. For instance, changes in gene frequencies and behavior of individuals over fourth dimension affect the way in which populations respond to environmental conditions. Time 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 difficult to demonstrate in population models, and its importance to population dynamics is a topic of debate. 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 phase abundances but was strongly influenced past cluttered behavior at certain values for the coefficient of adult cannibalism of pupae (Fig. half dozen.8).

Several studies suggest that insect population dynamics can undergo recurring transition betwixt stable and chaotic phases when certain variables take values that place the arrangement near a transition point between order and chaos (Cavalieri and Koçak 1995a, b, Constantino et al. 1997) or when influenced past a generalist predator and specialist pathogen (Dwyer et al. 2004). Cavalieri and Koçak (1994, 1995b) found that small changes in weather-related parameters (increased mortality of pathogen-infected individuals or decreased natality of uninfected individuals) in a European corn borer, Ostrinia nubilalis, population dynamics model caused a regular population cycle to become erratic. When this chaotic state was reached, the population reached higher abundances than it did during stable cycles, suggesting that pocket-sized 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 between deterministic or stochastic phases and chaotic phases may facilitate prediction of brusk-term dynamics (Cavalieri and Koçak 1994, Cushing et al. 2003, Logan and Allen 1992).

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Terrestrial Arthropods

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

Local Extinction and Metapopulation Dynamics

Population dynamics are often visualized by assuming that individuals alive in a unmarried locale isolated from other populations. Withal, this is rarely the example and individuals of many arthropod species drift between spatially isolated patches of suitable habitat. Populations inhabiting spatially segregated habitat patches can also go locally extinct, and these patches are afterward colonized by future immigrants. This way of conceptualizing population dynamics is known as metapopulation dynamics. The central concept here is that different populations within the metapopulation are connected by dispersal merely are undergoing dissimilar dynamics, just are connected by dispersal. Still, the density-independent and density-dependent dichotomy described before may all the same be of import and play a major role in the fluctuations and persistence of the metapopulation. The full office of metapopulation dynamics is discussed in another article (see Metapopulation Models).

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Novelty and Synthesis in the Evolution of Population Dynamics

Peter Due west. Price , Mark D. Hunter , in Population Dynamics, 1995

I. Introduction

Although population dynamics is a centerpiece in ecology, there is less emphasis in the field than should exist expected. In a recent review, research papers in population environmental as a broad area outnumbered those on communities and ecosystems 5 to one in some major journals during the years 1987–1991. Withal, the subcategory of population dynamics/regulation was represented past only v% of all papers [51.five% of all papers were in population ecology, 5.v% in population regulation, 9.4% in community ecology, and nine.vi% in ecosystems ecology ( Stiling, 1994)]. Areas favored past researchers in population environmental were contest (6.8% of papers), predation (six.iii%), plant–herbivore interactions (8.4%), habitat selection (6.viii%), and life-history strategies (9.0%).

If population dynamics is at the cadre of the ecological sciences, why is information technology so poorly represented in the current literature? The field necessitates an integration of virtually of the areas favored by ecologists mentioned here. In addition, such integration is essential for an acceptable 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 equally found–herbivore interactions, theory appears to have been of modest concern. The pressing needs to understand the dynamics of pest species in agriculture and forestry, vectors of disease, the pathogens themselves, and the biology of mutual and rare species should all fuel an energetic discipline in population dynamics. Perhaps the cause of the underrepresentation of papers in population dynamics lies in the maturing of the science into a multifaceted discipline. Synthesis of ecological, behavioral, and evolutionary aspects of population dynamics is developing rapidly, with ii consequences for the literature. Outset, relevant literature is likely to appear outside the chief 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 tiresome to sally because population modify is more complicated than it outset appears. After all, population change is determined ultimately by only four factors: nativity, decease, immigration, and emigration. This apparent simplicity is deceptive. It is easy to underestimate the complication of biotic and abiotic interactions in the natural world that can influence these four population parameters. Indeed, we volition argue in this chapter that the development of related fields such as plant-animal interactions, chemical environmental, and life-history evolution has proven to be a prerequisite for a realistic synthesis in population dynamics. These related fields provide the mechanistic basis, and therefore the predictive ability, underlying the birth, decease, 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 report, such as by Howard (1897), which had obvious impact on the development of early on theory by Lotka (1924). The development of life tables for field populations and their assay gave a tremendous heave to the field (e.grand., 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). But every bit the area of population dynamics prospered, the fledgling fields of evolutionary ecology (fostered by Robert MacArthur), coevolution, chemic ecology, life-history evolution, and plant–herbivore interactions were gaining basis, as noted in Chapter one (e.g., Sondheimer and Simeone, 1970). They flourished in the 1970s (e.g., Pianka, 1974; Gilbert and Raven, 1975; Rosenthal and Janzen, 1979; Collins, 1986). Our view is that these highly tractable fields eclipsed the cadre of population dynamics, which was bogging down: "the ecologist'due south phlogiston theory" (Krebs, 1979, p. 351) was proving to exist intractable (McIntosh, 1985). "Because MacArthur's approach often began with the assumption that populations were at a steady state, the study of population dynamics was pushed into the background" (Kareiva, 1989, p. 71).

From these newer fields of environmental, the area of population dynamics has gained a new importance, and a new ability. Its importance lies in the potential that population dynamics has to provide a central conceptual basis for the fusion of these newer fields, which seem to be growing apart rather than together. Fusion and synthesis is likewise inevitable when population dynamics encompasses behavior and phylogenetic relationships. By the aforementioned token, population dynamics is gaining enormous explanatory ability as the newer fields reveal key mechanisms driving population change. "The ecologist's phlogiston theory" is being replaced past breaths of fresh air (and oxygen), as the newly constructed science develops.

The new synthesis in population dynamics may be as important to ecology as a like 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 evolution. Many disciplines in biology are becoming integrated under one umbrella. Scientists from many countries bring their own special talents and contributions. As the union proceeds, new debates are generated that advance the stride of science and discovery, and one-time debates are resolved.

In the rest of this chapter, we explore what nosotros consider to be modern approaches to population dynamics. First, we consider the various elements that make up a synthetic approach to the study of population change. Section II is the backbone of the affiliate and in it we offer a list of components that nosotros regard as important to the study of population dynamics. Some of these elements are related fields of research, such as microbial ecology, whereas others are conceptual approaches, such as international cooperation in field research along important ecological gradients. 2nd, we describe how population dynamics has changed since its emergence equally a field. Finally, we describe three broad scales of approaches to questions of population dynamics, and some of the difficulties in integrating the diverse elements of population biological science together.

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Population Ecology

In Insect Environmental (Second Edition), 2006

Population dynamics reflect the net effects of differences amidst individuals in their physiological and behavioral interactions with the surroundings. Changes in individual success in finding and exploiting resource, mating and reproducing, and avoiding bloodshed agents determine numbers of individuals, their spatial distribution, and genetic composition at whatever point in time. Population structure is a component of the environs for the members of the population and provides information that affects private physiology and behavior, and hence fettle (meet Section I). For case, population density affects competition for food and oviposition sites (equally well every bit other resources), propensity of individuals to disperse, and the proximity of potential mates.

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Population Dynamics of White Sharks in S Africa

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

Summary

Population dynamics of white sharks C. carcharias were studied along the Due south African coast from Oct 1991 through August 1994. Written report areas included Dyer Island and Struis, False, and Mossel bays. During the study, 255 white sharks were recorded, 147 of which were tagged. Of the latter, xxx individuals were resighted 59 times; one shark (AGT) was resighted 10 times. Resighting intervals of individual sharks ranged from 1 to 545 days. Although gender separation was not 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 affluence overall showed no substantial change during the study. Abundance, however, fluctuated at sites over brusk periods.

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Eco-Evolutionary Dynamics

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

3.two.3 Population dynamic experiments

Population dynamic experiments involve monitoring free-running populations over multiple generations. Such experiments have been started in different ways depending on the purpose of the experiment. Where the purpose was to investigate the timescale of parental furnishings, populations were started with controlled numbers of eggs from parents of different 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 (north = 75–150/sex activity) and juveniles (northward = 500–1000), approximately at stable stage distribution to minimise transient dynamics. To investigate the links between ecological plasticity and life-history change, populations were initiated with mites recently collected from the wild to maximise genetic diversity (n = 150 adult/sexual practice and 1000 juveniles).

In the population experiments, we have oftentimes manipulated stochasticity by varying the timing and corporeality of nutrient supplied, while trying to maintain other factors every bit close to constant as possible. Our rationale for this is that many natural environmental factors will either vary the absolute nutrient supply (e.g. the atmospheric condition), the requirement for food (eastward.one thousand. temperature) or the availability of food (e.g. patchiness, territoriality, inter-specific contest). Each treatment supplied food at the aforementioned mean daily rate (equivalent to one or two balls of yeast per day), simply at a variable amount on unlike days. The algorithms we developed were to supply balls of yeast randomly, or periodically, within each window of time, such that over repeating window lengths, the cultures received a constant number of balls of yeast. Other populations were maintained on abiding food regimes either to act every bit contrasts to those in the variable environments, or on their own for some parental event experiments. Furnishings 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 Ecology (Second Edition), 2008

Abstract

Population dynamics are straight affected by dispersal through the immigration of individuals into populations and by the emigration of individuals out of populations. Much of what we understand virtually 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.east., unproblematic reaction-diffusion models) to incorporating explicit information about multiple parameters into a detailed model (i.e., complex cellular automata models). Field measurements of dispersal tin 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 only one or a few parameters of dispersal, depending on resources available to the researcher. Methods including mark–recapture, seed traps, and genetic estimates of dispersal tin can be used to collect dispersal data. Each method has its strengths and weaknesses, which should exist carefully evaluated by the researcher prior to utilization. As methods for modeling and detecting dispersal events improve, our ability to predict population responses to ecology perturbations will further benefit a wide spectrum of biological sciences.

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Giraffe Demography and Population Ecology

D.East. Lee , M.K.L 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 illustration to medical exercise. A patient (the population) is examined to appraise its condition, the cause of a problem is diagnosed, a treatment is prescribed to address the problem, and continued monitoring determines the outcome of handling.

Population cess is a thing of determining the population growth rate (λ). Assessment may be accomplished past examining changes in population numbers or by marking–recapture studies. Mark–recapture provides data for estimation of demographic rates that tin can be used to construct a population model, and the model'due south ascendant eigenvalue is equivalent to asymptotic λ. Assessment can be made without cognition 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-tabular array response experiments (LTREs; Caswell, 2001). Given the data from a population during time periods with different population trajectories or the data from multiple populations with variation in population trajectories, LTRE will quantify the contributions of demographic rates to the change in λ.

If data for LTRE are lacking, the proper tool for evaluating potential management prescriptions is prospective perturbation assay (Caswell, 2001). This technique uses a population model to investigate the theoretical furnishings of irresolute dissimilar demographic rates on overall population growth rate. Prospective perturbation assay is generally made past sensitivity or elasticity calculations. Sensitivity is the incremental alter in λ due to an incremental modify in a demographic rate. Elasticity is the proportional change 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 menstruum (e.thousand., Lee, 2015; Marmontel et al., 1997). PVAs tin appraise local extinction or extirpation adventure, and PVAs guide management by offering predicted outcomes from hypothetical treatments. PVAs tin can guide decisions on minimum reserve size, minimum number of individuals for translocation to plant 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 effort to alter λ. Treatments should exist informed by results from LTRE, perturbation analyses, PVA, or a combination of all three diagnosis tools and should exist implemented within an experimental or adaptive management and monitoring framework. Monitoring handling implementation and consequence should be done in a manner that permits robust evaluation of the effectiveness of the management activeness(s). We recommend a before–later control–touch 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 actions 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 permit unlike conservation scenarios to be evaluated using a common method. Population models also provide a framework for exposing information gaps where nosotros accept insufficient information and allow us to evaluate the significance of these uncertainties. Two population models have been developed for giraffes, a stage-structured model (Strauss et al., 2015) and an age-structured model (Lee, 2015). Both provide important and useful starting points for evaluating bug of conservation biology for giraffes. Conservation of a species or population ultimately entails agreement why a population is growing or shrinking and enacting management activities that touch population growth in the desired manner. Quantitative population environmental, as we accept described here, provides the all-time 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 animals Population Dynamics

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

2.one Introduction

Population dynamics is concerned with changes in affluence, as well as the factors that influence those changes ( Gotelli 2001). Components of population cess include an evaluation of status and vitality. Population condition refers to the current country of the population and considers factors such as abundance, age and sex construction, and health (i.e., nutritional and physiological condition). In contrast, population vitality, which is ordinarily expressed every bit the relative change in population size from ane year to the next, refers to the demographic wellness of the population and the power of the population to be self-sustaining.

A bones agreement of the theories governing animal population dynamics is warranted before application of the sex- and age-ratio techniques discussed in this volume. Because our primary involvement is to assess population status and vitality from sex- and age- ratio data, nosotros begin our discussion with basic principles of population growth. Equally the chapter proceeds, we build in complexity past relaxing assumptions nearly how populations grow. Eventually, we consider population growth of age- and stage-structured populations and how knowledge of age- or stage-specific rates tin help guide management activities. Because we use harvest data for many applications, we conclude the chapter with an overview of population harvest theory, including a discussion of the concepts of the annual surplus model, sustained yield harvesting, and condiment and compensatory bloodshed. This chapter is not meant to be exhaustive; rather, information technology is intended to provide context for using sex, age, and count data to evaluate the status and trends of beast populations discussed later in this book. Johnson (1994), Caughley (1977), Caswell (2001), Donovan and Welden (2001), and Gotelli (2001) offering more than comprehensive discussions of these topics.

At the near fundamental level, the number of animals one time footstep in the future (Northward_t _+ane) is affected by the electric current population size (Northward_t ), the number of additions to the population (i.east., 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,

(2.i) ${Northward}_{t + 1} = {Due north}_{t} + (B + I) - (D + Eastward) .$

To simplify our cess of local populations, we often assume a population airtight to motility (i.e., no clearing or emigration). Thus, nosotros driblet I and Eastward from Eq. (2.1).

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