HARALD ATMANSPACHER and STEFAN ROTTER
- Published in print:
- 2011
- Published Online:
- January 2013
- ISBN:
- 9780197264898
- eISBN:
- 9780191754074
- Item type:
- chapter
- Publisher:
- British Academy
- DOI:
- 10.5871/bacad/9780197264898.003.0006
- Subject:
- Philosophy, Philosophy of Mind
This chapter analyzes the different ways to describe brain behaviour with the goal to provide a basis for an informed discussion of the nature of decisions and actions that humans perform in their ...
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This chapter analyzes the different ways to describe brain behaviour with the goal to provide a basis for an informed discussion of the nature of decisions and actions that humans perform in their lives. The chapter is organized as follows. Section 2 outlines a number of concepts exhibiting how many subtle details and distinctions lie behind the broad notions of determinacy and stochasticity. These details are necessary for a discussion, in Section 3, of particular aspects relevant for the characterization of brain states and their dynamics. The descriptions of brain behaviour currently provided by neuroscience depend on the level and context of the descriptions. There is no clear-cut evidence for ultimately determinate or ultimately stochastic brain behaviour. As a consequence, there is no solid neurobiological basis to argue either in favour of or against any fundamental determination or openness of human decisions and actions.Less
This chapter analyzes the different ways to describe brain behaviour with the goal to provide a basis for an informed discussion of the nature of decisions and actions that humans perform in their lives. The chapter is organized as follows. Section 2 outlines a number of concepts exhibiting how many subtle details and distinctions lie behind the broad notions of determinacy and stochasticity. These details are necessary for a discussion, in Section 3, of particular aspects relevant for the characterization of brain states and their dynamics. The descriptions of brain behaviour currently provided by neuroscience depend on the level and context of the descriptions. There is no clear-cut evidence for ultimately determinate or ultimately stochastic brain behaviour. As a consequence, there is no solid neurobiological basis to argue either in favour of or against any fundamental determination or openness of human decisions and actions.
Russell Lande, Steinar Engen, and Bernt-Erik Saether
- Published in print:
- 2003
- Published Online:
- April 2010
- ISBN:
- 9780198525257
- eISBN:
- 9780191584930
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198525257.001.0001
- Subject:
- Biology, Ecology
All populations fluctuate stochastically, creating a risk of extinction that does not exist in deterministic models, with fundamental consequences for both pure and applied ecology. This book ...
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All populations fluctuate stochastically, creating a risk of extinction that does not exist in deterministic models, with fundamental consequences for both pure and applied ecology. This book provides an introduction to stochastic population dynamics, combining classical background material with a variety of modern approaches, including previously unpublished results by the authors, illustrated with examples from bird and mammal populations, and insect communities. Demographic and environmental stochasticity are introduced with statistical methods for estimating them from field data. The long-run growth rate of a population is explained and extended to include age structure with both demographic and environmental stochasticity. Diffusion approximations facilitate the analysis of extinction dynamics and the duration of the final decline. Methods are developed for estimating delayed density dependence from population time series using life history data. Metapopulation viability and the spatial scale of population fluctuations and extinction risk are analyzed. Stochastic dynamics and statistical uncertainty in population parameters are incorporated in Population Viability Analysis and strategies for sustainable harvesting. Statistics of species diversity measures and species abundance distributions are described, with implications for rapid assessments of biodiversity, and methods are developed for partitioning species diversity into additive components. Analysis of the stochastic dynamics of a tropical butterfly community in space and time indicates that most of the variance in the species abundance distribution is due to ecological heterogeneity among species, so that real communities are far from neutral.Less
All populations fluctuate stochastically, creating a risk of extinction that does not exist in deterministic models, with fundamental consequences for both pure and applied ecology. This book provides an introduction to stochastic population dynamics, combining classical background material with a variety of modern approaches, including previously unpublished results by the authors, illustrated with examples from bird and mammal populations, and insect communities. Demographic and environmental stochasticity are introduced with statistical methods for estimating them from field data. The long-run growth rate of a population is explained and extended to include age structure with both demographic and environmental stochasticity. Diffusion approximations facilitate the analysis of extinction dynamics and the duration of the final decline. Methods are developed for estimating delayed density dependence from population time series using life history data. Metapopulation viability and the spatial scale of population fluctuations and extinction risk are analyzed. Stochastic dynamics and statistical uncertainty in population parameters are incorporated in Population Viability Analysis and strategies for sustainable harvesting. Statistics of species diversity measures and species abundance distributions are described, with implications for rapid assessments of biodiversity, and methods are developed for partitioning species diversity into additive components. Analysis of the stochastic dynamics of a tropical butterfly community in space and time indicates that most of the variance in the species abundance distribution is due to ecological heterogeneity among species, so that real communities are far from neutral.
Jan Vijg
- Published in print:
- 2007
- Published Online:
- April 2010
- ISBN:
- 9780198569237
- eISBN:
- 9780191728242
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198569237.003.0009
- Subject:
- Biology, Evolutionary Biology / Genetics
This epilogue presents some concluding thoughts from the author. Increased stochasticity, beginning at the level of the genome, may work its way through the system and eventually translate into aging ...
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This epilogue presents some concluding thoughts from the author. Increased stochasticity, beginning at the level of the genome, may work its way through the system and eventually translate into aging as a pattern of cells and tissues acting increasingly out of tune. It is now possible to explore a wealth of ever-more sophisticated tools in both cell and molecular biology and the computational sciences to systematically study these cascades of stochastic variation in the various model systems for aging that are now available. This is likely to finally unlock the door to a full understanding of how we grow old.Less
This epilogue presents some concluding thoughts from the author. Increased stochasticity, beginning at the level of the genome, may work its way through the system and eventually translate into aging as a pattern of cells and tissues acting increasingly out of tune. It is now possible to explore a wealth of ever-more sophisticated tools in both cell and molecular biology and the computational sciences to systematically study these cascades of stochastic variation in the various model systems for aging that are now available. This is likely to finally unlock the door to a full understanding of how we grow old.
E. J. Milner-Gulland and Marcus Rowcliffe
- Published in print:
- 2007
- Published Online:
- January 2008
- ISBN:
- 9780198530367
- eISBN:
- 9780191713095
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198530367.003.0005
- Subject:
- Biology, Biodiversity / Conservation Biology
The effective management of natural resources use requires a mechanistic understanding of the system, not just correlations between variables of the kind discussed in Chapter 4. Understanding may ...
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The effective management of natural resources use requires a mechanistic understanding of the system, not just correlations between variables of the kind discussed in Chapter 4. Understanding may simply be in the form of a conceptual model, but is much more powerful when formalized as a mathematical model. This chapter introduces methods for building a model of the system that can be used to predict future sustainability with or without management interventions. The emphasis is on the simulation of biological and bioeconomic dynamics, for which step-by-step worked examples are given. These examples start with conceptual models, then show how to formalize these as mathematical equations, build these into computer code; test model sensitivity, validity, and alternative structures; and finally, explore future scenarios. Methods for modelling stochasticity and human behaviour are also introduced, as well as the use of Bayesian methods for understanding dynamic systems and exploring management interventions.Less
The effective management of natural resources use requires a mechanistic understanding of the system, not just correlations between variables of the kind discussed in Chapter 4. Understanding may simply be in the form of a conceptual model, but is much more powerful when formalized as a mathematical model. This chapter introduces methods for building a model of the system that can be used to predict future sustainability with or without management interventions. The emphasis is on the simulation of biological and bioeconomic dynamics, for which step-by-step worked examples are given. These examples start with conceptual models, then show how to formalize these as mathematical equations, build these into computer code; test model sensitivity, validity, and alternative structures; and finally, explore future scenarios. Methods for modelling stochasticity and human behaviour are also introduced, as well as the use of Bayesian methods for understanding dynamic systems and exploring management interventions.
Franck Courchamp, Luděk Berec, and Joanna Gascoigne
- Published in print:
- 2008
- Published Online:
- May 2008
- ISBN:
- 9780198570301
- eISBN:
- 9780191717642
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198570301.003.0006
- Subject:
- Biology, Biodiversity / Conservation Biology
This closing chapter first recapitulates the main notions and information seen in this book, then discusses the main problems related to the definition and to the demonstration of Allee effects; for ...
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This closing chapter first recapitulates the main notions and information seen in this book, then discusses the main problems related to the definition and to the demonstration of Allee effects; for example with regards to demographic stochasticity or to confounding variables. It also considers interesting ideas that are slightly off the mainstream of Allee effect reflections, such as ecosystem shifts or similar processes in some other scientific disciplines (such as mathematics, social sciences, linguistics). Finally, it considers the likely future of studies on this concept and proposes some avenues for new research, including finding Allee effects in new species, looking for new model taxa, for new types of Allee effects, and considering the broader perspective of community and ecosystem level consequences of this process.Less
This closing chapter first recapitulates the main notions and information seen in this book, then discusses the main problems related to the definition and to the demonstration of Allee effects; for example with regards to demographic stochasticity or to confounding variables. It also considers interesting ideas that are slightly off the mainstream of Allee effect reflections, such as ecosystem shifts or similar processes in some other scientific disciplines (such as mathematics, social sciences, linguistics). Finally, it considers the likely future of studies on this concept and proposes some avenues for new research, including finding Allee effects in new species, looking for new model taxa, for new types of Allee effects, and considering the broader perspective of community and ecosystem level consequences of this process.
David M. Wilkinson
- Published in print:
- 2006
- Published Online:
- September 2007
- ISBN:
- 9780198568469
- eISBN:
- 9780191717611
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198568469.003.0005
- Subject:
- Biology, Ecology
Ecological hypercycles are auto-catalytic processes by which different organisms (or guilds) improve each others environments. For example, autotrophs producing material of use to decomposers and the ...
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Ecological hypercycles are auto-catalytic processes by which different organisms (or guilds) improve each others environments. For example, autotrophs producing material of use to decomposers and the decomposers releasing nutrients which are reused by the autotrophs. Such an auto-catalytic process clearly has a positive Gaian effect, and is one of several reasons for expecting life to cover quickly most of a planet's surface once it has evolved. Environmental stochasticity makes it unlikely that ecologies restricted to a small area of a planet will survive for a geological period of time. However, once widespread, life may have a long drawn out end on a dying planet, with some cells surviving for at least millions of years after the biogeochemical cycles returned to being mere geochemical cycles.Less
Ecological hypercycles are auto-catalytic processes by which different organisms (or guilds) improve each others environments. For example, autotrophs producing material of use to decomposers and the decomposers releasing nutrients which are reused by the autotrophs. Such an auto-catalytic process clearly has a positive Gaian effect, and is one of several reasons for expecting life to cover quickly most of a planet's surface once it has evolved. Environmental stochasticity makes it unlikely that ecologies restricted to a small area of a planet will survive for a geological period of time. However, once widespread, life may have a long drawn out end on a dying planet, with some cells surviving for at least millions of years after the biogeochemical cycles returned to being mere geochemical cycles.
Tim M. Blackburn, Julie L. Lockwood, and Phillip Cassey
- Published in print:
- 2009
- Published Online:
- September 2009
- ISBN:
- 9780199232543
- eISBN:
- 9780191715983
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199232543.003.0003
- Subject:
- Biology, Ornithology, Biodiversity / Conservation Biology
Establishment is the stage that has been the focus of most invasion studies in birds. However, the research on this topic has not always been congruent in its findings, and considerable disagreement ...
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Establishment is the stage that has been the focus of most invasion studies in birds. However, the research on this topic has not always been congruent in its findings, and considerable disagreement exists over the causes of successful establishment. One point on which most writers agree is that the numbers of individuals contributing to an introduction event (i.e., propagule pressure) is an important contributory factor. This chapter presents reasons why this would be expected, which relate to the problems of survival that face small populations of any organism. Evidence for a relationship between propagule pressure and establishment success is assessed. The chapter concludes by considering some of the other associations of propagule pressure, especially those which may affect the conclusions of studies of establishment that do not account for it.Less
Establishment is the stage that has been the focus of most invasion studies in birds. However, the research on this topic has not always been congruent in its findings, and considerable disagreement exists over the causes of successful establishment. One point on which most writers agree is that the numbers of individuals contributing to an introduction event (i.e., propagule pressure) is an important contributory factor. This chapter presents reasons why this would be expected, which relate to the problems of survival that face small populations of any organism. Evidence for a relationship between propagule pressure and establishment success is assessed. The chapter concludes by considering some of the other associations of propagule pressure, especially those which may affect the conclusions of studies of establishment that do not account for it.
Michael J. Angilletta
- Published in print:
- 2009
- Published Online:
- May 2009
- ISBN:
- 9780198570875
- eISBN:
- 9780191718748
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198570875.003.0005
- Subject:
- Biology, Ecology, Animal Biology
Many organisms adjust their thermal sensitivities in response to environmental conditions. This form of phenotypic plasticity, referred to as acclimation, involves potentially reversible changes in ...
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Many organisms adjust their thermal sensitivities in response to environmental conditions. This form of phenotypic plasticity, referred to as acclimation, involves potentially reversible changes in the quantity and quality of cellular structures. Although these adjustments impose energetic costs, a more serious cost results from the time lag required to acclimate following changes in temperature. Optimality models, based on the costs imposed by time lags, can be used to predict the acclimation of performance curves in fluctuating environments. Contrary to current theory, acclimation of the thermal optimum rarely occurs in laboratory experiments. Furthermore, a genotype's capacity for acclimation rarely correlates with the magnitude or predictability of thermal heterogeneity in its natural environment. Even with their shortcomings, current optimality models constitute a major advance over verbal models, such as the beneficial acclimation hypothesis.Less
Many organisms adjust their thermal sensitivities in response to environmental conditions. This form of phenotypic plasticity, referred to as acclimation, involves potentially reversible changes in the quantity and quality of cellular structures. Although these adjustments impose energetic costs, a more serious cost results from the time lag required to acclimate following changes in temperature. Optimality models, based on the costs imposed by time lags, can be used to predict the acclimation of performance curves in fluctuating environments. Contrary to current theory, acclimation of the thermal optimum rarely occurs in laboratory experiments. Furthermore, a genotype's capacity for acclimation rarely correlates with the magnitude or predictability of thermal heterogeneity in its natural environment. Even with their shortcomings, current optimality models constitute a major advance over verbal models, such as the beneficial acclimation hypothesis.
André Nies
- Published in print:
- 2009
- Published Online:
- May 2009
- ISBN:
- 9780199230761
- eISBN:
- 9780191710988
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199230761.003.0007
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
Martingales form the mathematical counterpart of betting strategies. This chapter studies computable randomness, where the tests are computable martingales, and separates it from both Martin–Löf ...
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Martingales form the mathematical counterpart of betting strategies. This chapter studies computable randomness, where the tests are computable martingales, and separates it from both Martin–Löf randomness and Schnorr randomness. The chapter shows that each high degree contains a properly computably random (Schnorr random) set. It varies computable martingales, discussing stochasticity and non-monotonic betting strategies.Less
Martingales form the mathematical counterpart of betting strategies. This chapter studies computable randomness, where the tests are computable martingales, and separates it from both Martin–Löf randomness and Schnorr randomness. The chapter shows that each high degree contains a properly computably random (Schnorr random) set. It varies computable martingales, discussing stochasticity and non-monotonic betting strategies.
Paul W. Glimcher
- Published in print:
- 2010
- Published Online:
- January 2011
- ISBN:
- 9780199744251
- eISBN:
- 9780199863433
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199744251.003.0010
- Subject:
- Psychology, Neuropsychology
The central premise of the neuroeconomic endeavor is that the iterative process of reductively linking neuroscience, psychology, and economics through theoretical modifications to each discipline ...
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The central premise of the neuroeconomic endeavor is that the iterative process of reductively linking neuroscience, psychology, and economics through theoretical modifications to each discipline will maximize predictive power. This chapter examines further neurobiological, psychological, and economic constraints on the choice mechanism to test that premise. First, it examines in greater detail the relationship between expected subjective value and expected utility, focusing on the interrelationship between neuronal and behavioral stochasticity as revealed by existing psychological models. Second, it looks at the precise nature of cortical representation in the nervous system. Theories of cortical representation anchored to normative models of efficient coding identify constraints all neural representations must acknowledge. These constraints predict a specific class of choice behaviors that violate traditional Soft-REU, behaviors that have already been observed but not yet linked to the structure of the choice mechanism. This suggests that a version of Hard-REU that incorporates these constraints has significantly greater predictive power at both the neural and behavioral levels than a model more closely aligned with traditional Soft-REU. These are the final issues that need to be engaged.Less
The central premise of the neuroeconomic endeavor is that the iterative process of reductively linking neuroscience, psychology, and economics through theoretical modifications to each discipline will maximize predictive power. This chapter examines further neurobiological, psychological, and economic constraints on the choice mechanism to test that premise. First, it examines in greater detail the relationship between expected subjective value and expected utility, focusing on the interrelationship between neuronal and behavioral stochasticity as revealed by existing psychological models. Second, it looks at the precise nature of cortical representation in the nervous system. Theories of cortical representation anchored to normative models of efficient coding identify constraints all neural representations must acknowledge. These constraints predict a specific class of choice behaviors that violate traditional Soft-REU, behaviors that have already been observed but not yet linked to the structure of the choice mechanism. This suggests that a version of Hard-REU that incorporates these constraints has significantly greater predictive power at both the neural and behavioral levels than a model more closely aligned with traditional Soft-REU. These are the final issues that need to be engaged.
Russell Lande, Steinar Engen, and Bernt-Erik SÆther
- Published in print:
- 2003
- Published Online:
- April 2010
- ISBN:
- 9780198525257
- eISBN:
- 9780191584930
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198525257.003.0001
- Subject:
- Biology, Ecology
This chapter defines and formulates demographic and environmental stochasticity, and illustrates statistical methods for estimating them from field data. Population fluctuations in most species are ...
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This chapter defines and formulates demographic and environmental stochasticity, and illustrates statistical methods for estimating them from field data. Population fluctuations in most species are produced by demographic and environmental stochasticity, rather than by internally driven cycles or chaos. Demographic stochasticity results from chance independent events of individual mortality and reproduction, causing random fluctuations in population growth rate, primarily in small populations. Environmental stochasticity results from temporal fluctuations in mortality and reproductive rates of all individuals in a population in the same or similar fashion, causing population growth rate to fluctuate randomly in populations of all sizes. In populations much larger than the ratio of the demographic variance to the environmental variance, demographic stochasticity can be neglected. The demographic variance can be estimated from data on individual survival and reproduction, and using this, the environmental variance can then be estimated from population time series. Under density-independent growth in a random environment, the eventual rate of increase or decrease of population size is given by the long-run growth rate, the mean rate of increase of log population size, which is reduced by stochasticity. Density-dependent population growth in a stochastic environment produces temporal autocorrelation in population size, even in the absence of temporal autocorrelation in the environment. Time series for terrestrial populations of birds and mammals show little evidence of temporal environmental autocorrelation.Less
This chapter defines and formulates demographic and environmental stochasticity, and illustrates statistical methods for estimating them from field data. Population fluctuations in most species are produced by demographic and environmental stochasticity, rather than by internally driven cycles or chaos. Demographic stochasticity results from chance independent events of individual mortality and reproduction, causing random fluctuations in population growth rate, primarily in small populations. Environmental stochasticity results from temporal fluctuations in mortality and reproductive rates of all individuals in a population in the same or similar fashion, causing population growth rate to fluctuate randomly in populations of all sizes. In populations much larger than the ratio of the demographic variance to the environmental variance, demographic stochasticity can be neglected. The demographic variance can be estimated from data on individual survival and reproduction, and using this, the environmental variance can then be estimated from population time series. Under density-independent growth in a random environment, the eventual rate of increase or decrease of population size is given by the long-run growth rate, the mean rate of increase of log population size, which is reduced by stochasticity. Density-dependent population growth in a stochastic environment produces temporal autocorrelation in population size, even in the absence of temporal autocorrelation in the environment. Time series for terrestrial populations of birds and mammals show little evidence of temporal environmental autocorrelation.
Russell Lande, Steinar Engen, and Bernt-Erik SÆther
- Published in print:
- 2003
- Published Online:
- April 2010
- ISBN:
- 9780198525257
- eISBN:
- 9780191584930
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198525257.003.0006
- Subject:
- Biology, Ecology
This chapter reviews recent analytical models of sustainable harvesting of fluctuating populations without age structure that incorporate the risk of population collapse or extinction. Using ...
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This chapter reviews recent analytical models of sustainable harvesting of fluctuating populations without age structure that incorporate the risk of population collapse or extinction. Using diffusion theory it compares three classical harvesting strategies and one new strategy in terms of their harvest statistics and the mean time to population collapse or extinction. It uses simple analytical models to derive general principles and then apply these principles to more realistic, age-structured models of particular species to derive by simulation the optimal harvesting strategies.Less
This chapter reviews recent analytical models of sustainable harvesting of fluctuating populations without age structure that incorporate the risk of population collapse or extinction. Using diffusion theory it compares three classical harvesting strategies and one new strategy in terms of their harvest statistics and the mean time to population collapse or extinction. It uses simple analytical models to derive general principles and then apply these principles to more realistic, age-structured models of particular species to derive by simulation the optimal harvesting strategies.
Russell Lande, Steinar Engen, and Bernt-Erik SÆther
- Published in print:
- 2003
- Published Online:
- April 2010
- ISBN:
- 9780198525257
- eISBN:
- 9780191584930
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198525257.003.0008
- Subject:
- Biology, Ecology
Most of conservation biology deals with one species at a time. However, increasing attention is being devoted to the conservation and restoration of landscapes, communities and ecosystems, and how ...
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Most of conservation biology deals with one species at a time. However, increasing attention is being devoted to the conservation and restoration of landscapes, communities and ecosystems, and how their dynamics are altered by human activities. One reason there is relatively little discussion of community dynamics in conservation is the absence of any guiding theory. This chapter develops a general model of community dynamics that allows species to differ in their basic ecology and responses to environmental fluctuations. It investigates the extent to which species in a community differ consistently in abundance because of differences in their ecology, and explores how environmental stochasticity influences community composition in space and time. This theory is applied to analyze the dynamics of a tropical butterfly community sampled intensively through a limited space and time, emphasizing the advantages of studying taxa with short generations to facilitate sampling on temporal scales longer than the generation times of species in the community.Less
Most of conservation biology deals with one species at a time. However, increasing attention is being devoted to the conservation and restoration of landscapes, communities and ecosystems, and how their dynamics are altered by human activities. One reason there is relatively little discussion of community dynamics in conservation is the absence of any guiding theory. This chapter develops a general model of community dynamics that allows species to differ in their basic ecology and responses to environmental fluctuations. It investigates the extent to which species in a community differ consistently in abundance because of differences in their ecology, and explores how environmental stochasticity influences community composition in space and time. This theory is applied to analyze the dynamics of a tropical butterfly community sampled intensively through a limited space and time, emphasizing the advantages of studying taxa with short generations to facilitate sampling on temporal scales longer than the generation times of species in the community.
Sahotra Sarkar
- Published in print:
- 2011
- Published Online:
- September 2011
- ISBN:
- 9780199574131
- eISBN:
- 9780191728921
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199574131.003.0021
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
This chapter defends a stochastic dynamical interpretation of evolution under which drift does not emerge as an evolutionary cause, unlike mutation and selection. Rather, whether drift occurs in a ...
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This chapter defends a stochastic dynamical interpretation of evolution under which drift does not emerge as an evolutionary cause, unlike mutation and selection. Rather, whether drift occurs in a population model depends on its constitutive assumptions, namely, whether the population size is finite. The same amount of selection makes quantitatively and qualitatively different predictions in finite and infinite population models: this is all that there is to drift. This argument is illustrated through the explicit solution of a haploid model in which differences in vital parameters lead to drift in the presence of selection. In the absence of these differences, the model reduces to a neutral model. In the infinite population limit, the standard results without drift also obtain. The stochastic dynamical interpretation is contrasted with the views that evolution is a theory of forces and the statistical interpretation of evolution.Less
This chapter defends a stochastic dynamical interpretation of evolution under which drift does not emerge as an evolutionary cause, unlike mutation and selection. Rather, whether drift occurs in a population model depends on its constitutive assumptions, namely, whether the population size is finite. The same amount of selection makes quantitatively and qualitatively different predictions in finite and infinite population models: this is all that there is to drift. This argument is illustrated through the explicit solution of a haploid model in which differences in vital parameters lead to drift in the presence of selection. In the absence of these differences, the model reduces to a neutral model. In the infinite population limit, the standard results without drift also obtain. The stochastic dynamical interpretation is contrasted with the views that evolution is a theory of forces and the statistical interpretation of evolution.
Douglas W. Morris and Per Lundberg
- Published in print:
- 2011
- Published Online:
- December 2013
- ISBN:
- 9780198568797
- eISBN:
- 9780191774690
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198568797.003.0006
- Subject:
- Biology, Evolutionary Biology / Genetics
Although we must understand the mechanical nuts and bolts of inheritance, evolution occurs through changes in the population frequencies of traits, trait values, and genes in time and space, values ...
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Although we must understand the mechanical nuts and bolts of inheritance, evolution occurs through changes in the population frequencies of traits, trait values, and genes in time and space, values that depend at least as much on the dynamics of populations as on the mechanics of inheritance. This chapter demonstrates the crucial roles of ecology in evolutionary change. There are two major types of dynamics that are essential to model evolution. The chapter develops the principles of strategy dynamics to examine the processes responsible for the success and failure of some traits, and trait values, over others. Different traits and their values represent competing strategies to be tested by adaptive evolution. The success of each strategy depends on the spatial and temporal dynamics of populations, and their respective influences on those dynamics. The chapter merges strategy and population dynamics to evaluate the evolutionary stability of competing strategies.Less
Although we must understand the mechanical nuts and bolts of inheritance, evolution occurs through changes in the population frequencies of traits, trait values, and genes in time and space, values that depend at least as much on the dynamics of populations as on the mechanics of inheritance. This chapter demonstrates the crucial roles of ecology in evolutionary change. There are two major types of dynamics that are essential to model evolution. The chapter develops the principles of strategy dynamics to examine the processes responsible for the success and failure of some traits, and trait values, over others. Different traits and their values represent competing strategies to be tested by adaptive evolution. The success of each strategy depends on the spatial and temporal dynamics of populations, and their respective influences on those dynamics. The chapter merges strategy and population dynamics to evaluate the evolutionary stability of competing strategies.
Ilkka Hanski
- Published in print:
- 2012
- Published Online:
- December 2013
- ISBN:
- 9780199608898
- eISBN:
- 9780191774560
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199608898.003.0023
- Subject:
- Biology, Ecology, Evolutionary Biology / Genetics
This chapter focuses on the study of dispersal in butterfly populations, in particular the Glanville fritillary butterfly. Many of the processes that influence, and are influenced by, dispersal in ...
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This chapter focuses on the study of dispersal in butterfly populations, in particular the Glanville fritillary butterfly. Many of the processes that influence, and are influenced by, dispersal in the Glanville fritillary are likely to be equally significant in other species of insects and invertebrates. Butterflies are small bodied and ectothermic; they are strongly influenced by the prevailing environmental, and especially thermal, conditions, which increase the impact of environmental stochasticity on their population dynamics. Butterflies also often exhibit high host plant and habitat specificities. In many landscapes, the host plants occur in habitats that have a highly fragmented distribution, and hence the respective butterfly species have spatially structured populations, often in the form of a network of local populations called a metapopulation.Less
This chapter focuses on the study of dispersal in butterfly populations, in particular the Glanville fritillary butterfly. Many of the processes that influence, and are influenced by, dispersal in the Glanville fritillary are likely to be equally significant in other species of insects and invertebrates. Butterflies are small bodied and ectothermic; they are strongly influenced by the prevailing environmental, and especially thermal, conditions, which increase the impact of environmental stochasticity on their population dynamics. Butterflies also often exhibit high host plant and habitat specificities. In many landscapes, the host plants occur in habitats that have a highly fragmented distribution, and hence the respective butterfly species have spatially structured populations, often in the form of a network of local populations called a metapopulation.
Bernt-Erik Sæther, Steinar Engen, Marlène Gamelon, and Vidar Grøtan
- Published in print:
- 2019
- Published Online:
- September 2019
- ISBN:
- 9780198824268
- eISBN:
- 9780191862809
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198824268.003.0007
- Subject:
- Biology, Ornithology, Animal Biology
Climate variation strongly influences fluctuations in size of avian populations. In this chapter, we show that it is difficult to predict how the abundance of birds will respond to climate change. A ...
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Climate variation strongly influences fluctuations in size of avian populations. In this chapter, we show that it is difficult to predict how the abundance of birds will respond to climate change. A major reason for this is that most available time series of fluctuations in population size are in a statistical sense short, thus often resulting in large uncertainties in parameter estimates. We therefore argue that reliable population predictions must be based on models that capture how climate change will affect vital rates as well as including other processes (e.g. density-dependences) known to affect the population dynamics of the species in question. Our survey of examples of such forecast studies show that reliable predictions necessarily contain a high level of uncertainty. A major reason for this is that avian population dynamics are strongly influenced by environmental stochasticity, which is for most species, irrespective of their life history, the most important driver of fluctuations in population size. Credible population predictions must therefore assess the effects of such uncertainties as well as biases in population estimates.Less
Climate variation strongly influences fluctuations in size of avian populations. In this chapter, we show that it is difficult to predict how the abundance of birds will respond to climate change. A major reason for this is that most available time series of fluctuations in population size are in a statistical sense short, thus often resulting in large uncertainties in parameter estimates. We therefore argue that reliable population predictions must be based on models that capture how climate change will affect vital rates as well as including other processes (e.g. density-dependences) known to affect the population dynamics of the species in question. Our survey of examples of such forecast studies show that reliable predictions necessarily contain a high level of uncertainty. A major reason for this is that avian population dynamics are strongly influenced by environmental stochasticity, which is for most species, irrespective of their life history, the most important driver of fluctuations in population size. Credible population predictions must therefore assess the effects of such uncertainties as well as biases in population estimates.
G. F. Roach, I. G. Stratis, and A. N. Yannacopoulos
- Published in print:
- 2012
- Published Online:
- October 2017
- ISBN:
- 9780691142173
- eISBN:
- 9781400842650
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691142173.003.0013
- Subject:
- Mathematics, Applied Mathematics
This chapter addresses the problem of controllability for stochastic complex electromagnetic media. The starting point is the stochastic integrodifferential equations that model the evolution of the ...
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This chapter addresses the problem of controllability for stochastic complex electromagnetic media. The starting point is the stochastic integrodifferential equations that model the evolution of the fields, with a control procedure to be selected so that the system is driven to a desired final state. The controllability problem for stochastic media is more complicated than that for deterministic media and includes subtleties that must be addressed to reach a satisfactory answer. Hence, this chapter sets the model and then discusses the subtle issues introduced by the stochasticity in the controllability problem. It then proposes two different approaches towards controllability: an approach using PDEs with random coefficients and an approach using backward stochastic evolution equations (BSEEs). Afterward, the chapter lists several comments concerning boundary controllability and optimal control.Less
This chapter addresses the problem of controllability for stochastic complex electromagnetic media. The starting point is the stochastic integrodifferential equations that model the evolution of the fields, with a control procedure to be selected so that the system is driven to a desired final state. The controllability problem for stochastic media is more complicated than that for deterministic media and includes subtleties that must be addressed to reach a satisfactory answer. Hence, this chapter sets the model and then discusses the subtle issues introduced by the stochasticity in the controllability problem. It then proposes two different approaches towards controllability: an approach using PDEs with random coefficients and an approach using backward stochastic evolution equations (BSEEs). Afterward, the chapter lists several comments concerning boundary controllability and optimal control.
Francesca Merlin
- Published in print:
- 2016
- Published Online:
- May 2017
- ISBN:
- 9780226401744
- eISBN:
- 9780226401911
- Item type:
- chapter
- Publisher:
- University of Chicago Press
- DOI:
- 10.7208/chicago/9780226401911.003.0008
- Subject:
- Philosophy, Philosophy of Science
The concept of chance, when used to characterize genetic mutation, has often been analyzed and defined from the evolutionary point of view, i.e., looking at the relationship between mutation, ...
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The concept of chance, when used to characterize genetic mutation, has often been analyzed and defined from the evolutionary point of view, i.e., looking at the relationship between mutation, selection, and adaptation. More precisely, chance mutation in this sense means that the mutation is not specifically provoked with a view to the adaptation of the organism concerned. However, genetic mutations, as other sources of biological variation (e.g., recombination), are said to be “chancy” or “random” events from the molecular point of view as well, and no philosophical analysis of this discourse has been developed until now. Which notion of chance is invoked in this context? The present chapter provides an answer to this question by introducing and defining two notions of randomness: “strong randomness” and “weak randomness”. On the basis of recent research advances on the mutational process and its biased character, I show that all genetic mutations are “weakly random” molecular events. I conclude the chapter by replying to three possible objections that might be raised against my view.Less
The concept of chance, when used to characterize genetic mutation, has often been analyzed and defined from the evolutionary point of view, i.e., looking at the relationship between mutation, selection, and adaptation. More precisely, chance mutation in this sense means that the mutation is not specifically provoked with a view to the adaptation of the organism concerned. However, genetic mutations, as other sources of biological variation (e.g., recombination), are said to be “chancy” or “random” events from the molecular point of view as well, and no philosophical analysis of this discourse has been developed until now. Which notion of chance is invoked in this context? The present chapter provides an answer to this question by introducing and defining two notions of randomness: “strong randomness” and “weak randomness”. On the basis of recent research advances on the mutational process and its biased character, I show that all genetic mutations are “weakly random” molecular events. I conclude the chapter by replying to three possible objections that might be raised against my view.
David Sepkoski
- Published in print:
- 2012
- Published Online:
- February 2013
- ISBN:
- 9780226748559
- eISBN:
- 9780226748580
- Item type:
- chapter
- Publisher:
- University of Chicago Press
- DOI:
- 10.7208/chicago/9780226748580.003.0006
- Subject:
- Biology, Paleontology
Paleobiology transformed from a loose movement with a fairly uncoordinated set of goals to a bonafide sub-discipline with an active institutional and intellectual agenda. The dynamics of this shift ...
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Paleobiology transformed from a loose movement with a fairly uncoordinated set of goals to a bonafide sub-discipline with an active institutional and intellectual agenda. The dynamics of this shift are complex, and involve theoretical, institutional, and pedagogical factors. One of the striking features of the history of paleobiology is the extent to which the movement came to be so closely associated with a few signature theories. Over the time paleobiology emerged as a fairly casual label used to describe a broad approach to an array of questions surrounding the application of fossil data to problems in evolutionary biology. There is a set of practices that can be associated with this approach (such as quantitative analysis, modeling, and ecological/biogeographical theory), there is no unifying theoretical basis for paleobiology, nor did the term “paleobiology” have a distinct disciplinary identity within paleontology. However, paleobiology became closely associated with a prominent theory—known as the theory of punctuated equilibria—and the study of macroevolution as a dominant conceptual framework. Punctuated equilibria have two important roles. First, its invocation of Wrightian stochasticity provided inspiration for a number of studies that more deeply probed the influence of random or nondirectional elements in evolution as seen in the fossil record. Second, it acted as a model of a typoe of paleontology that could break the grip of Darwin's dilemma and could offer a route to bringing paleontology into the mainstream of evolutionary biology.Less
Paleobiology transformed from a loose movement with a fairly uncoordinated set of goals to a bonafide sub-discipline with an active institutional and intellectual agenda. The dynamics of this shift are complex, and involve theoretical, institutional, and pedagogical factors. One of the striking features of the history of paleobiology is the extent to which the movement came to be so closely associated with a few signature theories. Over the time paleobiology emerged as a fairly casual label used to describe a broad approach to an array of questions surrounding the application of fossil data to problems in evolutionary biology. There is a set of practices that can be associated with this approach (such as quantitative analysis, modeling, and ecological/biogeographical theory), there is no unifying theoretical basis for paleobiology, nor did the term “paleobiology” have a distinct disciplinary identity within paleontology. However, paleobiology became closely associated with a prominent theory—known as the theory of punctuated equilibria—and the study of macroevolution as a dominant conceptual framework. Punctuated equilibria have two important roles. First, its invocation of Wrightian stochasticity provided inspiration for a number of studies that more deeply probed the influence of random or nondirectional elements in evolution as seen in the fossil record. Second, it acted as a model of a typoe of paleontology that could break the grip of Darwin's dilemma and could offer a route to bringing paleontology into the mainstream of evolutionary biology.
