Hal Caswell

This open access book shows how to use sensitivity analysis in demography. It presents new methods for individuals, cohorts, and populations, with applications to humans, other animals, and plants. The analyses are based on matrix formulations of age-classified, stage-classified, and multistate population models. Methods are presented for linear and nonlinear, deterministic and stochastic, and time-invariant and time-varying cases. Readers will discover results on the sensitivity of statistics of longevity, life disparity, occupancy times, the net reproductive rate, and statistics of Markov chain models in demography. They will also see applications of sensitivity analysis to population growth rates, stable population structures, reproductive value, equilibria under immigration and nonlinearity, and population cycles. Individual stochasticity is a theme throughout, with a focus that goes beyond expected values to include variances in demographic outcomes. The calculations are easily and accurately implemented in matrix-oriented programming languages such as Matlab or R. Sensitivity analysis will help readers create models to predict the effect of future changes, to evaluate policy effects, and to identify possible evolutionary responses to the environment. Complete with many examples of the application, the book will be of interest to researchers and graduate students in human demography and population biology. The material will also appeal to those in mathematical biology and applied mathematics.; This open access book provides a comprehensive presentation of sensitivity analysis for demographic models Applicable to populations of humans, other animals, and plants Develops mathematical theory and shows examples of application Considers all types of population models (linear and nonlinear, deterministic and stochastic, age-classified and stage-classified) This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

This open access book shows how to use sensitivity analysis in demography. It presents new methods for individuals, cohorts, and populations, with applications to humans, other animals, and plants. The analyses are based on matrix formulations of age-classified, stage-classified, and multistate population models. Methods are presented for linear and nonlinear, deterministic and stochastic, and time-invariant and time-varying cases. Readers will discover results on the sensitivity of statistics of longevity, life disparity, occupancy times, the net reproductive rate, and statistics of Markov chain models in demography. They will also see applications of sensitivity analysis to population growth rates, stable population structures, reproductive value, equilibria under immigration and nonlinearity, and population cycles. Individual stochasticity is a theme throughout, with a focus that goes beyond expected values to include variances in demographic outcomes. The calculations are easily and accurately implemented in matrix-oriented programming languages such as Matlab or R. Sensitivity analysis will help readers create models to predict the effect of future changes, to evaluate policy effects, and to identify possible evolutionary responses to the environment. Complete with many examples of the application, the book will be of interest to researchers and graduate students in human demography and population biology. The material will also appeal to those in mathematical biology and applied mathematics.; This open access book provides a comprehensive presentation of sensitivity analysis for demographic models Applicable to populations of humans, other animals, and plants Develops mathematical theory and shows examples of application Considers all types of population models (linear and nonlinear, deterministic and stochastic, age-classified and stage-classified) This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

This open access book shows how to use sensitivity analysis in demography. It presents new methods for individuals, cohorts, and populations, with applications to humans, other animals, and plants. The analyses are based on matrix formulations of age-classified, stage-classified, and multistate population models. Methods are presented for linear and nonlinear, deterministic and stochastic, and time-invariant and time-varying cases. Readers will discover results on the sensitivity of statistics of longevity, life disparity, occupancy times, the net reproductive rate, and statistics of Markov chain models in demography. They will also see applications of sensitivity analysis to population growth rates, stable population structures, reproductive value, equilibria under immigration and nonlinearity, and population cycles. Individual stochasticity is a theme throughout, with a focus that goes beyond expected values to include variances in demographic outcomes. The calculations are easily and accurately implemented in matrix-oriented programming languages such as Matlab or R. Sensitivity analysis will help readers create models to predict the effect of future changes, to evaluate policy effects, and to identify possible evolutionary responses to the environment. Complete with many examples of the application, the book will be of interest to researchers and graduate students in human demography and population biology. The material will also appeal to those in mathematical biology and applied mathematics.

ButIacceptedtheinvitation.Key?tz'ssuggestionthatweincorporatesome of my earlier work on matrix population models seemed like a good way to complement the methods presented in the book, and to expand the range of their applications. I am a demographer of plants and nonhuman animals. Some would call this an oxymoron, since the Greek rootdemos refers to people, and that's us. But there are precedents for taking more inclusive de?nitions of the Greek.Ecologyandeconomics,forexample,bothcomefromtherootoikos, referring to the household. Interpretingdemos as referring to individuals, whether they are persons or not, lets demography apply across species. There is a long tradition of such crossover. Alfred J. Lotka is acknowledged as a founding father of both demography and ecology. Raymond Pearl used demographic methods to analyze the e?ects of toxic substances and cro- ing on fruit ?ies. Lee (1987) compared the density-dependence of the vital rates of human and non-human animals. Today, in studies of senescence, reproduction, and individual heterogeneity, the boundaries between animal and human studies are becoming increasingly blurred (Wachter et al. 1997, Carey 2003, Wachter and Bulatao 2003, Carey and Tuljapurkar 2003).

This book provides a complete treatment of matrix population models and their applications in ecology and demography. It is written for graduate students and researchers in ecology, population biology, conservation biology and human demography.

ButIacceptedtheinvitation.Key?tz'ssuggestionthatweincorporatesome of my earlier work on matrix population models seemed like a good way to complement the methods presented in the book, and to expand the range of their applications. I am a demographer of plants and nonhuman animals. Some would call this an oxymoron, since the Greek rootdemos refers to people, and that's us. But there are precedents for taking more inclusive de?nitions of the Greek.Ecologyandeconomics,forexample,bothcomefromtherootoikos, referring to the household. Interpretingdemos as referring to individuals, whether they are persons or not, lets demography apply across species. There is a long tradition of such crossover. Alfred J. Lotka is acknowledged as a founding father of both demography and ecology. Raymond Pearl used demographic methods to analyze the e?ects of toxic substances and cro- ing on fruit ?ies. Lee (1987) compared the density-dependence of the vital rates of human and non-human animals. Today, in studies of senescence, reproduction, and individual heterogeneity, the boundaries between animal and human studies are becoming increasingly blurred (Wachter et al. 1997, Carey 2003, Wachter and Bulatao 2003, Carey and Tuljapurkar 2003).