Matrix Population Models: Construction, Analysis, and Interpretation
Matrix Population Models, Second Edition, is a comprehensive treatment of matrix population models and their applications in ecology and demography. It begins with simple cases, presented in detail so that beginning students can learn how to use these powerful models. It goes on to cover advanced topics in stochastic and nonlinear models. Analytical methods and theoretical issues are illustrated with empirical examples throughout. The decade since the publication of the First Edition of this book has seen enormous progress in the theory and application of matrix population models. The new edition includes greatly expanded treatment of stochastic and density-dependent models, sensitivity analysis, and statistical inference, and new chapters on parameter estimation, structured population models, demographic stochasticity, and applications of matrix models in conservation biology. Matrix Population Models, Second Edition, is an indispensable reference for graduate students and researchers in ecology, population biology, conservation biology, and human demography.
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AgeClassified Matrix Models
StageClassified Life Cycles
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adult age class age distribution age-classified analysis applied approach approximation assume begin bifurcation birth bootstrap calculated called Caswell changes Chapter column complex Consider contributions convergence corresponding cycle cycle graph death defined demographic density depends derivatives described determined distribution dominant dynamics effects eigenvalues eigenvectors elasticity entries environment environmental equation equilibrium estimate et al example expected extinction fate females fertility Figure function given gives grow important increase independent individuals initial interaction interval loop matrix mean measure methods mortality observed obtained offspring parameters periodic perturbation plants population growth rate positive possible predicted probability problem produce projection proportional random reduces reproductive sample seeds selection sensitivity shows species stable stage statistical stochastic structure Suppose survival tion transition variable variance vector vital rates zero