Population dynamics for conservation / Louis W. Botsford, J. Wilson White, and Alan Hastings
- Oxford scholarship online
- Machine generated contents note: 1.Philosophical approach to population modeling -- 1.1.Simplicity versus complexity, and four characteristics of models -- 1.2.Logical basis for population modeling -- 1.2.1.Deductive reasoning and the scientific uses of modeling -- 1.2.2.Inductive reasoning and practical applications of modeling -- 1.2.3.Consequences of deductive and inductive logic for population dynamics -- 1.3.The state of a system -- 1.3.1.Models of i-states and p-states -- 1.3.2.Individual based models (IBM) -- 1.4.Uncertainty and population models -- 1.5.Levels of integration in ecology -- 1.6.State of the field -- 2.Simple population models -- 2.1.The first population model---the rabbit problem -- 2.2.Simple linear models (exponential or geometric growth) -- 2.3.Simple nonlinear models (logistic-type models) -- 2.3.1.Continuous-time logistic models -- 2.3.2.Discrete-time logistic models -- 2.4.Illustrating population concepts with simple models -- 2.4.1.Illustrating dynamic stability with simple, linear, discrete-time models -- 2.4.2.Dynamic stability of simple nonlinear models -- 2.4.3.Quasi-extinction in random environments with a discrete-time linear simple model -- 2.4.4.What does the simple logistic model tell us about managing for sustainable fisheries? -- 2.5.What have we learned in Chapter 2? -- 3.Linear, age-structured models and their long-term dynamics -- 3.1.The continuity equation and the M'Kendrick/von Foerster model -- 3.1.1.Solving the M'Kendrick/von Foerster model -- 3.2.The renewal equation---Lotka's model -- 3.3.The Leslie matrix -- 3.3.1.Solving the Leslie model -- 3.3.2.The stable age distribution -- 3.4.Mathematical theory underlying the Leslie matrix -- 3.4.1.The Perron-Frobenius theorem -- 3.5.Sensitivity and elasticity of eigenvalues: the Totoaba example -- 3.6.Handling the oldest age classes: age-lumping, terminal age classes, and post-reproductive ages -- 3.7.What have we learned in Chapter 3? -- 4.Age-structured models: Short-term transient dynamics -- 4.1.The other eigenvalues -- 4.1.1.An example of cyclic transient dynamics -- 4.2.How the dependence of reproduction on age influences these cycles -- 4.2.1.Semelparous species and imprimitive Leslie matrices -- 4.2.2.Cycle period: the mean age of reproduction and the echo effect -- 4.2.3.How age structure influences the occurrence of cycles -- 4.2.4.Convergence to the asymptotic dynamics -- 18.104.22.168.Rate of convergence to the stable age distribution: the damping ratio -- 22.214.171.124.The distance to the stable age distribution -- 126.96.36.199.Example: adaptive management of marine protected areas -- 4.3.Transient responses to ongoing environmental variability -- 4.3.1.Determining the equilibrium of a nonlinear age-structured population -- 4.3.2.The frequency response of a population -- 4.3.3.Cohort resonance -- 188.8.131.52.Analysis of cohort resonance -- 184.108.40.206.Cohort resonance: effects of life history, fishing, and eigenvalues -- 4.3.4.Extreme period-T cycles: cyclic dominance in sockeye salmon -- 4.4.What have we learned in Chapter 4? -- 5.Size-structured models -- 5.1.The size-structured M'Kendrick/von Foerster model -- 5.1.1.The solution to the size-structured M'Kendrick/von Foerster model -- 5.1.2.Adding reproduction to obtain a complete population model -- 5.2.Stand distributions -- 5.3.Cohort distributions -- 5.4.Numerical methods -- 5.4.1.Grid-based method -- 5.4.2.The escalator--boxcar train -- 5.4.3.Integral projection models -- 5.5.What have we learned in Chapter 5? -- 6.Stage-structured models -- 6.1.Biological processes -- 6.2.History of development of stage-structured matrix models -- 6.2.1.Early development of stage models -- 6.2.2.Early successes in stage-structured modeling -- 6.2.3.Early applications -- 6.2.4.Stochastic stage-structured models -- 6.3.Problems with stage-structured models -- 6.4.Possible better alternatives to stage-structured models -- 6.5.Replacement in stage-structured models -- 6.6.Delay equations -- 6.7.What have we learned in Chapter 6? -- 7.Age-structured models with density-dependent recruitment -- 7.1.Local stability and 2T cycles -- 7.1.1.Local stability analysis -- 7.1.2.An example: 2T cycles in Dungeness crab -- 7.2.The simplest general model of age-structured density dependence -- 7.3.Cycles in Dungeness crab: models and data -- 7.4.An intertidal barnacle, Balanus glandula -- 7.5.Cannibalism and the flour beetle, Tribolium -- 7.6.Effects of equilibrium conditions -- 7.6.1.Single-sex harvest -- 7.6.2.Multiple equilibria -- 7.7.What have we learned in Chapter 7? -- 8.Age-structured models in a random environment -- 8.1.The small fluctuation approximation (SFA) -- 8.2.The first crossing solution -- 8.3.A more general version of the growth of variability -- 8.4.Does the SFA/diffusion approximation work? Totoaba as an example -- 8.5.Color of the random environmental variability -- 8.6.Application of SFA to population data -- 8.7.State of the science quantifying extinction risk at the turn of the century -- 8.8.Perils of using stage models to characterize extinction risk -- 8.9.What have we learned in Chapter 8? -- 9.Spatial population dynamics -- 9.1.Modeling the spread of a population -- 9.1.1.The reaction-diffusion model -- 9.1.2.The asymptotic rate of spread -- 9.1.3.Leptokurtic dispersal -- 9.1.4.When diffusion is not a good representation of movement -- 9.2.Population persistence in aquatic habitats -- 9.2.1.The KISS model: persistence of a patch of plankton -- 9.2.2.The drift paradox -- 9.3.Metapopulations -- 9.3.1.The Levins model -- 9.3.2.Incidence function models -- 9.3.3.Patch value in the incidence function model -- 9.4.Models with internal patch dynamics: structure in space and age -- 9.4.1.Metapopulation persistence: replacement over space -- 9.4.2.Population persistence in heterogeneous space -- 9.5.Spatial variability across populations -- 9.6.What have we learned in Chapter 9? -- 10.Applications to conservation biology -- 10.1.Lessons from earlier chapters -- 10.2.Probabilities of extinction: the problem of measurement uncertainty -- 10.3.Probabilities of extinction: the importance of environmental spectra -- 10.4.Replacement as an extinction metric -- 10.5.An example with abundance, replacement, and measurement error -- 10.6.Comparative studies: Pacific salmon -- 10.7.Addressing exogenous variability: drivers and errors -- 10.8.Population diversity -- 10.9.What have we learned in Chapter 10? -- 11.Population dynamics in marine conservation -- 11.1.Three models from the 1950s -- 11.1.1.The logistic fishery model -- 11.1.2.The single cohort model (also known as the dynamic pool model, yield-per-recruit model) -- 11.1.3.The stock and recruitment model -- 11.1.4.Complete age-structured models: linking cohorts with a stock-recruit curve -- 11.2.Replacement in fully age-structured fishery models -- 11.2.1.Stock-recruit curves, lifetime egg production (LEP), and spawning per recruit (SPR) -- 11.2.2.Replacement and optimal fishery yield -- 11.3.The precautionary approach and modern fishery management -- 11.3.1.Precautionary management and reference points -- 11.3.2.Managing to avoid overfishing -- 11.4.Spatial management: marine protected areas -- 11.4.1.Strategic models of MPAs -- 11.4.2.Tactical models of marine protected area design -- 11.4.3.Other types of models used in MPA design -- 11.4.4.Adaptive management of MPAs -- 11.5.What have we learned in Chapter 11? -- 12.Thinking about populations -- 12.1.Modeling philosophy and approach -- 12.2.Replacement, an organizing principle -- 12.3.Population responses to time scales of environmental variability -- 12.4.Applying the lessons of population dynamics -- 12.5.What next?.
- This work provides a coherent overview of the theory of single population dynamics, discussing concepts such as population variability, population stability, population viability/persistence, and harvest yield while later chapters address specific applications to conservation and management.
- 9780191818301 (ebook)
- Audience Notes:
- This edition also issued in print: 2019.
- Bibliography Note:
- Includes bibliographical references and index.
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