Frequentist analysis of hierarchical models for population dynamics and demographic data

Valpine, Perry de

doi:10.1007/s10336-010-0642-5

Cited by 24 publications

(31 citation statements)

References 91 publications

(96 reference statements)

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“…[43], [44]). Although random-effects models can be analyzed using frequentist or Bayesian methods [45], [46], the frequentist point of view may have a number of advantages [44].…”

Section: Methodsmentioning

confidence: 99%

Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method

et al. 2014

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The differences in demographic and life-history processes between organisms living in the same population have important consequences for ecological and evolutionary dynamics. Modern statistical and computational methods allow the investigation of individual and shared (among homogeneous groups) determinants of the observed variation in growth. We use an Empirical Bayes approach to estimate individual and shared variation in somatic growth using a von Bertalanffy growth model with random effects. To illustrate the power and generality of the method, we consider two populations of marble trout Salmo marmoratus living in Slovenian streams, where individually tagged fish have been sampled for more than 15 years. We use year-of-birth cohort, population density during the first year of life, and individual random effects as potential predictors of the von Bertalanffy growth function's parameters k (rate of growth) and (asymptotic size). Our results showed that size ranks were largely maintained throughout marble trout lifetime in both populations. According to the Akaike Information Criterion (AIC), the best models showed different growth patterns for year-of-birth cohorts as well as the existence of substantial individual variation in growth trajectories after accounting for the cohort effect. For both populations, models including density during the first year of life showed that growth tended to decrease with increasing population density early in life. Model validation showed that predictions of individual growth trajectories using the random-effects model were more accurate than predictions based on mean size-at-age of fish.

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“…[43], [44]). Although random-effects models can be analyzed using frequentist or Bayesian methods [45], [46], the frequentist point of view may have a number of advantages [44].…”

Section: Methodsmentioning

confidence: 99%

Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method

et al. 2014

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“…Despite being very flexible, these computational methods require sufficient training to be applied correctly. As an alternative to the Bayesian approach, several approaches are being developed (Lele et al ., ; Lele & Dennis, ; De Valpine, , ) that deserve further exploration.…”

Section: Discussionmentioning

confidence: 99%

Digging through model complexity: using hierarchical models to uncover evolutionary processes in the wild

Buoro

Prévost

Giménez

2012

J of Evolutionary Biology

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The growing interest for studying questions in the wild requires acknowledging that eco-evolutionary processes are complex, hierarchically structured and often partially observed or with measurement error. These issues have long been ignored in evolutionary biology, which might have led to flawed inference when addressing evolutionary questions. Hierarchical modelling (HM) has been proposed as a generic statistical framework to deal with complexity in ecological data and account for uncertainty. However, to date, HM has seldom been used to investigate evolutionary mechanisms possibly underlying observed patterns. Here, we contend the HM approach offers a relevant approach for the study of eco-evolutionary processes in the wild by confronting formal theories to empirical data through proper statistical inference. Studying eco-evolutionary processes requires considering the complete and often complex life histories of organisms. We show how this can be achieved by combining sequentially all life-history components and all available sources of information through HM. We demonstrate how ecoevolutionary processes may be poorly inferred or even missed without using the full potential of HM. As a case study, we use the Atlantic salmon and data on wild marked juveniles. We assess a reaction norm for migration and two potential trade-offs for survival. Overall, HM has a great potential to address evolutionary questions and investigate important processes that could not previously be assessed in laboratory or short time-scale studies.

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“…These models make a distinction between observed data and true system states by incorporating multiple sources of variation, in particular, environmental stochasticity (process noise) and sampling variation (observation error) (de Valpine andHastings 2002, Calder et al 2003). The main challenge of estimating state-space models has been the development of algorithms that appropriately explore the space of possible true system states from which the data could have been sampled.…”

Section: Introductionmentioning

confidence: 99%

Fitting complex population models by combining particle filters with Markov chain Monte Carlo

Knape

Valpine

2012

Ecology

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Abstract. We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state-space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo (Macropus rufus) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis-Hastings algorithm. We fit three population models to these data; a density-dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random-walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random-walk model is the most parsimonious model. The particle filter Metropolis-Hastings algorithm is a brute-force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm.

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Frequentist analysis of hierarchical models for population dynamics and demographic data

Cited by 24 publications

References 91 publications

Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method

Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method

Digging through model complexity: using hierarchical models to uncover evolutionary processes in the wild

Fitting complex population models by combining particle filters with Markov chain Monte Carlo

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