10.5061/DRYAD.DB428
Elderd, Bret D.
Louisiana State University of Alexandria
Miller, Tom E. X.
Rice University
Data from: Quantifying demographic uncertainty: Bayesian methods for
integral projection models (IPMs)
Dryad
dataset
2015
Opuntia imbricata [Hawarth] D.C.
hierarchical Bayes
process error
model selection
parameter estimation
Markov chain Monte Carlo
2020-06-27T00:00:00Z
en
https://doi.org/10.1890/15-1526.1
237258 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Integral projection models (IPMs) are a powerful and popular approach to
modeling population dynamics. Generalized linear models form the
statistical backbone of an IPM. These models are typically fit using a
frequentist approach. We suggest that hierarchical Bayesian statistical
approaches offer important advantages over frequentist methods for
building and interpreting IPMs, especially given the hierarchical nature
of most demographic studies. Using a stochastic IPM for a desert cactus
based on a 10-year study as a worked example, we highlight the application
of a Bayesian approach for translating uncertainty in the vital rates
(e.g., growth, survival, fertility) to uncertainty in population-level
quantities derived from them (e.g., population growth rate). The best-fit
demographic model, which would have been difficult to fit under a
frequentist framework, allowed for spatial and temporal variation in vital
rates and correlated responses to temporal variation across vital rates.
The corresponding posterior probability distribution for the stochastic
population growth rate (λS) indicated that, if current vital rates
continue, the study population will decline with nearly 100% probability.
Interestingly, less-supported candidate models that did not include
spatial variance and vital rate correlations gave similar estimates of λS.
This occurred because the best-fitting model did a much better job of
fitting vital rates to which the population growth rate was weakly
sensitive. The cactus case study highlights several advantages of Bayesian
approaches to IPM modeling, including that they: (1) provide a natural fit
to demographic data, which are often collected in a hierarchical fashion
(e.g., with random variance corresponding to temporal and spatial
heterogeneity); (2) seamlessly combine multiple data sets or experiments;
(3) readily incorporate covariance between vital rates; and, (4) easily
integrate prior information, which may be particularly important for
species of conservation concern where data availability may be limited.
However, constructing a Bayesian IPM will often require the custom
development of a statistical model tailored to the peculiarities of the
sampling design and species considered; there may be circumstances under
which simpler methods are adequate. Overall, Bayesian approaches provide a
statistically sound way to get more information out of hard-won data, the
goal of most demographic research endeavors.
Quantifying demographic uncertainty: Bayesian methods for Integral
Projection Models (IPMs) ParametersComma delimited file (csv) containing
parameter estimates used to construct the posterior distributions for
calculating the stochastic population growth rate and the sensitivities of
the associated vital rates. The first row of the file contains the names
of the parameters.ElderdMiller_cholla.all.params.post.csv
New Mexico
Sevilleta National Wildlife Refuge