10.5061/DRYAD.RQ7T3
Paniw, Maria
Depto de Biología - ceiA3; Univ. de Cadiz; Campus Río San Pedro ES-11510
Puerto Real Spain
Quintana-Ascencio, Pedro F.
University of Central Florida
Ojeda, Fernando
University of Sheffield
Salguero-Gómez, Roberto
University of Queensland
University of Sheffield
Data from: Accounting for uncertainty in dormant life stages in stochastic
demographic models
Dryad
dataset
2016
Bayesian models
stochastic demography
Integral Projection Models
2020-07-13T00:00:00Z
en
https://doi.org/10.1111/oik.03696
1563177 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Dormant life stages are often critical for population viability in
stochastic environments, but accurate field data characterizing them are
difficult to collect. Such limitations may translate into uncertainties in
demographic parameters describing these stages, which then may propagate
errors in the examination of population-level responses to environmental
variation. Expanding on current methods, we 1) apply data-driven
approaches to estimate parameter uncertainty in vital rates of dormant
life stages and 2) test whether such estimates provide more robust
inferences about population dynamics. We built integral projection models
(IPMs) for a fire-adapted, carnivorous plant species using a Bayesian
framework to estimate uncertainty in parameters of three vital rates of
dormant seeds – seed-bank ingression, stasis and egression. We used
stochastic population projections and elasticity analyses to quantify the
relative sensitivity of the stochastic population growth rate (log λs) to
changes in these vital rates at different fire return intervals. We then
ran stochastic projections of log λs for 1000 posterior samples of the
three seed-bank vital rates and assessed how strongly their parameter
uncertainty propagated into uncertainty in estimates of log λs and the
probability of quasi-extinction, Pq(t). Elasticity analyses indicated that
changes in seed-bank stasis and egression had large effects on log λs
across fire return intervals. In turn, uncertainty in the estimates of
these two vital rates explained > 50% of the variation in log λs
estimates at several fire-return intervals. Inferences about population
viability became less certain as the time between fires widened, with
estimates of Pq(t) potentially > 20% higher when considering
parameter uncertainty. Our results suggest that, for species with dormant
stages, where data is often limited, failing to account for parameter
uncertainty in population models may result in incorrect interpretations
of population viability.
dataDroso - census dataDemographic transitions of Drosophyllum lusitanicum
populations recorded in annual censuses (from 2011 to 2015) in five
populations. These data are used to quantify vital rates of above-ground
individuals.dataDroso.csvdataDrosoSB - seed bankSeed fates (in a binary
format) inferred from two experiments. These data are used to quantify the
transitions related to the seed bank and associated parameter
uncertainties.dataDrosoSB.csvBayModel - Bayesian vital rate GLMMsExecutes
and saves the results of a Bayesian model quantifying all vital rates;
illustrates basic diagnostics that can be run on the results of an MCMC
run (i.e., the posterior parameter distribution) to check for model
convergence and autocorrelation of the posterior samples.BayModel.RmcmcOUT
- parameter samplesIn case the reader wishes to forego the step of fitting
the Bayesian models, we provided a mcmcOUT.csv file with 1000 posterior
parameter values for each of the parameters estimated with Bayesian models
using uninformative priorsmcmcOUT.csvmakeIPMDemonstrates how to construct
IPMs including continuous and discrete (seed bank) transitions for (A)
mean parameter values and (B) from the parameter distributions of the
Bayesian models; saves IPMs for all parameters related to seed-bank
ingression, stasis, and ingression. The code is based on the supporting
material in Ellner and Rees (2006), Am. Nat., 167, 410-428perturbVR -
vital rate perturbationsDemonstrates how to construct IPMs from perturbed
vital rates. Each IPM is obtained by (a) perturbing a vital rate by its
mean or standard deviation (see makeVRmu.R on constructing mean vital-rate
kernels) and (b) constructing a new IPM kernel incorporating the perturbed
vital rateperturbVR.RmakeIPMmufunction to constructs IPMs for average
environmentsmakeVRmufunctions to constructs vital-rate kernels for average
environments.sLambdaSimul - stochastic lambda simulationsRuns simulations,
based on different fire return intervals, of the stochastic population
growth rate using IPMs constructed (A) from mean parameter values, (B)
from perturbed vital rates, and (C) for each posterior sample of the
parameters describing seed-bank ingression (goSB), stasis (staySB) and
egression (outSB); calculates the stochastic population growth rate, its
elasticities, and the probability of quasi-extinction at time t. The
structure of the code is based on Tuljapurkar et al. (2003), Am. Nat.,
162, 489-502 and Trotter et al. (2013), Methods Ecol. Evol., 4,
290-298.sLambdaSimul.RsLambdaRmpi - stochastic simulations on parallel
processorsImplements the simulations of the stochastic population growth
rate using parallel processing, where simulations are split into different
processors of a supercomputer to greatly speed up computational
time.sLambdaRmpi.R