DOI: 10.5061/dryad.rq7t3
title: Data from: Accounting for uncertainty in dormant life stages in stochastic demographic models

description: 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 &gt; 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 &gt; 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.
description: 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