Premium
Accounting for uncertainty in dormant life stages in stochastic demographic models
Author(s) -
Paniw Maria,
QuintanaAscencio Pedro F.,
Ojeda Fernando,
SalgueroGómez Roberto
Publication year - 2017
Publication title -
oikos
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.672
H-Index - 179
eISSN - 1600-0706
pISSN - 0030-1299
DOI - 10.1111/oik.03696
Subject(s) - vital rates , population , statistics , econometrics , population model , bayesian probability , ecology , population viability analysis , mathematics , population growth , biology , habitat , demography , endangered species , sociology
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 λ s ) to changes in these vital rates at different fire return intervals. We then ran stochastic projections of log λ s 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 λ s and the probability of quasi‐extinction, P q(t) . Elasticity analyses indicated that changes in seed‐bank stasis and egression had large effects on log λ s across fire return intervals. In turn, uncertainty in the estimates of these two vital rates explained > 50% of the variation in log λ s estimates at several fire‐return intervals. Inferences about population viability became less certain as the time between fires widened, with estimates of P q ( 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.