RT journal article
T1 Accounting for uncertainty in dormant life stages in stochastic demographic models
A1 Simpson Paniw, María
A1 Quintana Ascencio, Pedro F.
A1 Ojeda Copete, Fernando
A1 Salguero Gómez, Roberto
A2 Biología
AB Dormant life stages are often critical for population viability in stochastic environments, but accurate field data characterizingthem are difficult to collect. Such limitations may translate into uncertainties in demographic parameters describing thesestages, 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 ofdormant life stages and 2) test whether such estimates provide more robust inferences about population dynamics. Webuilt integral projection models (IPMs) for a fire-adapted, carnivorous plant species using a Bayesian framework to estimateuncertainty in parameters of three vital rates of dormant seeds – seed-bank ingression, stasis and egression. We used stochasticpopulation projections and elasticity analyses to quantify the relative sensitivity of the stochastic population growth rate(log ls) to changes in these vital rates at different fire return intervals. We then ran stochastic projections of log ls for 1000posterior samples of the three seed-bank vital rates and assessed how strongly their parameter uncertainty propagated intouncertainty in estimates of log ls and the probability of quasi-extinction, Pq(t). Elasticity analyses indicated that changesin seed-bank stasis and egression had large effects on log ls across fire return intervals. In turn, uncertainty in the estimatesof these two vital rates explained50% of the variation in log ls estimates at several fire-return intervals. Inferences aboutpopulation 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 isoften limited, failing to account for parameter uncertainty in population models may result in incorrect interpretations ofpopulation viability.
PB Blackwell Publishing Ltd
SN 1600-0706
YR 2017
FD 2017
LK http://hdl.handle.net/10498/36671
UL http://hdl.handle.net/10498/36671
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-nov-2026