Data from: Accounting for uncertainty in dormant life stages in stochastic demographic models

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.在随机环境中，生物的休眠生命阶段通常对种群存续至关重要，但精准表征这类生命阶段的野外实地数据往往难以获取。此类数据局限可能导致描述这些生命阶段的种群统计参数存在不确定性，进而在探究种群对环境变化的响应时引发误差传播。本研究在现有方法基础上进行拓展：1）采用数据驱动方法估算休眠生命阶段的生命率（vital rates）参数不确定性；2）检验此类估算能否为种群动态提供更稳健的统计推断。本研究以一种适应火生境的食肉植物物种为研究对象，构建积分投影模型（Integral Projection Models, IPMs），借助贝叶斯框架（Bayesian framework）估算休眠种子三项生命率的参数不确定性：种子库迁入（seed-bank ingression）、种子库留存（seed-bank stasis）与种子库迁出（seed-bank egression）。本研究通过随机种群投影与弹性分析（elasticity analyses），量化不同火间隔期下，随机种群增长率（log λs）对上述三项生命率变化的相对敏感性。随后，针对三项种子库生命率的1000个后验样本开展log λs的随机投影，并评估这些参数的不确定性在多大程度上传播至log λs估算值与准灭绝概率（quasi-extinction probability, Pq(t)）的不确定性之中。弹性分析结果显示，在所有火间隔期下，种子库留存与种子库迁出的变化对log λs均具有显著影响。进一步分析表明，这两项生命率的估算不确定性在多个火间隔期下，可解释log λs估算值变异的50%以上。随着火间隔期延长，关于种群存续的推断不确定性随之升高，当考虑参数不确定性时，准灭绝概率Pq(t)的估算值最高可高出20%。本研究结果表明，对于存在休眠阶段、且野外数据往往较为匮乏的物种而言，若种群模型未纳入参数不确定性考量，可能会导致对种群存续的解读出现偏差。