Ecologically realistic estimates of maximum population growth using informedBayesian priors

Abstract

SummaryPhenomenological density‐feedback models estimate parameters such as carrying capacity (K) and maximum population growth rate (rm) from time series of abundances. However, most series represent fluctuations aroundKwithout extending to low abundances and are thus uninformative aboutrm.We used informative prior distributions of maximum population growth rate,p(rm), to estimateBayesian posterior distributions inRicker andθ‐logistic models fitted to abundance series for 36 mammal species. We also used state‐space models to account for observation errors.We used two data sets of population growth rates from different mammal species with associated allometry (body mass) and demography (age at first reproduction) data to predictrmprior distributions.We assessed patterns of differences in posterior means () from models fitted with and without informative priors and used the deviance information criterion (DIC) to rank models for each species.Differences in posteriorfrom models with informative vs. vague priors co‐varied with the prior mean () forRicker models, but only posteriorco‐varied with priorinθ‐logistic models. Informative‐priorRicker models ranked higher than (81% of species), or equivalent to (all species), those with vague priors, which decreased to 70% ranking higher for state‐space models. Prior information also improved the precision ofby 13–45% depending on model and prior.Posteriorwere highly sensitive topriors forθ‐logistic models (halving and doubling prior mean gave −56% and 95% changes in, respectively) and less sensitive forRicker models (−25% and 35% changes in).Our results show that fitting density‐feedback models without prior information gives biologically unrealisticestimates in most cases, even from simpleRicker models. However, sensitivity analysis shows that highrm − θcorrelation inθ‐logistic models means the fit is largely determined by the prior, precluding the use of this model for most census data. Our findings are supported by applying models to simulated time series of abundance. Prior knowledge of species' life history can provide more ecologically realistic estimates (matching theoretical predictions) of regulatory dynamics even in the absence of detailed demographic data, thereby potentially improving predictions of extinction risk.