Use of Integrated Modeling to Enhance Estimates of Population Dynamics Obtained from Limited Data

TLDR

Demographic data of rare and endangered species are often too sparse to estimate vital rates and population size with sufficient precision, but combining different sources of demographic data into one statistical model holds promise. We applied Bayesian integrated population modeling to data from a colony of the endangered greater horseshoe bat, incorporating dusk counts of subadults and adults, newborns from 1991–2005, and recapture data from 2004–2005. The integrated model revealed constant survival across sexes and time, high survival rates (first year 0.49, adults 0.91), low fecundity (0.74), a 4.4 % annual growth, 92 adults in 2005, and demonstrated that separate analyses would have produced imprecise or biased estimates.

Abstract

Abstract: Demographic data of rare and endangered species are often too sparse to estimate vital rates and population size with sufficient precision for understanding population growth and decline. Yet, the combination of different sources of demographic data into one statistical model holds promise. We applied Bayesian integrated population modeling to demographic data from a colony of the endangered greater horseshoe bats (Rhinolophus ferrumequinum). Available data were the number of subadults and adults emerging from the colony roost at dusk, the number of newborns from 1991 to 2005, and recapture data of subadults and adults from 2004 and 2005. Survival rates did not differ between sexes, and demographic rates remained constant across time. The greater horseshoe bat is a long‐lived species with high survival rates (first year: 0.49 [SD 0.06]; adults: 0.91 [SD 0.02]) and low fecundity (0.74 [SD 0.12]). The yearly average population growth was 4.4% (SD 0.1%) and there were 92 (SD 10) adults in the colony in year 2005. Had we analyzed each data set separately, we would not have been able to estimate fecundity, the estimates of survival would have been less precise, and the estimate of population growth biased. Our results demonstrate that integrated models are suitable for obtaining crucial demographic information from limited data.