There is a vigorous debate about the capacity of conservation biology, as a scientific discipline, to effectively contribute to actions that preserve and restore biodiversity. Various factors may be responsible for the current great divide that exists between conservation research and action. Part of the problem may be a lack of involvement by conservation scientists in actually conducting or helping implement concrete conservation actions, yet scientists' involvement can be decisive for successful implementation, as illustrated here by the rapid recovery of an endangered hoopoe population in the Swiss Alps after researchers decided to implement the corrective measures they were proposing themselves. We argue that a conceptual paradigm shift should take place in the academic conservation discipline toward more commitment on the part of researchers to turn conservation science into conservation action. Practical implementation should be regarded as an integrated part of scientific conservation activity, as it actually constitutes the ultimate assessment of the effectiveness of the recommended conservation guidelines, and should be rewarded as such.

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.

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