| Estimating and projecting population trends using population viability analysis (PVA) is crucial to identifying species at risk of extinction and for informing conservation management strategies. The reliability of PVA is highly susceptible to the underlying variability present in the system, the level of error in the data and the type of model used. Furthermore, since evidence indicates that generation time influences population dynamics by altering the variability present in the system, it is expected that PVAs perform differently across species with different life histories. In this study, we evaluated the reliability of predictions from PVA for hypothetical species with different generation times under different levels of environmental variability and uncertainty. PVA predictions were generated from simulated data using both matrix and scalar models and assessed with respect to Rule A.3 under the Red List of the IUCN. We found that the reliability of PVA predictions depended upon the interaction between generation time and growth rate. Specifically, both matrix and scalar models exhibited decent performance for "slow" life histories ( long generation time and low growth rates), although matrix models exhibited a more precautionary tendency. In contrast, we found that for "fast" life histories with short generation times and high growth rates PVA predictions exhibited less reliability, especially under high process and measurement error. In conclusions, our results indicate that caution must be taken when interpreting the results of PVAs with short generation times and high intrinsic growth rates. |