Can population models be a useful tool for evaluating the status of data-deficient species?

• Published in: Global ecology and conservation Vol. 48; p. e02708
• Main Authors: Cisterne, A., Pay, J., Crates, R., Stojanovic, D.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2023
• Subjects: Data-deficient; Owl; Population viability analysis; Rodenticide; Sensitivity testing; Sensitivity testing; Data-deficient; Owl; Rodenticide; Population viability analysis
• ISSN: 2351-9894; 2351-9894
• DOI: 10.1016/j.gecco.2023.e02708

Population and life history data remains sparse for many species, with low prospects for improvement due to the costs of research and difficulties associated with gathering field data. However, data-deficient species may nevertheless face substantial threats. Effective approaches are required to quantitatively assess threats and the effects of data uncertainty in the assessments. We explore the use of population models to assess the impact of uncertain threatening processes in the data-deficient Tasmanian masked owl Tyto novaehollandiae castanops. We addressed data-deficiency with a multi-parameter sensitivity analysis. This showed that reproductive output, adult mortality and juvenile mortality had the greatest impact on population size and growth. Using literature on a related but better studied species, the barn owl (Tyto alba), we then used population models to simulate the impacts of rodenticide exposure by increasing stochastic variation in reproductive output, adult, and juvenile mortality. We investigated rodenticide exposure because, although there is evidence of secondary poisoning of Tasmanian masked owls through consumption of contaminated prey, the effects of potentially widespread exposure on the population are unclear. The effect on population size and growth from increased stochasticity in the rodenticide sensitive parameters was negative. However, the magnitude of impacts depended on the baseline juvenile mortality rate used in each model, highlighting the importance of this uncertain parameter on our assessment of risk. Our study serves as an example of the use of population models for exploring hypothetical scenarios for data-deficient populations. Population models are an important tool for setting priorities for research and sounding a precautionary alarm where potential threats may be concealed by uncertainty.