Prior scientific knowledge inspires ecological research, hypotheses and debate but it’s rarely used to explicitly formulate predictive models. Bayesian statistics provide a formal way to include informative priors and evaluate their influence on parameter estimates. In this study, Tara Martin and colleagues use case studies of the influence of overabundant deer on the abundance of bird species on the Gulf Island, San Juan and Haida Gwaii archipelagos of western North America. They demonstrate the utility of informative priors and Bayesian modelling to determine the consequences of overabundance. They found that by including informative priors about deer browsing impacts on bird species from a study undertaken in Haida Gwaii, the precision of estimates from a similar study undertaken in the Gulf and San Juan archipelagos could be significantly increased.
Uncertainty about regional ecological impacts underpins the failure of many agencies to take management actions. The researchers demonstrate that informative priors, when used logically and transparently, can be a highly cost effective way to increase understanding of ecological processes. In some cases, it may be the only way to inform decision-making when scarce resources limit long-term field research or the threat is so great that immediate action is required.
For several bird species examined here, the inclusion of informative priors strengthened the conclusion that their populations were negatively affected by changes in vegetation structure caused by deer browsing. Their findings suggest that deer browsing in these island archipelagos must be managed if the risk of local extinctions among native flora and fauna is to be avoided.
More info: Tara.Martin@csiro.au
Martin TG, P Arcese, PM Kuhnert, AJ Gaston & JL Martin (2013). Prior information reduces uncertainty about the consequences of deer overabundance on forest birds. Biological Conservation 165: 10-17.