Integration of national demographic-disturbance relationships and local data can improve caribou population viability projections and inform monitoring decisions

Across fifty-eight boreal caribou study areas in Canada, survival and recruitment decrease with the percentage of the study area that is disturbed. There is variation in demographic rates among study areas, particularly where anthropogenic disturbance is low, but no populations inhabiting areas with high anthropogenic disturbance are considered viable. Demographic projections derived from local population-specific data are uncertain for populations with limited monitoring. We propose a simple Bayesian population model that integrates prior information from a national analysis of demographic-disturbance relationships with available local demographic data to improve population viability projections, and to reduce the risk that a lack of local data will be used as a reason to delay conservation action. The model also acknowledges additional uncertainty and potential bias due to misidentification of sex or missing calves, through a term derived from a simple model of the recruitment survey observation process. We combine this Bayesian model with simulations of plausible population trajectories in a value of information analysis framework to show how the need for local monitoring varies with landscape condition, and to assess the ability of alternative monitoring scenarios to reduce the risk of errors in population viability projections. Where anthropogenic disturbance is high, reasonably accurate status projections can be made using only national demographic-disturbance relationships. At lower disturbance levels where initial uncertainty is high local data improve accuracy but each additional year of monitoring provides less new information. The estimated probability of viability indicates whether more information is needed to improve accuracy of population viability projections. • Across fifty-eight boreal caribou study areas in Canada, both survival and recruitment decrease with the percentage of the study area that is disturbed. • We show how Bayesian integration of local data and prior information from a national analysis of demographic-disturbance relationships can reduce uncertainty about the viability of sparsely monitored populations. • Integrating prior information about demographic-disturbance relationships clarifies that no additional information is needed to project population viability where disturbance is high and there are no actions to support recovery. • At lower disturbance levels where uncertainty is higher there are diminishing returns on additional monitoring, and the estimated probability of viability indicates whether more information is needed. • We introduce a method for acknowledging potential errors in recruitment surveys to help clarify where and when these errors are likely to alter conclusions about population viability.