When canmodel-based estimates replace surveys ofwildlife populations that span many discretemanagement units?

Published online
14 Jun 2022
Content type
Journal article
Journal title
Ecological Solutions and Evidence

Priadka, P. & Brown, G. S. & Fedy, B. C. & Mallory, F. F.
Contact email(s)

Publication language
Canada & Ontario


1. Monitoring widely distributed species on a budget presents challenges for the spatio-temporal allocation of survey effort. When there are multiple discrete units to monitor, survey alternatives such as model-based estimates can be useful to fill information gaps but may not reliably reflect biological complexity and change. The spatio-temporal allocation of survey effort that minimizes uncertainty for the greatest number of units within a budget can help to ensure monitoring is optimized. 2. We used aerial survey-based population estimates of moose (Alces alces) across 30 WildlifeManagement Units (WMUs) in Ontario, Canada to parameterize simulated populations and test the performance of different monitoring scenarios in capturing WMU-specific annual variation and trends. Firstly, we tested scenarios that prioritized conducting a survey for a unit based on one of three management criteria: population state, population uncertainty or number of years between surveys. Also incorporated in the decision framework were WMU-specific costs and annual budget constraints. Secondly, we tested how using model-based estimates to fill information gaps improved population and trend estimates. Lastly, weassessedhow the utility (based onminimizing population uncertainty) of using a model-based estimate rather than conducting a survey was impacted by population density, severity of environmental stressors and years since the last survey. 3. Interval-based monitoring that minimized the number of years between surveys captured accurate trends for the highest number of WMUs, but annual variation was poorly captured regardless of management criteria prioritized. Using modelbased estimates to fill information gaps improved trend estimation. Further, the utility of conducting a survey increased with time since the last survey and was greater for populations with low densities when the severity of environmental stressors was high, while being greater for populations with high densities when environmental severity was low.

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