DOI: 10.5061/dryad.k6djh9w8s
title: Data from: When can model-based estimates replace surveys of wildlife populations that span many discrete management units?

description: 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 efforts are optimized.
 We used aerial survey-based population estimates of moose (Alces alces)
 across 30 Wildlife Management 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, we
 assessed how the utility (based on minimizing 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. 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 model-based 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. Overall, the utility of aerial survey monitoring was
 strongly associated with WMU-specific monitoring precision and the
 predictive power of model-based estimates. If long-term trends are evident
 then there is greater value in using alternatives such as model-based
 predictions to replace surveys, but model-based estimates may be a poor
 substitute when there is strong annual variation and when using a simple
 model.
description: This file includes moose density (moose/km2) estimates for 30
 Wildlife Management Units (WMUs) derived from plot-based aerial-surveys
 conducted by the Ministry of Northern Development, Mines, Natural
 Resources, and Forestry (formerly called the Ontario Ministry of Natural
 Resources and Forestry, OMNRF) over 25 years (1991 – 2015). The data
 presented here represents derived estimates per WMU and year. The original
 moose aerial inventory data are available upon request from the Ministry
 of Northern Development, Mines, Natural Resources, and Forestry.