Mark-resight brown bear survey methods

Improving non-invasive survey techniques can improve sustainability for monitoring into the future.

NPS/Jared Hughey

The Arctic Inventory and Monitoring Network uses a non-invasive mark-resight aerial survey approach to monitor brown bear abundance in the network's parks. During these surveys, bear groups are "marked" using a combination of location, coloration, and group characteristics, allowing them to be reliably identified or "resighted" during a subsequent survey. The mark-resight information is then used to determine how many groups were missed during the entire survey. In this paper, the authors extend the original mark-resight model to include spatial information to help explain variation in where bears occur on the landscape. Incorporating these extra sources of information allowed them to reduce sampling in areas where bears were less likely to occur, without biasing the results. They determined that the implementation of the new model could be used to reduce overall survey cost by ~20-30%, making brown bear monitoring more sustainable into the future.

Non-invasive mark–resight surveys for brown bears: Incorporating spatial information to improve landscape-scale monitoring of density and distribution

Abstract

1. Effective wildlife monitoring and management are reliant on unbiased estimators of population parameters. Most standard survey approaches ignore spatial pattern in the data although spatial relationships can be leveraged to produce more efficient designs for future surveys, thereby reducing project cost. Landscape-scale surveys for brown bears are notoriously expensive and difficult to complete, leading to the development of a non-invasive mark–resight aerial survey approach as a practical alternative to more commonly employed approaches for estimating brown bear density across large areas.

2. Here we extend that model to accommodate spatial covariate information and explicitly model residual spatial autocorrelation. Using brown bear survey data from northwestern Alaska, USA from 2015 to 2021, we demonstrate how our extension can be useful in exploring ecological factors related to spatial variation in bear density and detection probability.

3. We found that detection probability was higher in more rugged terrain, and occurrence probability was higher at mid-elevations where denning habitat was more prevalent and in areas with more salmon streams. The descriptive model of residual autocorrelation confirmed that additional unmeasured factors were associated with bear distribution. While these findings were not unexpected, they are very useful when considering future survey effort.

4. Using this information, we explored a hypothetical redesign of the 2021 brown bear survey that removed 50% of the survey subunits with elevation values below the mean, where bears were least likely to occur. We demonstrated that the resulting estimates were unbiased, suggesting that adopting the spatial approach to analysis could reduce overall project costs by ~20%–30% going forward.

5. Overall, our work indicates that directly accounting for spatial pattern within the non-invasive mark–resight framework has substantive advantages over the original non-spatial approach and may be useful in increasing the amount of information available for brown bear populations.

Schmidt, J. H., W. W. Deacy, L. J. Hughes, and D. T. Schertz. 2023. Non-invasive mark-resight surveys for brown bears: Incorporating spatial information to improve landscape-scale monitoring of density and distribution. Ecological Solutions and Evidence 4(4): e12288.