Wildlife management is based on understanding how many individuals exist in a population and whether that number is increasing or decreasing over time and why. However, it is usually not possible to simply count every single individual because animals may be difficult to see and can be spread out across a very large area that is impractical to cover completely. Therefore, most wildlife surveys rely on sampling theory and associated statistical methods that allow us to count a subset of the individuals and then generate a valid estimate of how many occur in the entire area. Given our reliance on sampling, it is important to make sure our estimation techniques are as accurate and efficient as possible. This paper describes a new approach that improves upon the standard method of moose population estimation in use throughout much of Alaska and portions of Canada.
In Alaska and Canada, moose management relies on aerial surveys that are analyzed using what is called the geospatial population estimator (GSPE). The main advantage of the GSPE is that it leverages the spatial autocorrelation among count units (e.g., if one unit has a lot of moose, the one next to it is more likely to have more moose too) to improve estimation. The currently available GSPE software is relatively easy to use and widely employed, but it is relatively inflexible when it comes to incorporating habitat characteristics that influence moose abundance and distribution or including knowledge gained from previous surveys. This in turn limits the types of questions that we can ask of the data (e.g., Is there a trend? What habitat factors affect population size?) and reduces estimator precision. We care about estimator precision because it indicates how confident we are in our estimate of abundance. In order to increase precision (i.e., become more confident in our estimate of abundance), we generally must survey more units. This means that the inherent inflexibilities of the GSPE software could have the effect of making surveys more expensive than would otherwise be necessary.
In this paper, we present a spatial modeling approach that is much more flexible, addresses the above limitations, and provides a robust alternative for analyzing the data from ongoing moose monitoring programs. Using data from six moose surveys in Yukon-Charley Rivers National Preserve conducted between 2003 and 2019, we compared outputs from the GSPE to those based on our new approach. We found that the two approaches produced equivalent estimates of abundance when model structures were the same (i.e., independent surveys, spatial autocorrelation only). However, when we used the new method to consider all six surveys together and included spatial habitat characteristics such as fire history, we were able to establish that the population grew at a rate of 2.3%/year, tracking the increase in previously burned areas within the study area. In addition, the estimator was >40% more precise when using the new approach, which has huge implications for reducing survey cost and effort in the future. Our hope is that the increased flexibility afforded by the new method will help make moose monitoring and management programs more efficient and effective while simultaneously reducing overall cost.
Source: U.S. Department of the Interior, National Park Service
