Getting More from Wildlife Datasets

Bears can cover long distances moving between habitat types to maximize their access to food resources. Surveys that take this into consideration and can more accurately account for population abundance and distribution can help managers see changes over time.

Wildlife management relies on monitring programs that provide accurate estimates of population abundance over time. But wildlife surveys can be challenging to design and carry out so that they are accurate (logistically and costly). Often, there are datasets collected using different methods that may or may not be useful on their own. Thus, finding ways to rigorously and accurately use data from multiple approaches can improve accuracy and utility of wildlife abundance estimates.

In this paper, we present a framework for formally combining distance sampling survey data with meadow count indices to squeeze more information out of each dataset than would be possible if we treated them separately. In addition to helping to get a more precise estimate of trend in abundance, we show how the temporary immigration process captured by meadow index counts can be extracted. Doing so provides much more in-depth information on the processes captured by the meadow counts (i.e., trends in both abundance and temporary immigration). Overall, the approach allows us to leverage the strengths of both survey types (i.e., landscape-scale distance sampling vs. annual index counts) and provide more information to managers. Although applied to bears in Lake Clark National Park and Preserve, the principles we develop could be applied to a variety of wildlife survey datasets.

Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration

Abstract

Managers rely on accurate estimators of wildlife abundance and trends for management decisions. Despite the focus of contemporary wildlife science on developing methods to improve inference from wildlife surveys, legacy datasets often rely on index counts that lack information about the detection process. Data integration can be a useful tool for combining index counts with data collected under more rigorous designs (i.e., designs that account for the detection process), but care is required when datasets represent different population processes or are mismatched in space and time. This can be particularly problematic in cases where animals aggregate in response to a spatially or temporally limited resource because individuals may temporarily immigrate from outside the study area and be included in the abundance index. Abundance indices based on brown bear (Ursus arctos) feeding aggregations within coastal meadows in early summer in Lake Clark National Park and Preserve, Alaska, USA, are one such example. These indices reflect the target population (brown bears residing within the park) and temporary immigrants (i.e., bears drawn from outside the park boundary). To properly account for the effects of temporary immigration, we integrated the index data with abundance data collected via park-wide distance sampling surveys, the latter of which properly addressed the detection process. By assuming that the distance data provide inference on abundance and the index counts represent some combination of abundance and temporary immigration processes, we were able to decompose the relative contribution of each to overall trend. We estimated that the density of brown bears within our study area was 38–54 adults/1,000 km² during 2003–2019 and that abundance increased at a rate of approximately 1.4%/year. The contribution of temporary immigrants to overall trend in the index was low, so we created 3 hypothetical scenarios to more fully demonstrate how the integrated approach could be useful in situations where the composite trend in meadow counts may obscure trends in abundance (e.g., opposing trends in abundance and temporary immigration). Our work represents a conceptual advance supporting the integration of legacy index data with more rigorous data streams and is broadly applicable in cases where trends in index values may represent a mixture of population processes.

Schmidt, J. H., T. L. Wilson, W. L. Thompson, and B. A. Mangipane. 2022. Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration. The Journal of Wildlife Management 86(3): e22185.