Density from pellet groups: Comparing methods for estimating dung persistence time

Abstract

Effective wildlife management often relies on estimates of animal density, and cue counting is a viable estimation strategy.A key component of density estimation from dung, a form of cue counting, is estimation of the persistence time, t ^, of dung piles. However, differences between observers on what constitutes a dung pile may alter subsequent density estimates.Additionally, many researchers studying white-tailed deer (Odocoileus virginianus) have substituted for t ^the number of days between the date in which 98% of deciduous trees shed leaves in autumn and field sampling. To address these 2 concerns, we compared 3 methods for estimating t ^of whitetailed deer pellet groups: (1) a common modelling approach based on observations from a single observer (single-observer method), (2) a method that accommodates interobserver variation on the status of dung during field surveys (interobserver method), and (3) the days elapsed since 98% of deciduous trees shed autumn leaves (leaf-off method). We then applied these 3 t ^estimates to distance-sampling data on pellet groups from white-tailed deer that we collected along transects during 3 sampling seasons from 2019-2021 in westcentral Indiana. We estimated habitat-and year-specific deer densities. Persistence probability of pellet groups varied across habitats and years, positively with age and number of pellets, and negatively with precipitation and temperature. In several instances, we found strong or marginal differences between densities estimated using the leaf-off method and the other

Imperfect detection and wildlife density estimation using aerial surveys with infrared and visible sensors

“…Indeed, concordance between our two viewers dropped to as low as 30% in areas that did not have snow cover, regardless of detection history, even though both viewers had undergone extensive training to examine IR video that we captured from aerial platforms. Other studies have reported lack of concordance between the classifications of multiple viewers of both aerial video (Beaver et al, 2020;Preston et al, 2021) and other types of population data (Delisle et al, 2022). In instances when high certainty cannot be obtained due to the natural history of the target species (e.g.…”

“…We sampled deer populations in Indiana's Deer Regional Management Units 3, 4 and 9 (Delisle et al., 2022; Swihart et al., 2020). We surveyed two different 6.4 × 6.4‐km areas within each Regional Management Unit (hereafter, RMU), resulting in six total areas flown (Fig.…”

Using cost‐effectiveness analysis to compare density‐estimation methods for large‐scale wildlife management

“…Specifically, we used a weighted habitat‐specific persistence rate for each RMU, with weights based on the sampling effort in each habitat type in each sub‐area (Estimation of persistence, available in Supporting Information). We used the same defecation rate of 26.8 fecal‐pellet groups/deer/day for density estimates in each RMU (Delisle et al 2022 b ). We included variation from the persistence rate but not the defecation rate in the final density estimates from pellet sampling because we did not experimentally estimate the defecation rate.…”

“…We included variation from the persistence rate but not the defecation rate in the final density estimates from pellet sampling because we did not experimentally estimate the defecation rate. Instead, we used the defecation rate from previous research in similar study areas (Delisle et al 2022 b ). Similarly, to model the detection process and select a final model, we used the methods in Delisle et al (2022 b ).…”

“…Instead, we used the defecation rate from previous research in similar study areas (Delisle et al 2022 b ). Similarly, to model the detection process and select a final model, we used the methods in Delisle et al (2022 b ). We fit all detection functions using the Distance package in R (Miller 2021).…”