Very small collars: an evaluation of telemetry location estimators for small mammals

Abstract

Background Fine-scale tracking of animals such as Peromyscus spp. is still done with micro-very high frequency collars due to the animal’s small size and habitat usage. In most cases, tracking micro-very high frequency collars requires manual telemetry, yet throughout the literature, there is little reporting of individual telemetry methods or error reporting for small mammal spatial analyses. Unfortunately, there is even less documentation and consensus on the best programs used to calculate fine-scale animal locations from compass azimuths. In this study, we present a strategy for collecting fine-scale spatial data on Peromyscus spp. as a model species for micro-very high frequency collars and assess multiple programmatic options and issues when calculating telemetry locations. Results Mice were trapped from April to October 2018–2019 with Sherman traps in Howard County, Maryland, USA. Collars were placed on 61 mice, of which 31 were included in the analyses. We compared the two most cited location estimator programs in the literature, location of a signal software and Locate III, as well as the Sigloc package in program R. To assess the programmatic estimates of coordinates at a fine scale and examine programmatic impacts on different analyses, we created and compared minimum convex polygon and kernel density estimator home ranges from locations produced by each program. We found that 95% minimum convex polygon home range size significantly differed across all programs. However, we found more similarities in estimates across calculations of core home ranges. Kernel density estimator home ranges had similar patterns as the minimum convex polygon home ranges with significant differences in home range size for 95% and 50% contours. These differences likely resulted from different inclusion requirements of bearings for each program. Conclusions This study highlights how different location estimator programs could change the results of a small mammal study and emphasizes the need to calculate telemetry error and meticulously document the specific inputs and settings of the location estimator.

Zoonotic implications of white-footed mice habitat selection and territoriality in fragmented landscapes

“…Each mouse had, on average, 56.9 plottable, quality locations (sd ± 44.1). Mean telemetry error distance was 7.29 m (sd ± 2.62 m) and mean-bearing error was 13 degrees (sd ± 3.18 degrees; Hummel et al 2022). Twenty-six of the 58 mice (45.0%) collared tested positive for Borrelia burgdorferi .…”

“…We used a maximum likelihood estimator to calculate our locations, and only mice with at least 20 quality locations were used for subsequent analyses. A detailed coordinated triangulation method has been reported previously by Hummell et al (2022).…”

“…Collar retrieval started approximately eight weeks after collar deployment. The detailed collaring method has been reported previously by Hummell et al (2022).…”

Linking small mammal capture probability with understory structural complexity using a mobile laser scanning‐derived metric: A case study

“…Continuous information about forest structure from laser scans provides an opportunity to consider multiple spatial scales with a single survey, enabling wildlife ecologists to test the relevance of measures at several scales to determine which is most applicable to the species under study. This could be particularly applicable to fine‐scale animal movement data, which due to technological advances is available at shorter intervals and for smaller animals than even a few years ago (Hummell et al, 2022; Kays et al, 2015; Wild et al, 2022). Current indices of understory complexity would likely not be good candidates for such telemetry studies.…”