Study area
This study was conducted in Howard County, Maryland, USA. Howard County is in the Piedmont region of Maryland and received average annual precipitation of 116.6 cm [47, 48]. Howard County is currently a mixed hardwood forest dominated by oak/hickory [47, 49]. The total 2019 population for Howard County was estimated to be 325,690 persons [50]. More specifically, this study was conducted within a fragmented suburban/urban county park in Howard County, Maryland: Blandair Regional Park (60.7 ha). Blandair Regional Park (Blandair) had a substantial number of single-family homes bordering the park boundary and fell within the defined highly suburban landscape of the Howard County metropolitan zone [51, 52]. Blandair consisted of very small grasslands with a developing forest and some historical buildings. Dominant plant species within the park consisted of autumn olive [Elaeagnus umbellate], black cherry [Prunus serotina], black walnut [Juglans nigra], grapevines [Vitis spp.], Japanese stiltgrass [Microstegium vimineum], mile-a-minute [Persicaria perfoliate], oaks [Quercus spp] and wine berry [Rubis spp.].
Trapping
Small mammal trapping occurred from April to October in both 2018 and 2019. We randomly located two trapping grids in Blandair park along homeowner’s lawn/forest edge specifically for conducting mouse telemetry. Our focal species was Peromyscus leucopus (white-footed mice). Within Maryland, white-footed mice are one of the most abundant species, and we used morphologic characteristics to distinguish between other species [53, 54]. Within each individual trapping grid, the transects (n = 6) were spaced 15 m apart (Fig. 1). Individual trap placement on each transect (6 traps per transect) started from the homeowner’s lawn/forest edge and moved into the forest interior. Therefore, each trapping grid consisted of 36 traps for a total of 72 traps in Blandair. Trap locations were recorded with a Garmin GPSMAP 64ST Handheld GPS unit (Olathe, Kansas, USA) and marked with tree flagging and a numbered ground flag. Sherman live traps (3 × 3.5x9″, H. B. Sherman Traps, Inc. Tallahassee, FL, USA) were baited with apples and a mixture of peanut butter, nuts, and rolled oats. To minimize stress and exposure of captured small mammals, traps were set after 3 pm and checked a half-hour before sunrise [55]. Mice were transferred to a clean clear bell jar that contained Isoflurane-soaked cotton balls in a separated chamber, approximating a dosage of 0.08–2.5%mg/kg [56]. Breathing rate was monitored for reduction by 50% from pre-anesthesia levels (80–100 breaths/min) [56, 57].
Collaring methods
Only mice weighing > 17 g were considered for collaring to ensure collars weighed ≤ 5% of body mass [14]. Mice were collared with Holohil Systems Ltd model BD-2XC VHF collars. The micro-VHF Collars were distributed with the goal of tracking two mice at each trap distance interval, regardless of sex. Additional collars were placed as appropriately sized mice were captured (Fig. 2). Given the small, often overlapping home range size of mice, collars were at least 0.40 MHz apart. The strap of each mouse collar was modified to a braided fiber fishing line that fit through flexible plastic tubing (Attachment Instructions, Holohil systems Ltd, Ontario, Canada). In total, the micro-VHF collars weighed 0.75 g and the estimated battery life was approximately 10 weeks.
When fitting collars, first, cotton yarn was used to measure the specific circumference of the mouse’s neck. Then, the collar’s length was trimmed to fit the specific mouse, and the antenna was wrapped through the plastic tubing to ensure a strong signal. The collar was put on the mouse and tightened to allow for limited rotation around the neck, but loose enough to have movement. The collar was able to rotate but not loose enough to allow the mouse to chew the antenna or slide a leg through the collar. Technicians made careful note of the mouse’s general eye appearance before and after being collared, specifically noting any bulging of the eyes, which typically indicated an overly tight collar (Fig. 2). Once the collar had a proper fit, a crimp bead was used to secure the collar strap at the determined size (Holohil systems Ltd, Ontario, Canada). Finally, the magnet was removed, and the VHF signal was checked. Mice were placed in a mesh enclosure with extra cotton, bait, and a hand warmer to recover for up to 20 min to ensure no negative reaction to the collar. Mice that were lively and not focused on the collar were released back in the original trapping location. Mice that were hyper-focused on the collar had their collar removed and were released at the original trapping location.
Telemetry
Before collaring mice, technicians listened to collars from varying distances until the signal was consistently not detected to determine general signal strength. This allowed for the creation of a dimensional area around where mice were collared that ensured hearing the VHF signal while limiting disturbance of mice. After collaring, mice were tracked for approximately 6 weeks from May to July and again from August to October in both 2018 and 2019. Each week, nightly telemetry was done at each trapping grid, allowing each individual mouse to be tracked for ≥ 3 nights over each 6-week period. To ensure capture of large foraging movements of mice each night, telemetry started one hour before civil sunset. Each telemetry session lasted approximately 5 h from dusk until midnight. Telemetry entailed three or more technicians working in conjunction standing at fixed corners of the grid (100 × 100 m square). When in their consistent, initial position outside of the trapping grids, which was GPS recorded, they used synchronized stopwatches and recorded bearing angles for all mice in sequential order by mouse radio frequency. This process was initiated in 40-min intervals. During each interval, ≥ 3 bearings were obtained for each mouse within the plot. Although rare, if a mouse within the plot was not able to be detected, a technician would shift halfway down (50 m) the length of the telemetry transect and again attempt to collect locations on the missing collar. Telemetry was concluded each night when we noted the obvious decrease or end of major movements via consistent bearing angles over one 40-min interval. After the 6 weeks of tracking concluded, recovery of all collars was attempted.
To assess technician telemetry accuracy, 5 mouse collars were hidden in randomly selected general areas and specifically placed in areas that mice might regularly inhabit such as downed woody debris, buried in leaf litter, or in tree cavities. The location of the error collars was recorded to within ≤ 2 m accuracy via GPS. Technicians were directed to within approximately 100 m of the hidden collar and asked to perform triangulation. The 10 technicians triangulated ≤ 5 collar locations, depending on their work schedule. To calculate the technician error, error polygons were created by creating waypoints (points) and bearings (polylines) using ArcGISⓇ Distance and Direction Editor Tool, and the centroid of the resulting polygon was calculated using ArcGISⓇ Calculate Geometry tool [58]. The error was recorded as the distance from the centroid of the error polygon to the true collar location. The weighted average of all telemetry error measurements was considered the measurement of telemetry bias or accuracy; weights were based on the frequency of telemetry performance by each technician. Then, differences in the bearing angles recorded by the technician versus the bearings that would have produced the exact true collar location were back-calculated using ArcGISⓇ Distance and Direction Editor Tool. Those angles represented the precision of the telemetry error of this study.
Location comparisons
We calculated locations from bearings in LOAS, Locate III, and the Sigloc package. LOAS and Locate III are stand-alone commercial programs and the Sigloc package, along with all other statistical analyses, were run in program R [42]. Given the almost complete lack of reported project-specific error and programmatic settings in the literature, we assumed that most researchers let the program estimate the error rate. Furthermore, many researchers created error polygons around estimated points after calculation, and incorporating errors across programs was not equivalent. For example, LOAS allowed error to be input and created different outputs, Sigloc-R required creation of unique code to incorporate error, and LOCATE was unclear how it was incorporating the only error it allowed for, bearing error. So, for initial comparisons, we did not adjust the default setting, no adjustment, for bias error and accuracy error within the programs. All three programs created confidence ellipses for each location. However, they are not ever accounted for in the literature, nor are they comparable to a manual error polygon, which has no firm statistical error estimate.
The only other setting that was an option to adjust was the output file type and the actual estimator. For all programs, we chose the Maximum Likelihood Estimator (MLE) as the location estimator given its popularity in the literature [14, 59, 60]. MLE is a statistical inference method, in this case an optimization method of estimating the location, that maximizes a likelihood function so that the observed data are most probable [59, 60]. MLE is widely used because the estimation method is repeatable and produces nearly optimal inference [23, 39, 61,62,63]. However, MLE can be sensitive to outlier bearing error, caused by issues like signal rebounding [13, 14, 59]. So, the three programs were comparable in all metrics that the user could control. For a mouse or its locations to be included in further analyses, we selected only mice that had ≥ 3 complete nights of telemetry data. Locations produced from all programs were visually evaluated in ArcGISⓇ. Any obvious outlier locations, such as a single point that fell significantly beyond the trapping grid (> 200 m), were assumed a recording error and assessed for input error and considered for removal. Individual mice were considered for exclusion if their predicted home ranges were nonsensical in size (> 10,000m2) [29, 57, 64,65,66,67] or more than one program failed to converge.
Several comparisons were made to assess the default program performance in a known situation. Using the known “true” locations, the original bearings for all test collars were input in LOAS, Locate III, and Sigloc, creating three sets of locations of the test collars. Then, the distance between the program outputs and the known collar locations was calculated. Next, we calculated the difference between the manual calculations of the centroids of the technicians’ error polygons to the actual program estimates. Then, to better assess the magnitude of impacts of bias and precision issues, we input back-corrected, now “perfect”, bearings for the known collar locations from one random set of waypoints for each technician into each program. Three sets of corrected locations were produced. The distance between those back-corrected outputs and the known collars was calculated. Finally, beyond the descriptive statistics calculated, we compared all outputs with Friedman tests.
Home range comparisons
Using the actual mouse data, a home range was created for each mouse using the three sets of geographic locations produced by each program. We did this using two different functions in the adehabitatHR package in Program R [68]. First, 95% and 50% Minimum Convex Polygon (MCP) home ranges were created because they are still a commonly reported home range approximation in the small mammal literature [28, 33, 65]. However, due to the advancement in home range analytics, we also create 95% and 50% kernel density estimates (KDE) for comparison of basic, downstream analyses [69, 70]. The KDE reference bandwidth (href) was selected as the most basic bandwidth based on the total lack of reporting of bandwidths used for very small mammals and to maximize the convergence of more limited data [70]. We tested home ranges individually as well as on average.
Based on the complex ecology of tick-borne diseases, differences in mouse home range size or shape and different preferences for yards, trails, or interior forest may influence the usage of rodent-targeted IPM devices and their placement. Given that knowledge, we first tested for simple differences in average home range size (for both MCP and KDE) using Friedman tests. Then, we analyzed differences in average home range size between pairs of programs using Wilcoxon sign-rank tests (for both MCP and KDE). Next, we calculated perimeter-to-area ratios, overlap, and land cover makeup for the KDE home ranges. Then we used a Levene’s test to understand the variability in the perimeter-to-area ratios between pairs of programs. Finally, we compared the land cover categories that each program identified for the composite KDE home ranges using Friedman tests. Shapiro–Wilk tests and all other statistical tests were conducted in program R. Spatial GIS data came from the high-resolution 2013–2014 Chesapeake Bay watershed land cover dataset (1 m resolution) [71]. All spatial calculations were done in ArcGIS® [58]. Generally, values were found to be non-normal, so non-parametric tests were used along with a significance threshold of alpha = 0.05.