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and medium-sized carnivore, ungulate, rodent and bird species (Ramesh and Downs 2015b).
Large carnivores (> 15 kg) were extirpated from the region before 1900 (Skinner and Chimimba 2005).
Data collection
We captured and collared individuals of the three mongoose species (large grey, water and white-tailed) between August 2016 and May 2017. Trap and camera trap locations were placed based on areas frequented by the target species, which were lured with bait. Step plate traps had dimensions 50 x 50 x 100 cm. Five traps were rotationally set in dense vegetative areas close to water sources and grasslands (wetlands, vleis and streams) and baited with a combination of chicken hearts and chicken mala. The traps were camouflaged with surrounding indigenous vegetation (grasses, branches and surrounding trees) because of a high failure rate associated with uncovered traps (Maddock 1988). We set traps each morning and checked them in the afternoon, and again the following morning because of differential activity period of the respective target species. Trap success was calculated using the number of individuals captured divided by the total effort, multiplied by 100 (Caceres et al. 2011).
A veterinarian sedated each trapped mongoose with a mixture of Anaket (0.8 mg/kg, Bayer, South Africa) and Domitor (0.5 mg/kg, Pfizer, South Africa) based on the individual’s approximate body mass. Once completely immobilised, individuals were removed from the trap, sexed and morphometric measurements were taken. These included: total length, body length, chest girth, foot length, canine length, body mass and head length. Individuals which met the minimum weight requirement (collar mass < 3% of body mass) (Boitani and Fuller 2000; Kenward 2001) were fitted with a collar with a GPS-GSM/UHF transmitter (Animal Trackem, Pietermaritzburg, South Africa) weighing ~52 g. A finger gap was left under each collar to prevent discomfort and allow for additional neck growth while at the same time
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preventing removal or excessive movement of the device. Once the individual mongoose was collared, it was given a reversal anaesthetic of Antisedan (0.5 mg/kg, Zoetis, South Africa).
Four trapped individuals (one large grey mongoose and three water mongooses) had to be released without a collar because they did not meet the minimum weight requirements. Ethical clearance to trap, sedate and GPS/UHF collar individuals of the three species of mongooses was provided by the University of KwaZulu-Natal (No. 020/15/animal). A portable solar- powered based station receiver was permanently set up at each study site to download telemetry data from the GPS-GSM/UHF transmitter. Data from the base station receiver were continually sent via the cellular network (GSM) and later accessed via Wireless Wildlife as a *csv.
document.
Data screening
Home range estimations are affected by the number and precision of GPS fixes. That is, small terrestrial animal behaviour is associated with higher inaccuracy and failed GPS fixes when compared with larger-bodied animals (Laver et al. 2015). Errors in GPS fixes are pronounced in small animals through reduced satellite reception, which generally diminishes GPS fix accuracy (Laver et al. 2015). Before any home range analyses, data were screened using packages adehabitatLT’ version 0.3.20, ‘adehabitatMA’ version 0.3.10, ‘ade4’ version 1.7e4 and ‘sp’ version 1.2e3 in R software (version 3.1.2) to remove inaccurate data points (Calenge 2006; RStudio 2015; Drabik-Hamshare and Downs 2017). Autocorrelation generally results from a lack of statistical independence between subsequent GPS points in both time and space (Legendre 1993). This violation typically results from too frequently obtained GPS fixes over a short time interval. To reduce the effects of autocorrelation of GPS points, GPS fixes were scheduled to record a geographic location point at 4-h intervals during 12-h periods for all tagged mongoose individuals. As a consequence of activity time differences between the
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three species (large grey mongoose diurnal; water and white-tailed mongoose species nocturnal) GPS fixes were set to record from 06:00–18:00 for the large grey mongoose and 18:00–06:00 for water and white-tailed mongoose species.
Home range and core area utilisation analyses
We imported the GPS coordinated data into ArcGIS 10.4 (ESRI, Redlands, CA, USA), and they were projected in UTM (WGS 1984 UTM Zone 35S and 36S). We determined home range estimations following the criteria set by Laver and Kelly (2008). We estimated home range size using three home range methods: Kernel Density Estimate method (KDE), Maximum Convex Polygon (MCP) and Local Convex Hull (LoCoH). R package rhr was used in user interface R studio (1.2.909 ) to estimate 50% (core area utilisation estimates) and 95% from the three home range estimate methods (RStudio 2015; Signer and Balkenhol 2015). To date, there is no optimal smoothing parameter for KDE, thus, we used the reference bandwidth smoothing parameter (href) to prevent over-smoothing and excessive fragmentation of home ranges (Walter et al. 2011). The LoCoH method constructs a convex hull around each point and the points of its nearest neighbour (n) (Getz et al. 2007). UD surfaces are affected by the h and n parameters, notability high values of h or n generate uniform UD surfaces, reducing variability, whereas smaller values increase the resolution of valleys and peaks through a more precise fit of the dataset (Worton 1989; Fieberg 2007; Lichti and Swihart 2011). Buffer and resolution levels were manually manipulated based on visual assessment (Drabik-Hamshare and Downs 2017). We performed repeated-measures analyses of variance (RMANOVA) for large grey mongoose, water mongoose and white-tailed mongoose using Statistica (Statsoft Inc., Tulsa, OK, USA) for the three estimation methods to delineate differences in the measured home range sizes at both the 50% and 95% levels using the three home range methods. We established site fidelity for each individual using the Mean Square Distance (MSD) and Linearity index (LI)
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from Centre of Activity using the ‘rhr’ version 1.2.909 package in R (Signer and Balkenhol 2015). Site fidelity was tested using 10,000 bootstrap replicates at 95% confidence interval.
Home range overlap
Spatial overlaps of mongoose home ranges were estimated through static analyses outlined in Kernohan et al. (2001). Home range measures of MCP and KDE were used to estimate home range overlap at both the core area 50% and 95% level. We calculated spatial overlap for all collared mongooses in the three study sites. Spatial overlaps were only estimated for mongooses that were tracked over a parallel time period (exception individual LG1 whose device failed after 47 days). Spatial overlaps of the mongooses’ home ranges represented an estimate because only a proportion of the population of mongooses was collared and tracked between the sites (Carter et al. 2012)