Fosa spatial dynamics and activity patterns 839
with Telonics TGW-4277-4 GPS Iridium collars (220 g; Telonics, Mesa, USA).We programmed collars to take quick fix pseudoranging spatial data at 20-minute intervals, to mitigate a high failure rate anticipated because of forest cover density. Quick fix pseudoranging allows for data to be transmitted to satellites much faster than traditional GPS methods, which is ideal for tracking small mammals in densely forested habitat. We placed collars only on adults weighing at least 4,400 g, so that collars comprised#5%of body mass. Animals were placed in a dark recovery cage and monitored until they recovered from anaesthesia, then released at the capture site.
Spatial and temporal analyses
We used only resolved locations (defined by Telonics as those with an accuracy within 30 m 98% of the time and within 10 m 80% of the time) for spatial analyses. Ranging patterns were calculated for kernel density estimates follow- ing Worton (1989), and Brownian bridge movement models following Horne et al. (2007). Kernel density estimates de- termine the probability of finding a given animal in a certain area, whereas Brownian bridge movement models incorpo- rate pathways and temporal data to calculate where indivi- duals spend the most time within a given home range. We calculated kernel density estimates of 50, 90, and 95% with ad hoc smoothing parameters using the adehabitatHR package in R 3.3.2 (Calenge, 2015).We used these estimates to compare fosa home ranges in Ankarafantsika National Park with those documented in a different dry forest habitat in Kirindy Forest (Lührs & Kappeler, 2013). We computed 50 and 95% Brownian bridge movement models using the R package BBMM (Nielson et al., 2015), which allowed us to characterize individual home ranges using high resolu- tion spatial data (20-minute intervals) without the assump- tion of independence that other home range estimates re- quire. We also combined spatial and temporal data using Brownian bridge movement models, to examine path- ways used by individuals within their home ranges, particu- larly in areas of heavy deforestation. We calculated overlaps between individuals for home ranges derived from both kernel density estimates and Brownian bridge movement models. Wedetermined habitat selection within each individual’s
home range using the semi-automatic classification plugin in QGIS 2.18.3 (Congedo, 2016). Landsat imagery from June 2016 (U.S. Geological Survey, 2016) was rasterized to delineate forest, water, grassland, and agricultural areas into a land-cover grid with cells of 30 × 30 m. We defined agricultural areas as those used as crop fields or livestock pastures, with clearly visible boundaries. We considered areas that lacked forest cover but did not have clearly visible boundaries in aerial images to be grasslands. Given the small
size (often , 30 × 30 m) and scattered nature of the village areas within the Park, we drew polygons of village areas at a scale of 1:10,000 and overlaid those on the land-cover grid. Wedefined village polygons as areas with at least two build- ings visible in satellite imagery, and extended village bound- aries to the surrounding forest edge or clear boundaries of other land-cover types. Thus, settlements officially recog- nized as a single village could comprise multiple polygons, allowing us to distinguish between settled areas and other land-cover types that separated different parts of a village. Secluded settlement locations where multiple buildings were visible in satellite imagery but that were not associated with official villages were also included in the village land- cover type. We then categorized recorded fosa locations by the land-cover type of their position on the grid. We used step selection functions to generate 10 random movements from every recorded position to calculate Manly selection ratios (Manly et al., 2003), using the R package adehabitat (Calenge, 2015). With the widesIII function we computed habitat use vs availability for each individual, using a χ2 test. To calculate Manly selection ratios, we condensed GPS data into temporal windows with a resolution of 8 hours, to minimize temporal and spatial autocorrelation (Frair et al., 2004). All GPS collars were equipped with a three-axis
accelerometer to record activity counts simultaneously with geographical coordinates at 20-minute intervals. The accelerometers detected acceleration relative to gravity, with activity counts calculated as the total number of active seconds (where tilt or acceleration/deceleration occurs in any combination of three planes of motion) within each time interval.
Results
Home ranges We captured five adult and two juvenile fosas, and only the adults were fitted with GPS collars. Three of the collared individuals were captured in 2016 (two males, one female), and two in 2017 (one male, one female). We assigned indi- vidual identifiers based on sex and capture order (e.g. the first captured female is F1). We tracked fosas for 14–115 days, yielding 215–2,110 recorded locations per individual (Table 1). Fosas represented 58% (seven out of 12) of all carnivores
captured, with several non-native species (three cats Felis catus, one dog Canis familiaris and one small Indian civet Viverricula indica) also captured in our traps. Home range size was similar across individuals, with
home ranges calculated with Brownian bridge movement models consistently smaller than those estimated by kernel densities (Table 1). The home ranges of all individuals over- lapped, with more overlap occurring between dyads in 2016
Oryx, 2020, 54(6), 837–846 © 2020 Fauna & Flora International doi:10.1017/S0030605319000498
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