DOI: 10.5061/dryad.j6q573nnb
title: Coexistence from a lion’s perspective: Movements and habitat selection by African lions (Panthera leo) across a multi-use landscape

description: Diminishing wild space and population fragmentation are key drivers of
 large carnivore declines worldwide. Persistence of large carnivores in
 fragmented landscapes often depends on the ability of individuals to move
 between separated subpopulations for genetic exchange and recovery from
 stochastic events. Where separated by anthropogenic landscapes,
 subpopulations’ connectivity hinges on the area's socio-ecological
 conditions for coexistence and dispersing individuals' behavioral
 choices. Using GPS-collars and resource- and step-selection functions, we
 explored African lion (Panthera leo) habitat selection and movement
 patterns to better understand lions’ behavioral adaptations in a landscape
 shared with pastoralists. We conducted our study in the Ngorongoro
 Conservation Area, Tanzania, a multiuse rangeland, that connects the
 small, high density lion subpopulation of the Ngorongoro Crater with the
 extensive Serengeti lion population. Landscape use by pastoralists and
 their livestock varies seasonally, driven by the availability of pasture,
 water, and disease avoidance. The most important factor for lion habitat
 selection was the amount of vegetation cover, followed by the distance to
 human settlements and the interaction between those two variables, with
 selectivity strengths varying with season and time of day. All lions were
 more willing to approach human settlements at night and during the dry
 season, selecting strongly for cover when moving closer to humans during
 the day. Resident females most consistently used areas close to humans,
 but also relied more consistently on cover than males. Connectivity of
 lion subpopulations, facilitated by nomadic males, does not appear to be
 blocked by sparse pastoralist settlements and nomadic males avoided humans
 on the landscape more strongly than did resident lions. These results are
 consistent with lions balancing risk from humans with exploitation of
 livestock by altering their behaviors to reduce potential conflict. Our
 study lends some optimism for the adaptive capacity of lions to promote
 coexistence with humans in shared landscapes.
description: <em>Lion movements</em> To capture
 lion movements and use of habitat, we deployed satellite GPS collars
 (models GPS Plus or Vertex Lite by Vectronic Aerospace
 (www.vectronic-aerospace.com) on lions of both sexes. Between Oct. 2012-
 Mar. 2023, we had 22 different individuals collared: eight females (190
 months total) and 14 males (252 months total). On average, individual
 lions were collared 19.9 months (range: 1.7 to 56.4 months) with nine
 lions being collared for two years or more (S1 Table). The collars had
 Iridium satellite data transmission (for regular data transfer),
 VHF-beacon (for real-time manual tracking), remote drop-off function, and
 were scheduled to take positions every 1-2 hours continuously, day or
 night. The collar batteries lasted for 2-3.5 years, after which the collar
 was either removed (using remote drop-off) or replaced. We targeted lions
 for collaring from adult individuals of either sex from outside the
 Ngorongoro Crater, in NCA’s multiuse area, from different groups and
 areas, based on our knowledge of the usual range of their pride or group.
 We prioritized lions in areas with heightened risk of conflict. Apart from
 studying the lions’ behavior through fine-scale movements, the purpose of
 the collars was to provide early-warning to livestock herders in the
 area. Lions in NCA community lands are few and elusive,
 hence immobilization to deploy collars is challenging and done
 opportunistically, mainly at night, following observations of lions in the
 area. To attract lions closer to the vehicle for immobilization, we
 broadcasted a high-volume recording of feeding hyenas, a bleating buffalo
 calf, or the roars of a lion. The lions were immobilized and collared
 under permission from the Tanzania Wildlife Research Institute and the
 Ngorongoro Conservation Area Authority, whose veterinarians immobilized
 and supervised the collaring of all animals. Lions were immobilized using
 a dart-gun from a vehicle at 10–20-meter distance, and collars were fitted
 by an experienced field researcher. All lions were monitored until alert
 and deemed safe and well recovered after waking up from the
 immobilization. <em>Landscape
 variables</em> We modelled lion habitat selection
 using landscape variables (Table 1) representing habitat characteristics
 and conflict risk (i.e., humans on the landscape). Habitat covariates
 included the percent of shrub/forest habitat within a 50m radius
 (hereafter termed cover) to capture lion preference for sheltering and/or
 stalking under cover (Hopcraft et al., 2005; Loarie et al., 2013); a
 terrain ruggedness index (hereafter “TRI”) (Riley et al., 1999) to capture
 lions’ use of rugged, less accessible areas for shelter and/or stalking;
 an enhanced vegetation index (hereafter “EVI”; MODIS) to capture greenness
 as an indication of herbivore (lion prey) abundance; and the distance to
 the nearest riverbed (S1 Figure) which lions commonly use for stalking
 prey or resting in thick riverine vegetation (Hopcraft et al., 2005;
 Mosser et al., 2009). Although EVI values for the region are available
 monthly, to reduce computation time and model complexity, we summarized
 EVI for wet (Dec-May) and dry (Jun-Nov) seasons, respectively, by
 averaging over each year from 2012-2022. We used the month of April to
 represent the wet season and October to represent the dry season,
 typically the peak months of greenness for each season (Metzger et al.,
 2015). Human-related covariates included the distance to the nearest boma
 (hereafter “human activity; S1 Figure), the distance to the nearest water
 point, and the density of bomas within a defined radius (3km, 5km; see
 below) (hereafter, “intensity of human activity”). Since many bomas and
 water points shift seasonally, we created separate covariates for distance
 to human activity, intensity of human activity, and distance to water
 points to model wet and dry seasons separately.
 <em>Models</em> We used SSFs to
 quantify lions’ habitat selection at the local scale by comparing habitat
 covariates at locations that the lions had visited with habitat covariates
 at a random set of available locations (i.e. the habitats available along
 animal movement tracks) (Avgar et al., 2016). The available locations for
 SSF modelling are conditioned on the last step taken by the animal,
 meaning they are relative to the current location, resulting in a
 stratified dataset (Muff et al., 2020). Again we used the amt package
 (Signer et al., 2019) to estimate each animal’s movement parameters for
 step length and turn angle, then sampled from these parameter
 distributions to generate random available locations (n=50 locations)
 around each used location. We did not restrict this analysis to the study
 area, allowing inclusion of long-distance tracks around which we had
 targeted additional collection of covariate data. We extracted covariate
 vectors for each location and used the Poisson formulation of conditional
 logistic regression to estimate associated slope coefficients (Fieberg et
 al., 2021). We included random intercepts for each strata (i.e., set of
 used and available steps) (Muff et al., 2020) and used R package glmmTMB
 to run our models, fixing the variance of the random intercept for strata
 to 10<sup>3</sup> (Muff et al., 2020).
description: # Coexistence from a lion’s perspective: movements and habitat selection
 by African lions (Panthera leo) across a multi-use landscape
 The dataset represents
 movements of 22 collared lions in the Ngorongoro Conservation Area,
 Tanzania. The data have been processed and prepped for step-selection
 analysis by using the the amt package (Signer et al., 2019) in Program R
 to estimate each animal’s movement parameters for step length and turn
 angle, then sampled from these parameter distributions to generate random
 available locations (n=50 locations) around each used location. Used and
 available locations are denoted in the "case_" column. The
 available locations are conditioned on the last step taken by the animal,
 meaning they are relative to the current location, resulting in a
 stratified dataset with stratification noted in the "step_id_"
 column. ## Description of the data and file structure Column Descriptions:
 "case_": True/False whether the row represents a used step
 "id": The unique ID of the lion "step_id_": The
 ID_step stratification "Dry_season": 0/1 whether sample occurred
 in the Wet (Dec-May) or Dry (Jun-Nov) season "Night": 0/1 which
 sample occurred at night or during the day, with sunset determined using
 the suncalc package in Program R "Dispersal_movement": 0/1
 whether the sample was part of a long-distance dispersal movement
 "Male": 0/1 whether the lion is male (or female)
 "Land_cover": The landcover class of where the step ended
 (1=Open, 2=Forest, 3=Shrub) "ForShrub": The percent of a 50m
 radius around the step that is covered by either Forest or Shrub habitat
 "Dist_river": The distance to the nearest river (m)
 "TRI": Terrain roughness index produced from USGS EROS digital
 elevation model using the terrain ruggedness index of Riley et al., 1999
 "Dist_human": The distance to the nearest bomas/buildings (m)
 "Dist_water": The distance to the nearest source of water (m)
 "Dens_human_1k": The density of bomas/buildings in a 1km radius
 around the point "Dens_human_3k": The density of bomas/buildings
 in a 3km radius around the point "Dens_human_5k": The density of
 bomas/buildings in a 5km radius around the point "EVI": MODIS
 monthly vegetation Indices (MOD13A3) Version 6. We summarized EVI for wet
 (Dec-May) and dry (Jun-Nov) seasons, respectively, by averaging over each
 year from 2012-2022