Exposure to anthropogenic activities and age class mediate mesocarnivore habitat selection in a human-dominated landscape

doi:10.21203/rs.3.rs-213810/v2

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Exposure to anthropogenic activities and age class mediate mesocarnivore habitat selection in a human-dominated landscape

https://doi.org/10.21203/rs.3.rs-213810/v2

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Wildlife populations are increasingly challenged by human activities that disrupt landscape connectivity, animal movement, population dynamics and population persistence. Yet modified habitats may provide resource subsidies for generalist species resulting in increased selection of disturbed areas. Understanding how species adjust their space use and activity in human-modified landscapes is fundamental to conserving wildlife populations globally. To test three competing hypotheses explaining spatiotemporal responses to human activity, we investigated coarse (3 hour)- and fine (20 minute)- scale habitat selection, activity patterns, and measured home ranges in a human-dominated landscape. We GPS-collared 25 adults and subadult caracals (Caracal caracal) from three contiguous subpopulations in urban and wildland-dominated regions.

Caracals in the wildland-dominated region avoided proximity to urban areas while caracals in the urban-dominated region selected for close proximity to urban areas. Selection responses for vegetative cover, freshwater, vineyards, the coast, and burned areas was dependent on age class, diel period, and whether individuals were in close proximity to urban areas. Large home ranges demonstrated habitat selection results were not a result of limited landscape use. Caracals did not shift their temporal activity to avoid humans, but selected for closer proximity to urban areas at night, suggesting they seek spatial refugia during the day. Behavioral plasticity whereby individuals seek out spatial refugia promotes the coexistence of this carnivore in a human-dominated landscape. These data are essential to our understanding of what is viable wildlife habitat to a generalist mesocarnivore and provide opportunities to guide land acquisition and conservation by local management agencies.

Paleoecology

urbanization

functional response

step selection functions

caracal

fire

mesocarnivore

age class

Human activities affect nearly every terrestrial system on earth (Venter et al. 2016). Diverse consequences include habitat loss, changes in size, structure, and connectivity of natural habitat (Kareiva et al. 2007), and shifted ecosystem processes, disturbance regimes (e.g. fire; (Alberti et al. 2017), and resource availability (Rebele 1994). Human infrastructure directly impedes animal movement (Tucker et al. 2018), while human activity can elicit fear in individuals, causing them to shift movement and habitat selection (Frid and Dill 2002, Suraci et al. 2019a). For species that persist in human-impacted landscapes, they are also confronted with vehicles, poisons, and pathogens that can increase mortality (Collins and Kays 2011) and reduce fitness (Flesch 2017, Johnson et al. 2020). The Anthropocene is thus characterized by rapid loss in global biodiversity and ecosystem services (Dirzo et al. 2014, Ceballos et al. 2015). Yet some species are remarkably successful at adapting, persisting, and even thriving in human-modified landscapes (Dirzo et al. 2014). Understanding the mechanisms that facilitate persistence in human-dominated landscapes is fundamental to modern biodiversity conservation (Matthews et al. 2011).

An animal’s immediate response to disturbances is typically behavioral (e.g. altered habitat selection, activity, or vigilance; (Tuomainen and Candolin 2011); behavioural plasticity is thus essential to population persistence in changing environments (Wong and Candolin 2015). A critical determinant of landscape use by animals is the perception of risk, whereby individuals must balance the rewards (e.g., attaining food or shelter) with the potential costs (e.g., predation; (Frid and Dill 2002, Johnson et al. 2020). Human activity typically elicits fear responses in wildlife equal to or exceeding those caused by non-human predators (Ciuti et al. 2012, Clinchy et al. 2016). In some disturbed landscapes where humans are ecologically perceived as apex predators (Clinchy et al. 2016), consequent changes in animal behavior have important implications for wildlife communities and ecosystem processes (Suraci et al. 2019b).

Quantifying animal movement, space use, activity, and habitat selection across a range of human footprints informs the perceived risk-reward tradeoffs in anthropogenic landscapes (Ciuti et al. 2012, Wilmers et al. 2013, Nickel et al. 2020). Habitat selection and spatial avoidance of human activity depends on the specific behavior under consideration (e.g., forage, rest, reproduce, disperse; (Wilmers et al. 2013), while movement itself enables animals to fulfill these needs (Moorter et al. 2015). Functional responses (adjustments in selection of a resource or landscape features as a function of changes in the availability of that resource or particular landscape features) can reveal whether animals perceive particular habitats as risky and provide insight into the cumulative effects of anthropogenic development on wildlife populations (Matthiopoulos et al. 2011), and spatial distribution dynamics (Mason and Fortin 2017, Duparc et al. 2019). Coupling behaviors with spatial locations to understand behavioural responses to heterogenous landscape features is essential to define habitat suitability, which can be incorporated into land-use planning (Wittemeyer et al. 2019).

Studies examining wildlife habitat selection, occupancy, and activity in human-dominated landscapes have frequently found that wildlife species may mitigate anthropogenic disturbances by partitioning space, time, or microhabitats (Gaynor et al. 2018, Grilo et al. 2019, Nickel et al. 2020). By segregating the landscape spatially, sometimes at fine-scales (Muhly et al. 2011, Schuette et al. 2013, Suraci et al. 2019b), species can avoid direct spatial overlap with humans. Yet spatial refugia are not always available, and thus animals may avoid human activity through increased nocturnality (reviewed in (Gaynor et al. 2018). A growing body of literature is demonstrating that the presence of well-vegetated microhabitat can facilitate landscape use by wildlife even in areas with high human activity through mitigating the risk of detection (Grilo et al. 2019, Nickel et al. 2020, Suraci et al. 2020)

Here we investigate the behavioral response of a mesocarnivore to the interrelated effects of anthropogenic development and ecological variation in a fragmented, fire-prone landscape isolated by dense urbanization in the City of Cape Town, South Africa. The caracal (Caracal caracal) is a medium-sized, ecological generalist. Recent studies confirm the persistence of caracals in large and small reserves within Cape Town (Authors et al. 2019, Authors et al. 2020, Schnetler et al. 2020), which may act as an ecological trap where they are exposed to pathogens (Viljoen et al. 2020) and poisons (Authors et al. 2019). These studies point to both risks and rewards to life on the urban edge for caracals.

Here, we used GPS-collars to record both coarse- and fine-scale movements of caracals across two subregions: a fragmented urban-dominated region, and a largely intact, wildland-dominated region. We tested a suite of hypotheses concerning the influence of human activity on caracal behavior. First, we tested a complete-avoidance hypothesis, that caracals will mitigate risk of humans by avoiding human activity both temporally and spatially. Second, we tested a temporal-avoidance hypothesis that caracals will mitigate risk of humans by selecting for human-dominated habitats during periods of low human activity. Third, we tested a spatial-refuge hypothesis, that caracals will mitigate risk of humans by selecting for safe microhabitats within the human-dominated matrix. We tested these hypotheses independently for adults and subadults and we further investigate whether subadult males seek spatial or temporal refuge to avoid interactions with adults. Finally, because caracals moved more than expected, we estimated home ranges to demonstrate that habitat selection trends were not artefacts of limited landscape use. This study contributes a greater understanding of how a generalist species persists in the Anthropocene through behavioral plasticity and tolerance of human activities. These data are considered essential to promoting biodiversity conservation by informing land acquisition by local conservation and management authorities.

2.1 Study area

We assessed habitat selection, activity, and home ranges of free-ranging caracals on the Cape Peninsula and within a small nature reserve (False Bay Nature Reserve, Fig.1a), both of which are isolated from other populations by dense residential and commercial development within the municipal limits of the City of Cape Town (Fig. 1a). Within the Cape Peninsula, we primarily sampled individuals in Table Mountain National Park (TMNP), which encompasses approximately 320 km² of fragmented, natural habitat, which we split into two regions (Fig. 1).

The ‘urban-dominated region’ of the study area comprises five habitat patches that encompass 187 km² (northern half, Fig. 1), 78.4% of which is bordered by urban area. We sampled two additional individuals in an urban-dominated region within the small fragmented False Bay Nature Reserve (comprising <3 km²) surrounded by an extremely densely populated region of Cape Town (Cape Flats human population densities: 9,000–17,000 people/km²). The ‘wildland-dominated region’ (southern half, Fig. 1) encompasses 133 km², 46.2% of which is bordered by urban area. This region consists largely of contiguous protected national park areas and small patches of vineyards (3.9%). In both regions, land uses include low to high density urban development, light industrial and commercial areas, golf courses, vineyards, eucalyptus stands, and pine plantations (urban-dominated region only). The fynbos biome, comprised of low-growing, dense shrubland with a fire-dependent ecology, dominates the natural areas of both the urban- and wildland-dominated regions. Across the study period, 35.2% of the available wildlife habitat intensively burned. Extensive coastal sand dune fields are also a common natural features.

2.2 Capture and GPS-collaring

We captured 29 caracals spanning the period 2014-2016 using custom-built box traps. Individuals were sampled throughout the urban-dominated region, while in the wildland-dominated region, our permits constrained sampling to the southern-most location (Cape of Good Hope, Fig. 1a) furthest (15.8 km) from urban development. Individuals were chemically immobilized. We recorded age class, sex, weight, and morphological measurements. Individuals were classified as subadults (<2 years) or adults (>2 years) based on body size, weight, tooth wear and eruption, and reproductive status as in (Authors et al. 2019). Individuals were fit with Tellus 1C collars (Followit^TM, Lindesberg, Sweden) that collected GPS locations at three-hour intervals throughout the 24 hr cycle. To assess fine-scale movement patterns and the use of potential movement corridors, we increased the GPS-sample rate to 20-minute intervals every 10^th day for 24 – 36 consecutive hours (resulting in a target of 72-96 consecutive locations). Collars were equipped with drop-off mechanisms.

Animal handling was approved by the Authors’ Institution Animal Ethics Committee (2014/V20/LS), Cape Nature (AAA007-0147-0056), and South Africa National Parks (SANParks; 2014/CRC/2014-017, 2015/CRC/2014-017, 2016/CRC/2014-017, 2017/CRC/2014-017).

2.3 Landscape covariates

Caracals primarily used undeveloped habitat patches with intact shrubland vegetation (hereafter ‘natural areas’), and secondarily, areas within the fully transformed urban matrix (consisting of commercial, residential, altered open areas, etc.). To capture the influence of the urban matrix on caracal habitat selection, we used distance measure (URBAN.DIST) that fell along a continuum of negative to positive values, where 0 indicated the line (in our GIS layer, Supplemental Table S1) demarcating the urban matrix-natural area boundary (hereafter ‘urban boundary’). Negative values were measured as the distance inside the urban matrix from the urban boundary; positive values represented the distance into natural areas.

To control for the relative influences of natural and anthropogenic landscape features on caracal habitat selection, we tested the influence of natural features including topography (SLOPE, ELEVATION), distance from freshwater (WATER.DIST) and coastline (COAST.DIST), and whether individuals were located in sand dune fields (DUNES). We used three different measures of vegetation: i) Cover (COVER, binary), ii) normalized difference vegetation index (NDVI, continuous), and iii) habitat burn intensity (BURN, normalized burn ratio 2 [NBR2], continuous). We also tested whether the availability of COVER influenced URBAN.DIST selection (COVER x URBAN.DIST).

Because of frequent wildfires, we attempted to sample NDVI and NBR2 covariates within a two-week time point of each GPS-point (matching the frequency of Landsat Spectral 8 updates, usgs.gov), when cloud cover did not preclude our ability to do so (Supplemental Methods). The median time difference between location date and vegetation index date was seven days for 3-hour fix intervals (mean = 23 days, SD = 27) and six days for 20-min fix intervals (mean = 19 days SD = 26).

Anthropogenic features included pine plantations (PINE), eucalyptus stands (EUCALYPTUS), vineyards (VINEYARDS), and distance from arterial roads (ROAD.DIST, high traffic primary and secondary roads). In the wildland-dominated region, ROAD.DIST and URBAN.DIST were highly colinear (VIF > 20). We thus used only URBAN.DIST in model-fitting.

Each categorical variable was converted to indicator covariates (VINEYARD, EUCALYPTUS, PINE, DUNES, COVER). All covariates were standardized (Supplemental Methods; Gelmen and Hill 2007). We calculated variance inflation factors (VIF) to ensure that no two covariates were strongly colinear (VIF < 3.0; Zuur et al. 2010). Further details for covariates are reported in the Supplemental Methods and Supplemental Table S1.

2.4 Movement-explicit step selection functions

We analyzed movement-explicit habitat selection using step-selection functions (SSFs; Thurfjell et al. 2014). To ensure we used only movement relocations, we filtered consecutive GPS-locations to have ≥50-meter step lengths (for both fix intervals) because, for both fix intervals, a 50-meter step length was the validated criteria for classifying GPS-clusters that formed when caracals in our study area were engaged in feeding or resting (Authors et al. 2020).

Caracals in the urban and wildland-dominated regions were subset for analyses because: i) there were stark contrasts in the degree of fragmentation by urbanization in the two-regions, and ii) the distribution of the distance of available points from the urban matrix-natural boundary also varied substantially (Supplemental Table S2). Caracals were thus split into three subgroups: i) five adults in the wildland-dominated region, and ii) seven subadult males, and iii) 14 urban adults in the urban-dominated region. Two subadult females in the urban-dominated region were included in the adult group because, unlike subadult males, they moved and used the landscape similar to adult females in their respective areas (Supplemental Methods, Supplemental Table S3).

We performed SSFs on both 3-hour and 20-min interval data separately using a 1:20 match-case control empirical design. Strata were created where each individual’s end ‘used’ location (t) was paired with 20 ‘available’ locations (Fortin et al. 2005). ‘Available’ locations were created using random vectors originating from the location immediately preceding ‘used’ location t (i.e., location t-1; Supplemental Methods). Three resource-independent movement parameters (STEP, LOG.STEP, and DIR.PERSIST) were calculated for all steps within each stratum to control for inherent biases in animal movement that influence habitat use (Supplemental Methods; Forester et al. 2009). For the fine scale 20-min interval data subset, we additionally calculated terms to control for potential tendency for movement along the same topographic gradient (SLOPE.PERSIST).

To correct for autocorrelation inherent in movement datasets, we calculated robust standard errors for selection coefficients using generalized estimating equations (GEE; Supplemental Methods; Koper and Manseau 2009, Prima et al. 2017) by specifying intra-group (‘cluster’) correlation (Prima et al. 2017). We collected data during 31 collaring events from 25 unique individuals. Prima et al. (2017) recommend a minimum of 20 clusters per analysis group. Thus, we destructively sampled our data (by removing 54 hours [Supplemental Methods; Supplemental Fig. S1] of data between successive clusters) resulting in 1-6 clusters per collaring event (urban-dominated region: 31 adult and 27 subadult male clusters, wildland-dominated region: 24 adult clusters; range: 13–92 days/cluster). Because 20-min data collection occurred every 9^th-10^th day, we assigned clusters according to the individual ID and consecutive period of collection (n = 277 clusters). To assess whether habitat selection varied with differing degrees of exposure to human activities, we divided our data into diel period based on local sunset and sunrise times.

We estimated selection (ß) coefficients using the coxph function in the survival package (Therneau 2018) for R statistics software (R Core Team 2019). A primary objective was to evaluate the relationship between proximity to the urban matrix and habitat selection. Thus, we built our modelling framework around the URBAN.DIST covariate. For all subgroups, we tested whether the relationship was best-captured using a simple linear, quadratic-transformed, log-transformed URBAN.DIST, or a segmented linear spline regression approach by comparing quasi-likelihood under independence criterion (QIC) scores of each model and selecting that with the lowest QIC (Koper and Manseau 2009).

The segmented linear spline regression approach split the distance from the urban boundary into two (or three) covariates with different slopes on either side of a breakpoint (Kohl et al. 2018). We selected the optimal breakpoint (hereafter ‘knot’) using a grid search approach by comparing QIC scores of candidate spline models (Supplemental Fig. S2). We tested candidate knot values at one meter intervals ranging between the minimum and maximum standardized distances from the urban boundary (Supplemental Methods). The segmented approach performed best for caracal subgroups in the urban-dominated region; the quadratic transformed distance performed best for wildland-dominated region caracals.

Next, for urban-dominated region caracals, we split their data according to diel period, and performed the same grid search. Subadults and adults had three knot values each ranging between 44-153 m from the urban boundary. These values indicate that there exists a narrow (natural area) buffer zone around the urban matrix that caracals perceive as equal to the urban matrix itself (e.g., Fig. 2a-c). We hereafter refer to the urban matrix and the surrounding natural buffer zone as the ‘urban interface.’

For caracals in the urban-dominated region, we next considered that the relative probability of selecting certain landscape features was dependent on where individuals encountered this feature (urban interface vs. natural area). Candidate covariates included PINE, BURN.INDEX, COVER, ELEVATION, WATER.DIST, and COAST.DIST (hereafter ‘split’ covariates). We predicted the potential for split PINE, BURN.INDEX, and COVER because these features are indicative of available vegetative concealment that may be more strongly selected (i.e., COVER) or avoided (i.e., PINE, BURN.INDEX) within the urban interface where concealment would mitigate detection risk. Within the urban interface, we predicted that the relative importance of ELEVATION would diminish substantially. WATER.DIST within the urban interface may be linked with increased opportunity to find urban-associated prey (Authors et al., 2020). We predicted that caracals would avoid coastline closely bordered by residential or commercial development. We did not consider a split potential for NDVI, DUNES, VINEYARDS, EUCALYPTUS, or ROAD.DIST (hereafter ‘simple’ covariates), because with rare exception, roads, eucalyptus, and vineyards were primarily located within, in close proximity to, or adjacent to the urban interface. We could not think of a biological reason why selection for DUNES or NDVI would differ with proximity to the urban boundary.

To incorporate split covariates in urban-dominated region models, we created an indicator variable I, where I =1 when an individual was within natural areas, and I = 0 when individuals were within the urban interface. This allowed us to model parameters α_n representing the selection estimate when individuals were within the urban interface, and α_n+ ß_n representing the selection estimate when individuals were within natural areas. For caracals in the urban-dominated region, we calculated the relative probability of selection w for landscape covariates x as follows:

w(x) = exp(ß₁x₁ + (α₂+ ß₂I)x₂… ß_nx_n + (α_n+ ß_nI)x_n)

In contrast, for wildland-dominated region caracals, we calculated the relative probability of selection w for landscape covariate x using coefficients ß via the standard exponential model (Manly et al. 2002):

w(x) = exp(ß₁x₁ + … ß_nx_n)

For each of the three caracal subgroups, diel period-, and fix interval-specific data subset, we modeled the relative probability of a caracal selecting a particular location as a function of landscape covariates described above. For caracals in the urban-dominated region, we tested an additional interaction URBAN.DIST x ELEVATION to assess whether caracals select for proximity to urban areas because urban areas are generally at lower elevations in this region. We fit models consisting of every combination of terms and compared the QIC of all resulting models. In cases where >1 best-fit model was identified with a ∆QIC < 2, we selected the most parsimonious model. In cases where there were two models with the same number of terms and ∆QIC < 2, we report that with the lowest QIC in figures and results, and report both best-fit models in the Supplemental Tables. We report the selection coefficient ß, and 95% confidence intervals calculated using the robust standard errors (Supplemental Tables S4-S15).

2.5 Cross validation

To evaluate the robustness of all best-fit models we used 5-fold cross validation following (Fortin et al. 2009). See Supplemental Methods for details. We report the mean and standard deviation values of Spearman correlation coefficients (r_s) across all iterations (Table 1). We also report mean r_s values expected under random habitat selection (i.e., if models have no predictive power).

2.6 Activity in high risk areas

We assessed relative activity patterns to test whether caracals in the urban-dominated region changed activity patterns mitigate risk of detection in areas with high human activity (Gaynor et al. 2018). We additionally assessed whether subadults in the urban-dominated region alter activity patterns to avoid interactions with adults. We defined high risk as a function of whether: i) individuals utilized the urban-dominated region, and ii) individuals were of the subadult male demographic. We assessed effect size using log risk ratios (“RR”; Gaynor et al. 2018) calculated from 20-min fix interval data that we subsampled to one-hour intervals to reduce autocorrelation (Hertel et al. 2017). RR is a ratio of the mean nocturnality for high risk groups to the mean nocturnality of low risk groups. We defined each caracal observation as “active” if individuals moved ≥50 meters in the 20-minutes preceding each one-hour observation. This movement threshold is consistent with the threshold described above to isolate movement locations (Authors et al. 2020), and follows (Hertel et al. 2017).

We used bootstrap resampling to control for individual effects and maintain even sampling among individuals. We sampled without replacement 24 daytime and 24 nighttime observations for each caracal for 1000 replications. For each replicate, we calculated the log proportion of active observations that occurred during the night for each group. We report a weighted mean log RR with weights assigned according to the inverse variance of each replicate and the 95% bootstrap confidence interval based on the 25^th and 95^th greatest RRs (Supplemental Results). We back-transformed mean effect sizes and converted to unlogged risk ratios to assess percent shift towards nocturnality in areas of high risk. Confidence intervals that overlapped zero were not significant.

2.7 Home ranges

We calculated home ranges to demonstrate that avoidance of the urban edge in the wildland-dominated region was not an artefact of limited use of the landscape. For animals for which we had a minimum of 30 days of movement data, we calculated 95% LoCoH-a (adaptive) home ranges (Getz et al. 2007) using three-hour fix interval data implemented in T-LoCoH (Lyons et al. 2013; Supplemental Methods).

3.1 Sampling

We sampled 29 individuals and GPS-collared 25 unique individuals (Supplemental Results). However, one urban-dominated region individual was initially collared as a subadult, and subsequently recollared as an adult. His movement data were classified according to their respective age class. Of the collared individuals, six individuals (2 adult males, 4 adult females) were captured in the wildland-dominated region. Nineteen unique individuals (but data for 7 adult males, 7 subadults males, 6 females) were sampled across the urban-dominated region. One wildland-dominated region female presented a unique case (Supplemental Results). She and was reclassified as an urban-dominated region individual after she was relocated (on the day of capture) 21 km north to the urban-dominated region for management reasons. We excluded the first 23 days (during which time her movements stabilized) of her data but included subsequent locations in SSFs and her home range calculation. Thus, in total, we obtained data for five wildland-dominated region adults and 20 urban-dominated region individuals.

Individuals were collared for an average of 123.0 days (SD = 49.6, median = 132.0). We collected a total of 26,292 3-hour GPS locations (Figure 1b, 17,424 filtered locations), and 26,639 20-minute locations (8,962 filtered locations).

3.2 Relationships with the urban boundary

Urban-dominated region caracals exhibited functional responses with respect to the relative probability of selecting distance from the urban matrix-natural area boundary (Fig. 2-3, Supplemental Tables 4-12); segmented models were optimal for both adults and subadults. The quadratic-transformed urban distance was the best-fit model for wildland-dominated region caracals (Figure 4, Supplemental Tables 12-15). In the urban-dominated region, adults were on average 469.3 m (median = 353.5, SD = 463.2) from the urban boundary; subadult males were on average only 45.1 m (median = 83.5, SD = 563.1) from the urban boundary. Wildland-dominated region caracals were on average 8,014.7 m (median = 8,044.0, SD = 5,499.8) from the urban boundary. In the urban-dominated region, (controlling for sample size) there were 4.7 times as many subadult locations within the urban matrix itself compared with adults (subadults = 30.7 %, adults = 6.5 % of locations).

The sparsity of adult (especially) and subadult caracal locations within the urban matrix itself suggests the matrix is suboptimal habitat, whereas natural areas were strongly preferred. However, the segmented models revealed that a narrow buffer zone of natural area surrounding the urban matrix was avoided equally to the urban matrix itself (Fig. 2a-c). The width of this buffer zone (determined via the knot value) shifted marginally according to diel period and dataset used (Table 1). The average buffer zone width was 116.7 m for subadults, and 68.2 m for adults.

3.3 Coarse-scale habitat selection varies across age class, region, and diel period

Unique habitat selection trends emerged across subgroups (Fig. 2-7, Supplemental Tables S4-S15). We expect that the habitat selection of caracals in the wildland-dominated region (Fig. 4, Supplemental Tables S13-15) represents caracal baseline habitat selection trends (with little human influence). In cases where the habitat selection of urban-dominated region adults deviates from that of wildland-dominated region adults, we posit these differences represent behavioral adaptations to frequent exposure to anthropogenic activities. Selection for sand dunes was universal across all best-fit models; selection for greenness (NDVI) was near universal. Observed cross-validation scores were high, particularly for urban-dominated region caracals (Table 1). Observed r_s scores for best-fit models for wildland-dominated region caracals were lower, possibly as an artifact of smaller sample size, but still markedly outperformed r_s values expected under random habitat selection.

In the wildland-dominated region, caracals generally avoided proximity to the urban boundary, although this avoidance relaxed when cover (in close proximity to the urban matrix) was available at night during periods of low human activity. Proximity to the coast was the strongest driver of wildland caracal movement (Fig. 4-5, Supplemental Tables S13-S15), although topographical covariate elevation was not in the composite or night top models, indicating that caracals selected for coastlines whether they were steep and rocky, or low sandy beaches. Indeed, caracals marginally selected for higher elevations during the day (p = 0.089), although selection for the coastline was strongest during the day. Wildland-dominated region caracals avoided vegetative cover, preferring instead open areas with high visibility. We were unable to assess the influence of vineyards and eucalyptus stands in this region because neither used nor available locations fell within these land use types.

Caracals in the urban-dominated region exhibited remarkably different trends in habitat selection, with some trends that also differed among age classes (Fig. 2-3,5-6, Supplemental Tables S4-S12). For adults in the urban-dominated region, they strongly avoided the ‘urban interface’ (the urban matrix surrounded by a narrow buffer of natural area, Figure 2a-b, Supplemental Tables S4-S7). However, there were multiple lines of evidence that adults explicitly selected for close proximity to the urban interface even after controlling for elevation. At night, adults selected for close proximity to the urban interface. When vegetative cover was available and they were situated in natural areas, adults caracals selected for close proximity to the urban interface (Fig. 3, Supplemental Table S4). A marginally (p = 0.054) significant interaction between URBAN.DIST and ELEVATION demonstrated that caracals avoided high elevations less if high elevations brought them into close proximity to the urban boundary. Further, selection for proximity to freshwater was 2-3 times stronger when individuals were inside the urban interface. Contrary to our expectations, adults strongly selected for proximity to the coast when located in the urban interface; when in natural areas, the relationship with the coast was neutral. In natural areas, selection for low elevations was strong, whereas inside the urban interface, elevation had no effect. Yet at night, selection for low elevation areas was stronger when this selection brought them into closer proximity to the urban interface. Adults in the urban-dominated region selected for always cover (but see 20-min data models below). When situated within the urban interface, adults avoided burned areas, but in natural areas, the relationship with burned areas was neutral (but see 20-min data models below, Fig. 6). Adults in the urban-dominated region avoided vineyards and roads. Avoidance of roads was strongest during the day when vehicle traffic was greatest.

Habitat selection differed among urban adults and subadults, and we detected evidence of subadult males avoiding adults (Fig. 2-3, 5; Supplemental Tables 8-12). Subadult avoidance of the urban interface itself was 3.6 times lower than that of adults. Similar to adults, we observed subadult selection for close proximity to the urban interface even after controlling for elevation. In addition to explicit selection for close proximity to the urban interface (composite dataset), at night, when cover was available, selection for proximity to the urban interface shifted from neutral to positive selection (Fig. 3, 6a). Selection of low elevation areas was stronger when it brought subadults in closer proximity to the urban interface. Subadults also strongly selected for proximity to freshwater most strongly when located within the urban interface. Yet in contrast with adults in both regions, subadults strongly avoided the coast (Fig. 5). At night, they selected for low elevations (irrespective of their location), whereas during the day, selection for low elevations was limited to when individuals were located in natural areas. Yet when the use of high elevation areas brought subadults into closer proximity to the urban interface, they used higher elevations. Subadults selected for cover and avoided burned areas when they were within the urban interface (e.g. Fig. 7). Unlike adults, subadults selected for vineyards, particularly at night when human activity was lowest.

3.4 Fine-scale habitat selection

We also assessed fine-scale movement-specific habitat selection at 20-min because at this scale, the relative importance of natural landscape features in habitat selection has been shown to increase (Suraci et al. 2019a) and could provide important information to retaining viable corridors through fragmented habitat. Median step length across all individuals during 20-min movement intervals was 136.8 meters, indicating that habitat use decision-making is often on the scale of <150 m. Overall, the avoidance of anthropogenic features tended to relax, while selection for natural features tended to increase (Fig. 6b, 7). However, a few important new trends emerged (Supplemental Tables S4-S6, S8-15).

For adults in the wildland-dominated region, the most notable differences we observed included selection for burned areas (Figure 7), lower slopes, and avoidance of eucalyptus (Supplemental Tables S13-S15). In the urban-dominated region, the most important difference detected was that adults selected for burned areas when they were in natural areas and avoided burned areas when they were within the urban interface (Figure 7; Supplemental Table S4, S6). Cover was strongly selected when adults were within the urban interface at night, whereas when in natural areas, adults avoided vegetative cover (Supplemental Table S6). During the day, for adults, the interaction between URBAN.DIST and COVER was particularly pronounced (Figure 6b). For subadults, primary differences included that avoidance of the coast was included in the best-fit model, and at night, subadults selected against NDVI.

3.5 Activity

We filtered a total of 26,278 20-min locations to 8,582 hourly locations collected from 19 individuals (Supplemental Results). We found no significant differences in nocturnality among subgroups (Supplemental Results). Overall, caracals were most active during crepuscular hours (Supplemental Fig. S3).

3.6 Home ranges

We collected sufficient (≥1 month) of monitoring data from 25 individuals (Supplemental Table S3). Mean home range size varied substantially across subgroups but were substantially larger than expected a priori (adult male: mean = 74.1 km², SE = 7.5; adult female: mean = 17.4 km², SE = 5.3; subadult male: mean = 33.0 km², SE = 11.8; Supplemental Tables S3, S16; Supplemental Results). For example, in one case, an adult male used 83% of the wildland-dominated region. Overall, the exceptionally large home ranges, particularly for males, indicates that our observed habitat selection results were not an artefact of limited use of the study area or biased sampling of individuals.

Human activities increasingly isolate and disrupt wildlife populations. In fragmented, disturbed habitats, highly mobile species must navigate a broad spectrum of habitat suitability (Chetkiewicz et al. 2006) often with increased risks. Yet this habitat modification may also increase resource abundance for generalist species. We tested three hypotheses regarding whether caracals: i) completely (spatially and temporally) avoid human activities, ii) temporally avoid human activities, or iii) seek spatial refugia to shelter from human activities. We observed stark differences in habitat selection among caracals differing in (i) age class and (ii) living across urban- and wildland-dominated landscapes. We found most support for the spatial refugia hypothesis, particularly when considering the role of well-vegetated microhabitats as refuges within the urban boundary. Further, the tolerance of anthropogenic disturbances differed depending on subgroup exposure to them.

4.1 Differing selection for the urban edge among caracal subgroups

Anthropogenic disturbances can shift species’ space use to habitats perceived to have the most optimal suitability (Hawlena et al. 2010, Suraci et al. 2019a, Nickel et al. 2020). Here we focused on movement-explicit habitat selection because movement ties various life history behaviors together (Wilmers et al. 2013). Disturbances in our system were not only manifest in habitat transformation but also recreational activities, fires, road traffic, and poaching (Author, unpubl.data). Caracals in the wildland-dominated region, with access to large tracts of natural vegetation, used areas an average of 16-fold further from urban areas than caracals living in smaller patches fragmented by urbanization. In the urban-dominated region, 75% of subadult male locations were within ~270 m of the urban boundary and 75% of adult locations were within ~690 m of the urban boundary. In the wildland-dominated region, only 9% of adult locations were within ~690 m from the urban boundary. A similar trend of stronger avoidance of anthropogenic landscapes by individuals less exposed to anthropogenic activities was observed for pumas (Puma concolor) inhabiting rural and wilderness areas (Knopff et al. 2014). In addition to documenting selection for close proximity to the urban boundary itself, we observed that caracals also more strongly (or only) select for other landscape features (freshwater and the coast) when those features themselves are within the urban interface. Together, these observations strongly suggest that valuable resources lie within close proximity to the urban boundary, particularly in the urban-dominated region, thus driving urban-dominated region caracals to selectively remain at the urban edge.

4.2 Movement explicit habitat-selection

Among the most consistent trends across subgroups was positive selection for greenness (NDVI). Yet the response to dense vegetation structure itself (COVER) was context-dependent. Adults in the wildland-dominated region generally avoided dense cover. In contrast, adults and subadults in the urban-dominated region selected for cover when within the urban interface, whereas they sometimes avoided cover when in natural areas further from the urban boundary. The adult response to COVER in the wildland-dominated region may represent baseline behavior for the species, and contribute to their success in extremely arid landscapes (Weisbein and Mendelssohn 1990). Complex vegetation offers the best cover for stalking prey (Smith et al. 2019), but in the context of the disturbed landscape, selection for cover may rather reflect selection for concealment from humans (Suraci et al. 2020).

Fires are an important driver of habitat disturbance in our study system and play a key role in shaping ecosystem structure and function, resource availability (Haslem et al. 2011) and faunal populations (Eby et al. 2014). The general expectation is that carnivores will avoid burned areas due to decreased prey availability and increased detection probability by humans (Lino et al. 2019), despite the fact that fires can increase the nutritive value of post-fire successional vegetation, and increase species abundance of potential prey (Sensenig et al. 2010). We found that caracals in the urban-dominated region exhibited a dichotomous selection-avoidance of burned areas; individuals strongly avoided burned areas when inside the urban interface but selected for them in natural areas (e.g., Fig. 7). Fynbos vegetation that dominates our study area has little nutritive value for mammals (Campbell 1986), yet the seeds released post-fire can be a valuable resource for small mammals (Bond and Breytenbach 1985). Small mammals in fynbos ecosystems typically only leave burned areas several weeks after a fire (Hensbergen et al. 1992). With reduced cover as a result of fire, they may be more easily detected and preyed upon. We conclude that caracals are attracted to recently burned areas outside the urban interface because of greater prey availability but avoid burned areas within the urban interface because they are too exposed to humans, which may be an example of sacrificing optimal foraging to avoid risky areas.

4.3 Subadults pushed to the edge

Young carnivores (particularly males) in populations disperse from their natal range (Bowler and Benton 2005). Habitat selection for this age cohort is essential to understanding landscape features that promote connectivity (O’Neill et al. 2020). In our peninsular system isolated by a dense urban matrix, successful dispersal out of the system appears near-impossible, and those young males that we observed attempt to leave the Peninsula and venture into the urban matrix were frequently hit by cars or turned back (Author, unpubl.data). Nevertheless, while connectivity appears largely obstructed, our models yielded interesting differences in habitat selection that may be indicative of strategies that subadults use to avoid competition with adults.

Adults avoided the urban interface much more strongly than subadults suggesting that subadults are likely being pushed into marginal and risky habitat. Similar effects have been documented in numerous other carnivore species that inhabit human-dominated landscapes (Hinton et al. 2016, O’Neill et al. 2020), including medium-sized felids such as bobcats (Lynx rufus, (Riley et al. 2003) and Iberian lynx (Lynx pardinus, (Palomares et al. 2000). Yet although ~31 % of subadult male locations were within the urban matrix, the relative probability of selection for the urban matrix remained low. Subadults also selected for vineyards (which were not considered part of the urban matrix), while adults in the urban-dominated region avoided this land use.

Beyond utilizing human-modified areas to a greater degree, subadults avoided the coastline, which may be further evidence of avoiding conflict with adults. Adults from both subpopulations selected for the coast where they regularly hunt coastal seabirds (Authors et al., 2020). We did not capture subadults in the wildland-dominated region and thus are unsure whether this trend holds in the wildland-dominated region. However, the lack of subadult sampling may reflect subadult avoidance of the coast. In the wildland-dominated region, our trapping permits constrained us to access only to areas within several hundred meters of the coastline. Thus, if subadults in the wildland-dominated region also avoid coastline because of dominant adult use of these areas, we were unlikely to sample the subadult demographic. Interestingly, we detected one opportunistic wildland-dominated region subadult at a feeding cluster after it was killed and consumed by an adult male in the area (Author et al., 2020). The threat imposed by competing with adults is substantial for younger individuals.

4.4 Spatial refugia, not temporal refugia

The mechanisms by which wildlife persist amongst human development are debated, but dominant hypotheses pivot on how individuals balance risk and energy gain (Lowry et al. 2013). Carnivore success in anthropogenic landscapes is considered to hinge on flexible behavior that accommodates human disturbance through functional responses, shifted activity patterns, and altered habitat selection (Athreya et al. 2013, Knopff et al. 2014, Suraci et al. 2019a, 2019b). Yet, carnivore habitat selection is largely driven by prey abundance (Palomares et al. 2000) and perceived vulnerability to mortality risk (Smith et al. 2020). In human-dominated landscapes, carnivores perceive humans as apex predators (Clinchy et al. 2016, Smith et al. 2017), and will adjust their behavior to avoid risk of conflict with people (Lowry et al. 2013, Gaynor et al. 2018). While we expected caracals in the urban-dominated region to avoid human encounters via increased nocturnality, we did not find shifts in activity patterns. Rather we detected overlapping bimodal activity patterns across all subgroups. Similar activity patterns have been documented in urban bobcats and were attributed to natural behavior which includes rest during the day, travel to foraging areas at dusk, forage and rest during the night, and travel to resting areas at dawn (Tigas et al. 2002). Yet we have also observed caracals in the urban-dominated region to not only selectively forage at the urban edge, but also to initiate the majority of their kills (61%) during the day (Authors et al. in review), likely because some of their favored prey are diurnal (e.g. vlei rats, Otomys irroratus, Helmeted guineafowl, Numida meleagris; Authors et al. 2020). Thus, the combination of ample vegetative cover and diurnal prey near the urban edge may rather drive our observed activity patterns.

Rather than adopting a strategy of temporal refugia from human activity, caracals in the urban-dominated region may adopt spatial avoidance patterns that vary as a function of surrounding land-cover availability and proximity to perceived apex (human) predators. During the day, urban-dominated region adults avoided areas close to the urban interface that lacked thick cover (e.g., Fig. 6b). Adults in the urban-dominated region also avoided roads only during the day when vehicle traffic is heaviest. When situated in areas of high human disturbance, animals may choose more protected areas or microhabitats (Dupke et al. 2016), or if individuals are located near refuge habitat, they may tolerate closer approaches by potential predators (Frid and Dill 2002). Alternatively, the near constant exposure to human activities for caracals in the urban-dominated region may habituate them to non-lethal human disturbance (Rodriguez-Prieto et al. 2009), or they may reduce antipredator behavior with increasing exposure to high-risk situations (risk-allocation hypothesis, Lima and Bednekoff 1999). The majority of human encounters in the natural portions of our study area are with recreational hikers or cyclists, and thus nonlethal, while lethal encounters with poachers or domestic dogs are far rarer.

4.5 Conservation and management implications

The mechanisms that facilitate carnivore population persistence and coexistence in anthropogenically disturbed landscapes include spatial avoidance of humans (Lamb et al. 2020), or temporal avoidance of human activity where they spatially overlap (Gaynor et al. 2018). We have demonstrated that caracals in the wildland-dominated region appear to spatially avoid humans. We found some evidence that urban-dominated region caracals avoid humans as well, but at finer scales and with a greater overall tolerance for human disturbance than the wildland-dominated subpopulation. Yet the urban matrix is inhospitable for adults– the reproductive demographic of the population. The urban matrix presents a near absolute barrier to movement (Fig. 1b), and thus, the caracal subpopulations are extensively isolated, and effectively a closed population. The Cape Peninsula is a dynamic system not only because of frequent fires, but also advancing anthropogenic development. Consequently, the long-term persistence of this population will be threatened by ongoing habitat transformation, and it is essential that this development be mitigated where feasible, including through the establishment of movement corridors. Further, the restoration of pine plantations (that were marginally avoided) could increase the amount of viable habitat for caracals and other wildlife. Charismatic carnivores are widely reported to have profound influence on ecosystem dynamics, provide socioeconomic benefits to society, and earn disproportionate attention in the media (Ripple et al. 2014). Local land managers and the general public have expressed interest in the conservation of this species, and thus management action is needed to prevent the defaunation of Table Mountain’s wildlife extending to caracals. Achieving this goal, and conserving fauna globally throughout the rapidly expanding urban-wildland interface, requires new conservation strategies and initiatives that better accommodate the nuances for wildlife of living in human-dominated landscapes.

Acknowledgments

We are grateful for the logistical support of SANParks, the City of Cape Town (CoCT), and the SPCA. We thank D.Winterton, L.Mossop, J.Broadfield, and numerous volunteer interns for field assistance. Essential veterinarians were B.Stevens, A.Knight, E.Jordan, T.Hepburn. We thank the Claude Leon Foundation, Cape Leopard Trust, the University of Cape Town, Botanica Wines, Stellenbosch University, the National Research Foundation, the Institute for Communities and Wildlife in Africa, Wildlife ACT, CoCT, Experiment, Big Cat Rescue, and numerous private donors for funding.

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Table 1. Optimal knot values and the results of K-fold cross validation values across all modeled datasets and diel periods. Knot breakpoints (represented as the distance from the urban boundary) were determined using segmented regressions only for urban-dominated region adults and subadults that indicated the functional shift in avoidance-selection trends from the urban interface to natural areas.

				K-Fold Validation r_s
Demographic	Diel period	Movement dataset	Knot distance (m)	Observed mean (SD)	Random mean (SD)
Urban-dominated region adults	Composite	3-hour	63	0.97 (0.01)	-0.004 (0.23)
	Day	3-hour	57	0.95 (0.02)	-0.01 (0.23)
	Night	3-hour	74	0.93 (0.03)	-0.04 (0.22)
	Composite	20-min	56	0.88 (0.04)	0.02 (0.24)
	Day	20-min	44	0.88 (0.05)	-0.03 (0.21)
	Night	20-min	115	0.77 (0.07)	0.02 (0.24)
Urban-dominated region subadult males	Composite	3-hour	142	0.91 (0.03)	0.01 (0.22)
	Day	3-hour	153	0.88 (0.04)	-0.01 (0.24)
	Night	3-hour	126	0.81 (0.07)	-0.02 (0.22)
	Composite	20-min	91	0.71 (0.10)	-0.004 (0.21)
	Day	20-min	89	0.53 (0.16)	0.01 (0.26)
	Night	20-min	99	0.60 (0.13)	0.02 (0.24)
Wildland-dominated region adults	Composite	3-hour	NA	0.62 (0.08)	-0.03 (0.25)
	Day	3-hour	NA	0.65 (0.08)	0.003 (0.22)
	Night	3-hour	NA	0.47 (0.11)	-0.03 (0.23)
	Composite	20-min	NA	0.63 (0.10)	-0.02 (0.24)
	Day	20-min	NA	0.42 (0.12)	0.03 (0.24)
	Night	20-min	NA	0.74 (0.08)	0.02 (0.22)

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Exposure to anthropogenic activities and age class mediate mesocarnivore habitat selection in a human-dominated landscape

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