DOI: 10.5061/dryad.x0k6djhqp
title: Rain, recreation, and risk: Human activity and ecological disturbance create seasonal risk landscapes for the prey of an ambush predator

description: Predation risk and prey responses exhibit fluctuations in space and time.
 Seasonal ecological disturbances can alter landscape structure and
 permeability to influence predator activity and efficacy, creating
 predictable patterns of risk for prey (seasonal risk landscapes). This may
 create corresponding seasonal shifts in antipredator behavior, mediated by
 species ecology and trade-offs between risk and resources. Yet, how human
 recreation interacts with seasonal risk landscapes and antipredator
 behavior remains understudied. Goals – In South Florida, we investigated
 the impact of a seasonal ecological disturbance, specifically flooding,
 which is inversely related to human activity, on interactions between
 Florida panthers (Puma concolor coryi) and white-tailed deer (Odocoileus
 virginianus). We hypothesized that human activity and ecological
 disturbances would interact with panther-deer ecology, resulting in the
 emergence of two distinct seasonal landscapes of predation risk and the
 corresponding antipredator response. We conducted camera trap surveys
 across southwestern Florida to collect detection data on humans, panthers,
 and deer. We analyzed the influence of human site use and flooding on deer
 and panther detection probability, co-occurrence, and diel activity during
 the flooded and dry seasons. Flooding led to decreased panther detections
 and increased deer detections, resulting in reduced deer-panther
 co-occurrence during the flooded season. Panthers exhibited increased
 nocturnality and reduced diel activity overlap with deer in areas with
 higher human activity. Supporting our hypothesis, panthers' avoidance
 of human recreation and flooding created distinct risk schedules for deer,
 driving their antipredator behavior. Deer utilized flooded areas to
 spatially offset predation risk during the flooded season while increasing
 diurnal activity in response to human recreation during the dry season.
 Significance to the field – We highlight the importance of understanding
 how competing risks and ecological disturbances influence predator and
 prey behavior, leading to the generation of seasonal risk landscapes and
 antipredator responses. We emphasize the role of cyclical ecological
 disturbances in shaping dynamic predator-prey interactions. Furthermore,
 we highlight how human recreation may function as a 'temporal human
 shield,' altering seasonal risk landscapes and antipredator responses
 to reduce encounter rates between predators and prey. 
description: Study Site We conducted our study in the Big Cypress
 Swamp physiographic region of southwestern Florida, encompassing the
 Florida Panther National Wildlife Refuge (FPNWR) and Big Cypress National
 Preserve, including Bear Island (BI) and the Addition Lands (AL; Fig 1,
 Supporting Information 1). This region served as the primary restoration
 zone for Florida panthers (US Fish and Wildlife 2008). It experienced
 distinct flooded and dry seasons (Fig 1), along with seasonal storms and
 hurricanes (Duever<em> et al.</em> 1994; Abernathy<em>
 et al.</em> 2019). The regional topography exhibited minimal relief,
 characterized by slight ridges separating flat basins interspersed with
 depressions that could retain standing water throughout the dry
 season. Public access
 varied across FPNWR, BI, and AL, with hiking trails being present in all
 areas and experiencing human site use. BI allowed off-road vehicle (ORV)
 access and hunting, while AL permitted public recreational access and
 limited hunting but prohibited ORV use. FPNWR, on the other hand, did not
 allow public access or hunting but allowed ORV use for management
 purposes. Importantly, while human access levels varied across our study
 area, representing a gradient of human disturbance, the overall level of
 human presence was relatively low compared to other studies examining the
 influence of humans on wildlife (Riley<em> et al.</em> 2003;
 Gehrt, Anchor & White 2009). In recent years,
 concerns over excessive deer harvest have led to regulatory changes aimed
 at limiting access, reducing the total harvest of adult males, and
 eliminating the harvest of spotted fawns and females
 (Schortemeyer<em> et al.</em> 1991; Florida Wildlife Code,
 68A-13.004). Adult males that do not meet antler criteria, which require
 at least one antler to be five inches or longer and have at least two
 points, are not allowed to be hunted. All hunting activities occur during
 the flooded season (Florida Wildlife Code, 68A-13.004). While infrared-flash camera traps are
 commonly used in wildlife research, recent studies have suggested that
 white-flash cameras may not significantly affect detection probabilities
 for many species. Further, white-flash cameras may even provide advantages
 such as color nighttime photos that improve species identification, an
 important aspect in determining species' diel activity patterns
 (Herrera<em> et al.</em> 2021; Ladd, Meek & Leung
 2022). Therefore, we deployed 180 motion-triggered white-flash trail
 cameras (HCO Outdoor Products, model SG565FV, Norcross, GA, USA) in FPNWR,
 BI, and AL, creating three camera grids within our study area. Each camera
 grid covered an area of more than 29 km<sup>2</sup>, and the
 grids were spaced more than 13 km apart. Within each grid, we positioned
 40 trail cameras on trails, approximately 700 m apart, and an additional
 20 camera traps around 250 m from each other, located off the trails (Fig
 1, Supporting Information 1). We monitored the cameras from January 1,
 2015, to December 31, 2017, following the maintenance and data retrieval
 protocols outlined in Crawford et al. (2019). Spatial co-occurrence and detection
 probability We aimed to
 investigate panther-deer co-occurrence and its relationship with human
 recreational use and flooding across hydrological seasons. Co-occurrence
 captures the shared site use by both predators and prey, providing
 insights into their spatial interactions. To achieve this, we utilized the
 hierarchical co-occurrence model developed by Rota et al.
 (2016). The Rota et al.
 (2016) model extends MacKenzie's et al. (2002) single-species model
 by linking a detection model with a partially observed latent process
 co-occurrence model. This hierarchical model comprises three sub-models:
 species-specific conditional detection probability
 (<em>p<sub>sit</sub></em>), marginal occupancy
 (ψ<sub>i</sub>), and species co-occurrence
 (ψ<sub>11</sub>). To estimate detection probabilities
 (<em>p<sub>sit</sub></em>), we required repeated
 sequential visits to the same camera site (MacKenzie et al. 2017). Since
 our camera data was continuous, we summed species- and sex-specific camera
 detections across seven continuous days to represent distinct sampling
 occasions. For each occasion, we categorized each camera location as
 occupied (1) or not occupied (0) for male and female deer and panthers. We
 developed competing models incorporating anthropogenic and environmental
 variables that we believed influenced the detection probabilities,
 marginal occupancy, and co-occurrence of male and female deer and panthers
 (Table 1). To assess the
 impact of recreational activity on detection probability, we summed
 weekly, effort-corrected human detections per camera trap night as a
 measure of human activity rate. Flooding effects on detection were
 evaluated using a weekly mean estimate of a ‘surface water index’ as
 represented by spatially-explicit, daily water levels previously developed
 for our study area (Abernathy<em> et al.</em> 2021).
 Additionally, since panthers are more likely to be detected on trails
 (Crawford <em>et al.</em> 2019), we included the on-or-off
 trail status as a variable in the species and sex-specific detection
 models. To examine the
 influence of flooding on deer-panther co-occurrence, we averaged surface
 water levels across each hydrological season for each camera-year. Next,
 we averaged effort-corrected human detections per trap night across each
 hydrological season for each camera-year to account for species responses
 to human use. Since our primary focus was deer-panther co-occurrence, the
 sex and species-specific occupancy (ψ<sub>i</sub>) models
 included the year of our study to account for potential differences in
 species occupancy or site use across years. Additionally, to address the
 lack of comparability across camera locations (Supporting Information 2),
 we treated each unique camera-year combination as a separate
 'camera' in our analysis (Fuller, Linden & Royle
 2016). It is worth noting
 that occupancy models assume geographically closed populations during
 sampling periods, but this assumption can be relaxed in cases where
 population changes occur due to random chance or when ψ is interpreted as
 the probability of animal use within the sampling area
 (MacKenzie<em> et al.</em> 2017). In our study, we selected a
 sampling area based on deer home range size, which is relatively small
 compared to the panther's home range (US Fish and Wildlife 2008).
 Thus, marginal occupancy (ψ<sub>i</sub>) and co-occurrence
 (ψ<sub>11</sub>) in our models represent individual and shared
 site use rather than true occupancy in the formal sense. In our modeling framework, detection
 probabilities were used to estimate site co-occurrence (shared site use),
 considering imperfect detection. Therefore, we initially developed
 competing models to explain species-specific detection while keeping
 occupancy constant (intercept-only models). We compared these models using
 Akaike's information criterion (AIC) to determine the best variables
 explaining variation in detection probabilities (Burnham & Anderson
 2002). The top detection model was then used to develop
 competing models to explain female and male deer co-occurrence with
 panthers (Table 1). Similarly, to determine which variables best-explained
 variation in species shared site use, we compared competing models using
 AIC (Burnham & Anderson
 2002). All explanatory variables used in detection and
 co-occurrence modeling were scaled and centered, and we observed no strong
 correlations among the variables (Table 1, Supporting Information 1). We
 performed data curation and analyses in R version 4.2.2 and implemented
 our detection and co-occurrence modeling using the 'unmarked'
 package (version 1.2.5; Fiske & Chandler 2011). Temporal activity  We aimed to explore the temporal dynamics
 of panther-deer interactions in response to human activity and seasonal
 flooding. To investigate this, we employed several analytical approaches.
 First, we sought to quantify the effects of hydrological season and human
 use on male and female deer and panther temporal activity. To accomplish
 this we first classified each sex and species-specific camera detection
 into diurnal (one hour past sunrise to one hour before sunset),
 crepuscular (sunrise to one hour after sunrise and one hour before sunset
 to sunset), or nocturnal (sunset to sunrise) categories using the maptools
 package (version 1.1-5; Bivand
 & Lewin-Koh 2018). Next, we calculated the upper (≥ 66%
 distribution), median (&lt; 66% and &gt; 34% distribution), and
 lower (≤ 33% distribution) quantiles for human and vehicle detections for
 each camera grid (Hyndman & Fan 1996). We classified cameras within respective
 upper, median, and lower quantiles as high, median, and low human use,
 respectively, across all hydrological season-years. To ensure
 comparability, we excluded off-trail cameras when estimating human site
 use due to very low human detections in those areas. Next, we calculated the proportion of
 detections for each deer sex and species across diurnal, crepuscular, and
 nocturnal time periods during each hydrological season-year. We
 constructed a candidate set of beta regression models (Ferrari &
 Cribari-Neto 2004) to investigate the influence of hydrological seasons
 (flooded and dry) and human use (high, median, and low) on diel activity
 for each sex and species (Table 2, Supporting Information 1) utilizing the
 betareg package (version 3.1-4; Cribari-Neto & Zeileis 2010). We
 compared competing models using AIC corrected for small sample
 size (Burnham &
 Anderson 2002). To summarize the effects of hydrological
 seasons and human use factors on the proportions of detections across
 times of day (diurnal, crepuscular, nocturnal), we estimated least-squares
 means for the factor combinations within each sex and species top model
 using the emmeans package (version 1.8.2-090003; Lenth<em> et
 al.</em> 2021). Finally, we calculated 95% confidence intervals for
 estimated least-squares means using the Satterthwaite method.
 Additionally, we sought to understand
 how hydrological seasons and human recreational use influenced shared
 temporal activity between deer of both sexes and panthers. To achieve
 this, first we estimated diel activity overlap between panthers and deer
 at each camera location by fitting kernel density functions to timestamps
 of deer and panther detections. We then estimated the coefficient of
 overlap, a quantitative measure ranging from 0 (no temporal overlap) to 1
 (identical temporal activity patterns; Ridout & Linkie 2009),
 using the overlap package (version 0.3.4; Meredith, Ridout &
 Meredith 2018). We applied a smoothing parameter of one if we had 50 or
 fewer detections and Δ hat of four if we had more than 50 detections to
 reduce bias in coefficient and standard error estimates (Ridout &
 Linkie 2009). Next, we quantified human recreational activity at each
 camera by calculating annual mean human detections per total camera trap
 nights, while mean annual surface water at each camera location
 represented flooding. We constructed linear models investigating sex- and
 species-specific diel overlap using every combination of mean human
 activity and mean flooding serving as explanatory variables (Table 3,
 Supporting Information 1). We used AIC corrected for small sample size to
 determine our top model (Burnham & Anderson
 2002). We scaled and centered mean human activity and
 flooding values, and ensured they were not strongly correlated (r =
 -0.03). To address small
 sample size concerns at certain camera locations, we calculated diel
 activity overlap between male deer-panthers and female deer-panthers
 across all cameras within each level of human recreational use (high,
 medium, and low) for both hydrological seasons (flooded and dry).
 Additionally, considering the influence of life history phenology in deer
 activity and the potential impact of hunting on antipredator responses
 (Main, Weckerly & Bleich 1996; Ruckstuhl & Neuhaus 2000;
 Cromsigt<em> et al.</em> 2013), we estimated the coefficient
 of overlap for each sex-species combination across all cameras within each
 level of human recreational activity (high, medium, and low) for each deer
 biological season (fawn birthing [Jan – Mar]; fawning rearing/antler
 growth [Apr – Jun], rut [Jul – Aug], gestation/post-rut [Sep – Dec]).
 Confidence intervals for each coefficient of overlap estimate were
 generated using a smoothed bootstrap approach with 10,000 iterations in
 the overlap package (version 0.3.4; Meredith, Ridout & Meredith
 2018). We performed all analyses in R version 4.2.2 (R Core Team
 2022).
description: R version 4.2.2