Our results demonstrate a pattern of deer avoiding residential areas during the day, with core ranges primarily encompassing park lands. Deer movement expands outwards into residential areas primarily at night, with large periods of movement focused around crepuscular hours. These nightly movements become more intense during the winter months, with expanded home ranges that include more residential areas and a complementary shift toward residential building structures. Additionally, we note several sex-specific trends related to life-history patterns and tie these findings to deer management, both generally and specifically for tick-borne diseases.
Similar to past work, we see a high variability in home range size across individuals (Kilpatrick et al. 2011), possibly arising from factors such as age, sex, social status, or population density. Each of these factors can influence individual space use on the landscape during biologically-relevant seasons such as mating or parturition, making them more likely to defend resources or seek new habitat patches, which would influence home range size. Furthermore, the reduced urban and suburban summer home ranges we observed may be related to the increase in forage availability in natural spaces, enabling deer to travel less to obtain necessary resources (Walter et al. 2011; Massé and Côté 2013). Our home and core range sizes were larger than past studies involving urban and suburban deer (Kilpatrick and Spohr 2000a, b; Etter et al. 2002; Grund et al. 2002; Kilpatrick et al. 2011), possibly due to the use of fixed or adaptive kernel methods in past work, but they were still smaller than other studies in rural or exurban areas (Storm et al. 2007; Walter et al. 2009). The newer Autocorrelated Kernel Density Estimators are sensitive to any significant shifting of space use which can cause disjoint or bimodal home ranges and overestimate space use, though our removal of nearly 20% of home ranges that did not asymptote avoided overestimating home range size. However, bimodal home ranges can arise from deer exploiting disjoint patches in highly fragmented suburban landscapes resulting in multiple home range centers, and they have been reported before in Maryland, with distances as great as 6km between home range centers (Eyler 2001; Rhoads et al. 2010).
Home ranges of white-tailed deer predominantly contained park land; but residential land comprised a substantial portion of each home range level which increased during winter months (Fig. 2), alongside the general increase in home range size, however this interaction was not significant. Past studies have highlighted the increased use of residential areas during winter months (Kilpatrick and Spohr 2000a; Grund et al. 2002; Storm et al. 2007), as deer may be exploiting ornamental plants, bird feeders, food scraps, and gardens for palatable forage during winter months (Williams and Ward 2006). Year-round supplemental feeding and baiting, which was documented throughout our study area may have also impacted deer movements as well. Importantly, while management has largely been focused on public properties, a major portion of deer space use is on private lands (e.g. homeowner properties, school grounds, religious facilities). Focus in management is shifting to include or involve private residences (Peterson et al. 2003), and accounting for this private land use could greatly increase population management effectiveness.
Consistent with Rhoads et al. (2010), we documented that speed is increased directly after sunrise and sunset throughout the year, and that the dawn peak is more evident during non-winter months (Fig. 3a). Female speeds in this study were lowest at the end of May through the beginning of June, corresponding to abundant food sources and peak fawning season in this region (McGinnes and Downing 1977; Dion et al. 2020). Overall female speeds gradually increased starting in mid-June, especially during the day, until peaking in late September and early October then subsiding. The spike in speed for males corresponding to mating behavior is easily distinguishable during the first few weeks in November as they search for mates (Rhoads et al. 2010; Massé and Côté 2013).
Consistent with speed, activity peaks closely follow sunrise and sunset. Clear resting and likely bedding periods (low speed, low activity) directly followed the crepuscular peaks, perhaps attributed to ruminating behavior after foraging events (Massé and Côté 2013). However, following these periods, and in contrast to low levels of speed outside of crepuscular hours, we documented an increase in activity levels, potentially related to foraging. Moreover, this pattern is more exaggerated during the long summer days, particularly for females. An interesting contrast is the decrease in daytime winter activity yet increased overall winter movements compared to summer. Much of these trends can likely be attributed to changing distribution of forage and cover, but contrary to what Massé and Côté (2013) documented, we see inverse relationships between summer and winter movement and activity. Winter likely required more movement from place to place to find forage, but less activity and more bedding occurred due to reduced resources, lower quality resources, and conservation of energy behavior (Massé and Côté 2013).
Most notably, we documented deer moving into residential areas, shown by a decrease in distance to residential buildings, during nighttime hours, especially during winter. This is broadly in agreement with the trends in housing density and home range size through various seasons, illustrating that home range expansions during the winter season are driven by nocturnal movements into residential areas. Deer distances to residential buildings did not track with changes in the timing of sunrise and sunset as for speed and activity, perhaps because deer were responding to a decrease in human activity. Human activity levels are likely determined by school or work schedules and less dependent on photoperiod. Similarly, we could expect deer distance to buildings to increase before sunrise due to the tendency of using residential areas when it is dark, but during mid-summer, deer did not begin to leave residential areas until after sunrise, which may be attributed to humans maintaining similar timing of activity even if the sun rises early in the mornings or more available cover which may have reduced pressure to vacate. Though this timing did not change, deer did maintain greater distance from residential buildings from April to June likely because natural forage is abundant during these times allowing them to better avoid human conflict. As male movements increased searching for mates during breeding season, we might have expected males to be closer to residences as ranges expanded and naturally included more residential areas. Additionally, the breeding season is known to cause increases in bold or aggressive behavior in males (Ozoga and Verme 1985) which could increase movement near residential areas due to reduced fear. Deer winter movements into residential areas have been associated with available food resources, yet male cervids are known to starve or incur poor body condition during breeding seasons (Mysterud et al. 2008). Interestingly, we still documented a strong avoidance of residential areas during that time.
It should be noted that all three models of movement characteristics, especially speed and activity models, explained only a small amount of the overall variance. This is unsurprising, given that these behaviors are likely driven by very specific events (e.g., interactions with homeowners, park users, or pets). Nevertheless, while these analyses illustrated the need for more work on specific behavioral responses to specific events, we were able to document clear, if broad, trends in these patterns which help illustrate how deer utilize and move through urban landscapes.
Many government agencies use visual or camera surveys to collect data on deer populations. Our paper shows deer space use changes depending on time of day and time of year and surveys are only as good as the survey locations. Any such, survey should sample both residential and park areas at the same time when feasible. Many such population trend data and population estimators are often conducted at night due to increased visibility of deer. The data presented here shows nighttime estimates in residential areas may overestimate abundance. Any sampling design for deer population estimates should account for variable daily cycles in space use in suburban and urban areas.
Hunting or culling can be a successful management tool but requires deer to be accessible. This study has documented several nuanced movements and behaviors that can impact urban and suburban deer management and will be important information for managers planning culling or sharpshooting efforts. Hunting has often been perceived as best during crepuscular periods because generally deer are moving more during these periods; however, any increase in diurnal speed or activity during hunting seasons can increase chance encounters with hunters. Although this study supports those crepuscular peaks in speeds, we documented that hour by hour deer do not generally rest throughout the main parts of the day. We see a strong midday peak in activity especially during non-summer months, with midday speeds also increasing during mating periods and late winter for males. Additionally, the ‘October lull’ has been described by hunters as a period of low movement rates and activity in white-tailed deer, but previous research has generally not supported this (Tomberlin 2007; Simoneaux et al. 2016). We documented evidence for both a lull in daytime speed for males during October as well as an overall increase in speed and activity from previous months that is only manifested during crepuscular and nocturnal periods in these suburban areas. As increased deer movement during daylight increases hunter opportunity for harvest, managers may look to avoid planning hunts earlier than mid-October during the periods of lower movement in this region.
Safe locations for hunting or sharpshooting in suburban areas are highly limited, especially the required distance from occupied residences (Maryland > 91m). As 66% of our locations were closer than 91m to residential buildings, frequently reassessing hunting safety zones when feasible and encouraging hunting methods that utilize archery equipment would likely increase management efficiency. Lastly, sharpshooting operations often occur at night on park properties as a more discrete and efficient method to reduce deer populations in sensitive or highly populated areas. However, our study shows that deer often move out of park areas and into residential yards at night. Furthermore, this movement of deer into residential yards is often intensified during typical hunting months, even in areas that are not routinely harvested. Managers might consider moving any culling operations, with appropriate sharpshooting tactics and permissions, closer to residential areas in fall and winter or operate male culling efforts during summer periods away from residences.
Vector-borne zoonotic diseases such as Lyme disease are increasingly a major public-health problem. Our results illustrate that each individual deer has the potential to interact with hundreds of residential properties, emphasizing their potential for transporting ticks and other parasites. In our study, average male core ranges contained more residential properties than females (Table 3), that is just a byproduct of males having larger home ranges. In fact, female deer core ranges contained greater average housing densities and were consistently in closer proximity to residential buildings. Regardless of sex, we found a much greater number of individual residential properties within deer core ranges compared to past research (Kilpatrick and Spohr 2000a, b; Storm et al. 2007; Kilpatrick et al. 2011).
When considering the multi-stage life cycle of ticks in Maryland, tick activity has several distinct components relevant to deer activity and movement, especially as they relate to distance to residential buildings and spreading of ticks to homeowner’s backyards. There are two major peaks in adult tick activity among the three disease-carrying tick species in Maryland. In spring, Lone star and American dog ticks are extremely active, while the adult black-legged tick becomes very active in October and November (Orr et al. 2013). This spring and fall activity coincide with times of major deer movements, potentially leading to increased tick dispersal. Winter months pose the greatest risk for deer transporting ticks to residential areas, with female deer posing the greatest risk of increasing ticks near homes. Increased use of residential areas during winter months combined with prolonged tick activity and lessened tick mortality due to climate change may increase or intensify chances of people becoming exposed to tick bites and tick-borne disease in their own backyards (Ogden and Lindsay 2016; Dumic and Severnini 2018). Further, while deer use of residential areas during summer is less intense than winter, the majority of deer still place approximately 35% of their home ranges in residential spaces. Summer is a very high tick activity time concurrent with increased human outdoor activity, and likely leads to increased risk of encountering ticks. Because of these high-risk periods during summer and winter combined with peak adult tick activity seasons occurring in fall and spring we recommend considering tick management year-round.