Human-Induced Fear in Free-Living Raccoons 

By Ruby Sanger 

Abstract 

This past June and July, I spent time at Norcross Wildlife Sanctuary in Wales, Massachusetts. Norcross is a large intact forest of approximately 4200 ha with restricted human access and a small 17ha area near the visitor center, where human access is permitted for hiking along 4km of natural trails. 

I intended to study the effects of human-induced fear on the foraging effects of free-living white-tailed deer, both in the human-accessible area and the forest. This design included audio treatments (human talking and birdsong) and food treatments (plain corn and plain corn mixed with molasses) at eight different stations across the sanctuary. However, after a few weeks of experimentation, there was no deer activity at any of the stations. While thorough background research was conducted to decide the most appealing foods to bait deer with, and there was known to be a high density of deer at the sanctuary, it is possible that because the forest is so dense with natural browse and plants the deer eat, they simply not interested in what was being offered. As a result, Dr. Sherriff and I decided to adapt the experiment to study the foraging behaviors of raccoons, as there were reoccurring raccoon visits at select feeding stations on the public trails. 

None of the stations with molasses had traffic, nor did any of the stations in the deep woods, so we cut the molasses treatment while adding two audio treatments (human yelling and dog barking) for a total of four treatments. The experiment was limited to the public trails of the sanctuary. 

Introduction 

While predators can kill prey, they can also alter prey fitness through non–consumptive effects. The risk of predation can subsequently alter prey’s behavior, morphology, and physiology, which may all impact prey survival and reproduction. These effects may then influence prey population sizes (Sheriff et al., 2020). For example, a study by Cherry et al. (2016) showed that the presence of coyotes led to reduced lactation and ovulation in white-tailed deer, and an absence of coyotes related to an increase in feeding, lactation, and ovulation. These non-consumptive effects may reduce prey population size due to a lack of reproductive success and fecundity (Say-Sallaz et al., 2019). 

The term “landscape of fear” is defined as the spatial variation in prey perception of predation risk. These “landscapes” combine the elements of the physical environment that prey may inhabit or forage in, the predation risk and how it varies across locations, and a prey’s response to predation risk. There are generally two methods of prey response, one being avoiding areas perceived as higher risk, and the other being changes in behaviors while in the areas perceived as higher risk. The perception and fear of predation may be able to drive community-level changes within ecosystems, such as trophic cascades. (Gaynor et al., 2019). A common way to study the spatial variation resulting from risk response is by looking at giving-up densities (GUDs) and analyzing foraging behaviors within the context of risk. GUDs are used to provide insight into metabolic and predation costs of foraging by determining when an individual may stop foraging (Brown, 1987). 

In a study by Darimont et al. (2015), it has been shown that human predators kill far more prey than non-human predators, as well as killing carnivores nine times more than natural predators (Smith et al. 2017). While humans kill at an unparalleled rate, they more often affect prey behavior through disturbance (Frid, Dill 2002). Fear of humans as a “super predator” is also known to lead to behavioral changes in both predators and prey, and the subsequent effects on populations and communities may be larger than those resulting from non-human predators (Crawford et al. 2022). Experimental non-consumptive behaviors from humans have even led to a decrease in feeding times for pumas, an animal without any natural predators (Smith et al. 2017). A landscape of fear of the perception of humans can result in significant changes in wildlife behavior and community dynamics. Suraci et al. (2019) conducted studies with free-living mountain lions, bobcats, medium-sized carnivores (such as opossums and skunks) and deer mice in the Santa Cruz mountains. 

Human predation risk was simulated by using playbacks of human vocalization. The carnivore groups all experienced behavioral changes in response to perceived predation risk: avoiding areas, making temporal changes, and being less efficient in foraging. 

However, deer mice seemingly benefited from human presence; they increased space use as well as foraging intensity. The fear that the carnivores perceived affected lower trophic levels, influencing the surrounding wildlife system (Suraci et al., 2019).

Methods 

i. Feeding Station Set-ups 

Figure 1: Map of the 8 feeding stations, marked on the GAIA app. 

 

Figure 2: Camera and speaker set-ups. The cups were used as a shield for the exposed speakers from the rain. 

 

Eight feeding stations were chosen among the public trails of Norcross Wildlife Sanctuary in Wales, Massachusetts (Figure 1). Each station consisted of a painted feeding bin, a field camera, and a speaker (Figure 2). 

The speaker and camera were programmed to be used together, using Arduino, so that when the camera was triggered by motion, the speaker was triggered to play a programmed playback. Two speakers were programmed to play conversational human speaking, two to play dogs barking, two to play bird songs native to the area, and two to play humans yelling. Each speaker was programmed to play at about 65-70 decibels. The camera-speaker set-ups were programmed for the speaker to trigger 20 seconds after the camera was triggered. 

80 ounces of dried whole grain corn (5 lbs) were set into the feeding tubs. Each empty tub, when closed with the lid, weighed 70 ounces. 

ii. Daily Protocol 

The stations were filled with 80 ounces of corn, and the cameras and speakers were switched on June 30th. Every morning from July 1st to July 10th, the combined weight of the corn and the tub were taken at each of the eight locations by closing the tub and weighing it with a digital fishing scale. If the weight was below 120 ounces, the corn was later refilled to the base weight of 150 ounces (80 ounces of corn plus the weight of the tub). Additionally, the SD cards in the cameras were checked to see if there was any raccoon activity or other significant animal activity during the night. The battery levels of the cameras and speakers, as well as the SD card storage amounts, were also checked every morning to ensure proper performance for the following night. In the case of heavy rain, the tubs were covered to prevent the corn from being waterlogged, which could provide inaccurate weights. 

Table 1: Key of auditory playbacks per feeding station

Feeding Station	Treatment 6/30-7/05	Treatment 7/05-7/10
1	Talking	Birdsong
2	Yelling	Dog barking
3	Dog barking	Yelling
4	Birdsong	Talking
5	Talking	Birdsong
6	Yelling	Dog barking
7	Birdsong	Yelling
8	Dog barking	Talking

On July 5th, after 5 nights of data collection, the playback treatments were changed to different locations (Table 1). At the end of data collection, the SD cards were collected, and the feeding stations were broken down. 

iii. Data Analysis 

The footage captured from the feeding stations are currently being analyzed manually. I will be scoring for behaviors including fleeing (running/leaving quickly), leaving (walking away), looking up, head-up foraging, head-down foraging, playing, freezing, and leaving a group. There are many cases of corn being eaten by mice, chipmunks, squirrels, and occasionally deer. The footage of these animals will be used to separate their consumption from the raccoon’s consumption. Additionally, statistics of each night of the experimental run are being gathered. I am counting the total number of foraging events as well as the time spent eating each night. The footage is currently being analyzed. 

Conclusion 

While the results are still being analyzed, information regarding how raccoons react to auditory playbacks will provide useful insight into the effectiveness of using sound as a method of pest control, as well as how small mammals such as raccoons are affected by the presence of humans. I hope to continue in this line of study and resume with the original design for studying white-tailed deer in the future. 

Works Cited 

Sheriff MJ, Peacor SD, Hawlena D, Thaker M. (2020). “Non‐consumptive predator effects on prey population size: A dearth of evidence.” Journal of Animal Ecology vol 89. 

Cherry, M. J., K. E. Morgan, B. T. Rutledge, L. M. Conner, and R. J. Warren. (2016). “Can coyote predation risk induce reproduction suppression in white-tailed deer?” Ecosphere 7(10):01481. 

Say-Sallaz, E., Chamaille-Jammes, S., Fritz, H., Valeix, M. (2019). “Non-consumptive effects of predation in large terrestrial mammals: Mapping our knowledge and revealing the tip of the iceberg.” Biological Conservation vol.235: 46-52. 

Suraci, J.P, Clinchy, M., Zanette, L.Y., Wilmers, C.C. (2019). “Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice.” Ecology letters vol. 22,10: 1578-1586. 

Gaynor, K.M., Brown, J.S., Middleton, A.D., Power, M.E., Brashares, J.S. (2019). “Landscapes of Fear: Spatial Patterns of Risk Perception and Response.” Trends in ecology & evolution vol. 34,4: 355-368. 

Darimont, C.T, Fox, C.H, Bryan, H.M, Reimchen, T.E. (2015). “HUMAN IMPACTS. The unique ecology of human predators.” Science vol. 349,6250: 858-60. 

Smith J.A., Suraci J.P, Clinchy M., Crawford A., Roberts D., Zanette L.Y., Wilmers C.C. (2017). “Fear of the human ‘super predator’ reduces feeding time in large carnivores.” Proceedings. Biological sciences vol. 284,1857: 20170433. 

Frid, Alejandro, and Dill, L. (2002). “Human-Caused Disturbance Stimuli as a Form of 

Predation Risk.” Conservation Ecology, vol. 6. 

Crawford, D.A., Conner, M.L, Clinchy, M., Zanette, L.Y., Cherry, M.J. (2022). “Prey tells, large herbivores fear the human ‘super predator’.” Oecologia vol. 198,1: 91-98. 

Gaynor K., Hojnowski C., Carter N., Brashares J. (2018). “The influence of human disturbance on wildlife nocturnality.” Science vol. 360,6394 (2018): 1232-1235.