Anthropogenic Road Noise Effects on Small Mammals

by Alyssa Giordano

Over the summer I worked on an experiment that investigated the effects of chronic road (anthropogenic) noise on free-living small mammals. I was a recipient of the 2020 summer research award from the Office of Undergraduate Research (OUR), which helped me greatly to pursue this research under the supervision of Dr. Michael Sheriff. I have always felt a strong connection to ecology and conservation biology, and this project has captivated me and rooted me further to this field, propelling me to pursue this kind of research in my career. This opportunity has helped me in deciding to continue my education to earn a master’s degree. I hope to pursue further projects related to anthropogenic effects, with implications to ecology and wildlife management.

Background:

Predators can alter prey populations through direct killing and consumption, but also through non-consumptive, risk effects (Peacor et al. 2020; Sheriff et al. 2020). Such effects include changes to behavior, physiology, fecundity, and survival. Small mammals for example are known to respond to predation risk by changing their habitat use, activity patterns, and foraging behavior (Lima 1998).

Chronic traffic (anthropogenic) noise has shown dramatic increases over the last few decades with the expansion of resources and transportation (Shannon et al. 2016). For example, the United States’ population increased by about one third and traffic nearly tripled between 1970 and 2007 (Barber et. al. 2010). Road noise can affect prey’s ability to perceive predation risk cues and, thus, alter their risk responses which can be critical to survival (Francis and Barber 2013; Shannon et al. 2016). Road noise is hypothesized to alter prey responses to predation risk in three distinct ways, i) mask auditory cues of predation resulting in greater antipredator responses, as prey have more difficulty detecting their predators and find the area riskier (Barber et. al. 2010), ii) mask and distract prey due to the excess of auditory signals resulting in a reduction in antipredator responses, as prey do not perceive the area as risky given they cannot detect their predators (Blumstein 2014, Chan et. al. 2010), or iii) be perceived as a threat itself, with increased antipredator responses above that with which prey respond to risk alone (Shannon et. al. 2016, Tyack et. al. 2011). Within this research I tested each of these hypotheses by examining the food intake and foraging behavior of free-living deer mice concurrently exposed to both road noise and predation risk. I predicted that if road noise resulted in an increase in antipredator behavior, small mammals would find the area to be riskier and forage less. If road noise resulted in a decrease in antipredator behavior, small mammals would be distracted by the excess sound and forage more. If road noise resulted in an increase in antipredator behavior when not concurrently treated with predation risk, small mammals would find the road noise itself risky and forage less.

Methods:

To conduct my experiment, I used audio playbacks to manipulate the acoustic environment of free-living deer mice and other small mammals. My audio treatments consisted of non-predatory control, avian predators, road noise, and road noise + avian predators. Each treatment was played for three days, with a two day buffer between treatments to avoid contamination of effects from one treatment to the next. To measure foraging activity, I set up giving-up density (GUD) trays. GUDs are based on the marginal value theorem (Charnov 1976), such that the return of a foraging patch diminishes and the cost increases the more an animal forages, ultimately the animals ‘give up’ and move on (Brown et. al. 1999). The point at which an animal gives up has been shown to be impacted by predation risk (Brown 1988). Plastic foraging trays were filled with 2.5g of millet seed and 2 cups of sand (figure 1). Six trays were placed into the field for 2 consecutive 24 periods beginning at 0700h on days 2 and 3 of a treatment. Motion detecting cameras (purchased as part of the OUR grant) were deployed at 3 of the GUDs to measure foraging behavior. Each treatment was replicated three times and the total duration of the experiment occurred from July 1st to September 13th, 2020.

I analyzed the foraging data using 2-way ANOVAs and a tukey test with treatment and night as fixed effects (RStudio Desktop 1.3.1093).

Results:

I found that there was a significant effect of treatment and of night (Table 1) on the amount of food eaten by small mammals (Fig. 2). When exposed to predation risk small mammals decreased the amount of food eaten, when exposed to predation risk and road noise small mammals ate a similar amount, and when exposed to road noise alone small mammals slightly increased the amount that they ate compared to the control treatment.

I also found that small mammals ate more on night 2 compared with night 1 (Fig. 2).

Discussion:

This data supports the hypothesis that road noise will cause prey to reduce their antipredator responses to predation risk. (Chan et al. 2010) found something similar, that during noise exposure Caribbean hermit crabs allowed simulated predators to get closer, suggesting they had an impaired ability to respond. This may occur because prey have a harder time perceiving risk cues when also exposed to noise, creating a clouded soundscape. This will have consequences to prey by exposing them to predators more, as the prey will not engage in antipredator responses as much or as fast.

I still need to analyze the behavioral data from the motion detecting cameras with footage taken over 2.5 months. The video footage will be analyzed for the number of visits to the GUDs, the time spent during each visit, and the time spent being vigilant. This portion of the project will delve deeper into the specific effects on animal risk response when exposed to chronic road noise and predation. I have determined the general effect of chronic road noise, but it will be very interesting to see their true behavior, and if it shows anything that the preliminary foraging data cannot.

A special thanks to the Office of Undergraduate research and the College of Arts and Sciences for providing me with funding for my project. Though the analysis is not yet fully complete, the funding has given me an invaluable opportunity to explore an important wildlife conservation topic in depth. Finally, I’d like to acknowledge and thank Dr. Michael Sheriff for the support and guidance throughout my project.

References:

Barber, J. R., Crooks, K. R., & Fristrup, K. M. (2010). The costs of chronic noise exposure for terrestrial organisms. Trends in ecology & evolution, 25(3), 180-189.

Blumstein, D. T. (2014). Attention, habituation, and antipredator behaviour: implications for urban birds. Avian urban ecology, 41-53.

Brown, J.S., Laundre, J.W., & Gurung, M. 1999. The ecology of fear: optimal foraging, game theory, and trophic interactions. Journal of Mammalogy 80: 385-399.

Brown, J.S. 1988. Patch use as an indicator of habitat preference, predation risk, and competition. Behavioral Ecology and Sociobiology 22: 37-47.

Chan, A. A. Y. H., Giraldo-Perez, P., Smith, S., & Blumstein, D. T. (2010). Anthropogenic noise affects risk assessment and attention: the distracted prey hypothesis. Biology letters, 6(4), 458-461.

Charnov, E. L. (1976). Optimal foraging, the marginal value theorem.

Francis, C. D., & Barber, J. R. (2013). A framework for understanding noise impacts on wildlife: an urgent conservation priority. Frontiers in Ecology and the Environment,11(6), 305-313.

Lima, S.L. 1998. Nonlethal effects in the ecology of predator-prey interactions. What are the ecological effects of anti-predator decision-making? BioScience 48: 25-34.

Peacor, S. D., Barton, B. T., Kimbro, D. L., Sih, A., & Sheriff, M. J. (2020). A framework and standardized terminology to facilitate the study of predation‐risk effects. Ecology, e03152.

Shannon, G., Crooks, K. R., Wittemyer, G., Fristrup, K. M., & Angeloni, L. M. (2016). Road noise causes earlier predator detection and flight response in a free-ranging mammal. Behavioral Ecology, 27(5), 1370-1375.

Sheriff, M. J., Peacor, S. D., Hawlena, D., & Thaker, M. (2020). Non‐consumptive predator effects on prey population size: A dearth of evidence. Journal of Animal Ecology.

Tyack, P. L., Zimmer, W. M., Moretti, D., Southall, B. L., Claridge, D. E., Durban, J. W., … & McCarthy, E. (2011). Beaked whales respond to simulated and actual navy sonar. PloS one, 6(3).

Neuronal Protection Effects of Blueberries through Inhibition of Key Enzymes involved in the Neurogenerative Diseases

By Chelsea Spitz

This past summer, I was awarded a grant from the Office of Undergraduate Research (OUR) to conduct research in the Chemistry Department under the guidance of Dr. Shuowei Cai. The purpose of this research is to study the neuronal protection effects of blueberries against neurodegenerative diseases and develop the extraction method for blueberries. I also planned on identifying active compounds in blueberries and develop a LC-MS based method to fingerprint the blueberry extract from other extraction methods. Unfortunately, due to the ongoing pandemic, I could not get access to LC-MS system, therefore, my research is mainly focusing on development of extraction method and study the neuronal protection effects of blueberries through inhibition of key enzymes involved in the neurodegenerative disease, including the inhibition kinetics, to understand the mechanism of the neuronal protections of blueberries.

Alzheimer’s disease (AD), the most common form of dementia, is a neurodegenerative disease affecting the structural integrity of the brain. Individuals suffering from AD undergo both steady memory loss and significant cognitive decline as a result of the progressive neuronal damages leading to the death of neurons in brain. AD accounts for 60 to 80 percent of dementia cases, while vascular dementia, due to microscopic bleeding and blood vessel blockage in the brain, is the second most common cause of dementia (Alzheimer’s Association, Alz.org). It is estimated that one in 10 Americans over 65 years of age is currently living with symptomatic AD, and worldwide, 50 million people live with symptomatic AD. AD puts a huge burden on both caregivers and the health system. In 2018, the direct costs to American society for caring of those with AD totaled $277 billion, and it is projected to over $1 trillion by 2050 (Alz.org). Yet, there is no cure for AD and only a handful of drugs have been approved by FDA to manage the symptoms that includes cholinesterase inhibitors and N-methyl-D-aspartate receptor (NMDA) receptor antagonist (i.e. memantine). There is even no treatment that can slow down the progresses of the disease. This may be partly due to the lack of knowledge of the mechanism of AD.

Based on the differences seen in AD’s brains, several potential causes have been hypothesized: deficits in the cholinergic transmission; beta-amyloid plagues (Aβ); tau tangles; oxidative damage and mitochondrial dysfunction; neuronal inflammation; synapse loss; vascular changes; endosomal abnormalities, among others. Several of those hypotheses have been found to be connected to each other, and collectively, they lead to the neuron death. For example, acetylcholinesterase (AChE) is a critical enzyme to regulate the level of the neurotransmitter, acetylcholine. Both Aβ and abnormally hyperphosphorylated tau (p-tau) can increase AChE expression. The increased AChE further influences PS1 and tau-protein kinase GSK-3β. GSk-3β induces hyperphosphorylated tau (P-tau), while PS1 affects the APP processing and Aβ production. Inhibition of AChE not only can rescue the deficit of cholinergic transmissions, but also can potentially reduce Aβ and P-tau.

Tyrosinase is a key enzyme in the biosynthesis of melanin. It catalyzes two reactions: the hydroxylation of tyrosine to L-DOPA and the subsequent oxidation of L-DOPA to dopaquinone. This enzyme may also oxidize dopamine to form melanin pigments through the formation of dopamine quinone, a reaction results in the formation of highly reactive oxygen or nitrogen species (ROS) capable of inducing neuronal cell death. Oxidation stress links to both inflammation and endosomal abnormalities, which hold key for neurodegenerative diseases, including AD.

Our research, therefore, is focusing on examination of neuronal protection effects of blueberries through their inhibition of AChE and tyrosinase. Most phytochemicals are extracted from plants using methanol-based solvent. Residual methanol is highly toxic for human consumption. To explore a safer solvent for extraction of phytochemicals from blueberries, we investigate using ethanol as the extraction solvent. Over the course of the summer, we extract the polar components from blueberries using three different types of solvent system: ethanol alone; methanol alone, and methanol/acetone/water/formic acid (40/40/19/1). Our lab has been used the methanol/acetone/water/formic acid extraction system for extraction of blueberries, and our aim is to compare the biological activities of the blueberry extract using ethanol with those with methanol-based extraction. The activity of AChE was examined using the Ellman method, the tyrosinase activity is determined using L-DOPA as the substrate and monitored the enzymatic product at 490 nm. Both assays were carried out using 96-well microplate, and each sample was run triplicate. To further study the mechanism of inhibition on tyrosinase, we carried out the inhibition kinetics with the blueberry extract from ethanol. The kinetics of tyrosinase was measured every 20 s in 3 min to obtain the initial velocity rate. The contraction of blueberry extract used for inhibition kinetic measurement was 0.25 mg/ml and 0.15 mg/ml.

As shown in Figures 1,2 and 3, the extracts from all three solvent systems showed strong inhibition on AChE and tyrosinase. The extract from methanol/acetone/water/formic acid solvent showed the strongest inhibition on both enzymes. The USDA solvent mixture inhibited the enzymes approximately double that of the other two solvents while the USDA MDS and USDA EDS extracts inhibited relatively close to one another. The methanol-based extract however was still slightly stronger than the ethanol-based extract but overall, they were relatively the same. The IC50s show that the USDA Solv mixture is a much stronger inhibitor than the other two solvents because it requires a lower concentration of the extract to inhibit 50% of the enzyme.

Table 1: IC50 Values For Tyrosinase and AChE

Solvent Mix MDS EDS
Tyrosinase 0.17 mg/mL 1.66 mg/mL 1.14 mg/mL
AChE 0.72 mg/mL 3.48 mg/mL 6.89 mg/mL

 

While blueberry extract from ethanol showed less potent as that from methanol-based solvent, it still showed strong inhibition on two key enzymes that related to neurodegenerative diseases. Here, we demonstrated that ethanol can be a safe alternative to extract the bioactive phytochemicals. We further examined the inhibition kinetics of blueberry extract from ethanol to understand the inhibition mechanism on tyrosinase. As shown in Figure 4, the blueberry extract inhibits tyrosinase in a non-competitive manner (mixed mode inhibition). The inhibition constant Ki and Ki’ are 0.056 mg/ml and 0.82 mg/ml, respectively (Table 2). This suggested that the compounds in blueberry both directly interact with the active site of tyrosinase the substrate-enzyme complex.

Table 2: Inhibition Constant of Blueberry Extract

KI (mg/ml) KI’ (mg/ml)
0.25 mg/ml 0.060 0.079
0.15 mg/ml 0.051 0.085
Average 0.056 (0.006)* 0.082 (0.005)

*: the figure in parenthesis is the standard deviation from the two concentrations of blueberry extract

 

Future Plan:

The plan to continue this project consists of continuing to perform more kinetics assays using USDA MDS extract and to try and see if the data is reproducible. We also plan to work on modeling and studying the structure of the enzyme more closely as well as the compounds found in the extracts from the LC-MS data. Once we gain the access to LC-MS instrument, we will identify the compounds in the ethanol extract, and compare with those from methanol-based extracts. We will further be using NMR to confirm the compounds identified from LC-MS.

The research grant I received from the Office of Undergraduate Research allowed me to learn new skills in the lab such as the enzyme kinetics assays as well as help me find my footing for my research goals. I would like to thank my research advisor Dr. Shuowei Cai for guiding me along the way. As well as the Dean to the College of Arts and Sciences, Dean Entin, and the Office of Undergraduate Research for funding my research this summer.