Hunting and Hooves Hit the Highway:

Transcription

1 : Analysis of the relationships between hunting and vehicle collisions with deer and elk in a West-Central Idaho game management unit University of Idaho McCall Outdoor Science School 1800 University Ln, McCall, ID moss0035@vandals.uidaho.edu

2 1 Abstract Human impacts on animal movements can be a complex relationship to track and quantify. Studies have shown that both elk and deer are more vigilant, more likely to constrain themselves to a smaller area, and less likely to cross roads during the fall hunting seasons (August 15-November 30) as a defense adaptation against human predation. As a result, it might be inferred that there would be a lower chance of vehicle-wildlife collisions with these species during hunting seasons. This study analyzed Idaho Department of Fish and Game hunting and roadkill data for an area in West-Central Idaho between the years of 2011 and 2016 to investigate how hunting potentially impacts the frequency of vehicle collisions with deer (Odocoileus spp.) and elk (Cervus canadensis). Results showed that during hunting season, the number of average daily vehicle collisions with elk and deer were significantly higher than all other seasons of the year. Additionally, between years, an increase in days spent hunting was strongly and significantly correlated with an increase in average daily road-killed elk and deer during hunting season. This study challenges some previous literature, as it seems to indicate a positive correlation between hunting activity and vehicle-wildlife collisions. This is a useful insight into the movement and behavior of these animals during times of high hunting activity, and leaves room for further, more focused research. Additionally, this may be applicable as an educational resource for motorists.

3 2 Introduction Elk and deer-vehicle collisions can have significant ecological, social, and economic impacts. Conover et al. (1995) estimated that over 1 million deer-vehicle collisions occur across the U.S. annually. In Utah alone, deer-vehicle collisions cost approximately $7.5 million annually, with costs coming in the form of human injury and death, property damage, and deer fatalities (Bissionette et al. 2008). The risk of wildlife-vehicle collisions (WVCs) can be influenced by many environmental factors, including vegetation type, elevation, and distance to food or water sources (Gunson et al. 2010, Clevenger et al. 2003). The likelihood and frequency of motor vehicle collisions with wildlife can also be affected by anthropogenic factors, such as traffic speed, traffic volume, type of road, and density of roads (Ng et al. 2008; Clevenger et al. 2003). Collisions with elk and deer make up 82.8% of all reported roadkill on the Idaho Department of Fish and Game s website (Roadkill & Salvage, 2017). Directly to the west of Idaho in Washington state, 415 elk and nearly 15,000 deer carcasses were recovered from state and federal highways between 2000 and 2004 (Myers et al. 2008). Additionally, Decker et al. (1990) estimates that in a New York county, there are 5 unreported deer collisions for every 1 deer collision reported, so actual numbers are likely higher than those reported. Thousands of elk and deer hunters purchase hunting licenses each year, and they spend between 27,000 and 39,000 days hunting each year (Harvest Statistics, 2017). Studies have shown that both elk and deer are more vigilant, more likely to constrain themselves to a smaller area, and less likely to cross roads during the fall hunting seasons as a defense adaptation to human predation (Paton et al. 2017; Little et al. 2016). Gilbert et al. (2016) created a model showing that the recolonization of cougars in the eastern United States could result in a 22% decrease in deer-vehicle collisions due to predation. If humans play a similar predatory role in the ecosystem in the form of elk and deer hunting, it is

4 3 reasonable to think that hunting seasons can lead to a decrease in elk and deer populations, and therefore a decrease in WVCs with elk and deer. This study asked how WVCs with elk and deer are related to elk and deer hunting season dates and hunting intensity. It was hypothesized that WVCs with elk and deer would decrease during hunting season compared to other times of the year, because the animals are decreasing movement in response to hunting. It was also hypothesized that as hunting pressure and number of animals harvested increased, deer and elk VWCs would decrease. Materials and Methods Study Area This study utilized data referencing Idaho Department of Fish and Game (IDFG) game management unit (GMU) 24. This area encompasses parts of Valley County, Idaho, USA, including the towns of McCall and Cascade and a stretch of state highway 55. Parts of Payette and Boise National Forests make up 42.37% of the area of GMU 24. Around 75% of GMU 24 is forest, and 15% is irrigated land. ( a. b.

5 4 Figure 1. Details of IDFG Game Management Unit 24 (a) and location of GMU 24 in the state of Idaho (b). Maps obtained from Data Collection and Organization Roadkill data was accessed through the Idaho Department of Fish and Game s website ( Idaho drivers are required to report roadkill to this database if they intend to salvage any part of the animal. IDFG and Idaho Transportation Department workers also report found roadkill to this database. This database was downloaded as a.csv file and filtered to only display deer, mule deer, whitetail deer, and elk in reported IDFG game management unit (GMU) 24. There were no entries between the years of 1986 and 2009, and 2009 and 2010 appeared to be missing data from parts of those years. For these reasons, the period of was chosen for analysis. Occurrences of road-killed elk and deer in GMU 24 were separated into groups by year and season. The seasons were December 21 March 19 (winter), March 20 June 20 (spring), June 21 August 14 (summer before hunting season), August 15 November 30 (hunting season), and December 1 December 20 (autumn after hunting season). The time period to be labeled as hunting season was chosen by the earliest opening of a deer or elk season in GMU 24 to the latest closing of a deer or elk season in GMU 24. Average daily occurrence of roadkill was figured for each season of each year in order to account for the differences in number of days per season. Hunting data was accessed from the IDFG website ( This included the total number of days hunters spent pursuing deer and elk in GMU 24 each year and the total number of animals harvested each year. Data Analysis

6 5 All statistical analyses were performed using R (version 3.4.1) software. To test the hypothesis that number of days spent hunting would have a positive correlation with in-season roadkill, I conducted a linear regression analysis of the two variables. Similarly, a regression was done comparing total number of animals harvested each year to the total number of road-killed deer or elk during hunting season each year to test the hypothesis that there would be a positive correlation. To test the hypothesis that collisions are less frequent during hunting season, an analysis of variance (ANOVA) test compared the average daily roadkill occurrences during each season. A pairwise t-test was also performed on this dataset to determine how significantly different each season was from each of the others. Results Linear regression tests compared total days hunted and total animals harvested each year to the number of road-killed elk and deer during the hunting season (figs. 2 and 3). For days hunted and inseason collisions, a strong (R 2 = 0.672), positive, significant (P = 0.028) relationship was detected. For total animals harvested and in-season collisions, P = and R 2 = 0.309, indicating that there is neither a significant nor very strong relationship between these variables. Figure 4 shows the average daily road-killed elk and deer for each season. It is notable that the daily average during hunting season is over three times higher than any other season s daily average. A pairwise t-test was used to test for significant differences in daily average roadkill between the five seasons identified for this study (table 1). Hunting season was significantly different from all other seasons, with low p-values ranging from to

7 6 Figure 2. Scatterplot displaying the relationship between the number of days hunted by all hunters in GMU 24 and the average daily occurrence of elk or deer roadkill in GMU 24 during the associated hunting season. For this model, R 2 = and P = Figure 3. Scatterplot displaying the total number of animals harvested from GMU 24 per hunting season compared to the average daily occurrence of elk or deer roadkill in GMU 24 during the associated hunting season. For this model, P = and R 2 =

8 Average Daily Roadkilled Animals 7 Average Daily Roadkilled Elk and Deer, by Season ( ) Winter (Dec 21-Mar19) Spring (Mar20-June20) Summer before Hunting (June21-Aug14) Season Active Hunting Seasons (Aug15-Nov30) Autumn after Hunting (Dec1-Dec20) Figure 4. The average daily number of road-killed deer or elk reported during each season, averaged across the six years from Vertical black bars represent standard error for the dataset. Figure 5. Boxplot displaying the average daily road-killed elk or deer per season in GMU 24 between 2011 and The vertical axis represents the daily average roadkill. On the horizontal axis, from left to

9 8 right, the seasons are hunting, autumn after hunting, winter, spring, and summer before hunting season. Boxes represent the middle 50% of the data for that season, and bold horizontal black lines in boxes represent median values for that season. Table 1. Results of a pairwise t-test used to observe statistical difference between each season individually. Scores of >0.05 are considered not significantly different. Aug15Nov30 Dec1Dec20 Dec21Mar19 June21Aug14 AvgDaily - Roadkill Dec1Dec Dec21Mar June21Aug Mar20June Discussion and Conclusion The results from this study did not support the original hypotheses, but instead showed evidence completely opposite of what was expected. Interestingly, days of hunting and number of animals harvested had positive relationships with the number of road-killed elk and deer during hunting season. As the number of animals harvested by hunters went up, so did the number of VWCs with elk and deer. This contradicts the idea of human hunters as predators that would lower populations and, consequently, VWCs (Gilbert et al. 2016). However, it may be the case that harvest increased because

10 9 the populations of deer and elk increased the previous year. If this was the case, then hunting isn t decreasing the population, but rather just holding it steady. Therefore, as population increased, the number of animals to be harvested increased, and the number of animals to be hit by vehicles increased. The correlation between harvest and roadkill may not be causal. Similarly, hunting pressure and roadkill may not be causal. It could be that more hunting licenses were issued which would increase the number of days hunted in response to a higher harvestable elk or deer population, which would also lead to a higher chance of an elk or deer being hit by a vehicle. It is also possible that higher numbers of days hunted resulted in more movement by deer and elk in response to hunting pressure, leading them to cross more roads and move more rapidly (Cleveland et al. 2012; Preisler et al. 2013). Collisions during hunting season were significantly different from all other seasons, indicating that there is something significant about the hunting and/or autumn season when it comes to occurrence of elk and deer roadkill. However, with this dataset, collisions during hunting season were over three times higher than in any other season. This challenges the logic supported by Paton et al. (2017) and Little et al. (2016) that deer and elk collisions would decrease because the animals are more timid and less mobile during hunting season. However, Cleveland et al. (2012) showed that elk that are being hunted will move more rapidly than elk that are not being hunted. Additionally, Preisler et al. (2013) concluded that elk will increase their movement to avoid human disturbances such as hiking, horseback riding, and ATV riding all modes of travel that may be employed by hunters. This rapid movement may explain part of the increase. However, it is important to consider that correlation does not always equal causation. Especially in this case, the sharp increase during fall may be able to be partially attributed to natural movement patterns. Roadkill numbers will vary by season naturally (Conard & Gipson 2006). More specifically, during autumn, forage species such as elk and deer will increase their movement naturally (Skovlin 1967; Ager et al. 2003; Cleveland et al. 2012). A significant limitation of this study was that the natural movement of elk and deer during autumn was not

11 10 accounted for when analyzing roadkill per season. Utilizing a mathematical normalization to account for natural movement patterns would be useful in future research to isolate hunting as the controlled variable. Another limitation was the inconsistency in reporting on the IDFG website. Roadkill was selfreported by citizens, and the data sets were relatively small. With more data and a bigger area, this study could maybe reveal new patterns or insights. Regardless of whether or not road kills were due to hunting, this study is useful and applicable as education for drivers in Idaho GMU 24, as there is consistently a sharp increase in WVCs from August 15 to November 30. Works Cited Ager, A. A., Johnson, B. K., Kern, J. W., & Kie, J. G. (2003). Daily and seasonal movements and habitat use by female rocky mountain elk and mule deer. Journal of Mammology, 84(3), Bissionette, J. A., Kassar, C. A., & Cook, L. J. (2008). Assessment of costs associated with deer-vehicle collisions: human death and injury, vehicle damage, and deer loss. Human-WIldlife Conflicts, 2(1), Cleveland, S. M., Hebblewhite, M., Thompson, M., & Henderson, R. (2012, September). Linking Elk Movement and Resource Selection to Hunting Pressure in a Heterogeneous Landscape. Wildlife Society Bulletin, 36(4), Clevenger, A. P., Chruszcz, B., & Gunson, K. E. (2003). Spatial patterns and factors influencing small vertebrate fauna road-kill aggregations. Biological Conservation, 109, Conard, J. M., & Gipson, P. S. (2006, December). Spatial and seasonal variation in wildlife-vehicle collisions. The Prairie Naturalist, 38(4), Conover, M. R., Pitt, W. C., Kessler, K. K., DuBow, T. J., & Sanborn, W. A. (1995). Review of human injuries, illnesses, and economic losses caused by wildlife in the United States. Wildlife Society Bulletin, 23, Gilbert, S. L., Sivy, K. J., Pozzanghera, C. B., Dubour, A., Overduijn, K., Smith, M. M., & Prugh, L. R. (2016). Socioeconomic benefits of large carnivore recolonization through reduced wildlife-vehicle collisions. Conservation Letters, 10(4), Gunson, K. E., Mountrakis, G., & Lindi, Q. J. (2010). Spatial wildlife-vehicle collision models: A review of current work and its application to transportation mitigation projects. Journal of Environmental Management,

12 11 Little, A. R., Webb, S. L., Demarais, S., Gee, K. L., Riffell, S. K., & Gaskamp, J. A. (2016). Hunting intensity alters movement of white-tailed deer. Basic and Applied Ecology, 17(4), Myers, W. L., Chang, W. Y., Germaine, S. S., Vanger Haegen, W. M., & Owens, T. E. (2008). An analyis of deer and elk-vehicle collision sites along state highways in washington state. Olympia, WA: Washington Department of Fish and Wildlife. Ng, J. W., Nielson, C., & Cassady St. Clair, C. (2008). Landscape and traffic factors influencing deer-vehicle collisions in an urban environment. Human-Wildlife Conflicts, 2(1), Paton, D. G., Ciuti, S., Quinn, M., & Boyce, M. S. (2017). Hunting exacerbates the response to human disturbance in large herbivores while migrating through a road network. Ecosphere, 8(6). Preisler, H. K., Ager, A. A., & Wisdom, M. J. (2013, March). Analyzing animal movement patterns using potential functions. Ecosphere, 4(3), Skovlin, J. M Fluctuations in forage quality on summer range in the Blue Mountains. United States Department of Agriculture, Forest Service, Pacific Northwest Research Station, Research Paper PNW-44: R: Foundation for Statistical Computing Platform (2017). Version Vienna, Austria.