Water depth and flow rate effects on black bear movements across the Ocmulgee River in Middle Georgia.
Abstract: In Georgia, there are three distinct populations of black bears (Ursus americanus, including two subspecies americanus and floridanus). The Middle Georgia population has been shown to exhibit high genetic similatities within the population and we wanted to determine if the Ocmulgee River was a barrier to bear movements. One out of 9 collared females and 7 of 17 collared males crossed the Ocmulgee River. River flow (bear = 70.7 cubic m/sec, random = 92.7 cubic m/sec) and river depth (bear = 2.6 m, random = 2.8 m) were significantly lower when bears crossed the river than random samples. The river did appear to be a barrier to females but not males. Females may be less likely than males to cross the river because of behavioral differences (e.g., cub rearing) and smaller home ranges.
Article Type: Report
Subject: Black bear (Physiological aspects)
Black bear (Environmental aspects)
Human mechanics (Research)
Hydraulic measurements (Research)
Rivers (Environmental aspects)
Authors: Bond, Bobby T.
Balkcom, Gregory D.
McDonald, J. Scott
Bewsher, Jeff M.
Pub Date: 04/01/2012
Publication: Name: The American Midland Naturalist Publisher: University of Notre Dame, Department of Biological Sciences Audience: Academic Format: Magazine/Journal Subject: Biological sciences; Earth sciences Copyright: COPYRIGHT 2012 University of Notre Dame, Department of Biological Sciences ISSN: 0003-0031
Issue: Date: April, 2012 Source Volume: 167 Source Issue: 2
Topic: Event Code: 310 Science & research
Geographic: Geographic Scope: United States Geographic Code: 1USA United States
Accession Number: 287956863
Full Text: INTRODUCTION

Throughout North America, American black bear populations have been reduced from past historical records (Dickson, 2001; Pelton, 2001). Habitar conversion, degradation, fragmentation and past unregulated harvest have contributed to the decline of the species (Pelton, 2000; 2001). However, in many areas of the country, the species has recovered and currently thrives in several locations (Pelton, 2000; 2001; 2003). In Georgia, there are three geographically separated bear populations (North, Middle, and South; Fig. 1). The North population (Ursus americanus americanus) is the largest and is associated with the Mountains and Ridge and Valley physiographic regions and northward, along the Appalachian Mountain range and is considered a contiguous population to Canada. The South population (U. a. floridanus) is the second largest and is associated with the flatwoods in the Lower Coastal Plain surrounding the Okefenokee National Wildlife Refuge and is contiguous with bear populations in North Florida. The Middle population (U. a. americanus) is the smallest population, is associated with the Upper Coastal Plain, and is not contiguous with any other state or intrastate population.

The Middle population is known to have high genetic similatities within the population (Miller, 1995; Sanderlin, 2009). Therefore, understanding potential physical barriers that may hinder bear movements in this population impacting geneflow is important. Potential physical barriers may include roads and rivers. Regarding roads, traffic volume at certain levels may impede gene flow at the individual and population level, and at greater levels may become an obstacle (Brody and Pelton, 1989; McCown et al., 2004). For example, mortalities from bear-vehicle collisions and observations from previous research on the Middle population suggest that Interstate 16 may be a barrier to bear movements (Cook, 2007). Additionally, the Ocmulgee River, which bisects this population, may act as a barrier. White et al. (2000) expressed concern that rivers could be possible barriers to bear movements and dispersal in fragmented bear populations particularly with regard to female movements. With the Middle population's high genetic similarities, we needed to examine the ability of bears to freely move within this population. Our objective was to investigate whether water depth and flow rate of the Ocmulgee River affected bear movements across this river.

METHODS

STUDY AREA

We monitored bears in portions of Bleckley, Houston, Laurens, Pulaski, and Twiggs counties (137,305 ha) in the Upper Coastal Plain physiographic region of Georgia (Fig. 2). Bear trapping effort occurred on Oaky Woods (7851 ha) and Ocmulgee (8597 ha) Wildlife Management Areas (WMA) with additional trapping on proximal private properties. The trapped area was composed of 29% wetlands (forested = 23%, open water = 1%, emergent = 1%, and shrub/scrub = 4%; GDNR, 1996; NWI, 1999) along the Ocmulgee River. The average width of the Ocmulgee River within our study area was 53.3 m. Duting our 5 y study, mean annual temperature was 18.5 C and mean annual rainfall was 113.5 cm, which was similar to their long-term means of 18.2 C and 113.9 cm, respectively (1948-2010; NCDC, 2011).

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

CAPTURE

We annually trapped bears using Fremont bear foot snares from Apr. through Aug. 2003 through 2005. We anesthetized bears using a mixture of 4.4 mg/kg of ketamine HCL (Ketaset[R], Burns Vetetinary Supply Incorporated, Farmers Branch, Texas) and 2.0 mg/kg of xylazine HCL (Rompun, Haver-Lockhart Incorporated, Shawnee, KS; Kreeger, 1996). Immobilized bears were then given 1.8 mg/kg of Tolazoline at 1 to 1.5 h after induction m reverse the effects of xylazine HCL. Bears were fitted with a 650 g very high frequency (VHF) radio telemetry collar and some male bears were fitted with a GPS equipped radio-telemetry collar. All collars were 51 mm wide and were equipped with mortality and activity sensors [Advanced Telemetry Systems (ATS), Inc., Isanti, MN]. We attached leather spacers (4 mm thick, 4 cm wide and 8.9 cm in length) between the two loose ends of the collar so the collars would break free after approximately a year to reduce the chance of heck injuries (Bond et al., 2009).

MONITORING

Bears were located 3-4 times/wk by ground radio-telemetry from Apr. 2003 through Aug. 2004. We triangulated bear locations using the loudest signal method, with the midpoint (i.e., bearing) between the azimuths at the two edges of the signal (Springer, 1979). We used a minimum of two beatings as close to a 90[degrees] angle intersection as possible (i.e., no less than a 60[degrees] and no more than 120[degrees] angle intersection; White and Garrott, 1990). We attempted to get as close as possible to the bear using roads when performing triangulation (usually <1 km). We did not hike to bears for locations so as not to disturb them and bias locational data. We collected all bearings <15 min apart. Data from GPS collars was collected when the spacers broke and the collar dropped or the bear slipped the collar (location data was retrieved from Apr. 2003 through Jun. 2007). For this study, we lumped GPS data with both biangulation and triangulation data. Because this was not an intensive habitat use study that would require highly accurate data at a fine scale, inaccuracies introduced by combining data will not impact overall results. Our study design was only reliant in determining the date and what side of the river bears were on; therefore, our data only had to be of a coarse scale.

ANALYSIS

We used a 2-step process to determine which collared bears had the potential to cross the river (Bond et al., 2001). The probability that bears were close enough to cross the river was examined by buffering the river with the second largest home range diameter for males (23,094 m) and females (6028 m). We did not use the largest home range diameter to avoid the influence of a possible outlier. Next, we overlaid the bear locations and the river buffer. If any locations for each individual bear were within that buffer, we considered that bear as having the potential to cross the river and included it in our sample for analysis.

River data was collected from a United States Geological Survey (USGS) National water information system station in Macon, GA (8.3 miles north of our bear locations; USGS, 2010). We used flow rate (cubic m/sec) and water depth in our statistical analyses to determine if these factors affected the bear's probability to cross the river. On the day the bear crossed the river we collected flow rate and water depth from the weather station. We selected the median date between locations if an animal crossed and more than a day passed between locations. For comparison, we randomly selected a day during the time period that bears were monitored and collected flow rate and depth data. In other words, if bear A crossed the river two times and was monitored from Apr. 2003 to Aug. 2003, then we used data from the weather station for two randomly selected days within the same time period that bear A was monitored. We selected an equal number of random samples to compare to our bear crossing samples. We performed a one-tailed t-test to determine if bears crossed when river flow rate and depth were significantly ([alpha] = 0.05) less than average random samples during the same time period (Sokal and Rohlf, 1981).

RESULTS

Telemetry locations (12 males and 9 females) were collected from Apr. 2003 to Aug. 2004 and GPS data was collected from GPS collars (5 males) from 2003 to 2007 for a total of 26 bears [17 males (mean age = 4.6 y, se = 0.6) and 9 females (mean age = 7.7 y, se = 1.2)]. We collected 4107 locations for males and 1298 locations for females. All males and females had locations within the buffered area around the river, indicating that they had the potential to cross the river during the study period. We observed only 1 of 9 femmes cross the river. She crossed once and then returned for a total of two crossings. However, we observed 7 out of 17 males cross the river for a total of 97 crossings. For all the bears combined, we observed a total of 99 crossings for an average of 12.4 crossings/bear (SE = 2.1; range of 2 to 20).

Examination of both flow rate [bear = 70.7 cubic ms/sec (SE = 4.5), random = 92.7 cubic m/sec (SE = 11.0); t = -1.84, P = 0.03] and depth [bear = 2.6 m (SE = 0.1), random = 2.8 m (SE = 0.1); t = -1.65, P = 0.05] were significantly different between dates of bear river crossings and random. Therefore, bears crossed at significantly lower flow rates and water depths.

DISCUSSION

The Middle Georgia bear population has been found to have the second highest within population genetic similarity of all Southeastern bear populations in the Coastal Plain (Miller, 1995), and this similarity has been a management concern for this population. Currently there appears to be multiple barriers, both proven and potential., to bear movements within this population's geographic range. Previous research on this population has shown that no wild-caught bears crossed Interstate 16 and that bears' home ranges bordered the interstate, but they did not cross it (Cook, 2007). At current traffic levels of 7700-8800 cars/d Highway 96 does not appear to be a barrier to bear movements (Cook, 2007). However, the Georgia Department of Transportation plans to expand Highway 96; therefore, the current traffic rates are assumed to increase. Within our study we have observed that the Ocmulgee River is not a barrier to male movements but is a barrier to female movements.

While not a part of our study's analysis, it's important to know the impacts of roads as possible barriers to bears. Interstates have been reported to reduce bear movements (Brody and Pelton, 1989; Beringer et al., 1990; McCowen et al., 2004) and lower the potential for genetic exchange among black bear populations (Thompson et al., 2005; Dixon et al., 2006). Roads and highways have affected spatial dynamics (Brody and Pelton, 1989; Betinger et al., 1990; Percy, 2003; McCowen et al., 2004; McCoy, 2005). Within the Middle population, Cook (2007) observed that the probability of habitat use, and bear movement were constrained by highway and county/residential road density and traffic volume. Cook (2007) also observed that crossing frequency within the Middle Georgia population was greater for highways with 2 cars per min (cpm) than that for highways with 3, 4 and 5 cpm. Evidence suggests that bears cross heavily trafficked roads less frequently, shift their home ranges in avoidance of intolerable road densities or they are obligated to cross roads more frequently than they would normally prefer in search of food resources (Brody and Pelton, 1989; Beringer et al., 1990; McCowen et al., 2004; Simek et al., 2004). None of the wild-caught research bears in the Middle Georgia population was detected to cross highways that had a volume of 8810 cars per day or to have crossed Interstate 16 (Cook, 2007). Therefore, some roads within this population appear to be barriers to bear movements.

From our study, it appears the Ocmulgee River acts as a barrier to female movements but not for males, which is similar to the results observed by White et al. (2000). White et al. (2000) is the only other study to examine river crossing by black bears. Whereas the depth of the river was statistically significant, there was a relatively small measurable difference between crossings and random (0.2 m); however, there was a greater difference in flow rate between crossings and random (22 cubic m/sec). White et al. (2000) did not collect or analyze river depth or flow in their study so it is hard to compare. White et al. (2000) followed 40 bears of which 23 crossed the river 67 times for an average of 2.9 crossings/bear, whereas we followed 26 bears of which eight crossed the river 99 times for an average of 12.4 crossings/ bear. The difference in bear river crossings between these studies may be a function of the differences in width between their river (~200 m) and our river (~53 m).

White et al. (2000) did note that most male crossings were at times (i.e., season) when the river level was at its lowest, potentially leading to narrower widths for bears to cross and slower currents (White et al., 2000). They noted that river width, current velocity, and barge traffic affected the pattern of crossings between different river courses (White et al., 2000). They speculated that females crossed due to severe flooding and logging activity, but at locations where the river was 33% narrower than average (White et al., 2000). All of the crossings they observed were on river courses 200 m wide or less, and they never observed any collared bears that crossed the Mississippi River (1600 m; White et al., 2000). Even though our river was much narrower than their rivers we still did not observe as many females (1 out 9) crossing as they did (8 out of 21). We have periodic flooding of the river bottoms along the Ocmulgee River, but we assume it is not as extensive as White et al. (2000) experienced along the White and Arkansas Rivers that led to their larger number of female crossings than we observed.

It appears that males can traverse the river, bur do so when river flow rate is below average. Female bears exhibited home ranges of one-tenth the size of males in this population (Cook, 2007) and may have also restricted their movements due to cub rearing. Even though females were within close proximity to the river, smaller home range size and behavioral differences may be two reasons they did not cross the river as often as males. Future research should test if the male movements across the Ocmulgee River are enough to ensure that genetic material interchanges within this population, more thoroughly examine all barriers to bear movements (especially after Highway 96 is expanded) and determine if there are other factors that are leading to this population's high genetic similarity.

Acknowledgments.--We acknowledge DNR personnel who assisted in bear captures: R. Beard, J. Chapman, R. Jones, T. Shover, B. Vickery, and R. Wood. We also acknowledge the Warnell College of Natural Resources, University of Georgia for assistance with telemetry. We appreciate comments from J. Bowers, J. Bowman, J. Jacquot, and two anonymous reviewers on earlier drafts. Funding and support were provided by the Georgia Department of Natural Resources, Wildlife Resources Division through the Wildlife Restoration Program (GA W-66).

LITERATURE CITED

BERINGER, J. J., S. G. SEIBERT AND M. R. PELTON. 1990. Incidence of road crossing by black bears on Pisgah National Forest, North Carolina. Internat. Conf. Bear Res. Manage., 8:85-92.

BOND, B. T., L. W. BURGER JR., B. D. LEOPOLD, J. C. JONES AND K. D. GODWIN. 2001. Habitat use by cottontail rabbits across multiple spatial scales in Mississippi. J. Wildl. Manage., 66:1171-1178.

--, G. D. BALKCOM, J. S. McDONALD, J. M. BEWSHER AND J. W. BOWERS. 2009. Estimating Retention Rates of Leather Spacers on Radio Collars for Black Bears in Georgia. Proc. Ann. Conf Southeast. Assoc. Fish Wildl. Agen., 63:70-74.

BRODY, A. J. ANO M. R. PELTON. 1989. Effects of roads on black bear movements in western North Carolina. Wildl. Soc. Bull., 17:5-10.

COOK, K. L. 2007. Space use and predictive habitar models for American black bears (Ursus americanus) in central Georgia, USA. M.S. Thesis. University of Georgia, Athens, USA. 255 p. DICKSON, J. G. 2001. Early History, p. 20-30. In: J. G. Dickson (ed.). Wildlife of Southern Forests: Habitat and Management. Hancock House Publishers, Blaine, Washington, USA. 480 p.

DIXON, J. D., M. K. OLI, M. C. WOOTON, T. H. EASON, J. W. McCOWN AND D. PAETKAU. 2006. Effectiveness of a regional corridor in connecting two Florida black bear populations. Conserv. Bio., 20:155-62.

GEORGIA DEPARTMENT OE NATURAL RESOURCES (GDNR). 1996. State of Georgia landcover statistics by county. Georgia Department of Natural Resources. Project Report 26, Atlanta, Georgia, USA. 57 p.

KREEC, T.J. 1996. Handbook of wildlife chemical immobilization. International Wildlife Veterinary Services, Inc, Laramie, Wyoming, USA. 340 p.

McCOWN, W, P. KUBLIS, T. EASON AND B. SCHEICK. 2004. Black bear movements and habitar use relative to roads in Ocala National Forest. Final Report Contract BD-016. Florida Fish and Wildlife Conservation Commission, Tallahassee, Florida, USA. 118 p.

McCOY, K. 2005. Effects of Transportation and Development on Black Bear Movement, Mortality, and Use of the Highway 93 Corridor in NW Montana. Ph.D. Dissertation. University of Montana, Mansfield, USA. 132 p.

MILLER, D. A. 1995. Systematic classification of black bears in the southeastern United States. M.S. Thesis. Virginia Polytechnical Institute and State University. 103 p.

NATIONAL CLIMATE DATA CENTER (NCDC). 2011. . Accessed 02 Aug. 2011.

NATIONAL WETLAND INVENTORY (NWI). 1999. . Accessed 12 May 2009.

PELTON, M. R. 2000. Black Bear. p. 389-408. In: S. Demarais and P. R. Krausman (eds.). Ecology and management of large mammals in North America. Prentice-Hall, Inc, Upper Saddle River, New Jersey, USA. 778 p.

--. 2001. American Black Bear, p. 224-233. In: J. G. Dickson (ed.). Wildlife of Southern Forests: Habitat and Management. Hancock House Publishers, Blaine, Washington, USA. 480 p.

--. 2003. Black Bear (Ursus americanus), p. 547-555. In: G. A. Feldhamer, B. C. Thompson and J. A. Chapman (eds.). Wild Mammals of North America: Biology, Management and Conservation. 2nd ed. The John Hopkins University Press, Baltimore, Maryland, USA. 1216 p.

PERCY, M. P. 2003. Spatio-temporal movement and road crossing patterns of wolves, black bears and grizzly bears in the Bow River Valley of Banff National Park. Ph.D. Dissertation. University of Alberta, Edmonton, Canada. 254 p.

SANDERLIN, J. L. S. 2009. Integrated demographic modeling and estimation of the central Georgia, USA, black bear population. Ph.D. Dissertation. University of Georgia, Athens, USA. 329 p.

SIMEK, S. L., P. S. KUBILIS, S. A. JONKER AND T. H. EASON. 2004. Non-Invasive Assessment of Black Bear Movements and Abundance Relative to U.S. 98 within the Aucilla Wildlife Management Area. Final Report Contract BD568. Florida Fish and Wildlife Conservation Commission, Tallahassee, Florida, USA. 69 p.

SOKAL., R. R. AND F. J. ROHLF. 1981. Biometry, 2nd ed. W. H. Freeman and Company. San Francisco, California, USA. 859 p.

SPRINGER, J. T. 1979. Some sources of bias and sampling error in radio triangulation. J. Wildl. Manage., 43:926-935.

THOMPSON, L. M., F. T. VAN MANEN AND T. L. KING. 2005. Geostatistical analysis of allele presence patterns among American black bears in eastern North Carolina. Ursus, 16:59-69.

UNITED STATES GEOLOGICAL SURVEY (USGS). 2010. . Accessed 8 Mar. 2010.

WHITE, T. H., JR., J. L. BOWMAN, B. D. LEOPOLD, H. A. JACOBSON, W. P. SMITH AND F. J. VILELLA. 2000. Influence of Mississippi alluvial valley rivers on black bear movements and dispersal: implications for Louisiana black bear recovery. Biol. Cons., 95:323-331.

WHITE, G. C. AND R. A. GARROTT. 1990. Analysis of wildlife radio-tracking data. Academic Press, San Diego, California, USA. 383 p.

BOBBYT. BOND, (1) GREGORYD. BALKCOM, J. SCOTT McDONALD AND JEFF M. BEWSHER, Georgia Department of Natural Resources, Wildlife Resources Division, Game Management Section, 1014 Martin Luther King, Jr. Boulevard, Fort Valley, Georgia 31030. Submitted 2 August 2011; Accepted 4 November 2011.

(1) Corresponding author: Telephone: (478) 825-6354; FAX: (478) 825-6421; e-mail: bobby.bond@dnr. state.ga.us
Gale Copyright: Copyright 2012 Gale, Cengage Learning. All rights reserved.