PROGRESS REPORT for COOPERATIVE BOBCAT RESEARCH PROJECT. Period Covered: 1 April 30 June Prepared by

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1 PROGRESS REPORT for COOPERATIVE BOBCAT RESEARCH PROJECT Period Covered: 1 April 30 June 2013 Prepared by John A. Litvaitis, Gregory Reed, Tyler Mahard, and Marian K. Litvaitis Department of Natural Resources University of New Hampshire Durham, NH 1 August 2013

2 1 SUMMARY BY STUDY OBJECTIVES OBJECTIVE I -- DEVELOP PROTOCOL TO ESTIMATE CURRENT ABUNDANCE OF BOBCATS AND TRACK POPULATIONS STATEWIDE. Approach #1: Use of transmitter-equipped bobcats to model suitable habitats and generate density estimate based on area requirements. A draft of Gregory Reed s MS thesis is attached. Chapters II and IV of that document address these topics. A final version of the thesis will be submitted during the next reporting period. Approach #2: Development of a method to monitor abundance of bobcat populations based on trail cameras and citizen scientist volunteers. Determining an effective set of attractants Monitoring of bobcat populations using trail cameras and citizen scientists requires a set of attractants that are both amenable to volunteer use and effective in generating adequate detection rates (Litvaitis et al. 2013). Two study areas were established in Barrington and Rochester to test the efficacy of catnip oil and a visual attractant. Each study area was centered about the core area of a collared bobcat monitored in prior research (Broman 2012). Fifteen cameras (Bushnell Trophy Camera Brown, Model ) were deployed in the Barrington study area between June 21 and 28. Eleven cameras were deployed in the Rochester study area between June 26 and 29. Camera stations were spaced approximately 0.4 km. A vial of catnip oil (Harrison 2006) and CD (Litvaitis et al. 2012) were used as attractants (Fig. 1). Catnip oil and CDs can be efficiently distributed to citizen scientists and are pleasant to work with whereas meats, urine, and gland-based lures generally produce foul odors and attract a variety of carnivores other than bobcats. Subsequent trials will use a wick soaked in catnip oil to maximize oil surface area exposure to wind, thus increasing dispersal of scent. Further, pie tins are a less expensive alternative to CDs and are likely just as effective. Detection rate will be calculated by dividing the number of bobcat detection events by the number of trap-nights. A bobcat detection event is counted for each bobcat photograph taken >30 minutes apart from prior bobcat photographs on the same camera. The number of trap-nights is the summation of 24-hour periods during which each camera is active. Analyzing indices of bobcat abundance Data from documented bobcat mortalities, public sightings, and hunter surveys were summarized in a Geographic Information System (GIS) to provide insight on current bobcat abundance and distribution in New Hampshire (Fig. 2). Wildlife Management Unit

3 2 (WMU) A, B, E1, E2, D2E, and F have produced low numbers of bobcat detections, whereas D1, D2W, and several southern WMUs have generated abundant detections. Public sightings and reported mortalities lack a practical measure of data collection effort. Thus, a lack of recorded observations for a given region may indicate either low abundance of bobcats or lack in ability to obtain records. Because hunter surveys also document the number of hours hunters spend in the field, these are a better index for inferring bobcat abundance. Figure 1. Detection station set-up. Vials contained ~3mL catnip oil and were inserted into soil. CDs were suspended at a height of m by string from low tree branches or shrubs. Attractants were located 3-5m from cameras Hunter-survey data has been analyzed for spatial and temporal variation in the abundance of bobcat observations by WMU (Fig. 3, Table 1 and 2). WMUs vary substantially in number of bobcats observed, hours hunted, hunting intensity, and bobcat observation rate. Observation rates should be interpreted with consideration of number of bobcats observed and hours hunted (effort). For example, WMU E3 has produced a relatively high observation rate of 3.33 bobcats per hour, but this

4 3 is calculated from one bobcat observation and 444 hunting hours across all four years. Other WMUs were more heavily hunted, but had low observation rates (e.g., M, A2). Because substantial effort to collect bobcat observations occurred in these regions, the data suggest that bobcat abundance may have been lower compared to regions where substantial effort and high observation rates occurred (e.g., D2W, H2N, K, J2). The results of this analysis may be used to select study areas for trials of camera surveys involving citizen scientists. Positioning study areas across a gradient of bobcat Figure 2. Bobcat mortalities, public sightings, and hunter sightings for New Hampshire townships from 2009 through Values in legend indicate number of records for each index summed over all four years. Hunter and public sighting records may include multiple sightings reported by one observer in a restricted timeframe. In these instances, observations may have involved one or multiple individuals. Bobcat mortalities include deaths due to motor vehicle collisions (n = 64), incidental trappings (n = 31), being shot (n = 3), euthanasia (n = 1), deliberate trapping due to nuisance (n = 1), and a predator (n = 1). Reported or discovered mortalities were documented by New Hampshire Fish and Game Department (NHF&G). Public sightings were collected by UNH researchers. Hunter sightings of bobcats were collected by NHF&G via survey cards.

5 4 observation rates should result in a comparable gradient of measured bobcat abundance if bobcat observation rates are directly proportional to bobcat abundance. This inference seems to be supported by the most recent habitat suitability model (Fig. 4). Figure 3. Hunters observation rates of bobcats from 2009 through 2012 by WMU. Values are expressed as number of bobcat observations per 1000 hunting hours. WMU for which less than 2,000 hunting hours and less than 2 hours per km 2 were reported across all four years are outlined in red. Survey effort among these WMUs was substantially less and rates are based on low numbers of bobcat observations. See Tables 1 and 2 for annual values and details of data analysis.

6 5 Table 1. Hours hunted, number of bobcat observations, and bobcat observation rates for each Wildlife Management Unit (WMU) from 2009 through In some cases, hunters may have generated simultaneous bobcat observations by encountering multiple bobcats at once (e.g., mother with kittens). In other cases, hunters may have generated multiple bobcat observations by observing a bobcat on > 1 occasions within a single hunting outing. Columns of four-year totals of hunting hours and number of bobcats observed are provided. Four-year average bobcat observation rates have been calculated for each WMU and are expressed in the rightmost column as the number of bobcat observations per 1000 hunting hours. The data in this table has been summarized from records of 75,249 hunting outings across all four years. Records of hunting outings containing contradicting entries in town and WMU fields have been excluded from analysis (n = 591). Records for which WMU entries were not fully specified (e.g., C rather than C1 or H2 rather than H2N ) were specified to WMU designations used in this table according to town of hunting outing when possible. Records with WMU designations that were not fully specified and were ambiguous due to town occupancy of multiple WMUs (n = 2,643) were excluded from analysis. Determining Optimal Survey Period Bobcat detectability may vary according to behavioral change with season. Seasonal influence on the amount of effort required to detect bobcats should be considered when selecting a timeframe for camera surveys. Bobcats are thought to be most likely to encounter detection stations during the time of year when they are travelling most. Because public sightings and mortality indices are collected year-round, monthly frequency of reported observations may be positively correlated with bobcat travel rates. Winter months have yielded the most public sighting and road mortality records over the past four years (Fig. 5). However, this has not been the case for each year separately, as fall of 2009 and 2012 produced more public sightings than the winters of those years. Data from collared bobcats used in research conducted by Broman (2012) may be analyzed as an additional source of information on seasonal variation in bobcat travel rates.

7 6 However, survey period may also be constrained by periods when the greatest numbers of volunteers are able and willing to participate. Table 2. Four-year totals of reported hunting hours, total area, and hunting intensity for each WMU.

8 Figure 4. Hunters observation rates of bobcats from 2009 through 2012 by WMU (left) juxtaposed with the habitat suitability model (right). Observation rates are expressed as number of bobcat observations per 1000 hunting hours. WMUs for which there are less than 2,000 reported hunting hours and less than 2 hours per km 2 have black hashing. Survey effort among these WMUs was substantially less and rates are based on low numbers of bobcat observations. See Tables 2 and 3 for annual values and details of data analysis. 7

9 8 Figure 5. Counts of road mortality and public sighting records by season for years Gauging camera survey effort Literature was analyzed to provide an estimate of expected bobcat density in New Hampshire. Understanding bobcat density will be useful for estimating the number of individuals expected to occupy a study area of a given size. This will allow for design of camera surveys that sample bobcat populations large enough to confidently infer differences in abundance between regions or over time. Because bobcat density has not been directly measured in New England, female bobcat home range (HR) estimates in New Hampshire were compared to a scaling of bobcat densities and female home range sizes measured across the United States (Fig. 6). Telemetry studies by Litvaitis et al. (2011) included five collared female bobcats located in southwestern (n = 1) and southeastern (n = 4) New Hampshire. Estimated female HRs ranged from to km 2, (μ = 23.8 km 2 ). For

10 9 comparison, a telemetry study in northwestern Vermont involving 4 collared female bobcats generated female HR estimates ranging from 7.0 to 34.5 km 2 (μ = 22.9 km 2 ) (Donovan et al. 2011). A telemetry study in western Maine calculated female HRs that ranged from 18.2 to 41.1 km 2, (μ = 32.5 km 2 ) (Litvaitis et al. 1986). Based on the 5 female HR estimates in New Hampshire and the power regression equation generated through HR scaling (Fig. 6), we suggest that densities of 4.8 (HR = μ +SE = 26.7 km 2 ) to 6.2 (HR = μ -SE = 20.9 km 2 ) individuals/100 km 2 may be typical for the state. These figures may be used to provide general estimates of the study area size needed to incorporate bobcat populations large enough to make valid inferences of abundance gradients. Figure 6. Correlation of average female home range sizes and bobcat density. Data come from multiple telemetry studies of bobcat distribution across the U.S. as summarized by Anderson and Lovallo (2003). Equation and trend line were fit using a power regression. After a study area has been established, the desired magnitude of detection events Z may be used to estimate the required survey duration D (in days) by the following equation: D = Z R -1 C -1 (Equation 1) C is the number of cameras in the survey and R is the detection rate, or number of bobcat detection events per trap-night. Therefore, survey duration and number of cameras involved will depend largely on rates at which bobcats are detected in New Hampshire, which are expected to be within the range of 0.01 to 0.04 detection events per trap-night (Litvaitis et al. 2013). Bobcat density and

11 10 detection rate will be taken into account in the consideration of occupancy (MacKenzie 2006) and capture-recapture surveys involving citizen scientists. As an example, envision a 1000-km 2 area (average WMU size) across which an occupancy study is to take place. In occupancy studies, a study area is subdivided into sample units that are surveyed for presence of the target species on multiple occasions. For each sample unit, researchers wish to survey with enough effort to provide a detection probability of 0.6. In other words, researchers wish to achieve an average of 0.6 detections per occupied sample unit (Z = 0.6). Let us assume that detection rates are 0.02 detection events per trap-night (R = 0.02), as previously found in New Hampshire (Litvaitis et al. 2013). Let us also assume that 5 cameras are available per sample unit. In this situation, the required survey duration, D, would be equivalent to Z R -1 C -1 = 0.6* *5-1 = 6 days (Eq. 1). Developing a method to identify individual bobcats As occupancy modeling assesses organism distribution, and indices measure parameters correlated to abundance (e.g., hunter sightings, public sightings), capture-recapture estimation is a more direct abundance measure (White et al. 1982). Non-invasive capture-recapture methods of abundance estimation have been applied to felid populations using camera trapping and analysis of coat pattern as a method of identifying individuals (Ocelot: Trolle & Ke ry 2003, Tiger: Karanth et al. 2006, Iberian lynx: Garrote et al. 2011). At least two studies have used these techniques to successfully assess bobcat abundance (Heilbrun et al. 2006, Larrucea et al. 2007). These studies used non-baited active infrared camera stations placed along trails. Thus, the majority of photographs were lateral views of bobcats travelling. Markings on the inner and outer legs, flanks, and faces were commonly used in image comparisons. Variation in photograph angle, and positions of legs and faces within photographs influenced the ability to compare individuals (Heilbrun et al. 2003). Because of these variations and asymmetry in pelage markings, several photographs of the same individual were often required to obtain a successful recapture. Sampling in both studies occurred in regions where dense or chaparral vegetation was present, which is thought to encourage bobcat use of trails and high detection rates (Heilbrun et al. 2006). Because bobcat use of trails in New Hampshire s woodlands may not be as prevalent, lower capture density and higher variation in the angles at which bobcats are photographed may lower the ratio of successful recaptures to total captures. The efficacy of these techniques in New Hampshire is unknown, but worthy of investigation.

12 11 Images from camera trap photographs of bobcats ed to UNH by public observers in New Hampshire are being analyzed using methods similar to those of Larrucea et al. (2007). Depending on criteria, image pairs are classified as representative of the same individual, representative of different individuals, or indeterminate. When images display corresponding areas of the pelage of each bobcat, distinct marking arrangements are compared (e.g., inner front left leg spot arrangement, markings above eyes, and stripes on lower right flank). If distinct markings can be matched in three locations, the two images will be considered to be of the same individual. Thus far, this has not occurred, as photographs are mailed from observers throughout New Hampshire. However, two photographs ed by the same public observer include bobcat images that are a very near match (Fig. 6). If marking arrangements are distinctly different in three corresponding locations, the images are considered to represent two different individuals (Fig. 7). If three marking comparisons can neither be confidently matched nor determined to not match, the comparison is classified as indeterminate. If the two images are from different angles, the general overall patter of pelage markings, presence and size of ear tufts, overall conformation of the bobcat, and locations and times between photographs are considered. Figure 6. These images were captured approximately 30 hours apart at the same camera station and likely represent the same individual. Circles may represent locations of identical markings, but stretching of the animal s right forelimb in the image on the right hinders comparison. Brightness and contrast have been adjusted to ease analysis.

13 12 Figure 7. Two photographs of different individuals and three corresponding locations on the pelage of each individual used for comparison. Note spots vs. stripes on the right hind limb, distinctly different markings on the inner left forelimb, and different marking arrangements on the necks. Brightness and contrast have been adjusted to ease analysis. Approach #3: Evaluate the application of population genetics using tissue from road-killed bobcats. To date, DNA has been extracted from all carcass samples provided by NHF&G, and reaction conditions for microsatellite analyses have been optimized. McIntire-Stennis funding has been secured to conduct a genetic study to estimate the effective population size of NH bobcats, and to determine the effects of habitat discontinuities on gene flow among contemporary populations. Furthermore, a teaching-assistantship supported MS graduate student (Rory Carroll) has been recruited, who will start working on the population genetics in September OBJECTIVE II -- COMPARE ABUNDANCE OF BOBCATS IN NEW HAMPSHIRE TO POPULATIONS IN ADJACENT STATES. Chapter IV of the attached draft thesis addresses this objective. OBJECTIVE III -- IDENTIFY POTENTIAL WILDLIFE CORRIDORS. Chapter III of the attached draft thesis addresses this objective.

14 13 LITERATURE CITED Anderson, E. M., and M. J. Lovallo Bobcat and lynx. Pages in G. A. Feldhamer, B. C. Thompson, and J. A. Chapman, editors. Wild Mammals of North America. Second Edition. The Johns Hopkins University Press, Baltimore, Maryland, USA. Broman, D. J. A A comparison of bobcat (Lynx rufus) habitat suitability models derived from radio telemetry and incidental observations. MS Thesis, University of New Hampshire, Durham 76pp. Donovan, T. M., M. Freeman, H. Abouelezz, K. Royar, and A. Howard Quantifying home range habitat requirements for bobcats (Lynx rufus) in Vermont, USA. Biological Conservation 144: Garrote, G., R. P. Ayala, P. Pereira, F. Robles, N. Guzman, F. J. Garcia, M. C. Iglesias, J. Hervas, I. Fejardo, M. Simon, and J. L. Barroso Estimation of the Iberian lynx (Lynx pardinus) population in the Donana area, SW Spain, using capture-recapture analysis of cameratrapping data. European Journal of Wildlife Research 57: Harrison, R. L A comparison of survey methods for detecting bobcats. Wildlife Society Bulletin 34: Heilbrun, R. D., N. J. Silvy, M. E. Tewes, and M. J. Peterson Using automatically triggered cameras to individually identify bobcats. Wildlife Society Bulletin 31: Heilbrun, R. D., N. J. Silvy, M. J. Peterson, and M. E. Tewes Estimating bobcat abundance using automatically triggered cameras. Wildlife Society Bulletin 34: Karanth, U. K., J. D. Nichols, N. S. Kumar, and J. E. Hines Assessing tiger population dynamics using photographic capture-recapture sampling. Ecology 87: Larrucea, E. S., G. Serra, M. M. Jaeger, and R. H. Barrett Censusing bobcats using remote cameras. Western North American Naturalist 67: Litvaitis, J. A., J. A. Sherburne, and J. A. Bissonette Bobcat habitat use and home range size in relation to prey density. Journal of Wildlife Management 50: Litvaitis, J. A., D. Broman, G. Reed, C. Merrill, and M. K. Litvaitis Progress Report for Cooperative Bobcat Research Project, University of New Hampshire, 1 June 31 December Litvaitis, J. A., G. Reed, C. Merrill, and M. K. Litvaitis Progress Report for Cooperative Bobcat Research Project, University of New Hampshire, 1 January 31 March Litvaitis, J. A., G. Reed, T. Mahard, and M. K. Litvaitis Progress Report for Cooperative Bobcat Research Project, University of New Hampshire, 1 January 31 March 2013.

15 14 MacKenzie, D. I Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Trolle, M., and M. Ke ry Estimation of ocelot density in the Pantanal using capture-recapture analysis of camera-trapping data. Journal of Mammology 84: White, G. C., D. R. Anderson, K. P. Burnham, D. L. Otis Capture-recapture and removal methods for sampling closed populations. Los Alamos National Laboratory.