Poster Sessions, pages 567-640 - ICOET

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An Assessment of Field Method Efficacy to Monitor Wildlife Presence Near Interstate 70 at Vail Pass Abstract Paige Bonaker (970-620-0079, mmeeko@gmail.com) and Len Broberg, Ph.D., Environmental Studies Department, University of Montana, 230 Burlington Avenue, Missoula 59801 USA Chris Haas, Department of Fishery and Wildlife Biology, Colorado State University, Fort Collins, CO 80523 USA The 2002 National Cooperative Highway Research Project found a need among many state transportation departments for the development of “standard analytic techniques for assessing wildlife ecology and transportation.” These agencies use several methods to assess wildlife activity in areas of highway construction projects. However, there have been very few comparison studies of the different techniques and none have been conducted near a high traffic roadway. For this study, I am ascertaining the utility of various field methods to monitor wildlife near Interstate 70 at Vail Pass, Colorado. Interstate 70 is a major east-west transportation route running through the Colorado Rockies. The associated human development that comes with this transportation corridor has resulted in varying degrees of habitat fragmentation across the region and represents a potentially significant barrier to wildlife movement. To alleviate this potential barrier effect, the Colorado Department of Transportation is proposing to build a wildlife bridge across I-70 just west of Vail Pass. The Southern Rockies Ecosystem Project (SREP) and other Colorado non-profit organizations are developing a monitoring strategy that will inform the placement of the wildlife bridge and determine baseline movement patterns and activity levels of various wildlife species before, during, and after the project. In order to gather a greater wealth of data with this strategy, a Citizen Science Wildlife Monitoring program was created in 2006 by these organizations. My study is aimed at assessing which monitoring techniques are most effective at documenting species presence across this important wildlife linkage. In this study, “effective” is used to define any method: 1) that detects a mammal, and 2) by which the user is able to identify the mammal to the species level. This paper analyzes four sampling techniques (remote sensing digital cameras, track transect surveys, scat transect surveys, and hair snare surveys) during baited and non-baited sampling sessions. Results from this study will be used by SREP to develop a cost-effective monitoring strategy for the Vail Pass region. In July and August 2006, eight lines were placed perpendicular to a two-lane dirt road called Shrine Pass Road (SPR) which runs relatively parallel to I-70. Each line consisted of four plots; two directly on the roadway shoulder (roadway sites) and two 100-150m (328’-492’) from the road (approach sites). Each plot had a 100m (328’) long x 2m (6.6’) wide track and scat transect that ran as parallel as possible to SPR. At the midway point in each approach site transect, there was a hair snare station, a track bed and a camera station. The roadway sites only had a camera station at the midway point. Two study sessions were completed. An unbaited study period ran for two weeks and included both roadway and approach stations. On day one of this session, data from all the stations and transects were collected and the survey areas were cleared. On the final day of this session, the stations and transects were again walked and all track, scat and camera data were collected. For the baited session, only the approach sites were used and the hair snares were baited with a non-rewarding scent lure. The stations and transects were sampled every day for ten consecutive days. The hair snares were re-baited every third day. All tracks, scat and hairs found were recorded and any scat and hair samples were collected. Any samples that could not be positively identified by species in the field were labeled “unknown.” Preliminary results indicate that species detection varies greatly depending on the sampling method and whether a scent lure is present. Twelve different species were positively identified by the cameras, four by scat surveys and two by track surveys. No species could be positively identified using the hair snares without genetic analysis. Interestingly, for deer and elk, preliminary analysis indicates that detections with track surveys were significantly greater than those with camera and scat surveys. In contrast, scat was a better indicator of American marten presence compared to most other techniques. In fact, preliminary results suggest that detections of marten with scat and camera surveys are significantly greater than detections with track surveys. No difference was found between scat and camera surveys. Overall, however, scat surveys were a fairly ineffective technique without genetic analysis as several scat samples were unidentifiable in the field. Furthermore, it seems that the baited session was more effective than the non-baited session for monitoring wildlife. For instance, all twelve species identified at camera stations were recorded during the baited session whereas only six species were recorded during the non-baited period. Non-baited cameras did not detect certain rodent species, domestic dogs, gray fox, porcupine, and American marten. In addition, scat detections for marten were significantly higher for the baited session than for the unbaited session. Finally, activity indices were higher for domestic dog, mule deer, red and gray fox, mice, chipmunks and squirrels at the baited camera stations. Comparatively, during the nonbaited session, the activity index was notably higher solely for rabbits. These results will aid in assessing what sampling methods are most appropriate for certain species given time constraints, seasonal environmental conditions, and availability of funding for monitoring equipment. The results from the field study will be reinforced by additional research on each method to evaluate their effectiveness in other studies. In the end, this study will contribute to developing an appropriate long-term monitoring strategy for the Vail Pass linkage. Posters 586 ICOET 2007 Proceedings
The Salmon Resource and Sensitive Area Mapping Project: Integrating a Natural Resource GIS With Field Operations Via Handheld Computer Applications Robert G. Carson (503-224-3445, bcarson@masonbruce.com) and Wendy H. Wente, Mason, Bruce & Girard, Inc., 707 SW Washington Street, Portland, OR 97205 USA Milton Hill, Oregon Department of Transportation, Transportation Data Section, 555 13th Street NE, Suite 2, Salem, OR 97301 USA Abstract: The Salmon Resource and Sensitive Area Mapping (SRSAM) project was a unique effort undertaken by the Oregon Department of Transportation (ODOT) to develop a Geographic Information System (GIS) of sensitive natural resource sites integrated with high-resolution digital color infrared imagery for the entire Oregon state highway system (approximately 9,000 miles). SRSAM data allow ODOT to plan maintenance and roadway/bridge project activities with up-to-date environmental resource data by providing maintenance workers, biologists, and transportation planners with access to a current, updateable database of sensitive environmental features. Taking full advantage of the SRSAM GIS for ODOT’s transportation planning uses required development of an effective system for delivering information to individual users in the field. To this end, ODOT contracted with Mason, Bruce & Girard, Inc. (MB&G) to develop two handheld computer applications that integrate spatially referenced data, including SRSAM’s sensitive resource data, with field data collection forms, thereby allowing users to view, manipulate, and enter data in the field. Use of these applications requires no specialized knowledge of GIS software, empowers users by providing access to an extensive database of environmental information, and through the use of standardized ArcPad forms for routine tasks improves the efficiency of field data collection and management. The first application addresses ODOT’s requirements for Mitigation Site Assessment, and enables biologists to spatially identify areas where maintenance or remediation is necessary. This allows a more rapid and efficient response when regulatory performance standards are not being met. The second application focuses on Environmental Scoping, the process by which ODOT identifies environmental issues likely to be associated with proposed projects. This coarselevel assessment requires numerous sources of environmental information. ODOT’s Environmental Scoping Application allows users to view over 20 reference data layers, including project-site imagery, while in the field. Other data layers within the Environmental Scoping application are dynamic, allowing users to update and correct spatially referenced environmental information based on their observations. The computer-based forms for both applications obviate the need to transcribe field data collected on paper, thus eliminating a time-consuming and error-prone procedure. Overall, SRSAM has provided a mechanism for ODOT to deliver sensitive natural resource data to maintenance crews, biologists, and transportation planners making field decisions that could impact sensitive resources. ODOT’s commitment to completing the SRSAM project state-wide was a key reason that ODOT’s routine road maintenance activities received a programmatic exemption under the Federal Endangered Species Act (ESA). The cost to ODOT of not obtaining the programmatic permit for maintenance activities has not been calculated, but surely would have been substantial (millions of dollars). Furthermore, the handheld computer applications, as well as the SRSAM GIS, offer a solution to a difficult ODOT challenge by standardizing data collection and storage techniques throughout the state, thereby streamlining ODOT’s efforts to protect sensitive resources. In sum, the SRSAM project represents an innovative, multifaceted solution to ODOT’s challenge of environmental compliance and stewardship. The SRSAM Project During the late 1990s the Oregon Department of Transportation (ODOT) recognized that it could play a central stewardship role in protecting and enhancing Oregon’s natural resources. ODOT also realized that attention to natural resource issues as a routine procedure during transportation-related development or maintenance activities could reduce the incidence of unnecessary negative impacts to those resources and the associated costs of mitigation or special permitting. To this end, ODOT began an effort to integrate natural resource management with its transportation system maintenance and development activities. ODOT identified two primary components that would be instrumental to its efforts: 1) a comprehensive inventory of sensitive natural resources along ODOT’s transportation network, and 2) capability to produce maps, primarily to be used during maintenance activities, to indicate the locations of sensitive resources and associated restrictions. ODOT contracted with Mason, Bruce & Girard, Inc. (MB&G), a natural resources consulting firm, to collect the desired natural resource information along ODOT’s transportation corridors and develop the associated Geographic Information System (GIS) for storing and updating the statewide inventory. ODOT referred to this effort as the Salmon Resources and Sensitive Area Mapping (SRSAM) project. To build the GIS, MB&G acquired high resolution color infrared imagery of the entire highway system across the state of Oregon. These imagery data were coupled with existing and field- verified sensitive resource data to form the basis of the SRSAM GIS (Carson et al. 2001, Carson et al. 2003). By the end of the SRSAM project development phase in 2005, MB&G had built a GIS inventory of sensitive natural resource data along all state-maintained highways in Oregon, covering approximately 9,000 roadway miles. Current Uses of SRSAM Data The SRSAM data corridor extends at least 500 feet from the centerline along each side of the roadway. This corridor approach accurately captured the data needed to produce the maps ODOT originally desired for use during maintenance and project planning activities. ODOT uses the SRSAM GIS to produce two types of maps that depict: 1) Resource Areas (i.e., “RES” maps), and 2) Restricted Activity Zones (i.e., “RAZ” maps). Bridging the Gaps, Naturally 587 Posters
Page 1 and 2: Poster Sessions Introduction Ecolog
Page 3 and 4: metamorphs. Road mortality is likel
Page 5 and 6: eproductive abilities and survivors
Page 7 and 8: that the strongest stimuli for immo
Page 9 and 10: hectic and substantial task of crea
Page 11 and 12: Forman, R. T. T. 2000. Estimate of
Page 13 and 14: Lehtinen, R. M. S. M. Galatowitsch,
Page 15 and 16: Sealy, J. B. 2002. Ecology and beha
Page 17 and 18: Abstract Assessing the Stone Marten
Page 19: Abstract Lessons and Experiences Fr
Page 23 and 24: Environmental Scoping Application E
Page 25 and 26: Recommendations for future research
Page 27 and 28: Use of a GIS-Based Model of Habitat
Page 29 and 30: Effects of a Purpose-Built Underpas
Page 31 and 32: Background Major Objectives for Roa
Page 33 and 34: Iuell, B., H. (G. J.) Bekker, R. Cu
Page 35 and 36: Forest Service Back Roads: Utilizat
Page 37 and 38: Abstract Development of a Bald Eagl
Page 39 and 40: An Alternative to the Openness Rati
Page 41 and 42: Abstract Effectiveness of Black Bea
Page 43 and 44: Highway Median Impacts on Wildlife
Page 45 and 46: Taxa groups (largely based on Calif
Page 47 and 48: Long-Term Consequences of Winter Ro
Page 49 and 50: Abstract Effects of a Highway Impro
Page 51 and 52: Introduction Roadkill and Landscape
Page 53 and 54: The fact that we have never met two
Page 55 and 56: Figure 3. Valley grassland unit, an
Page 57 and 58: Abstract An Analytical Framework fo
Page 59 and 60: Abstract Measuring Gene Flow Across
Page 61 and 62: Introduction Wildlife Use of Open a
Page 63 and 64: automatically photograph animals th
Page 65 and 66: Our track plates did not find any s
Page 67 and 68: Abstract An Overview of Recent Deer
Page 69 and 70: Abstract Riparian Restoration Plan
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Relating Vehicle-Wildlife Crash Rat
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Simulation-Optimization Framework t
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