Bat Surveys on USFS Northern Region 1 Lands in Montana: 2006 Prepared for: USDA Forest Service Northern Region Prepared by: Susan Lenard, Bryce A. Maxell, Paul Hendricks and Coburn Currier Montana Natural Heritage Program Natural Resource Information System Montana State Library June 2007 MONTANA Natural Heritage Program Bat Surveys on USFS Northern Region 1 Lands in Montana: 2006 Prepared for: USDA Forest Service, Northern Region P.O. Box 7669 Missoula, MT 59807 Agreement Numbers: 06-CS-11015600-031 and 04-CS-11015600-036 Prepared by: Susan Lenard, Bryce A. Maxell, Paul Hendricks and Coburn Currier MONTANA Natural Heritage Program >V rtunsiABA /"/jtkk MONTANA ^^ T htate /!■ ^Natural Resource ^^ Library Ugjjff Information System © 2007 Montana Natural Heritage Program P.O. Box 201800 • 1515 East Sixth Avenue • Helena, MT 59620-1800 • 406-444-5354 This document should be cited as follows: Lenard, S., B. A. Maxell, P. Hendricks, and C. Currier. 2007. Bat Surveys on USFS North- ern Region 1 Lands in Montana: 2006. Report to the USDA Forest Service, Northern Region. Montana Natural Heritage Program, Helena, Montana 23 pp. plus appendices. ii Executive Su m m a r y The distribution and status of bats in Montana remain poorly documented on US Forest Service Northern Region lands. The Northern Region recognized the need for additional documentation of bats on Forest Service lands and initiated bat surveys in 2005 across the Region on selected National Forest (NF) Ranger Districts (RD). In Montana, these included Bozeman RD -Gallatin NF, Swan Lake RD-Flathead NF, Townsend RD- Helena NF, Libby RD -Kootenai NF, and Judith RD-Lewis & Clark NF. In 2006, the second year of the project, increased number of surveyors in the field resulted in greater survey effort with both mist-net and acoustic sampling in the following RDs: Butte and Dillon RD - Beaverhead-Deerlodge NF, Sula and West Fork RD - Bitterroot NF, Ashland, Beartooth, and Sioux RD - Custer NF, Tally Lake RD-Flathead NF, Helena, Lincoln, and Townsend RD-Helena NF, Fortine and Rexford RD-Kootenai NF, Mussellshell RD - Lewis & Clark NF, and Superior RD - Lolo NF. Following a modified protocol based on the Oregon Bat Grid system, crews surveyed non-randomly chosen suitable habitats within randomly chosen 10 km 2 sample units in each RD; for a total of 75 sites surveyed on Northern Region lands in Montana. This approach was primarily targeted at identifying species richness within grid cells; inferences on rates of occupancy are limited to the percent of 10 x 10 km ! grid cells where a species was detected within each sampled RD. The 2006 field survey filled important gaps in documented distributions in Montana, adding new county records. However, a summary of all existing bat records across the region continues to show large distribution gaps for all species, underscoring the need for additional surveys. In particular, large portions of the Beaverhead- Deerlodge NF, Custer NF, Flathead NF, Gallatin NF, and Lewis and Clark NF lack records for any bat species. Even with two years of surveys only two Districts (Beartooth RD-Custer NF and Libby RD-Kootenai NF) have documented the full compliment of species predicted to occur there. Ten species of bats were captured by mist net or detected by acoustic recording during the USFS surveys between late June and early September 2006. Species recorded included Little Brown Myotis (Myotis lucifugus) at 34 sites, Western Long-eared Myotis (M. evotis) at 37 sites, Fringed Myotis (M. thysanodes) at nine sites, Long-legged Myotis (M. volans) at 25 sites, California Myotis (M. californicus) at four sites, Western Small- footed Myotis (M. ciliolabrum) at 17 sites, Big Brown Bat (Eptesicus fuscus) at 23 sites, Hoary Bat (Lasiurus cinereus) at 38 sites, Silver-haired Bat (Lasionycteris noctivagans) at 28 sites, and Spotted Bat (Euderma maculatum) at three sites. California Myotis was detected by acoustic recording at three sites outside their known distribution; these observations are considered tentative until the species is captured with mist nets in the area. Call analysis has yet to be performed on seven sites. Genetic analysis is needed for species identification for single individuals netted at three sites. Surveys at four sites detected no bats during mist-netting efforts; no acoustic sampling was done on these sites. Tentative identification was made for Yuma Myotis at mist-netting sites, but no acoustic recordings produced calls definitive for the species and no genetic analysis has been performed that confirm the species presence in the state. All previously recognized observations of Yuma Myotis appear to be misidentifications of Little Brown Myotis given recent acoustic analysis at a number of sites previously identified Yuma Myotis roost sites. The presence of this species in the state is highly questionable given the lack of definitive documentation. Detection probabilities for bats with multiple survey types (acoustic and mist-netting surveys) and survey duration were investigated as a pilot project to: (1) compare naive site occupancy rates with estimates adjusted because all species are not detected at all sites where they are present; and (2) plan future inventory and monitoring. in Models that best fit the resulting data indicated that acoustic monitoring generally does a better job of detecting most bat species compared to mist netting and acoustic surveys outperformed mist- net surveys in the number of species documented per site. The average naive site occupancy rate as determined from acoustic sampling was 38.2% while the average naive site occupancy rate as determined from mist-netting totaled 18.0%. Thus, detection probabilities are clearly higher for acoustic sampling methods and allocating resources for equipment and supplies to increase acoustic monitoring efforts is an important next step in monitoring bat species in Montana. Models which best fit the data also indicated that duration of surveys has an important influence on detection of species; although not to the extent of the importance of acoustic sampling. Estimates of recommended minimum or maximum duration of surveys were not a product of this analysis. Naive site occupancy rates (range 21.2 to 78.8%) were lower than robust estimated occupancy rates (Psi) resulting from multiple surveys of grid cells (33.7 to 100%) for all species for which this comparison could be made. Lower estimates of detection probability or insufficient data for calculation of estimates were associated with a number of species with limited distributional information. Pilot surveys need to be conducted to evaluate baseline levels of site occupancy and detection probability for these and other bat species in Montana not evaluated with this pilot effort. Pilot surveys also need to address how detection probabilities vary with sampling covariates such as type and duration. This pilot survey work will place future inventory and monitoring efforts on a sound base for supporting management decisions and evaluating changes in status. We recommend the USFS Northern Region continue with a grid-based random sampling scheme stratified by ecoregion or Ranger District, with multiple surveys per grid cell allowing for valid inference of grid cell occupancy rates across each sampling stratum. While the Oregon- based 10 km 2 grid sampling protocol may be appropriate, other grid systems could be employed to accomplish landscape-scale bat monitoring. A bat sampling grid based upon the latilong concept would fit well with other current and historical wildlife distribution studies in Montana and would greatly simplify implementation of the sampling because 1:24,000 scale quadrangle maps fit within this scheme and could be used directly as the sampling unit. It is important to note, however, that the detection analysis shows strong support for a grid scale smaller than either the Oregon bat scheme or the latilong scheme so that a greater number of sample units could be surveyed with multiple surveys. Further investigation of the appropriate sampling unit and sampling scheme is still needed. However, a grid-based sampling scheme is an important monitoring approach that should be considered beyond USFS lands and coordinated with other partner agencies and organizations to guide effective bat management across the state. Up-to-date distribution maps for Montana's species can be queried and viewed with a variety of map layers on the Montana Natural Heritage Program's Tracker website at: http : //m tnhp . org/Tracker . IV Acknowledgements We thank Fred Samson and Jenny Taylor (USFS) for initiating and promoting the project through the USFS Regional Inventory and Monitoring (RIM) program and overseeing its implementation. Jenny and Kristi DuBois organized the training session, run by Joe Szewczak (Humboldt State University) and Pat Ormsbee (USFS). Pat developed the sampling grid for survey locations. We thank Joe Szewczak for providing additional assistance during call analysis and interpretation. On-the-ground 2006 surveys were conducted on the Beaverhead-Deerlodge NF by Paul Hendricks, Coburn Currier, Bryce Maxell, and Susan Lenard (MTNHP); on the Bitterroot NF by Nate Schwab (USFS), Dave Romero (Bitterroot NF), Scott Eggeman, and Joe Butsick; on the Custer NF by Barb Pitman and Tawni Parks (Custer NF), Jenny Holifield (Kootenai NF), Bill Kranland, and Coburn Currier, Bryce Maxell, and Susan Lenard (MTNHP); on the Flathead by Nate Schwab (USFS), Lewis Young, Pat Shanley, and Jenny Holifield (Kootenai NF); on the Helena NF by the 2006 Beartooth WMA Bat training crew, Nate Schwab (USFS), Pat Shanley, and Bryce Maxell (MTNHP); on the Lewis and Clark NF by Eric Tomasik (Lewis & Clark NF), Coburn Currier and Bryce Maxell (MTNHP); on the Kootenai NF by Jenny Holifield (Kootenai NF) and Lewis Young; and on the Lolo NF by Sarah Kaufman, Karina Mahoney, and Bryce Maxell (MTNHP). Scott Blum (MTHNP) entered survey data into the Montana Natural Heritage Program's Point Observation Database, facilitating the production of new distribution maps and the updating of element occurrence data in the Montana Natural Heritage Program's Biotics database. Table of Contents Introduction 1 Methods 3 Grid Cell Identification 3 Focus of 2006 Efforts 3 Field Methods 7 Detection Probability Analysis and Program PRESENCE 7 Results and Discussion 10 Overview 10 Species Captured During Mist-netting and Acoustic Surveys 10 Naive Detection Rates by Survey Method 12 Number of Species Detected by Survey Method 13 Survey Coverage with Sampling Grid 14 Detection Probability Analysis and Results 16 Need for a State Bat Grid 19 Recommendations 20 Literature Cited 21 Appendix A. Global/State Rank Definitions Appendix B. Distribution Maps for Bats in Montana Appendix C. Application of Oregon Bat Grid to Montana - Cell Ownership and Accessibility Appendix D. Site Locations for USFS 2006 Bat Surveys Appendix E. Documented Species List per Forest/District Appendix F. Site Occupancy and Detection Probability Analysis List of Tables Table 1. Oregon Bat Grid Cell Count for USFS Forests in Montana 3 Table 2. Model Descriptions 8 Table 3. Number of Survey Sites per District in 2006 10 Table 4. Species list for 2006 and Site Survey Detection Method 11 Table 5. Overall percent detection rate for species during acoustic surveys versus mist- netting surveys on eight Region 1 National Forests in Montana, 2 July - 28 September, 2006 12 Table 6. Average Number of Species per Detection Method 13 Table 7. Total Number of Cells per Forest with Multiple Surveys (Fit Protocol) for 2006 and combined 2005 & 2006 as Percentage of Overall Total Cells 14 Table 8. Comparison of All Montana Bat Data (MTHNP Point Observation Database) and data collected in Multiple Survey Cells in 2006 with Overall Predicted Number of Species 16 Table 9. Bat Detection Probability Summary 17 VI List of Figures Figure 1. Montana Bat Sampling Grid 4 Figure 2. Land Status and Accessibility of Oregon Bat Grid Applied to Montana 5 Figure 3. 10km x 10km Grid Overlay with 2006 USFS Survey Locations 6 Figure 4. Grid Cell Survey Status 15 vn Introduction Recognition of a general lack of basic natural history information on native bat species (Hayes 2003), widespread disturbance, alteration, and/or complete removal (Fenton 1997, Pierson 1998) of habitats traditionally used by bats for roosting and foraging have contributed to increasing concern in recent decades about the status of bats throughout North America. As a result, six species or subspecies of bats in the continental United States are currently classified as endangered under the United States Endangered Species Act of 1973 (O'Shea et al. 2003). While none of these federally listed bats occur in Montana, six other species are recognized by the state as Species of Concern (Eastern Red Bat - Lasiurus borealis (G5 S2S3); Fringed Myotis - Myotis thysanodes (G4G5 S3); Northern Myotis - Myotis septentrionalis (G4 S2S3); Pallid Bat - Antrozous pallidas (G5 S2); Spotted Bat - Euderma maculatum (G4 S2); Townsend's Big-eared Bat - Corynorhinus townsendii) (G4 S2) (See Appendix A for Rank Definitions) (MTNHP and MTFWP 2006). While conservation and protection of roosts are important long-term management considerations for many North American bat species (Sheffield et al. 1992), efforts to conserve bats in Montana are often hampered by a lack of data on general habitat requirements. For example, the little data available from Montana on foraging behavior and diet of bats have largely been obtained at water sources (Jones et al. 1973), with no knowledge of where the foraging bats are roosting. Conversely, studies of bat roosts in Montana (e.g., Worthington 1991a, 1991b, Hendricks et al. 2000, 2004) lack information on where and how far the roost members go to feed and drink. Additionally, patterns of roost selection and fidelity (e.g., Sherwin et al. 2003) have not been studied in Montana, even though it is understood that suitable summer and winter roosts may limit the local and regional distribution and abundance of many temperate-zone bats (Humphrey 1975, Dobkin et al. 1995), especially cave- and crevice -dwelling taxa. Most bat species use a variety of localized habitats for roosting, whether natural sites (e.g., caves, trees, rock crevices) or man-made sites (e.g., buildings, mines, bridges). Sites may be used only for specific purposes during specific seasons of the year. Recent research on bat roosts in Montana has followed the national pattern of inventorying and monitoring roosts in caves, abandoned mines, and bridges (e.g., Worthington 1991a, 1991b, Hendricks et al. 2000, 2004, 2005; Hendricks and Kampwerth 2001), and remains an important activity for a state bat conservation plan. Nevertheless, sampling bats across the landscape at foraging sites continues to be critical for filling gaps in documented distribution, assessing relative abundance of local populations, and ultimately identifying roost locations. Efforts over the past two years have improved understanding of the distribution and status of bats on US Forest Service Northern Region lands in Montana. The effort has generally followed the Oregon Bat Grid Protocols designed to inventory the presence of bat species using a standardized effort and sample unit (a 10 x 10 km 2 grid) across the state. The protocol consists of collecting baseline data on acoustic, morphologic, and genetic characteristics for bats species in the Region. While important information has been gathered on Montana's bats more work needs to be done to continue filling in distribution holes and identifying important roosting locations. A summary of all existing bat records across the region clearly shows large distribution gaps for all species, further underscoring the need for additional surveys (see Appendix B). In particular, large portions of the Beaverhead-Deerlodge NF, Custer NF, Flathead NF, Gallatin NF, and Lewis and Clark NF still lack records for any bat species. Insufficient data may affect bat populations and the habitat they use for roosting and foraging because of potential unintended consequences from a variety of management activities. The Northern Region recognized the need for additional documentation of bats on Forest Service lands to address inventory and monitoring requirements, and initiated bat surveys in 2005 across the Region on selected National Forest Ranger Districts. Given the large areas of the Region lacking bat data, a second year of surveys, generally following the 2005 protocols, was conducted in 2006 to fill in data gaps. The primary objective of the 2006 survey was to document bat species richness (number of species) within sample units for areas with limited or no bat data. The longer-term objective was to infer sample unit occupancy for each species across entire Ranger Districts by implementing a grid- based sampling methodology. While our primary goals for the 2006 field season were to fill in data gaps for as many bat species as possible, we also completed some ground work for future inventory, monitoring, and predictive habitat modeling. We evaluated detection probabilities for bats at 33 different grid survey cells from 2005 and 2006 throughout the USFS Region 1 Forests in Montana. This was done in order to: (1) compare naive site occupancy rates with robust estimates of site occupancy that correct for the fact that species are not always detected at all sites where they are present; and (2) take steps to model species' occupancy rates in different habitats while simultaneously addressing the issue that detection probabilities may vary by a variety of site (e.g., elevation and temperature) and sampling (e.g., duration of survey and survey type - acoustic or mist-netting) covariates. Explicitly addressing the fact that species are detected imperfectly in the context of various site and sampling covariates is important in order to ensure that: (1) species that appear to be rare from naive estimates of site occupancy resulting from single surveys of sites truly are rare; (2) managers have a sound basis for making management decisions with regard to the status of species in various habitats and across various portions of the species' range where their status may be quite different; (3) monitoring programs are adequately designed (i.e. enough visits of enough sites) to detect biologically meaningful changes in the occupancy rates of different habitats; and (4) predictive distribution models account for variable rates of occupancy of different habitats. Methods Grid Cell Identification One of the first steps in applying the Oregon bat grid for USFS Region 1 was to identify cell ownership and the associated effort required to sample the sites. Cell ownership of the 3983 cells covering the state was identified by assigning cells to specific landowners if the entity occupied 50 percent or more of the land area in a cell. Of the 3983 cells overlaying the state, a total of 821 cells covered the Region 1 lands in Montana based on this specific criterion. Seven hundred eighty-two cells were further assigned to specific individual Forests within the state (see Table 1 and Figure 1); cells shared between Forests and/or between RDs and other public lands were given separate status and can be found in Appendix C. The remaining cells were assigned to other federal agencies, tribal, or private landowners. All cells were then categorized as "road accessible"' or "road inaccessible" by visual evaluation of the extent of existing roads within each cell using topographic layers and aerial images (see Figure 2). As with all survey efforts, on-the-ground assessment to determine overall accessibility needs to be made at the time of survey. Five hundred eighty five of the Region 1 cells were estimated to be road accessible; the remaining 197 cells were identified as inaccessible by roads and would need different logistical effort (see Table 1). Evaluating accessibility of cells by roads was necessary to Table 1. Oregon Bat Grid Cell Count for USFS Forests in Montana Forest Grid Cell Count Cells Road Accessible Beaverhead- Deerlodge 151 128 Bitterroot 60 41 Custer 50 37 Flathead 104 69 Gallatin 86 39 Helena 43 36 Kootenai 114 110 Lewis and Clark 79 41 Lolo 95 84 TOTAL 782 585 highlight the logistical differences required to sample cells with and without roads. As in 2005, the areas selected for survey during 2006 followed the framework of the Oregon Bat Grid from which random-selected grid cells in Region 1 were drawn. Using ArcGIS 9.2 the grid of square blocks, each 10 Ian on a side (100 km 2 in area), was overlaid on each RD to create a target population of sampling units (grid cells) to which inferred occupancy rates could be made. In order to fill in data gaps, general geographic areas with limited or no existing bat data were identified for survey in 2006. Each qualifying cell within these regions was randomly selected using randomly generated numbers. Sample units were selected from those with the lowest random numbers with reasonable access to potential survey sites. Focus of 2006 Efforts Surveys for bats in Montana were conducted during summer (primarily early July to late August) 2006 on Ranger Districts (RD) in each of eight National Forests (NF) of the Northern Region: Beaverhead- Deerlodge NF-Butte and Dillon RD, Bitterroot NF- Sula and West Fork RD, Custer NF-Ashland and Beartooth RD, Flathead NF-Tally Lake RD, Helena NF-Helena, Lincoln, and Townsend RD, Kootenai NF-Fortine and Rexford RD, Lewis & Clark NF- Mussellshell RD and the Lolo NF-Superior RD. The Flathead, Kootenai, Lolo, Bitterroot, and one of the Helena NF RDs (Lincoln) sampled are west of the Continental Divide. The remaining sampled RDs are in the central and south central portions of Montana east of the Continental Divide. Survey sites spanned a range of elevations: 2980-6251 ft west of the Divide and 3960-8307 ft east of the Divide. The number of sample units surveyed differed among Forests as did the number of survey nights per cell. 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Sites usually contained features that might concentrate bat activity; primarily water sources such as ponds and streams, less often bridges over streams, caves and mines, and least often at or near abandoned buildings. Bats were captured using mist nets of various lengths and configurations; the number of nets deployed varied from site to site. Nets were deployed at twilight and left open for at least 3.5 hours, weather permitting, or until one hour passed with no acoustic detections. Species physical identification was based on published keys and species accounts (van Zyll de Jong 1985, Nagorsen and Brigham 1993, Adams 2003). Standard measurements (weight, forearm length, ear length) and sex, age, and reproductive status were obtained for each individual. Wing punch tissue samples were also collected from each captured bat until five punches per species were accumulated from each site. Tissue was taken using sterile procedures and stored in biopsy tubes containing desiccant and/or ethanol. Tissues are to be used for genetic identification of species pairs difficult to distinguish in the field (especially Little Brown Myotis - Myotis lucifugus versus Yuma My otis - M. yumanensis); genetic analysis was initiated before the writing of this report but was not completed. The survey protocol also called for acoustic monitoring at each site using a Pettersson D-240x detector and an MP3 recording device. Acoustic surveys were conducted either by hand or by remote recording; remote recordings off-site from the netting location by at least one kilometer were counted as separate surveys. Recorded calls were subsequently analyzed using Sonobat software and, primarily, an unpublished bat species identification key provided during the 2006 training session (Szewczak, personal communication, July 2006). Calls collected by the Montana Natural Heritage Program were identified by Heritage Program staff. Call data collected by USFS survey teams is currently being analyzed; analysis was not completed before the writing of this report. Data was recorded on standardized data sheets, and later transcribed to a Point Observation Database housed at the Montana Natural Heritage Program, Helena where it is available for agency and public use. Detection Probability Analysis and Program PRESENCE We used program PRESENCE (Mackenzie et al. 2002, 2005) to compare the fit of a priori developed candidate models to the pilot bat detection data. The specific goals of the modeling effort were to: (1) estimate detection probabilities (p) for individual species; (2) identify the extent to which detection probabilities differ between survey types; (3) identify the extent to which detection probabilities differ between survey duration; (4) compare estimated site occupancy rates (Psi) to the naive percentage of sites where species were detected; and (5) use estimates of (p) to identify the number of sites needed and number of surveys per site needed to achieve various confidence intervals for estimates of site occupancy in future inventory and monitoring efforts. It is worth noting the assumptions associated with this modeling effort using program PRESENCE (Mackenzie et al. 2005) and the extent to which these assumptions may have been violated. Key assumptions and the degree to which they were likely violated include: (1) Sampled patches are representative of unsampled patches so that inferences can be correctly made to the entire population of interest. Water bodies were targeted across all sites where the pilot detection probability surveys were performed so inferences are probably limited to areas near water. (2) Species do not emigrate from or immigrate to the sample units between surveys (also known as the closure assumption). This assumption is clearly violated as our surveys occurred across two years. It is possible the occupancy rates were different between years. For this analysis we assume these rates to be constant, this may or may not be true. (3) Surveys are independent of one another (e.g., detections at one site do not depend on the detections at another site). There is no evidence the presence of mist-net or acoustic stations at one location affect detections at mist-net or acoustic stations at other locations so, the assumption of independent surveys does not appear to have been violated. (4) Species are correctly identified so that there are no false detections. Species and/or calls not definitively identified were not included in the analysis and were essentially treated as non-detections so this assumption does not appear to have been violated. (5) All sources of heterogeneity are modeled. This assumption is almost certainly violated because a number of site (e.g., elevation) or sampling (e.g., start and end temperature) covariates were not incorporated into the candidate models. However, we do not consider this violation to be important in the context of the specific goals of this analysis. That is, we were largely focused on understanding approximate site occupancy and detection rates, difference between naive site occupancy rates and estimates involving correction for detection probability, and planning for future inventory and monitoring efforts, not specific questions about how individual species respond to differences in habitat or habitat conditions. A set of 8 simple a priori candidate models was developed in order to address these questions. More complex models were not considered because the limited pilot data gathered was not suitable for estimating large numbers of parameters. Table 2. Model Descriptions Model Notation Model Description Psi(.),p(.) Site occupancy rate (Psi) is constant across all sites surveyed. Detection probability (p) is constant across all surveys. Psi (.), p(s) Site occupancy rate (Psi) is constant across all sites surveyed. Detection probability (p) varies by individual survey. Psi (.), p(type) Site occupancy rate (Psi) is constant across all sites surveyed. Detection probability (p) varies by survey type. Psi (.), p(duration) Site occupancy rate (Psi) is constant across all sites surveyed. Detection probability (p) varies by survey duration. Psi (.), p(s*type) Site occupancy rate (Psi) is constant across all sites surveyed. Detection probability (p) varies by individual survey and survey type. Psi (.), p(s*duration) Site occupancy rate (Psi) is constant across all sites surveyed. Detection probability (p) varies by individual survey and survey duration. Psi (.), p(type*duration) Site occupancy rate (Psi) is constant across all sites surveyed. Detection probability (p) varies by survey type and survey duration. Psi (.), p(s*type*duration) Site occupancy rate (Psi) is constant across all sites surveyed. Detection probability (p) varies by individual survey, survey type, and survey duration. Relative fit of the a priori models to the data was evaluated using Akaike Information Criteria (AIC) which balances the fit of the model to the data with a penalty for the number of parameters used in the model in order to arrive at the most parsimonious model (Burnham and Anderson 2002). The best fitting model has the lowest AIC value and models within 2 AIC values of one another essential have the same level of support in terms of how well the)' describe the data given the number of parameters involved. The Simulations module in program PRESENCE was used to examine different scenarios for future inventory and monitoring efforts. For these analyses, the true proportion of sites occupied was varied in order to encompass the range of site occupancy rates (0.3, 0.5, 0.7, and 0.9) and detection probabilities (0.2, 0.4, 0.6, and 0.8) observed during the pilot study and likely to be encountered with bat species in other regions of Montana. For each combination of site occupancy rate and detection probability three major levels of survey effort and/or funding were considered; (1) 100 sampling days = 400 site surveys which is approximately equivalent to twice the level of effort made during the 2005 and 2006 field surveys, (2) 50 sampling days = 200 site surveys which is approximately equivalent to the level of effort made during the 2005 and 2006 field surveys, and (3) 25 sampling days =100 site surveys which is approximately equal to half the level of effort made during the 2005 and 2006 field survey. A number of scenarios were considered for each level of survey effort in which the number of sites surveyed multiple times (M), the number of times those multiple survey sites where surveyed (S), and the number of roost sites surveyed a single time (Roost) were varied in order to examine the effect different allocations of the same level of effort had on the standard error (SE) of the estimate of the site occupancy rate (Psi). Results and Discussion Overview The summer 2006 survey helped fill a number of distribution gaps, highlighted the importance of including acoustic sampling in bat survey efforts, and produced several new county records. In addition, new locations were recorded for the Spotted Bat, a Region 1 Sensitive Species, including one on the Helena RD of the Helena NF which represents a westward range extension in the state of approximately 260 km. Limited success at capturing other USFS Region 1 Sensitive Species and State Species of Concern (see Appendix A) suggests the need for specific methodology targeting these species. The only other Species of Concern recorded during the 2006 survey efforts was the Fringed Myotis which was detected at nine of the 75 sites (six sites through acoustics and three sites by mist-net). Species Captured During Mist- Netting and Acoustic Surveys Seventy-five sites were sampled for bats across the eight USFS Northern Region 1 Forests in Montana in 2006 (see Table 3). The Custer NF and Lewis and Clark NF had the greatest number of surveys, Table 3. Number of Survey Sites per District in 2006 19 and 11, respectively. Thirty-two sites were west of the Continental Divide, while 43 sites were east of the Divide (Figure 3). Bats were detected at 71 of the 75 sites (see Appendix D for site locations and species detected at each location). Ten bat species were recorded during acoustic surveys and nine during mist-netting efforts (Table 4). Nine species were captured at sites west and ten species at sites east of the Continental Divide. The Spotted Bat was the only species encountered during the 2006 surveys not detected west of the Divide and it was documented by acoustic recording only. The summer 2006 Northern Region survey resulted in new county records for nine species (see maps in Appendix B): Big Brown Bat (Eptesicus fuscus) (Mineral and Meagher), Hoary Bat (Golden Valley and Meagher), Spotted Bat (Lewis and Clark), Western Small-footed Myotis (Myotis ciliolabrum) (Stillwater), Western Long-eared Myotis (Stillwater), Fringed Myotis (Beaverhead, Powell, and Stillwater), Long-legged Myotis (Meagher and Powell), and Little Brown Myotis (Stillwater). With the addition of four species, Stillwater County (Custer NF) received the most new records. Forest Ranger Districts surveyed in 2006 Number of survey sites 2006 Beaverhead-Deerlodge Butte 4 Dillon 5 Bitterroot Sula 5 West Fork 1 Custer Ashland 1 Beartooth 19 Sioux 1 Flathead Tally Lake 7 Rexford 1 Helena Helena 1 Lincoln 3 Townsend 1 Kootenai Fortine 7 Lewis and Clark Mussellshell 11 Lolo Superior 8 Total 75 10 Table 4. Species list for 2006 and Site Survey Detection Method Species List for 2006 sites Number of Surveys where Species was Detected Total # of sites where Species was Detected Acoustic Mist-net Spotted Bat (Euderma maculatum) o J Big Brown Bat {Eptesicus fuscus) 13 12 22 Hoary Bat {Lasiurus cinereus) 28 13 35 Silver-haired Bat (Lasionycteris noctivagans) 12 20 27 California Myotis {Myotis californicus) 3* 5 8 Western Small-footed Myotis (M. ciliolabrum) 13 5 16 Long-eared Myotis (M. evotis) 23 20 36 Little Brown Myotis (M. lucifugus) 30 9 33 Fringed Myotis (M. thysanodes) 6 9 Long-legged Myotis (M. volans) 7 20 25 * all acoustic data show characteristics definitive to Myotis californicus, yet these observations remain tentative due to lack of in-hand evidence of the species in these regions which are quite distant from previously documented localities for the species. The Spotted Bat detection was the first detection of a Spotted Bat during the USFS survey efforts. Two species captured in 2005, the Townsend's Big- eared Bat and Yuma Myotis, were not observed in 2006. No Townsend's Big-eared Bats were documented acoustically or by mist-net during the 2006 survey efforts. Tentative identification was made for Yuma Myotis at mist-netting sites, but no acoustic recordings confirmed their presence and no genetic analysis has been performed to confirm the presence of this species in the state. The presence of this species in Montana, therefore, is highly questionable given the lack of definitive documentation through genetic data from tissue samples or acoustic data (Montana Bat Working Group, annual meeting, February 2007). All previously recognized observations of Yuma Myotis appear to be misidentifications of Little Brown Myotis given recent acoustic analysis at a number of roost sites. No Townsend's Big-eared Bats were documented acoustically or by mist-net during the 2006 survey efforts. Tentative identifications of California Myotis were made at three sites across two counties during the 2006 season. Recordings of call characteristics consistent with Myotis californicus were made at two locations in Beaverhead County approximately 70 km east of previously documented California Myotis observations (e.g., Ravalli, Missoula and Lake Counties [Hendricks and Maxell 2005]). A third call series was identified as Myotis californicus in the Pryor Mountains in Carbon County in 2006 which would represent an eastward range expansion of approximately 400 kilometers. Interestingly, tentative identification was made for a California Myotis during a mist-netting effort in the Bighorn Canyon National Recreation Area in 2004 (Keinath 2004, 2005) approximately 20 kilometers east of the 2006 USFS site. Although several individuals identified the 2004 specimen as Myotis californicus while in hand, without genetic analysis the species is considered unconfirmed at this location (Doug Keinath, personal communication). Szewczak (personal communication 2007) confirmed the identification of the calls recorded on the Custer National Forest, yet agreed they should be considered tentative until genetic confirmation of the species in this general area has been made. Without in hand evidence of California Myotis at the three locations, these observations remain tentative. 11 Additional bat inventory work conducted in 2006 by the Montana Natural Heritage Program in eastern Montana resulted in numerous additional county records for these and other species. Up-to- date distribution maps for Montana's species can be queried and viewed with a variety of map layers on the Montana Natural Heritage Program's Tracker website at: http ://mtnhp . or g/Tracker . Naive Detection Rates by Survey Method Sixty three mist-net and 43 acoustic surveys were conducted in 2006 (32 sites were mist-net-only surveys, 12 sites were acoustic-only surveys, and 31 sites were both). With the exception of the Spotted Bat acoustic detection, all species were recorded using both survey methods. However, the percent of sites at which species were detected varied between the two survey methods (see Table 5). Acoustic surveys outperformed mist-net surveys in the number of species documented per site and overall naive estimates of site occupancy. The average detection rate for acoustic sampling was 38.2% (range = 8.3 to 83.3%; median = 34.7%), while the average naive detection rate for mist-netting was 18.0% (range = 0.0 to 33.3%; median = 18.4%). These results are supported by the best-fitting candidate models which showed that acoustic sampling boosted detection probabilities. The most abundant species as determined by the acoustic sampling was the Little Brown Myotis, which was detected at 83.3% of the acoustic surveys. However, this species was only detected at 15.0% of the mist-net sites. The Hoary Bat (Lasiurus cinereus) was the second most abundant species detected acoustically (77. 8% of sites), but were only detected at 21.7% of the mist-net stations. Based on the analyzed call data, the Long-legged Myotis {Myotis volans) was the only species detected more frequently during mist- net surveys than acoustic surveys (33.3% versus 19.4%). Two other species, Silver-haired Bat (Lasionycteris noctivagans) and Western Long- Table 5. Overall percent detection rate for species during acoustic surveys versus mist-netting surveys on eight Region 1 National Forests in Montana, 2 July - 28 September, 2006. Forty-three acoustic surveys and 63 mist-netting surveys were conducted across 75 sites. State Species of Concern are in bold. Species Overall Percent Detection Rate Acoustic n=36 a Mist-net n=60 b Little Brown Myotis {Myotis lucifugus) 83.3 15.0 Western Long-eared Myotis (Myotis evotis) 63.9 Fringed Myotis {Myotis thysanodes) 16.7 5.0 Long-legged Myotis (Myotis volans) 19.4 California Myotis (Myotis californicus) 8.3* 8.3 Western Small-footed Myotis (Myotis ciliolabrum) 36.1 8.3 Silver-haired Bat (Lasionycteris noctivagans) 33. J *5 o o Big Brown Bat (Eptesicus fuscus) 36.1 21.7 Hoary Bat (Lasiurus cinereus) 77.8 21.7 Spotted Bat (Euderma maculatum) 8.3 0.0 " analysis is not complete for all acoustic surveys b three mist- netting locations resulted in capture of single individuals needing genetic analysis for identification. *the presence of this species at three survey sites is in question although the calls for Carbon County were verified by J. Szewczak (personal communication 2007). This record would represent a significant eastward expansion of previously documented range in Montana. While an individual was documented in-hand east of this location during a 2004 unrelated study, genetic analysis needs to be performed to confirm its identification. 12 eared Myotis (M. evotis), shared the same overall mist-net detection rate (33.3%). The Silver-haired Bat was detected at the same rate on the acoustic surveys (33.3%) while the Western Long-eared Myotis was detected at nearly twice the rate during the acoustic surveys (63.9%). Acoustic data for seven site locations has yet to be analyzed so acoustic detection rates will increase for some species when the analysis is completed. Number of Species Detected by Survey Method The detection success rate of acoustic and mist- net surveys, measured as the average number of species detected, differed among all sites pooled, as well as sites where both survey methods were employed. Acoustic surveys only produced an average of 4.44 species per site. Sites with combined acoustic and mist-net surveys resulted in an average of 3.96 species per site. The average number of species recorded during mist-net surveys alone was only 2.03 species (see Table 6). Failure to detect any species occurred only at those sites where mist-net surveys were the only survey method employed (four sites). Acoustic surveying has great potential to provide rapid assessment of species distributions over many sites (Hayes 1997, O'Farrell and Gannon 1999) as well as to identify areas of significant concentrations of species and individuals. Remote acoustic monitoring stations also have an advantage over traditional capture methods by greatly enhancing the number of bat species documented in an area while requiring less field effort. It is important, however, to have equipment available and field crews trained in the use of this technology well in advance of field surveys. Even with a training session designed to familiarize attendees with the technology, slightly more mist- netting surveys (32) occurred without acoustic sampling in 2006 than those with (3 1), suggesting that some field personnel were not comfortable employing the acoustic sampling methods. While mist-nets have been used as the traditional method for documenting bat species in an area, mist-net surveys alone probably under-represent total bat species richness in a sample unit more often than not. With an increasing ability to identify calls to species level, acoustic sampling can be used, under some circumstances, not only to augment mist- netting efforts, but as a primary data-gathering tool. We consider acoustic surveys an integral component of future inventory and monitoring schemes to be used to augment more traditional capture methods. The Montana Natural Heritage Program has begun building a collection of calls for bats recorded in Montana. This is the first step in building a library of reference calls from individuals within the state whose identity is definitive through morphologic and genetic measurements. The three sets of data (acoustic, morphologic, genetic) will provide future workers using acoustic monitoring the reference tools needed to identify and account for regional differences in calls. Table 6. Average Number of Species per Detection Method Capture Method # of sites Average # of Species Standard Deviation Acoustic only 9* 4.44 2.74 Acoustic and mist-net 27** 3.96 1.76 Mist-net only 36 2.03 1.48 * this data is based upon only 9 of the 12 acoustic only sites. Call data for 3 of the sites has not yet been analyzed. ** Thirty-one sites were sampled by both acoustic and mist-netting techniques. Call data for 4 sites has not been analyzed; these sites were included in the mist-net only analysis. 13 Survey Coverage with Sampling Grid Multiple surveys were conducted in 16 different grid cells (See Figure 4) in 2006, representing 37 of the 75 sites surveyed that year. These fit the protocol requirement designed for Montana of at least two surveys per sample unit (see Table 7) (Montana Bat Grid Draft Protocol, unpublished document, 2006). Combined with data from 2005, 33 cells fit the protocol requirement and were surveyed at two or more locations per cell. The Forest with the greatest overall cell coverage (as a percent of the total number) is the Lewis and Clark at 8%, followed by the Helena (7%) and the Flathead (6%). All other Forests have had 4% or fewer cells surveyed using the Montana Bat Grid Protocol. One of the requirements of the bat grid protocols involves identifying species predicted to occur in the grid cells based upon existing information on the general distributions of species. The success rate, i.e. the percentage of species detected compared to the predicted species for that location, for 2005 and 2006 ranged from 27% to 92% (see Table 8). While the predicted species lists are generated from general distribution maps, the data from the Heritage Point Observation Database indicates that the full compliment of predicted species has been documented on only two Districts, Beartooth RD-Custer NF and Libby RD-Kootenai NF While it might be anticipated that not all species will ultimately be documented where predicted, the limited success rates for 2005 and 2006 suggests much greater effort needs to be employed to adequately survey all Districts for bat species. Only when species are documented by field surveys will we gain better understanding of their distribution and habitat needs rather than relying solely on predicted presence (see Appendix E for a list of documented bat species per Region 1 USFS Districts). As in 2005, the 2006 survey efforts focused on and identified numerous areas where bats concentrate their activity while seeking food and water resources. Some of these sites, especially those used by several bat species, may be useful in the future for monitoring efforts across Forest Districts. While these sites could be used to develop a comprehensive survey and monitoring scheme, both for the Northern Region and all of Montana, one of the important next steps is to adopt a sampling grid that is both easily implemented and broadly applicable. While the Oregon Bat Grid can be useful and provides a uniform basis from which sampling sites can be selected, the application of this grid is somewhat cumbersome. The grid's orientation is skewed (trending northwest to southeast) and follows no standard lines of orientation. While the uniform Table 7. Total Number of Cells per Forest with Multiple Surveys (Fit Protocol) for 2006 and combined 2005 & 2006 as Percentage of Overall Total Cells. Forest Cell Count Fit Protocol 2006 Fit Protocol for years 2005 & 2006 Percent of "Protocol cells" surveyed in Forest Beaverhead-Deerlodge 151 4 4 •20/ J /O Bitterroot 60 2 2 3% Custer 50 2 2 4% Gallatin 86 1 1% Flathead 104 2 6 6% Helena 43 o j 7% Kootenai 114 1 5 4% Lewis and Clark 79 4 6 8% Lolo 95 1 1 1% TOTAL 782 16 30 4% 14 CO O a u u - C £ - il a a -5 u bo BO ve c v i. o ft. c 3 *f *^ 09 3 ^ s u. s 5 P -S a 4— f 0> '- s. * V ua -J i< = — u < - c ^_ i - 1 V 5* • -J U J la $ 15 Table 8. Comparison of All Montana Bat Data (MTHNP Point Observation Database) and data collected in Multiple Survey Cells in 2006 with Overall Predicted Number of Species. Forest District Predicted Number of Species Point Observation Database as % of Predicted 2005 & 2006 Protocol Data as % of Predicted Beaverhead/Deerlodge Butte 10 70% 60% Dillon 10 80% 60% Bitterroot Sula 11 45% 36% Custer Beartooth 12 100% 92% Flathead Swan Lake 11 45% 45% Tally Lake 11 55% 55% Gallatin Bozeman 10 70% 60% Helena Helena 11 82% 64% Lincoln 11 55% 27% Lownsend 11 91% 91% Kootenai Fortine 11 73% 27% Libby 11 100% 73% Lewis and Clark Judith 10 70% 70% Mussellshell 10 70% 70% Lolo Superior 11 82% 55% grid cell size (10x10 km 2 ) may be desirable, identifying one's location on the ground, or its associated identifying label is impossible without a GIS grid overlay in hand. This makes field organization and navigation somewhat problematic, especially when the number of surveys conducted per cell, as described in the draft Oregon protocols, is important. A more useful sampling scheme for a broad-scale bat inventory would converge with the Oregon Bat Grid which incorporates elements of a typical state bird atlas to help guide sampling efforts to each sample unit. In the Oregon scheme, the primary objective is to document all species on a list of expected species generated for each sample unit. Each sample unit is surveyed using multiple detection methods, as we attempted to do in 2005 and 2006, but also is visited as many times (up to 12) as it takes to achieve the species richness goal, rather than limiting the survey effort to two or fewer visits, as was done in 2005 and 2006. Even for roost monitoring of a species like Townsend's Big-eared Bat, there is so much detection variability during any single visit (due to a variety of site and sampling covariates) that as many as nine visits to a site may be necessary to identify a non-roost (Sherwin et al. 2003). Although the 2006 survey helped to further fill in distribution gaps and generated much useful data, limited human and monetary resources kept the survey from achieving the objective of determining species richness for most sample units visited, largely because too few site visits were made. This failure greatly limits or prohibits the ability to infer sample unit occupancy across Districts. Detection Probability Analysis and Results Data for estimating site occupancy rates and detection probabilities was gathered for 8 of the 10 species detected during the 2005-2006 USFS Region 1 bat surveys (Table 9). Two species detected during these surveys had insufficient data for estimates: California Myotis is typically of limited range and unlikely to be encountered across all Forests and Spotted Bat is presumed a relatively rare species of limited distribution. An additional five species known to occur in Montana were 16 p = Estimated Probability of Detection (SE) o in o o o in o ^D O O 0\ in o OO o o en o ^o o o in oo o Os r- o o CM in CM O in i^ o o 1^ '*■ in o on o o CM CM O o o o- ■<*■ o Psi = Estimated Proportion Sites Occupied (SE) o o o 00 o o oo oo o in ro O 1^ en en o 00 o o 1^ o oo o i (a On O O o 1^ o o o o s ON o o in oo o Naive Estimate Proportion of Sites Occupied 00 o en CM 1^ o O en CM 1^ o ^3 en O en NO O On en On en O o On 3 s Si •S s •3 a FM Tl T) a W 1> ! > k) 2; H w ■^3 00 — n FM - £ 'i i^ U a> d o +J . -H >. W u ? f> u 1> d, rl Ui. l/i VI G DO rj P< C/3 3 — kJ - o W 17 either not encountered or were not encountered with enough frequency across the two years to be considered for analysis. Therefore, alternative methods appear to be justified for detection and monitoring species with specific habitat requirements, limited distributions, or general rarity to the state. For those species with sufficient data, estimated detection probabilities ranged from a low of 0.252 to a high of 0.596 with mean = 0.43 1 and median = 0.480 (see Table 9). The estimated detection probability for the only Species of Concern was 0.314 for Myotis thysanodes. Abundant species with easily distinguished acoustic call characteristics had higher detection probabilities (range = 0.474 - 0.596). Improved techniques in call identification would likely result in higher detection rates. Best fitting models for five of the eight species analyzed indicate type of survey was important in explaining detection probability (mist- net sampling having lower detection probabilities than acoustic sampling). Thus, there is strong evidence that increasing acoustic sampling efforts will improve detection of species in inventory and monitoring efforts. Estimated site occupancy rates that took probability of detection into account were all higher than naive percentage of sites where species were detected (mean = 0.188, range = 0.080 to 0.606). Thus, evaluating detection probability is clearly important for identifying the success of field efforts and should influence the type of survey methods employed, especially when presence/non-detection is the goal of the study. Simulations of standard error (SE) for site occupancy rates (Psi) resulting from a number of scenarios for survey effort, detection probability (p), number of sites surveyed multiple times (M), number of times those multiple survey sites where surveyed (S), and number of Roosts surveyed a single time (Roost), identified a number of combinations that resulted in unacceptable levels of precision for confidence intervals (Appendix F). We considered acceptable confidence interval widths to have a maximum SE < 0.097 (i.e., a total confidence interval width of 0.388). However, even this may not be an acceptable confidence interval for evaluating some management or status questions. When acceptable confidence interval widths were achieved, we highlighted scenarios in gray in Appendix F when they allowed the greatest number of sites to be surveyed for each level of survey effort. In some cases we highlighted multiple scenarios associated with the same level of survey effort in order to highlight tradeoffs that might be faced (e.g., using a smaller grid cell size as a sampling unit versus using a grid cell comparable to the Oregon bat grid or the area covered by a 1:24,000 scale topographic map). When no scenarios resulted in acceptable confidence intervals under a given level of survey effort and Psi and p, then no scenarios were highlighted. In general, simulations (Appendix F) showed that: (1) When site occupancy rates are > 0.3, detection probabilities need to be > 0.4 before current levels of sampling effort result in acceptable confidence intervals. (2) Sampling with approximately half of the existing level of effort (approximately 25 days or 100 surveys) only achieves acceptable confidence intervals when site occupancy rates are > 0.3 and detection probabilities are > 0.6. Thus, this level of effort would certainly not be enough to derive confidence intervals acceptable for monitoring the one Species of Concern (Fringed Myotis) for which site occupancy and detection probabilities were estimated in this pilot study and this is likely the case for other Species of Concern as well. (3) While the existing level of sampling effort (approximately 50 days or 200 surveys) is adequate for monitoring most individual species when site occupancy rates are > 0.3 and detection probabilities are > 0.4, it is probably inadequate for all Species of Concern. It also may be inadequate for monitoring larger groups of species across larger regions because specific habitats/ regions of the state may need all sampling effort in order to achieve the desired confidence intervals. (4) Doubling the sampling effort from existing levels (approximately 100 days or 400 surveys) allowed acceptable confidence intervals to be calculated with site occupancy as low as 0.3 when detection probabilities were as low as 0.2. Furthermore, this level of sampling effort allows two sets of species with non-overlapping ranges in at least two different parts of Montana to be monitored simultaneously as long as detection probabilities are at least 0.2. Need for a State Bat Grid While it is beyond the scope of this report to explore all the details of what comprises a state bat grid, the scheme eventually developed should include a hierarchical scale of data collection allowing inference of grid cell occupancy rates for all species. The objectives of a state bat grid would be: 1) to inventory the presence of bat species using a standardized survey effort and sampling unit across the survey region; 2) collect baseline data on acoustic, morphologic, and genetic characteristics that serve as reference for bat species identification, and; 3) to provide a baseline inventory that would allow future monitoring to assess changes over time. Inventorying and monitoring bat distributions and trends at this scale will place us in a better position to address conservation issues as they arise. To date, none of these objectives has been thoroughly addressed in Montana, although the 2005 and 2006 surveys of selected Districts of the Northern Region represent an admirable pilot effort toward satisfying these objectives. We do recommend a bat grid be developed and applied to all of Montana. While the Oregon Bat Grid offers a scheme from which to design a statewide bat grid, we recommend investigating the use of the Latilong concept (latitude- and longitude -defined polygons). The Latilong concept was pioneered by Dr. P.D. Skaar in the late 1960s and has been the foundation of wildlife distribution applications in the state since then (Lenard et al. 2003). While the size of the Latilong blocks varies slightly from north to south (blocks at the border with Canada are approximately 5% smaller than those along the Wyoming border), defining the sampling unit to 1:24,000 scale quad maps (representing 1/32 of a Latilong block) would provide much greater utility in field planning, preparations, and protocol execution (easier to locate cells on the ground than the Oregon grid). A bat sampling grid based upon the Latilong concept would also fit well with other current and historical wildlife distribution studies in Montana. Although we support the Latilong approach, the detection probability analysis indicates that there may be a need to move to a smaller scale (e.g., Section scale). Thus, investigation of a smaller scale grid cell should be carefully considered as plans move forward for a statewide inventory and monitoring effort. 19 Recommendations 1. Emphasize acoustic sampling in all future inventory efforts. Greater numbers of surveys with higher detection rates and total numbers of species detected will clearly enhance any bat inventory scheme. 2. Include alternative methods for detection of all species (e.g. species- specific targeted surveys and specific habitat surveys). Low estimates of detection probability or insufficient data for calculation of estimates were associated with a number of rare or limited distribution species. 3. Conduct pilot surveys to evaluate baseline levels of site occupancy and detection probability for the remainder of the bat species in Montana not evaluated with this pilot effort. Pilot surveys also need to address how detection probabilities vary with site (e.g., elevation, cover type, forest management regime) and sampling (e.g., weather, survey duration, survey methods) covariates. 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Journal of Mammalogy 80:24-30. Olson, D. H., W. P. Leonard, and R. B. Bury (eds). 1997. Sampling amphibians in lentic habitats: methods and approaches for the Pacific Northwest. Northwest Fauna 4: 1- 134. O'Shea, T J., M. A. Bogan, and L. E. Ellison. 2003. Monitoring trends in bat populations of the United States and territories: status of the science and recommendations for the future. Wildlife Society Bulletin 31:16-29. Pierson, E. D. 1998. Tall trees, deep holes, and scarred landscapes: conservation biology of North American bats. Pp. 309-325, In Bat biology and conservation (T. H. Kunz and P. A. Racey, eds.). Smithsonian Institution Press, Washington, DC. 365 pp. Pierson, E. D., Wackenhut M.C., Altenbach J.S., Bradley P., Call P., Genter D.L., Harris C.E., Keller B.L., Lengus B., Lewis L., Luce B., Navo K.W., Perkins J.M., Smith S., and Welch, L. 1999. Species conservation assessment and strategy for Townsend's Big-eared Bat (Corynorhinus townsendii townsendii and Corynorhinus townsendii pallescens). Idaho Conservation Effort, Idaho Department of Fish and Game, Boise, ID. 68 pp. Montana Natural Heritage Program and Montana Fish, Wildlife, and Parks. 2006. Montana Animal Species of Concern. Helena, Montana: Montana Natural Heritage Program and Montana Department of Fish, Wildlife, and Parks. 17 pp. Nagorsen, D. W., and R. M. Brigham. 1993. Bats of British Columbia. UBC Press. Vancouver, BC. 164 pp. Sheffield, S. R, J. H. Shaw, G. A. Heidt, and L. R. McClenaghan. 1992. Guidelines for the protection of bat roosts. Journal of Mammalogy 73:707-710. Sherwin, R. E., W. L. Gannon, and J. S. Altenbach. 2003. Managing complex systems simply: understanding inherent variation in the use of roosts by Townsend's Big-eared Bat. Wildlife Society Bulletin 31:62-72. Nicholson, A. J. 1950. A record of the Spotted Bat (Euderma maculatum) for Montana. Journal of Mammalogy 31:197. Shryer, J. and D. L. Flath. 1980. First record of the Pallid Bat (Antrozous pallidas) from Mon- tana. Great Basin Naturalist 40: 1 15. 22 Swenson, J. E. 1970. Notes on the distribution of My otis leibii in eastern Montana. Blue Jay 28:173-174. Swenson, J. E., and J. C. Bent. 1977. The bats of Yellowstone County, southcentral Montana. Proceedings of the Montana Academy of Sciences 37:82-84. Swenson, J. E., and G. F. Shanks, Jr. 1979. Noteworthy records of bats from northeastern Montana. Journal of Mammalogy 60:650-652. Thomas, D. W. 1988. The distribution of bats in different ages of Douglas-fir forests. Journal of Wildlife Management 52:619-626. van Zyll de Jong, C. G. 1985. Handbook of Canadian mammals. 2. Bats. National Museum of Natural Sciences. Ottawa, ON. 212 p. Worthington, D. J. 1991a. Abundance and distribution of bats in the Pry or Mountains of south central Montana and northeastern Wyoming. Report for the Bureau of Land Management Billings Resource Area and Custer National Forest. Montana Natural Heritage Program, Helena, MT 23 pp. Worthington, D. J. 1991b. Abundance, distribution, and sexual segregation of bats in the Pry or Mountains of south central Montana. Master's Thesis, University of Montana, Missoula, MT. 41 pp. 23 Appendix A. Global/State Rank Definitions Heritage Program Ranks The international network of Natural Heritage Programs employs a standardized ranking system to denote global (range-wide) and state status. Species are assigned numeric ranks ranging from 1 to 5, reflecting the relative degree to which they are "at-risk". Rank definitions are given below. A number of factors are considered in assigning ranks — the number, size and distribution of known "occurrences" or popula- tions, population trends (if known), habitat sensitivity, and threat. Factors in a species' life history that make it especially vulnerable are also considered (e.g., dependence on a specific pollinator). Global Rank Definitions (NatureServe 2003) Gl Critically imperiled because of extreme rarity and/or other factors making it highly vulnerable to extinction G2 Imperiled because of rarity and/or other factors making it vulnerable to extinction G3 Vulnerable because of rarity or restricted range and/or other factors, even though it may be abundant at some of its locations G4 Apparently secure, though it may be quite rare in parts of its range, especially at the periphery G5 Demonstrably secure, though it may be quite rare in parts of its range, especially at the periphery Tl-5 Infraspecific Taxon (trinomial) — The status of infraspecific taxa (subspecies or varieties) are indicated by a "T-rank" following the species' global rank State Rank Definitions 51 At high risk because of extremely limited and potentially declining numbers, extent and/or habitat, making it highly vulnerable to extirpation in the state 52 At risk because of very limited and potentially declining numbers, extent and/or habitat, making it vulnerable to extirpation in the state 53 Potentially at risk because of limited and potentially declining numbers, extent and/or habitat, even though it may be abundant in some areas 54 Uncommon but not rare (although it may be rare in parts of its range), and usually widespread. Apparently not vulnerable in most of its range, but possibly cause for long-term concern 55 Common, widespread, and abundant (although it may be rare in parts of its range). Not vulnerable in most of its range Combination Ra n k s G#G# or S#S# Range Rank — A numeric range rank (e.g., G2G3) used to indicate uncertainty about the exact status of a taxon Qualifiers NR Not ranked Q Questionable taxonomy that may reduce conservation priority — Distinctiveness of this entity as a taxon at the current level is questionable; resolution of this uncertainty may result in change from a species to a subspecies or hybrid, or inclusion of this taxon in another taxon, with the resulting taxon having a lower-priority (numerically higher) conservation status rank Appendix A - 1 X Presumed Extinct — Species believed to be extinct throughout its range. Not located despite intensive searches of historical sites and other appropriate habitat, and virtually no likelihood that it will be rediscovered H Possibly Extinct — Species known from only historical occurrences, but may never- the- less still be extant; further searching needed U Unrankable — Species currently unrankable due to lack of information or due to substan- tially conflicting information about status or trends HYB Hybrid — Entity not ranked because it represents an interspecific hybrid and not a species ? Inexact Numeric Rank — Denotes inexact numeric rank C Captive or Cultivated Only — Species at present is extant only in captivity or cultivation, or as a reintroduced population not yet established A Accidental — Species is accidental or casual in Montana, in other words, infrequent and outside usual range. Includes species (usually birds or butterflies) recorded once or only a few times at a location. A few of these species may have bred on the one or two occa- sions they were recorded Z Zero Occurrences — Species is present but lacking practical conservation concern in Montana because there are no definable occurrences, although the taxon is native and appears regularly in Montana P Potential — Potential that species occurs in Montana but no extant or historic occurrences are accepted R Reported — Species reported in Montana but without a basis for either accepting or rejecting the report, or the report not yet reviewed locally. Some of these are very recent discoveries for which the program has not yet received first-hand information; others are old, obscure reports SYN Synonym — Species reported as occurring in Montana, but the Montana Natural Heritage Program does not recognize the taxon; therefore the species is not assigned a rank * A rank has been assigned and is under review. Contact the Montana Natural Heritage Program for assigned rank B Breeding — Rank refers to the breeding population of the species in Montana N Nonbreeding — Rank refers to the non-breeding population of the species in Montana Appendix A - 2 Appendix B. Distribution Maps for Bats in Montana .5 A e d ~ 3 ^ fi tJ s « 'J — i_ c r-i .= Li. s / ft c u :/: ~ r 13 a p < - * * -J Appendix B - 1 o 3 (A TfTc^ -J e q § -2 <*> as a O c pa 23 ^ i/ oa *^ P -*- c '— a en IT: '- U. u Appendix B - 4 VI ** ^-» ^i c ^ • p*4 o O Ss On K S S O ^ ftj 08 Nj, >• Sm ■«W 0* C3 Gfl PQ -Q O *0 u • ■« C3 J3 £ 0> > Appendix B - 5 Appendix B - 6 C5 -© > 1i u +-* QJ © IS) © pO '*- o 1 05 E c^ = :- v Appendix B - 7 Appendix B Appendix B - 9 Appendix B - 10 a 5/5 C *s 88 5/5 Q ju *> '£ "5 « rt t) ■a a v» a s © a © © o sa « m ™ O i- w ^ ?< i 2 © -2 ~ fa Q ? SO GO £ fa fa « 5Q tZ3 D § Appendix B - 11 Appendix C. Application of Oregon Bat Grid to Montana - Cell Ownership and Accessibility Cell Count per Forest by Ranger District FOREST DISTRICT NUMBER OF CELLS BEAVERHEAD-DEERLODGE Butte 6 Butte- Jefferson 2 Dillon 32 Dillon-WiseRiver 2 Jefferson 19 Jefferson-Madison 1 Madison 26 Pintler 22 Pintler-WiseRiver 1 Wisdom 19 Wisdom-Dillon 1 Wisdom-WiseRiver 1 WiseRiver 18 WiseRiver- Wisdom-Dillon 1 BITTERROOT Darby 15 Darby-Stevensville 2 Stevensville 13 Sula 10 WestFork 19 WestFork-Darby 1 CUSTER Ashland 22 Beartooth 24 Sioux 4 FLATHEAD GlacierView 16 HungryHorse 17 HungryHorse-SpottedBear 1 SpottedBear 37 SwanLake 19 SwanLake-HungryHorse 1 SwanLake-HungryHorse-SpottedBear 1 TallyLake 12 GALLATIN BigTimber 12 B igTimber-Gar diner 2 Big Timber-Livingston 2 Bozeman 18 Bozeman-Livingston 2 Gardiner 19 HebgenLake 19 Livingston 11 Livingston-Gardiner 1 Appendix C - 1 FOREST DISTRICT NUMBER OF CELLS HELENA Helena 14 Helena-Townsend 3 Lincoln 16 Townsend in KOOTENAI Cabinet 26 Fortine 17. Libbv 29 Rexford 15 ThreeRivers 32 LEWIS AND CLARK BeltCreek 7 Judith 14 Judith-Mussel shell 2 White Suphur Sprine 11 White Sulphur Spring-Musselshell 2 Musselshell 9 RockvMountain 34 LOLO Missoula IX Ninemile 17 Plains/ThompsonFalls 20 SeelevLake 17. Superior 28 TOTAL 787. Mixed Forest Cells FORESTS DISTRICTS CELL COUNT Beaverhead-Deerlodge_Bitterroot Pintler_Darby 1 Beaverhead-Deerlodge_Bitterroot Pintler_Darby-Sula 1 Beaverhead-Deerlodge_Bitterroot Wisdom_Sula 1 Beaverhead-Deerlodge_Lolo Pintler_Missoula 3 Custer_Gallatin B eartooth_Gardiner 3 Gallatin_LewisandClark Livingston_Musselshell 1 Helena_Beaverhead-Deerlodge Helena_Jefferson 1 Helena_Beaverhead-Deerlodge Helena_Pintler 1 Total 12 Appendix C - 2 Forest and Other Public Lands Mixed Cells FOREST AND OTHER PUBLIC LANDS DISTRICT(S) CELL Beaverhead-Deerlodge_BLM Dillon 8 Beaverhead-Deerlodge_BLM Madison 2 Beaverhead-Deerlodge_BLM WiseRiver 1 B eaverhead-Deerlodge_S tateLands Madison 4 B eaverhead-Deerlodge_S tateLands WiseRiver 1 Custer_BLM Beartooth 4 Flathead_S tateLands GlacierView-TallyLake 1 Gallatin_S tateLands Gardiner-Livingston 1 Helena_BLM Helena 1 Helena_BLM Townsend 2 Helena_S tateLands Helena 2 Total 27 Appendix C - 3 Appendix D. Site Locations for USFS 2006 Bat Surveys < < < O o - > g • Ph < | o z < hJ < U U g u < > > < s u < 1 < < < > ^ < § § < « O > > > p ffi u > H < fa u o > o > >^ u CD Z < pj s g J 2 ^ < S *- o u gi CD Q '3 eu a tZ3 XI j S S 2 2 S S s < sf < < < < < u << S < < § § S 1 §£ nl P < 5 J U -* o p P P P P < d > p J P z hJ hJ p z > > o o z < h-, Ph hJ . > J j j J w J pj pj > >* >H P P P P ^ ^ >H ^ p p ^ P >H si % s s s S S25 S2l s s S s S so J j J J o P < o p < a p o p o D a p < o p < a p o D o p o p r5 Q a 5 p p »—5 < < oo < On < < in < so < cc w CM en ■* SO CM en ^ H 00 ON CM c *^ / — s O o en so O o in o O t- o '+ m o 00 in o 00 o ^f o en en ■0 — o '— 0/ i CD CD CM CD CD CD ■a a o "S CD CD 4^ o W CZ! ^" ^ ID c CD .M CD CD CD u On CD > O XI aj ^ - CD >T 8 x: C/3 3 Eh " U z _o E rn d e U CD CD c CD u M tH s z CD (U CD T3 U on rj U g U 2 o u a. CD CD ez> CD u s— CD '55 U £ 00 00 3 3 o o "2 *H CD u M o CD XI CD M CD CD u s u CM 00 c CD CD ^ u 8 -a „2 CD P a u u ^J 1 §• ^ 2 CD <5 ^ 3 •B -^ o u Cj CD u c c3 C S-i o CO CD 55 ■r; M ^ > o J P ■S o« Q Ph Z o ll 4-1 o o CD o CD CD CD CD CD CD 5 a pq $H pq CD PQ CD J3 CD CD J3 CD CD S g x: XJ XJ Xj s CD CD > CD > > > > > "3 13 "3 3 3 3 o U > > Kj > C3 nj Kj B a > > > > > > CD CD CD CD CD CD C3 C3 a a C3 oi CM iz> m Cfl PQ PQ PQ PQ PQ Pi rt & Pi rd Oi oo ■*-» ^ Xj u C c c c c O o p s CD pq PQ CD pq CD PQ s o Q s Q o s 1 _ca on a "3 CO 13 oo _C3 en c3 3 CO CD PQ o o CD CD CD CD CD CD CD 00 00 00 00 00 00 00 00 00 -d T3 tJ -a ■a -o -a -d -a o o O o o o o o o »H lH (H t-i i-* i-t (H i-t CD CD CD CD CD CD CD CD CD o 35 CD CD CD CD CD CD CD p p 9 9 9 9 9 9 9 § § i § § t3 i § ^ CS « c3 a C3 rt ca 03 a C/2 X! i-i CD XI CD CD CD CD J3 CD CD -G CD O o o p o o o g o o o p ^ > > > > > > > > > a) CD CD CD CD CD a B cd 03 C3 03 rt CS as CO O o CD CD CD CD CD CD CD CD ■ VH .■t^ ."3, ."^3 ,y ."ti =3 ^ m PQ PQ PQ PQ PQ PQ PQ PQ PQ PQ PQ PQ PQ PQ a Appendix D - 1 u CU Q '3 cu a aa < K H >< < > g §3 < u < P < u g < p P g S < < P < u g S > g S < o 2; < ^< < p -> E ■-) Ph < u < p < > < p p g S < p E Ch w < o z < 3< ^^ < o > g < g u 2 W MYLU, MYEV, MYTH, MYVO, MYCA, MYCI, LANO, EPFU, LACI, EUMA (all Acoustic) >H O F ^ ^ r i S^ P p » E 5 Ph P M P u g >H S S MYLU (M), MYEV (M), MYVO (M), EPFU (M), EUMA (audible, no recording) < H < _ >< P M >^ U S ^ P W P u s s t5 o c C3 cd -d CJ CO 3 O CJ < '-' -d O u > ^ S C3 MYEV (M), MYVO (M), MYCI (M), EPFU (M) Acoustic data not yet analyzed = a ci CZ3 w P CN P P in CM m p m p NO cs p p •—I NO P P CN P P CO CN p p •—I 00 CM P 00 CM p 00 CM O P < 00 CM O P < On Ol C H-* , ■■ O o On in o m On m O in o On in CO m m o o in m O 00 en O 00 en NO CN O ON o en o en 00 CM en m CM r~ NO NO en CM O in 0) s « z cu 55 C3 l a a, a e M ft a o 1o •s S so H s* S "3 *- o 9 so 3 >> w 2 ? Z tn 1 s »-i CO 3 U u g •a ■" oo <" " 5 so . 03 ft o u -t- 1 O-i > 35 Vh H cd > 3 | U E 3 U X D O o ° l « E > CO X CO ^" ^ O CD CD CD -o ^ ■8 1 I- 8 *j ^ CO C C3 o W a, CO CD Pi C3 >l CD CD u CD 00 o J CD a) CD b CO CD C C O 2 g U CD T3 Pi 3 so X CD CD c CO —J ° n, * s co c 03 03 W co S3 CD c -d c o a< ccf CD ca ^ ^ CD B o U -o W 2 00 c o ~3 -a a 3 O & 00 5 N i-i Ph ■d CD Pi cd" C C3 y 4=i c S o 03 ^H co i; 00 _d 'C n, CO E c CD c« C3 M CD CD (h u CD C s CJ Q Z Z NO ON en o in P CN On r- NO o NO CN Z m t- NO en o m P en On en m o NO CM en Tf On O O in m" On in ON o NO eN Z ON r^ o o m W c3 U C o X H 03 u c o X H 03 u c o XI 03 u c o X lH u c o X a u c o 03 U c o 1 U u "C s xj o o H C3 CD m x o o a CD CO XJ o o m xj o o 03 CD pq xj o o c3 CD PQ X o o a CD CQ o o 03 CD 03 X o o e 03 CD m X o o 03 CD PQ xj o o fi 03 CD 03 X o o CD 03 X o o c3 CD 03 o o 3 CD 03 X o o CD 03 a; •_ O CD CO 3 U Q _0) '3 cu ft B5 MYVO (M), LANO (M), EPFU (M), LACI (M); Acoustic data not yet analyzed 73 CD N >i 13 c ct >i o c c3 "3 T3 O GO o ej < "cd o c 03 1 o C/j o o < HH CD H >-> 2 cs < o < X 3p ^& 2 pq < p Oh W o z < © Ph ^ Ph ^> m PJ „ s e < U Ig ^& s g CO ^< pq „ si O > >H S > PU s < u s o > >h >^ pq „ P © S 6 1 1 u < i o Z < i u < i e Ph pp O z MYLU (A), MYEV (M), MYTH (M), MYVO (M), MYCI (A, M), LANO (H), EPFU (A), LACI (A, M), EUMA (A) < < < o z < < > S o > >H S § H s p z PP S = a n O P < o en o p < en Ph W CM Cm H oo 00 CN Ph w On hJ 5 00 hJ po po H-S o CM o < o < CM o < cn 5 CN po PS NO CM o po < o o P < CN CN o p < cn CN 'W , ■■ o> en in o o NO r- ON o o o 00 o O NO ON cn m in cn cn m m m NO cn NO o CM cn o 00 cn 00 00 o NO m CN O o 00 It m NO CM ■>*■ ■>*■ cn in in s 01 55 c 3 O s o >N Ph X" "" Ti u e> (0 "* 00 c oj 'a cn 5 > u CD CJ op s oo & OO c 1 U GO J-H CJ & s 60 CD "H -a CD a M CD CD i-t u '-§ a; 1 C3 S3 00 c & O0 on o 3 a 3 S d > o ■a M S u ^ OJ GO ^ O C3 hJ hJ c '3 cl 3 O s a) > o X a M ° E oo O0 T3 4-1 C s | o g Ph 5 c I o m P^ ,2 5 1 o T3 +J' 03 q o "d ^ o u a 03 i* hJ 1 03 OO o E 03 B C/j C T3 13 M J 0) o oQ oo E o o a * CD CD n >* 1 H ? E •^<§ S E H c/j ra q. a) 3 X o o 1 c o >1 c cU u M CD CD u CD q2 CD CD u _o 1 M CD CD U CD 60 O O s -2 3 M CD CD tH u tH CD & & O U Q Z Z in r- On o o in pq" r- no o On NO CN z o o 5- o o mi W t> cn ^f -tf o t- CN Z o NO cn NO o o in W NO NO 00 00 On NO CN Z o 00 -* r- m o m W 00 m in cn cn z ^* 00 ON r~ r~ o m W oo oo cn (N ^* m cn z NO in On in cn in W o NO cn 00 NO z CN m m cn m W NO o r~ 00 NO Z o NO NO cn m W oo cn cn 00 NO Z ■* NO cn m w o m NO 00 NO Z o cn 00 r- 00 cn ■n pii o CM in o r- NO z r- NO OO oo cn m W On cn m r- r- NO z CM cn cn m W On O NO r- NO Z 00 NO m ON m W o o cn cn CN z CM O cn in W o m oo it CM z in ON cn ON in W f- r~ oo NO cn CM z NO o 00 ON in W o 00 ON 00 in cn CM z cn cn ON in CM in pq 00 On cn cn r- cn CN a = o U c o 03 u c o XI — 03 u C O X lH 03 u CD > 5 -o o PL, u ■a OJ Xi t3 E T3 C3 tu X! E T3 03 0) Xj E T3 03 xs 03 E 1 X a E T3 03 CD XI E M CD XI o3 E •a 03 u CO CD 53 & T3 03 O pq •a 03 U CD 13 o Ph 03 u C/j '■? CD *q u "C s XJ o o H 03 CD xj o o J-H 03 cj oa X o o J-H 03 CJ m T3 1 GO X 3 O OO ■a hJ c2 ■a hJ >N ■i x> r2 ■a to 1 CD ■s CD ■a 03 c CD r CD a T3 C CD 60 c c O CD c o CJ C 2 a; 0) •_ O *h CD 60 3 U &H CJ GO 3 u *H CJ GO 3 u 3 u GO 3 u ■a x; Ph T3 (U xl "S E T3 03 XI E T3 03 to XS E -d 03 CD X E T3 03 CD XI E ■a 03 CD XI Ph 03 C CD 03 C o 13 X 03 C CD 03 C 13 03 C 13 X Appendix D - 3 u OS Q _0) '3 u a aa o > 1 < s > PJ S < 1 z < hJ O > P P -1 hJ P z > PJ i p hJ p >i pj p 2 pj S j > Q PJ l - J s s !l ^.z ^< >^ < < 2 hJ 1 S > S < < z Is < z < hJ < > < PJ HH 3p ^& 2 pj O Z < hJ < > PJ g S l< -. ,— n = a ci CZ3 w hJ p P m tN hJ no tN r- tN O P < p < m p < yD p < hJ hJ hJ p H-i tN tN hJ en tN 5 en tN hJ p H-s tN P m tN p O P < tN C ^ , N no no o m en 00 en tN cs NO yD 00 ON NO en 00 ON O ON NO O O 00 in O CO O in O O m O O O NO 00 NO NO NO NO O in NO s z 55 -d 03 o GO Ph CJ c-i a) .9 u (N CD 'c« CD CD £h u > O (h CD d Eh O CJ CD" ■a hJ CD »H O Ph -0 * d a G CJ -H ^ pj JS T3 H CJ O ™ CJ CL CJ !-h CJ , w =d ^ ^ 3 cj ss Sou M CD CD tH -2 "3 Oh 4^ (H O Ph .d Eh O z 00 d "Jh Oh OO CD 00 -3 '£ M ■2 CD < O >N & d C/3 CD hJ If CJ S-i Cfl .5 ll [3 3 & S M CD CD £h O CD 42 | H 44 Lh Ph 1 M CD CD !h O *H CD 43 E H ■a Ph CO a PJ >N 44 CJ 3 hJ 44 CD CD nJ d 2 CO .9 CnI 43 -H O O Ph 1 Q ■a T3 O 3 * O CD 43 CJ CD > lj CD CD WJ 43 H-i g a r s p- Ph CD CD ^H Q Z Z m 00 ■ CD hJ 1 '5 CD -1 •a -3 U > CD hJ •a -3 O > CD hJ -a 03 u GO > CD hJ Appendix D - 4 -*— > J— > 01 Q u u ft B5 s* "S ° C/j ^ a o >- s z u 00 01 q o 'B S +j oa Q Z 8 O U V. s •- o > - S 1 < <-.i o ID u -1- in -t M u -i 3 o J3 w oo ° ^ ' en Z i Z o -t- -+ UJ r- -1- c-. -1- c -i 3 It UJ O z < > UJ -i- o a, . O J Z J z o -i- -i- UJ r- -t- -i- -I- o o UJ o z < § 3 tu 00 £> Q M O P 5 u ° = u UJ n O > O gz 3 rl s c; ^ — C) XI S ;>, ^ n a o J z I/ - ! o UJ — ci T> -t O o O Z < 5 CO O .5 £ » H o J_) q 2 as s 8 So o - O UJ < o — -T O o 3, Q U w UJ "* m \ \D re O 00 .2 aa > aj s 3 U J ^3 Si ~ CO 3 3 >, =1 S a P >, < 3 E o u ■s. S O * GO s q — * j= 3 ) 1) J -c > !* S ". 3 a -^ 3 — n > 01 — q ^, n ^3 ^ 3 dj n j 00 --« aa 3 O — a o a V. UJ — > o J '^j oo u o o < S T5 ^ '. ,"■ o ?3 n > OJ • = ■f< 13 •— E > UJ CM — ' or q n aa s > > 3 3 CO to "j 3 c S B o I s ^ 5 a aj — 1 u tt. 0- UJ O >> 3 '•— u. ^O •J — .o 00 3 ■a '— O 3 O 3 O ^3 3 Q ■. o- X so ■£ o o o o o o r/i u > S 3 -O >. -3 s 3 E 3 3 o T3 3 3 J= ■ o .o 3 3 a Oh O i 1* so 'cfl 11 < 11 * ■ Appendix D - 5 Appendix E. Documented Species List per Forest/District BUTTE Beaverhead/Deerlodge Big Brown Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis evotis Myotis lucifugus Dillon Big Brown Bat Hoary Bat California Myotis* Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Fringed Myotis Long-legged Myotis Jefferson Big Brown Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Fringed Myotis Madison Townsend's Big-eared Bat Long-eared Myotis Eptesicus fuscus Lasiurus cinereus Myotis californicus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis thysanodes Myotis volans Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis thysanodes Corynorhinus townsendii Myotis evotis Pintler (Philipsburg/Deer Lodge) Big Brown Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis Yuma Myotis Wisdom Little Brown Myotis Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis volans Myotis yumanensis+ Myotis lucifugus Appendix E - 1 BITTERROOT Darby Townsend's Big-eared Bat Big Brown Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Stevensville Townsend's Big-eared Bat Little Brown Myotis Sula Big Brown Bat Silver-haired Bat California Myotis Long-eared Myotis Long-legged Myotis Corynorhinus townsendii Eptesicus fuscus Myotis ciliolabrum Myotis evotis Myotis lucifugus Corynorhinus townsendii Myotis lucifugus Eptesicus fuscus Lasionycteris noctivagans Myotis californicus Myotis evotis Myotis volans West Fork Little Brown Myotis Myotis lucifugus CUSTER Ashland Townsend's Big-eared Bat Big Brown Bat Spotted Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis Corynorhinus townsendii Eptesicus fuscus Euderma maculatum Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis volans Beartooth Pallid Bat Townsend's Big-eared Bat Big Brown Bat Spotted Bat Silver-haired Bat Hoary Bat California Myotis* Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Fringed Myotis Long-legged Myotis Antrozous pallidus Corynorhinus townsendii Eptesicus fuscus Euderma maculatum Lasionycteris noctivagans Lasiurus cinereus Myotis californicus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis thysanodes Myotis volans Appendix E - 2 Sioux Townsend's Big-eared Bat Big Brown Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis Corynorhinus townsendii Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis volans FLATHEAD Hungry Horse Big Brown Bat Little Brown Myotis Spotted Bear Long-legged Myotis Eptesicus fuscus Myotis lucifugus Myotis volans Swan Lake Hoary Bat California Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis Lasiurus cinereus Myotis californicus Myotis evotis Myotis lucifugus Myotis volans Tally Lake Big Brown Bat Silver-haired Bat Hoary Bat California Myotis Long-eared Myotis Long-legged Myotis Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis californicus Myotis evotis Myotis volans GALLATIN Big Timber Big Brown Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Little Brown Myotis Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis lucifugus Bozeman Big Brown Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis volans Appendix E - 3 Gardiner Big Brown Bat Hoary Bat Long-eared Myotis Little Brown Myotis Hebgen Lake Little Brown Myotis Livingston Little Brown Myotis Long-legged Myotis Eptesicus fuscus Lasiurus cinereus Myotis evotis Myotis lucifugus Myotis lucifugus Myotis lucifugus Myotis volans HELENA Helena Townsend's Big-eared Bat Big Brown Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Fringed Myotis Long-legged Myotis Corynorhinus townsendii Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis thysanodes Myotis volans Lincoln Big Brown Bat Silver-haired Bat Western Small-footed Myotis Long-eared Myotis Fringed Myotis Long-legged Myotis Eptesicus fuscus Lasionycteris noctivagans Myotis ciliolabrum Myotis evotis Myotis thysanodes Myotis volans Townsend Townsend's Big-eared Bat Big Brown Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Fringed Myotis Long-legged Myotis Yuma Myotis Corynorhinus townsendii Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis thysanodes Myotis volans Myotis yumanensis+ Appendix E - 4 KOOTENAI Cabinet Townsend's Big-eared Bat Big Brown Bat Silver-haired Bat Hoary Bat California Myotis Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis Yuma Myotis Corynorhinus townsendii Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis californicus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis volans Myotis yumanensis+ Fortine Townsend's Big-eared Bat Big Brown Bat Silver-haired Bat Hoary Bat California Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis Libby Pallid Bat Townsend's Big-eared Bat Big Brown Bat Silver-haired Bat Hoary Bat California Myotis Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Fringed Myotis Long-legged Myotis Yuma Myotis Rexford Townsend's Big-eared Bat Big Brown Bat Silver-haired Bat Hoary Bat California Myotis Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis Corynorhinus townsendii Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis californicus Myotis evotis Myotis lucifugus Myotis volans Antrozous pallidus Corynorhinus townsendii Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis californicus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis thysanodes Myotis volans Myotis yumanensis+ Corynorhinus townsendii Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis californicus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis volans Appendix E - 5 Three Rivers Townsend's Big-eared Bat Big Brown Bat Silver-haired Bat Hoary Bat California Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis LEWIS AND CLARK Belt Creek Townsend's Big-eared Bat Judith Big Brown Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis White Sulphur Spring Long-eared Myotis Fringed Myotis Yuma Myotis Corynorhinus townsendii Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis californicus Myotis evotis Myotis lucifugus Myotis volans Corynorhinus townsendii Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis volans Myotis evotis Myotis thysanodes Myotis yumanensis+ Musselshell Big Brown Bat Silver-haired Bat Hoary Bat Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis volans Rocky Mountain Silver-haired Bat Hoary Bat Long-eared Myotis Little Brown Myotis Long-legged Myotis Yuma Myotis Lasionycteris noctivagans Lasiurus cinereus Myotis evotis Myotis lucifugus Myotis volans Myotis yumanensis+ Appendix E - 6 LOLO Missoula Big Brown Bat Silver-haired Bat Hoary Bat California Myotis Long-eared Myotis Little Brown Myotis Plains/Thompson Falls Townsend's Big-eared Bat Silver-haired Bat California Myotis Long-eared Myotis Long-legged Myotis Superior Townsend's Big-eared Bat Big Brown Bat Silver-haired Bat Hoary Bat California Myotis Western Small-footed Myotis Long-eared Myotis Little Brown Myotis Long-legged Myotis Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis californicus Myotis evotis Myotis lucifugus Corynorhinus townsendii Lasionycteris noctivagans Myotis californicus Myotis evotis Myotis volans Corynorhinus townsendii Eptesicus fuscus Lasionycteris noctivagans Lasiurus cinereus Myotis californicus Myotis ciliolabrum Myotis evotis Myotis lucifugus Myotis volans * tentative identification + species presence in the state in question Appendix E - 7 Appendix F. Site Occupancy and Detection Probability Analysis Psi = 0.3 & p = 0.2 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 2 4 2 4 8 8 16 16 Roost 25 25 25 100 SE 0.269 0.147 0.349 0.146 0.086 0.083 0.092 0.084 - 50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 S 2 4 2 4 8 Roost 13 SE 0.335 0.227 0.394 0.231 0.137 25 8 13 50 0.139 . 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.388 0.302 0.383 0.309 Psi = 0.3 & p = 0.4 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 2 4 2 8 8 16 16 Roost 25 25 25 100 SE 0.081 0.054 0.156 0.053 0.063 0.061 0.086 0.081 - 50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 25 S 2 4 2 4 8 8 Roost 13 13 SE 0.151 0.082 0.245 0.080 0.094 0.082 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.240 0.133 0.331 0.143 50 Appendix F - 1 Psi = 0.3 & p = 0.6 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 100 50 50 25 25 * » 4 2 8 8 16 16 Roost 25 25 25 100 SE 0.043 0.048 0.065 0.043 0.061 0.057 0.091 0.075 - 50 Sampling Days (1 day = : 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 25 S 2 4 2 8 8 Roost 13 13 50 SE 0.065 0.063 0.114 0.061 0.094 0.082 ■ 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.113 0.095 0.212 0.094 Psi = 0.3 & p = 0.8 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 2 4 2 8 8 16 16 Roost 25 25 25 100 SE 0.036 0.044 0.057 0.042 0.064 0.056 0.090 0.071 - 50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 S 2 4 2 4 8 Roost 13 SE 0.057 0.066 0.069 0.060 0.089 25 8 13 50 0.079 . 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 . 4 2 4 » Roost 6 25 SE 0.069 0.096 0.138 0.083 Appendix F - 2 Psi = 0.5 & p = 0.2 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 2 4 2 4 8 8 16 16 Roost 25 25 25 100 SE 0.214 0.140 0.270 0.133 0.099 0.096 0.101 0.095 - 50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 25 S 2424880 Roost 13 13 50 SE 0.269 0.195 0.318 0.194 0.142 0.135 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.321 0.248 0.346 0.254 Psi = 0.5 & p = 0.4 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 S 2 4 2 4 8 Roost 25 SE 0.094 0.070 0.150 0.056 0.072 50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 S 2 4 2 4 8 Roost 13 SE 0.149 0.093 0.198 0.088 0.097 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.200 0.135 0.258 0.129 50 25 25 8 16 16 25 25 100 0.064 0.099 0.090 - 25 8 13 50 0.098 . Appendix F - 3 Psi = 0.5 & p = 0.6 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 ■ 4 2 8 8 16 16 Roost 25 25 25 100 SE 0.050 0.055 0.069 0.048 0.070 0.067 0.099 0.082 - 50 Sampling Days (1 day = : 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 25 S 2 4 2 8 8 Roost 13 13 50 SE 0.072 0.076 0.111 0.066 0.100 0.092 - 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.111 0.102 0.163 0.097 Psi = 0.5 & p = 0.8 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 2 4 2 8 8 16 16 Roost 25 25 25 100 SE 0.043 0.053 0.052 0.048 0.069 0.062 0.097 0.077 0.088 50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 S 2 4 2 4 8 Roost 13 SE 0.055 0.069 0.076 0.067 0.101 25 8 13 50 0.080 0.088 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M S(i 25 25 25 S 4 2 4 Roost 6 25 SE 0.099 0.107 0.091 0.127 Appendix F - 4 Psi = 0.7 & p = 0.2 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 2 4 2 4 8 16 16 Roost 25 25 25 100 SE 0.191 0.135 0.230 0.133 0.099 0.096 0.098 0.095 - 50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 S 2 4 2 4 8 Roost 13 SE 0.227 0.174 0.263 0.176 0.133 25 8 13 50 1.136 . 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.261 0.209 0.290 0.211 Psi = 0.7 & p = 0.4 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 2 4 2 4 8 8 16 16 Roost 25 25 25 100 SE 0.100 0.062 0.137 0.061 0.066 0.064 0.090 0.084 - 50 Sampling Days (1 day = = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 25 S 2 4 2 4 8 8 Roost 13 13 50 SE 0.135 0.087 0.170 0.088 0.092 0.086 - 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.171 0.126 0.210 0.126 Appendix F - 5 Psi = 0.7 & p = 0.6 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 100 100 50 50 25 25 * » 4 2 8 8 16 16 Roost 25 25 25 100 SE 0.053 0.049 0.074 0.048 0.066 0.061 0.093 0.082 0.093 50 Sampling Days (1 day = : 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 25 S 2 4 2 8 8 Roost 13 13 50 SE 0.074 0.068 0.108 0.065 0.094 0.081 0.101 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.109 0.097 0.145 0.094 0.138 Psi = 0.7 & p = 0.8 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 4 2 4 8 8 16 16 Roost 25 25 25 100 SE 0.036 0.046 0.051 0.043 0.063 0.057 0.097 0.075 0.126 50 Sampling Days (1 day = : 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 25 25 ■ ' 4 2 8 8 Roost 13 13 50 SE 0.049 0.062 0.073 0.060 0.091 0.076 0.137 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.071 0.092 0.101 0.084 0.151 Appendix F - 6 Psi = 0.9 & p = 0.2 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 2 4 2 4 8 8 16 16 Roost 25 25 25 100 SE 0.143 0.102 0.174 0.105 0.075 0.077 0.068 0.067 - 50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 25 S 2424880 Roost 13 13 50 SE 0.175 0.119 0.203 0.128 0.101 0.098 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.201 0.155 0.242 0.158 9.856 Psi = 0.9 & p = 0.4 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 2 4 2 8 16 16 Roost 25 25 25 100 SE 0.081 0.054 0.101 0.055 0.046 0.047 0.060 0.061 0.079 50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 25 S 2424880 Roost 13 13 50 SE 0.104 0.071 0.124 0.073 0.062 0.064 0.082 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.124 0.098 0.151 0.092 0.114 Appendix F - 7 Psi = 0.9 & p = 0.6 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 100 100 50 50 25 25 S » 4 2 8 8 16 16 Roost 25 25 25 100 SE 0.049 0.034 0.064 0.034 0.043 0.042 0.059 0.058 0.135 50 Sampling Days (1 day = : 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 25 S 2 4 2 8 8 Roost 13 13 50 SE 0.063 0.048 0.082 0.048 0.059 0.058 0.147 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.080 0.068 0.106 0.067 0.158 Psi = 0.9 & p = 0.8 100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 200 100 100 100 50 50 25 25 S 2 4 2 8 8 16 16 Roost 25 25 25 100 SE 0.027 0.029 0.038 0.028 0.042 0.039 0.059 0.055 0.091 50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 100 50 50 50 25 S 2 4 2 4 8 Roost 13 SE 0.039 0.043 0.054 0.042 0.057 25 8 13 50 0.057 0.103 25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) M 50 25 25 25 S 2 4 2 4 Roost 6 25 SE 0.054 0.059 0.070 0.057 Psi - Estimated Proportion of Sites Occupied (species specific) p - Estimated Probability of Detection (species specific) M - Multiple Sites (Cell Count) S - Number of Surveys per site (4 = one mist-net and three acoustic stations) Roost - Number of Roost sites surveyed (this would occur in conjunction with individual cell surveys) SE - Standard Error Appendix F - 8