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Squires Et Al 2012 Estimating detection probability for Canada lynx Lynx canadensis using snow-track surveys in the northern Rocky Mountains, Montana, USA Author(s): John R. Squires, Lucretia E. Olson, David L. Turner, Nicholas J. DeCesare & Jay A. Kolbe Source: Wildlife Biology, 18(2):215-224. Published By: Nordic Board for Wildlife Research DOI: http://dx.doi.org/10.2981/10-105 URL: http://www.bioone.org/doi/full/10.2981/10-105 BioOne (www.bioone.org) is a nonprofit, online aggregation of core research in the biological, ecological, and environmental sciences. BioOne provides a sustainable online platform for over 170 journals and books published by nonprofit societies, associations, museums, institutions, and presses. Your use of this PDF, the BioOne Web site, and all posted and associated content indicates your acceptance of BioOne’s Terms of Use, available at www.bioone.org/page/terms_of_use. Usage of BioOne content is strictly limited to personal, educational, and non-commercial use. Commercial inquiries or rights and permissions requests should be directed to the individual publisher as copyright holder. BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, research libraries, and research funders in the common goal of maximizing access to critical research. Wildl. Biol. 18: 215-224 (2012) Short communication DOI: 10.2981/10-105 Ó Wildlife Biology, NKV www.wildlifebiology.com Estimating detection probability for Canada lynx Lynx canadensis using snow-track surveys in the northern Rocky Mountains, Montana, USA John R. Squires, Lucretia E. Olson, David L. Turner, Nicholas J. DeCesare & Jay A. Kolbe We used snow-tracking surveys to determine the probability of detecting Canada lynx Lynx canadensis in known areas of lynx presence in the northern Rocky Mountains, Montana, USA during the winters of 2006 and 2007. We used this information to determine the minimum number of survey replicates necessary to infer the presence and absence of lynx in areas of similar lynx density (approximately 2.8 lynx/100 km2) with confidence. The probability of detecting lynx in mountainous habitats that support resident populations was 0.80-0.99 when surveys were conducted on an 838km2 grid with 10 km of search effort per cell. Snow-track surveys were highly successful at detecting the presence of Canada lynx over large landscapes. Two survey replicates established absence of Canada lynx with 95% certainty. The high probability of detection associated with snow-track surveys makes this method useful for documenting populations of Canada lynx in areas where their status is uncertain. Key words: Canada lynx, detection probability, forest carnivores, Lynx canadensis, monitoring, snow-track surveys John R. Squires & Lucretia E. Olson, U.S. Forest Service, Rocky Mountain Research Station, 800 E. Beckwith, Missoula, Montana 59801, USA - e-mail addresses: [email protected] (John R. Squires); [email protected] (Lucretia E. Olson) David L. Turner, U.S. Forest Service, Rocky Mountain Research Station, 860 North 1200 E. Logan, Utah 84321, USA - e- mail: [email protected] Nicholas J. DeCesare, College of Forestry and Conservation, University Of Montana, Missoula, Montana 59812, USA - e- mail: [email protected] Jay A. Kolbe, Montana Fish, Wildlife and Parks, P.O. Box 1288, Seeley Lake, Montana 59868, USA - e-mail: jkolbe@mt. gov Corresponding author: John R. Squires Received 1 October 2010, accepted 2 October 2011 Associate Editor: Aure´lien Besnard Canada lynx Lynx canadensis are federally listed in rare or sensitive species are often based on anecdotal the contiguous United States as a threatened species occurrence data (unverifiable observations or sign; under the Endangered Species Act (U.S. Fish and McKelvey et al. 2008). Using anecdotal data to de- Wildlife Service 2000). Therefore, determining the lineate species occurrence leads to errors of omission presence or absence of lynx across broad landscapes, and commission that result in a misallocation of such as wilderness areas or national forest units, is limited funding and inefficient conservation actions important to conservationists and land managers. (McKelvey et al. 2008). Snow-tracking to detect The presence of carnivores can be especially difficult carnivores is particularly effective because it does not to establish as they are often cryptic, exhibit crepus- require a solicited response from the animal, it do- cular activity patterns and occur at low densities cuments the presence of individuals over a span of across large spatial scales (Wilson & Delahay 2001, several days if snow conditions permit, and it is O’Connell et al. 2006, Kolbe & Squires 2007). generally low cost compared to live-trapping or re- Delineations of current and historical ranges for mote cameras (Reid et al. 1987, Halfpenny et al. Ó WILDLIFE BIOLOGY 18:1 (2012) 215 1997, Foresman & Pearson 1998). Snow-track sur- tions (Squires et al. 2004). In that study, we used veys have been used successfully to determine the computer simulations to model the probability of presence of a variety of carnivore species, including detecting lynx snow-tracks depending on the amount wolverine Gulo gulo, river otter Lutra canadensis,red of search effort. Our goal in this study is to expand on fox Vulpes vulpes and short-tailed weasel Mustela this earlier work by calculating the empirical prob- erminea (Reid et al. 1987, Edelmann & Copeland ability of detecting lynx based on snow-track surveys 1999, Forsey & Baggs 2001, Ulizio et al. 2006, in areas of known presence. We focus on detectability Heinemeyer et al. 2008). However, a chronic weak- within areas occupied by resident populations of lynx ness of monitoring strategies based on snow-tracking rather than in areas occupied by single dispersing is species misidentification. This shortcoming can be individuals to ensure that underlying lynx densities addressed to meet evidentiary standards of species are relevant for conservation. We address the fol- identification by considering the snow-track a lowing two questions: 1) What was the probability of ’collection device’ for obtaining genetic samples detecting lynx with snow-track surveys conducted in (Squires et al. 2004), a method demonstrated to be areas with confirmed resident populations? 2) Given effective for lynx (McKelvey et al. 2006) and other this estimated detection probability, what level of forest carnivores (Ulizio et al. 2006). survey effort would be necessary to infer the absence Monitoring efforts based on snow-track surveys of resident lynx populations with confidence? Based conducted on a standardized survey grid can be used on these results, we offer management recommenda- to delineate local distributions of lynx (Squires et al. tions to optimize survey methods for monitoring lynx 2004). In the Northern Rocky Mountains, Montana, using snow-tracking methodology. USA, delineations of local lynx distributions as de- termined by snow-tracks overlapped the distribution of lynx as estimated by radio-telemetry by 97% Material and methods (Squires et al. 2004). Track counts can also estimate animal population density if track age and estimated We conducted our study in two regions of western daily travel rates are known (Stephens et al. 2006). Montana: the Purcell Mountains in the northwest However, using snow-track surveys to establish corner of the state and the Seeley Lake area, either the presence or absence of species in potential including the Garnet Range, located approximately habitat may be more difficult. Although species 50 km to the southwest (Fig. 1). The boreal forests in presence can be ascertained by a single detection the Seeley Lake area and the Garnet Range are (assuming correct identity), absence is difficult to composed mainly of subalpine fir Abies lasiocarpa establish with statistical certainty. A variety of sta- and Engelmann spruce Picea engelmannii,witha tistical concepts, including occupancy models, spe- smaller component of lodgepole pine Pinus contorta cies distribution models and mark-recapture models, and western larch Larix occidentalis; ponderosa pine provide statistical frameworks for modeling species Pinus ponderosa and Douglas-fir Pseudotsuga men- occurrence across landscapes (Pollock et al. 1990, ziesii were dominant at low elevations. Forests in the McKenzie et al. 2003, Elith et al. 2006). These models Purcell Mountains are similar with the addition of are data intensive and require significant financial western red cedar Thuja plicata and western hemlock and time commitments. Since our primary goal was Tsuga mertensiana in mixed stands. Elevations to provide managers with a relatively accessible ranged from 800 to 2,300 m a.s.l. in the Purcell method of estimating probability of detection and Mountains and from 1,200 to 2,500 m in the Seeley presence/absence of lynx, we chose to use a more Lake area and Garnet Range. tractable model by McArdle (1990) and Reed (1996). During 2006 and 2007, we conducted winter snow- This model uses the estimated probability of detect- track surveys for lynx on a grid of 8 3 8kmsquares ing a given species to determine the number of following Squires et al. (2004). This grid size roughly sampling events necessary to establish presence or equals an average female home range (of 72 km2)for absence with known statistical certainty, and has lynx from southern boreal forests (United States and proven to be effective in determining the number of southern Canada; Aubry et al. 2000, Squires et al. sampling events necessary to detect species that occur 2004). We used VHF-telemetry to verify the presence at different densities (Kery 2002, Jackson et al. 2006). of lynx in our study areas prior to surveys; however, In a previous study, we demonstrated the efficacy the technicians did not know the actual location of of snow-tracking for delineating local lynx distribu- lynx when they conducted their surveys. We selected 216 Ó WILDLIFE BIOLOGY 18:1 (2012) Figure 1. Locations of the survey grids used in the Purcell Mountains, near Libby, Montana, and the Seeley Lake area, near Missoula, Montana.
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