Occurrence and activity of bats at three national monuments in Central Arizona.
Some bats that echolocate can detect capture devices and avoid them causing some individuals to never be captured (O'Farrell, 1997; O'Farrell and Gannon, 1999). Big-eared bats (Corynorhinus) are maneuverable and adept at avoiding mist-nets (Cockrum and Cross, 1964). In two studies conducted in Ontario, Canada, neither Fenton et al. (1987) nor Hickey and Neilson (1995) captured hoary bats (Lasiurus cinereus)in mist-nets, but they did detect them using acoustic monitoring. size of a water source also can determine whether a species is captured (Cross, 1986). Molossid bats need large surfaces of water to drink while myotis can drink from areas <1 [m.sup.2] (Cross, 1986). Mist-netting also is time consuming, expensive, and requires constant attention, thereby, limiting the number of sites that can be sampled at a given time (O'Farrell, 1997; O'Farrell and Miller, 1999). Experience of a researcher also affects success of sampling with mist-nets (Kunz and Kurta, 1988).
Acoustic sampling with bat detectors can overcome some of the potential biases associated with sampling using mist-nets or traps (O'Farrell, 1997). Most bats emit ultrasonic pulses that have species-specific characteristics permitting identification without handling (Fenton and Bell, 1981; O'Farrell, 1997; O'Farrell and Miller, 1999; O'Farrell et al., 1999). Also, acoustic monitoring allows for efficient collection of data over large areas (O'Farrell, 1997; O'Farrell and Gannon, 1999) and provides more accurate surveys of communities than traditional sampling (Kalko et al., 1996; O'Farrell and Gannon, 1999; Ochoa et al., 2000). For example, five additional species of bats were identified using acoustic monitoring on Barro Colorado Island, Panama, after 15 years of sampling with traditional methods (i.e., direct capture; Kalko et al., 1996) and short-term acoustic surveys added two to nine species to diversity lists at four localities in Venezuela (Ochoa et al., 2000).
Additionally, the ability to detect and record vocalizations of bats offers researchers new opportunities to study behavior and ecology of bats (Fenton, 1988). sampling using acoustic monitoring provides information about presence of species and can be used to determine distribution (Fenton et al., 1983; Thomas, 1988), habitat selection (Krusic et al., 1996; Vaughan et al., 1996; Jung et al., 1999; Zimmerman and Glanz, 2000), foraging areas (Furlonger et al., 1987; Crome and Richards, 1988; Vaughan et al., 1996; Grindal and Brigham, 1999), and relative activity (Hayes, 1997; Gaisler et al., 1998; Grindal et al., 1999; Humes et al., 1999). Information regarding bats beyond their roosts is an important factor in their conservation (Fenton, 1997).
Acoustic sampling has limitations. For example, acoustic sampling did not adequately detect phyllostomids in Akumal, Mexico, and mist-netting was a better method of sampling to monitor their distribution and abundance (Fenton et al., 1992). To maximize inventories, a combination of methods should be used (Fenton et al., 1992; O'Farrell and Gannon, 1999).
We used a multifaceted approach to conduct surveys at Montezuma Castle, Tonto, and Tuzigoot national monuments. We attempted to form comprehensive lists of species to determine species richness for each of the monuments by employing acoustic monitoring and mist-netting. We used acoustic monitoring to gather information about overall activity and use of vegetational associations, we used mist-netting coupled with acoustic sampling to determine number of species of bats that each method was capable of detecting, and we determined if there were differences in detectability of species for each method.
Methods and Materials--Montezuma Castle National Monument, Yavapai County, Arizona, is 112 km N Phoenix. The monument consists of the Montezuma Castle unit and Montezuma Well unit, located ca. 6.5 km apart. We identified three vegetational associations at Montezuma Castle unit; desert (i.e., vegetation typical of the transition zone between the upper Sonoran life zone and true Sonoran Desert), riparian (i.e., deciduous riparian forest along Beaver Creek), and the transitional zone between riparian and desert areas (i.e., mesquite Prosopis juliflora bosque; Brown, 1994) and two at Montezuma Well unit; desert and riparian. Tonto National Monument, Gila County, Arizona, is 160 km E Phoenix. Three vegetational associations also occurred at Tonto National Monument; talus-slope (i.e., vegetation typical of upland desert scrub and semi-desert grassland within a canyon), bajada (i.e., vegetation typical of upland desert scrub and semi-desert grassland along gentle slopes), and riparian (Brown, 1994). Tuzigoot National Monument, Yavapai County, Arizona, is 146 km N Phoenix. it encompasses 324 ha ranging in elevation from 1,024 to 1,036 m. We identified two vegetational associations at Tuzigoot National Monument; desert and marsh (i.e., vegetation typical of interior marshlands; Brown, 1994).
At Montezuma Castle unit, Montezuma Well unit, and Tonto National Monument during 2002 and 2003, and Tuzigioot National Monument in 2003, we randomly selected three points in each vegetational association (i.e., stratified-random design) using Arc-View (Environmental Systems Research Institute, Redlands, California). We sampled each point in a single vegetational association 4 times/night (e.g., 1930-2130, 2200-2400, 0030-0230, and 0300-0500 h) for 20 min to account for bats foraging at different times during the night (Kunz, 1973). Each vegetational association was sampled on consecutive nights of similar weather conditions. We sampled each site 6 times/summer (May-September) because activity tends to be low during winter (Kuenzi and Morrison, 2003). At Tuzigoot National Monument in 2002, we sampled both vegetational associations in a single night by sampling 2 points/vegetational association for 15 min, 4 times/night (e.g., 1930-2130, 2200-2400, 0030-0230, and 0300-0500 h) because we had fewer personnel available. sampling occurred five times during summer 2002 at Tuzigoot National Monument because of fire at Tavasci Marsh.
Vocalizations were sampled using the Pettersson D240x (Pettersson Elektronik AB, uppsala, Sweden), a broadband, time-expansion, bat detector, to detect all frequencies simultaneously in areas where species composition was unknown. We linked the detector to a tape recorder to record vocalizations and sampled the signal from the microphone by a fast analogue-to-digital converter to produce an output signal 1/10 the original rate of sampling while retaining nearly all characteristics of the original signal (i.e., time-expanded signal). Time-expansion recordings are desirable because they present more accurate vocalizations than real-time recordings (Fenton et al., 2000) making identification of closely related species, such as myotis, easier because of the ability to detect subtle differences in structure of calls.
We used Bat sound (Pettersson Elektronik AB, Uppsala, Sweden) and Sonobat (DND Design, Jacksonville, Florida) to generate sonograms from recorded calls to visualize and analyze vocalizations (i.e., determine species; Fenton and Bell, 1981). Discriminate analysis (SPSS, inc., Chicago, Illinois), was used to identify Myotis to species because the similarity of calls among species restricted identification to genus (i.e., unknown myotis; Vaughan et al., 1997; Gannon et al., 2004). We measured duration (msec), high frequency (kHz), low frequency (kHz), characteristic low frequency (kHz), bandwidth (kHz), frequency at maximum amplitude (kHz), maximum amplitude (percent of duration), slope (kHz/msec), heel (percent of duration), upper slope (kHz/msec), lower slope (kHz/msec), and frequency of second fundamental harmonic (kHz) of all calls of Myotis to classify calls to species. Ninety-four calls (8 southwestern myotis Myotis auriculus, 22 California myotis M. californicus,15 western small-footed myotis M. ciliolabrum, 13 Arizona myotis M. occultus, 14 cave myotis M. velifer, and 22 Yuma myotis M. yumanensis), where species was identified from captured individuals and calls were recorded (i.e., known myotis), were used as the training set in the discriminate analysis. Discriminate functions for the classification of unknown calls of Myotis were derived from known calls of Myotis and used to classify known calls of Myotis to determine a rate of correct classification for unknown calls of Myotis. We conducted a Student's t-test (two levels) or an analysis of variance (ANOVA, >2 levels) in JMPIN (SAS Institute, Inc., Cary, North Carolina) to compare overall activity of bats (mean passes/min) between vegetational associations (Ramsey and Schafer, 1997) and Tukey-Kramer multiple-comparison tests to determine which vegetational associations differed when results of ANOVA were significant (i.e., P < 0.05; Ramsey and Schafer, 1997). A pass was defined as a 34-s download of a 3.4-s time-expanded call that automatically set off the bat detector. Data were log transformed to meet assumptions of equal variance (Ramsey and Schafer, 1997) when necessary; however, means are reported on untransformed data. We conducted a Student's t-test (two levels) or a Kruskal-Wallis test (>2 levels) in JMPIN to compare average number of calls collected for each species across vegetational associations (Ramsey and Schafer, 1997). The number of foraging buzzes, rapid emission of pulses produced by a bat as it nears a prey item (Griffin et al., 1960), also were counted in each vegetational association to determine key foraging areas.
We set four-shelved, 38-mm-mesh mist-nets along suspected foraging flyways and across selected water sources. Mist-nets were opened at dusk and remained open 1-6 h depending on weather, time of year, and number of personnel present, and checked for bats at 5-10-min intervals. All captured bats were placed in a cloth bag and examined [less than or equal to] 15 min of capture (Kunz and Kurta, 1988), identified to species, and released after measurements were taken and observations recorded.
We used acoustic sampling on some nights to record bats flying in the area (Fenton, 1988) in conjunction with mist-netting. Bats were recorded every other 0.5 h in 2002 and every other 10 min in 2003 to obtain a better representation of presence of species during the hour. We did not accompany mist-netting with acoustic sampling at Tuzigoot National Monument because we did not have personnel available to operate recording equipment and check mist-nets simultaneously. Species diversity and percentage occurrence of species detected was compared for acoustic monitoring and mist-netting to determine differences in detectability between methods.
We set mist-nets at Montezuma Castle unit, Montezuma Well Unit, Tonto National Monument, and Tuzigoot National Monument (i.e., Tavasci Marsh) 24, 19, 35, and 9 times, respectively, during 2001-2003. We accompanied mist-netting with acoustic monitoring 13, 12, and 10 times at Montezuma Castle unit, Montezuma Well Unit, and Tonto National Monument, respectively.
Results--Seven of the 94 known calls of Myotis used as the training set for discriminate analysis were incorrectly classified (one California myotis, one western small-footed myotis, one Arizona myotis, two cave myotis, and two Yuma myotis). Five discriminate functions were derived from the analysis. The first discriminate function accounted for 74.5% of variation ([[lambda].sub.1] = 5.97) and the second for 15.6% of variation ([[lambda].sub.2] = 1.25). The last three discriminate functions accounted for 9.9% of variation ([[lambda].sub.3] = 0.49, [[lambda].sub.4] = 0.22, [[lambda].sub.5] = 0.09). We were confident that a large number of the unknown calls of Myotis were classified correctly because of the high rate of success classifying known Myotis.
Overall activity (passes/min) at Montezuma Castle Unit was similar in the desert ([bar.x] = 0.10, n = 36, 95% CI = 0.06-0.14), transition ([bar.x] = 0.11, n = 36, 95% CI = 0.07-0.15), and riparian ([bar.x] = 0.11, n = 36, 95% CI = 0.07-0.15) vegetational associations ([F.sub.2,105] = 0.09, P = 0.910). We recorded 937 calls attributed to 14 species of bats in 144 h of acoustic monitoring. Forty-eight calls (5.1%) contained foraging buzzes (12 in desert, 6 in riparian, and 30 in transition vegetational associations). Brazilian free-tailed bats (Tadarida brasiliensis) accounted for 41.0% of calls recorded. Each of the 13 other species comprised < 10% of calls. We detected Brazilian free-tailed bats most often in the desert vegetational association ([chi square]2 = 12.06, P = 0.002; Table 1). Five species were detected most often in the riparian vegetational association: western small-footed myotis ([chi square]2 = 7.28, P = 0.030), fringed myotis M. thysanodes ([chi square]2 = 9.41, P = 0.010), hoary bats ([chi square]2 = 11.54, P = 0.003), big brown bats Eptesicus fuscus ([chi square]2 = 17.54, P = 0.001), and pallid bats Antrozous pallidus ([chi square]2 = 5.23, P = 0.070; Table 1). California myotis ([chi square]2 = 13.95, P = 0.001) and Yuma myotis ([chi square]2 = 16.54, P = 0.001) were detected most often in the transition vegetational association (Table 1). We did not detect a difference in the six other species across vegetational associations (P > 0.100; Table 1). We captured 67 bats of eight species during 138 h of mist-netting and recorded 565 calls of 15 species during 27.3 h of acoustic monitoring at mist-nets (Fig. 1).
There were 0.06 more bat calls/min collected in the riparian vegetational association ([bar.x] = 0.12, n = 36) than in the desert vegetational association ([bar.x] = 0.06, n = 36) at Montezuma Well unit ([t.sub.70] = 3.32, P = 0.002, 95% CI = 0.03-0.10). We recorded 530 calls of 17 species in 96 h of acoustic monitoring. Twenty-one calls (4.0%) contained foraging buzzes (6 in desert and 15 in riparian vegetational associations). Brazilian free-tailed bats, canyon bats (Parastrellus hesperus), and Arizona myotis accounted for 34.2, 11.5, and 11.1%, respectively, of calls recorded. Each of the 14 other species made up <10% of calls with southwestern myotis, fringed myotis, Townsend's big-eared bats (Corynorhinus townsendii), spotted bats (Euderma maculatum), and big free-tailed bats (Nyctinomops macrotis) each accounting for <1% of calls recorded. Six species were detected most often in the riparian vegetational association: Arizona myotis ([t.sub.70] = -2.62, P = 0.010), cave myotis ([t.sub.70] = -2.59, P = 0.010), Yuma myotis ([t.sub.70] = -1.94, P = 0.060), western red bats Lasiurus blossevillii ([t.sub.70] = -2.12, P = 0.040), pallid bats ([t.sub.70] = -2.24, P = 0.030), and Brazilian free-tailed bats ([t.sub.70] = -1.77, P = 0.080; Table 1). We did not detect a difference in the 11 other species across vegetational associations (P> 0.100; Table 1). We captured 221 bats of 13 species during 99.5 h of mist-netting and recorded 408 calls of 15 species during 25.3 h of acoustic monitoring at mist-nets (Fig. 2).
Overall activity (passes/min) at Tonto National Monument differed across vegetational associations ([F.sub.2,105] = 18.37, P < 0.001). The riparian vegetational association ([bar.x] = 0.25, n = 36, 95% CI = 1 0.19-0.32) had higher activity than the bajada ([bar.x] = 0.06, n = 36, 95% CI = 0.05-0.08) and talus-slope ([bar.x] = 0.10, n = 36, 95% CI = 1 0.07-0.13) vegetational associations (Tukey-Kramer honestly significant difference P > 0.050). The bajada and talus-slope vegetational associations did not differ significantly (Tukey-Kramer honestly significant difference P > 0.050). We recorded 1,205 calls of 13 species of bats in 144 h of acoustic monitoring. Forty calls (3.3%) contained foraging buzzes (6 in bajada, 22 in riparian, and 12 in talus-slope vegetational associations). Cave myotis, Brazilian free-tailed bats, Arizona myotis, California myotis, and western small-footed myotis accounted for 24.1, 19.5, 12.5, 12.0, and 11.5%, respectively, of calls recorded. Each of the eight other species made up < 10% of calls with southwestern myotis, big free-tailed bats, and greater bonneted bats (Eumops perotis) each accounting for <1% of calls recorded. Pocketed free-tailed bats (Nyctinomops femorosaccus) were most often in the bajada vegetational association ([chi square]2 = 10.39, P = 0.006; Table 1). Seven species were detected most often in the riparian vegetational association: California myotis ([chi square]2 = 25.88, P < 0.001), western small-footed myotis ([chi square]2 = 30.82, P < 0.001), Arizona myotis ([chi square]2 = 21.68, P < 0.001), cave myotis ([chi square]2 = 45.75, P < 0.001), Yuma myotis ([chi square]2 = 36.32, P < 0.001), big brown bats ([chi square]2 = 14.69, P = 0.001), and pallid bats ([chi square]2 = 5.30, P = 0.070; Table 1). We detected Brazilian free-tailed bats most often in the talus-slope vegetational association ([chi square]2 = 5.41, P = 0.070; Table 1). We did not detect a difference in the four other species across vegetational associations (P > 0.100; Table 1). We captured 74 bats of six species during 231.7 h of mist-netting and recorded 275 calls of six species during 22 h of acoustic monitoring at mist-nets (Fig. 3).
There were 0.13 more calls/min collected in the marsh vegetational association ([bar.x] = 0.23, n = 28) than the desert vegetational association ([bar.x] = 0.09, n = 28) at Tuzigoot National Monument ([t.sub.54] = 4.53, P < 0.001, 95% CI = 0.07-0.19). We recorded 648 calls of 16 species of bats in 68 h of acoustic monitoring. Forty-four calls (6.8%) contained foraging buzzes (8 in desert and 36 in marsh vegetational associations). Brazilian free-tailed bats, canyon bats, Yuma myotis, and western small-footed myotis accounted for 24.7, 19.9, 11.3, and 10.0%, respectively, of calls recorded. Each of the 12 other species made up < 10% of calls with southwestern myotis, hoary bats, and Townsend's big-eared bats each accounting for <1% of calls recorded. Nine species were detected most often in the marsh vegetational association: California myotis ([t.sub.54] = -3.55, P = 0.001), western small-footed myotis ([t.sub.54] = -2.98, P = 0.004), Arizona myotis ([t.sub.54] = -3.66, P = 0.001), cave myotis ([t.sub.54] = -2.82, P = 0.010), Yuma myotis ([t.sub.54] = -4.27, P = <0.001), western red bats ([t.sub.54] = -3.04, P = 0.004), hoary bats ([t.sub.54] = -2.42, P = 0.020), Townsend's big-eared bats ([t.sub.54] = -1.80, P = 0.080), and pocketed free-tailed bats ([t.sub.54] = -1.99, P = 0.050; Table 1). We did not detect a difference in the seven other species across vegetational associations (P > 0.100; Table 1). We captured 33 bats of 10 species during 29.5 h of mist-netting (Fig. 4).
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Discussion--Overall activity of bats was lowest in the desert vegetational association and highest in riparian or marsh vegetational associations at all three national monuments. our findings were similar to other studies that reported riparian and marsh areas had higher activity than surrounding areas (Brosset et al., 1995; Grindal et al., 1999; Zimmerman and Glanz, 2000; Everette et al., 2001). Although overall activity was greater in riparian vegetational associations at Montezuma Castle unit, we detected no significant difference between the three vegetational associations, possibly because the riparian corridor winds throughout the monument rather than in a relatively straight path.
We did not detect high percentages of foraging buzzes. Number of passes by bats is correlated with number of foraging buzzes, and therefore, with foraging by bats (Russo and Jones, 2003). We could have missed foraging buzzes because bats moved out of range of the bat detector while emitting a buzz, the detector only collected a small portion of the vocalization and did not capture the foraging buzz, some bats did not emit foraging buzzes while foraging (i.e., substrate gleaners), the number of foraging buzzes was not equivalent to foraging activity (Hayes, 2000), or bats may have been commuting rather than foraging.
Use of acoustic monitoring during mist-netting added three to eight species to lists of diversity, depending on site, and acoustic monitoring in vegetational associations added two to six species, depending on site. our results are consistent with other studies that detected additional species in areas by using acoustic monitoring in combination with mist-netting (Everette et al., 2001; Sedlock, 2001). However, acoustic monitoring without the use of mist-netting would have detected fewer species in each monument, except Tuzigoot National Monument. We captured one additional species at Montezuma Castle unit, one at Montezuma Well Unit, and three at Tonto National Monument that were not documented by acoustic monitoring at mist-nets or in vegetational associations. our results were similar to the study at Mount Makiling in the Philippines where two species were detected only by mist-nets (Sedlock, 2001).
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The differences we documented between mist-netting and acoustic monitoring highlight the variability between techniques. Many factors can contribute to reliability of each method. Acoustic monitoring can be biased by intensity of calls (Fenton, 2003). Some species are detected easily at [greater than or equal to] 10 m, while other species can be detected only at <2 m (Fenton, 2003). To further complicate matters, some species can vary structure of calls depending on complexity of vegetational structure (Kalko and Schnitzler, 1993). Although we assumed that all bats were equally detectable by ultrasonic equipment, there were clear indications in our data showing unequal detectability among species. At Montezuma Castle Unit and Tonto National Monument, we did not detect Townsend's big-eared bats using acoustic monitoring; however, these bats were captured during mist-netting at both sites.
Successful use of mist-nets requires placement in areas where bats tend to aggregate (Kunz and Kurta, 1988). At Montezuma Castle Unit and Montezuma Well Unit, we set nets in areas we could navigate safely, and the only permanent source of water at Tonto National Monument was a small spring lined by dense riparian vegetation that limited mist-netting opportunities. it was probable that some species were not captured because of placement of mist-nets. Size of surface of water also can influence species captured in mist-nets, as opposed to species present in the area, because some species are highly maneuverable and others need large surfaces of water because they are less maneuverable (Cross, 1986). Our data indicate that mist-nets probably were not set in appropriate locations to capture molossids at Montezuma Castle Unit and Tonto National Monument. At Montezuma Castle Unit, molossids made up a significant proportion of bats recorded acoustically, but none was captured in mist-nets. At Tuzigoot National Monument, our mist-netting effort was limited because of difficulties mist-netting a large body of water. With the limited amount of time available, we probably caught only a small proportion of species. We detected five species using acoustic monitoring in the area that were never captured in mist-nets.
Temporal and spatial variation can affect outcome of studies using echolocation for assessments (Hayes, 2000; Sherwin et al., 2000; Gannon et al., 2003). We were not able to account for nightly temporal variation through replication within a site and among sites because of limitations in equipment and time. There were high levels of variability in activity on consecutive nights at the same site, possibly because of weather, abundance of insects, distribution of insects, interactions among individual bats, and other complex relationships and factors (Hayes, 1997). During this study, we were capable of sampling only a single site (i.e., vegetational association) and single point within a site at a time; therefore, possibly not accounting for overall variability among nights. We attempted to find some measure of variability within a site by breaking the evening into four sampling periods; however, it would have been more appropriate to sample all points within a site simultaneously to account for variability in activity at a site.
We successfully established comprehensive lists of diversity for species of bats through use of acoustic monitoring accompanied by mist-netting for each of the monuments. We were able to demonstrate that some species were detected by only a single method, further illustrating the need to use methods simultaneously. Activity was highest in riparian and marsh areas, possibly because of higher density of insects. However, desert areas were important because many species (e.g., molossids) are not able to maneuver in cluttered environments.
We thank K Davis and S. Hoh for cooperation and support with field logistics, safety issues, lodging, and assistance during this study. We thank the Desert Southwest Cooperative Ecosystem Studies Unit, United States Geological Survey, Sonoran Desert Research Station, University of Arizona, and the Sonoran Desert Inventory and Monitoring Network for funding this project.
Submitted 31 October 2005. Accepted 3 June 2009.
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Associate Editor was Philip D. Sudman.
Melanie Bucci, * Yar Petryszyn, and Paul R. Krausman
School of Natural Resources, University of Arizona, Tucson, AZ 85721 (MB)
Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721 (YP)
Wildlife Biology Program, University of Montana, Missoula, MT 59812 (PRK)
* Correspondent: email@example.com
Table 1--Average number of calls of bats collected by species during acoustic monitoring in vegetational associations at Montezuma Castle (Montezuma Castle Unit and Montezuma Well Unit), Tonto, and Tuzigoot national monuments, Arizona, 2002-2003. Montezuma Castle National Monument Montezuma Castle Unit n = 36 Species Desert Riparian Transition Southwestern myotis -- -- -- California myotis 0.11 0.19 0.69 Western small-footed myotis 0.17 0.86 0.36 Arizona myotis 0.28 0.81 0.39 Fringed myotis 0 0.47 0.06 Cave myotis 0.25 0.56 0.58 Yuma myotis 0.11 0.33 1.22 Western red bat 0.03 0.17 0.14 Hoary bat 0.06 1.00 0.22 Canyon bat 1.03 0.31 1.11 big brown bat 0.17 1.11 0.11 Spotted bat -- -- -- Townsend's big-eared bat -- -- -- Pallid bat 0.06 0.31 0.06 Brazilian free-tailed bat 5.22 1.97 3.47 Pocketed free-tailed bat 0.47 0.75 0.50 big free-tailed bat 0.17 0.11 0.06 Greater bonneted bat -- -- -- Montezuma Well Unit n = 36 Species Desert Riparian Southwestern myotis 0.03 0.03 California myotis 0.31 0.53 Western small-footed myotis 0.33 0.56 Arizona myotis 0.31 1.33 Fringed myotis 0.08 0 Cave myotis 0.31 0.78 Yuma myotis 0.14 0.47 Western red bat 0.11 0.42 Hoary bat 0.19 0.56 Canyon bat 0.75 0.94 big brown bat 0.36 0.44 Spotted bat 0.03 0.03 Townsend's big-eared bat 0.03 0.06 Pallid bat 0 0.17 Brazilian free-tailed bat 1.61 3.42 Pocketed free-tailed bat 0.19 0.17 big free-tailed bat 0 0.06 Greater bonneted bat -- -- Tonto National Monument n = 36 Species Bajada Riparian Talus slope Southwestern myotis 0 0.56 0.56 California myotis 0.28 3.33 0.39 Western small-footed myotis 0.31 3.03 0.53 Arizona myotis 0.44 2.33 1.39 Fringed myotis -- -- -- Cave myotis 0.53 6.64 0.89 Yuma myotis 0.22 2.72 0.25 Western red bat -- -- -- Hoary bat -- -- -- Canyon bat 0.58 0.62 0.64 big brown bat 0.08 0.50 0.06 Spotted bat -- -- -- Townsend's big-eared bat -- -- -- Pallid bat 0.02 0.28 0.06 Brazilian free-tailed bat 2.08 0.97 3.47 Pocketed free-tailed bat 0.42 0.08 0.25 big free-tailed bat 0.08 0 0.06 Greater bonneted bat 0.03 0 0 Tuzigoot National Monument n = 28 Species Desert Marsh Southwestern myotis 0 0.04 California myotis 0.21 1.46 Western small-footed myotis 0.50 1.82 Arizona myotis 0.18 1.46 Fringed myotis 0 0.46 Cave myotis 0.29 1.50 Yuma myotis 0.39 2.21 Western red bat 0 0.36 Hoary bat 0 0.18 Canyon bat 1.79 2.82 big brown bat 0.32 0.39 Spotted bat -- -- Townsend's big-eared bat 0 0.11 Pallid bat 0.14 0.11 Brazilian free-tailed bat 2.71 3.00 Pocketed free-tailed bat 0.07 0.32 big free-tailed bat 0.11 0.18 Greater bonneted bat -- --
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|Author:||Bucci, Melanie; Petryszyn, Yar; Krausman, Paul R.|
|Date:||Jun 1, 2010|
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