occupancy rates by swift foxes (vulpes velox) in eastern ... - BioOne

THE SOUTHWESTERN NATURALIST 52(4):541–551

DECEMBER 2007

OCCUPANCY RATES BY SWIFT FOXES (VULPES VELOX) IN EASTERN COLORADO DANIEL J. MARTIN, GARY C. WHITE,*

AND

FRANCES M. PUSATERI

Colorado Division of Wildlife, 317 West Prospect, Fort Collins, CO 80536 (DJM, FMP) Department of Fishery and Wildlife Biology, Colorado State University, Fort Collins, CO 80523 (GCW) *Correspondent: [email protected] ABSTRACT—We conducted a rigorous monitoring program to develop appropriate conservation plans for swift foxes (Vulpes velox). We set 20 cage traps on 51 grids to estimate rates of occupancy in eastern Colorado. Every 50th grid was selected systematically with a randomly selected starting grid from a list of 2,566 grids ranked by percentage of shortgrass prairie. We trapped 31 August 2004–12 February 2005, with each grid trapped 3 nights. Upon capture, we marked individual foxes with a unique identifier, and determined gender prior to release. We centered a 6.4 by 8.0 km-rectangle over each grid trapped and calculated percentage of shortgrass prairie using a GIS. We caught 136 swift foxes on 40 grids, including 12 recaptures; 71% of captures were in grids with .50% shortgrass prairie. We estimated the proportion of 4.8 by 6.4 km-grids in eastern Colorado occupied by swift foxes to be yˆ 5 0.711 (SE 5 0.069, 95% CI 0.576–0.846), with no evidence of a decline from the 1995–1997 surveys. RESUMEN—Realizamos un programa riguroso de monitoreo para desarrollar me´todos de conservacioń apropiados para Vulpes velox. Pusimos 20 trampas de jaula en 51 cuadrıćulas para estimar la tasa de ocupacioń en el este de Colorado. Empezando con una cuadrıćula escogida al azar, escogimos cada quincuage´sima cuadrıćula sistema´ticamente de una lista de 2,566 cuadrıćulas puestas en orden de porcentaje de pradera de pasto corto. Atrapamos desde el 31 agosto 2004 hasta el 12 febrero 2005 y atrapamos por tres noches en cada cuadrıćula. Al capturar cada zorro, le colocamos una identificacioń exclusiva y determinamos el sexo antes de liberarlo. Utilizamos un sistema de informacioń geogra´fica (SIG) para calcular el porcentaje de pradera de pasto corto en un rectańgulo de 6.4 3 8.0 km centrado sobre cada cuadrıćula. Atrapamos 136 V. velox en 40 cuadrıćulas incluyendo 12 recapturas; 71% de las capturas ocurrio´ en cuadrıćulas con .50% de pradera de pasto corto. Estimamos la proporcioń de cuadrıćulas de 4.8 3 6.4 km en el este de Colorado ocupadas por V. velox comoyˆ 5 0.711 (EE 5 0.069, IC 95% 0.576–0.846) sin evidencia de una disminucioń comparada a los muestreos de 1995–1997.

The swift fox (Vulpes velox) is a small canid indigenous to shortgrass prairies of North America, including much of eastern Colorado (Samuel and Nelson, 1982; Scott-Brown et al., 1987; Allardyce and Sovada, 2003). The swift fox is closely related to the kit fox (V. macrotis), and there is evidence leading some to suggest combining these species taxonomically (Dragoo and Wayne, 2003; but see Mercure et al., 1993). However, many wildlife agencies, including the Colorado Division of Wildlife, still consider swift and kit foxes separate species due to the relatively small area of overlap in their geographic range. Both species have declined in number and geographic range likely due to degradation and fragmentation of habitat, predation (mainly by coyotes, Canis latrans), com-

petition from expanding populations of the red fox (V. vulpes), and predator-control efforts of the past century (Allardyce and Sovada, 2003). While most current predator-control operations no longer use methods with broad effects (e.g., Compound 1080), habitat degradation and fragmentation from urban sprawl and some agricultural practices continue to reduce available habitat for swift foxes and many of their species of prey, including prairie dogs (Cynomys; Allardyce and Sovada, 2003). In addition, these factors can be compounded by the relatively limited dispersal distances of swift foxes (Sharps, 1984, in litt.; Uresk and Sharps, 1986). The continued decline in some populations led to a 1992 petition of the United States Fish and Wildlife Service to list the swift fox under the

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Endangered Species Act of 1973 (United States Fish and Wildlife Service, 1995). In 1994, the United States Fish and Wildlife Service concluded the swift fox was warranted for listing but precluded by higher-priority species (Federal Register, 1994). Legal harvest of swift foxes in Colorado ended in 1998 when the trapping season was closed by the Wildlife Commission, and the species was designated as ‘‘Nongame’’ and listed as a Species of Special Concern (Colorado Division of Wildlife Rules and Regulations, http://wildlife.state.co.us/regulations/ ch10.pdf). This designation added legal protection of swift foxes by allowing take only of individuals causing damage. Because of the large area of shortgrass prairie remaining in the state, Colorado is believed to have a wider range of distribution of swift foxes than any other state (Finley at al., 2005). However, no formal estimate of numbers or distribution of swift foxes had been made throughout the state prior to or since the 1992 petition. Therefore, an initial estimate of size of population and development of a monitoring program for swift foxes in eastern Colorado took place during 1995–1997 (Finley et al., 2005). Personnel of the Colorado Division of Wildlife decided to continue monitoring populations of swift foxes in the state at intervals of 5 years. Thus, we began a monitoring effort based on the methods of Finley et al. (2005) in August 2004. Goals of this project were to 1) estimate occupancy rates of 6.4 by 8.0 km-grids, 2) relate occupancy to presence of shortgrass prairie, 3) estimate size of population, and 4) compare estimates of parameters to those of Finley et al. (2005) METHODS—Study Area—Our study area was primarily east of Interstate 25 and is dominated by shortgrass prairie, lands in the Conservation Reserve Program, and agricultural development (Fig. 1). The terrain varies widely, from flat to rolling upland plains in the east-central to high plains and canyons in the Southeast. Agricultural cropland included primarily center-pivot irrigated corn and wheat (Colorado Department of Agriculture, http://www.ag.state.co.us/ commissioner/archived/2002annual.PDF). Dominant species of plants in areas with shortgrass prairie (including livestock range) are blue grama (Bouteloua gracilis), buffalograss (Buchloe dactyloides), scarlet globemallow (Sphaeralcea coccinea), plains prickly-pear (Opuntia polyacantha), rubber rabbitbrush (Chrysothamnus nauseosus), broom snakeweed (Gutierrezia sarothrae), and spreading buckwheat (Eriogonum effusum; Shortgrass Steppe LTER, http://sgs.cnr.colostate.edu). Intensity of grazing by cattle varied greatly. Lands

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enrolled in Conservation Reserve Program (ca. 3,232 km2) contain a variety of native and non-native vegetation. Dominant vegetation types in the Conservation Reserve Program in eastern Colorado include western wheatgrass (Pascopyrum smithii), switchgrass (Panicum virgatum), blue grama, sand bluestem (Andropogon hallii), yellow Indiangrass (Sorghastrum nutans), prairie sandreed (Calamovilfa longifolia), and green needlegrass (Nassella viridula), depending on location (E. Backhaus and D. Martin, in litt.). Pinyon pine (Pinus edulis) and one-seeded juniper (Juniperus monosperma) are common on some locations in the southeastern part of the state (Schauster et al., 2002). Humidity usually is low (Colorado Climate Center, http://ccc.atmos.colostate.edu/climateofcolorado.php), mean temperature from August to January for the state is 5.7uC, and mean precipitation is 18.03 cm (National Oceanic and Atmospheric Administration National Climatic Data Center, http://www.ncdc.noaa.gov/oa/ climate/research/cag3/co.html). Selection of Grids—We estimated rates of occupancy based on methods described by Finley et al. (2005), who demonstrated that composition of shortgrass prairie in a grid was a reliable predictor for both probability of occupancy (y) and probability of detection (p). Therefore, we sorted the 2,566 available grids by the percentage of shortgrass prairie contained in the grid based on data from the Colorado Gap Analysis Program and then systematically selected every 50th grid for sampling foxes (with a random starting point between 1 and 51). This procedure resulted in a sample of 51 grids, and was repeated six times without replacement to provide alternate grids in case landowners denied access for primary grids. The sample size of n 5 51 was determined based on power calculations provided by Finley et al. (2005). After completion of the trapping effort, we used ArcView 3.2 (ESRI, Redlands, California) to create and center a 6.4 by 8.0 km-buffer over each grid, with the edge expansion buffer based on one-half of the intertrap distance. The percentage of shortgrass priarie was recalculated for each buffer in ArcGIS 9.0 (ESRI, Redlands, California) using data from the Gap Analysis Program. Effort—We conducted trapping September–February to take advantage of dispersing animals and to maximize probabilities of capture (Finley et al., 2005). Each 4.8 by 6.4 km-grid contained 20 singledoor cage-traps measuring about 81.3 by 25.4 by 30.5 cm (model 1079, Woodstream Havahart, Lititz, Pennsylvania, and model 108 Tomahawk Live Trap, Tomahawk, Wisconsin). Traps with wire mesh .2.5 by 2.5 cm were modified by attaching 1.3 by 1.3 cm-mesh hardware cloth to cover the sides and top, in an attempt to reduce the chance of foxes injuring teeth or jaws. Each trap was set for 3 trap nights providing 60 trap nights of effort for each grid. We baited traps with either a turkey poult (Longmont Foods, Longmont, Colorado) or a raw chicken leg or wing. Bait also was tied inside the trap at a point furthest from the entrance. We lured foxes to traps by placing a small piece of bait, Powder River Paste Bait (O’Gorman Enterprises, Broadus, Montana), canned mackerel, or cat food within 1 m of each entrance to a trap. Each trap was placed near intersections of land-

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Martin et al.—Swift foxes in eastern Colorado

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FIG. 1—Location of grids used to monitor the swift fox (Vulpes velox) in eastern Colorado, showing percentage category of shortgrass prairie and number of foxes captured on each (including recaptures), August 2004– February 2005. survey section borders, and was protected if possible from the elements by placing next to vegetation. If needed, a 22.9 by 30.5-cm piece of plastic was attached to the top of traps to provide shade and as a weather barrier if there was inadequate natural cover. We checked traps each morning to reduce both the time animals were held captive and the possibility of scaring

active animals away from traps because foxes primarily are nocturnal (Kitchen et al., 1999; Moehrenschlager et al., 2003). Once captured, we transferred each swift fox to a canvas bag and weighed it to the nearest 0.1 kg using a calibrated spring scale (Pesola AG, Baar, Switzerland). We marked swift foxes using a unique identifier,

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usually by implanting a Passive Integrated Transponder (PIT) tag (Digital Angel Corporation, South Saint Paul, Minnesota) subcutaneously between the scapulae, and always a number marked on or in at least one pinna with a nontoxic permanent pen. Capture and handling of swift foxes was performed in a humane manner and followed guidelines of the American Society of Mammalogists. Statistical Analysis—We estimated probability of occupancy (y) using the occupancy model of MacKenzie et al. (2002) in Program MARK (White and Burnham, 1999). We considered a set of a priori models that incorporated the month variable (time of trapping) as sine and cosine functions to model probabilities of detection (p), and the percentage of shortgrass prairie on the trapping grid to model both probabilities of detection and occupancy, y (psi). Finley et al. (2005) demonstrated time of trapping to be an important factor influencing p, but because times of trapping for this study were substantially different than those of Finley et al. (2005), we did not expect the same models to apply. Time-specific and time-trend models of p also were considered. We followed Burnham and Anderson (2002) to perform selection of models with information-theoretic methods using the corrected Akaike Information Criterion (AICc). To estimate proportion and number of grids occupied by swift foxes in eastern Colorado, we used the logistic equation with estimates from the minimum Akaike Information Criterion (AICc) model corrected for sample size with the covariate short ^ grid-specific ^ ^ ~ exp b0 z b1 xi , where the covariate grass prairie y i

^ ^ 1 z exp b0 z b1 xi

value of shortgrass prairie for grid i (i 5 1, …, 2,566) is xi, and the estimates from Program MARK are the intercept (bˆ0) and slope parameter (bˆ1). The number 2,566 P ^ y with of occupied grids is estimated by O^ ~ i~1

i

variance estimated by the sum of the elements of the 2,566 3 2,566 estimated variance-covariance matrix of ˆ i. Elements of this matrix are the y h i2 ^ ~ y ^ y ^ 1{y ^ Var i i i h i ^ z2x Cov ^ ^b , b^ , Vâr b^0 zxi 2 Vâr b i 1 0 1

and

^ ,y ^ 1{y ^ ~y ^ y ^ ^ 1{y C ôv y i j i i j j h ^ ^b1 ^ ^b0 z xi z xj V ar V ar i ^ ^ ^b0 , b z xi z xj C ov , 1

where Var(.) indicates the variance of the enclosed estimator, and Cov(.,.) indicates the covariance of the two enclosed estimators. These formulae differ from those presented in Bowden et al. (2003) because they were using a covariate to predict an estimated size of population using a ratio estimator with correlated estimates, whereas here the covariate is used to estimate directly the correlated estimates of rate of occupancy. In addition, we ignored the negligible, finite population correction.

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FIG. 2—Histogram showing the close relationship between the grids included in the sample compared to the sampling frame. To be comparable to the frequencies of grids in the sampling frame, frequencies of sampled grids are 50. A representative sample relative to availability of the variable for shortgrass prairie was selected.

We also followed the analysis methods of Finley et al. (2005) to estimate size of the population of foxes using a trapping grid, using the Huggins estimator (Huggins, 1989, 1991). We performed selection of models with information-theoretic methods following Burnham and Anderson (2002).

RESULTS—Selection of Grids—We selected grids randomly to approximately represent habitat percentages of shortgrass prairie proportional to their availability in the study area (Figs. 1 and 2). When we multiplied frequency of the sample by 50, we obtained a close relationship between the sampling frame and the grids sampled (Fig. 2). We trapped 51 grids during 31 August 2004–12 February 2005. Most grids (n 5 46) had to be shifted from their original locations to obtain an area with adequate permission of landowners. In addition, two grids were set with ,20 traps (one grid in Las Animas County was set with 15 traps, one in Pueblo County was set with 18 traps) due to either a lack of permission from landowners or rough terrain (e.g., steep canyon terrain). Further reflecting difficulty in access to land, 11 traps (1–5 traps/grid) in six grids were not located within the buffer boundary used to calculate percentage of shortgrass paririe (range 5 8–994 m, x¯ 5 328 m from nearest edge of buffer). These changes were necessary to accommodate ownership and terrain of land. Because only small changes were required, we do not believe these modifications of the sampling design would bias estimates of p or y.

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TABLE 1—Efficacy of capture (captures/100 trap nights) of swift foxes (Vulpes velox) in each percentage category of shortgrass prairie in eastern Colorado, 31 August 2004–12 February 2005. Swift foxes captured and captures/ 100 trap nights include recaptures. Category of Mean percentage Number of gridsa percentage of of shortgrass shortgrass prairie paririe Effective Total 0–20 21–40 41–60 61–80 81–100 Overall

14 28 48 66 91 55

4 7 4 4 17 36

9 11 4 8 19 51

Sampling effort (trap nights)a

Swift foxes capturedb

Captures/100 trap nightsb

Effective

Total

Initial

All

Initial

All

239 417 232 233 1,009 2,130

539 650 232 458 1,129 3,008

8 24 12 10 70 124

8 25 14 10 79 136

3.4 5.8 5.2 4.3 6.9 5.8

1.5 3.9 6.0 2.2 7.0 4.5

a Effective 5 the value only for those grids in which y ¨ swift foxes were captured; Total 5 the value for all grids including those with no fox captured. b Initial 5 includes only initial captures; All 5 includes recaptures. All grids sampled are included.

Effort—We set 1,013 traps on 51 grids during 31 August 2004–12 February 2005. We had 23 trap nights with inoperable traps (e.g., mechanical failure or vegetation blocking entrance of trap), and 29 trap nights in which traps were closed for weather events and thus open for ,3 trap nights each. In addition, one entire grid was deemed inoperable due to ice-cover on traps; this grid was run 3 additional trap nights. All trap nights lost to inoperable traps or traps not set were excluded from calculations. Therefore, the

total effort was 3,008 trap nights on 51 grids. Mean daily capture efficacy (5 number of captures/100 trap nights) varied from 1.5 to 6.0 depending on amount of shortgrass prairie in the grid (Table 1). Efficiency of capture decreased from 5.1 (n 5 1,008) to 4.2 (n 5 1,004) and 3.1 (n 5 996) for trap nights 1 to 3, respectively (Fig. 3). We captured 136 swift foxes on 36 (71%) grids, including 12 recaptures (Table 1). No swift fox was captured more than twice in this

FIG. 3—Success of capturing swift foxes (Vulpes velox) by trap night in eastern Colorado, September 2004– February 2005. Total effort for trap nights 1 to 3 was 1,008, 1,004, and 996 trap nights, respectively. Error bars represent 61 SE.

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TABLE 2—Frequency of species captured in traps set for swift foxes (Vulpes velox) in eastern Colorado, showing number of grids, 31 August 2004–12 February 2005. Species Swift fox Cottontail Domestic cat Striped skunk Raccoon Unknown rat Jackrabbit American badger Coyote Unknown bird Ord kangaroo rat

Vulpes velox Sylvilagus Felis catus Mephitis mephitis Procyon lotor Rattus Lepus Taxidea taxus Canis latrans Aves Dipodomys ordii

study. Of all captures, 71% were in grids with .50% shortgrass prairie (Table 1). However, there was a weak correlation between percentage of shortgrass prairie habitat and probability of capture or recapture (see results below from capture-recapture analysis). The mean value of shortgrass prairie estimated for all grids trapped was 55.3% (Table 1), while the same value for grids in which swift foxes were captured was 62.4%. In addition to swift foxes, we captured 10 other species (Table 2). We captured 55 females, 61 males, and 8 in which sex was not determined, with 7 males and 5 females recaptured. We recorded mass for 78 foxes with known sex. Mean masses and SE of males and females were 2.50 6 0.03 kg (n 5 41) and 2.30 6 0.04 kg (n 5 37), respectively. For recaptured individuals that were weighed at each capture (n 5 5), we used the average value of each mass in calculating overall mean mass. Of the 82 swift foxes with injuries recorded, 39 (48%) had visible injuries (Fig. 4). Estimation of Occupancy—Compared to the top p model with constant probabilities of detection, the sine and cosine functions for the month variable did not improve fit of model for probabilities of detection, nor did percentage of shortgrass prairie improve estimates of probabilities of detection (Table 3). However, percentage of shortgrass prairie did provide an important predictor of occupancy (Fig. 5) with the logit predictive equation: Probability of Occupancy ~ h i exp b^0 z b^1 ðSGP%Þ h i, ^ z b^ ðSGP%Þ 1 z exp b 0 1

Number of individuals

Number of grids

130 25 14 14 8 4 3 2 1 1 1

35 16 9 8 7 3 2 2 1 1 1

where bˆ0 5 20.287 (SE 5 0.624, 95% CI 21.510– 0.936) and bˆ1 5 2.775 (SE 5 1.299, 95% CI 0.229–5.322). The estimated probability of occupancy using the average amount of shortgrass prairie on the ˆ 5 0.777 (SE 5 0.0786, 51 grids sampled was y 95% CI 0.589–0.894). When the estimated occupancy was summed across the 51 grids using the observed amount of shortgrass prairie on each grid, yˆ 5 0.742 (SE 5 0.087, 95% CI 0.572– 0.912). Finally, the entire population of 2,566 grids from which the 51 sampled grids were drawn was used to compute the proportion of eastern Colorado occupied by swift foxes, 1,824.6 grids estimated to be occupied (SE 5 177.0, 95% CI 1,477.5–2,171.5). Thus, the estimated proportion of grids occupied in eastern Colorado is yˆ 5 0.711 (SE 5 0.069, 95% CI 0.576–0.846). Estimation of Population—Results of selection of models for estimation of size of population (Table 4) lead us to suggest a behavioral effect in response to initial capture, with probabilities of capture a function of month (sin function) and shortgrass prairie. Initial probabilities of capture (Fig. 6) and probabilities of recapture (Fig. 7) from the minimum-AICc model are a function of month through a sin transformation, and shortgrass prairie. We estimated mean number of animals/grid for all 51 grids as 4.83 (SE 5 1.990, 95% CI 0.933–8.735), with a range of 0–26. As Finley et al. (2005) cautioned, mean number of animals estimated/grid cannot be extrapolated to a estimate of size of population for eastern Colorado because grids attract foxes from some unknown distance outside the trapping grid, and thus would be positively biased. The potential attrac-

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FIG. 4—Of the 136 swift foxes (Vulpes velox) captured in eastern Colorado during September 2004–February 2005, 82 had ‘‘visible injuries’’ recorded. Records were not kept for 54 swift fox that were captured and are not included in this figure. Lacerations on digits usually involved torn nails; lacerations of mouth were injured gums or lips. Teeth reported as broken .50% of their length were considered large breaks; all lesser breaks were listed as chips/small breaks. Multiple tooth fractures were combined if they fit into the same category. Injuries to incisors and premolars ranged from small chips to missing entire teeth. Eight foxes were reported to have injuries in .1 category.

tion of foxes to a grid also might cause a positive bias for estimates of occupancy in grids with little or no shortgrass prairie, but with shortgrass prairie outside the grid buffer but still in the vicinity of the grids.

FIG. 5—Prediction of probability of occupancy by swift foxes (Vulpes velox) with 95% confidence intervals as a function of percentage of shortgrass prairie on the 30.7-km2 trapping grid. Crosses on the 0 and 1 lines indicate status of the 51 trapping grids, with 36 grids recording foxes that were captured.

DISCUSSION—Comparison of estimates of the percentage of 4.8 by 6.4 km-grids occupied by swift foxes in eastern Colorado does not appear to have changed since a comparable sample was taken of 72 grids in March 1995–January 1997 (Finley et al., 2005). Using average percentage of grids in shortgrass prairie with the minimumˆ 5 0.821 AICc model, the earlier estimate was y (SE 5 0.066), compared to the current estimate ˆ 5 0.777 (SE 5 0.079). The estimated change of y is 20.044 (SE 5 0.103, 95% CI 20.245–0.157). Summing the predicted values for occupancy across the sampled grids for the respective studies provides a similar conclusion; Finley et ˆ 5 0.790 (SE 5 0.057), al. (2005) reported y whereas our study yielded yˆ 5 0.742 (SE 5 0.087), providing an estimate of the change of 20.048 (SE 5 0.104, 95% CI 20.252–0.156). When the same values of shortgrass prairie for the 2,566 grids in the sampling frame are used for the minimum-AICc model of Finley et al. (2005), we estimate 1,736.2 grids are occupied (SE 5 201.8, 95% CI 1,340.7–2,131.7). The estimated increase is 88.4 (SE 5 268.4). These

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TABLE 3—Results of selection of an occupancy model for 51 grids where swift foxes (Vulpes velox) were trapped in eastern Colorado, August 2004–February 2005. Variables are: p 5 probability of detection, t 5 time-specific occasion of detection, T 5 trend in occasion of detection, SGP 5 percentage of shortgrass prairie on grid, and Month 5 month trapping took place with either a sine or cosine transformation.

Model

AICc

DAICc

Weights of AICc

{p(.) y¨(SGP)} {p(sinMonth) y¨(SGP)} {p(SGP) y¨(SGP)} {p(cosMonth) y¨(SGP)} {p(.) y¨(.)} {p(sinMonth + cosMonth) y¨(SGP)} { p(T) y¨(.)} {p(cosMonth + cosMonth2) y¨(SGP)} {p(t) y¨(.)}

196.785 198.882 198.891 199.133 200.412 201.176 201.242 201.522 203.449

0 2.097 2.107 2.349 3.628 4.392 4.457 4.737 6.664

0.397 0.139 0.138 0.123 0.065 0.044 0.043 0.037 0.014

differences are well within the sampling variation of the estimates, so we are not able to detect a change in populations of the swift fox in eastern Colorado. However, given the close association of swift foxes with shortgrass prairie shown in this study and others (Kamler et al., 2003), monitoring changes in shortgrass prairie would provide an indicator of probable changes to populations of swift foxes. But, the relationship between shortgrass prairie and populations

Number of Likelihood parameters used of model in each model 1 0.351 0.349 0.309 0.163 0.111 0.108 0.094 0.036

3 4 4 4 2 5 3 5 4

Deviance 190.274 190.012 190.022 190.264 196.162 189.843 194.731 190.189 194.579

of swift foxes is not 1:1, and there may be a time lag in response of the population to changes in habitat. Based on the estimated SE of 268.4 for change in number of occupied grids in the sampling frame, power can be computed to detect a decline in the rate of occupancy (Fig. 8). For a decline in number of occupied grids in eastern Colorado from 1,825 to 1,125, the expected power to detect this change is about 0.8.

TABLE 4—Results of the selection of a model for the closed-population estimator for 51 grids where swift foxes (Vulpes velox) were trapped in eastern Colorado, August 2004–February 2005. Variables are: p 5 initial probability of capture, c 5 probability of recapture, t 5 time-specific occasion of trapping, T 5 trend in occasion of trapping, and Month 5 month trapping took place with sine or cosine transformation.

DAICc

Number of parameters Weights Likelihood used in of AICc of model each model Deviance

Model

AICc

{p(SGP + sinMonth) 5 c(SGP + sinMonth) + additive effect} {p(SGP + sinMonth + cosMonth) 5 c(SGP + sinMonth + cosMonth) + additive effect} {p(SGP) 5 c(SGP) + additive effect} {p(sinMonth) 5 c(sinMonth) + additive effect} {p(cosMonth) 5 c(cosMonth) + additive effect} {p(cosMonth + sinMonth) 5 c(cosMonth + sinMonth) + additive effect} {p(.)c(.)} {p(T) 5 c(T)} {p(.) 5 c(.)} {p(T) 5 c(T) + additive effect} {p(t) 5 c(t) + additive effect} {p(g*t) 5 c(g*t)}

331.785

0

0.421

1

4

323.666

333.739

1.954

0.159

0.377

5

323.560

334.006 334.421 336.195 336.322

2.221 2.636 4.410 4.537

0.139 0.113 0.047 0.044

0.329 0.268 0.110 0.104

3 3 3 4

327.935 328.350 330.124 328.204

337.474 337.658 338.619 339.211 339.840 537.810

5.689 5.872 6.833 7.426 8.054 206.024

0.025 0.022 0.014 0.010 0.008 0

0.058 0.053 0.033 0.024 0.018 0

2 2 1 3 4 108

333.439 333.622 336.607 333.140 331.721 220.762

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FIG. 6—Changes in initial probability of capture (p) for swift foxes (Vulpes velox) trapped in eastern Colorado on 30.7-km2 grids, by percentage of shortgrass prairie on grids, September 2004 (upper line) through February 2005 (lower line).

Denial of access by landowners to grids selected to be in the sample is a form of nonresponse bias, and could bias the resulting estimates of rates of occupancy. We believe this bias is small because foxes are not limited in their movements only to the grid to be sampled, and so shifting the grid to accommodate the denial of access to a portion of the original grid probably is not going to change the probability of occupancy by much. However, some bias may exist where large landowners manage their property differently than the average. For example, landowners that eliminate colonies of prairie dogs on their land and owns contiguous blocks larger than the sampling grid used in this study may deny access, and force us to select a com-

FIG. 7—Changes in probability of recapture (c) for swift foxes (Vulpes velox) trapped in eastern Colorado on 30.7-km2 grids, by percentage of shortgrass prairie on grids, September 2004 (upper line) through February 2005 (lower line).

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FIG. 8—Approximate power to detect a change in number of occupied grids in the sampling frame of 2,566 grids. With an estimated 1,825 grids currently occupied, a decline of 700 (38%) to 1,125 could be detected with a power of 0.8 with two surveys.

pletely new grid. Such a scenario may cause some bias with our sampling approach. As discussed by Finley et al. (2005), capturing animals allows researchers to collect data to estimate maximum number of individuals, determine habitat association, spatial distribution, geographic range, relative health (i.e., disease monitoring), and demographics of populations in the local sampled. Data for many of these factors cannot be collected using more traditional methods for indexing populations of swift foxes such as scent stations, tracking plates, or spotlight surveys (Gese, 2001, 2004; Schauster et al., 2003). The ability of an agency to determine trends in populations of swift foxes over large areas will continue to be essential for proper management of this species. Biologically significant declines in any area sampled can then be addressed (Haight et al., 2004). However, capture of foxes is an invasive technique that can cause injury to individuals (Moehrenschlager et al., 2003), so we suggest an alternative, noninvasive technique similar to that reported by Harrison et al. (2002) for future monitoring efforts. Rather than setting traps over each grid, bait shown to elicit a defecation response in swift foxes should be used (Kimball et al., 2000). Scats could then be collected daily and properly stored for later extraction and analysis of DNA (see Paxinos et al., 1997, for more on extraction of DNA from scat, and Ralls et al., 2001, for information on determining kinship). Harrison et al. (2002) reported difficulties in identifying individual swift foxes from DNA extracted from scat. However, K. Ralls

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(pers. comm.) recently has had good success in identifying individuals from scats, and while processing is more expensive than with hair samples, costs of processing continue to decline. In addition to increased size of samples and accuracy of estimates, this less-invasive approach would effectively negate both injury to animals (Fig. 4) and incidental capture of other species, and would greatly decrease number of supplies needed and amount of time spent in the field. Furthermore, because individual foxes would be identified with a given degree of accuracy from scats deposited at bait-stations, problems associated with traditional scent-station techniques (Sargeant et al., 2003) would be reduced. However, if concurrent disease monitoring is required to fulfill study objectives, capturing animals will be necessary. Collection of DNA from scats still requires access to the sampling grid, so it does not avoid the potential nonresponse bias associated with denial of access by a landowner. We thank the field crew, K. Adams, A. Longanecker, and A. Wastell for their effort, plus the many others that assisted on occasion. L. Wolfe and G. Schroeder trained the field crew in PIT-tag injection and bloodcollection procedures. J. Kindler and M. Cowardin helped with GIS processing. J. Kamler and R. Matlack provided useful recommendations prior to the trapping effort. L. DiFeo and E. Backhaus provided data on enrollment and seed mixtures in the Conservation Reserve Program for Colorado. We thank K. Huyvaert for producing the Spanish translation of the abstract. This project was funded by the Colorado Division of Wildlife.

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