Western North American Naturalist

Volume 63 Number 4 Article 8

12-3-2003

Habitat features and predictive habitat modeling for the Colorado chipmunk in southern New Mexico

Marina Rivieccio New Mexico State University

Bruce C. Thompson USGS New Mexico Cooperative Fish and Wildlife Research Unit, Las Cruces, New Mexico

William R. Gould New Mexico State University, Las Cruces, New Mexico

Kenneth G. Boykin New Mexico State University, Las Cruces

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Recommended Citation Rivieccio, Marina; Thompson, Bruce C.; Gould, William R.; and Boykin, Kenneth G. (2003) "Habitat features and predictive habitat modeling for the Colorado chipmunk in southern New Mexico," Western North American Naturalist: Vol. 63 : No. 4 , Article 8. Available at: https://scholarsarchive.byu.edu/wnan/vol63/iss4/8

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HABITAT FEATURES AND PREDICTIVE HABITAT MODELING FOR THE COLORADO CHIPMUNK IN SOUTHERN NEW MEXICO

Marina Rivieccio1,4, Bruce C. Thompson2,5, William R. Gould3, and Kenneth G. Boykinl

ABSTRACT.-Two subspecies ofColorado chipmunk (state threatened and federal species of concern) occur in south" em New MeXico: Tamias quadnvittatus australis in the Organ Mountains and T. q. oscuraensis in the Oscura Mountains. We developed a GIS model ofpotentially suitable habitat based on vegetation and elevation features, evaluated site clas" sifications ofthe GIS model, and determined vegetation and terrain features associated with chipmunk occurrence. We compared GIS model classifications with actual vegetation and elevation features measured at 37 sites. At 60 sites we measured 18 habitat variables regarding slope, aspect, tree species, shrub species, and ground cover. We used logistic regression to analyze habitat variables associated with chipmunk presence/absence. All (100%) 37 sample sites (28 pre­ dicted suitable, 9 predicted unsuitable) were classified correctly by the GIS model regarding elevation ap.d vegetation. For 28 sites predicted suitable by the GIS model, 18 sites (64%) appeared visually suitable based on habitat variables selected from logistic regression analyses, of which 10 sites (36%) were specifically predicted as suitable habitat via logistic regression. We detected chipmunks at 70% of sites deemed suitable via the logistic regression models. Shrub cover, tree density, plant proXimity, presence oflogs, and presence ofrock outcrop were retained in the logistic model for the Oscura Mountains; litter, shrub cover, and grass cover were retained in the logistic model for the Organ Mountains. Evaluation of predictive models illustrates the need for multi"stage ahalyses to best judge performahce. Microhabitat analyses indicate prospective needs for different management strategies between the subspecies. Sensitivities of each population ofthe Colorado chipmunk to natural and prescribed fire suggest that partial bUImngs ofareas inhabited by Colorado chipmunks in southern New MeXico may be beneficial. These partial bUrnings may later help avoid a fire that could substantially reduce habitat ofchipmunks over a mountain range.

Key words: Colorado chipmunk, GIS, habitat modeling, New Mexico, Tamias quadrivittatus.

The Colorado chipmunk, Tamias quadrivit­ found in various habitats such as mixed conifer, tatus, occurs in Arizona, Colorado, New Mex­ mesic woodland, and montane scrub in 1998 ico, and Oklahoma (Best et al. 1994). There (New Mexico Natural Heritage Program, Post~ are 3 subspecies, all of which occur in New fire ecological studies reported to US. Army Mexico (Findley et al. 1975, Sullivan 1996). The Corps ofEngineers, 1998), which contradicted 2 subspecies that occur in central and south~ previous indications that they occur only in coniferous~ ern New Mexico are the Organ Mountains chip­ spatially restricted and fragmented munk (T. q. australis) and the Oscura Moun­ forest habitats (US. Department of Defense [DoD] 1998, NMDGF Biota Information Sys~ tains chipmunk (T. q. oscuraensis; Patterson tern of New Mexico, onUne database). Previous 1980a, 1980b, Sullivan 1996). They are restricted surveys found Oscura Mountains chipmunks to their corresponding mountain range and primarily along west-facing slopes (R.M. Su1li~ are listed as threatened by New Mexico and as van and R. Smartt unpublished report, Sulli­ a species of concern by the US. Fish and Wild­ van 1996) in pinyon pine~juniper (Pinus edulis~ life Service (Patterson 1980a, Sullivan 1996, Juniperus) habitat, although Sullivan (personal New Mexico Department of Game and Fish communicatiOn 1998) identified north, north~ [NMDGF] 1988, New Mexico Administrative west, and northeast slopes as critical habitat to Code 19.33.1). be protected for this population. Little is lmown about the 2 southern sub­ Both southern subspecies occur primarily species. The Organ Mountains chipmunk was on military installations and were selected for

INew Mexico Cooperative Fish and Wildlife Research Unit and Department ofFishery and Wudlife Sciences. New Mexico State University, Box 30003 MSC 4901. Las Cruces. NM 88003. 2USGS New Mexico Cooperative Fish and Wildlife Research Unit. Box 30003 MSC 4901, Las Cruces. NM 88003. 3University Statistics Center, New Mexico State University, Box 30001 MSC 3CQ. Las Cruces. NM 88003. 4Present address: 1005 Foothill Drive. Fulmore, CA 93015. 5Present adress: New Mexico Department ofGame and Fish. Box 25112. Santa Fe, NM 87504.

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species-at-risk evaluation on White Sands 1) are 1500~2700 m in the Oscura Mountains Missile Range (WSMR) and Fort Bliss (Boykin on WSMR (Lincoln and Socorro Counties, et al. 2001). Both subspecies are considered NM) and 1300-2700 m in the Organ Moun~ vulnerable due to geographic isolation and tains on Fort Bliss (Dona Ana County, NM). confinement to mesic, higher-elevation habi­ Vegetation communities in which Colorado tats that can support only a limited number of chipmunks have been found in New Mexico in­ chipmunks (NMDGF 1988, Mehlhop et al. clude ponderosa pine (Pinus ponderosa) forest, 1994). Because federally listed species can pinyon~juniper woodland, montane scrub, and affect military operations in a variety of,ways coniferous and mixed woodland (Patterson 1980a, (DoD Directive 4715.3, Schreiber 'and Reed Dick-Peddie 1993, Mehlhop et al. 1994, Sullivan 1998), development of information that im~ 1996, Thompson et al. 1996). proves conservation practices and stabilizes these populations may preclude future special METHODS classification, thus being beneficial to the mili­ GIS-based Habitat Model tary installation and to the species a~ risk. For the Organ Mountains chipmunk, Mehl­ We used elevation and vegetation associa~ hop et al. (1994:13) recommende-d "verification tions to develop the GIS-based habitat model of suspected habitat affinities and subsequent with information obtained from technicalllter­ protection of these habitats in the mountain ature. Vegetation associations used in the model range." Previous habitat modeling of the Col­ for the Organ Mountains chipmunk were orado chipmunk in New Mexico did not pre­ montane shrubland, pinyon~juniper woodland, dict chipmunk occurrence in the Oscura Moun­ and ponderosa pine forest (Patterson 1980b, tains (Thpmpson et al. 1996), because model­ NMDGF Biota Information System of New ing was completed before information was Mexico). Vegetation associations used for the available from Sullivan (1996). We used a GIS", Oscura Mountains chipmunk were ponderosa based model of potentially suitable habitat to pine forest, juniper woodland, pinyon pine~ classify and sample prospective Colorado chip­ Gambel's oak (Quercus gambelii), and mon­ munk habitat in the Organ and Oscura Moun­ tane scrub (Sullivan 1996). Dominant plant tains. We tested the hypothesis that occur~ species associated with these vegetation com~ rence of the Organ Mountains chipmunk and munities included pinyon pine, juniper, pon­ the Oscura Mountains chipmunk corresponds derosa pine, oak (Quercus), and mountain to predictions of suitable habitat based on mahogany (Cercocarpus breviflorus). For both existing literature and GIS modeling from models, elevations used were > 1380 m (Best available spatially referenced information. Our et al. 1994). research objectives were to (1) develop and Aspect and slope were not used as model evaluate a GIS-based habitat model of poten­ variables for 2 reasons. In the Organ Mountains, tial suitable habitat for each subspecies based chipmunks occurred on a variety of aspects on existing information and GIS-compatible and slopes (New Mexico Natural Heritage Pro­ information, (2) detect, through field observa­ gram unpublished data). In the Oscura Moun~ tions, presence or absence of chipmunks and tains no information was available regarding their habitats in areas predicted as suitable slope and data pertaining to aspect were con­ and unSuitable, and (3) measure and describe tradictory. vegetation and terrain features (microhabitat) We used 4 measures to evaluate predictive relating to use ofhabitat. ability of GIS~based habitat models. The 1st measure of performance was the degree to STUDY AREA which elevation and vegetation variables used in the GIS model characterized sites. A 2nd White Sands Missile Range and Fort Bliss measure was the degree to which model~pre­ are located in south central New Mexico (Fig. dicted sites corresponded to visual assessments 1). These areas comprise 714,000 ha on WSMR ofhabitat suitability. For 3rd and 4th measures and 446,000 ha on Fort Bliss within the Great of performance, we examined the degree to Basin Conifer Woodland and the Chihuahuan which model-predicted suitable sites were biogeographic provinces (Brown 1982). Approx­ determined to be suitable either by detection imate elevations ofthe 2 mountain ranges (Fig. or prediction of chipmunks via a logistic 2003] HABITAT MODELING FOR COLORADO CHIPMUNK 481

Fig. 1. Map of Colorado chipmunk study area in the Oscura and Organ Mountains of New Mexico within White Sands Missile Range (horizontal lines) and Fort Bliss (vertical lines); gray shading illustrates habitat predicted from the GIS model; dots indicate sites examined for chipmunks and measured for habitat variables. One dot in the Oscura Mountains represents 2 sites; 2 dots in the Organ Mountains (1 inside modeled habitat, 1 outside modeled habitat) rep­ resent 2 sites each. regression model based primarily on detec~ using a random number generator in ArcView tions ofchipmunks independent ofmodel-pre­ (Environmental Systems Research Institute, dicted sites. Inc. [ESRI], Redlands, CA) applied to center coordinates ofnumbered analytical cells (30 x Surveys 30-m pixels) in the spatial data. Survey sites were a random subset of sites We conducted field surveys at 28 model­ where the GIS model predicted Colorado chip~ predicted stiitable and 9 unstiitable sites dur­ munk habitat should (model-predicted stiitable) ing July~October 1999 and March-August or should not (model-predicted unsuitable) 2000. Each site surveyed was 2.1 ha, the home occur. These 37 sites were randomly selected range of the Colorado chipmunk (Bergstrom 482 WESTERN NORTH AMERICAN NATURALIST [Volume 63

1988). Two observers spent about 20 minutes Analysis surveying at each site for chipmunks. This We used confusion matrices and a Kappa time was judged sufficient for detection, based statistic (K) to evaluate the degree to which on activity patterns of chipmllnks discussed the GIS-based habitat model predicted habitat with M. Bogan (U.S. Geological Survey, personal in context with visual assessments of site suit­ communication, 1999) and previous research ability, logistic regression analyses, and detec­ (Sullivan unpublished field data). tion of chipmunks (Fielding and Bell 1997). Habitat Variables Sites initially were categorized as suitable or unsuitable based on congruence with GIS chip~ We considered habitat characteristics of variables. Sites were also visually inspected munks at landscape and micro~scales. We sur­ and judged to be suitable or unsuitable based veyed for presence ofchipmunks and measured on our biological knowledge of the species. microhabitat features (Fig. 1) at 37 model-pre­ Subsequently, sites were classified as suitable dicted sites, 14 previously known locations with or unsuitable habitat based on logistic regres­ chipmunks (New Mexico Natural Heritage Pro­ sion models of presence for each mountain gram unpublished data, Sullivan unpublished range. Those sites with estimated probability field data), and 9 opportunistic sightings (32 of detection 2:0.5 were considered to be suit­ sites in the Oscura Mountains and 28 sites in able habitat. Akaike's Information Criterion the Organ Mountains). We obtained habitat (AlC, Akaike 1973) and our biological knowl­ data directly related to verified occurrence of edge were used to select a parsimonious and chipmunks at 30 sites. Opportunistic sites estimated "best approximating model" (Bum­ were those at which a chipmunk was detected hain and Anderson 1998). Logistic regression by an observer while traveling to and among is the preferred statistical technique when con­ field sites. Detection or nondetection of chip­ tinuous and discrete variables are contained in munks was used to construct a logistic regres­ a data set (Block et al. 1998) and when describ­ sion model for each mountain range surveyed. ing locations ofpresence or absence (Manly et The majority (>75%) of 30 sites with chip­ al. 1993, Alldredge et al. 1998). We used a chi­ munks used in developing the logistic model square test to assess overall significance of the were independent from the GIS model-pre­ logistic regression model for each subspecies. dicted sites. Therefore, our use of microhabi~ We used quantitative variables except for pres­ tat features to interpret predictions derived ence or absence ofrock outcrop and logs, which from the GIS-based habitat model is effica­ were dichotomous variables. In constructing cious and relatively unique among other stud­ the logistic regression model, we considered ies ofhabitats ofsmall mammals. previously known locations as positive for chip­ We sampled 3 vegetation plots at each site munks, even if a chipmunk was not detected surveyed. The center of the 1st plot was the during our specific survey. We were assessing randomly selected UTM coordinate for the habitat predictions, and it is known that detect­ survey site. The remaining 2 nonoverlapping ing animals in occupied habitat can have prob~ plots were randomly placed within the site ability substantially less than 1.0 (Karl et al. (Skalski 1987). Each plot consisted of 2 inde­ 2002, Kery 2002). We examined perfortnance pendent sampling units modified from Dueser . ofthe logistic model using a jackknife approach and Shugart (1978): 2 perpendicular 20-m line because it is less biased than the resubstitu­ transects and a circular plot with a 5-m radius tion method in which the same data used to (Buell and Cantlor 1950, Dueser and Shugart construct a model are used to test it (Olden et 1978, Bonham 1989). We recorded slope and al.2002). aspect (Higgins et al. 1996) at the center ofthe 1st plot and presence or absence ofa rock out­ RESULTS crop within each site. We used point-inter­ cepts (every 1 m) along line transects to mea~ Performance ofHabitat Model sure features ofground and shrub cover (table the GIS-based habitat model predicted a 1). The circular plot was used to enUmerate total of79,900 ha as potentially suitable habitat features of shrubs and trees (Table 1). Counts for both subspecies (Fig. 1). Of the 37 modeh of shrubs and trees were converted to density predicted sites we examined, all (Kappa = 1.0) values for subsequent analysis. were correctly classified as predicted suitable 2003] HABITAT MODELING FOR COLORADO CHIPMUNK 483

TABLE 1. Description ofvariables and methods used to measure habitat ofthe Colorado chipmunk in southern New Mexico. Variable Description Measured along line transects" Canopy cover Vegetation ;;,,2 m tall (Dueser and Shugart 1978) Ground cover Point having litter, bare soil, or plant cover (Dueser and Shugart 1978) Shrub cover Vegetation <2 m tall (Dueser and Shugart 1978). Shrub cover was further broken down into 2 categories: tall (>1 m) and short (s1 m) Closest herbaceous plant Species ofclosest herbaceous ramet (grass or forb) from each point. Individuals were not counted twice (absence ofany ramet within 5 m was noted as "no plant") Measured within 5 m circular plot Tree species Single stemmed and > 1.75 m tall Tree size Diameter oftrees (in centimeters) Tree count Number oftrees by species Shrub species Multi-stemmed or single stemmed and s1.75 m tall Shrub count Number ofshrubs by species: tall (>1 m) and short (s1 m) in height Vegetation species Identification ofspecies occurring within the plot Logs Number oflogs (converted to present or absent for analysis) Measured at site Aspect Direction ofslope measured at center of1st plot Slope Measured using a clinometer at center of1st plot Elevation Measured from topographic maps and ArcView Rock outcrop Presence ofa rock outcrop within the site aThese variables ;;ere meaSured as percentage ofpoints with the variable from 3 plots consisting of41 point samples along 2 perpendicular line transects (Dueser and Shugart 1978). or unsuitable according to vegetation and ele­ predicted-suitable sites. We visuallY/al.ldibly de­ vation variables in our GIS model (Table 2). tected chipmunks during our 20~minute surveys The degree of agreement (Kappa = 0.47) be~ at 'i ofthese sites in the Oscura (415) and Organ tween classifications of sites based on GIS (3/5) Mountains. No chipmunks were detected modeling versus our visual assessment indi~ at any ofthe 9 model~predicted unsuitable sites cated a moderately successful ch~acterization surveyed, and none of those sites appeared to of sites using the landscape-level varia,bles be suitable chipmunk habitat. (Table 2). OSCURA MOUNTAINS CHIPMUNK.~Chip~ Logistic modeling identified 10 sites as suit­ munks were detected at 2001-2565 m elevation able habitat of the 28 predicted-sllitable sites on all aspects and slopes ranging up to 30°. A from the GIS habitat model. All 9 predicted­ combination of the variables vegetation cover unsuitable sites were identified as unsuitable. and terrain features was significant for predict­ Superficially, the corresponding Ka,ppa statis­ ihg chipmunk presence or absence in the logis­ tic of 0.2,1 indicated poor overall performance tic regression model (Table 3). The model with of the GIS model. However, there were 16 of the 2nd lowest AlC value was selected over 20 previously known and opportunistic detec~ the lowest (33.815 versus 33.400) because the tion sites that occurred in predicted suita,ble latter model excluded the closest herbaceous habitat. These 16 sites were not included in plant and tree density variables, both ofwhich calculating the Kappa statistic; if they had represent important cover and food. The over~ been included as model predicted and occu­ all test of the model (X2 = 22.422, 5 elf, P < pied, the Kappa would have been higher. Three 0.001) indicated the model was significant in other historic and opportunistic sites occurred explaining chipmunk presence or absence. outside the GIS coverage area, although micro­ As shrub cover, tree density, and herbaceous habitat data were obtained at these sites. cover increased (the condition of "no plant" decreased), likelihood for presence of chip­ Detection ofChipmunks Relative munks increased. Shrub Cover averaged 14.5% to Predicted Habitat at nondetection sites and 24.7% at detection SUitable habitat features derived from logis­ sites. Tree densities were < 17.8 trees . 100 tic regression analysis were present at 10 ofthe m-2 at all sites in the Oscura Mountains, with 484 WESTERN NORTH AMERICAN NATURALIST [Volume 63

TABLE 2. Performance of GIS and logistic regression predictive models of habitat of Colorado chipmunks for 37 sample sites in the Organ and Oscura Mountains, New Mexico, July 1999-August 2000. For all comparisons, n = 37. Actual observed Comparison derivation Model predicted Kappa Suitable Unsuitable All sites; GIS model variables only 1.00 Suitable 28 o Unsuitable o 9b All sites; GIS model variables and visual classification with 0.47 variables based on expert opinion Suitable 18 10 Unsuitable o 9b All sites; GIS model variables and classification with logistic 0.21 regression model ofmeasured variables Suitable 10' 18 Unsuitable o 9b 'Chipmunks were visually/audibly detected at 7 ofthese sites. hChipmunks wre not detected at any ofthese sites. densities :57.6 trees . 100 m-2 at nondetection presence or absence. As litter increased, likeli­ sites. Presence oflogs and rocks also was asso~ hood of chipmunk presence increased, but as ciated with increased likelihood of chipmunks shrub cover and grass increased, likelihood of being present. Maximum likelihood estimates chipmunk presence decreased (Table 3). Shrub ofcoefficients corresponding to vegetation mea~ cover was similar on average at detection and sures and associated logistic regression odds nondetection sites (x = 17.8% and 17.3%, re~ ratios are provided in Table 3. The odds ratio spectively), but variation among sites produced is the ratio of probability for occurrence of a differences in likelihood of presence. Litter chipmunk at a site to probability for absence was more abundant at detection sites than non­ of a chipmunk from that site, given the values detection sites (x = 32.1% vs. 8.3%), whereas for variables used in the model. An odds ratio grass cover was less at detection sites (x = of 1.0 indicates no change relative to unit 65.9%) than nondetection sites (x = 91.2%). change in explanatory variables (e.g., odds ofa Rocks did not enter into the model because all chipmunk being present at an Oscura Moun­ but 1 site had rock outcrops present. Logs were tains site increases 11.6% when shrub cover present at about half(14) ofthe sites, and chip­ increases by 1%; Table 3). munks were present at 7 of these sites. The The logistic model correctly predicted pres­ model correctly predicted 23 of 28 sites (82%) ence or absence of habitat associations for 25 using a jacklmife approach to assessing the of 3,2 sites (78%) using a jackknife approach to model. The logistic model predicted presence assessment of the model. The logistic model in .2 sites where chipmunks were not detected. predicted presence in 4 sites where none were Chipmunks were detected at 3 sites in which detected. Chipmunks were detected at 3 sites the logistic model predicted absence of chip­ munk habitat. However, these 3 sites were his~ in which the logistic model predicted absence toric sites in which presence of chipmunks of habitat. However, 2 of these sites were his­ was assumed for construction of the model, toric sites in which presence was assumed for but at which no chipmunk was detected on construction of the model, but at which no our surveys. chipmunk was detected on our surveys. ORGAN MOUNTAINS CHIPMUNK.-Chipmunks DISCUSSION were detected at elevations ranging from 1542 m to 2374 m, on all aspects, and on slopes of Our comparative assessment ofgeneralized 0 0 10 =30 • The logistic regression model with and quantitative habitat models illustrates the the lowest AlC value included litter, grass, importance ofconsidering several scales when and shrub coVer (Table 3). The overall test of qualifying predicted habitat as suitable or un~ the model (;x2 = 24.058, 3 df, P < 0.001) indi­ suitable. The GIS-based habitat model is a cated the model was Significant in explaining helpful tool for predicting suitable habitat for 2003] HABITAT MODELING FOR COLORADO CHIPMUNK 485

TABLE 3. Vaiiables included in logistic-regression models to predict habitat features of2 subspecies ofColorado chip­ munk in the Oscura and Organ Mountains, New Mexico.

Vaiiable Parameter estimate (8x) Odds ratio 90% CI ofodds ratio Oscura Mountains Shrub cover +0.1099 (0.0547) 1.116 1.02-U~2 Rock +2.2458 (1.4179) 9.448 0.91-97.3 Log +4.9908 (~.3200) 147.058 3.32-6682.2 Tree density (per 100 m2) +0.5876 (0.4368) 1.80 0.88-3.69 No plant" -6.2605 (4.3727) O.OO~ 0.0-2.54 Organ Mountains Grass cover -0.0838 (0.0420) 0.920 0.85-0.95 Shrub cover -0.1796 (0.1355) 0.836 0.67-1.04 Litter cover +0.0967 (0.0470) 1.102 1.02-1.19 aRefers to absence ofany-herbaceous plant;5 m from asampling point.

several reasons. The habitat model helped nar­ 2002). As a result, it is important to assess row our search area, given size of the general habitat at several scales when attempting to landscape of interest. The GIS~based habitat describe siJitable habitat as discussed in detail model was profiCient in describing landscape in Scott et al. (2002). features of elevation and vegetation based on Our work differs from other studies because our Kappa value of1.0 for landscape features at we relied on visual detections as opposed to model-predicted sites, where K < 0.4 indicates sampling with traps (Dueser and Shugart 1978, a poor model and K > 0.15 indicates an excel­ Kitchings and Levy 1981, Bowers 1995, Sulli­ lent model (Landis and Koch 1917, Fielding van 1996). We made that decision based on and Bell 1997). personal experience with trapping (M. Bogan; None ofthe model-predicted unsiJitable sites U.S. Geological Survey, personal communica­ contained habitat features considered suitable tion, 2000; B. Thompson personal observation) for Colorado chipmunks, and we detected no and prior indication that the Organ Mountains chipmunks at any of these sites. Thus, the chipmunk occurs where it is difficult to trap model performed well at predicting appar­ chipmunks safely (New Mexico Natural Her~ ently unsiJitable habitat for chipmunks. How­ itage Program unpublished data). We acknowl~ ever, the GIS-based model was a limited pre­ edge that some of our study sites could have dictor of occurrence detectable by visual and contained undetected chipmunks. However, auditory evidence of chipmunks. The GIS­ we prelimin~yevaluated trapping for detec­ based habitat model predicted siJitable habitat tion of Colorado chipmunks during 2 days of based on information obtained from technical trapping (afternoon ofday 1 through afternoon literature. Chipmunks use habitat features at of day 3) with 30 traps set in an area with several scales, and the GIS model did not in­ known chipmunk presence. That trapping effort corporate all ofthese features or scales. Micro­ captured only 1 of 4 chipmunks that we visu­ habitat features that are important for chip­ ally detected in the area. Further, we visually/ munks, based on the logistic regression model, audibly detected chipmunks on every occa­ were not represented in spatial data included sion during multiple visits to 10 sites that con­ in the GIS=based habitat model. However, it is tained chipmunks. All but 2 of the chipmunks crucial to consider that such models predict we recorded on our randomly selected study habitat features first and foremost; actual sites Were detected within 20 minutes. The occurrence ofan animal at a specific place and other 2 chipmunks were detected within 25 time is conditioned by factors (e.g., behavior minutes while we conducted vegetation mea­ and demography) independent of physical surements. Thus, we believe our detection habitat features. Additionally, not detecting a process was sufficient for purposes of this chipmunk at a predicted siJitable site does not research. mean the site is unoccupied. Documenting We detected more chipmunks at previously occurrence of a species in predicted suitable known locations surveyed in the Oscura Moun~ habitat without long-term survey is an acknowl­ tains than in the Organ Mountains. This dif~ edged difficulty (Edwards et al. 1996, Karl et al. ference may relate to detection of chipmunks 486 WESTERN NORTH AMERICAN NATURALIST [Volume 63 along roads in the Oscura Mountains, whereas chipmunks appear to use a combination ofthese the Organ Mountains had no road accessible 3 microsite features. to us. Considerably more h:iking was needed The scale at which we measured vegetation to teach sites m the Organ Mountains than in variables is the scale at which vegetation man­ the Oscura Mountains, thus altering condi~ a,gement usually is applied, and habitat models tions for making detections and reducing developed at this scale can be incorporated overall opportunity for detection in different into habitat management plans (Block et al. areas. 1998). The GIS layers that we used are at a Chipmunks were detected at a wider range scale different from the vegetation variables, of eleva,tions than previously reported in the although both are important in developing Organ Mountains (1845=2225 m; Patterson management plans. This allowed us to exam­ 1980a). In the Oscura Mountains, chipmunks me factors related to why chipmunks may not were not restricted to northwest-facing slopes be present at some of the predicted-suitable as reported by Sullivan and Smartt (unpub­ sites. It also allowed us to obtain a more accu­ lished field data 1990) or associated only with rate description of chipmunk habitat. As more north, northwest, or northeast slopes as indi~ spatially and thematically resolved GIS infor­ cated by previous surveys (Sullivan unpub­ mation becomes available, the predictive lished field data). model can be updated to enhance future pre­ Our logistic regression models can be used diction ofhabitat ofchipmunks. to explain microhabitat differences between sites where chipmunks were or were not de­ IMPLICATIONS tected but were predicted as suitable habitat. Our research revealed different environmen­ In the Organ Mountains, model-predicted suit­ tal variables likely to influence distribution of able sites where chipmunks were not detected Colorado chipmunks in .2 mountain ranges in had greater grass cover, less litter, and an ab­ southern New Mexico. These areas involve 2 sence oflogs compared with sites where chip~ distinct subspecies (pa,tterson 1980a, Sullivan munks were detected. These differences indi­ 1996) that use different microhabitat configu­ cate that perhaps there Was too much cover at rations. This difference in variables signals these sites. In the Organ Mountains, 68% of future researchers to be cautious in extending the plots had high grass cover (>80%), whereas habitat features to taxa that occur in different in the Oscura Mountains only 34% ofthe plots vegetation types, mountain ranges, or geogra­ had high grass cover. tt has been shown fot phic regions. Although some researchers (e.g., chipmunks that "dense undergrowth inhibits Kitchings and Levy 1981) repeated a, study the flow of visual signals across the communi­ elsewhere and found similar results, extrapola­ cation channel" (Svendsen andYahner 1979). tion of habitat features among areas must be In the Oscura Mountains, at all model-pre­ done ca,utiously. dicted suitable sites where a chipmunk Was Variation in importance of variables in the detected, rocks and logs were present, and the different mountain ranges indicates that dif­ sites had greater density of trees. At the 16 ferent management strategies may be needed. predicted~suitable sites in which a chipmunk Sensitivities of each population of Colorado was not detected (based on GIS model), 44% chipmunks to natural and prescribed fire need had a rock outcrop, 25% had logs present, and to be considered, The Organ Mountains chip­ 13% had both features. This illustra,tes the munk may have an affinity for habitats that importance of rocks and logs as cover in the have been burned (New Mexico Namral Her­ Oscura Mountains. Rocks, logs, shrubs, and ita,ge Program; unpublished data, 1998). After trees provide cover for chipmunks and play a bum, shrub, herbaceous, and grgss cover are different roles in predictive models depending reduced. Fires in the Organ Mountains may on their dispersion at sampled sites. Chipmunks be important and necessary to survival of chip~ dig burrows under rocks and logs (Svendsen mUMS by producing desirable grass and shrub and Yahner 1979). Because rocks were present cover. In the Oscura Mountains, fire may have at most sites in the Organ Mountains, shrub a different effect on the population because cover and ptesence of logs may not be as im­ occurrence of chipmunks increases with in­ portant there as in the Oscltra Mountains, where creasing herbaceous and shrub cover. Partial 2003] HABITAT MODELING FOR COLORADO CHIPMUNK 487 burnings of areas inhabited by Colorado chip~ DICK-PEDDIE, WA. 1993. New Mexico vegetation past, munks in southern New Mexico may be a ben­ present and future. University ofNew Mexico Press, Albuquerque. eficial management experiment to help avoid DUESER, R.D.; AND H.H. SHUGART, JR. 1978. Microhabi­ destructive fires that might sweep extensive tats in a forest-floor small mammal fauna. Ecology parts of'a mountain range. 59:89-98. EDWARDS, TC., JR., E.T DESHLER, D. FOSTER, AND G.G. MorSEN. 1996. Adequacy ofwildlife habitat relation ACKNOWLEDGMENTS models for estimating spatial distributions of terres­ trial vertebrates. Conservation Biology 10:263-210. Funding for this project was provided FIELDING, A.H., AND J.E BELL. 1997. A review ofmethods through Army Environmental Center Legacy for the assessment of prediction errors in conserva­ funding and T&E, Inc. New Mexico State tion presence/absence models. Environmental Con­ University's Agricultural Experiment Station servation 24:38-49. FINDLEY, J.A., A.H. HARRIS, D.E. WILSON, AND C. JONES. provided additional financial support. We 1975. Mammals of New Mexico. University of New thank Richard Suarez for his valuable assis­ Mexico Press, Albuquerque. tance with fieldwork. Mike Morrison, Bruce HIGGINS, KE, J.L. OLDEMEYER, KJ. JENKINS, G.K CLAM­ Patterson, Jerry Scrivner, and 3 anonymous BEY, AND RE HARLOW. 1996. Pages 562-591 in T reviewers provided numerous helpful com~ Bookhout, editor, Research and management tech­ niques for wildlife and habitats. Wildlife Society, ments on earlier manuscript drafts. Bethesda, MD. KARL, J.W, LX SVANCARA, P.J. HEGLUND, N.M. WRIGHT, LITERATURE CITED AND J.M. SCOTT. 2002. Species commonness and the accuracy of habitat-relationship models. Pages 573­ AKAIKE, H. 1973. Infonnation theory and an extension of 580 in TM. Scott, P.J. Heglund, M.L. Morrison, J.B. the maximum likelihood principle. Pages 267-281 in Haufler, M.G. Raphael, W:A. Wall, and EB. Samson, B.N. Pelran and E Csaaki, editors, Second Internation­ editors, Predicting species occurrences: issues of al Symposium on Infonnation Theory. Akademiai accuracy and scale. Island Press, Washington, DC. Kiado, Budapest, Hungary. KERY, M. 2002. Inferring the absence ofa species-a case ALLDREDGE, J.R, D.L. THOMAS, AND L.L. McDONALD. study of snakes. Journal of Wildlife Management 1998. Survey and comparison of methods for study 66:330-338. KITCHINGS, J.T, AND DJ. LEVY. 1981. Habitat patterns in a ofresource selection. Journal ofAgriculture, Biologi­ small mammal community. Journal of Mammalogy cal, and Environmental Statistics 3:231-253. 62:815-820. BERGSTROM, B.J. 1988. Home ranges of three species of LANDIS, J.R, AND G.C. KOCH. 1977. The measurement of chipmunks (Tamias) as assessed by radiotelemetry observer agreement for categorical data. Biometrics and grid trapping. Journal ofMarnmalogy 69:190-198. 33:159-174. BEST, TL., S.L. BURT, AND J.L. BARTIG. 1994. Tamias MANLY, B.EJ., L.L. MCDONALD, AND D.L. THOMAS. 1993. quadrivittatus. Mammalian Species 466:1-7. Resource selection by animals. Chapman and Hall, BLOCK, W:M., M.L. MORRISON, AND P.E. SCOTT. 1998. London. Development and evaluation of habitat models for MEHLHOp, P.M., E. MULDAVIN, AND E. DEBRUIN. 1994. A herpetofauna and small mammals. Forest Science survey ofsensitive species and vegetation communi­ 44:430-437. ties in the Organ Mountains ofFort Bliss: final report, BONHAM, C. 1989. Measurements for terrestrial vegeta­ volume II animals. Prepared for Directorate of En­ tion. John Wiley & Sons, New York. vironment, Fort Bliss, New Mexico Natural Heritage BOWERS, M.A. 1995. Use of space and habitats by the Program, Albuquerque. eastern chipmunk, Tamias striatus. Journal of Mam­ NEW MEXICO DEPARTMENT OF GAME AND FISH. 1988. malogy 76:12-21. Handbook of species endangered in New Mexico. BOYKIN, KG., J. BAK, A.J. KROLL, M. RMECCIO, RA. DEIT­ Report G-149:1-2, Santa Fe. NER, B.C. THOMPSON, AND M.C. ANDERSEN. 2001. OLDEN, J.D., D.A. JACKSON, AND ·P.R PERES-NETO. 2002. Application and assessment ofspecies at risk conser­ Predictive models of fish species distributions: a vation approaches at Fort Bliss and White Sands note on proper validation and chance predictions. Missile Range, New Mexico and Texas. Research Transactions of the American Fisheries Society 131: Completion Report, Research Work Order 32, New 329-336. Mexico Cooperative Fish and Wildlife Research PATTERSON, B.D. 1980a. A new subspecies of Eutamias Unit, Las Cruces. quadrivittatus (Rodentia: Sciuridae) from the Organ BROWN, D.E., EDITOR. "1982. Biotic communities of the Mountains, New Mexico. Journal of Mammalogy American Southwest-United States and Mexico. 61:455-464. Desert Plants 4:1-342. ___. 1980b. Montane mammalian biogeography in New BUELL, M., AND J. CANTLOR. 1950. A survey of two com­ Mexico. Southwestern Naturalist 25:33-40. munities ofthe New Jersey Pine Barrens and a com­ SCHREIBER, E.R, AND M.A. REED. 1998. Installation sum­ parison ofmethods. Ecology 31:567-586. maries from the 1997 survey of threatened and en­ BURNHAM, KP., AND D.R ANDERSON. 1998. Model selec­ dangered species on anny lands. U.S. Anny Construc­ tion and inference: a practical infonnation"theoretic tion Engineering Research Laboratory (USACERL), approach. Springer-Verlag, New York. Technical Report 99/78:1-54.

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SCOn; J.M., P.l HEGLUND, M.L. MORRISON, lB. HAUFLER, biological diversity conservation in New Mexico M.G. RAPHAEL, WA. WALL, AND RB. SAMSON, EDI­ using geographic information systems. Research TORS. 2002. Predicting species occurrences: issues of Completion Report, Research Work Order 13, New accuracy and scale. Island Press, Washington, DC. Mexico Cooperative Fish and Wildlife Research SKALSKI, J.R 1987. Selecting a random sample ofpoints in Unit, Las Cruces. \ circular field plots. Ecology 68:749. U.S. DEPARTMENT OF DEFENSE. 1998. Integrated natural SULLIVAN, RM. 1996. Genetics, ecology, and conservation resources management plan, Fort Bliss, Texas and of montane populations of Colorado chipmunks New Mexico. Prepared by Fort Bliss Directorate of (Tamias quadrivittatus). Journal of Mammalogy 71: Environment, Geo-Marine Inc., Science Applica­ 951-975. tions International Corp., Center for Ecological SVENDSEN, G.E., AND RH. YAHNER. 1979. Habitat prefer" Management of Military Lands, U.S. Army Corps of ence and utilization by the eastern chipmunk Engineers. (Tamias striatus). Kirtlandia, Cleveland Museum of Natural History 31:1-14. Received15July 2002 THOMPSON, B.C., P.J. CRIST, l PRIOR-MAGEE, R DElTNER, Accepted10January 2003 D. GARBER, AND M.A.HuGHES. 1996. Gap analysis of