Reframing the Grazing Debate: 

Evaluating Ecological Sustainability 

and Bioregional Food Production 

Matthew R. Loeser1 

Thomas D. Sisk1 

Timothy E. Crews2 

Kurt Olsen1 

Craig Moran 1 

and 

Christina Hudenko1 

1 Center for Environmental Sciences and Education 

Northern Arizona Universiry 

POBox5694 

Flagstqff,AZ 86011-5694 

2

Environmental Studies Program 

Prescott College 

220 Grove Avenue 

Prescott, AZ 86301 

Abstract. The semi-arid grasslands of the Colorado Plateau are productive, diverse, and 

extensive ecosystems. The majority of these ecosystems have been altered by human land 

use, primarily through the grazing of domestic livestock, yielding a plethora of environmental and social consequences that are tightly interconnected. From an agroecological 

perspective, untangling these issues requires both an understanding of the role of livestock 

grazing in bioregional food production and the effect of that grazing on ecological 

sustainability. To address the former, we discuss the importance of cattle ranching as a 

bioregional food source, including estimates of meat production and water use in Arizona. 

To address the latter, we present data from a long-term project addressing changes in native 

plant community composition, under a range of alternative livestock management strategies. Our study site near Flagstaff, AZ includes four different management treatments: (1) 

conventional low-intensity, long-duration grazing rotations; (2) high-intensity, short-duration rotations; (3) very high-impact, very short-duration grazing (to simulate herd impact); 

and, ( 4) livestock ex closure. Preliminary results suggest belowground properties are responding more quickly to grazing treatments than aboveground properties. Particular 

Email: MatthewR. Loeser, matthew.loeser@nau.edu 

3 
4 REFRAMINGTHEGRAZINGDEBATE 

response variables, such as cyanobacteria and diatoms, show a marked short-term response 

to very high-impact, short-duration grazing, but long-term implications are as yet unknown. 

Key words: ecological sustainability, bioregional food production, livestock grazing, 

biological diversity, participatory research. 

INTRODUCTION 

Two years ago, we addressed the 4th Biennial Conference of Research on the 

Colorado Plateau on the issue of the ecological sustainability of cattle grazing on arid 

rangelands (Sisk et al. 1999). At that time, our focus was on the role of science in 

helping to resolve the contentious and often bitter social batde over grazing policy 

and practices, and the opportunities presented by public participation in the scientific 

process (Sclove 1998). We demonstrated that the current level of understanding of 

grazing impacts in the Southwest often lacked a rigorous scientific foundation, and 

we suggested an approach for designing research efforts to address scientific issues 

underlying environmental conflict. The centerpiece of our efforts has been an experiment designed to test a set of hypotheses derived from differing claims voiced by 

ranchers, resource managers, and environmentalists about the ecological impacts of 

livestock grazing on the Colorado Plateau. Here we provide an update, expanding on 

the scientific and policy themes that are so closely interwoven in the grazing debate. 

Currendy, consensus on the issue of livestock grazing in the Southwest does 

not appear to be on the near horizon of the socio-political landscape. This impression is particularly apparent in the mainstream media that tend to emphasize the 

contentiousness of environmental issues (e.g., Rotstein 1999). However, deeper 

investigation into the ecological literature provides some evidence of a broad agreement on livestock impacts. For example, Belsky et al. (1999) summarized roughly 

100 papers from the scientific literature that measured the effects of cattle grazing on 

riparian zones in the western U.S. Their review found considerable evidence that 

cattle grazing often has negative effects on stream channel morphology, soils, vegetation, and wildlife. This review and others (e.g., Platts 1991, Kauffman and Krueger 

1984, Armour et al. 1994), make a compelling case that livestock grazing should be 

carefully controlled, if not altogether eliminated, along riparian zones. 

Riparian ecosystems, however, represent only a fraction of grazed lands in the 

Southwest, and information from this sensitive habitat-type does not necessarily 

pertain to other ecosystems. Upland grasslands, which constitute the majority of 

grazed lands, differ substantially from riparian ecosystems in structure, function, and 

evolutionary history, and the impacts of livestock grazing on these two ecosystems 

may be very different. Although we know of no rigorous scientific comparison of 

Southwestern riparian and upland responses to similar grazing systems, the literature suggests that the response of upland systems are more varied. Rambo and 
LoESER ET AL. 5 

Faeth (1999), studied semi-arid grasslands that had been excluded from grazing for 

over eight years, and showed that ungrazed grasslands had fewer plant species than 

adjacent, grazed plots. Insect species richness, however, showed no significant difference. In studies of ground-foraging birds, Bock et al. (1984) found that grazed areas 

and adjacent exclosures had similar abundances in years of average rainfall, but 

exclosures supported nearly 3 times as many birds as the grazed areas following two 

consecutive drought years (Bock and Bock 1999). This complexity of organismal 

responses to grazing, as well as an overall paucity of rigorous scientific information, 

has motivated our efforts to address relevant ecological questions through manipulative experiments conducted in concert with ranch management teams. We provide 

a brief retrospective on our involvement with two such groups that include environmental advocates and policy makers, and explain how this experience has provided a 

broader context for considering trade-offs associated with livestock grazing in the 

Southwest. 

Ground Zero for Grazing Policy 

For several decades, the center of conflict regarding grazing policy has focused on 

whether grazing degrades "the land." Fifty years of research provides clear, but 

equivocal evidence: it does in some places and at some times, and at other times and 

places it does not. In fact, there is also compelling evidence that livestock grazing can 

speed the recovery of certain degraded sites (van Wieren 1991 ), and that grazing may 

increase productivity in some ecosystems (11cN a ugh ton et al. 1997, Milchunas et al. 

1989). Clearly, further efforts to characterize grazing as "good" or "bad" are overly 

simplistic and, we believe, problematic. Instead, two broad questions emerge: (1) 

how and where can grazing be practiced in an ecologically sustainable manner; and (2) 

how do we, the public, wish to manage our public grasslands in the Southwest? The 

answers to the former question will come from greater collaborative interaction among 

ranchers, research scientists, environmental groups, and the public who plan and 

apply on-the-ground management. We are optimistic that the collaborative groups, 

being founded with increasing frequency across the West, will be at the forefront of 

collaborative decision-making. The latter question however, is less tractable. Extreme, polarizing views are propagated daily through the media as demonstrated by 

the well-circulated jingle "cattle-free by '93" (now "2003") and the direcdy opposing 

political views espoused through the ranching industry. In fact, the contest has 

become so mythologized and self-referential that it is easy to lose sight of the real 

questions, such as whether regional agriculture is important to the four-comers states, 

what lands can be grazed sustainably and profitably, and what alternative land management should replace grazing in areas where it is unsustainable or not desired by 

the public. 

Bioregional Perspective of Food Production 

The scientific debate over livestock grazing has focused primarily on single species' responses (such as endangered species) and overall forage production. Ecosystems grazed by livestock have justifiably been compared to ungrazed areas to ascer-
6 REFRAMINGTHEGRAZINGDEBATE 

tain human impacts on natural systems. Interestingly, few comparisons are made 

between the biological diversity and ecosystem function of grazed ecosystems and 

other agroecosystems. In other words, if we assume that humans are going to 

impact natural ecosystems to produce food and fiber through agriculture, it seems 

appropriate to consider the relative ecological impacts of different agricultural practices in the arid Southwest. 

Inherent to conventional agroecosystems dedicated to annual crop production 

is the nearly total replacement of native plant and animal communities. They generally consist of non-native plants (both crops and weeds), and fauna (especially birds, 

mammals, and arthropods) that can exist in communities that experience disturbance at high frequencies and intensities through actions such as plowing soils, which 

often increase erosion rates and contribute to a decline in soil organic matter (Davidson 

and Ackerman 1993). Rarely do modern agricultural systems generate sufficient 

nutrients internally to balance nutrients exported in crops, thus most farms depend 

on large inputs of synthetic fertilizers (Doerge et al. 1991 ). The crop uptake of these 

fertilizers however is fairly inefficient, often not higher than 50%, with residue nutrients often making their way into waterways, or the atmosphere (M:atson et al. 1998). 

Inputs of pesticides including insecticides, herbicides and fungicides are also commonplace in conventional agroecosystems. While the pesticides applied today are 

less persistent in the environment than those used in previous decades, they are 

nonetheless highly toxic and relatively indiscriminate in the species that they affect. 

Finally, modern agroecosystems require substantial fossil fuel subsidies in the production process. The energy used to cultivate, harvest, synthesize and apply fertilizers, and irrigate, primarily comes from fossil fuels. The energy return on each fossilfuel calorie invested in agriculture tends to be quite low (Pimentel and Pimentel 

1996). 

"When compared with agro-ecosystems dedicated to annual agriculture, plant 

species diversity in grazed, upland agroecosystems in the Southwest appear relatively 

intact (Hughes 1996, Rambo and Faeth 1999). The specific ecological impacts of 

cattle grazing are often difficult to estimate, given the lack of non-grazed ecosystems 

that can be used as controls. However, this is not to say that livestock grazing is 

innocuous, because there is strong evidence that grazing can alter community composition of particular ecosystems through mechanisms such as selective biomass 

removal, alteration of soil properties, fire suppression, and transport of exotic species (Fleischner 1994). Indirect consequences of livestock grazing, such as the introduction of grasses for forage, especially Lehmann lovegrass (Eragrostis lehmanniana) 

and buffelgrass (Pennisetum cifiare), have had profound impacts on community dynamics in the Southwest (Bock and Bock 1998, Burquez and Martinez-Y rizar 1997). 

"Where exotics have not been intentionally introduced, however, grazed ecosystems 

are generally dominated by native, perennial species (Rambo and Faeth 1999). 

Estimating total costs of any agriculture is challenging given the gulf that exists 

between actual and perceived costs of natural resources. But without accurate cost 

estimates, the grazing debate remains awash with ambiguous statements. In 1990, 

crop agriculture in Arizona used approximately 5.2 million acre feet of water (Eden 
LoESER ET AL. 7 

and Wallace 1992). Livestock in Arizona consume approximately 15 gallons animalunit -

1 day -1

, which translates into an estimated annual water consumption by all 

range-fed Arizona livestock of only 8,384 acre-feet of water (1 acre-foot water = 

1233.482 m3

; Table 1). When ranchers manage their livestock using horses, livestock 

grazing on Western rangelands may represent the only food production system in 

the United States that is based largely on solar energy rather than fossil fuel inputs. In 

other words, the work performed and inputs used to grow crops or raise animals in 

most agroecosystems involves a very significant reliance on commercial energy (Pimentel 

and Pimentel1996). Producing livestock on western rangelands, however, relies 

heavily on native rates of net primary productivity, while using wind, gravity and/ or 

solar panels to provide water. 

Tradeoffs 

Livestock grazing may have lower ecosystem impacts than annual agriculture, 

but it is also much less productive. A critical question, therefore, is whether the 

production of food from rangelands balances the tradeoffs in native ecosystem 

diversity and productivity that may occur with livestock grazing. To begin to address 

this question, it is important to develop a sense of arid rangeland food productivity. 

Following, we estimate levels of meat produced by catde grazing on Arizona rangelands, excluding feedlot productivity. While these estimates are crude, we believe they 

provide a reasonable, approximate understanding of potential protein production. 

Table 1. Estimated annual meat production and livestock water consumption according 

to ecosystem type in Arizona. 

Ha animal edible beef water 

Acres Area % unit-1 prod.3

·

4 protein5 consumed6 

Ecosystem AUM-1

• 1 (ha) cover year·1 kg year1 kg year1 m3 year·1 

Chaparral 12.5 1,303,452 4 61 1,871,842 411,805 442,808 

Grassland 4.1 5,793,686 24 20 25,376,344 5,582,795 6,003,092 

Pinon-Juniper 12.5 5,164,781 18 61 7,416,964 1,631,732 1,754,576 

Ponderosa 19.8 885,079 3 96 807,634 177,679 191,056 

Desert 20.0 9,143,387 31 97 8,257,226 1,816,611 1,953,374 

Total 22,290,385 80 43,730,113 9,620,625 10,344,905 

1 AUM =animal unit month= the area (in acres) required tofeed one steer or cow/calf unit for 1 month. AUMs based on 

actual stocking rates for different Arizona ecosystems reported in USFW ( 1999) 

2 D. Brown (pers. comm.) 

3 In an animal's first year on the range, it will gain -190 kg, and if it is left for a second year, it will gain -330 kg in a good 

(wet) year and as low as 165 in a dry year. On average, therefore, an animal gains approximately 219 kg yr1 (A. Kessler 

and D. Moroney, pers. comm.) 

4 Edible meat constitutes -40% ofthe total animal weight 

5 Beef is -22% protein (Ensminger et al. 1983) and the average yearly protein requirement for a person is -23.7 kg 

6 One cow or steer requires 15 gallons of water per day (Naeser and St. John 1998) 
8 REFRAMINGTHEGRAZINGDEBATE 

Stocking rates of livestock on lands in Arizona range between 4 and 20 acres 

AUM-1 (an animal unit month is either one steer or cow-calf pair) for desert, chaparral 

and woodland ecosystems (USFW 1999). This range in stocking rates reflects the 

variation in herbaceous, aboveground net primary productivity of the different ecosystems. By making conservative assumptions about stocking rates, we estimate 

that the current grazing of 80% of Arizona's land surface (Mayes and Archer 1982) 

results in sufficient protein production to supply one million people with 40% of 

their annual requirements (assuming 65g protein capita-1 day -

1

). Alternatively, if 

livestock numbers were decreased by 50% across all ecosystem types, then the 40% of 

Arizona land that would remain grazed could supply one million people with approximately one-fifth of their annual protein requirements. This latter level of food 

production is large enough that we believe the value ofbioregional food production 

needs to be considered in the debate regarding livestock grazing in the arid Southwest. Elimination of livestock grazing in the Southwest would substantially impede 

any regional movement toward greater reliance on bioregional food production, and 

would shift agricultural activity, as well as the concomitant environmental impacts, to 

other regions. The potential socio-economic implications of such a proposal are 

beyond the scope of this paper, but undoubtedly warrant further consideration. 

Reshaping the Debate 

Native Habitats as the Endpoint 

Although plant surveys have been a mainstay of the vast majority of grazing 

studies, the emphasis has often been placed on total forage, without regard for the 

particular species that make up the community (e.g., Holechek et al. 1999). Increasing 

public recognition of the value of native habitats and native species has made this an 

issue of contention in the current grazing debate. Dramatic declines in native habitats, such as the degradation or loss of 80% of Western riparian ecosystems (U.S. 

Department of Interior 1994), underscore the rapidity of change wrought by humans. Moreover, the list of nonnative plant species in Arizona has doubled in the 

past 50 years to roughly 330 and continues to grow (Burgess et al. 1991). Complicating this issue is the fact that the establishment of many nonnative plants in grasslands was aided in the early 1900s by government -subsidized seeding programs that 

intentionally (and unintentionally) included nonnative plants (Bahre 1995, Cox and 

Ruyle 1998). This trend in the loss of native habitats and native species is the product 

of multiple land-use actions, many of which are historically associated with, but not 

inherently necessary to livestock production (e.g., road building, erosion of 

streambanks, extensive fencing, chaining of trees, etc.). 

Many examples of landscapes severely degraded by overgrazing exist and the 

mismanagement of rangeland has fueled a widespread anti-grazing sentiment. Many 

environmental groups have advocated the complete removal of cattle from large 

tracts of land, and this approach has been implemented on many National Park 

Service lands (Anderson 1993). The responses of arid and semi-arid grasslands to 

exclusion from cattle grazing have been mixed, with changing richness of native 

species ranging from dramatic increases (Brady et al. 1989) to slight decreases (Rambo 
LoESER ET AL. 9 

and Faeth 1999). When interpreting vegetation responses to livestock removal, 

however, it is important to recognize that virtually all lands that are accessible to cattle 

or sheep in the Southwest have been grazed intensively at one time or another. 

Lands currendy excluded from grazing do not necessarily represent the state of semiarid grassland ecosystems prior to the introduction of domestic livestock (Milton et 

al. 1994), an ecological state that remains poorly understood and whose restoration is 

beyond current technical capacity. Instead, lands where grazing has been eliminated 

represent the likely endpoint of cattle removal from similar ecosystems that are 

currendy being grazed. Thus, constructive approaches to resolving the present grazing debate will include the assessment of expected outcomes of different levels and 

styles of rangeland management (including livestock removal), rather than a restricted 

and largely theoretical choice between current conditions and those that predominated prior to the arrival of domestic grazers. 

A Role for Research 

Clearly, a broad range of land management options currendy exists, and many 

are being implemented and evaluated across the Southwest. Science provides a framework for measuring and interpreting the environmental implications of each option. 

To assess some key elements of the ecological sustainability of grazing in semi-arid 

grasslands, we asked the following question: Do belowground and aboveground 

variables affecting grassland composition and function, respond in a predictable 

manner to increasing grazing intensity? For belowground properties, we measured 

soil compaction and specific members of the microbiotic community, whereas we 

measured plant cover and macro-arthropods as aboveground properties. 

METHODS 

Meaningful application of science to grazing issues will require comparisons of 

the effects of actual management practices, as well as experimental treatments designed to elucidate the relationships between grazing and ecosystem sustainability. 

In 1997 we initiated a study of grazing impacts in a semi-arid grassland in Arizona. 

Our experimental design, replicated in time and space, consists of four treatments in 

three blocks on the landscape (a total12 study plots; see Sisk et al. 1999). The four 

treatments are as follows: (1) conventional low-density, long-duration grazing rotations (CON); (2) high-density, short-duration rotations inspired by Savory (1988) 

Holistic Resource Management (HRM); (3) very high-impact, short-duration grazing 

to simulate herd impact (VHI); and ( 4) livestock exclosure (EX C). Stocking rates and 

rotations for the first two treatments are determined by ranchers and land management agencies on adjoining pastures, while the latter two treatments are implemented on fenced 1-ha experimental plots created and managed by researchers. The 

timing of the graze event for each of the three cattle treatments falls within the 

months of May-October, but specific dates vary between years due to fluctuating 

environmental conditions and ranching logistics. Of the four treatments, only the 

VHI treatment does not represent a current grazing policy, but it does simulate herd 

behavior, and serves as a critical upper-end treatment to study the potential spectrum 
10 R EFRc\ ,\II NG TI-lE GR:IZING D EB:ITE 

of responses. In all CON and HRM plots, we have carefully matched elevation, 

exposure, soil type, and vegetation type so that spatially and temporally extensive 

treatment effects can be complemented with the exclosure and VHI treatments implemented on 1-ha plots. For the purpose of this paper, we present data from one 

study site on the southern edge of the Colorado Plateau. 

Site D escription 

Located at 2160m elevation in north-central Arizona, our primary field site is 

Reed Lake, characterized by Upper Great Basin grassland (Brown 1994) surrounded 

by Ponderosa Pine forest. Dominant perennial grasses are Agropyron smithii (western 

wheat grass) and Ejymus ejymoides (squirrel-tail grass). Soil type is fairly homogeneous 

among study plots and across our study site of approximately 25 ha, with a standard 

error ofless than 10% for each soil particle size class. The top 8 em of soil is, on 

average, comprised of 42% sand, 12% silt, and 44% clay (Fig. 1). Annual precipitation averages between 300 mm and 460 mm with the majority generally falling as 

monsoonal rains between June and September (Brown 1994). 

Response Variables 

Soil Compaction 

As the intensity of cattle grazing increases, the amount of trampling increases. 

We measured soil compaction in the top 5 em of the soil surface with a pocket soil 

penetrometer (Ben Meadows Company, Atlanta, GA 30341). In October of 1999, 

50 

45 

40 - r-- + c 

.52 35 

-

~ 30 c. 

~ 25 CJ 

-

c 20 Cl) 

CJ 

.. 

Cl) 15 c.. 

10 + 

5 

0 

SAND SILT CLAY 

Figure 1. Soil composition for 12, 1-ha plots at the Reed Lake study site. Consistency 

in proportional representation of particle size classes suggests that results from the 

grazing experiment are not confounded by differences in soil type. 
LOESER ET AL. 11 

each plot was measured in three locations that were haphazardly selected (except for 

one of three plots in the conventional treatment that was missed due to a rainstorm). Within each of these locations the average of three readings was used as a 

final soil compaction measurement. This sampling event followed the conclusion 

of grazing for 1999, and was chosen to represent the cumulative compaction for that 

year. Data were analyzed withANOVA. 

Soil Microbiotic Community 

Alterations of soil quality can have effects on cyanobacteria populations and, 

consequendy, on their role as nitrogen fixers (Evans and Belnap 1999). In 1999, we 

employed a slide-incubation technique to assess cyanobacteria and diatoms (Rossi 

and Riccardo 1927, Rossi et al. 1935). Prior to the 1999 grazing season, five microscope slides were placed in each corner of EXC and VHI plots, which minimized 

potential disruption due to researchers in the plot. Slides remained in the ground for 

26 days to incubate microbes and were subsequendy transported to the laboratory. 

Cyanobacteria filaments and diatoms were then counted at 20X magnification with a 

phase contrast microscope. Data were analyzed with a nested AN OVA. 

Plant Cover 

Beginning in 1997, before the EXC and VHI treatments were initiated, we 

conducted annual ground cover (both basal and foliar cover) surveys using the modified-Whittaker plot design (Stohlgren et al. 1995). A modified-Whittaker plot was 

placed within each of the 12 plots and permanendy marked, so that the researchers 

can return annually to conduct surveys. Data were analyzed for 1997-99 with a 

repeated measures AN OVA. 

Arthropods 

In 1998 we conducted sweep-net surveys of plots in the EXC and VHI treatments before and after the VHI grazing event. Total abundance of these vegetationdwelling arthropods was calculated for each plot. Data were analyzed with a repeated 

measures AN OVA. 

RESULTS 

Soil Compaction 

In comparison with the EXC and CON treatments, the HRM and VHI treatments showed greater soil compaction (df=3, F=15.308, P=0.006; Fig. 2). These 

increases are likely to have effects on other soil properties, including bulk density and 

infiltration rates, but the extent of these effects will depend on the persistence of 

these differences, which can only be determined through longer-term study. 

Soil Microbiotic Community 

Our VHI treatment had roughly 50% less colonization by cyanobacteria and 

diatoms, in comparison with the EXC plots (df=1, F=8.98, P=0.0047; Fig. 3). Because these organisms alter soil structure and fix nitrogen, these declines in abundance may portend further ecological consequences. 
12 R EFRc\ ,\ II NG TI-lE GR:IZING D EB:ITE 

16 - l 

b 

14 

12 

rl- b 

a a 

ri-

"' 10 < 

E 

CJ 8 + 

c, 

..:.: 6 

4 

2 

0 

EXC CON HRM VHI 

Figure 2. Soil compaction under four grazing treatments, following three years of 

treatment. EXC = livestock exclosure; CON= conventional, low-density, long-duration 

grazing; HRM =high-intensity, short-duration grazing; VHI =very high-intensity, shortduration grazing to simulate herd impact. Different letters denote significant 

differences in the degree of soil compaction (df=3, F=15.308, P=0.006). 

Plant Cover 

We found plant cover to be fairly similar among treatments, ranging from 78% 

to 88% (Fig. 4). Year-to-year variation in total plant cover was not significant, whereas 

treatment type was a significant factor (df=3, F=9.87, P < O.OOOl) . .At a flner scale of 

inspection, total plant cover measurements showed the HRM treatment to be consistendy lower than the EXC and CON treatments by about 7-9%. Furthermore, the 

1.6 

1.4 

1.2 

(]) 

:2 

Ui 

'- 0.8 (]) 

Cl. 

Vl 0.6 E 

Vl 

·c: 0.4 ru 

Ol 

'-

0 0.2 

0 

a b 

-r--

• 

CYANOBAClERIA 

ioEXCl 

~ 

a b 

DIATOMS 

Figure 3. Abundance of cyanobacteria and diatoms in exclosures (EXC) and very 

high-intensity (VHI) plots following two years of treatment. Different letters denote 

significant differences in abundances (df=1 , F=8.98, P=0.0047). 
L OESER ET :IL. 13 

95 

ab ab ab 

90 - ab 

:;; 85 > 

0 

u 

c: 80 -

"' ~ 

a. "' 75 

70 -

65 ' 

1997 1998 1999 

Figure 4. Percent plant cover for fou r grazing treatments (see Fig. 2 and text for 

treatment descri ptions). EXC and VHI treatments were initiated following data 

collection in 1997, while CON and HRM treatments were put in place more than 12 

years prior to initiation of this study. Between-year differences were not significant 

for any treatment; significant within-year differences among treatments are denoted 

by different letters (df=3, F=9.87, P<0.0001 ). 

VHI treatment exhibited an 8% decline in plant cover after one year of treatment, but 

this difference did not persist into 1999. In general, short-term effects of treatments 

were measured, but their long-term implications remain unclear. Finer resolution 

measures, such as comparisons of community composition, are addressed in a separate paper (Loeser et al. in prep.). 

Arthropods 

Pre-graze and post-graze sampling ofEXC and VHI plots showed a decline of 

greater than 50% in arthropod abundance following the VHI grazing event in 1998 

(df=1, F=5.95, P=0.07; Fig. 5). In contrast to this short-term response, the pre-graze 

abundance, which is a measure of response since the 1997 grazing event, did not 

differ between treatments, suggesting that long-term effects may be negligible. 

DISCUSSION 

Although we are in the early stages of a long-term study, we have detected shortterm differences among four treatments reflecting a gradient of grazing intensity. In 

general, it appears that soil properties and belowground processes are more sensitive, 

over the short-term, to differences in grazing treatments than are aboveground properties. This supports similar conclusions drawn by Anderson (1995) who argued 

that belowground organisms may be keenly susceptible to land-use change. Measurements of short-term changes in above- and belowground communities due to 

grazing were not unexpected, however, the more ecologically and policy relevant 

questions involve long-term shifts in biological diversity and ecosystem productivity. 
14 R EFRc\ ,\II NG TI-lE GR:IZING D EB:ITE 

350 

300 

:§250 

a. 

~200 en 

""C 

g_150 

e 

..c 

~ 100 

50 

0 

a a 

T 

1 

~ I 

a b 

T 

l 

T 

i 

1 

Post-Gaze 

Figure 5. Arthropod abundance in exclosures (EXC) and very high-intensity (VHI) 

plots, before and after the 1998 grazing event, as determined by sweep net samples. 

Different letters denote a significant difference (df=1, F=5.95, P=0.07). 

While these questions will only be answered with longer-term datasets, the shortterm changes we have detected indicate that the experimental treatments have had 

significant, measurable effects that capture relevant impacts along a gradient of grazing intensities. 

Belowground Properties 

If the fundamental structure of the soil is being altered by the more intensive 

grazing treatments, as suggested by an increase in compaction in HRM and VHI 

plots, we would expect belowground soil organisms to respond. Furthermore, soil 

structural changes will likely affect other abiotic parameters, such as water penetration 

and retention. Preliminary results from our soil moisture measurements suggest 

that more heavily compacted sites have 1-5% less soil moisture (Loeser et al. unpub.). 

These alterations in soil abiotic parameters likely explain the nearly two-fold decrease 

in cyanobacteria and diatoms in the VHI compared to EXC treatments. Soil microorganisms in particular have limited mobility and are known to be sensitive to compaction (Whitford et al. 1995). Preliminary results from other ongoing studies at this 

site suggest tl1at soil microarthropod abundance is roughly 40% lower in VHI plots 

than EXC plots (Loeser et al. unpub.). 

Aboveground Properties 

While belowground properties appear to be responding quickly to treatment 

effects, aboveground organisms, including plants and arthropods, have not yet demonstrated clear trends. Plots of the HRM treatment consistently showed lower 
LOESER ET AL. 15 

ground cover than EXC plots, but because this was evident at the time that experiment began, it cannot be ascribed to the treatment itself. A treatment effect did occur 

in VHI plots after only one year, resulting in a loss of 10% of the live plant cover, but 

this difference did not persist into subsequent years. When we tested the possible 

relationship between arthropod samples and plant data, we did not ftnd significant 

correlations (R?=0.01, P=0.12). Arthropod samples collected shortly after the VHI 

grazing event showed a significant decline in total arthropod abundance, but samples 

from 1999, collected prior to grazing, did not differ significandy among treatments. 

While this suggests rapid recovery of the arthropod fauna, future collections over 

larger areas will be needed to determine long-term trends. Although our initial 

results are not conclusive, they indicate that alternative grazing treatments, such as the 

EXC and VHI treatments, have mixed effects on plants and arthropod communities. 

Although aboveground measurements, such as plant cover and species richness, tend to dominate the grazing literature, we have demonstrated that measurements at multiple trophic levels offer additional information and provide a tractable 

approach for investigating grazing impacts on underlying ecosystem processes. A 

traditional animal- or forage-based approach would likely conclude that these treatment effects do not differ significandy, but clearly the impacts are more complicated, 

particularly within the soil. While additional data over an extended time period will 

be required to untangle grazing impacts and their ecological consequences, significant 

short-term differences in particular response variables between the two most extreme 

treatments indicate the methods that we employed to measure changes in this system are robust, and that long-term research efforts are justified. 

Assessing the multi-faceted environmental implications of livestock grazing in 

the Southwest requires objective quantification of grazing impacts. We believe that 

an assessment of the environmental impacts of grazing should also examine grazing policy in the context of the increasing need for ecologically sustainable agriculture. 

Our research demonstrates short-term negative effects of very high grazing events on 

soil fauna and arthropods, but has not yet demonstrated long-term patterns in 

aboveground properties. As one of the very few bioregionally significant food 

production systems on the southern Colorado Plateau, grazing provides a significant 

source of edible protein that utilizes grassland communities comprised largely of 

native species. Efforts to generate more detailed and credible information on catde 

and grassland community production levels might serve as common ground for 

opposing parties to discuss real-world compromises and the inclusive environmental impacts of livestock grazing versus increasing reliance on food, water, and energy 

imports to support the region's growing human population. We strongly believe 

that future research should move beyond the simplistic approach of grazed-versusungrazed comparisons to address a wider range of grazing practices, in order to more 

effectively determine whether an ecologically sustainable and socially acceptable level 

of grazing may exist for the publicly owned semi-arid grasslands of the Colorado 

Plateau. 
16 REF'RAMING THE GRAZING DEBATE 

AcKNOWLEDGEMENTS 

We wish to acknowledge the cooperation and support of the Flying M, Bar-TBar, Hart, and Orme ranches, the Diablo Trust, and the Orme working group. Numerous undergraduate researchers have contributed to this project, including, R. 

Eisfeldt, M. King, E. Stanley, A. Keller, C. Moran, H. Greer, K. Coyle, T. Keeler, K. 

Olsen, C. Hudenko, L. Dunlop, S. Mezulis, G. Kendall,]. Chirco, and]. White. We 

also acknowledge the contributions of P. West, A. Tomei, T. Wojtowicz,]. Wolf, D. 

Rowland, C. Meyer, J. Battin, L. Ries, and S. Kelly. This study has been supported 

through a NAU Organized Research Grant and in-kind donations from the U.S. 

Forest Service and the Diablo Trust. 

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of Arizona Press, Tucson, AZ. 
Computer Simulation for Rafting Traffic 

on the Colorado River 

Catherine A. Roberts 

Department of Mathematics and Computer Science 

College of the Hofy Cross 

Worcester, Massachusetts, USA 01610 

and 

Randy Gimblett 

School of Renewable Natural Resources 

The University of Arizona 

Tucson, Arizona, USA 85721 

Abstract. A computer program called the Grand Canyon River Trip Simulator 

(GCRTSim) has been developed for use by managers at the Grand Canyon National 

Park. GCRTSim consists of a database, simulator, and extensive analysis tools. The 

database will eventually contain approximately 500 trip diaries, collected in 1998 and 

1999, that report stops for activities and camping along 226 miles of the Colorado 

River corridor within the park. The simulator provides park managers with the 

opportunity to set prospective launch schedules for rafting trips and simulate rafting 

seasons using these launch calendars. Both the trip diary database and the results of 

the simulations can be analyzed using graphing tools. The analysis can provide 

insight into use levels that could impact both the recreational experiences andresources along the Colorado River corridor. 

Key words: Colorado River, Grand Canyon National Park, rafting, simulation, 

management, launch schedule, model. 

Email: Catherine A. Roberts, croberts@mathcs.holycross.edu 

19 
20 COMPUTER SIMULATION FOR RAFTING TRAFFIC 

INTRODUCTION 

The 1989 Colorado River Management Plan (National Park Service 1989) governs the recreational rafting traffic on the Colorado River within Grand Canyon 

National Park. This document provides guidance for park managers in charge of 

supervising and governing both commercial and noncommercial river rafting use. 

To help supplement the ability of managers to understand the complex humanenvironment interactions in this setting, a team of faculty and students from the 

University of Arizona's School of Renewable Natural Resources, and from Northern 

Arizona University's Department of Mathematics and Statistics (senior author's previous affiliation), have worked since April1998 to develop the Grand Canyon River 

Trip Simulator Project (GCRTSim) (Bieri 2001, Cherry 1997, Gimblett et al. 2000). 

The goal of GCRTSim is two fold: (1) to improve understanding of the current 

rafting traffic conditions, and (2) to predict possible outcomes of changes to the 

current set of rafting traffic regulations. To accomplish the first objective, we collected 

trip diaries from rafting parties, and used these data to inform the National Park 

Service (NPS) about the use frequency of various camping and attraction sites. The 

data from these trip reports, coupled with extensive expert interviews, were used to 

develop an artificial-intelligence and statistical-based computer simulation model of 

rafting traffic along the Colorado River (Brian and Thomas 1985,] albert 1990, 1991, 

O'Brien and Roberts 2000, Roberts 1998, Shelby and Nielsen 1976a, 1976b, 1976c, 

1976d). The simulator can approximate the behavior of rafting trips under a wide 

range of natural or imposed conditions. Thus, GCRTSim can consider how a 

proposed set of new regulations would influence an imaginary launch schedule, and 

simulate river trips over multiple seasons. The resultant data can subsequently be 

analyzed to provide insight into the potential consequences proposed set of new 

regulations. The intent is to provide NPS managers with more information about 

existing conditions on the Colorado River, and enable them to gain insight into the 

potential consequences of any new proposed management actions. 

Computers have provided a venue for investigating human recreational use 

since the mid-1970's (Bishop and Gimblett 1999, Borkan and Underhill1989, Schechter 

1975, Schechter and Lucus 1978, Underhill et al. 1986, Van Wagendonk 1979). With 

recent advances in computing, and the development of artificial intelligence algorithms, the potential to make real progress in this area has grown immensely. While 

a natural "next step" in the management of natural resources is to take advantage of 

the potential offered by these recent advances, to date little has been done in this 

arena. Some recent work has developed a related intelligent -agent based program to 

study the interactions between jeep tours, bicyclists and hikers in a recreational setting 

in Sedona, Arizona (Gimblett et al. 1996). Our efforts have been to design a computer simulation model that examines the complex interactions between humans 

and the natural environment. Each rafting trip is designed as an intelligent agent, 

imbued with the intelligence to respond dynamically to its environment and to 

modify its plans accordingly. This represents a new approach for managers in the 

National Park system, in that we are able to combine statistical analysis, artificial 

intelligence and tools from mathematical modeling in a cutting edge fashion. 
ROBERTS AND GIMBLETT 21 

METHODS 

Data Collection Methodology 

To develop a detailed picture of river use, we needed to gain an understanding 

of: (1) the popularity of various camping and attraction sites along the Colorado 

River corridor; and, (2) how various trip leaders make decisions about where to stop, 

when to stop, and how long to remain at a given location. To obtain this information, trip leaders were asked to complete trip itineraries during the 1998 and 1999 

rafting seasons. These itineraries listed the time in and time out for each location (250 

sites between the launch area at Lee's Ferry and the end of the Park's tracking of river 

use at Diamond Creek). The trip diaries represent trips of all lengths and propulsion 

types (i.e., motorized or non-motorized). 

The authors worked with various constituent groups to help gain support for 

this data collection effort. To reach private boaters, presentations were given at the 

annual meetings the Grand Canyon Private Boater Association, and email notification was sent to their members, encouraging participation in our study. In addition, 

the permit office at the Grand Canyon National Park sent information direcdy to 

permit holders. At orientation on the day of launch, the ranger at Lee's Ferry provided our survey materials to the permit holders. 

To reach the commercial trips, presentations were given at Grand Canyon River 

Outfitters Association meetings. The outfitters made individual decisions as to how 

to implement their support of this project. Some, for example, required their guides 

to complete trip reports. Others distributed the trip reports to their guides with a 

request to participate. The authors also met with the Grand Canyon River Outfitters 

Association to solicit support of the river guide community (a summary of the 

meeting dates and locations can be found at http:// mathcs.holycross.edu/ ~croberts/ 

research). Although completing the trip diaries was optional, we recognize that the 

data collected are, nonetheless, far more comprehensive than anything previously 

available. A statistical analysis is currendy underway to more precisely determine the 

extent to which this database is representative and reasonable. 

During 1998, more than 15 river guides were interviewed to learn as much as 

possible about the logic employed by a river guide when taking a trip down the 

Colorado River. These guides, recommended by the Grand Canyon River Outfitters 

Association, the Private Boaters Association and the Grand Canyon River Guides 

Association, collectively represented many years of experience running the Colorado 

River, either privately (i.e., non-commercially) or as guides for commercial outfitters. 

They had experience at various river flow regimes and with all types of watercraft (e.g, 

oars, paddle boats, dories, motor boats). Questions were open-ended and extensive. For example, to understand how a guide might choose a campsite, we asked 

questions such as, ''\Vhen do you start thinking about camping for the evening?", 

''\Vhat campsites do you like and why?", ''\Vhich ones do you try to avoid and 

why?", and "List every factor that goes into the selection process of choosing a 

campsite, and explain why each factor is important." The result was a complex matrix 

of possibilities for campsite selection based on several scenarios or situations that 
22 COMPUTER SIMULATION FOR RAFTING TRAFFIC 

might be faced by a river guide. The scenarios could either be the result of human 

interactions and decisions, or could be the result of responding to the natural environment. For example, a trip might avoid a campsite because a conversation earlier in 

the day revealed that another trip was planning to select that site (result of a human 

interaction and decision); alternatively, a trip might avoid a campsite because when 

they arrive, a recent rainfall has rendered the area too small for their group size (result 

of responding to the natural environment). 

Simulation Engine Development 

The simulation engine represents a hybrid program that uses statistical data 

from the trip diary database, along with artificial-intelligence algorithms developed 

from the expert interview process. As development of the simulation engine proceeded, additional analysis of the database, or additional querying of expert guides, 

has been utilized as needed. The simulation engine is constructed as an objectoriented system that uses elements of fuzzy logic in the decision structure (Gimblett 

et al. 2000, Manneville et al. 1989, Reghis and Raventa 1998, Tecuci and Dybala 1998). 

Fuzzy logic is an artificial-intelligence construct that permits a decision to be made by 

weighing several factors or variables in an appropriate manner. Fuzzy logic theory 

provides a robust and full range of decision-making tools that are suitable for capturing much of the nuances inherent in making complex decisions in the natural environment of the Colorado River. For example, when a trip is choosing a campsite, the 

current conditions of the river and the individual trip play a role, as does the campsite's 

historical popularity. Fuzzy logic takes into account all these factors and weighs them 

appropriately, so that each trip's campsite decision represents a reasonable outcome 

for that particular set of circumstances. 

A launch schedule (e.g, the current launch schedule or a prospective calendar 

created by the user) is entered into the simulation engine, which outputs simulated 

trips from Lee's Ferry. These simulated trips execute days on the river by choosing 

attraction sites for hikes or other activities, stopping for lunch, and selecting an 

appropriate campsite each night. Certain trips must be at given locations on certain 

times (e.g., some trips exchange passengers at Phantom Ranch), and the trips are 

managed by the simulator to meet these fixed points as scheduled. Moreover, a 

sophisticated planning algorithm helps each simulated trip plan out an optimal 

schedule that will include stops at key attraction sites and ensure that campsite selections are appropriate. A comprehensive record is developed for each simulated trip, 

including where and when it encounters other trips, where it chooses to engage in an 

activity or to stop for camp, and the duration of time spent engaged in each activity or 

camp stop. 

Simulation Engine Use 

After running a simulation, the created database can be queried to investigate 

outcomes of that particular launch schedule. For example, one could query the top 

10 attraction sites, and compare the simulation output with data from the real1998 

and/ or 1999 trip diaries, to observe if any major differences exist. There are a 
ROBERTS AND GIMBLETT 23 

number of standard and non-standard queries possible to help the user of GCRTSim 

judge whether the outcome of a simulation represents an improvement over the 

current conditions in Grand Canyon National Park. 

GCRTSim has the ability to run simulations representing new prospective launch 

calendars. It is also possible for the user to manipulate other conditions along the 

river corridor. For example, the user could restrict camping or activities at any number 

of sites. In this instance, a user could compare data from the 1998 trip diaries, as well 

as from simulations run off of the 1998 launch schedule, both with or without the 

added camping/ activity restrictions. A judgement could then be made about the 

possible consequences of such a management action on the dynamics of the river 

rafting traffic on the Colorado River. 

RESULTS 

The Grand Canyon River Trip Simulator Project (GCRTSim) can create numerous types of graphs and charts from a database (real or simulated) to provide insight 

into Colorado River rafting traffic dynamics. To help illustrate some of the uses for 

GCRTSim, it is important to note that the trip report database represents a wealth of 

valuable information. Approximately 500 trip diaries were collected, representing 

about a 50% return rate for the commercial and 30% return rate for private trips. To 

date, only the 1998 trip reports are available for analysis. Not only is it useful to 

examine the "real data" from trip reports, but comparisons are also possible between 

these "real data" and various simulation runs. Simulations were run using a launch 

calendar regarded as typical by the 1989 Colorado River Management Plan. Simulations were run at both 100% and 50% use levels. Comparisons were made between 

the simulations and real data, and the results are presented herein. It must be noted 

that the 1998 trip reports represent approximately 40% of the actual launches, whereas 

a simulation of 100% use level represents a complete launch calendar. At the 50% 

use level, half of the launches were removed from the standard launch calendar, the 

remainder of which represents an even cut of all trip types. 

The authors caution the reader that the results presented here are illustrative 

only. It would be unwise to draw conclusions regarding management of future 

launch schedules based soley on data presented in this paper. First, the 100% and 

50% use levels were arbitrarily created and cannot be presumed to necessarily illustrate 

management decision scenarios. Second, in order to evaluate the potential impact of 

an alternative scenario (such as a 50% use level), it is necessary to examine multiple 

outputs from the simulation model- only a few such indicators are presented here. 

The graphs simply illustrate the types of output that are available to users of the 

simulation model. 

A user can easily compare the popularity among key attraction and camping sites 

along the river corridor. In Figure 1, the most popular attraction sites are presented 

from each of three data sets: (1) the "real" data from the 1998 trip reports, (2) the 

"simulated" data from 100% use level, and (3) the "simulated" data from 50% use 

level. Thus, it appears that key attraction sites remain popular, regardless of the 

number of trips on the river (Fig. 1). Simulated trips chose the same top attraction 
24 C OZI IP UTER SEII UL\TION FO R R :IFl'ING T!L\ FFIC 

Activity Sites 

1.00 .------------------------, 0.90 >- 0.80 c.J 0.70 

a; 0.60 ::::l 0.50 

I ~l~ 1 f f f 0. 00 +-..J....L,.a..JLL,-Ja..L,_,_.L-LL,.--.L.L,---.L~LLl.,.-...L.J.,..-..J...L,-JLLJ.1 

Site Name 

•100% use 

o50% use 

D real data 

Figure 1. Comparison of recreational visitation at 1 0 key attraction sites along the 

Colorado River corridor within the Grand Canyon National Park. Real data corresponds 

to trip diaries collected during the 1998 river running season. The 100% and 50% 

results arise from simulations conducted from scenarios representing 100% use and 

a 50% reduction in people, boats and user days. 

sites, but at a lower frequency, than that reported in the real data. Some of this error 

can be explained by d1e fact that d1e real data does not represent full river use. Still, 

efforts are underway to refine the model to better reflect current conditions on the 

river. Data, such as is presented in Figure 1, provides some insight into how reducing 

the number of launches might affect the selection of attraction sites. Again, the 

historical popularity of these sites keep them as key attractions, regardless of the use 

level, although the amount of use does change. It is interesting to note d1at this 

same dynamic does not hold for campsites. While the popularity of some campsites 

remain high under any use level, others fall into less use when there is less competition on the river. 

Comparison between "Real" and "Simulated" Data 

An important distinction between "real" and "simulated" data is illustrated in 

Figures 2a and b. In each case, graphs show the distribution of all trips along the river 

corridor on a particular day. The horizontal axis shows river mile, while the vertical 

axis represents the number of trips reported to be at each location on that particular 

day. Clearly, several trips were on the river 15 July, but we did not receive trip reports 

from all parties. While the complete launch schedule simulation does not match up 

perfecdy with the trip diary data, it still provides an accurate representation for the 

distribution of parties along the river corridor. N ote that the real data are incomplete, 

whereas the simulated data represent a complete scenario where every trip is repre-
R OBERTS :I ND G L\ IBLETT" 25 

Real Data for 

Distribution of Trips Over a Day 

Wednesday. J ul 15 1998 

C =Commercial Trips 

P =Private Tri s 

.~ 4+-------------------------------------~f------ir--------

.... 

~ 3nr------,-------------------------.,-~ ------~~-------­

D 

~ 2~--nr .. l~-rr--~~,_--.,~---rr--1~-4~----j~-----.r , 

"' 

Simulated Data for 

Distribution of Trips Over a Day 

Wednesday, Jul151998 

C =Commercial Trips 

P =Private Trips 

Figure 2a-b. a. Distribution of rafting trips along the Colorado River corridor within 

the Grand Canyon National Park over one day, as reported in diaries collected during 

the 1998 river running season ("real data"). The horizontal axis marks the river mile, 

and the vertical axis indicates the number of trips reported to be at that river mile on 

July 15, 1998. Note that not all trips on the river submitted a trip diary. b. Distribution 

of rafting trips along the Colorado River corridor within the Grand Canyon National 

Park over one day; results are from a simulation of a scenario that represents the 

typical 100% use-level ("simulated data"). The horizontal axis marks the river mile, 

and the vertical axis indicates the number of trips that were simulated as being at that 

river mile on July 15, 1998. 

sen ted. The higher peaks represent more trips having been at those locations on that 

same day. 

15-day Trips 

All of the records for 15-day trips (using real data) were compiled to illustrate an 

"average" 15-day trip on the river; these data were used to generate simulated 15-day 

trips at both the 100% and 50% use levels (Fig. 3). The slope of the lines provides a 
26 COMPUTER SIMULATION FOR RAFTING TRAFFIC 

sense of trip velocity as they travel down the river. Comparisons among the lines on 

this graph indicate the accuracy of our simulator. For example, the slope of the line 

for the real data and the 100% simulation data are closely matched. After day 10, 

however, the real data average trip speed is slower, and the average trip location 

distance is less than the simulated data. This disparity suggests that the simulation 

might have some error that accumulates to become obvious only after running trips 

of more than 10 days. 

250 

200 

~ 150 i 

... 

Q) 

> 100 ii: 

50 

0 

2 3 4 5 6 7 8 9 10 11 12 13 14 

Day Number 

-+-50% use 

~100% use 

--+--- real data 

Figure 3. The average trip profile for rafting trips of 15-day duration. This graph 

provides a comparison between the real data and various simulated scenarios. It 

can be used both to calibrate the simulator in the development stage and also as a 

tool for interpreting outcomes of multiple scenarios. 

Trips of Yarying Lengths 

We next provide output that takes the average of all the trips, not just those of 

15-day duration (Fig. 4). Each line captures the average location of each trip on a daily 

basis, but also represents trips of many different lengths. The anomalous decrease 

after day 6 is not due to trips backtracking along the river, but rather shows the effects 

of shorter trip lengths that travel the entire river corridor (all250 miles) in six or seven 

days. These trips, because of early completion, are then removed from the data set. 

A more useful query might involve separating out shorter, motorized trips for individual analysis. On day-7, only trips that are greater than or equal to seven days are 

shown. The simulation provides an indication that it is capturing the real data flow 

of rafting trips in some sort of "average" sense. The similarities between Figures 3 

and 4 suggest a certain robustness in the simulator's ability to capture the basic flow 

of rafting traffic on the Colorado River. The simulator appears, however, to result in 

trip itineraries that are further down river than the real data indicates. This is another 

area of focus for improving the next version of our simulator. 
ROBERTS AND GIMBLETT 27 

300 

250 

200 ~ ____..._50% use i 

... 150 ---<>---1 00% use Q) 

> -real data ii: 100 

Q) "" "f!> ~ ~ "Q) 

Day Number 

Figure 4. The average trip profile for trips of all lengths. This provides a comparison 

between real data and various simulated scenarios. 

In addition to specific queries, GCRTSim provides a comprehensive report that 

can be compared to Management Objectives established in the Colorado River Management Plan (National Park Service 1989). These management objectives are the 

guidelines that the National Park Service employs in order to evaluate proposed 

launch scenarios and determine whether or not a given scenario results in acceptable 

river traffic conditions. For example, one management objective specifies that there 

should be an 80% probability that a trip will make contact with seven or fewer river 

parties per day, with up to 90 minutes in sight of less than 125 other people (National Park Service 1989). A simulation run based on a 100% use level, showed that 

this particular management objective resulted in an average probability of 54.53% 

that party contacts will remain within the management standards. A simulation run 

based on a 50% use level, resulted in an average probability of 91.09% that party 

contacts will remain within the management standards. Queries such as these will 

enable users of GCRTSim to better judge alternative management scenarios. 

DiscussioN AND CoNcLusioNs 

GCRTSim, in addition to being a repository for an extensive database of trip 

reports completed during 1998 and 1999, is also an integrated statistical and artificial 

intelligence-based computer simulator that models complex, dynamic human-environment interactions in the Colorado River corridor. It will be used by managers at 

Grand Canyon National Park to help understand the potential impact of various 

alternative management scenarios for rafting trips on the Colorado River. 

These results are preliminary because the 1999 trip diary data are not yet available, 

and additional improvements and refinements for the simulation engine are still 
28 COMPUTER SIMULATION FOR RAFTING TRAFFIC 

underway. The real test will be subsequent to this, when the model is used extensively to examine potential outcomes of various alternative launch schedules. The 

insight that can be provided by GCRTSim is expected to be a valuable contribution 

to a complex situation: managing rafting traffic on the Colorado River in an optimal 

way for both recreators and for the natural resource itself. For up-to-date information on the status of this project, visit the websites at http:// mathcs.holycross.edu/ 

~croberts/ research or http:// odin.math.nau.edu/ ~msl. 

AcKNOWLEDGEMENTS 

The authors would like to acknowledge the team members involved in this 

project: Dr. Terry Daniel, Dr. :Michael I. Ratliff, :Micheal Meitner, Susan Cherry, Doug 

Stallman, Rian Bogle, Rob Allred, Joanna Bieri, Gary O'Brien and Dana Kilbourne. 

This project would not be possible without funding from the Grand Canyon N ational Park, the University of Arizona and N orthem Arizona University (including 

the NAU-NASA Space Grant). The authors also wish to thank the coundess people 

who have contributed to our understanding of the complex nature of running 

rafting trips down the Colorado River. This includes Linda Jalbert, Laurie Domler 

and others from Grand Canyon National Park, the Grand Canyon River Outfitters 

Association, the Grand Canyon Private Boaters Association, the Grand Canyon River 

Guides and several social science researchers involved with the Grand Canyon National Park. 

LITERATURE CITED 

Bieri, J.A., and C.A. Roberts. 2000. Using the Grand Canyon River Trip Simulator to 

Test New Launch Schedules on the Colorado River. AWlS Magazine, Association 

for Women in Science, Washington DC. 29: 6-10. 

Bishop, I.D., and HR. Gimblett. 1999. Management of Recreational Areas: Geographic 

Information Systems, Autonomous Agents and Virtual Reality. Environment and 

Planning B. Planning and Design 2000 27: 423-435. 

Barkan, R.E., and A.H. Underhill. 1989. Simulating the Effects of Glen Canyon Dam 

Releases on Grand Canyon River Trips. Environmental Management 13: 347-354. 

Brian, N.J., and JR. Thomas. 1984. The 1983 Colorado River Beach campsite inventory. 

National Park Service Division of Resource Management, Grand Canyon National 

Park, Grand Canyon, 56 pp. 

Cherry, S. 1997. Modeling of River Trips on the Colorado River in Grand Canyon 

National Park. Unpublished Paper. School of Renewable Natural Resources. 

University of Arizona. Tucson, Arizona. 

National Park Service. 1989. Colorado River Management Plan. U.S. Department of the 

Interior. 

Gimblett, HR., C.A. Roberts, T. Daniel, M. Mitner, S. Cherry, D. Kilbourne, M. Ratliff, 

D. Stallman, R. Bogle. 2000. Intelligent Agent Modeling for Simulating and 

Evaluating River Trip Scheduling Scenarios for the Grand Canyon National Park. 

Pages 245-275. in HR. Gimblett HR. (ed.) Integrating GIS and Agent based 

modeling techniques for Understanding Social and Ecological Processes, Oxford 

Press, London. 
ROBERTS AND GIMBLETT 29 

Gimblett, H.R., B. Durnota and R.M. Itami. 1996. Spatially Explicit Autonomous 

Agents for Modeling Recreation Use in Complex Wilderness Landscapes. Complex 

International Journal 3. 

Jalbert, L.M. 1990. Monitoring Visitor Distribution and Use Patterns Along the 

Colorado River Corridor: River Contact Survey and Attraction Site Monitoring. 

Resource File in Grand Canyon Science Center, Grand Canyon National Park. 

47pp. 

Jalbert, L.M. 1991. Monitoring Visitor Distribution and Use Patterns Along the 

Colorado River Corridor: River Contact Survey and Attraction Site Monitoring. 

Resource File in Grand Canyon Science Center, Grand Canyon National Park. 

32pp. 

Jalbert, L.M. 1992. The Influence of Discharge on Recreational Values including 

Crowding and Congestions and Safety in Grand Canyon National Park. Division of 

Resources Management in Cooperation with Glen Canyon Environmental Studies. 

Grand Canyon National Park. 

Jalbert. L.M. 1993. Colorado River Management; Resource Monitoring Program: 

Recreational Impacts to Camp and Attraction Site Quality, River Rehabilitation 

and Visitor Experience, Program Summary. Resource File in Grand Canyon Science 

Center, Grand Canyon National Park. 14pp. 

Kaplinski, M., J. Hazel, M. Manone, R. Parnell and A. Dale. 1998. Colorado River 

sediment storage in Grand Canyon during calendar year 1997, Final Report to the 

U.S. Bureau of Reclamation, Grand Canyon Monitoring and Research Center, 

Flagstaff, AZ, 65 pp. 

Kearsley, L., and R. Quartaroli. 1997. Effects of a beach/habitat building flow on 

campsites in Grand Canyon: Final Report of Applied Technology Associates for the 

Glen Canyon Environmental Studies, 18 pp. 

Manneville, P., N. Boccara, G.Y Yichniac and R. Bidaux. 1989. Cellular Automata and 

Modeling of Complex Physical Systems. Springer, New York. 

O'Brien, G.O., J. Jansen, K.S. Thompson, A.R. Potochnik and K.J. Burke. 1997. Effects 

of the Effects of Continuous High Flows and Daily Fluctuating Flows from Glen 

Canyon Dam on Grand Canyon Beaches, 1997 and 1998: A Continuation of a 

Repeat Photography Study by Grand Canyon River Guides, Inc. (Adopt-a-Beach 

Program) Administrative report for Grand Canyon Monitoring And Research 

Center by the Grand Canyon River Guides Adopt-a-Beach program. 

O'Brien, Gary and C.A. Roberts. 2000. Evaluation of river beach carrying capacity 

information utilized by the Grand Canyon River Trip Simulator: Analysis and 

recommendations for future study. Unpublished Paper. Northern Arizona University. Flagstaff, AZ. 

Reghis, M., and E. Reventa. 1998. Classical and fuzzy concepts in mathematical logic 

and applications. CRC Press, Boca Raton. 

Roberts, C.A, 1998. Expert Interviews with Grand Canyon River Guides. Unpublished 

Paper. Available at CPFS, Northern Arizona University. Flagstaff, AZ. 

Schechter, M. 197 5. Simulation model of wilderness area use. Resources for the 

Future, Washington, D.C. pp. 172. 

Shelby, B., and J.M. Nielsen. 197 6a. Design and Method of the Sociological Research in 

the Grand Canyon. Technical Report No. 1, Colorado River Research Program 

Report Series, U.S. Department of Interior, National Park Service. 
30 COMPUTER SIMULATION FOR RAFTING TRAFFIC 

Shelby, B., andJ.M. Nielsen. 1976b. Motors and Oars in the Grand Canyon. Technical 

Report No.2, Colorado River Research Program Report Series, U.S. Department 

of Interior, National Park Service. 

Shelby, B., andJ.M. Nielsen. 1976c. Use levels and Crowding in the Grand Canyon. 

Technical Report No.3, Colorado River Research Program Report Series, U.S. 

Department of Interior, National Park Service. 

Shelby, B., and J.M. Nielsen. 197 6d. Private and Commercial Trips on the Grand Canyon. 

Technical Report No.4, Colorado River Research Program Report Series, U.S. 

Department of Interior, National Park Service. 

Shechter, M., and R.L. Lucus. 1978. Simulation of Recreational Use for Park and 

Wilderness Management. Johns Hopkins University Press for Resources for the 

Future, Inc., Washington, D.C. 220 pp. 

Tecuci, G., and T. Dybala. 1998. Building intelligent agents: an apprenticeship multistrategy learning theory, methodology, tool and case studies. Academic Press, San 

Diego. 

Underhill, A. H., A.B. Xaba and R.E. Barkan. 1986. The Wilderness Simulation Model 

Applied to Colorado River Boating in Grand Canyon National Park, USA. 

Environmental Management. 10: 367-374. 

Van Wagendonk,J. W. 1979. Use of a wilderness simulator for management decisions. 

Proceedings of the 1st Conference of Scientific Research in the National Parks. 

Transactions and Proceedings Series 5: 1039-1040. 
(Insert sketch: Biological Resources) 

The Influence of Habitat Types, 

Water Sources, and Movement Barriers 

on Pronghorn Antelope Home Ranges 

in Northern Arizona 

Jill L. Bright 

Arizona Game and Fish Department 

1910AvenueA, #28-B 

Yuma,AZ 85364 

and 

Charles van Riper III 

USGS Forest and Rangeland Eco.rystem Science Center 

Colorado Plateau Field Station 

Box 5614, Northern Arizona University 

Flagstqff, AZ 86004 

Abstract. We studied distribution and movement patterns of 17 radio-collared pronghom antelope within the environs of Wupatki National Monument in northern Arizona. 

Aside from pronghorn gender differences, individual animal and herd movements were 

specifically influenced by fencing along main thoroughfares, historical presence of animals, 

forage succulence and permanendy available water sources. From data analyzed and modeled with an Arc Info Geographic Information System, the extreme fragmentation that we 

observed in our study animals in northern Arizona leads us to believe that rights-of-way 

fences are a major factor affecting pronghorn movements. To facilitate movement and 

interchange among herds, it is imperative to reduce the effect of fenced rights-of-way so 

that pronghorn can freely move as perturbations occur (e.g., winter storms, droughts, fire). 

Another factor affecting localized movement and influencing homerange is permanendy 

available water, particularly within Wupatki NM. Draw down of the water table by wells, 

along with anthropogenic manipulation of former natural watering sites, have negatively 

influenced locations where animals historically watered. This has resulted in no permanent 

water sources remaining within Wupatki NM. In fact, we found greatest movement out of 

the park to secure water during September, that time of year when pronghorn are most 

heavily harvested in northern Arizona. If wildlife managers desire to better manage and 

coordinate pronghorn populations over a large fragmented landscape in northern Arizona, 

they will have to pay closer attention to fenced transportation corridors and to the distribution of water sources. 

Key words: pronghorn antelope, Anti!ocapra americana, movements, homeranges, 

fences, livestock grazing, GIS, highways,Wupatki National Monument. 

Email: Jill L. Bright, JBright@gf.state.az.us 

33 
34 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

INTRODUCTION 

Pronghorn antelope (Antilocapra americana americana) are widely distributed across 

northern Arizona and occur in isolated patches of habitat throughout the central and 

southern state. Historically, this species ranged over a large portion of Arizona but in 

the early 1900s were extirpated from many areas (Nelson 1925). Surveys found only 

700 pronghorn in Arizona in 1924, but primarily due to transplants from neighboring states, this number had increased to over 10,000 by the mid 1980's. Despite 

increases in state-wide numbers, northern Arizona herds appear to have experienced 

a recent decline (Ockenfels 1994), raising concern for the long-term welfare of pronghorn in Arizona. Additional information about pronghorn home ranges, movements and habitat requirements is needed in order to better manage the present 

herds and to help ensure their continued survival. 

Pronghorn home ranges and movement patterns have been studied in many 

areas of western N orthAmerica (e.g., Bayless 1969, Tucker and Garner 1984). Clemente 

et al. (1995) found that adult pronghorn home ranges averaged 22.5 km2 in southern 

New Mexico. Ockenfels et al. (1994) reported home ranges in central Arizona averaged 88 km2 with some individual animals migrating between northern and southem areas. Based on a review of the literature, Allen et al. (1984) concluded that 

pronghorn movements are direcdy controlled by the basic habitat requirements of 

water and forage as affected by seasonal weather. They felt that pronghorn move 

large distances only if forced to do so by extreme weather or habitat conditions. 

O'Gara (1978) stated that "sizes of home and seasonal ranges vary so much with 

habitat and weather conditions that results of studies seldom have application to 

another area, or even another year." Thus, to better manage pronghorn in northern 

Arizona there is a need for research into the basic habitat requirements of these herds. 

Pronghorn require a variety of habitats for their essential life activities. They use 

land forms typified by low, rolling expansive terrain, and although known to occur 

mainly in grasslands, they also use drier shrub-grass plains, steppes and deserts 

(Yoakum 197 4). Studies of feeding habits have found that pronghorn select forbs 

when available, turning to browse and grass at other times of the year (Dirschl1963, 

Hoover 1966, Taylor 1972, Mitchell and Smoliak 1971, Hailey 1979, Barrett 1980, 

Roebuck et al. 1982, Howard et al. 1982 and Koerth et al. 1984). Beale and Smith 

(1970) found that during summers of above average rainfall, forbs made up over 

90% of the pronghorn diet. Grass is commonly utilized in early spring and occasionally at other times if new growth appears. Other summaries of dietary preference 

(Sundstrom et al. 1973, Autenrieth 1978, Allen et al. 1984) agree that pronghorn are 

opportunistic and selective, taking the most palatable and succulent forage available 

at each season. 

In addition to necessary forage requirements, pronghorn require adequate water 

sources. Water distribution may restrict movements or cause animals to move into 

less suitable areas. Ranges that produce and maintain high pronghorn densities have 

water available every 1.6 km-8.0 km. In Wyoming, 95% of more than 12,000 

pronghorn were within a 4.8 km- 6.4 km distance of water (Sundstrom 1968). Boyle 
BRIGHT AND VAN RIPER 35 

and Alldredge (1984) found that pronghorn numbers observed within 6.4 km of 

water sources increased through late spring and summer to a maximum of 92% in 

August- just after measurements of forage moisture content were lowest. Despite 

the importance of water to productive antelope herds, we found only one study in 

Arizona that addressed permanent water sources in relation to animal distributions. 

Ockenfels (1994) found that the majority of pronghorn locations were within a 1.6 

km radius of water; however, most of the study area was also within 1.6 km of 

water. 

In addition to biotic factors influencing pronghorn numbers and distributions, many anthropogenic factors influence distribution and population patterns. 

Human encroachment, in the form of residential and commercial development, as 

well as road construction reduces and fragments suitable pronghorn habitat. Range 

management practices of livestock fencing can further fragment and isolate adjacent 

populations. Overgrazing and trampling from cattle reduces suitable forage and may 

reduce cover that would serve as pronghorn fawn shelter and seclusion from predators. In addition, overgrazing may allow more rapid tree encroachment into grassland areas, thereby reducing suitable habitat (Neff 1986, Ockenfels 1994). These 

various limits to pronghorn movements may result in decreased genetic interchange, 

ultimately leading to low genetic diversity. Populations that drop below minimum 

viable levels could experience lower fertility, higher fawn mortality and may be more 

greatly influenced by severe weather, disease or random catastrophic events. 

Our study was initiated on land surrounding Wupatki National Monument, to 

provide much needed information on northern Arizona pronghorn antelope. 

Wupatki National Monument contains grassland habitats that have not been grazed 

for over 10 years but are immediately adjacent to currently grazed grasslands, providing a unique opportunity to study pronghorn using multiple habitat types. Monument staff have noted that pronghorn were frequently sighted within the Monument boundaries during fall and winter, but were not as often observed in the spring 

and summer. Since visitors have indicated that large animal sightings greatly increased their enjoyment of a trip to the Monument (Lee and Stephens, 1995), the 

park was interested in knowing if and why the animals were leaving. The objectives 

of our study were to: 

Determine pronghorn home range sizes and core use areas in and 

around Wupatki National Monument. 

Document the effects of roads and fences on pronghorn movement 

patterns. 

Determine pronghorn habitat use and their selection of vegetation, 

slope and aspect variables. 

Determine the effects of water distribution on pronghorn movements. 

Determine the abundance and moisture content of forbs, grasses 

and shrubs. 

Determine if there are monthly differences in pronghorn distributions and habitat preferences inside and outside the Monument. 
36 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

STUDY AREA 

Our study area was centered on Wupatki National Monument, located in north 

central Arizona, approximately 35 kilometers northeast of Flagstaff (Fig. 1). Terrain 

was flat to rolling in the north to steeper mountainous areas further south. Chinle 

badland formations were common in the east while basalt rock outcrops were scattered throughout the study area. Elevations range from 1300 meters north of the 

Monument to over 2700 meters on O'Leary Peak to the south. Elevations within 

Wupatki National Monument are generally between 1500 and 1800 meters. 

Local climate regimes within the study area vary with elevation. Lower elevations 

are located in a "rain shadow," northeast of the San Francisco Peaks, where summers 

are hot, with average high temperatures around 30°C, and lows in the teens. Annual 

precipitation is 21 em and most occurs during July and August in the form of brief, 

heavy but local thundershowers (monsoons). Winters are cooler with high temperatures around 5°C and lows below freezing, with one or two isolated snow showers 

occurring during this time. The higher elevations to the south are much cooler with 

considerably more precipitation, mosdy in the form of winter snows. 

Vegetation varied with elevation, with lower elevations characterized by Great 

Basin Cold Desert shrublands and grasslands, while middle elevations consisted 

mosdy of open juniper woodlands. Coniferous forests interspersed with open grassland parks occurred in the higher elevations. 

The Great Basin Cold Desert Shrub community comprised the lowest elevations (1320 to 1535 meters) of our study area. Topography was rough and broken by 

several major drainages. Shrubs occurred mainly on scattered hummocks separated 

by intervening empty areas of deep, black cinders. Dominant shrub species occurring 

in this community were four-wing saltbush (Atriplex canescens), broom snakeweed 

(Gutierrezia sarothrae), Apache plume (Fallugia paradoxa), shadscale (Atriplex cotifertifolia), 

Mormon teas (Ephedra spp.), sand sage (Artemesia ftlofolia) and several species of 

flythicket (Brickellia spp.). Grasses constituted less than 5% of ground cover and 

included galleta (Pieuraphis spp.), threeawns (Aristida spp.), and bush mulhy 

(Muhlenbergia porten). The principal forbs were globemallow (Sphaeralcea subhastata), 

buckwheats (Eriogonum spp.), spurges (Euphorbia spp.), and prince's plume (Stanlrya 

pinnata). 

Grasslands within the study area were generally flat to rolling terrain. These 

grasslands were made up of a mixture of grasses dominated by galleta, black grama 

(Bouteloua eriopoda) and New Mexican feathergrass (Stipa neomexicana). Rubber rabbitbrush ( Chrysothamnus nauseosus) and broom snakeweed were the most common 

shrubs. Other shrubs included threadleaf groundsel (Senecio longilobus) and fourwing saltbush. Winter fat (Krascheninnikovia lanata), an important forage plant for 

wildlife, occurred in low density, particularly in the ungrazed National Monument. 

Common forbs in this community were Russian thisde (Sa/sola kaft), globemallow, 

spurge and several species of aster (Asterspp.). 
Legend 

Roads 

1---1 5 kilometers 

Figure 1. Study area. 

BRIGHT ,\ N D \1,\ N RIPER 37 

Navajo 

Indian 

Reservation 
38 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

Woodlands occurred on elevations above the grasslands, 1800 meters and higher, 

where slopes were steeper and the land broken in several areas by deep ravines. The 

main vegetative components were open and closed stands of one-seed juniper 

(juniperus monosperma). Snakeweed and rabbitbrush also occurred interspersed 

throughout these woodlands with a variety of grasses of which galleta grass was the 

most dominant. Black grama, Fendler threeawn (Aristida fendleriana), mesa dropseed 

(Sporobolus flexuosus) and Indian ricegrass (Oryzopsis lymenoides) were also common. 

Coniferous forests occurred at the highest elevations of our study area, dominated by ponderosa pine (Pinus ponderosa) with some pinyon pine (Pinus edulis). 

Cliffrose (Purshia stansburiana) and apache plume were common shrubs among the 

pines. Common grasses were little bluestem (Andropogon scoparius), sand bluestem 

(Andropogon halliz) and blue grama (Bouteloua gracilis). Several species of penstemon 

(Penstemon spp.) and skyrocket ( Gilia aggregata) were the dominant forb species, with 

many other forbs present (Bateman 197 6). 

METHODS 

Capture and Relocation 

Using a net -gun fired from a helicopter, 17 pronghorn antelope were captured in 

October 1992,. Each animal (13 females and 4 males) was fitted with a radio transmitter collar and individually numbered eartags. Pronghorn were then aerially located 

twice a month until September 1994 and located on the ground from January 1993 

until September 199 5. Locations were plotted on 7.5' U.S. G.S. topographical maps 

and Universal Transverse Mercator coordinates (UTMs) derived to the nearest 0.1kilometer from mapped locations. Global Positioning System equipment was used 

during ground surveys to record animal locations. Data collected at each ground site 

included date, time, dominant vegetation type, slope, aspect, weather variables such 

as wind speed, temperature, and precipitation. We also recorded pronghorn group 

size, structure and activity. Data were entered into a computer using FoxPro, verified 

and then imported into an Arc View Geographic Information System. 

Home Ranges and Movements 

Home Ranges 

Relocations of collared pronghorn were analyzed using features of the program 

TELEM (McKelvey 1997). Using the adaptative kernal method (Worton 1989), the 

95% contour was used as an estimate ofhome range size while the 50% contour was 

used to determine core use areas. Including both aerial and ground locations, pronghom were located once a week for home range calculations in order to reduce the 

possibility of auto-correlated data from more frequent observations (White and 

Garrott 1990). To determine if pronghorn were using the National Monument and 

the adjacent ranch differently throughout the year, we tested numbers oflocations in 

and out of the Monument by month using chi-square tests. Gender related differences in home range size and differences between home range sizes of animals 
BRIGHT AND VAN RIPER 39 

captured inside and outside the Monument were tested with t-tests. Interactions 

between gender and capture location were tested with an AN OVA. 

Movements 

Pronghorn movements were analyzed by calculating the distance between consecutive locations for individual animals. Consecutive locations were most often 

between 5 and 9 days apart, with a few more than 10 days. To determine if length of 

time between locations had an effect on mean distance moved, we tested the movements of a random selection of 5 (of 1 7) animals to determine whether there was a 

difference between total relocations and only those between 5 and 9 days. Distance 

moved was tested for gender and seasonally related differences with AN OVA. Additionally, distance moved between consecutive locations in the Monument and consecutive locations on the ranch were tested seasonally to determine if average consecutive movements differed between the Park and adjacent habitats. 

Paved roads and fences were classified by type, digitized and imported into GIS 

coverages. Number of times pronghorn crossed these potential movement barriers 

was determined by sorting the data file by individual animal and date, and then 

counting all movements across roads and fences. 

Habitat Mapping 

For habitat preference analyses (vegetation, slope and aspect), in order to have 

adequate numbers of animal observations in each cell for chi-square tests, we divided 

the calendar year into three seasons based on local temperature and precipitation 

regimes. Spring comprised the months March through June and was characterized 

by warm days, cool nights and low precipitation. Average daily high was 24 ec, 

average low gee, and precipitation averaged 4 em. Summer was classified as July 

through October. Both day and night temperatures were considerably higher with 

more precipitation. Average daily high was 30eC, low 15ec and precipitation averaged 9 em. The third seasonal category was winter (November through February), 

characterized by cool days and below freezing nights. Average high temperatures were 

gee , low -3 ec while precipitation averaged 6 em. 

Vegetation Mapping 

This portion of the study tested whether pronghorn use habitats randomly or 

if they preferentially select habitats, based on the premise that visibility and mobility 

are important selection factors. A detailed vegetation map was created for Wupatki 

and the surrounding area by ground-truthing an existing vegetation map for the 

National Monument and a map of the Babbitt (CO Bar) Ranch, taking into account 

shrub heights, densities, and density of juniper cover. The map was digitized into a 

Geographic Information System and then existing polygons corrected from field 

data. New vegetation polygons were created in the field, when necessary, using a 

Global Positioning System to produce a final vegetation coverage. The map encompassed over 90% of our pronghorn locations. 

We compared the number of times radio-collared pronghorn were observed in 

each habitat type to expected frequencies based on the area of that vegetation class, 
40 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

using chi-square tests. When the null hypothesis was rejected (i.e., that all habitat 

types were not used equally), simultaneous 90% Bonferroni confidence intervals 

were made for the proportion of times animals used a specific type. To determine 

whether a habitat type was preferred or avoided, the confidence interval was checked 

for overlap with the availability proportion of the corresponding habitat type (N eu et 

al. 197 4, Byers et al. 1984, White and Garrott 1990). Differences in preferences for 

habitat types were analyzed between sexes and among seasons. 

Slope and Aspect Mapping 

We analyzed slope and aspect preferences by creating coverages and overlaying 

pronghorn relocations. This was done using USGS digital elevation models and 

converting them to a grid. We then reclassified the grids into classes: slopes were 

grouped into three classes (0-9% slope, 10-19% slope and over 20% slope); aspects 

were grouped into nine classes, north, northeast, east, southeast, south, southwest, 

west, northwest and no aspect where slopes were less than one percent. These grid 

coverages were then converted into final polygon coverages for use in Arc View. 

Number of times radio collared pronghorn were relocated within each slope 

and aspect class was compared to expected numbers using chi-square tests, based on 

the relative abundance of total area for each class. Preference or avoidance of slope 

and aspect classes were determined using simultaneous Bonferroni confidence intervals. Differences in preferences between sexes and seasons were also analyzed. 

Forage Abundance and Succulence 

During the active growing season (March through August), abundance and 

moisture content of forbs, grasses and shrubs within grassland habitat were collected to determine moisture content differences. 

Forage Abundance 

Using vegetation coverage and GIS random plotting technology, six random 

points a week (three in the Monument and three on adjacent habitats) were selected. 

We located these points using the navigator feature on the global positioning system. 

At each point, using a tape measure, two 50 meter straight lines were laid out along 

the ground in random directions from the point based on spinning a compass dial. 

We used the line intercept method of Canfield (1941) to determine relative abundance of forbs, grasses and shrubs, by summing up the distance (in em) of each of 

these vegetation classes intercepting the tape. Total distance of each vegetation class 

from a line was averaged and used for analyses. At each random point, we ocularly 

estimated percent grass cover. Differences in abundance of each vegetation class 

between transects inside and outside the Monument were analyzed using t-tests. We 

used ANOVA to test for monthly differences in abundance, linear regressions to 

determine the relationship between monthly precipitation and average high temperature on the abundance of forage. 

Succulence 

Forage moisture content was determined by collecting one sample of each vegetation class (forb, grass, shrub) at the six points each week. Samples were clipped 
BRIGHT AND VAN RIPER 41 

with scissors, placed in individual brown paper bags, weighed immediately in the 

field and recorded. Samples were then allowed to air dry in the bags and weight was 

recorded weekly until3 consecutive weights were equal, ensuring that each sampled 

had dried completely, then the final dry weight was recorded. Differences between 

wet and dry weight, divided by wet weight was used to determine percent moisture 

content of each sample (Kitchen 1974, Rowlands pers. comm. 1995). Percent moisture content for each vegetation class was used to test for differences inside and 

outside the Monument and for monthly differences. We utilized linear regressions 

to determine the relationship between monthly precipitation and average high temperature on the succulence of forage. 

Ulater Sources 

A GIS coverage of available water was created by locating all water sources within 

our study area, determining if they were accessible to pronghorn, and if they contained seasonal or year-round water. Accessible waters were digitized in the field 

using a GPS. Concentric buffers of2 km were drawn around each water source up to 

a distance of 10 km. This final GIS coverage was used to analyze pronghorn preferences around water sources, comparing numbers oflocations by season within each 

buffer, to expected numbers based on relative areas of each buffer. 

Using the near command in Arc View, the distance to the closest water source 

was calculated for every pronghorn location. These distances were analyzed by AN OVA 

to determine differences by sex and season. To determine significant factors affecting 

pronghorn distances to water, forage abundance, forage succulence, precipitation and 

average daily high temperature were plotted against each other and analyzed with a 

forward stepwise regression. 

RESULTS 

Capture and Relocation 

Seventeen pronghorn antelope (13 females and 4 males) were captured and 

outfitted with radio transmitters. Four females and one male were captured inside 

Wupatki National Monument while nine females and three males were captured on 

the CO Bar Ranch. These animals were relocated a total of 1,831 times during the 

course of this study. 

Pronghorn did not use the ranch and the Monument equally (X2=158, P<0.05, 

Fig. 2). Pronghorn were located within the Monument as often or more often from 

November through March. During the remainder of the year, pronghorn were 

significantly more common outside the Monument. 

HomeRang~andMorem~~ 

Home Ranges 

Analysis for normality indicated that home range and core use area size tended 

to come from a normally distributed population, thus t-tests and ANOVA were 

used to analyze these data. Home range size varied from 83.6 km2 to 359.0 km2

• 
42 I NFLUENCES ON PRONGHORN ANTELOPE J-IO,\!E R:INGES 

1/) 

c: 

0 

::;::::; 

m 

(.) 

0 

~ 

m 

-.9 

-

c 

'?ft. 

100-

80 

60 

40 

20 -

JAN FEB tu!A.R APRIL lu\IW JUNE JULY AUG SEPT OCT NOV DEC 

D Inside Monument • outside Monument 

Figure 2. Pronghorn occurrences (% of total locations) inside and outside Wupatki 

National Monument by month . 

Most home ranges were between 80 - 150 km2 (Fig. 3a). Average home range size for 

all animals was 169.85 (SE 20.4) km2

• There were no differences in home range size 

by sex. Females tended to have larger home ranges, 181.2 (SE 25.1) km2

, versus 132.8 

(SE 25.4) km2 for males but this difference was not statistically significant (t=1 .4, 

P>0.05). However, because there were only 4 male versus 13 female pronghorn, 

unequal sample sizes may have influenced our ability to detect differences. 

Average home range size for animals captured within the Monument (n=5) was 

162.6 (SE 36.6) km2 versus 172.8 (SE 25.5) km2 for those captured outside (n=12). 

However, these were not significant differences (t=0.22, P>0.05). Of the 17 pronghorn studied, 15 had home ranges encompassing parts of both the grazed ranch and 

ungrazed Monument. Two of the pronghorn had home ranges exclusively on the 

ranch property. 

Core use areas of territories also did not differ by sex or capture location. Core 

use size averaged 27.68 (SE 4.5) km2

, ranging from 8.8 km2 to 72.6 km2, and clustered 

around 11 - 30 km2 (Fig. 3b). Females tended to have larger core use areas, 31.4 (SE 

5.4) km2 compared to 15.6 (SE 3.2) km2 for males but this was not significant (t=1.5, 

P>0.05). Animals captured outside the Monument had larger core use areas than 

those captured inside, 28.8 (SE 6.1) km2 and 24.9 (SE 5.0) km2 respectively, but this 

also was not a statistically significant difference (t=0.43, P>0.05). 
BRIGHT ,\ N D \1,\ N RI PER 43 

5 

4 

!!! 

"' 

c 

~ 3 

'-

"' 

0 

0> 

_Q 2 

E 

z "' 

0 

0" 100 100" 150 150" 200 200" 250 250" 300 <300 

Home range size (square kilometers) 

Figure 3a. Frequency of home range sizes for pronghorn. 

Movements 

Since no significant difference was detected between using all relocations versus 

using only those between 5 and 9 days, all relocations were used to determine mean 

distance moved between consecutive locations. Normality tests revealed that mean 

distance moved was likely sampled from normally-distributed populations. Mean 

distance moved by females was 3.42 (SE 0.1) km and males 3.12 (SE 0.1) km and 

these distances were not significantly different (t=1.75, P>O.OS). 

4 

0" 10 11 "20 21 "30 31 "40 41 "50 <50 

Size of core use areas (square kilometers) 

Figure 3b. Frequency of core use area sizes for pronghorn . 
44 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

Mean distance moved by month of the year was tested using AN OVA and was 

significandy different (F=2.0, P<O.OS). Tukey-HSD test indicated that the only significant monthly differences were between April, with the highest mean distance 

(3.85 km) and October with the lowest mean distance (2.54 km). 

When pronghorn were in the National Monument, they did not move as far 

between consecutive locations during the spring and summer seasons as when on 

the ranch (t=2.95, P<O.OS, t=4.43, P<O.OS; Fig. 4). Average distance moved between 

consecutive locations during spring on the Monument was 2.35 (SE 0.14) km compared to 2.98 (SE 0.15) km on the ranch. During the summer season, movements 

on the Monument averaged 2.01 (SE 0.14) km compared to 2.90 (SE 0.14) km on the 

ranch. Movements between consecutive locations during the winter did not differ 

between animals on the ranch and Monument (t=0.14, P>O.OS, ranch 2.82 ±0.17 

km, Monument 2.85 ± 0.12 km). 

Our study area was bounded on the west by US Highway 89, which is a paved 

two-lane highway with fenced rights-of-way. During the course of the study, no 

crossings of this highway were recorded for any pronghorn. In fact, several home 

ranges appeared to be bounded by this highway. The Wupatki-Sunset Crater loop 

road is a paved, two-lane road without any fences. Pronghorn crossed this road 230 

times during the course of the study, and several pronghorn had home ranges 

straddling this road. 

The livestock fence on the north of Wupatki National Monument has been 

modified to pronghorn standards suggested by O'Gara and Yoakum (1992). It has 

three strands of barb wire, with the lowest strand smooth wire, and at least 50 em 

3.5 

--,--. I 

I -- -~--- --,--- w I I 

I en 3.0 • I 

N I I I 

+I I • I 

E' I I 

I • ~ I 

"E 2.5 I I 

Q) ---·-- E 

Q) 

I > 0 

E 

c 

ro 2.0 

Q) ~Wupatki :a: 

-r· 

1.5 -~·Ranch N= 426 329 79 275 214 164 

Spring Summer Winter 

Figure 4. Average distance between consecutive locations within the Monument 

and consecutive locations on the Babbitt (CO Bar) Ranch by season. 
BRIGHT AND VAN RIPER 45 

above the ground. Pronghorn were located within one kilometer of this fence 189 

times during the course of the study and crossed the fence 238 times. The fence on 

the southern boundary of Wupatki National Monument has not been modified to 

allow easier access for pronghorn. It is four-strand barbed wire fence with the lower 

strand only 32 em above the ground. Pronghorn were located within one kilometer 

of this fence 117 times but crossed this fence only 75 times. Additional four strand 

barbed wire interior pasture fences occurred on the CO Bar Ranch, but did not appear 

to pose crossing problems for pronghorn. 

Habitat Selection 

Vegetation Selection 

Using a vegetation map created for this study (Fig. 5), the following classes 

existed within the area encompassing more than 90% of our pronghorn relocations: 

Grassland: grasses were the main component with less than 20% cover of 0-60 

em high shrubs. 

Shrub-grassland: shrubs were between 20-30% cover but still less than 60 em 

high. The main shrubs in this category were either rubber rabbitbrush, snakeweed 

or shadscale. 

Open Juniper grassland: juniper cover was 5-20% and the understory was 

primarily grasses, with shrubs having less than 20% cover. 

Open Juniper shrubland: juniper cover was 5-20% and the understory was 

made up of more than 20% shrub cover. 

Closed juniper woodland: juniper cover was greater than 20%. 

Cold Desert Shrubland: shrubs were the main vegetation (greater than 30% 

cover) and typically greater than 60 em high. Common shrubs were Mormon 

tea, Apache plume, squawbush, snakeweed, rabbitbrush, four-wing saltbush. 

Chinle Badlands/ Rock Outcrops: bare ground or deep cinders predominated. 

In testing pronghorn relocations against expected numbers, based on area of 

each vegetation type, we found that animal use of vegetation types differed from 

expected based on area by sex and by season (Figs. 6 and 7). During the spring season 

both females and males preferred the grassland type (females X2= 198.8, 6 df, P<0.05, 

males X2 = 73.3, 6 df, P<0.05; Tables 1a and 1b). Males and females utilized the 

closed juniper woodlands, cold desert shrublands and Chinle badlands less than 

expected. Females also preferred the shrub-grasslands, while males used this type as 

expected. Both sexes avoided the open juniper grasslands but used open juniper 

shrublands as expected. 

During the summer season, pronghorn use differed from availability ofhabitat 

types (females X2 = 191.5, 6 df, P<0.05; males X2 = 54.9, 6 df, P<0.05; Tables 2a and 

2b). Males and females preferred the grassland type. Females used shrub-grasslands 

more than expected while males used this type as expected. Both sexes avoided the 

closed juniper woodlands, cold desert shrublands and Chinle badlands. Females 

avoided the open juniper habitat, but males used this type as expected. 
46 I NFLUENCES ON PRONGHORN ANTELOPE J-IO,\!E R:INGES 

0 Wupatki boundary 

N Roads 

~ Chinle badlands/rock outcrop 

EJ Closed juniper woodland 

I Cold desert shrubland 

Grassland 

Open juniper grassland 

~ Open juniper shrubland 

t-;.;.;.;;.j Shrub-invaded grassland 

Figure 5. Vegetation map of study area. 

Wupatki Sunset Crater 

Loop Road 

N 

+ 

During the winter season, pronghorn habitat preference differed from availability (females X2=168.1, 6 df, P<O.OS; males X2=77.1, 6 df, P<O.OS; Tables 3a and 3b), 

with both sexes preferring the shrub-grasslands. Females also used the grassland 

type more than expected, while males used it as expected. Both sexes avoided the 

closed juniper woodlands, cold desert shrublands and Chinle badlands. Females 

avoided the open juniper grasslands and preferred open juniper shrublands, while 

males used both types as expected. 
250 

(/) 200 

c 

0 

~ 150 0 

...J 

0 

Cv 100 _.o 

E 

::::l 

z 50 

60 

~50 

0 

~40 0 

::30 

0 

Jl20 

E 

~10 

BRIGHT ,\ N D \1,\ N RI PER 47 

Grassland 

p 

p 

p 

Spring Summer Winter 

Shrub-grassland 

p 

p p 

Spring Summer Winter 

Cold desert shrubland 

~-- AT~~------~~~------ LQ~--

Spring 

• Female Observed 

0 Male Observed 

Summer Winter 

t:;2l Female Expected 

~ Male Expected 

Figure 6. Number of observed pronghorn locations versus expected locations in 

grassland , shrub-grassland and cold desert shrubland vegetation types. P denotes 

use greater than expected; A denotes use less than expected. 
48 I NFLUENCES ON PRONGHORN A NTELOPE J-IO,\!E R:INGES 

60 

50 (J) 

c 

0 

~ 40 0 

0 

_J 

0 30 

Cii 

_o 

E 20 :::l 

z 10 

0 

20 

<JJ 

§ 15 

~ 0 

_j 

010 

Q; 

.0 

5 5 z 

Open juniper grassland 

A 

A 

A 

A 

[ ~ Spring Summer Winter 

Open juniper shrubland 

p 

Spring Summer Winter 

Closed juniper woodland 

Spring Summer Winter 

• Female Observed 

D Male Observed 

E2J Female Expected 

8SJ Male Expected 

Figure 7. Number of observed pronghorn locations versus number of expected 

locations in open juniper grasslands, open juniper shrublands and closed juniper 

woodlands. P denotes use greater than expected; A denotes use less than expected. 
BRIGHT AND VAN RIPER 49 

Table 1 a. Use of vegetation classes by female pronghorn compared to availability of 

vegetation classes in spring (March-June). Use differed from availability (x2

=198.8, 

6 df, P<O.OS). 

Vegetation Observed# Expected # Proportion of Bonferroni 

Class of locations of locations area available 90% Cl Preference 

Grassland 220 135 0.444 0.65- 0.79 Prefer 

Shrub-grassland 18 6 0.019 0.02- 0.09 Prefer 

Open juniper grassland 17 53 0.176 0.02- 0.09 Avoid 

Open juniper shrubland 4 4 0.012 0.00- 0.03 

Closed juniper woodland 4 17 0.057 0.00- 0.03 Avoid 

Cold desert shrubland 6 53 0.176 0.00- 0.03 Avoid 

Rock outcrops 0 16 0.053 Avoid 

Slope and Aspect Selection 

Pronghorn did not use slopes as expected based on availability. In addition, use 

of slope-classes differed by sex and season. During spring season, females preferred 

gende slopes, used intermediate slopes equal to their availability, and avoided steeper 

slopes (X2=20.3, 2 df, P<O.OS). Males avoided steeper slopes and showed no preferences for either gende or intermediate slopes (X2=9.4, 2 df, P<O.OS; Table 4a). 

During the summer, females preferred gende slopes between 0-9% and avoided 

intermediate (10%-19%) and steeper slopes (X2=45.2, 2 df, P<O.OS). Males preferred 

intermediate slopes, avoided steep slopes and used gende slopes as expected (X2

=16.4, 

2 df, P<O.OS; Table 4b). 

During the winter season, females preferred gende while avoiding intermediate 

and steep slopes (X2=59.4, 2 df, P<O.OS). Males avoided steep slopes but demonTable 1 b. Use of vegetation classes by male pronghorn compared to availability of 

vegetation classes in spring (March-June). Use differed from availability (x2

=73.3, 6 

df, P<O.OS). 

Vegetation Observed# Expected# Proportion of Bonferroni 

Class of locations of locations area available 90% Cl Preference 

Grassland 82 515 0.444 0.60- 0.82 Prefer 

Shrub-grassland 13 6 0.019 0.00- 0.06 

Open juniper grassland 11 20 0.176 0.02 - 0.16 Avoid 

Open juniper shrubland 5 1 0.012 0.00- 0.09 

Closed juniper woodland 0 6 0.057 Avoid 

Cold desert shrubland 0 20 0.176 Avoid 

Rock outcrops 6 0.053 0.01 - 0.03 Avoid 
50 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

Table 2a. Use of vegetation classes by female pronghorn compared to availability of 

vegetation classes in summer (July-October). Use differed from availability (x2=191.5, 

6 df, P<O.OS). 

Vegetation Observed# Expected# Proportion of Bonferroni 

Class of locations of locations area available 90% Cl Preference 

Grassland 215 127 0.444 0.68 - 0.81 Prefer 

Shrub-grassland 18 5 0.019 0.03- 0.09 Prefer 

Open juniper grassland 25 50 0.176 0.04 - 0.13 Avoid 

Open juniper shrubland 1 3 0.012 0.00 - 0.01 Avoid 

Closed juniper woodland 0 16 0.057 Avoid 

Cold desert shrubland 2 50 0.176 0.00- 0.02 Avoid 

Rock outcrops 0 5 0.053 Avoid 

strated no preference for gentle or intermediate slopes (X2=13.2, 2 df, P<O.OS; Table 

4c,Fig. 8). 

No selection of any aspect classes was detected for pronghorn during the spring 

season (X2=7.4, P>O.OS; Table Sa), but they did not use aspect classes equal to availability during summer and winter seasons (X2=29.2 and X2

=44.6 respectively, P<O.OS; 

Tables Sb and Sc). During the summer season, pronghorn selected for or used, as 

expected, the cooler northern exposures but avoided hot and windy southerly exposures. During the winter season, pronghorn selected the northeast aspect or areas 

with no aspect (slope < 1%) and avoided southern aspects. All other slope aspects 

were used as expected. 

Forage Abundance and Succulence 

.Forage Abundance 

Forbs and grasses were significantly more abundant on our transects within 

Table 2b. Use of vegetation classes by male pronghorn compared to availability of 

vegetation classes in summer (July-October). Use differed from availability (x2=54.9, 

6 df, P<O.OS). 

Vegetation Observed# Expected# Proportion of Bonferroni 

Class of locations of locations area available 90% Cl Preference 

Grassland 65 47 0.444 0.49 - 0.74 Prefer 

Shrub-grassland 4 2 0.019 0.01 - 0.08 

Open juniper grassland 18 18 0.176 0.07- 0.26 

Open juniper shrubland 4 0.012 0.01 - 0.08 

Closed juniper woodland 1 5 0.057 0.01 - 0.03 Avoid 

Cold desert shrubland 0 18 0.176 Avoid 

Rock outcrops 0 5 0.053 Avoid 
BRIGHT AND VAN RIPER 51 

Table 3a. Use of vegetation classes by female pronghorn compared to availability of 

vegetation classes in winter (November-February). Use differed from availability 

(x2=168.1, 6 df, P<0.05). 

Vegetation Observed# Expected# Proportion of Bonferroni 

Class of locations of locations area available 90% Cl Preference 

Grassland 164 121 0.444 0.52- 0.68 Prefer 

Shrub-grassland 47 14 0.019 0.03- 0.08 Prefer 

Open juniper grassland 32 48 0.176 0.07 - 0.17 Avoid 

Open juniper shrubland 5 3 0.012 0.02- 0.04 Prefer 

Closed juniper woodland 2 15 0.057 0.00- 0.02 Avoid 

Cold desert shrubland 11 48 0.176 0.01 - 0.07 Avoid 

Rock outcrops 0 14 0.053 Avoid 

than outside Wupatki National Monument. Forbs inside were 12.3 em (SE 1.6) 

while they were only 6.4 em (SE 0.87) outside ( t=3.3, P<0.05). Grasses inside were 

60.5 em (SE 1.3) and 52.4 (SE 0.9) outside (t=5.0, P<0.05). Shrubs did not differ 

significantly in abundance being 6.2 em (SE 1.2) inside and 5.3 em (SE 1.3) outside 

(t=0.4, P>0.05; Fig. 9). 

With all vegetation classes (forb, grass and shrub), abundance differed significantly by month (F=5.8, P<0.01; F=2.4, P=0.04; F=2.5, P=0.03 respectively; Table 

6). There were significantly more forbs in March and April, while differences between 

other months were not significant. Grasses on our grazed transects were least abundant in May and June. However, in the Monument transects, grass abundance was 

lowest in March and April. 

Succulence 

Mean moisture content of forbs (t=1.8, P>0.05) and new growth on shrubs 

(t=1.6, P>0.05) did not differ inside and outside the Monument throughout the 

Table 3b. Use of vegetation classes by male pronghorn compared to availability of 

vegetation classes in winter (November-February). Use differed from availability 

(x2=77.1, 6 df, P<o.os). 

Vegetation Observed# Expected# Proportion of Bonferroni 

Class of locations of locations area available 90% Cl Preference 

Grassland 33 31 0.444 0.31 - 0.62 

Shrub-grassland 7 0.019 0.01 - 0.19 Prefer 

Open juniper grassland 15 12 0.176 0.09- 0.34 

Open juniper shrubland 1 1 0.012 0.00- 0.05 

Closed juniper woodland 0 4 0.057 Avoid 

Cold desert shrubland 4 12 0.176 0.01 - 0.12 Avoid 

Rock outcrops 0 4 0.053 Avoid 
52 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

Table 4a. Use of slope classes by pronghorn compared with slope availability 

during the spring (March-June) season. Use differed from availability for females 

(X2 = 20.3, 2 df, P<0.05) and males (X2 = 9.4, 2 df, P<0.05). 

Slope Observed# Expected# Proportion Bonferroni 

Sex Class {%) of locations of locations area available 90% Cl Preference 

Female 0-9 331 304 0.73 0.75- 0.84 Prefer 

10- 19 68 66 0.16 0.12- 0.20 

~ 20 18 46 0.11 0.02- 0.06 Avoid 

Male 0-9 117 108 0.73 0.71 - 0.86 

10- 19 27 23 0.16 0.11 - 0.25 

~ 20 5 17 0.11 0.00- 0.07 Avoid 

Table 4b. Use of slope classes by pronghorn compared with slope availability 

during the summer (July-October) season. Use differed from availability for females 

(X2 = 45.2, 2 df, P<0.05) and males (X2 = 16.4, 2 df, P<0.05). 

Slope Observed# Expected# Proportion Bonferroni 

Sex Class {%) of locations of locations area available 90% Cl Preference 

Female 0-9 364 305 0.73 0.83- 0.90 Prefer 

10- 19 42 67 0.16 0.07- 0.13 Avoid 

~ 20 13 47 0.11 0.02- 0.05 Avoid 

Male 0-9 121 122 0.73 0.64- 0.80 

10- 19 41 26 0.16 0.17-0.32 Prefer 

~ 20 6 19 0.11 0.00- 0.07 Avoid 

Table 4c. Use of slope classes by pronghorn compared with slope availability 

during the winter (November-February) season. Use differed from availability for 

females (X2 = 59.4, 2 df, P<0.05) and males (X2 = 13.2, 2 df, P<0.05). 

Slope Observed# Expected# Proportion Bonferroni 

Sex Class {%) of locations of locations area available 90% Cl Preference 

Female 0-9 336 273 0.73 0.86- 0.93 Prefer 

10- 19 37 60 0.16 0.06- 0.13 Avoid 

~ 20 3 42 0.11 0.00- 0.02 Avoid 

Male 0-9 98 92 0.73 0.69- 0.85 

10- 19 27 20 0.16 0.13- 0.29 

~ 20 2 14 0.11 0.00- 0.04 Avoid 
400 

350 

00 0 

~250 0 

=200 

0 

Jl150 

E 

:::J 100 z 

50 

0 

p 

BRIGHT ,\ N D \1,\ N RI PER 53 

Slope class 0 - 9% 

p 

p 

~ Spring Summer Winter 

70 Slope class 1 0 - 19% 

60 

<J) 

g50 

-ro 

g 40 --' 

0 Q) 

.0 

§20 

z 

10 

Spring 

A 

A 

Summer Winter 

50 Slope class >19% 

<J) 

§40 

~ 

15 30 

--' 

~20 A Q) 

.0 

§10 

z 

Spring 

• Female Observed 

D Male Observed 

Summer Winter 

r::2j Female Expected 

~ Male Expected 

Figure 8. Number of observed pronghorn locations versus expected number of 

locations in slope classes 0-9%, 10-19% and >19%. P denotes use greater than 

expected , A denotes use less than expected . 
54 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

Table Sa. Use of aspect classes by pronghorn compared witih aspect availability 

during the spring (March- June) season. Use did not differ from availability (X2 = 7.44, 

8 df, p > 0.05). 

Aspect 

North 

Northeast 

East 

Southeast 

South 

Southwest 

West 

Northwest 

No Aspect 

Observed # Expected # Proportion of 

of locations of locations area available 

115 

122 

106 

90 

51 

35 

34 

45 

18 

107 

123 

105 

90 

59 

31 

33 

53 

11 

0.17 

0.20 

0.17 

0.15 

0.10 

0.05 

0.05 

0.09 

0.01 

Bonferroni 

90% Cl Preference 

0.15- 0.22 

0.16- 0.24 

0.13- 0.21 

0.11 - 0.18 

0.05- 0.11 

0.03- 0.08 

0.03- 0.078 

0.05 - 0.10 

0.00- 0.07 

Table 5b. Use of aspect classes by pronghorn compared with aspect availability 

during the summer (July- October) season. Use did not differ from availability (X2 = 

29.2, 8 df, p > 0.05). 

Observed# Expected# Proportion of Bonferroni 

Aspect of locations of locations area available 90% Cl Preference 

North 136 98 0.17 0.20- 0.29 Prefer 

Northeast 118 112 0.20 0.17- 0.25 

East 88 95 0.17 0.12- 0.20 

Southeast 62 82 0.15 0.08 - 0.14 Avoid 

South 51 54 0.10 0.06 - 0.12 

Southwest 19 28 0.05 0.01 - 0.053 

West 22 30 0.05 0.02- 0.06 

Northwest 48 48 0.09 0.05- 0.11 

No Aspect 16 10 0.01 0.01- 0.05 

Table 5c. Use of aspect classes by pronghorn compared with aspect availability 

during the winter (November- February) season. Use differed from availability (X2 = 

44.6, 8 df, P<0.05). 

Observed# Expected # Proportion of Bonferroni 

Aspect of locations of locations area available 90% Cl Preference 

North 79 88 0.17 0.11-0.20 

Northeast 137 100 0.20 0.22- 0.32 Prefer 

East 89 86 0.17 0.13- 0.22 

Southeast 62 73 0.15 0.08 - 0.16 

South 27 48 0.10 0.03- 0.08 Avoid 

Southwest 21 25 0.05 0.02- 0.06 

West 28 27 0.05 0.02- 0.08 

Northwest 38 43 0.09 0.04 - 0.10 

No Aspect 22 9 0.01 0.02- 0.07 Prefer 
BRIGHT ,\ N D \1,\ N R IPER 55 

collection period. Average forb moisture content in the Monument was 44.8% (SE 

1.6%) and outside 40.9% (SE 1.4%). New growth on shrubs averaged 37.7% (SE 

0.9%) on grazed sites and 35.9% (SE 0.7%) on d1e ungrazed transects. Grasses were 

significantly more succulent in the grazed sites (27.2 SE 1.1%) than in ungrazed 

transects (21.2 SE 1.5%), (t=3.3, P<0.05; Fig. 10). 

March and April forb moisture content averaged 51 .9 ± 12.3%. Average succulence for the remainder of the collection period was 40.1 ± 7.9%. Differences between monilily forage succulence were significant (F=6.4, P<0.01). Tukey's HSD 

tests revealed that forbs had significantly more moisture in the early spring in ilie 

Monument and grazed sites. 

Average moisture content in July for shrubs was 40.1 ± 4.0%. It was lowest in 

March wiili an average of 32.8 ± 3.6%. Moisture content for new growd1 on shrubs 

differed by monili (F=4.0 P<0.01). Tukey's HSD test showed that new growth on 

shrubs had more moisture later in the summer than spring. 

Grasses also differed significantly in moisture content by monili (F=3.1, P=0.02). 

Tukey's HSD tests revealed that grasses were significantly more succulent in April 

(0=30.8 ± 8.9%). August had the lowest average succulence (0=19.8 ± 8.3%). 

14 

* 12 

E 

~10 

_c 

OJ 8 c 

Q) 

6 Q) 

e 4 Q) 

c 2 

0 

FORBS SHRUBS 

70 

60 

50 

~40 

0 

u 

:o?30 

0 

20 

10 

0 

GRASS 

• MO NUMENT D RANCH 

Figure 9. Forb, grass and shrub abundances in Wupatki National Monument compared 

to outside the Monument (in ems). * denotes significant difference. 
56 I NFLUENCES ON PRONGHORN ANTELOPE J-IO,\!E R:INGES 

Table 6. Monthly mean abundance of forbs , shrubs and grasses in the park and on 

the grazed CO Bar Ranch. 

Forbs (em) Shrub( em) Grass(%) 

Month Park Grazed Park Grazed Park Grazed 

March 21 .0 1.2 6.2 19.3 61 .6 57.5 

April 25.9 11 .5 12.6 5.0 51 .1 54.4 

May 10.2 3.7 3.3 1.0 63.3 45.5 

June 10.8 5.7 3.9 5.5 60.5 47.2 

July 5.8 5.22 4.0 1.9 63.9 55.2 

Aug 7.3 6.9 6.8 8.5 62.8 55.0 

Neither average daily high temperature (r2 == 0.44, P>.OS), nor precipitation 

(r2==0.62, P>.OS) was significantly related to the monthly abundance of forbs. However, monthly succulence level of forbs was inversely related to the average monthly 

high temperatures (r2

==0.66, P<.OS). As temperatures rose, succulence dropped in 

forbs. Precipitation levels were not statistically significant in determining succulence 

(r==0.53, P>.OS). 

Water Use 

2 km Concentric Buffers 

Pronghorn did not use the 2 km buffers around water sources as would be 

expected based on area within these buffers. Dming the year, 84% of all pronghorn 

locations were less than 6 km from a water source (Fig. 11 ). In the spring season, use 

of buffers differed from availability for females and males (females X2==86.5, 4 df, 

50 

t:40 

2 

c 

8 30 <!.) 

·c; 

~20 

E 

10 

0 

FORB GRASS SHRUB 

• MONUMENT D RANCH 

Figure 10. Forage moisture content of forbs , grasses and shrubs on Wupatki 

National Monument and on Babbitt Ranch . *=denotes significant difference. 
BRIGHT AND VAN RIPER 57 

P<0.05; males X2=103.586.5, 4 df, P<0.05; Table 7a). Females preferred the 0-1.99 

km buffer and avoided areas greater than 8 km, with all other buffers being used as 

expected. Males preferred 0-3.99 km buffers, and avoided areas greater than 6 km. 

During the summer season, buffer use differed from availability for females 

and males (females X2

=126.3, 4 df, P<0.05; males X2=74.4, 4 df, P<0.05; Table 7b). 

Throughout the summer they preferred buffers up to 3.99 km from water. Males 

avoided areas greater than 6 km while females avoided those greater than 8 km from 

water. 

During the winter season (Fig. 12), buffer area use differed from availability, but 

females and males used the buffers similarly (females X2=122.6, 4 df, p <0.05; males 

X2=64.4, 4 df, P<0.05; Table 7c). During winter, both sexes preferred the 2-3.99 km 

buffer and used the 0-1.99 km and 4-5.99 km buffers as expected, but avoided areas 

greater than 6 km from water. 

Mean Distance from Water 

Mean distance of pronghorn sightings to water did not differ significandy by 

year during this study (F=0.52, P>0.05), but mean distance to water differed by sex 

and season, and by location within and outside the Monument. Female pronghorn 

were found farther from water sources than males during the spring season (t = 3.43, 

P<0.01) but there was no difference between sexes during the summer or winter. 

Females were significandy (F= 13.7, P<0.05) closer to water during the summer (2845 

SE 91 m) than spring or winter, the latter not being significandy different (spring 

3,514 SE 102m, winter 3,475 SE 106m). Males were farther from water in the winter, 

(3,332 SE 145m) than either spring or summer (F=6.0, P<0.05), which did not 

differ (spring 2,732 SE 123m, summer 2,738 SE 148m) (Fig. 13). 

Pronghorn, when located within the Monument boundaries, were significandy 

farther from water than when located outside the Monument (t=9.47, P<0.05). 

Mean distance within was 4,305 ± 73.7 meters compared to 3,285 ± 78.5 meters 

outside the Monument. 

When forb succulence, forage abundance, monthly average high temperatures, 

and precipitation were used to determine relationships to distance-from-water, only 

monthly high temperature was significant (r2=0.66, P<0.05). As temperatures rose, 

pronghorn moved closer to available waters. Neither forb succulence (r2=0.21, P>.05), 

forage abundance (r2=0.1 0, P>0.05), nor precipitation (r2=0.30, P>.05) were significandy correlated with distance to water. 

DISCUSSION 

Home Ranges, Movements and Distribution 

Home range size for all radio-collared pronghorn that we studied averaged 

16,900 ha and were considerably larger than the 156 - 2300 ha reported as typical home 

range sizes by Kitchen and O'Gara (1982). They were almost twice the size of home 

ranges reported by Ockenfels et al. (1994) from central Arizona. Most of our radio-
58 I NFLUENCES ON PRONGHORN ANTELOPE J-IO,\!E R:INGES 

• 2 km concentric buffers 

• Spring and summer pronghorn locations 

0 Wupatki boundary 

N Roads * Available water source 3 0 

~ 

3 

-~ ... ' .. 

. •• . 

6 9 

I 

N 

+ 

Kilometers 

Figure 11. 2 km concentric buffers around water sources with pronghorn locations 

during spring and summer. 
BRIGHT AND VAN RIPER 59 

Table 7a. Distances of pronghorn locations from identified water sources compared 

with area within isometric 2.0 km concentric buffers around water sources during 

spring. Use differed from availability for females (X2 = 86.5, 74.4, df, P<0.05) and 

males (X2 = 1 03.5, 4 df, P<0.05). 

Distance Observed# Expected# Proportion Bonferroni 

Sex Class {km) of locations of locations area available 90% Cl Preference 

Female 0- 1.99 130 102 0.202 0.21 - 0.30 Prefer 

2.0- 3.99 134 117 0.232 0.22- 0.31 

4.0- 5.99 133 111 0.219 0.22- 0.31 

6.0- 7.99 102 90 0.179 0.16-0.24 

8.0- 9.99 7 84 0.166 0.00- 0.03 Avoid 

Male 0- 1.99 55 32 0.202 0.25- 0.43 Prefer 

2.0- 3.99 75 37 0.232 0.37- 0.56 Prefer 

4.0- 5.99 25 35 0.219 0.08- 0.22 

6.0- 7.99 5 28 0.179 0.00- 0.06 Avoid 

8.0- 9.99 0 26 0.166 Avoid 

collared pronghorn had home ranges encompassing parts of the grazed ranch and 

ungrazed Monument. 

Individual pronghorn varied in their tendency to move long distances. Two 

females moved from the lower elevation grassland area of the National Monument 

to open parks in high elevation ponderosa pine forest each spring, returning to the 

grasslands in the late fall. One other female moved from grassland habitats on the 

Table 7b. Distances of pronghorn locations from identified water sources compared 

with area within isometric 2.0 km concentric buffers around water sources during 

summer. Use differed from availability for females (X2 = 126.9, df, P<0.05) and 

males (X2 = 74.45, 4 df, P<0.05). 

Distance Observed# Expected# Proportion Bonferroni 

Sex Class {km) of locations of locations area available 90% Cl Preference 

Female 0- 1.99 157 91 0.202 0.29- 0.40 Prefer 

2.0- 3.99 133 105 0.232 0.24- 0.34 Prefer 

4.0- 5.99 92 99 0.219 0.16-0.25 

6.0- 7.99 67 81 0.179 0.11 - 0.18 

8.0- 9.99 3 75 0.166 0.00- 0.01 Avoid 

Male 0- 1.99 50 29 0.202 0.25- 0.44 Prefer 

2.0- 3.99 60 33 0.232 0.31 - 0.51 Prefer 

4.0- 5.99 27 31 0.219 0.10-0.26 

6.0- 7.99 7 25 0.179 0.00- 0.09 Avoid 

8.0- 9.99 0 23 0.166 Avoid 
60 I NFLUENCES ON PRONGHORN ANTELOPE J-IO,\!E R:INGES 

• 2 km concentric buffers 

• Winter pronghorn locations 

D Wupatki boundary 

N Roads * Available water source 

N 

+ 

3 0 3 6 9 Kilometers 

~E~--~~;;~~~~ 

Figure 12. 2 km concentric buffers around water sources with pronghorn locations 

during the winter season. 
BRIGHT AND VAN RIPER 61 

Table 7c. Distances of pronghorn locations from identified water sources compared 

with area within isometric 2.0 km concentric buffers around water sources during 

winter. Use differed from availability for females (X2 = 122.6, df, P<O.OS) and males 

(X2 = 64.3, 4 df, P<O.OS). 

Distance Observed# Expected# Proportion Bonferroni 

Sex Class (km) of locations of locations area available 90% Cl Preference 

Female 0- 1.99 91 82 0.202 0.17-0.27 

2.0- 3.99 168 95 0.232 0.35- 0.46 Prefer 

4.0- 5.99 94 90 0.219 0.18-0.28 

6.0- 7.99 53 73 0.179 0.08-0.17 Avoid 

8.0- 9.99 4 68 0.166 0.00- 0.02 Avoid 

Male 0- 1.99 26 25 0.202 0.12-0.29 

2.0- 3.99 63 29 0.232 0.39- 0.60 Prefer 

4.0- 5.99 26 27 0.219 0.12-0.29 

6.0- 7.99 12 22 0.179 0.03-0.15 Avoid 

8.0- 9.99 0 21 0.166 Avoid 

ranch, across the Little Colorado River, to desert shrub land habitat on the Navajo 

Reservation each spring prior to fawning, returning to the ranch in late summer. All 

of the other pronghorn showed migratory behavior but did not tend to move such 

large distances, having maximum movements between 10 km and 20 km, comparable to movements reported by Ockenfels (1994) in central Arizona. 

We found that the mean distance moved by pronghorn between consecutive 

locations averaged 3.3 km. Hailey (1979) reported mean distance moved as 1.2 km, 

3800 

3600 I r--r- w 

(/") 

C\1 3400 

+I 

E 3200 I 

Q) I (.) 3000 r-T- c ---y-- s 

(/) 

:0 2800 I 

c ' I Female ro 

Q) 2600 I 

~ __ .! __ I 

2400 -r2200 -~-Male 

N= 426 160 375 144 316 127 

Spring Summer Winter 

Figure 13. Mean distances to water (in meters) for male and female pronghorn, by 

season. 
62 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

and Ockenfels (1994) found average distance moved in central Arizona herds to be 

2.5 km, somewhat smaller than found in this study. However, pronghorn herds in 

central Arizona are much more confined by man-made barriers (e.g., roads, fences, 

housing developments), than are animals in the more remote area of our study. 

Ockenfels (1994) reported that the area he studied was "well watered" with very few 

areas farther than 1.6 km from water, while over 80% of our study area was greater 

than 1.6 km from water. 

Some fences (i.e., net wire or fences with a low bottom wire), highways and 

railroads may pose barriers to pronghorn movement, thereby fragmenting habitat, 

restricting movement and isolating populations (Buechner 19 50, O'Gara and Yoakum 

1992, van Riper and Ockenfels et al. 1998). Pronghorn move within habitats in 

response to drought, forage and water availability, winter storms, human disturbances and other changing conditions. Highway 89, a paved and fenced two-lane 

road was an effective movement barrier separating herds to the east and west. At no 

time, during the 3 years of this study, did we document any pronghorn crossing this 

highway. However, the paved but unfenced two-lane Monument road did not 

appear to pose movement problems for pronghorn. Pronghorn commonly crossed, 

and several home ranges straddled, this road. 

Livestock fences at Wupatki National Monument, with lower strands modified 

for pronghorn, as described by O'Gara and Yoakum (1992), did not appear to deter 

pronghorn from crossing pasture boundaries. Pronghorn did not cross a fence 

without lower strand modifications as often, but several pronghorn had home ranges 

encompassing both sides of these fences. However, we simply counted the number 

of times pronghorn crossed these fences. If pronghorn came to the fence and had 

trouble or decided not to cross, this would not be revealed by our data. In times of 

stress, such as pursuit by a predator or deep winter snow cover, these fences may still 

pose problems. 

Although pronghorn used the ranch and Monument, they used these habitats 

differently during the course of the year. During winter months animals were frequently located within the National Monument boundaries, but during the rest of 

the year were far more common on Babbitt Ranch property. From these distribution 

patterns, it is apparent that the pronghorn in this study need to utilize components 

of habitats on both the ranch and Monument in order to meet their annual essential 

life requirements. 

Aside from the grazing regimes, there are two main differences between Babbitt 

Ranch and the Monument: (1) the ranch has several water sources available to the 

pronghorn while the Monument has none; and (2) the Monument has more diverse 

habitat types. The ranch consists mostly of grassland (92%) with a few patches of 

shrub-grassland (6%) and juniper habitats (1 %) while the Monument contains grasslands (25%), shrub-grasslands (12%) and open juniper habitats (13%). Within the 

grassland vegetation type, we found that the Monument had significantly more 

forbs available during the spring and summer. 
BRIGHT AND VAN RIPER 63 

Forage Succulence 

Pronghorn are considered to be opportunistic and selective, taking the most 

palatable and succulent forage available at all seasons (Sundstrom et al. 1973, 

Authenreith 1978, Allen et al. 1984). Beale and Smith (1970) and Baker (1953) found 

that forbs, which provide high quality nutrition, digestibility and moisture, represent 86% or more of pronghorn summer diets in the Great Basin. Other authors 

have reported that forbs are the main component of pronghorn diets, with browse 

becoming increasingly important as forbs decrease in abundance (Mitchell and Smoliak 

1971, Koerth et al. 1984). Abundance of forage classes in our study differed between 

the grazed Babbitt Ranch and the ungrazed Wupatki National Monument. Throughout the collecting period, forbs were significantly more abundant within the Monument. Forbs were also more abundant in spring and early summer than in other 

months. Forbs are particularly important at this critical time of the year for late 

gestation, lactation and growth of fawns. 

Despite the fact that the Monument had more forbs available during late spring 

and summer, this is the time of the year that pronghorn are found more frequently 

on the ranch, perhaps needing to remain closer to permanent water sources. During 

the spring and summer seasons, pronghorn utilizing the ranch moved larger distances between consecutive locations than when the animals were in the Monument. 

This may be due to lower forb production on the ranch, forcing the pronghorn to 

move greater distances to find adequate forage. 

Besides abundance, succulence of forage is an important consideration for pronghom. Beale and Smith (1970) found that water consumption of pronghorn in Utah 

varied inversely with the quantity and succulence of available forage. Although forbs 

were more abundant on the Monument, moisture content did not differ between 

the Monument and ranch. Forbs and grasses were significantly more succulent 

during the spring season. Shrubs provided more succulence during the late summer 

as forbs dried out. 

water Use 

Although consumption of succulent forage can help pronghorn meet water 

requirements, the importance of dependable and accessible water sources is not 

disputed, especially in the arid Southwest. Beale and Holmgren (1975) concluded 

that pronghorn cannot live without water during hot weather, even if forage succulence is above average, and that fawns are most affected by a lack of water. Texas 

droughts brought about a reduction in vitality of antelope which resulted in decreased fertility (Autenreith 1978). Whisler (1984) states that diurnal, xeric-dwelling 

ungulates such as pronghorn must tolerate large solar radiation heat loads, since they 

are less able to behaviorally avoid daily and seasonal temperature extremes in the 

open, often windswept, habitats that they typically occupy. 

Placement of water sources plays an important role in determining pronghorn 

distributions and daily and seasonal movements. Boyle and Alldredge (1984) found 

that pronghorn in Wyoming began dispersing from winter ranges as snow cover 
64 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

receded in spring and by June concentrated on adjacent summer ranges where drinking water was more abundant and dependable. In this study, pronghorn began 

moving onto the Babbitt Ranch in late spring, as forbs became available and were 

most succulent, and remained on the ranch where water sources were available throughout the summer months. 

Kindschy et al. (1978) stated that the optimal water distribution was for all 

pronghorn areas to be within 1.6 km of water, although Sundstrom (1968) found 

ranges that produced and maintained high pronghorn densities had water available 

every 1.6 - 8.0 km. In Wyoming Sundstrom (1968) found that 95% of more than 

12,000 pronghorn locations were within 6.4 km of water. Ockenfels et al. (1994) 

found that nearly all pronghorn locations in central Arizona occurred < 1.6 km from 

water; however, there were very few areas in their study area that were more than 1.6 

km from water. 

In our study, the majority of pronghorn locations in all seasons ( 83% in spring, 

87% in summer and 85% in winter) were within a 6 km radius of a water source. 

However, during spring and summer, both male and female pronghorn used areas 

only up to 4 km from a water source more often than expected. During the spring 

and summer, water demands are most likely the greatest for pronghorn. Females 

need increased water for late gestation, fawning, and lactation. Later in the summer, 

temperatures rise and forage dries out, creating an increased need for free standing 

water for male and female pronghorn. We found that areas greater than 6 km were 

avoided by males, and females avoided areas greater than 8 km from water. 

During the winter, temperatures are much lower, and pronghorn became less 

dependant on permanent water sources. Both sexes ranged from 0- 6 km from 

water as expected based on area. Again, areas greater than 6 km from water were 

avoided. 

Although female pronghorn were located within 2 km of a water source more 

frequendy than expected during the spring season, average distance to water for 

females was greater in spring than either summer or winter. Females need increased 

water for lactation during the spring. Forb production and moisture content were 

also highest at this time, and the need for free standing water may be lessened by the 

consumption of succulent forage. Beale and Smith (1970) found that pronghorn in 

Utah did not drink water when forbs were abundant and moisture content 75% or 

greater. Moisture content in this study hovered around 52%, so pronghorn would 

most likely still need additional water. However, if water requirements during spring 

were met mosdy by forage consumption, females with young fawns may have ranged 

farther from water sources to avoid increased predation around the denuded vegetation closer to water sources. 

Both male and female pronghorn were closest to water sources during the 

summer, when temperatures were high and forage less succulent. Both sexes ranged 

farther from water during the winter. During the winter, temperatures were much 

lower in this area, and thus the need for free standing water was decreased. In 

addition, brief snow showers provided ephemeral sources of drinking water from 

snowmelt. During the hot summer, as forb production decreased and the plants 
BRIGHT AND VAN RIPER 65 

lost moisture, pronghorn were most likely driven closer to water due to an increased 

need for free standing water. 

"When pronghorn were located within the National Monument boundaries, 

they were significantly farther from water sources than when they were located outside the Monument. However, forage production was significantly higher on the 

National Monument. The fact that there are no permanent sources of drinking water 

available to supplement the pronghorns' water requirements in the Monument may 

force them into areas outside the Monument that have lower forage production. 

Once pronghorn leave the Monument seeking water, they have to move greater 

distances in order to find sufficient quantities of nutritious forage, as evidenced by 

their larger consecutive movements on the ranch when compared to the Monument. 

These trade-offs between forage quality and water availability may help explain the 

large home range sizes and movements between the ranch and Monument made by 

the pronghorn in our study. This may also explain why pronghorn have home 

ranges and core use areas encompassing both the ranch and Monument, as components of both are necessary for this animal to meet its life requirements. 

Habitat Selection 

In this study, pronghorn moved from Monument to ranch habitats in early 

spring, remained on the ranch throughout the summer, then returned to Monument habitat in the late fall and winter. During the winter, forb production is low 

and pronghorn must turn to browse and evergreen forage for food. Shrub and 

juniper habitats which provide this food source are much more common on the 

National Monument than the ranch. Additionally, temperatures are lower and snow 

provides an ephemeral source of water, lessening the need for free-standing water. 

Thus the pronghorn utilize Monument habitats more during the winter portion of 

the year. 

In spring, forbs become abundant and are at their greatest succulence in grassland habitats. Pronghorn move to these grasslands at the north edge of the Monument and the ranch to utilize forbs. However, as summer approaches and temperatures rise drastically, water from the forage is insufficient to satisfy pronghorn water 

needs. TheN ational Monument does not have any available permanent water sources. 

However, water impoundments for cattle that are accessible to pronghorn, are common on the ranch. Thus, during the summer pronghorn move onto the ranch in 

order to remain closer to water sources, which may leave them in areas ofless desirable forage. As winter approaches, the animals move back toward the Monument for 

winter browse. 

Because forb production is lower on the ranch during the spring and summer, 

pronghorn may need to range farther on the ranch to find adequate forage supplies. 

In fact, we documented larger consecutive movements when on the ranch at this time 

of the year, when compared to movements on the Monument. Making long movements and utilizing areas oflower quality forage could likely contribute to the large 
66 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

home ranges seen in this study, and may also result in decreased pronghorn productivity and increased mortality. 

In addition to forage and water needs, pronghorn utilize specific habitats throughout the year for other life requirements, such as mating and birthing, predator detection and avoidance and shelter from weather. Vegetation composition and structure 

have been long known to influence pronghorn use of an area (Yoakum 1980). Since 

vision and mobility contribute to predator detection and avoidance, pronghorn are 

thought to avoid tall, dense vegetation (Sundstrom et al. 1973, Kindschy et al. 1978, 

1982). Pronghorn typically inhabit open grasslands, shrub-grasslands, steppes and 

deserts that provide adequate forage supplies, shelter, and hiding cover for fawns 

(Yoakum 1974). Numerous studies have determined that pronghorn do not use 

vegetation types in proportion to their availability. Yoakum (1974, 1979, 1980) 

found that pronghorn prefer vegetation less than 60 em high. He considered vegetation over 60 em as suboptimal because it obstructs views of the surrounding area. In 

addition, he found that areas with a dense ground cover of shorter shrubs was less 

preferred because it reduces the mobility of pronghorn and increases their vulnerability to predation. Willis et al. (1988) also reported that pronghorn select areas oflow 

shrub volume. 

Pronghorn in this study did not use vegetation classes based on their availability, 

with males and females selecting different habitat classes at different times of the 

year. As expected, pronghorn avoided the taller, closed juniper woodlands and cold 

desert shrublands, during all seasons, where shrub and tree height and density gready 

reduce visibility and mobility. Chinle badland and rock outcrop classes where bare 

ground dominated were also avoided. Additionally, during all seasons, pronghorn 

preferred the grassland type, where shrubs made up less than 20% of the vegetation. 

Pronghorn preferences for shrub-grasslands (shrubs 20-30%) varied by sex and 

season. Females selected the shrub-grasslands during the spring and summer, when 

fawning is occurring in northern Arizona. The heavier shrub component in the 

grassland may provide better access to good quality fawning areas for females and 

more fawn hiding cover. The presence of numerous, rather than a few isolated 

shrubs may make fawn detection by predators more difficult, thereby allowing for 

higher fawn survival. 

During the winter, males and females used shrub-grasslands more than expected. This is most likely due to the fact that forb abundance was decreasing, and the 

pronghorn were turning to browse for forage. The importance of browse for pronghom during the winter has been documented by numerous authors (Hoover 1966, 

Bayless 1969, Mitchell and Smoliak 1971, Taylor 1972, Barrett 1980). In all these 

studies, browse made up at least 80% of the pronghorn winter diet. Hailey (1979) 

noted movements of pronghorn in winter to brushy areas in Texas. Beale and Smith 

(1970) contend that browse is required to sustain pronghorn during winters or 

droughts. 

Although open juniper is considered suboptimal habitat for pronghorn, because visibility and mobility are reduced (Yoakum 1980), female pronghorn used 

open juniper shrublands during the spring and winter. Although these habitats 
BRIGHT AND VAN RIPER 67 

increase risk of predation, Goldsmith (1989) found that pronghorn compensate for 

obstructed visibility and reduce the risk of predation by increasing vigilance. Pronghorn use of areas with taller vegetation in winter or early spring, when forb availability is low, has been documented in previous studies (Bayless 1969, Beale and Smith 

1970, Sundstrom et al. 1973, Neff and Woolsey 1979, Barrett 1980). Dirschl (1963) 

found that pronghorn were concentrated during winter in areas where sagebrush and 

junipers provided evergreen forage for the animals. In northern Arizona, Gay (1984) 

found areas with taller vegetation to be winter and early spring feeding sites. 

The female pronghorn in our study also used juniper shrublands during spring 

when fawning is occurring. The higher percentage of shrub cover may provide 

important fawning sites and hiding cover for developing fawns. Clemente et al. 

(1995) found that female pronghorn in southern New Mexico occupied mesquite 

vegetation classes, which would be considered suboptimal vegetation based on reduced visibility and mobility, in early spring and continued to occupy this type for 2-

3 months coinciding with the fawning period. 

Pronghorn typically inhabit open, gentle terrain because it provides enhanced 

visibility and mobility. Studies point out that the species is adapted to flat or undulating terrain of less than 20% slopes (Yoakum 1980, Kindschy et al. 1982). Pronghorn antelope in our study did not use slope classes equal to their availability. Male 

and female pronghorn avoided steep slopes (over 19%) during all seasons of the 

year. Female pronghorn preferred gentle slopes between 0 - 9% during all seasons. 

Males used both the gentle and intermediate slopes equal to their availability in all 

seasons, with the exception of summer when they favored the intermediate slopes, 

perhaps looking for cooler temperatures and shade from the intense summer sun. 

No selection of aspect classes was detected for pronghorn during our spring 

season. During the summer season, pronghorn either selected for, or used as expected, the cooler northern exposures and avoided the hot and windy southern 

exposures. During winter, pronghorn selected the northeast aspect and avoided the 

southern aspects, again most likely avoiding the prevailing southwesterly winds. 

SUMMARY AND MANAGEMENT REcoMMENDATIONS 

We found that pronghorn in northern Arizona had larger than average home 

ranges, and displayed longer than average movements than reported in the literature. 

Most of the pronghorn had home ranges utilizing the grazed Babbitt (CO Bar) 

Ranch property and the ungrazed Wupatki National Monument, but focused use of 

these areas at different times of the year. During the winter, pronghorn utilized the 

Monument as often or more frequently than the CO Bar Ranch. However during 

spring and summer, animals were more common on the ranch property. The ranch 

had permanent water sources available to animals while the Monument had none. 

Ranch vegetation was predominately grassland while the Monument contained grasslands, shrub-grasslands and open juniper habitats. These two differences and the 

seasonal requirements of pronghorn can readily explain movements between the 

ranch and Monument that we observed. 
68 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

It appears that pronghorn are utilizing the ranch as summer range in a large part 

due to the availability of free-standing water. However, they must also use the 

Monument for winter range, exploiting its browse habitats. Constructing permanent water sources in the National Monument, in areas frequented by the pronghom, would likely reduce long range movements. This may also increase fawn 

survival, as shrub habitats needed for fawn hiding cover are more abundant on the 

Monument. In addition, pronghorn might stay in or nearer the Monument boundaries during the early fall hunting season and during the summer months, which 

would enhance visitor's enjoyment of the Monument and perhaps increase visitor 

knowledge and appreciation for this animal. 

Management Implication 

There are several water sources on the CO Bar Ranch that are inaccessible to 

pronghorn due to fencing. These fences could be modified to allow pronghorn 

access in order to increase numbers of available water sources. One tank, just north 

of the Wupatki boundary, if made accessible to pronghorn, would provide essential 

water much closer to areas of superior forage within the Monument. 

The grazing status on National Forest lands south of Wupatki National Monument should be reviewed. Currendy there are no catde using this area and thus no 

need for the barbed wire fence along the southern park boundary. If it is determined 

that catde may use this area in the future, the fence should be modified to pronghorn 

standards (as described by O'Gara and Yoakum 1992) in order to enhance pronghorn 

movement. 

Park resource managers should coordinate with highway planners to reduce the 

impacts of Highway 89 on pronghorn populations in this area. Removing, modifying, or moving the fence further back from the highway are possible options that 

should be examined in order to allow pronghorn movement between isolated herds 

on both sides of this highway. The use of underpasses and overpasses constructed 

to facilitate pronghorn crossing of roadways would be a preferred alternative. Although Ward et al. (1980) found that pronghorn did not use underpasses along I -80 

in Wyoming, modifications such as widening an existing underpass to allow better 

visibility may prove successful to enhancing movement across Highway 89. 

Other possible mitigation features that could be undertaken by the national 

park service in northern Arizona could be: (1) removing fences along rights-of-way; 

(2) expanding rights-of-way dimensions by placing fences further away from the 

transportation corridor (e.g., road or railroad), then modifying the fences to permit 

better movement of pronghorn between fenced areas; (3) relocating rights-of-way 

out of pronghorn habitat; ( 4) consider relocating animals, particularly to isolated 

areas where pronghorn have been extirpated; (5) providing permanent water sources 

in Wupatki and Sunset Crater NM; and, (6) provide signs on unfenced park roads 

warning visitors of wildlife movement corridors. Careful attention should also be 

given to preventing any fencing of presendy unfenced roads. 

The issues confronting national park areas in dealing with pronghorn manage-
BRIGHT AND VAN RIPER 69 

ment in northern Arizona, is only an indication of a much larger problem facing 

managers of protected areas around the world. If managers wish to have their 

protected areas function as species reservoirs (i.e., 'sources' instead of 'sinks'), they 

have to: 1) begin to forge active partnership with contiguous land owners to manage 

resources on an ecosystem basis; 2) then decide to what degree they are willing to 

allow active management to occur when their managed lands can not adequately 

support a species; and finally, 3) standardize (or partition) the degree of management 

among managers of all lands within each ecosystem. 

AcKNOWLEDGEMENTs 

This cooperative research effort was supported by funds from the Federal Aid to 

Wildlife Restoration Act, administered by the Arizona Game and Fish Department, 

funds from the U.S. National Park Service and Geological Survey. We thank R. 

Ockenfels, R. Schiller, B. Kilgore G. Larson and M. Fletcher for assistance in procuring 

funding. The cooperation of NPS staffs at Wupatki-Sunset Crater National Monuments and Petrified Forest National Park (particularly Sam Henderson, Micki 

Hellickson, Karen Bepler)and Billy Cordasco of Babbitt Ranches was integral in 

completing the study. Numerous Arizona Game and Fish Department pilots provided aerial support for pronghorn captures and location efforts. We thank Terrance 

Arundel for providing GIS support, and Maureen Stuart for assistance with illustration and text production. 

LITERATURE CITED 

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Allen, A.W, J.G. Cook and M.J. Armbruster. 1984. Habitat suitability index models: 

Pronghorn. USDA Fish and Wildlife Service, FWS/OBS-82/10.65. Fort Collins, 

co 22pp. 

Baker, T. 19 53. Antelope movement and migration studies. Wyoming Wildlife 17: 31-

36. 

Barrett, M.W 1980. Seasonal habitat associations of pronghorn in Alberta. Proceedings 

of the Pronghorn Antelope Workshop 9: 174-195. 

Bateman, G.C. 1976. Natural Resource Survey and Analysis of Sunset Crater and 

Wupatki National Monuments. Office of Resource Management, Southwest 

Region, National Park Service. 

Bayless, S.R. 1969. Winter food habits, range use and home range of antelope in 

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Beale, D.M. and R.C. Holmgren. 1975. Water requirements for antelope fawn survival 

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Beale, D.M. and A.D. Smith. 1970. Forage use, water consumption and productivity of 

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Boyle, S.A. and A.W Alldredge. 1984. Pronghorn summer distribution and water 
70 INFLUENCES ON PRONGHORN ANTELOPE HOME RANGES 

availability in the Red Desert, Wyoming. Proceedings of the Pronghorn Antelope 

Workshop 11: 103-104. 

Buechner, H.K. 19 50. Life history, ecology and range use of the pronghorn antelope in 

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Mule Deer Demographic Responses 

to Select Climatic Variables in Arizona 

Brian F. Wakeling 

Arizona Game and Fish Department 

&search Branch 

2221 WestGreenwqy Road 

Phoenix, AZ 85023 

Abstract: To determine what effect climate has potentially had on recent demographic 

shifts and population declines of mule deer in Arizona, I studied the relationship of 

monthly precipitation and Palmer Drought Severity Index (PDSI) values with Arizona 

Game and Fish Department winter mule deer (Odocoi!eus hemionus) survey fawn:doe (FDR) 

and buck:doe (BDR) ratio data. Seven of 37 Game Management Units (GMUs) had no 

relationship between measured climatic variables and FDR, while 22 other GMUs had 

relationships with adjusted R2 of< 0.350. Thirteen of 37 GMUs had no relationship 

between measured climatic variables and BDR, while 19 GMUs had relationships with 

adjusted R2 of< 0.350. Pooling GMUs into similar habitats did not improve the modeled 

fit of relationships between demographic parameters and climatic variables. Habitats at 

climatic extremes (i.e., desertscrub and montane conifer habitats) demonstrated a predictable and superior model fit with FD R, more so than other habitats (i.e., Mohave desertscrub, 

chaparral and desertscrub, and grassland-woodland habitats), suggesting climate has a greater 

influence on recruitment in less moderate climates. 

Statewide mule deer population estimates showed a relationship with PDSI data with 

an adjusted R2 of 0.446. This apparent weak explanatory ability is probably the result of 

some combination of: (1) mule deer demographics responding to other confounding factors 

such as predation, habitat alterations or succession, or sport harvest, (2) climatic variation 

not driving population declines across Arizona, (3) other climatic variables, such as temperature, having a greater influence than precipitation or PDSI, ( 4) demographic parameters responding to a combination of climatic factors in addition to those I evaluated either 

directly or through vegetative influences (nutrition or cover), or (5) survey data is not 

accurately representative of the population. However, the explanation of 40-50% of the 

variation in statewide mule deer population numbers does suggest that climatic variables do 

have a strong influence in determining deer numbers throughout the state. 

Key words: Arizona, climate, mule deer, Odocoi!eus hemionus, Palmer Drought Severity 

Index, precipitation. 

Email: Brian F. Wakeling, bwakeling@gf.state.az.us 

73 
74 MuLE DEER AND CLIMATE 

INTRODUCTION 

Mule deer in Arizona have suffered population declines during the past two 

decades (Arizona Game and Fish Department, unpub. data). Resource managers 

have attributed much of this decline to either ultimate or proximate causal agents. In 

fact, the decline in mule deer numbers has occurred across the West, and the Western 

Association of Fish and Wildlife Agencies (\VAFWA) established an ad hoc committee in June 1998 specifically to examine this phenomenon. At their initial meeting, 

this committee identified climatic changes as one of eight factors (climatic changes, 

habitat alterations or succession, nutrition, disease, competition, predation, sport 

harvest, or urbanization) that likely has had substantial influence on mule deer populations across the West (\VAFWA Mule Deer Ad Hoc Committee, unpub. data). 

Connolly (1981a, b) identified similar putative causes for a West-wide decline in the 

1960s and 1970s. 

Various climatic factors influence ungulate populations. Precipitation in desert 

regions may influence mule deer productivity (Leopold and Krausman 1991, Smith 

and LeCount 1979) and movements (Kucera 1992, Rautenstrauch and Krausman 

1989). Low temperature may influence habitat occupation, as certain habitats provide 

needed thermal cover (Parker and Gillingham 1990). High temperatures influence 

mule deer during summer, and deer respond by altering activity patterns (Leopold 

and Krausman 1987). Yet the impact of many climatic variables on ungulates is 

realized through vegetative influences on nutrition and carrying capacity (Feldhamer 

et al. 1989, Langvatn et al. 1996, Leopold and Krausman 1991, Smith and LeCount 

1979). Winter snow accumulations can also dramatically influence survival (Mech et 

al.1987,Picton 1984). 

Because climate has the potential to influence mule deer populations, I studied 

climatic relationships in regard to demographic parameters measured routinely by the 

Arizona Game and Fish Department (AGFD). My objective was to compare mule 

deer demographic components with monthly precipitation data and Palmer Drought 

Severity Index (PDSI) (Palmer 1965), in an attempt to determine if these climatic 

variables had an influence on Arizona's mule deer population. 

METHODS 

The AGFD conducts winter deer surveys from ground (foot, vehicle, or horseback) or air (helicopter or fixed-wing) during winter (months of December and 

January). During these surveys, observers record the number of observed male, 

female, and young. Population estimates are then derived using annual buck:doe 

ratios (BDR), fawn:doe ratios (FDR), harvest estimates from mail out questionnaires, estimates of mean annual non-hunt mortality based on change-in-ratio estimates, and an initial estimate of the population size (Fig. 1). Sampling efforts were 

not equal among years. Consequently, I did not search for relationships in non-ratio 

data (raw counts) despite acknowledged inherent problems with ratio data (Atchley 

et al. 197 6, Packard and Boardman 1988). I was also unable to separate survey data by 

technique. 
WAKELING 75 

Big Game Population Model 

Survey buck:doe 

Survey fawn:doe j Byyear 

INPUT- Harvest (B, D) 

Mortality rates (B, D, F) 

Initial population estimate (B, D) 

I 

Survey fawn:doe 

B:D minus J 

survey B:D I ADULTS AT SURVEY I I FAWNS AT SURVEY I 

i I BUCKS DOES 

I MORTALITY I 

CORE HMORTALITYI I BUCKS I DOES I 

I PRE-HUNT ADULTS I 

I BUCKS DOES I 

I HARVEST : 

I BUCKS I DOES I 

I POST-HUNT ADULTS I 

I BUCKS DOES I 

Year = Year + 1 

I 

Pre-hunt population (B, D) 

OUTPUT- Post-hunt population j Byyear (B, D, F) 

Mortality totals (B, D, F) 

Figure 1. Conceptual framework of the Arizona Game and Fish Department model 

for estimating mule deer population numbers in Arizona. 

I examined relationships between mule deer demographic parameters using 

data from the AGFD, and climatic variables from the National Oceanic and Atmospheric Adrrllnistration data base. Specifically, I calculated FDR and BDR from AGFD 

winter surveys from 1957 to 1996. Monthly precipitation and Palmer Drought 

Severity Index (PDSI) data were taken from the most central and representative 

weather station of each individual GMU. I also included monthly weather data, 

including two years prior to mule deer surveys, to examine lag effects on populations. Because GMU shave been changed over the years, all surveys were pooled to 

the largest common unit (i.e., GMUs 1-10,12,13,15-24, 27-45). I used these data in 

an exploratory, forward step-wise, multiple linear regression (P to enter = 0.05, P to 

remove = 0.1 0) analysis to determine which climatic factors best predicted mule deer 

demographic parameters and population responses. 
76 MuLE DEER AND CLIMATE 

Cluster analysis was used to group similar GMU s based on proportions of 

habitat associations (Brown et al. 1979). I then pooled mule deer observations 

across similar GMU s and recalculated FDR and BDR. Climatic data were averaged 

across similar GMU s. Each pooled group of GMU s with similar habitat was then 

reanalyzed using forward step-wise, multiple linear regressions. 

Finally, I used statewide mule deer population estimates from 1970-1996 and 

climatic factors to examine larger scale relationships. For statewide climatic data, I 

averaged climatic data across the state. Again, I used forward step-wise multiple 

linear regression to evaluate this relationship. Because these analyses were exploratory 

in nature, I consciously ignored the potential for autocorrelation among climatic 

variables. 

RESULTS 

Monthly precipitation and PDSI values explained little of the variation in FDR 

and BDR data (Table 1). In addition, I found little consistency among GMUs in 

climatic factors that explained variation in demographic parameters. Seven of 3 7 

GMU s demonstrated no relationship with FDR and weather variables. Adjusted R2 

on FDR models ranged from 0.148-0.562, although 22 models had adjusted R2<0.350. 

Thirteen of 37 GMUs demonstrated no relationship with BDR and weather variables. Adjusted R2 on BDR models ranged from 0.096-0.520, although 19 models 

had adjusted R2<0.350 (Table 1). 

Cluster analysis grouped GMUs into 5 categories: (1) montane conifer GMUs 

(1-14, 19, 23, 25-27), (2) Mohave desertscrub GMU (15), (3) chaparral and desertscrub 

GMUs (17, 18, 20-22, 24, 37), (4) grassland-woodland GMUs (28-36), and (5) 

desertscrub GMU s (16, 38-46) (Figs. 2 and 3). Multiple linear relationships from the 

analysis of pooled GMU s yielded dissimilar relationships among categories, with 

relatively low adjusted R2 values. These relationships were generally dissimilar from 

many of the individual GMU s within each category (Table 2). The statewide mule 

deer population estimate was best described by greater PDSI values in the September 

before surveys and greater PDSI values in the October 2 years before surveys. The 

adjusted R2 value for this relationship was 0.446. 

DISCUSSION 

Relatively little variation in mule deer population parameters measured by AGFD 

was explained by multiple linear relationships with monthly precipitation or PDSI 

values. Several possible explanations exist for this lack of explanation: (1) mule deer 

demographics are responding to other confounding factors such as predation, habitat alterations, or sport harvest, (2) climatic variation is not driving population declines across Arizona, (3) other climatic variables, such as temperature, might have a 

greater influence than precipitation or PDSI, ( 4) demographic parameters might respond to a combination of climatic factors in addition to those that I evaluated, 

either directly or through vegetative influences (e.g., nutrition or cover), or (5) survey 

data does not accurately represent the true mule deer population numbers. 
Table 1. Significant climatic variables and adjusted f?2 values for multiple linear regression equations explaining fawn:doe ratios (FOR) and 

buck: doe ratios (BDR) in each Arizona Game Management Unit (GMU). 

GMU 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

12 

13 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

3 7-3 8 

39-4 0 

41 

42 

43-44-45 

FD R a 

No relationship 

-Jul PDSI 

+Jul PDSI, -Aug PDSI 

-P Jun ppt 

-Feb ppt, -Sep ppt, +P Jul ppt 

+Jan PDSI, -Aug ppt, -Feb ppt, +PP Jan ppt 

No relationship 

+Jul ppt, +P Jul ppt, +Nov PDSI, -P Dec ppt, -Mar ppt, -P Jun ppt 

+Nov ppt, +P Apr ppt, +P May ppt 

-Feb ppt, -P Feb PDSI 

+ P Dec P D S I, -Jan P D S I 

+Dec ppt, +P Apr ppt 

-P Jan ppt 

+Nov PDSI, -P Jan ppt 

+Nov PDSI 

+P Jun ppt, -Mar PDSI 

+Jul ppt 

+Jul ppt 

+Jul PDSI, -May PDSI 

+Oct ppt, +P Dec ppt 

+Sep ppt, +P Jun ppt, +P Jan PDSI, -Aug PDSI 

+Mar ppt, +P Apr ppt, +Jan PDSI, -Apr ppt, -May ppt, -P Aug ppt 

+Jul PDSI, +P Jul PDSI, -P Aug PDSI 

+Dec ppt, +P Apr ppt 

No relationship 

No relationship 

+Oct ppt, -P Apr PDSI 

+Jan PDSI, -Aug ppt, -P Aug PDSI 

No relationship 

No relationship 

+Aug ppt, +Sep PDSI 

-Oct ppt, -Apr ppt, -P Nov ppt, -P Apr ppt, -P Mar ppt 

+Oct ppt, -Jun ppt, -P Jul ppt, -P Apr ppt 

No relationship 

-Jan ppt, -P Sep ppt, -P Feb PDSI 

+Nov ppt, -P Mar PDSI 

-PMayPDSI 

0.2 3 5 

0.224 

0.1 4 8 

0.36 0 

0.3 82 

0.562 

0.2 76 

0.31 9 

0.246 

0.3 7 5 

0.1 71 

0.342 

0.1 71 

0.2 3 5 

0.1 36 

0.0 8 3 

0.2 71 

0.21 7 

0.3 92 

0.5 54 

0.2 9 7 

0.2 7 8 

0.1 53 

0.32 9 

0.2 59 

0.44 7 

0.3 7 0 

0.341 

0.1 84 

0.1 59 

BD R" 

+Oct ppt, +P Oct ppt., -P Oct PDSI 

-Jan ppt, -P Jan ppt 

-PP Jan PDSI 

No relationship 

-Feb PDSI 

+P Aug ppt, -May PDSI 

+P Apr ppt, -Mar ppt, +P Aug PDSI 

-P Aug PDSI 

-Aug P D S I 

No relationship 

-P Jul PDSI 

-Dec ppt, -P Oct PDSI 

+Jan ppt, +Feb ppt, +P Nov ppt, -Apr PDSI 

+P Sep ppt 

No relationship 

+P Dec ppt, -P Apr ppt, -May PDSI 

-Feb ppt 

No relationship 

+Jul PDSI, -P Dec ppt 

-P Jul PDSI 

No relationship 

No relationship 

+P Oct ppt, +P Nov ppt, -Jul PDSI 

No relationship 

+P Sep ppt 

-May ppt 

No relationship 

No relationship 

-P AprPDSI 

-Jan P D S I 

No relationship 

-Apr PDSI 

+P Sep ppt 

No relationship 

No relationship 

No relationship 

+P Sep PDSI 

0.3 9 5 

0.3 92 

0.1 0 8 

0.164 

0.244 

0.2 9 9 

0.2 55 

0.2 36 

0.116 

0.2 9 7 

0.52 0 

0.2 52 

0.31 9 

0.1 50 

0.3 7 2 

0.1 8 3 

0.464 

0.1 3 5 

0.126 

0.2 0 5 

0.1 2 7 

0.2 54 

0.1 3 3 

0.0 96 

a Abbreviations: ppt refers to precipitation, PDSI refers to Palmer Drought Severity Index, +refers to positive effect of factor, -refers to negative effect offactor, no modifier on month refers to data from during or 

immediately preceding the survey, P as a modifier on month refers to the year prior to the survey, and PP as a modifier on month refers to 2 years prior to the survey. 
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W :IKELI NG 79 

Kilometers 

50 100 

Figure 3. Arizona map indicating Game Management Units, pooled by cluster analysis, 

used in the evaluation of relationships among mule deer ratio and climatic data. 

Mule deer populations, like most wildlife, probably respond both directly and 

indirectly to many climatic factors, although developing consistent predictive relationships across their occupied range is virtually impossible. However, PDSI may be 

an important factor influencing statewide mule deer populations. PDSI values can 

be more indicative of favorable conditions for forage growth and development of 

suitable fawn hiding cover. Precipitation may be favorable in some habitats, such as 

the warmer portion of the state, whereas it may be detrimental where snow accumu-
80 MuLE DEER AND CLIMATE 

Table 2. Significant climatic variables and adjusted f?2 values for multiple linear 

regression equations explaining fawn:doe ratios (FOR) and buck:doe ratios (BDR) 

across similar Game Management Units in Arizona. 

Category FORa R2 BORa R2 

Montane -Feb ppt, -Nov PDSI 0.312 -Jul PDSI, -P Jun PDSI 0.292 

conifer 

Mohave -P Jan ppt 0.171 +Jan ppt, +Feb ppt, 0.520 

desertscrub +P Nov ppt, -Apr PDSI 

Chaparral and -PP Jan PDSI 0.083 No relationship 

desertscrub 

Grassland- +Dec PDSI, -P Jun PDSI 0.210 -Aug PDSI 0.115 

Woodland 

Desertscrub +Nov ppt, +Mar ppt, 0.410 +P Sep ppt 0.092 

-P Feb PDSI, -P Jan PDSI 

a Abbreviations: ppt refers to precipitation, PDSI refers to Palmer Drought Severity Index, +refers to positive effect of factor, 

-refers to negative effect of factor, no modifier on month refers to data from during or immediately preceding the survey, P 

as a modifier on month refers to the year prior to the survey, and PP as a modifier on month refers to 2 years prior to the survey. 

lations adversely impact mule deer populations. Rainfall in a given habitat is known 

to induce migration (Kucera 1992), whereas humidity may be the factor that induces 

movements in another habitat (McCullough 1964). In yet another ungulate species, 

increased rainfall can correlate with decreased population density (Latham et al. 1997). 

Each of these factors may be useful in understanding wildlife habitat relationships 

within a given community, however when applied to habitats beyond where the 

relationships were discovered, the relationships may be spurious. However, PDSI 

values may be better suited to indexing statewide populations. 

Environmental relationships tend to be complex, although simple models may 

approximate our understanding of animal-environment relationships. Climatic variables, in addition to those I used in my evaluation, influence vegetative development, succession, nutritive quality, and cover components of the habitat (Singer et al. 

1997, Post and Stenseth 1999). Mule deer densities in themselves have the potential 

to influence survival of young (Clutton-Brock et al. 1987). Prey densities influence 

predator densities, and ultimately habitat structure that both occupy. Neither habitat 

nor climate may be succinctly represented in a single, concise variable. 

"When examining population responses to climate, winter FDR may be a better 

variable than BDR because winter surveys occur after fall hunting seasons. Sport 

harvests influence BDR and may obscure, or be difficult to separate from, climatic 

effects. Yet, even with FDR, GMU s within each pooled habitat category did not 

exhibit consistent relationships among themselves or with pooled data sets. 
WAKELING 81 

Desertscrub habitats were favorably influenced by winter precipitation, probably for 

reasons elucidated by Smith and LeCount (1979). Similarly, decreased drought conditions and increased precipitation should create favorable forage and hiding cover 

within grassland-woodland and chaparral and desertscrub habitats. Conversely, explanations supporting the negative influence of winter precipitation in Mohave 

desertscrub are difficult to develop. Montane conifer habitats were negatively influenced by winter precipitation, presumably as a result of snow accumulations and 

resulting physiological stress. The superior fit of the regression models to the 

desertscrub and montane conifer habitats suggests that climate is more influential in 

determining FDRs within these potentially more extreme Arizona habitats. 

Mule deer survey data may in itself be problematic, in that small sample sizes are 

not uncommon within GMU s, and misclassification of sex and age classes can substantially alter estimated ratios. The probability of misclassification can increase with 

observer inexperience, survey speed, distance, and inclement weather. However, 

explaining 40-50% of the variation within mule deer population estimates, using 

measured climatic variables, may be adequate for large-scale modeling. This is particulary 

true if climate is proven to have the largest influence on mule deer populations. 

Predation, habitat structure, and relation to carrying capacity may be unable to explain 

as much of the variation in Arizona's mule deer population as does the climatic 

variables that were analyzed in this paper. 

AcKNOWLEDGEMENTs 

This research was funded by the Federal Aid in Wildlife Restoration Project W78-R through the Arizona Game and Fish Department. I am grateful to both the 

WAFWA Ad Hoc Mule Deer Committee and an informal local steering committee 

for direction on this exercise. M.J. Rabe conducted the cluster analysis for grouping 

the GMUs. S.R. Boe provided GIS support. R.M. Lee provided important, thoughtprovoking discussions in the development of this paper. The manuscript benefited 

from the reviews from D. Mattson and an anonymous referee. 

LITERATURE CITED 

Atchley, W. R., C. T. Gaskins, and D. Anderson. 1976. Statistical properties of ratios. I. 

Empirical results. Systematic Zoology 25: 137-148. 

Brown, D. E., C. H. Lowe, and C. P. Pase. 1979. A digitized classification system of 

the biotic communities of North America, with community (series) and association 

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Herpetofauna Survey of 

Petrified Forest National Park, Arizona 

Charles A. Drost 

Trevor B. Persons 

and 

Erika M. Nowak 

USGS Forest and Rangeland Eco.rystem Science Center 

Colorado Plateau Field Station 

Box 5614, Northern Arizona University 

Flagstqff,AZ 86011 

Abstract. We present the results of an amphibian and reptile inventory conducted in 

1997-1998 at Petrified Forest National Park. Using visual encounter surveys, pitfall 

trapping, artificial cover boards, and night driving techniques, we recorded 1,628 individual amphibians and reptiles (exclusive of larval amphibians) of 23 species. The species 

total includes seven amphibians, nine lizards, and seven snakes. Two of the lizard species 

(both of them whiptaillizards, genus Cnemidophorus) are new to the park. Small populations of the little striped whiptail are the first records for Petrified Forest, and also the first 

reported occurrences in Navajo and Apache counties. The New Mexico whiptail (probably 

introduced) is the first report for the state of Arizona. Petrified Forest National Park has 

a diverse herpetofauna, with species from varied biogeographic regions (Great Plains, 

Great Basin, and southern deserts and grasslands) occurring together in the park. The park 

is one of the few remaining large protected areas of grassland habitat on the southern 

Colorado Plateau, and supports regionally rare grassland amphibians and reptiles. 

Key words: amphibians, reptiles, Petrified Forest National Park, Arizona, Little 

Colorado River basin, grasslands, inventory, habitat associations, biogeography. 

Email: Charles Drost, Charles.Drost@nau.edu 

83 
84 HERPETOFAUNA OF PETRIFIED FoREST NATIONAL PARK 

INTRODUCTION 

Accurate inventory data, including information on the occurrence of plant and 

animal species, their distribution, abundance, and habitat relationships, is one of the 

essential starting points for informed protection and management of natural resources. In spite of long-standing policy (e.g. NPS-75; National Park Service 1992) 

and discussion on the importance of resource inventory, many National Park Service 

areas remain seriously deficient in even basic inventory data (e.g. Stohlgren and Quinn 

1992). The need for baseline natural resource data is especially acute in areas and 

habitats that have been extensively disturbed, as is the case with grassland habitats on 

the southern Colorado Plateau (Kearney and Peebles 1960, Lowe 1964, Brown 1994). 

Despite protecting a large area of native grassland and other habitats important 

to amphibians and reptiles, Petrified Forest National Park has never had a herpetofauna 

inventory. The grassland and grass-shrub habitats of the Petrified Forest area are 

known to support regionally rare species, such as Couch's spadefoot (S caphiopus 

couchit) and the milk snake (Lamprope!tis triangulum) (Stebbins 1985). For the southem Colorado Plateau region, there have been no previous intensive studies of grassland amphibian and reptile assemblages. The few published reports on amphibians 

and reptiles in the region only document species occurrence within a general area (e.g. 

Eaton 1935, Harris 1963). 

The goal of this study was to conduct a complete species inventory (after Scott 

1994) of amphibians and reptiles at Petrified Forest, compare sampling methods in 

grassland and grass/ shrub habitats, and describe the herpetofauna in terms of relative abundance, habitat associations, and biogeography. In addition to providing 

inventory data to park resource managers, this study is important for its contribution 

to understanding the distribution and ecology of amphibians and reptiles in the 

region. Because Petrified Forest is both geologically and floristically characteristic of a 

large portion of the southern Colorado Plateau, and in particular of the Little Colorado River basin of northeastern Arizona, results of this study contribute to understanding the fauna of the wider geographic region. 

METHODS 

Study Area 

Petrified Forest National Park is located on the south-central Colorado Plateau, 

straddling the border of Navajo and Apache counties east of Holbrook, Arizona 

(Fig. 1). The park encompasses 38,133 ha, and lies within the Litde Colorado River 

basin. Elevations vary from 1,617 m along the Puerco River, which bisects the park, 

to 1,900 mat the summit of Pilot Rock in the extreme northwestern comer of the 

park (USGS 1981, 1982). Topographically, the park ranges from rolling, sandy grasslands to mesas and extensively eroded badlands. 

Petrified Forest has warm summers and moderately cold winters. Average summer Quly) high temperature is 33.6°C, with overnight lows averaging 15.6°C. In 

January, average daytime high temperature is 8.4°C, with an average low of -6.6°C. 

Mean annual precipitation is 24.4 em, much of it coming in the form of monsoon 
N 

A 

NRoads 

0 Park Boundary 

• Puerco Site 

• Prairie Dog Site 

• Jasper Forest Site 

... Cottonwood Wash Site 

• lnornatus Site 

... Route 66 Site 

• Sage Site 

2--0--2-•4 Kilomr-e-te_rs __ __. 

• 

• 

DROST ET :IL. 85 

Location of Petrified Forest 

National Park in Arizona 

Figure 1. Outline map of Petrified Forest National Park, showing major roads and 

location of sampling sites during a herpetological inventory from May 1997- September 

1998. 

thunderstorms in July-September. Spring and early summer are dry, and often 

extremely windy. 

Petrified Forest lies within the Plains and Great Basin Grassland biome of 

Brown and Lowe (1980). Park vegetation is a complex interdigitation of arid grasslands, grass-shrublands, dune communities, and badlands and other barren habitats . .Areas of juniper woodland and riparian trees and shrubs are limited in area, but 

add significantly to the park's biological diversity. For analysis of amphibian and 

reptile distribution, we recognized a number of broad vegetation types: grasslands, 

shrub communities (primarily shrub-grasslands), dune communities, riparian com-
86 HERPETOFAUNA OF PETRIFIED FoREST NATIONAL PARK 

munities (Puerco River), and juniper woodland. Dominant and characteristic perennial plant species of these habitat types are described in Appendix 1. Two additional 

habitats for amphibians and reptiles - rocky areas (including rock outcrops and 

boulder fields) and developed areas- are not based on vegetation associations, but 

may support distinctive associations of amphibians and reptiles. We do not cover in 

detail badlands, desert pavement, and saline flats because of their limited amphibian 

and reptile fauna. 

Field Methods 

We used a variety of field survey methods at Petrified Forest during the spring, 

summer, and early fall, when amphibians and reptiles are most active. Sampling was 

initiated in July 1997 and completed in September 1998. We trapped amphibians 

and reptiles using four-liter (one gallon) pitfall traps (Fellers and Drost 1991 ), 19-liter 

(five gallon) pitfall arrays with drift fences (Campbell and Christman 1982,Jones 

1986, Corn 1994), and artificial cover boards (Fellers and Drost 1994). We placed 

combinations of traps and cover boards at seven sites located in different habitats 

throughout the Park. A typical installation consisted of an array of four 19-1 pitfall 

traps arranged in a 'Y' shape, with a metal flashing drift fence connecting the four 

traps Qones 1986). TheY-array was paired with a 135m transect of five 4-1 pitfall 

traps, alternating with five cover boards, each measuring 60 em x 120 em x 2 em (%-

inch plywood). Spacing between traps and boards was 15m. We sampled trap sites 

twice a month. During each session, pitfalls and arrays were open (lids removed) for 

four consecutive nights, and checked at least every other day. Cover boards were 

checked once per sampling session. 

We conducted night driving surveys (11endelson and Jennings 1992, Rosen and 

Lowe 1994, Shaffer and Juterbock 1994) on the main park road, which runs the entire 

length of the Park from north to south (Fig. 1). Surveys generally consisted of 

driving the length of this road ( 45 km one way), but occasionally included other 

paved and unpaved roads. Start time of surveys varied from official sunset to about 

10 p.m., with most surveys completed by 11 p.m. We completed at least eight night 

surveys each month, except in the early and late season, when amphibian and reptile 

activity was low. 

Visual encounter surveys (VES; Crump and Scott 1994) were primarily used to 

survey for diurnal lizard species. We selected areas for these surveys that would 

sample the range ofhabitats and geographic regions within the Park, with particular 

attention being given to searching areas not sampled by other methods. For example, we did not extensively trap sites or conduct night drives in much of the 

northern section of the Park because of its remoteness as part of a large wilderness 

area. Time-constrained searches and general surveys comprised our VES. Timeconstrained searches covered relatively small, predefined areas in single habitat types, 

each of which was searched for a period of about an hour. General surveys typically 

covered larger areas, were not restricted to single habitat types, and were not timelimited. These surveys were conducted in areas away from roads and away from our 
DROST ET AL. 87 

main sampling areas. All areas covered by visual encounter surveys were recorded on 

1 :24,000 scale maps, along with data on time, habitat, and species. 

Besides these specific sampling methods, we also recorded data from general 

field observations, which included turning natural and artificial cover, animals seen 

on the park roads during the day, and observations of amphibians and reptiles 

during the course of other fieldwork. We also solicited observations from personnel 

working in the park, including National Park Service staff and other field researchers. 

RESULTS 

Sampling Effort 

Distribution of sampling effort varied by month for the different sampling 

techniques (Table 1 ). Sampling effort for pitfall and array traps is measured in trapdays ((number of traps open) x (number of days they were open for)), and for 

artificial cover boards, number of times the boards were checked (i.e., turned to look 

for animals underneath). Sampling effort totaled 1,811 trap-days for 4-1 pitfalls, 

1,657 trap-days for 19-1 array traps with drift fences, and 915 checks of artificial cover 

boards. 

We spent 135 hours conducting visual encounter surveys, including general 

surveys and time-constrained searches (TCS). During the first year, we concentrated 

on general surveys of large areas of the park on foot, to provide a broad overview of 

the geographic extent of Petrified Forest National Park, and its range of habitats. 

Some of the more remote areas sampled by general surveys included Digger Wash 

and Pilot Rock in the northwest corner of the park, the area north of old Route 66 on 

the northeast park boundary, the Blue Mesa area along the park's eastern boundary, 

and the south Petrified Forest Wilderness area in the southeast corner of the park. 

Table 1. Monthly sampling effort for a survey of amphibians and reptiles at Petrified 

Forest National Park, Arizona, between July 1997 and September 1998. Sampling 

effort for pitfall and drift fence arrays is recorded as trap-days, and sampling effort 

for cover boards is measured as number of times the boards were checked (see 

text). Effort for visual encounter surveys, including time-constrained searches (TCS) 

and general surveys, is measured in hours. Effort for night driving is number of km 

driven. 

Jul Aug Sep Oct May Jun Jul Aug Sep 

97 97 97 97 98 98 98 98 98 

Pitfall traps 0 55 199 0 320 320 319 469 159 

Drift fence arrays 0 60 199 0 288 284 285 421 144 

Cover boards 0 0 30 10 145 160 240 210 120 

TCS 0.0 0.0 0.0 0.0 4.5 12.7 5.0 8.5 0.0 

General survey 13.9 17.7 5.6 0.4 6.3 24.4 14.0 8.4 14.2 

Night driving 222 968 1,022 142 87 713 628 1 '161 625 
88 HERPETOFAUNA OF PETRIFIED FoREST NATIONAL PARK 

TCS were initiated in the second year of the project. Just over 30 hours of timeconstrained searches were conducted in 1998. 

We began night drive surveys in July 1997, with a total of 2,354 km driven in 

1997, mostly along the main north-south park road. We drove a total of 3,214 km 

from May through September of 1998, primarily along the main park road. Over the 

2-year study period, total road survey sampling amounted to 5,567 km. 

Amphibian and Reptile Diversity 

We sighted or captured 1,628 individual amphibians and reptiles during this 

study, of 23 species. These included seven amphibians (one salamander and six 

spadefoot and true toads), nine lizards, and seven snakes (Table 2; nomenclature 

follows the federal interagency Integrated Taxonomic Information System or ITIS; 

see http:/ /www.itis.usda.gov/access.html). We found all but one of these 23 speTable 2. Amphibians and Reptiles of Petrified Forest National Park, based on an 

inventory study conducted in 1997 and 1998. Names follow ITIS (see text). Some 

familiar names have been changed recently, including: Pituophis melanoleucus 

changed to P catenifer, and Scaphiopus multiplicatus and Scaphiopus bombifrons 

changed to Spea multiplicata and Spea bombifrons. 

AMPHIBIANS 

Salamanders 

Tiger Salamander (Ambystoma tigrinum) 

Frogs and Toads 

Couch's Spadefoot ( Scaphiopus couch it) 

Plains Spadefoot ( Spea bombifrons) 

Southern Spadefoot (Spea multiplicata) 

Great Plains Toad (Bufo cognatus) 

Red-spotted Toad (Bufo punctatus) 

Woodhouse's Toad (Bufo woodhousit) 

REPTILES 

Lizards 

Collared Lizard (Crotaphytus collaris) 

Lesser Earless Lizard (Holbrookia maculata) 

Short-horned Lizard (Phrynosoma douglasit) 

Sagebrush Lizard (Sceloporus graciosus) 

Eastern Fence Lizard (Sceloporus undulatus) 

Side-blotched Lizard (Uta stansburiana) 

Little Striped Whiptail (Cnemidophorus inomatus) 

New Mexico Whiptail (Cnemidophorus neomexicanus) 

Plateau Striped Whiptail (Cnemidophorus velox) 

Snakes 

Glossy Snake (Arizona elegans) 

Night Snake (Hypsiglena torquata) 

Common Kingsnake (Lampropeltis getula) 

Milk Snake (Lampropeltis triangulum) 

Striped Whipsnake (Masticophis taeniatus) 

Gopher Snake (Pituophis catenifer) 

Western Rattlesnake (Crotalus viridis) 
DROST ET :IL. 89 

cies in the first month and a half of field work, by mid-August 1997. The last species 

found was the New Mexico whiptail (Cnemidophorus neomexicanus), which has a very 

restricted distribution in the park. This species was first discovered early in the second 

year, in mid-June 1998. The little striped whip tail ( Cnemidophorus inornatus) and the 

New Mexico whiptail represent first reports of these species in the Petrified Forest 

region, and the occurrence of the New Mexico whip tail at Petrified Forest is the first 

record of that species anywhere in the state of Arizona (Persons and Wright 1999a; 

Fig. 2). 

Comparison of Methods 

The different field techniques used in this study varied widely in their effectiveFigure 2. Cnemidophorus inornatus (above), and C. neomexicanus (below), two 

additions to the known herpetofauna of Petrified Forest National Park and the 

surrounding region (photos by T. B. Persons). 
90 HERPETOFAUNA OF PETRIFIED FoREST NATIONAL PARK 

ness in sampling different species of amphibians and reptiles (fable 3). In general, 

lizards were most effectively sampled by pitfall and array trapping and cover boards, 

and by visual encounter surveys (time-constrained searches and general surveys). 

Moderate numbers of some amphibian species were captured in array traps, though 

the highest numbers of all species were recorded during night driving surveys. Except for the striped whipsnake (Masticophis taeniatus), nearly all of the snakes were 

Table 3. Comparison of capture totals of different amphibian and reptile sampling 

methods at Petrified Forest National Park in 1997 and 1998. Methods and sampling 

effort are as listed in Table 1. Number of species and total number of individuals 

accounted for with each sampling method are listed for amphibians, lizards, and 

snakes. Animals not identified to species (e.g., Spea sp.) are not listed in the species 

total, unless they were the only members of that taxon recorded. 

Pitfall Array Cover TCS General Night 

Tiger Salamander 0 1 0 0 0 5 

Couch's Spadefoot 0 1 0 0 0 24 

Plains Spadefoot 1 14 0 0 0 53 

New Mexico Spadefoot 0 30 0 0 1 191 

Spadefoot, Spea species 0 8 0 2 7 1 

Great Plains Toad 0 8 1 0 2 70 

Red-spotted Toad 0 0 0 0 0 12 

Woodhouse's Toad 0 0 0 0 0 3 

Amphibian species: 1 5 1 1 2 7 

Total amphibians: 1 62 1 2 10 359 

Collared Lizard 2 5 8 9 23 2 

Lesser Earless Lizard 0 7 3 4 22 4 

Short-horned Lizard 0 0 3 1 2 2 

Sagebrush Lizard 31 45 41 1 19 0 

Eastern Fence Lizard 3 9 8 30 125 0 

Fence Lizards, Sce/oporus sp. 0 0 2 1 15 0 

Side-blotched Lizard 1 1 7 15 8 0 

Little Striped Whiptail 0 0 0 0 11 0 

New Mexico Whiptail 0 0 0 0 5 0 

Plateau Striped Whiptail 26 28 2 32 186 0 

Whiptails, Cnemidophorus sp. 0 0 0 8 8 0 

Lizard species: 5 6 7 7 9 3 

Total lizards: 63 95 74 101 424 8 

Glossy Snake 0 0 0 0 0 15 

Night Snake 0 0 0 0 0 33 

Common Kingsnake 0 0 0 1 2 6 

Milk Snake 0 0 0 0 0 7 

Striped Whipsnake 0 1 0 0 2 2 

Gopher Snake 0 0 0 1 2 47 

Western Rattlesnake 0 0 0 0 0 28 

Snake species: 0 1 0 2 3 7 

Total snakes: 0 1 0 2 6 138 

Total species: 6 13 9 12 17 18 

Total numbers: 64 158 75 105 440 505 
DROST ET AL. 91 

found during night driving surveys. Species recorded only during night driving 

surveys included the red-spotted (Btifo punctatus) and Woodhouse's toad (B. woodhousit), 

and glossy snake (Arizona elegans), night snake (Hypsiglena torquata), and milk snake 

(Lampropeltis triangulum). No other species were detected with only one sampling 

method, except for the isolated populations of little striped whiptail and New Mexico 

whiptail, which were only encountered during general surveys. 

Small ( 4-1) pitfall traps, drift fence arrays with large (19 1) pitfall traps, and 

artificial cover boards were all set out in the same areas, and generally sampled the 

same fauna. The arrays were much more effective in sampling amphibians, however 

(Table 3), presumably because the drift fences intercepted individuals moving from 

place to place. Artificial cover boards were about as effective as array traps for lizards, 

capturing as many or more individuals of all species except plateau striped whiptail, 

and recording one species not captured in the arrays (short-horned lizard, Phrynosoma 

douglasit). For approximately equal effort, the 4-1 pitfall traps were less effective than 

arrays or cover boards, capturing fewer species and fewer individuals. 

Habitat and abundance 

Table 4lists capture data for the most common species captured at each of the 

pitfall/ array/ coverboard sampling sites at Petrified Forest, and Figure 3 shows "capture" (i.e., encounter) rates for the most common species recorded on visual encounter surveys (combined time-constrained searches and general surveys). Both data 

sets are grouped into habitats as grassland, shrubland, sand/ shrub habitat, rock 

habitat (rocky cliffs and slopes, boulder fields), and riparian habitat. Capture rates 

varied among the different grassland sampling sites, probably due to wide variation 

in height and density of grass in the different areas. Jasper Forest and the Prairie Dog 

Table 4. Capture rates of amphibians and reptiles at pitfall and drift fence array sites 

at Petrified Forest National Park, Arizona, in 1997-98. Rates are captures per 1,000 

trap-nights (see text). Site codes are: COWA-Cottonwood Wash; INOR-Inornatus; 

JAFO-Jasper Forest; PRDO-Prairie Dog; PUER-Puerco River; RT66-Route 66; and 

SAGE-Sagebrush. Habitat at the site is in parentheses below the site code. For 

some sites, a secondary habitat is listed after the first, "main" habitat (e.g. Grass/ 

Rock). 

JAFO PRDO RT66 SAGE COWA I NOR PUER 

(Grass/Rock) (Short grass) (Tall grass) (Shrub) (Sand/Shrub) (Rock) (Riparian) 

Plains Spadefoot 0.0 0.0 0.0 2.1 13.2 0.0 4.7 

Southern Spadefoot 0.0 5.6 8.3 1.0 17.6 2.8 33.0 

Collared Lizard 0.0 0.0 0.0 1.0 2.2 11.1 0.0 

Lesser Earless Lizard 0.0 16.7 2.8 0.0 0.0 0.0 0.0 

Sagebrush Lizard 0.0 5.6 0.0 48.5 28.7 2.8 0.0 

Eastern Fence Lizard 2.8 0.0 0.0 0.0 3.3 0.0 37.7 

Side-blotched Lizard 5.6 0.0 0.0 0.0 0.0 0.0 0.0 

Plateau Striped Whiptail 2.8 0.0 0.0 23.7 29.8 5.6 4.7 
92 HERPETOFAUNA OF PETRIFIED FoREST NATIONAL PARK 

site are relatively open areas of short grass, while the Route 66 site has much taller, 

denser grassland. In addition, Jasper Forest has scattered surface rocks, and the 

presence of extensive burrows at the Prairie Dog site may be important to existing 

amphibian and reptile populations. Among all of the grassland sites, lesser earless 

lizard (Holbrookia maculata) at the Prairie Dog site had the highest capture rate (Table 

4). For visual encounter surveys in grassland, the highest encounter rates were for 

plateau striped whiptail (Cnemidophorus velox), followed by lesser earless lizard (Fig. 3). 

At the Sagebrush sampling site, sagebrush lizards (S celoporus graciosus) had the 

highest capture rate, followed by plateau striped whiptails. The same two species 

were most numerous in visual encounter surveys in shrub habitats. The Cottonwood Wash sampling site, in deep sand with scattered shrubs in the southern part of 

the park, was the most productive trapping site at Petrified Forest. We captured four 

lizard species and two spadefoot toad species at the site, with nearly equal numbers 

of plateau striped whiptails and sagebrush lizards. In visual encounter surveys in 

old dune and other sand substrate habitats, plateau striped whiptails and lesser 

earless lizards were encountered most frequendy, and eastern fence lizards (S celoporus 

undulatus) were somewhat less common. 

Collared lizards were captured most often in the rock habitat sampling site, 

while eastern fence lizards were most numerous in visual encounter surveys in rock 

habitats, followed by plateau striped whiptails and collared lizards. At the riparian 

sampling site, eastern fence lizards and southern spadefoots (Spea multiplicata) were 

the most common captures, while plateau striped whiptails were numerous in visual 

encounter surveys in riparian habitat (the highest numbers of any species werecorded in any habitat), and eastern fence lizards were also common. 

DISCUSSION 

Sampling Effort 

We conducted fieldwork at Petrified Forest for one week every other week, so 

that sampling was spread uniformly across the active period for amphibians and 

reptiles, with approximately two sampling sessions per month. Except for the 

project start-up period, sampling effort was consistent across months (Table 1). 

Sampling effort for visual encounter surveys and night driving surveys was more 

variable, with effort concentrated during the times when observed amphibian and 

reptile activity was greatest (Table 5). In particular, night-time temperatures in May 

and June at Petrified Forest are relatively cold, and we observed very litde activity at 

night, so we concentrated night driving sampling effort in the warmer months, from 

July through September. This also corresponded to the general time period of 

monsoon rains at Petrified Forest, which markedly increased amphibian activity. 

Amphibian and Reptile Diversity 

Petrified Forest has moderate numbers of amphibian and reptile species, and is 

of particular interest for its grassland species. Noteworthy aspects of the fauna 

include three species of spadefoot toads: the plains spadefoot, which is near the 

western extent of its range in the Petrified Forest area; theN ew Mexico spadefoot; 
Cnemidophorus inornatus 

9.00 

8.00 

7.00 

6.00 

5.00 

4.00 

3.00 

2.00 

1.00 

0.00 

-

-

-

-

-

-

-

-

Grass Shrub Sand -Rock Riparian 

Cnemidophorus velox 

45.00 .----------------..., 

40.00 -j----------------

35.00 -j----------------

30.00 -j----------------

25.00 -j----------------

20.00 -j----------------

15.00 

10.00 

5.00 

0.00 

Grass Shrub Sand Rock Riparian 

Sceloporus graciosus 

4.00 .-------------------, 

3.50+-----

3.00-t----

2.50+-----

2.00-t----

1.50-t----

1.00+-----

0.50+-----

0.00+----....--

Grass Shrub Sand Rock Riparian 

DROST ET AL. 93 

Crotaphytus collaris 

45.00 .-------------------, 

40.00 +-------------

35.00 +-------------

30.00 +-------------

25.00 +-------------

20.00 +-------------

15.00 

10.00 

5.00 

0.00 

Grass Shrub Sand Rock Riparian 

Holbrookia maculata 

12.00 

10.00 r8.00 r6.00 r4.00 f2.00 r0.00 fGrass Shrub - Sand Rock Riparian 

Sceloporus undulatus 

20.00 .-------------------, 

18.00 +-------------

16.00 +----------

14.00 +----------

12.00 -j-------------

10.00 +----------

8.00 +----------

6.00 -j-------------

4.00 +-------

2.00 -t-==---

0.00 +---.____-,----

Grass Shrub Sand Rock Riparian 

Uta stansburiana 

6.00.-------------------, 

5.00 +-------------------1 

4.00 -j--------------

3.00 +------------

2.00 +------------

1.00 +------------

0.00 +------.-------.------,--

Grass Shrub Sand Rock Riparian 

Figure 3. Encounter rates, by habitat, of lizard species at Petrified Forest National 

Park, in visual encounter surveys conducted in 1997 and 1998. Numbers are 

individuals seen per 10 hours search time. 
94 HERPETOFAUNA OF PETRIFIED FoREST NATIONAL PARK 

Table 5. Night drive sampling of amphibians and reptiles at Petrified Forest National 

Park, by month and year, in 1997-1998. Distance driven varied among different 

months, so data have been adjusted to number of individuals per 1 ,000 km of driving. 

1997 1998 

SPECIES JUL AUG SEP OCT MAY JUN JUL AUG SEP 

Tiger salamander 0.0 1.0 1.0 7.0 0.0 0.0 1.6 0.9 0.0 

Total: 0.0 1.0 1.0 7.0 0.0 0.0 1.6 0.9 0.0 

Plains spadefoot 9.0 6.2 2.0 0.0 0.0 0.0 49.4 9.5 1.6 

Couch's spadefoot 0.0 2.1 1.0 0.0 0.0 0.0 27.1 3.4 0.0 

New Mexico spadefoot 99.1 10.3 10.8 21.1 0.0 0.0 189.5 12.9 17.6 

Great Plains toad 36.0 13.4 4.9 7.0 0.0 0.0 38.2 15.5 1.6 

Red-spotted toad 18.0 2.1 0.0 0.0 0.0 0.0 6.4 0.9 1.6 

Woodhouse's toad 0.0 0.0 1.0 0.0 0.0 0.0 1.6 0.9 0.0 

Total: 162.2 34.1 20.5 28.2 0.0 0.0 313.7 43.9 22.4 

Glossy snake 0.0 4.1 2.9 0.0 0.0 2.8 1.6 0.9 6.4 

Night snake 9.0 8.3 4.9 7.0 11.5 7.0 8.0 4.3 1.6 

Common kingsnake 0.0 3.1 0.0 0.0 0.0 0.0 0.0 0.9 3.2 

Milk snake 0.0 4.1 0.0 0.0 0.0 0.0 0.0 1.7 1.6 

Striped whipsnake 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9 1.6 

Gopher snake 4.5 8.3 14.7 28.2 0.0 0.0 1.6 5.2 19.2 

Western rattlesnake 9.0 6.2 7.8 14.1 0.0 1.4 1.6 5.2 3.2 

Total: 22.5 34.1 31.3 49.3 11.5 11.2 12.7 18.9 36.8 

and the Couch's spadefoot, which occurs as a disjunct population at Petrified Forest, 

well north of the main part of the species' range in Arizona. Also of note are three 

species of whiptaillizards. One of these (the little striped whiptail) is bisexual, while 

the other two (plateau striped whiptail and New Mexico whiptail) are unisexual and 

reproduce by parthenogenesis. Only the plateau striped whiptail was known from 

Petrified Forest or the northeastern Arizona area prior to our study. The little striped 

whip tail was probably once more widespread in the region, as it has declined in other 

parts of its range where grassland habitats have been lost or degraded (Bogan et al. 

1998, Wright and Lowe 1965, 1968). The two small populations that we found at 

Petrified Forest are the only ones known from Apache and Navajo Counties in 

northeastern Arizona, and are separated from the nearest other populations by a 

distance of approximately 107 km (Persons and Wright 1999b). 

The small population of New Mexico whiptails along the Puerco River floodplain within Petrified Forest is far west of the species' range along the Rio Grande 

drainage in New Mexico (Persons and Wright 1999a). Skin-grafting studies indicate 

that the lizards at Petrified Forest are genetically identical to populations in New 

Mexico (Persons and Wright, in prep.). Since the New Mexico whiptail is parthenogenetic, the population at Petrified Forest could have arisen from a single individual 

transported on the Atchison-Topeka and Santa Fe railroad (an east-west line that 

runs just north of the Puerco River through the park), or escaped from a vehicle 
DROST ET AL. 95 

(Interstate Highway 40 links Petrified Forest with the heart of the New Mexico 

whiptail's range in central New Mexico). 

From a conservation standpoint, the population of milk snakes at Petrified 

Forest is of interest. This small snake is very rare in the state of Arizona, known 

from only a few restricted locations scattered across the state. As with some of the 

other amphibians and reptiles at Petrified Forest, the occurrence of the milk snake at 

this location may be linked to the isolated area of healthy grassland protected within 

the park. This species and the little striped whiptail stand out as the two rarest and 

most restricted species protected within Petrified Forest National Park. 

Completeness of Inventory 

That this study resulted in a nearly complete species inventory is supported by 

the fact that 22 of the 23 species documented (96%) were found after the first month 

and a half of field work, with varied field methods, intensive sampling effort, and 

extensive geographic coverage of the park. While there are a few additional species 

that could possibly occur at Petrified Forest (e.g., the many-lined skink, Eumeces 

multivir;gatus, in the Puerco River floodplain, and the secretive southwestern blackheaded snake, Tantilla hobartsmitht), only one species seems likely to have been missed 

during our surveys. The northern leopard lizard, Gambelia wislizenii, is known to 

occur in the general region ofPetrified Forest (Stebbins 1985), and has been seen by 

one of us (fBP) 8 km west of the park boundary, in contiguous habitat (Puerco 

River flood plain). If this species occurs in the park, it is evidently rare and locally 

distributed. We surveyed many areas of suitable open shrub and shrub-grassland 

habitat, and found only the related collared lizard, a species usually associated with 

rocky situations. 

Comparison of Methods 

To be most meaningful, comparisons of survey methods should include measures of cost. This provides a standard basis for comparison, and also reflects the fact 

that most surveys are conducted under limited budgets. The most expensive component of any survey is typically personnel time (e.g., Burbidge 1991), and that is 

particularly true of the observer-intensive methods used in this survey. For each 

sampling method, we recorded the amount of time that field personnel were actively 

engaged in that sampling method. We spent approximately 137 person-hours of 

effort on pitfall/ array/ coverboard sampling sites (including 25 hours for installation 

of the traps and boards), 136 hours on visual encounter surveys (combined timeconstrained searches and general surveys), and 182 hours on night-driving surveys. 

If travel time to and from sampling sites is added in for pitfall/ array/ coverboard and 

visual encounter surveys (we did not record this precisely, because we were frequently 

engaged in more than one activity at a time), the amount of time we spent on the 

different methods is relatively close, with a slight bias toward night-driving. 

Of the survey methods we used for amphibians and reptiles at Petrified Forest, 

the two most effective were visual encounter surveys and night driving. Night driving was by far the most effective method for amphibians and snakes (fable 3). Night 
96 HERPETOFAUNA OF PETRIFIED FoREST NATIONAL PARK 

driving surveys found all of the seven species of amphibians that we documented at 

Petrified Forest, including two (red-spotted toad and Woodhouse's toad) that were 

not found with any other survey technique. Likewise, night driving recorded nearly 

five times as many individuals as all of the other techniques combined. The only 

other method that captured appreciable numbers of amphibians was drift fence 

arrays, in which we captured five amphibian species and just over 60 individuals. We 

recorded more individuals of each amphibian species during night -driving surveys 

than we did with all other methods combined. 

The significant advantage of night-driving surveys for snakes was even more 

pronounced. Again, all seven species of snakes that we documented at Petrified 

Forest were recorded on night drives, including road-killed individuals of the diurnal 

striped whip snake. By comparison, all other methods combined recorded only three 

snake species (Table 3). In terms of numbers of animals, we recorded 138 individual 

snakes on night drives, compared to a total of nine with all other methods combined. 

Visual encounter surveys (combining time-constrained searches and general 

surveys) were most effective for lizards (Table 3). VES documented all nine lizard 

species known from Petrified Forest National Park, including two (little striped whiptail 

and New Mexico whiptail) that were not found with any other method. The drift 

fence/pitfall arrays and artificial coverboards provided comparable or better results 

for two relatively secretive species (the short-homed lizard and sagebrush lizard), but 

for most species we recorded substantially higher numbers of individuals during 

visual encounter surveys. This was especially true for the conspicuous, active, and 

wide-ranging whiptaillizards. 

Besides their effectiveness for survey purposes, a significant advantage of visual 

surveys is that they have almost no other associated cost, compared to the significant 

materials cost and installation and maintenance time required for pitfalls, drift fences, 

and coverboards. However, there are problems associated with visual encounter 

surveys that must be taken into account. Because most animals observed are not 

caught, some individuals may be misidentified, or individuals may not be identified 

to species level (e.g., Sceloporus sp. and Cnemidophorus sp. in Table 3). With adequate 

training and experience, this should not be a serious problem for inventory purposes. More serious, if one wishes to quantify numbers of different species, is 

observer bias. This is a notorious problem with visual surveys (e.g., McDonald 

1981 ), and extends both to differences among observers in the numbers of animals 

seen, and also (in unconstrained general surveys) to differences in the areas that 

different observers choose to focus their search efforts. 

Habitat and Abundance 

Sampling methods that we used for amphibians and reptiles at Petrified Forest 

provide information on relative abundance, but the different methods have their 

own distinct biases. Inferences about relative abundance are strongest when different 

sampling methods yield the same rank order of species. Night driving provided the 

most extensive information on amphibians and snakes. For amphibians, rank abundance of the different species was similar between night driving and drift-fence arrays 
DROST ET AL. 97 

(the only other method that yielded appreciable numbers of amphibians; Table 3). 

New Mexico spadefoots were the most abundant species encountered, followed by 

either Great Plains toad (night driving) or plains spadefoot (arrays). For snakes, no 

other method yielded more than six individuals, so night -driving data are the only 

yardstick that we have for species abundance. The four most numerous snakes 

encountered on night drives were, in order: gopher snake; night snake; western 

rattlesnake; and glossy snake. Relative abundance information from night driving 

has the important caveat that we do not know the propensity of different species to 

move onto the road, or to remain on the road for extended periods of time (one 

evident bias is that small, dark species such as the night snake are more likely to be 

overlooked than larger, lighter-colored species). Still, with appropriate caution in 

interpreting results, our night -driving data provides a good basis of comparison for 

future night -driving surveys. Night -driving surveys were not stratified by habitat, so 

we cannot discuss habitat associations for amphibians and snakes. 

Our VES provided the most extensive data for lizards, but a comparison with 

data from trapping methods (pitfalls, drift fence arrays, and artificial cover) showed a 

conspicuous difference for the sagebrush lizard. Otherwise, VES and trapping yielded 

the same rank abundance. For VES this was, in order: plateau striped whiptail; 

eastern fence lizard; collared lizard; lesser earless lizard; and sagebrush lizard. 

We broke down the data from VES and trapping by habitat (Table 4 and Fig. 3), 

and all species showed pronounced patterns of abundance in relation to habitat. 

Plateau striped whiptail, eastern fence lizard, and collared lizard had broad distributions across habitats, while side-blotched lizard was quite restricted. There were 

evident differences in abundance between the two methods, primarily for sagebrush 

lizard and plateau striped whiptail (Table 6). 

Table 6. Most numerous amphibian and reptile species recorded by trapping methods 

(including pitfall traps, drift fence arrays, and artificial cover) and visual encounter 

surveys in different major habitats at Petrified Forest National Park in 1997-98. See 

also Table 4 and Figure 3. 

Trapping VES 

Grass Lesser earless lizard, Plateau striped whiptail, 

Southern spadefoot Lesser earless lizard 

Shrub Sagebrush lizard, Plateau striped whiptail, 

Plateau striped whiptail Sagebrush lizard 

Sand Plateau striped whiptail, Lesser earless lizard, 

Sagebrush lizard Plateau striped whiptail 

Rock Collared lizard Eastern fence lizard, 

Plateau striped whiptail 

Riparian Eastern Fence Lizard, Plateau striped whiptail, 

Southern spadefoot Eastern fence lizard 
98 HERPETOFAUNA OF PETRIFIED FoREST NATIONAL PARK 

Biogeography 

Northeastern Arizona, including Petrified Forest, lies within a broad area of 

overlap of Great Basin and Great Plains biotic communities (Brown 1994). In 

addition, the region incorporates habitat elements derived from grasslands and deserts 

more characteristic of southern Arizona and northern Mexico. This mixing of biogeographic elements is reflected in the park's herpetofauna. The 23 species that we 

recorded can be categorized as having the main part of their range in either the Great 

Basin, Great Plains, the desert and grassland regions of the Southwest, eastern North 

America, or some combination of these (Table 7; Stebbins 1985). Species with more 

southern ranges dominate the fauna, while species characteristic of the Great Basin 

(sagebrush lizard), Great Plains (plains spadefoot), and eastern U.S. (milk snake) are 

near the limits of their range. Two of the species listed under "Other" in Table 7 have 

unique distributions: the New Mexico whiptaillizard is likely introduced (Persons 

Table 7. Biogeographic patterns of the amphibian and reptile fauna of Petrified 

Forest National Park, Arizona. Species are categorized as occurring primarily in the 

Great Basin, Great Plains, southern deserts and grasslands, or eastern North America. 

Species listed as "other'' have distributions that are widespread in western North 

America (tiger salamander, short-horned lizard, gopher snake, western rattlesnake), 

more restricted (Plateau Striped Whiptail on the southern Colorado Plateau), or probably 

introduced in the Park (New Mexico Whiptail). Species with distributions that are 

distinctly associated with two different areas are noted in both columns. 

Species Great Basin Plains Southern Eastern Other 

Tiger Salamander X 

Couch's Spadefoot X 

New Mexico Spadefoot X 

Plains Spadefoot X 

Woodhouse's Toad X 

Red-spotted Toad X 

Great Plains Toad X X 

Collared Lizard X X 

Short-horned Lizard X 

Lesser Earless Lizard X X 

Sagebrush Lizard X 

Eastern Fence Lizard X 

Side-blotched Lizard X X 

Little Striped Whiptail X 

Plateau Striped Whiptail X 

New Mexico Whiptail X 

Striped Whipsnake X X 

Gopher Snake X 

Common Kingsnake X X 

Milk Snake X 

Glossy Snake X 

Night Snake X X 

Western Rattlesnake X 

TOTAL 5 3 12 4 6 
DROST ET AL. 99 

and Wright 1999a), and the plateau striped whiptaillizard is largely restricted to the 

Colorado Plateau, having originated in this region through interspecific hybridization (Wright 1993). 

In contrast to the predominance of southern amphibian and reptile species in 

the region, the flora of the Petrified Forest area is generally considered to be most 

closely allied with the Great Basin (Kearney and Peebles 1960, Gleason and Cronquist 

1964, Brown 1994). This points to the role that other mechanisms, besides vegetation and climate, have played in the distribution of amphibians and reptiles that 

occur in the park. The most obvious factor is topographic barriers, such as the 

mountains of central Utah and the canyon of the Colorado River, which separate 

Great Basin species from northeastern Arizona. Hence, although the vegetation is 

primarily derived from Great Basin and Great Plains floras (Brown 1994), the close 

geographic proximity of Petrified Forest to the grasslands and deserts to the south 

has evidently been more important in determining the current composition of the 

park's herpetofauna. 

Conservation 

Petrified Forest National Park protects a large area of native grassland, and aside 

from a portion of Wupatki National Monument north of Flagstaff, contains the 

only federally protected grasslands in the Little Colorado River basin of northeastern 

Arizona. Livestock grazing has had a profound impact on grasslands in the Southwest (e.g., Lowe 1964), and through exclusion of grazing Petrified Forest may also 

play an important role in the conservation of a number of regionally rare amphibian 

and reptile species, including Couch's spadefoot, little striped whiptail, and milk 

snake. Besides exclusion of livestock grazing, the current policy of night-time road 

closure at Petrified Forest may also play an important role in protecting the park's 

herpetofauna. Road mortality can have significant impacts on amphibians and reptiles, even in National Park areas (Rosen and Lowe 1994). Although the policy of 

night-time road closure at Petrified Forest was initiated to protect the park's significant geologic and cultural resources, local populations of nocturnal amphibians and 

reptiles also benefit. 

AcKNOWLEDGEMENTS 

We thank the staff at Petrified Forest National Park, especially Superintendent 

Michele Hellickson, and Resource Management staff Mark DePoy, William Grether, 

Karen Bepler, and Pat Thompson, who supported this project from start to finish. 

We also thank park rangers Chuck Dorn, Rita Garcia, and Debbie Krueger for recording amphibian and reptile observations while they were on patrol. Many individuals 

helped with fieldwork for this study; Matthew Spille, Christoph Seger, and Claudia 

Seger-Thomschitz deserve special thanks for their time and effort in this regard. 

Roger Repp, the "herp king of southern Arizona," made the long trip north to help 

us look for milk snakes. Finally, we especially thank Maggs Rasmussen and Mary R. 
100 HERPETOFAUNA OF PETRIFIED FOREST NATIONAL PARK 

Delftno-Debold of the Colorado Plateau Field Station for helping us through the 

budgetary and administrative mazes. 

LITERATURE CITED 

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riversiana, on Santa Barbara Island, California. Herpetological Monographs 5: 28-78. 

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DROST ET AL. 101 

Kearney, T. H., and R. H. Peebles. 1960. Arizona Flora with Supplement by J. Howell, 

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master's thesis, Northern Arizona University, Flagstaff. 

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The Vertebrates of Arizona. University of Arizona Press, Tucson. 

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in Avian Biology no. 6, Cooper Ornithological Society. 

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a desert grassland. Journal of Herpetology 26: 38-45. 

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Herpetological Review 30: 207-209. 

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Herpetological Review 30: 109. 

Rosen, P. C., and C. H. Lowe. 1994. Highway mortality of snakes in the Sonoran desert 

of southern Arizona. Biological Conservation 68: 143-148. 

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Donnelly, R. W. McDiarmid, L. C. Hayek, and M. S. Foster ( eds.) Measuring and 

Monitoring Biological Diversity: Standard Methods for Amphibians. Smithsonian 

Institution Press, Washington, D.C. 

Shaffer, H. B., and J. E. Juterbock. 1994. Night driving. Pages 163-166 in: Heyer, W. R., 

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and Monitoring Biological Diversity: Standard Methods for Amphibians. 

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U.S. National Parks. Natural Areas Journal 12: 145-154. 

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National Park. GIS map and accompanying summary descriptions of vegetation 

categories. Applied Remote Sensing Program, Office of Arid Lands Studies, 

University of Arizona, Tucson. 

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Wright J. W., and L. J. Vitt (eds.) Biology of Whiptail Lizards (Genus 

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Denburgh. Journal of the Arizona Academy of Science 3: 164-168. 

Wright, J. W., and C. H. Lowe. 1968. Weeds, polyploids, parthenogenesis, and the 
102 HERPETOFAUNA OF PETRIFIED FOREST NATIONAL PARK 

geographical and ecological distribution of all-female species of Cnemidophorus. 

Copeia 1968: 128-138. 
Changes in Water Temperature 

of Backwaters During 

Fluctuating vs. Short-Term Steady Flows 

in the Colorado River, Grand Canyon 

Timothy L. Hoffnagle* 

Research Branch 

Arizona Game and Fish Department 

2221 W. Greenwqy Road 

Phoenix, AZ 85023 

Abstract. Discharge from Glen Canyon Dam, Arizona, fluctuates in a diel pattern 

which may affect native fishes and their habitats in the Colorado River, Grand Canyon. Differences in water temperature, turbidity, dissolved oxygen (DO) and pH in 

main channel and backwater habitats were compared between fluctuating and shortterm (3-day) steady discharge regimes. Mean temperature in the main channel and 

backwaters displayed regular diel fluctuations, but mean temperatures were warmer 

under steady flows in both habitats (P<0.01). Mean main channel temperature was 

8.36° C under fluctuating flows and increased to 8.92° C under steady flows. In 

backwaters, mean temperature increased from 11.91° C to 14.18° C, and minimum, 

maximum and diel temperature range were higher under steady flows (P<0.01). 

Mean and minimum DO decreased and range of DO increased (P~0.03), while 

mean, maximum and range of pH (P~0.03) increased in backwaters under steady 

flows. Mean turbidity did not significandy change (P;::.:0.35). These water quality 

changes may affect native fish populations through their influence on primary and 

secondary production and the potential for a change in non-native fish community. 

These factors should be more closely examined before implementation of a steady 

flow regime, construction of a temperature control structure or other changes that 

might increase water temperature in the Colorado River in Grand Canyon. 

Key words: temperature, dam discharge, fluctuating flow, steady flow, Colorado 

River, Glen Canyon Dam. 

Email: Timothy L. Hoffnagle, thoffnag@eou.edu. 

• Present address: Oregon Department of Fish and Wildlife, 211 Inlow Hall, Eastem Oregon University, La Grande, OR 97850 

103 
104 CHANGES IN BACKWATER WATER TEMPERATURES, COLORADO RIVER 

INTRODUCTION 

The flow of water in the Colorado River through Grand Canyon is predominantly regulated by hypolimnetic discharge from Glen Canyon Dam. The closure of 

Glen Canyon Dam, in 1963, turned a seasonally warm, muddy river into one that is 

typically clear and constantly cold. This change has greatly affected the river corridor 

biota, particularly native fishes. Alteration of spawning and rearing habitat, blockage 

of migration and introduced non-native species have contributed to the extirpation 

of four of the original eight native species (11inckley 1991 ). Reproducing populations of only four native species remain: humpback chub, Gila rypha (listed as endangered; U. S. Fish and Wildlife Service 1990), flannelmouth sucker, Catostomus latipinnis, 

bluehead sucker, Catostomus disco bolus and speckled dace, Rhinichtf?ys osculus. As many 

as 17 exotic fish species, most of which are predators and/ or competitors of native 

species, have been found within the Grand Canyon (Maddux et al. 1987, Valdez and 

Ryel1995, Arizona Game and Fish Department 1996). 

Glen Canyon Dam is operated as a peaking power hydropower facility. From 

1963 through July 1991, daily discharge fluctuated widely, with no restrictions on 

ramping rates. During this period, discharge peaked as high as 893 m3 / s (31 ,500 cfs) 

in the early afternoon, while discharge during the early morning could be as low as 28 

m3 / s (1,000 cfs) or 85m3

/ s (3,000 cfs), depending on the time of year. On 1 August 

1991, interim operations were implemented which restricted discharge to a maximum of 567m3 /s (20,000 cfs) and a minimum of227 m3 /s (8,000 cfs) from 07:00 

to 15:00, and 142m3

/ s (5,000 cfs) at night. Ramping rates were also restricted to 71 

m3 /s (2,500 cfs) per hour up and 43m3 /s (1,500 cfs) per hour down. 

Changing from an unregulated to a regulated stream has caused backwaters to 

become increasingly important as rearing areas for native fishes in the Colorado River 

system (Holden 1978, Valdez and Clemmer 1982, Carteret al. 1985, Maddux et al. 

1987, Arizona Game and Fish Department 1996). Backwaters, quiet pockets connected to the main channel (but with greatly reduced flow), are formed in areas of 

eddies where scouring occurs under higher flows. As water levels drop, a reattachment sand bar is exposed, partially isolating the eddy return channel and forming the 

backwater (Rubin et al. 1990). Not only do backwaters provide calm, sheltered water, 

they are also warmer and contain greater densities of aquatic invertebrates than the 

main channel (Cole and Kubly 1976, Arizona Game and Fish Department 1996). 

However, fluctuations in dam releases cause inundation and/ or dewatering ofbackwaters, reducing their ability to support larval and juvenile fish (Kennedy 1979). 

In an effort to improve habitat for native fish, a regimen of steady releases from 

Glen Canyon Dam has been proposed (U.S. Fish and Wildlife Service 1994). Stabilized river levels would prevent the daily loss and creation ofbackwaters. The present 

diel cycle of flow fluctuations forces juvenile fish to move into or out of many 

backwaters each day as these habitats are inundated and/ or dewatered with changes 

in river stage. J ourdonnais and Hauer (1993) speculated that forced movement, 

caused by alterations in river discharge, may increase predation on juvenile fish. Eliminating fluctuations could improve conditions for juvenile fishes. It is likely that 

backwaters, under steady flow conditions, would support increased planktonic and 

benthic invertebrate communities as a result of increased temperature and decreased 
HOFFNAGLE 105 

daily flushing (Kennedy 1979). A dramatic increase in benthic invertebrate populations has been seen in backwaters sampled under reduced fluctuations (Arizona 

Game and Fish Department 1996) when compared to those sampled under flow 

regimes designed to maximize power production (Cole and Kubly 1976, Haury 

1986, 1988). Conversely, turbidity, which is used as cover by native Colorado River 

fishes (Valdez and Ryel1995, Arizona Game and Fish Department 1996), will likely 

decrease under steady flows. Tbis would make backwaters and other near shore areas 

less hospitable to larval and juvenile native fishes. Additionally, other water quality 

parameters in backwaters, such as dissolved oxygen and pH will also be affected by 

steady flows and, subsequendy, primary and secondary production. 

Tbis study was conducted to determine differences in diel temperature changes 

in backwaters and the main channel between fluctuating vs. steady flow regimes in 

the Colorado River, Grand Canyon. Herein, I provide initial data concerning the 

effect of steady flows on temperature, pH, dissolved oxygen and turbidity of backwater and main channel larval and juvenile native fish habitat. I also examine the 

effects that any changes might have on native fishes should a steady flow be implemented in the Colorado River, Grand Canyon, Arizona. 

STUDY AREA 

Tbis study was conducted on the Colorado River, in Grand Canyon National 

Park, near the confluence of the Colorado and Litde Colorado (LCR) rivers (RK 99; 

RK = river kilometers in the Colorado River below Lees Ferry; Fig. 1 ). This reach was 

selected because of its importance to native fishes, as all four remaining native species 

reproduce in the LCR and rear in backwaters and the main channel of the Colorado 

River in this reach (Arizona Game and Fish Department 1996). The reach between 

Kwagunt Rapid (RK 90.1) and Lava Chuar Rapid (RK 105.4) was explored for 

suitable backwaters. 

Four backwaters, RK 94.6L, 95.9L, 97.8L and 102.5R ('L' and 'R' denote side of 

river when facing downstream), were selected based on the likelihood that they would 

persist under fluctuating vs. steady flow regimes (Fig. 1 ). The backwaters varied in 

many physical characteristics which affect warming and water chemistry, including 

surface area, depth, mouth dimensions, amount of algae and/ or aquatic vegetation 

and exposure to solar radiation. 

METHODS 

My sampling period was four days of fluctuating flows (25-28 May 1994) and 

three days of steady flows (29- 31 May 1994). Fluctuating flows ranged from 219.6-

370.2 m3

/s (7756-13075 cfs) while steady flows were approximately 236 m3/s 

(8,325 cfs). Steady releases from Glen Canyon Dam began at approximately 06:00 28 

May 1994 and reached my backwater sites, at the confluence of the Colorado and 

Litde Colorado rivers, at approximately 00:00 on 29 May 1994. Sampling was completed on 31 May 1994 when normal fluctuations resumed. 

One temperature gauge (Ryan Tempmentor1M) was placed in the middle of each 

backwater on 24 May 1994. In one backwater (RK 97 .8L) a Hydro Lab DataSonde 
Colorado River, Grand Canyon 

Lake Mead 

River 

RK99.0 

.. ··i<wagunt Rapid 

RK 90.1 

Lava 

Chuar 

Rapid 

RK 105.2 

Figure 1. Colorado River, Grand Canyon, Arizona, and the location of the backwater and main channel sites sampled in this study. Locations are 

given as river kilometers downstream from Lees Ferry and side of river when facing downstream. 
HOFFNAGLE 107 

IITM was used which also recorded dissolved oxygen (DO) and pH. Each gauge was 

shaded from direct sunlight and suspended approximately 25 em below the water 

surface. Turbidity was measured once each day in each backwater and the main 

channel using a Hach TM nephelometer. Main channel temperature and discharge 

data were obtained from the U.S. Geological Survey gauge on the Colorado River at 

RK 98.3R, above the mouth of the LCR. All instruments were set to record at 30 

minute intervals from 25 May- 1 June 1994. Available direct solar radiation (hours) 

for each site at the end of May was later measured using a Solar PathfinderTM. Differences in diel mean, minimum, maximum and range of temperature (

0 C), DO, pH 

and mean turbidity between steady vs. fluctuating river discharges in backwaters vs. 

the main channel were tested using at-test. Probability was accepted when P~0.05. 

RESULTS 

Flows and Backwater Sites 

Two backwaters, RK 94.6 Land RK 97.8 L, were well established. They had 

been sampled regularly by Arizona Game and Fish Department (unpub. data) and 

contained aquatic, emergent and terrestrial vegetation in and around them. The 

remaining two backwaters, RK 95.9L and RK 102.5 R, were bounded by clean sand 

bars and were more ephemeral. 

The backwater at RK 94.6 L was long (up to 61 m), wide (up to 7 m) and mostly 

shallow. Its size varied greatly with water elevation (Table 1). It also received the 

Table 1. Representative and maximum depth (em), surface area (m2) and hours of 

direct solar radiation in late May, for each backwater sampled, 25-31 May 1994, 

during fluctuating and steady flow regimes in the Colorado River, Grand Canyon, 

Arizona. Backwater location is given as river kilometer and side (left or right). 

Backwater Location 

Parameter 94.6L 95.9L 97.8L 102.5R 

Representative Depth (em) 

Fluctuating Flow 21-66 28-35 72-116 58-76 

Steady Flow 64 52 73 54 

Maximum Depth (em) 

Fluctuating Flow 32-85 56-61 107-170 70-94 

Steady Flow 124 81 112 70 

Surface Area (m2) 

Fluctuating Flow 178-335 32-19 140-248 40-67 

Steady Flow 324 57 165 145 

Maximum Length (m) 

Fluctuating Flow 52-58 12-14 28-31 10-16 

Steady Flow 61 19 34 18 

Maximum Width (m) 

Fluctuating Flow 6-7 2 5-8 7-8 

Steady Flow 6 6 5 10 

Hours of Solar Radiation 7.25 5.5 6.75 4.75 
108 CHANGES IN BACKWATER WATER TEMPERATURES, COLORADO RIVER 

greatest amount of direct solar radiation (7.25 hours from 09:00-16:15). Its mouth 

(connection with main channel) was wide and deep (> 1 m) and its location and 

dimensions varied gready with varying river stage. The foot (terminal end) of this 

site remains a backwater except under high discharges (510m3 /s = 18,000 cfs) not 

seen during this study, which would inundate the site. This backwater contained a 

dense mat of aquatic macrophytes, including Potamogeton andAnachatiswith Equisetum and 1)pha along its sides. 

The backwater at 95.9 L was very small (13m), narrow (2m) and shallow (fable 

1). Its mouth was also shallow and narrow and the size of this backwater did not 

vary gready with river elevation. This site would be inundated by flows barely exceeding those seen during this study. Due to its location, partially under an overhanging 

ledge, and the fact that the river there flows north to south, this backwater received 

litde direct solar radiation (5.5 hours; 11:00-15:30). The only aquatic vegetation in 

this backwater was Cladophora that had drifted in from the main channel. 

The backwater at RK 97.8 L was long (34m) and wide (8 m) with both deep and 

shallow sections (fable 1). The mouth was wide, but very shallow. However, a 

boulder along one shore provided a site for scouring under high flows which created 

a deep hole. This site would also require flows exceeding 510m3

/ s for inundation. 

It was also very exposed to solar radiation (6.75 hours; 08:45-15:30) and contained 

much aquatic vegetation, including Potamogeton and Equisetum in the shallow areas 

and Cladophora in the deeper areas. 

The backwater at RK 102.5 R was short (18 m), wide (1 0 m) and shallow with 

two arms and its mouth was wide and deep (fable 1). It was located in a narrow 

section of the canyon with Tapeats limestone walls that blocked much sunlight, 

resulting in only 4.75 hours of direct solar radiation each day (09:15-14:00). This 

backwater contained no aquatic vegetation except Cladophora that had drifted in. 

Temperature 

Water temperatures in the main channel and backwaters displayed regular, diel 

fluctuations under fluctuating and steady flow regimes (Fig. 2). Maximum backwater 

temperatures occurred in midday (09:00-16:00, depending on the site) while the main 

channel was warmest around 18:00. Minimum backwater temperatures occurred 

near 06:00 for most sites, but around 00:00 for the backwater at 94.6L and in the main 

channel between 06:00-09:00. 

In the main channel, mean temperature was 8.4 ° C under fluctuating flows but 

was significandy higher (P<0.01) at 8.92° C under steady flows (fable 2). Mean daily 

minimum temperature also significandyincreased from 7.8° C to 8.1° C. Mean daily 

maximum and diel temperature range were not significandy different (P::?:0.08) between flow regimes in the main channel. 

In backwaters, daily mean, minimum and maximum temperatures and diel 

temperature range were significandy greater under steady than fluctuating flows 

(P<0.01; Table 2). Daily mean temperature under fluctuating flows was 11.9° C, 

increasing to 14.2° C under steady flows. Mean daily minimum temperature increased from 10.5°C under fluctuating flows to 11.5° C under steady flows. Mean 
HOFFN:IGLE 109 

25 400 

· -, Mainchannel ----- Discharge 360 20 -- Temperature , .... \ 320 

15 

280 

10 240 

5 200 

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 

25 400 

·- 94.6l 

20 360 

a 320 

15 

280 

10 240 

5 200 ~ 

- 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 < CD (.) 25 400 ..., 0 95.9l - ·- g, ~ 20 360 

(/1 

:::l -·, 320 0 

- 15 :::r co ' 280 D) lo. ,, ..., Q) 10 240 co c. \ . .,/ ..... ______________________ ,. _ 

CD E ,, 

Q) 5 200 - 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 3 1- 25 400 ~ (/1 

·- 97.8l 

20 360 - , ., 320 

15 ' 

.-, ', ', 280 

10 '•· 240 '' --.......... -... ---... -.. -...... -----·- ,, ., 

5 200 

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 

25 400 

20 

•-, : ' 

102.5R 

360 

-·, · . .. ,~ 320 

15 

/ 

1 ... -\ 

~ 280 

10 \ ' \ \ : \ ..... ______________________ ., _ 240 . ' ,, 

5 200 

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 

25 26 27 28 29 30 31 

Date (May) and Hour 

Figure 2. Temperature changes, in main channel (RK 98.2) and backwaters (RK 

94.6L, 95.9l, 97.8L and 1 02.5R), and river discharge during fluctuating and steady 

flows in the Colorado River, Grand Canyon , 25-31 May 1994. Temperature gauge in 

backwater at 94.6l was dewatered (a) and resubmerged (b). 
110 CHANGES IN BACKWATER WATER TEMPERATURES, COLORADO RIVER 

Table 2. Mean, minimum and maximum temperature in the sampled backwaters and 

main channel during fluctuating and steady flows in the Colorado River, Grand Canyon, 

25-31 May 1994. 

Habitat/ Fluctuating Flow Steady Flow 

Location Mean Min Max Std Dev Mean Min Max Std Dev 

Backwaters 11.9 10.1 17.7 1.2 14.2 10.9 23.8 2.2 

94.6L 13.2 10.8 17.7 1.9 17.7 11.3 23.8 3.3 

95.9L 11.0 10.1 12.5 0.6 12.7 10.9 17.0 1.4 

97.8L 11.6 10.4 13.8 0.8 13.9 11.5 17.3 1.6 

102.5R 12.0 10.8 13.7 0.7 13.9 12.3 16.6 1.0 

Main Channel 8.4 7.6 9.4 0.5 8.9 8.0 9.6 0.5 

daily maximum backwater temperature under steady flows was 18.7° C, but only 

14.4° C under fluctuating flows. The mean diel temperature range was only 2.7° C 

under fluctuating flows, but increased to 5.6° C under steady flows. 

In individual backwaters, daily mean and maximum water temperatures were 

significantly higher (P<0.05) under steady flows in each backwater (Table 2). Daily 

minimum water temperature significantly increased (P<0.01) at all sites, except RK 

94.6L (P>0.07). 

Turbidity 

Mean turbidity decreased under steady flows, but not significantly (P~0.35). 

Mean main channel turbidity decreased from 6.936 to 2.681 Nephelometric Turbidity 

Units (NTU) under fluctuating and steady flows, respectively (Table 3). During 

fluctuating flows mean backwater turbidity was 9.396 NTU and decreased to 5.87 5 

NTU under steady flows. 

Table 3. Mean, minimum and maximum turbidity in the sampled backwaters and main 

channel during fluctuating and steady flows in the Colorado River, Grand Canyon, 

25-31 May 1994. 

Habitat/ Fluctuating Flow Steady Flow 

Location Mean Min Max Std Dev Mean Min Max Std Dev 

Backwaters 9.4 1.6 31.3 5.3 5.9 1.2 11.4 2.1 

94.6L 6.5 2.7 13.4 6.0 7.5 5.7 8.7 1.6 

95.9L* 6.5 6.5 6.5 6.4 3.4 9.4 4.3 

97.8L 6.1 1.6 10.0 4.2 2.1 1.2 3.0 0.9 

102.5R 18.6 5.9 31.3 18.0 7.5 5.0 11.4 3.4 

Main Channel 6.9 1.8 23.6 4.4 2.7 1.1 3.1 0.3 

·Turbidity was measured only once during fluctuating flows at this site. 
H OFFNciGLE 111 

Dissolved Oxygen 

Dissolved oxygen (DO) in the backwater at Rl<: 98.7L fluctuated daily under 

both flow regimes (Fig. 3). Maximum DO levels occurred in late afternoon and the 

minimum occurred in early morning under both flow regimes. Mean DO level was 

significantly lower (P<0.01) under steady flows (10.0 mg/ L) than fluctuating flows 

(10.8 mg/L). Mean minimum DO was 10.1 under fluctuating flows and decreased 

significantly (P<0.01) to 8.9 mg/ L under steady flows. Mean maximum DO level 

u 

~ 

~ :::l 

-~ Q) 

c. 

E 

~ 

:I: 

c.. 

18 

17 

16 

15 

14 

13 

12 

11 

10 

8.2 

8.1 

7.9 

7.8 

7.7 

7.6 

-Temperature 

----- Discharge 

" 

-pH 

-- --- Discharge 

·.J 

12.5 -r-----------------------,. 

~ 11~: !\\. '·'\ M-.---- ~:~~~ .. g~ Oxygen 

~ 11 /' ~ / \ / ~ ;(' 10.5 ', _: \ : \ 

~ 10 ·.: ... \ 

~ 9.5 

0 

~ 

C .5 -t-r,...,..,~.,-.,..,...,~.,-T"T"'l~rrrT"T"'l~rrrT"T"T~rrr.,..,...,~rrrT""T""T~rf12 

25 

12 

26 

12 

27 

12 

28 

12 

29 

Date (May) and Time 

12 

30 

12 

31 

380 

360 

340 

320 

300 

280 

260 

240 

220 

200 

380 

360 c 340 (ij' 

n 320 :::r 

Ill 300 ca 280 CD 

2603 w 

240 .§:: 220 

200 

380 

360 

340 

320 

300 

280 

260 

240 

220 

Figure 3. Changes in temperature, pH and dissolved oxygen in the backwater at RK 

97.8L and river discharge during fluctuating and steady flows in the Colorado River, 

Grand Canyon, 25-31 May 1994. 
112 CHANGES IN BACKWATER WATER TEMPERATURES, COLORADO RIVER 

decreased from 11.90 mg/L under fluctuating to 11.86 mg/L under steady flows, 

but was not significantly different (P=0.92). Diel DO range increased (P<O.OS) to 

2.97 mg/L under steady flows, from that seen under fluctuating flows (1.79 mg/L). 

pH 

pH levels showed regular diel variations in the backwater at RK 97.8L under 

fluctuating and steady flow regimes, with maximum pH occurring in the afternoon 

and minimum in early morning (Fig. 3). Mean, maximum, and diel range of pH 

were higher (P<O.OS) under steady than fluctuating flows, while mean minimum pH 

did not significantly change (P=0.15). Mean pH increased from 7.72 to 7.80, while 

maximum pH increased from 7. 9 under fluctuating flows, to 8.1 under steady flows. 

Diel range of pH also increased from 0.23 to 0.44. Minimum daily pH decreased 

from 7.63 to 7.61, but was not significantly different. 

DISCUSSION 

It is evident that a 64-hour (three periods of daylight) regimen of steady flows 

caused changes in water quality parameters in the Colorado River main channel and 

backwaters. Temperature and pH increased, while DO and turbidity decreased. These 

changes and the potential for greater biotic and abiotic changes, under longer term 

steady flows, have important implications for native fishes in the Colorado River in 

Grand Canyon. 

I found that the 3-day steady flow caused an increase in water temperature in 

backwaters and the main channel of the Colorado River during late May 1994. The 

full potential for backwater warming was probably not reached during this short 

period, and these data are insufficient to estimate the limit of warming or other water 

quality changes. The water did warm in both the backwaters and the main channel. It 

is, however, safe to say that temperatures would not have reached those of pre-dam 

conditions. 

The amount of warming in the backwaters monitored in this study varied and 

was likely influenced by ambient temperature, physical location (accessibility to direct 

solar radiation), main channel temperature, the amount of water exchange between 

the backwater and main channel and backwater morphometry (size of mouth and 

surface area and volume of the backwater). Under fluctuating flows, backwaters may 

warm, but daily flushing with main channel river water resets backwater temperatures 

to approximately that of the main channel. Under the steady flow regime, diel 

fluctuations in temperature were still influenced by solar radiation and diel changes in 

ambient temperature but were less influenced by the main channel. With minimum 

temperatures well above that of the main channel and no surge of main channel 

water under the steady flow regime, backwaters held heat better than under fluctuating flows, allowing them to warm further the next day. In all sites, except RK 94.6L, 

the highest daily mean and minimum temperatures occurred on the last day of 

steady flows, and at all sites the highest maximum temperature occurred on the last 

day, indicating an increase of temperature over time, which would probably continue 

if the steady flows were oflonger duration. 
HOFFNAGLE 113 

The diel cycle of flow fluctuations near the LCR is such that temperature variation in backwaters are maximized. During fluctuating flows, peak discharges reached 

the LCR gauge between 06:00 and 09:00, leaving the remainder of the day under 

steady or decreasing discharges. This permits backwaters in this area to warm considerably throughout the day due to little input of new, cold water from the main 

channel. In most other areas of the Colorado River in Grand Canyon warming 

should occur to a lesser degree since the timing of high and low discharge occurs at 

different times of the day. If low discharge occurs in the early to mid-morning, 

warming of backwaters should be gready diminished as they will be continuously 

filled with cold river water during daylight hours. 

Backwater temperatures that I recorded under fluctuating flows were not those 

preferred by native Grand Canyon fishes. Humpback chub prefer water temperatures 

of 21 - 24.4 ° C (Bulkley et al. 1982) and other native Colorado River fishes probably 

have similar preferences (Valdez and Ryel1995, Arizona Game and Fish Department 

1996). These preferred temperatures are far from the 7.6 - 9.6° C temperature range 

recorded in the main channel during this study under fluctuating and steady flows. 

Even in the monitored backwaters, maximum recorded temperature was 17.6° C 

under fluctuating flows. However, under the steady flow regime mean diel temperature in one backwater (RK 94.6L) reached 18.1° C, nearing the preferred temperature 

range for native fishes, and mean maximum temperature reached 23.8° C, well within 

the preferred range. Also, temperature in most backwaters showed indications of 

increasing with each day of steady flows. Therefore, it appears that under a regime of 

steady flows, temperature in backwaters will approach and may even exceed the preferred temperature range of native fishes. This is most likely to occur during warmer 

months, in shallow areas of backwaters and in lower reaches of the Colorado River 

in Grand Canyon. 

Through warming of the water and reduced mixing with the main channel, 

steady discharges also affect other water quality properties of backwaters, such as 

dissolved oxygen, pH and turbidity. The Colorado River was extremely clear during 

the entire study and turbidity decreased in both backwaters and the main channel, but 

not significandy. Decreased turbidity may result in increased predation on larval and 

juvenile fish and main channel turbidity is probably already sufficiendy low to affect 

fish behavior. Colorado River fishes evolved in a turbid system (11inckley 1991) and 

likely prefer turbid water. Arizona Game and Fish Department (1996) reported 

increased catches of juvenile humpback chub, flannelmouth sucker, speckled dace 

and fathead minnow under turbid conditions (>30 NTU). Valdez and Ryel (1995) 

reported increased catches of sub-adult and adult humpback chub in trammel nets at 

night and during periods of high turbidity (also >30 NTU) in the Colorado River. 

Sabo et al. (1991) found that high quality nursery ponds along the Mississippi River 

contained higher turbidity, dissolved oxygen and conductivity than low quality nursery areas. 

As seen with temperature, DO and pH also varied with regular diel fluctuations 

under both flow regimes. Daily mean, minimum and/ or maximum levels of these 

parameters changed under the steady flow regime due to increased photosynthetic/ 
114 CHANGES IN BACKWATER WATER TEMPERATURES, COLORADO RIVER 

respiratory activity by algae and macrophytes (\X'etzel1983). Under steady flows, daily 

maximum DO did not significantly vary between flow regimes. Dissolved oxygen 

was highest during the late afternoon when algal and macrophytic photosynthesis 

was greatest. Daily mean and minimum DO significantly decreased under steady 

flows with increases in water temperature and as biological oxygen demand during 

the night used 0 2 which was not replenished by the nightly influx of new water that 

occurs under fluctuating flows. However, DO levels recorded in these backwaters 

were never near 6 mg/L, below which fish growth and survival may be affected 

(Boyd 1979, Piper et al. 1982). 

During the late afternoon, pH was also highest, probably due to the use of CO2 

by algal and macrophytic photosynthesis (\X'etzel 1983). Although pH increased 

significantly, the changes were small and it is unlikely that pH is limiting fish in this 

system, since fish generally do well in waters with a pH of 6.5 - 9.0 (Boyd 1979, Piper 

et al. 1982). 

Therefore, it appears that fluctuations in river discharge also moderated the diel 

changes in DO and pH in this backwater caused by daily cycles of photosynthesis and 

respiration. However, as with changes in temperature, the limits of these changes 

under an extended period of steady flow cannot be predicted from these data. 

These results show that backwaters and the main channel (to a lesser extent) will 

warm under a steady flow regime. Several biotic changes may be expected to be 

caused by this warming and subsequent changes, which may include alterations in 

algal, invertebrate and fish communities and the possibility of an increase in the 

distribution and prevalence of diseases and parasites. These changes in habitat and 

the biotic community are complex and may be beneficial or detrimental for native fish 

populations. 

Algal and invertebrate communities in backwaters may change under steady 

flow conditions. It is likely that steady flows will cause an increase in backwater 

invertebrate densities in response to warmer temperatures and a lack of flushing. 

Increases in aquatic invertebrates under the current interim flow regime (small fluctuations) as compared to a peaking power flow regime (large fluctuations) have 

already been observed (Cole and Kubly 197 6, Haury 1986, 1988, Arizona Game and 

Fish Department 1996). Steady flows and increased invertebrate densities would 

further improve backwaters as feeding areas for juvenile fishes. Although not examined in this study, the short duration of these flows was probably not long enough 

for significant changes to occur in populations of even those invertebrates with the 

shortest life cycles. Leibfried and Blinn (1987) reported an increase in total benthic 

standing crop (based on drift) in the main channel Colorado River under five months 

of steady flows, as compared to fluctuating flows. Warmer water and increased food 

abundance should cause an increase in fish growth and survival in all native fish. 

Lupher and Clarkson (1994) reared humpback chub larvae in 1 0°C, 14 °C and 20°C 

water and found that length increased 10%, 3 7% and 83% and weight increased 28% 

195% and 951% over 30 days, in the respective groups. Similar, but less dramatic, 

results are expected in situ. However, it may be that increased use of backwaters by 

fish and subsequent changes in growth rates will not occur until invertebrate populations increase. 
HOFFNAGLE 115 

There are potential negative aspects to long periods of steady flows for native 

fishes. Main channel temperatures will increase, particularly in lower reaches of the 

river and may become hospitable to exotic warm and cool water predators and competitors already found in the Colorado River and/ or the reservoirs immediately 

upstream (Lake Powell) and downstream (Lake Mead) from Grand Canyon, (Maddux 

et al. 1987, Valdez andRyel1995,Arizona Game and Fish Department 1996). These 

predators include striped bass, Morone saxatilis, walleye, Stizostedion vitreum, smallmouth bass, Micropterus dolomieu, and channel catfish, Ictalurus punctatus. Exotic 

competitors include fathead minnow, Pimephales promelas, which is already common, 

plains killifish, Funduls zebrinus, and red shiner, yprinella lutrensis, which are becoming increasingly common, and green sunfish, Lepomis ryanellis, presendy found in low 

numbers within the system. Additionally, Blinn et al. (1989) found that epiphytic 

diatom communities from the Glen Canyon Dam tailwaters changed from large, 

upright forms to smaller, closely adnate forms with an increase in water temperature 

from 12°C to 18°C. Adnate forms of diatoms may be more difficult for grazing fish 

to consume. 

It is possible that backwater temperatures may rise too high during the late 

afternoon, making these areas unsuitable for juvenile fishes, particularly in the lower 

reaches of the Grand Canyon. Maximum backwater temperatures recorded under 

the current discharge regime of modified fluctuations reached as high as 26.6° C in 

May (Arizona Game and Fish Department 1996). It is also possible that increased 

algae, phytoplankton and macrophyte growth may make backwaters anoxic during 

darkness, further reducing their suitability to fish. This has been observed in the 

backwater at RK 88.86 (Arizona Game and Fish Department 1996). 

Increased temperature may also allow the invasion of new parasites and diseases. Fifteen species of parasites have been found in the lower LCR (Clarkson et al. 

1997, Brouder and Hoffnagle 1997, Hoffnagle et al. 2000). Increased mainstem 

temperatures may allow these parasites to expand their distributions within Grand 

Canyon. Bothriocephalus acheilognathi, the Asian fish tapeworm, is a thermophilic 

parasite of planktivorous cyprinid and cyprinodontid fishes and has been known to 

cause high mortality rates in fish (Hoffman and Schubert 1984). Cold temperatures 

in the mainstem Colorado River presendy appear to keep this parasite from being 

able to complete its life cycle in the Colorado River in Grand Canyon. In the LCR, it 

infects humpback chub, speckled dace, plains killifish, common carp, red shiner and 

fathead minnow (Clarkson et al. 1997, Hoffnagle et al. 2000). The maximum temperature recorded in this study was 23.8°C and maximum daily mean temperature 

was 18.07°C, very close to the 20°C needed by this parasite to complete its life cycle 

(Granath and Esch 1983). Brouder and Hoffnagle (1997) examined the distribution 

of B. acheilognathi in humpback chub, speckled dace, fathead minnow and plains 

killifish throughout the Grand Canyon in 1994- 1995 and found infected fish to be 

most common in and near the LCR. However, an infected speckled dace was found 

in the main channel Colorado River as far as 214 km downstream and in the mouth 

of Kanab Creek (132 km downstream). Hoffnagle and Landye (1999) reported the 

captured of speckled dace infected by B. acheilognathi in Kanab Creek in 1998. Increas-
116 CHANGES IN BACKWATER WATER TEMPERATURES, COLORADO RIVER 

ing Colorado River water temperatures to those preferred by humpback chub will 

likely increase the infection rate by B. acheilognathi in all susceptible fish. That, coupled 

with the continual displacement of fish downstream, will facilitate the invasion of B. 

achei!ognathi into other tributaries and possibly the main channel. Increased infection 

of humpback chub by B. achei!ognathi could threaten this endangered fish. Other 

parasites found in the LCR may be more dangerous to humpback chub, including 

some that may alter the behavior of their hosts (Hoffnagle et al. 2000). 

My results clearly demonstrate that water temperature will increase under aregime of steady flows during periods of warm weather. Additionally, dissolved 

oxygen and pH will be affected by this flow regime and turbidity changes may also be 

expected under longer-term steady flows. The effects of steady flows and changing 

river and backwater conditions on plankton, aquatic invertebrates and fishes were not 

tested but could be considerable. Therefore, it is apparent that further study is 

needed to assess the potential changes of long-term steady flows on larval and 

juvenile native fishes, their food sources, parasites and habitat before such changes 

are made. These studies, both laboratory and in situ, should provide significant 

information on the utility of steady releases for management of native fish populations in the Colorado River, Grand Canyon. 

AcKNOWLEDGEMENTS 

This project requires thanks to many individuals who supplied funding, logistics, equipment and time, mosdy on short notice. Thanks to Dave Wegner, U.S. 

Bureau of Reclamation, Glen Canyon Environmental Studies, for funding and supporting this project. Bill Vernieu and Jeanne Korn, U.S. Bureau of Reclamation, 

provided the DataSonde and some of the Tempmentors. Dr. Owen Gorman, U.S. 

Fish and Wildlife Service, provided the remaining Tempmentors, the nephelometer 

and the able field assistance of Dennis Stone. OARS, Inc. was contracted to provide 

field support in the form of Stuart Reeder and Steve Bledsoe, who were extremely 

helpful in making the field work run smoothly. Dr. Larry Stevens provided the Solar 

PathfinderTM data. Dr. Vicky Meretsky, U.S. Fish and Wildlife Service, provided 

statistical guidance. Lasdy, thanks to R. Bradford, M. Brouder, T. Dresser, D. Kubly, 

D. McGuinn-Robbins, W Persons, L. Riley, A. Robinson, M. Tuegel and]. Walters 

(Arizona Game and Fish Department biologists) and Dr. Richard Valdez, SWCA, 

Inc., for reviewing the manuscript. 

LITERATURE CITED 

Arizona Game and Fish Department. 1996. Ecology of Grand Canyon backwaters. 

Final report prepared for the U.S. Bureau of Reclamation, Upper Colorado Region, 

Glen Canyon Environmental Studies, Flagstaff, Arizona. Cooperative Agreement 

No. 9-FC-40-07940. Arizona Game and Fish Department, Phoenix. 

Blinn, D. W, R. Truitt and A. Pickart. 1989. Response of epiphytic diatom communities from the tailwaters of Glen Canyon Dam, Arizona, to elevated water temperature. Regulated Rivers: Research and Management 4: 91-96. 
HOFFNAGLE 117 

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of Colorado River endangered fishes to selected habitat parameters. Pages 185-241 

in Colorado River Fishery Project, Final Report, Part 3. U. S. Fish and Wildlife 

Service, Bureau of Reclamation, Salt Lake City, Utah. 

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118 CHANGES IN BACKWATER WATER TEMPERATURES, COLORADO RIVER 

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(Gila rypha) in the Colorado River, Grand Canyon, Arizona. Final Report to Glen 

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Contract No. 0-CS-40-09110, BIO/WEST Report No. TR-250-08. 

Wetzel, R. G. 1983. Limnology, second edition. Saunders College Publishing, Forth 

Worth, Texas, USA. 
Ponderosa Pine Tree Selection 

by Roosting Merriam's Turkeys 

inN orth-Central Arizona 

Brian F. Wakeling 

Arizona Game and Fish Department 

Research Branch 

2221 WestGreenwqy Road 

Phoenix, AZ 85023 

Sally Weimann 

Tim Jackson 

Ben Dowler 

Mechelle Eacret 

and 

Jennifer Gillis 

Grand Caf!Yon U niversi!J 

Department of Biology 

College of Science and Allied Health 

3 300 West Camelback Road 

Phoenix,AZ 85061-1097 

Abstract: We compared ponderosa pine (Pinus ponderosa) trees used for roosting by 

Merriam's turkeys (Meleagris gallopavo merriamz) in north-central Arizona with randomly 

selected ponderosa pine trees ~40 em diameter breast height (dbh) to determine which 

factors best identified trees that Merriam's turkey select for roosting. Roost trees were 

older (P= 0.007), greater in diameter (P < 0.001), taller (P <0.001), had greater surrounding basal area (BA; P= 0.086), and had a greater height to fttst limb (P= 0.063) than did 

randomly-located, ~ 40 em diameter trees. A logistic regression model, developed using a 

forward-stepwise approach, correctly classified 92.3% of all trees. Our model can be used 

to identify potential turkey roost trees by using only tree dbh and immediately surrounding 

BA. Used in conjunction with existing models, this model now allows managers to more 

accurately rank and prioritize potential roosting habitat for land management planning. 

Key words: Meleagris gallopavo merriami, Merriam's turkey, ponderosa pine, roost. 

Email: Brian F. Wakeling, bwakeling@gf.state.az.us 

119 
120 MERRIAM's TuRKEY RoosT TREES 

INTRODUCTION 

Merriam's turkeys consistendy select clumps of the largest ponderosa pine trees 

for roosting (Hoffman 1968, Scott and Boeker 1977, Rumble 1992, Mollohan et al. 

1995, Wakeling and Rogers 1996, 1998); therefore, habitat selected for roosting may 

be more characteristic and easily identified than other habitats that turkeys occupy. 

Most trees used for roosting exceed 40 em diameter breast height ( dbh) (Boeker and 

Scott 1969, Mackey 1984, Mollohan et al. 1995). In addition to large trees, high basal 

area (BA) (>20 m2/ha), dense overstory canopy (>50% closure), and steep slopes 

(> 30%) are other characteristics common to roosting habitat (Boeker and Scott 1969, 

Mackey 1984, Rumble 1992). Conversely, aspect and landform were selected less 

consistendy by roosting turkeys, depending on season and area (Schemnitz et al. 

1985,Rumble 1992,Mollohanetal.1995, WakelingandRogers 1996, 1998). Mollohan 

et al. (1995) found that trees with characteristics selected by roosting turkeys were 

extremely rare when compared with availability of those characteristics throughout 

the forests of north-central Arizona. 

Merriam's turkeys predominately limit diurnal activities to within 1.6 km of 

roosting sites (Wakeling 1997), and inadvertent removal or isolation of a roosting 

site, through management activities such as timber harvest, can be detrimental to 

Merriam's turkey populations (Scott and Boeker 1977). In recognition of this fact, 

protection of known roosting sites has been a practice of most land management 

agencies since the mid-1980s. But in many instances, documentation of roosting 

sites can be difficult and has not occurred. Numerous descriptions of roosting 

habitats have been developed (Boeker and Scott 1969, Phillips 1980, Rumble 1992), 

including qualitative (Mollohan et al. 1995) and mathematical models (Wakeling and 

Rogers 1996, 1998) that may be used to identify suitable roosting areas. Turkeys 

seem to select roosting sites in part by individual tree characteristics, yet no mathematical model is available to assist in the identification of suitable roost trees. 

We studied the differences between known roost trees at Merriam's turkey roosting 

sites in northern Arizona and paired, randomly located ponderosa pine trees ~ 40cm 

dbh. Our objective was to infer from this comparison if Merriam's turkeys differentiated among large trees when selecting roosts. A second objective was to develop a 

mathematical model that would assist resource managers to more easily identify 

suitable roosting trees in north-central Arizona. 

STUDY AREA 

We studied roost trees in ponderosa pine forests south of Flagstaff, Arizona, 

on the Coconino National Forest and south of Winslow, Arizona, on the ApacheSitgreaves National Forests. All roosting sites occurred within mixed conifer or 

ponderosa pine habitat associations, at elevations of1,850 to 2,450 m, within areas 

that had been previously studied by Mollohan et al. (1995) and Wakeling and Rogers 

(1998). Greater detail of habitat descriptions may be found in Wakeling and Rogers 

(1998). 
W AKELING ET AL. 121 

METHODS 

We measured characteristics associated with 13 ponderosa pine trees that had 

been used by roosting Merriam's turkeys and on 13 paired, randomly located :;::.:40 em 

dbh ponderosa pine trees. We randomly selected 1-2 roost trees from eight roosting 

sites that had been located during previous Merriam's turkey research studies (Mollohan 

et al. 1995, Wakeling and Rogers 1996, 1998). The random roost trees were selected 

by identifying the closest known roost tree on a computer-generated random bearing 

from the geographic center of a roosting site. The eight roosting sites represented 

three winter roosts, three summer roosts, and two roosts from yearlong range. We 

sampled two trees from each winter and yearlong roosting site and one tree from each 

summer roosting site because winter and yearlong roosting sites contained 2-3 times 

as many trees as summer roosting sites. 

We measured the following variables specific to each roost tree: dbh with a 

diameter tape; tree height and height to first limb by measuring a known distance 

perpendicular to the tree with a tape and using a clinometer to measure angles to the 

apex, first limb, and base of the tree; age of tree, by extracting a core sample from the 

tree with an increment bore (the sample was marked and tree rings enumerated later 

using a 1 OX magnification microscope in the laboratory); and presence or absence of 

horizontal branches > 3 em diameter within the upper third of the tree crown that 

approximated a level perching surface (i.e.,< 10% slope). 

In addition, we collected information pertaining to the site surrounding the 

roost tree. We measured slope(%) from 15m above to 15m below the roost tree 

using a clinometer. Stem density of trees >2.5 em dbh was enumerated on a 0.05-ha 

circular plot (12.6-m radius). We calculated percent canopy closure according to Strickler 

(1959) by taking readings with a spherical densiometer at site center and at 12.6 m 

from site center on four bearings, each 90 degrees from the previous, with the first 

bearing randomly oriented. We used a 10-factor prism to estimate basal area (BA) 

surrounding the roost tree. 

Immediately upon completing measurements on the roost tree, we randomly 

located a ponderosa pine tree >40 em dbh for comparison. These random trees 

included trees within and outside the roosting site because we were unable to determine conclusively if all trees within a site had been used for roosting. To locate 

random trees, we walked a computer-generated random bearing for a distance of 40-

500m. When we reached the random distance, we continued along the bearing until 

encountering the first ponderosa pine tree >40 em dbh. We then recorded the same 

measurements taken at the roost tree. 

We analyzed data using paired !-tests for continuous data (i.e., age, diameter, 

height, stem density, BA, slope, height to first limb, and canopy closure) and contingency tables for categorical data (i.e., horizontal branch presence). We then included 

all variables in a forward-stepwise logistic regression equation (Hosmer and Lemeshow 

1989) to determine what variables best predicted roost tree identification. 
122 MERRIAM's TuRKEY RoosT TREES 

RESULTS 

We found that many characteristics differed between roost and random trees: 

roost trees averaged greater age (P= 0.007), dbh (P< 0.001 ), height (P< 0.001 ), height 

to first limb (P= 0.063), and basal area (BA) (P= 0.086) surrounding the tree (fable 

1). Roost and random trees had the same number ofhorizontal branches (12 of13) 

Table 1. Mean (SD) and paired t-test probabilities (P) for characteristics of random 

ponderosa pine trees and those selected for roosting by Merriam's turkeys, in northcentral Arizona. 

Roost tree Random tree 

Characteristic (n = 13} (n = 13} p 

Age of tree (years) 161.5 (48.8) 104.6 (49.5) 0.007 

Diameter at breast height (em) 71.4 (12.0) 48.1 (7.6) <0.001 

Height of tree (m) 27.1 (4.0) 19.1 (5.1) <0.001 

Stem density on 0.5-ha circular plot 21.0 (9.8) 19.9 (12.2) 0.792 

Basal area surrounding roost tree (m2

/ha) 21.5 (8.3) 16.2 (6.7) 0.086 

Slope at roost tree (%) 15.8 (11.2) 16.2 (15.4) 0.931 

Height to first limb (m) 5.9 (2.5) 3.9 (2.7) 0.063 

Canopy closure (%) 49.3 (15.9) 44.2 (21.8) 0.503 

while slope and percent canopy closure did not differ between sites.The logistic 

regression model correctly classified 92.3% of roost vs. non-roost trees at all locations (fable 2). In this model, roost trees were associated with greater tree dbh and 

surrounding BA than were randomly located trees >40 em dbh. 

Table 2. Logistic regression model (legit scale) explaining ponderosa pine roost tree 

selection by Merriam's turkey in north-central Arizona. 

n x2 p 

Used Random 

Modela Percent correct 

Used Random Overall 

13 13 26.5 <0.001 Y=-20.266-+D.055BA-+D.274DBH 92.3 92.3 92.3 

a BA is the basal area (m2/ha) surrounding the roost tree, and DBH is the diameter at 

breast height (em) of the roost tree. 
W AKELING ET AL. 123 

DISCUSSION 

Although many studies have found that 40 em dbh was the minimum size 

roost tree used by Merriam's turkeys, and large, overmature ponderosa pine trees are 

most suitable for roosting, we speculate that 40 em dbh is not a functional minimal 

threshold for roost tree diameter. Rumble (1992) found Merriam's turkeys using 

ponderosa pine trees < 40 em dbh in the Black Hills, South Dakota, but these were 

the largest trees available on his study site. Because turkeys strongly favored trees that 

were older, taller, and larger diameter than the average ~ 40 em dbh tree in our study, 

we suggest that there may not be a critical minimum above which tree diameter 

becomes irrelevant. Although Merriam's turkeys inhabit areas that lack larger trees, 

they seem to consistently prefer the largest available trees. 

Basal area (BA) is apparently also influential in the selection of roost trees. 

Greater BA seems favorable to roosting site selection regardless of maximum available BA. Even in South Dakota second growth ponderosa pine, Rumble (1992) 

found Merriam's turkeys roosting in sites with >20 m2 /ha BA. Thus, selection of 

roosting sites by Merriam's turkeys consistently favors older, mature trees with high 

surrounding BA. 

Our logistic regression model can be used to assist in prioritizing potential 

roosting sites and roost trees when planning land management activities such as 

timber harvest or other impact developments. This roost tree model can be used in 

conjunction with broader scale models provided for roosting site selection during 

winter (Wakeling and Rogers 1996) or summer (Wakeling and Rogers 1998; Table 3). 

Roosting site models could be used to identify potential roosting habitat, whereas 

the roost tree model could be used to assist with ranking priorities on trees within 

those potential sites. Sites with similar scores from roosting habitat models could be 

protected and managed for roosting habitat regardless of the score, if Merriam's 

turkeys are a featured management species. In accordance with Wakeling (1997), 

roosting sites should be managed at tree densities of ~ 1.25 /km2

• 

Table 3. Logistic regression models (legit scale) describing Merriam's turkey roosting 

site selection during winter and summer in north-central Arizona. 

Season Modela Citation 

Winter Y = -21.290 + 9.803CPY + 0.742PD +0.386SLOPE Wakeling and Rogers (1996) 

Summer Y = -6.614 + 1.435CG + 0.160PBA + 0.101SLOPE Wakeling and Rogers (1998) 

a CPY is closed overhead canopy presence (absent= 0, present= 1 ), PD is the mean 

ponderosa pine diameter at breast height (em), SLOPE is slope at site (%), CG is 

conifer ground cover <46 em in height(%), and PBA is the basal area provided by 

ponderosa pine at the site (m2

/ha). 
124 MERRIAM's TuRKEY RoosT TREES 

AcKNOWLEDGEMENTS 

This research was funded by the Federal Aid to Wildlife Restoration Project W78-R through the Arizona Game and Fish Department. Grand Canyon University, 

Department of Biology, College of Science and Allied Health provided additional 

support. This manuscript benefited from a review by R. A. Ockenfels and two 

anonymous referees. 

LITERATURE CITED 

Boeker, E. L., and V E. Scott. 1969. Roost tree characteristics of Merriam's turkey. 

Journal of Wildlife Management 33: 121-124. 

Hoffman, D. M. 1968. Roost sites and habits of Merriam's turkeys in Colorado. 

Journal of Wildlife Management 32: 859-866. 

Hosmer, D. W,Jr., and S. Lemeshow. 1989. Applied logistic regression. John Wiley 

and Sons, Inc., New York. 307pp. 

Mackey, D. L. 1984. Roosting habitat of Merriam's wild turkeys in south-central 

Washington. Journal of Wildlife Management 48: 1377-1382. 

Mollohan, C. M., D. R. Patton, and B. F. Wakeling. 1995. Habitat selection and use by 

Merriam's turkey in northcentral Arizona. Arizona Game and Fish Department 

Technical Report 9, Phoenix. 46pp. 

Phillips, F. 1980. A basic guide to roost site management for Merriam's turkeys. 

Arizona Game and Fish Department Wildlife Digest 12, Phoenix. 6pp. 

Rumble, M. A. 1992. Roosting habitat of Merriam's turkeys in the Black Hills, South 

Dakota. Journal of Wildlife Management 56: 750-759. 

Schemnitz, S.D., D. L. Goemdt, and K H. Jones. 1985. Habitat needs and management of Merriam's turkey in New Mexico. Proceedings of the National Wild 

Turkey Symposium 5: 199-231. 

Scott, V E., and E. L. Boeker. 1977. Responses of Merriam's turkey to pinyon-juniper 

control. Journal of Range Management 30: 220-223. 

Strickler, G. S. 1959. Use of the densiometer to estimate density of forest canopy on 

permanent sample plots. USDA Forest Service Research Note PNW-180. Spp. 

Wakeling, B. F. 1997. Winter movement patterns of Merriam's turkeys in north-central 

Arizona. Proceedings of the Biennial Conference of Research on the Colorado 

Plateau 3: 93-100. 

Wakeling, B. F., and T. D. Rogers. 1996. Winter diet and habitat selection by Merriam's 

turkeys in north-central Arizona. Proceedings of the National Wild Turkey 

Symposium 7: 175-184. 

Wakeling, B. F., and T. D. Rogers. 1998. Summer resource selection and yearlong 

survival of male Merriam's turkeys in north-central Arizona, with associated 

implications from demographic modeling. Arizona Game and Fish Department 

Technical Report 28, Phoenix. 50pp. 
The Suitability of Butterflies 

as Indicators of Ecosystem Condition: 

A Comparison of Butterfly Diversity Across 

Stand Treatments in Northern Arizona 

Kerry L. Griffis 

Department of Fisheries and Wildlife 

Utah State University 

Logan, UT 84341-5210 

Sheri S. Mann 

Agriculture, Health, and Natural Resource Extension 

American Samoa Community College 

Pago Pago, AmericanS amoa 96799 

and 

Michael R. Wagner 

School of Forestry 

Northern Arizona University 

Flagstaff,AZ 86011 

Abstract. Past human activities have resulted in a broad spectrum of ponderosa pine (Pinus 

ponderosa) stand conditions on the Colorado Plateau. A team of researchers at Northern 

Arizona University's School of Forestry is using common experimental plots to evaluate how 

highly-variable forest conditions influence potential ecological indicator species and guilds. 

Butterflies are one of several insect guilds that we are evaluating. During 1997 and 1998, we 

monitored populations of adult butterflies within replicated unmanaged, thinned, thinned 

and prescribed-burned, and wildfire-affected ponderosa pine sites. We did not detect a 

significant effect of stand treatment on butterfly abundance or similarity at the family level 

after two years, despite our large plot size (20-80 ha). Important treatment effects may be 

masked by butterfly movement between plots, heterogeneous forest stand structure, time 

since treatment, number of stands sampled, climate, and our sampling of adult butterflies. We 

suggest that sampling effort should be increased and data analyzed at the species level to 

quantify butterfly response to stand treatment. 

Key words: Lepidoptera, Pinus ponderosa, indicator species, forest treatments, thinning, 

prescribed f:tre, wildf:tre 

Email: Kerry Griffis, kerryg@cc.usu.edu 

125 
126 BUTIERFLIES AS ECOSYS1EM CONDITION INDICATORS 

INTRODUCTION 

Past human activities (e.g, logging, grazing, fire suppression) have impacted 

stand conditions in ponderosa pine forests across the Colorado Plateau (Weaver 

1951, Covington et al. 1997). The combination of past human activities has lead to 

structural and functional forest changes, often resulting in dense stands with low 

understory plant diversity (Harrington and Sackett 1992, White 1985, Covington and 

Moore 1994, Fule et al. 1997). A team of researchers (STIFH, Stand Treatment 

Impacts on Forest Health) at Northern Arizona University's School of Forestry is 

evaluating the effects of past stand treatments on insects, fungi, understory plants, 

forest structure, and eventually ecosystem function. Butterflies (Lepidoptera) are one 

of several guilds that we are evaluating as a potential ecological indicator of forest 

condition in northern Arizona. Our goal is to find a group of species that is easy to 

evaluate, in addition to exhibiting changes in abundance and richness in response to 

differences in stand treatments. 

Indicator species are thought to either signal the presence/ abundance of other 

species, or to signal chemical/ physical changes in the environment through changes 

in their own presence or abundance (Landres et al. 1988, Simberloff 1998). The 

second of these types of indicators is referred to as an ecological indicator (McGeoch 

1998). One of the key goals in using an indicator is to simplify measurements of a 

complex system without losing important information (Ferris and Humphrey 1999). 

A number of authors have proposed criteria for selecting indicator species (e.g., 

Landres et al. 1988, Rodriguez et al. 1998, Ferris and Humphrey 1999). Recently, Hilty 

and Merenlender (2000) organized and compiled these criteria into a comprehensive 

list. They suggest that no indicator can meet all the suggested criteria, but should 

meet a majority of the standards. 

In many regions of the world, Lepidoptera are widely accepted as ecological 

indicators of ecosystem health (Rosenberg et al. 1986, New et al. 199 5, Beccaloni and 

Gaston 1995, Oostermeijer and van Swaay 1998), and meet a number of the criteria 

set forth by Hilty and Merenlender (2000). Butterflies have a fairly clear taxonomy, 

and their life history and biology are well defined (Nelson and Anderson 1994, Wood 

and Gillman 1998). Many of their physiological tolerances, such as light, temperature, and habitat requirements, have been quantified (Warren 1985, Thomas and 

Harrison 1992, Greatorex-Davies et al. 1993, Sparks et al. 1996, Oostermeijer and 

Swaay 1998, Pollard et al. 1998), and correlations with changes in ecosystem conditions have been demonstrated (Bowman et al. 1990, Thomas and Harrison 1992, 

Hill et al. 1995, Pullin 1996, Sparks et al. 1996, Spitzer et al. 1997, Pollard et al. 1998, 

Schultz 1998, Swengel1998). In addition, butterflies are small, have high reproductive rates, and are at a low trophic level that allow them to quickly respond to environmental stress. Many butterflies specialize on a specific plant species for oviposition or 

feeding (Ehrlich 1984, Oostermeijer and van Swaay 1998). Butterflies tend to be easy 

to find and measure. Also, they are charismatic, and the public tends to show interest 

in them. 

There are drawbacks to using butterflies as ecological indicators: (1) they are fairly 

mobile and may be able to tolerate some levels of disturbance because of their ability 

to move and find resources; (2) their ability to respond to change can be a hindrance 
G Rl FFI S ET :\L. 127 

in areas with high climatic variability, as changes detected in their abundance may be in 

response to a climatic condition instead of ecosystem structure (Pollard and Yates 

1993). 

We evaluated how the abundance and diversity of butterflies varied among four 

replicated forest treatments in northern Arizona. We hypothesized that butterfly 

abundance and diversity should be lower in unmanaged areas than in treated stands, 

and that high intensity fire (represented by stand-replacing wildfire) should correlate 

with high butterfly abundance and diversity. Another study, using our same stands 

and other stands from the STIFH project, found the abundance of nectar-bearing 

plants highest in stands that experienced wildfire, and lowest in stands that did not 

have applied silvicultural treatments (Griffis et al. 2001). We attempt, by showing 

correlations between butterfly abundance and diversity, to assess the suitability of 

using butterflies as environmental indicators in northern Arizona ponderosa pine 

forests. 

METHODS 

Study Site 

The study area, located on the Coconino Plateau in northern Arizona (Fig. 1 ), is 

approximately 2,000 to 2,450 m elevation in a ponderosa pine/ Arizona fescue (Pinus 

ponderosa Doug!. ex Laws / Festuca arizonica Vasey) association (USDA Forest Service 

1997). The dominant overs tory species was Pinus ponderosa (ponderosa pine) with a 

small component of Quercus gambe!ii (gam bel oak). The understory is characterized 

by the most common native species; Festuca arizonica, E!ymus e!ymoides (Raf.) Swezey, 

Figure 1. Location of STIFH research plots in ponderosa pine forests near Flagstaff, 

Arizona. Circles on inset illustrate individual plots. From Bailey et al. (this volume). 
128 BUTIERFLIES AS ECOSYS1EM CONDITION INDICATORS 

Cirsium wheeleri (Gray) Petrak., Carex spp., Muhlenbergia montana (Nutt.) A.S. Hitchc., 

Lupinus argenteus Pursh, and Achillea millefolium L.; and the most common exotic 

species; Sa/sola kali var. tenuifolia Tausch., Verbascum thapsus L., Poa pratensis L., Chenopodium graveolens Willd., Bromus tectorum L., and Descurainia obtusa (Greene) O.E. 

Schulz (Griffis et al. 2001 ). During the first year of the study (1997), three stand 

treatments were examined: unmanaged, thinned and burned, and wildfire. In the 

second year, 1998, thinned stands were sampled in addition to the 1997 treatments. 

Three stands per treatment were sampled in 1997; four stands per treatment were 

sampled in 1998. Stands ranged in size from 20-80 ha (50-200 acres). 

Stands were selected randomly from a larger pool of stands used in the STIFH 

project (Fig. 1). Our stands were composed of mature, even-aged blackjack (younger 

than approximately 125 years) ponderosa pine, with larger, yellow pine (older than 

approximately 125 years and larger than 64 em dbh) not exceeding 10 trees/ha. 

Thinned stands had greater than 30% of their basal area removed between 1987 and 

1993, with at least 50% of this coming from diameter classes greater than 30 em 

(pulpwood size). Thinned and prescribed burned stands additionally received a 

broadcast burn treatment within 3 to 4 years of thinning. Three of these stands were 

burned in 1991 and one in 1995. Overstory survival following the broadcast burn 

was greater than 90%. Unmanaged stands have not received a density altering treatment within the last 30 years, such that the stands have greater than 60% of maximum stand density index (and thus are actively self-thinning). Wildfire areas are 

stands in which greater than 90% of the basal area was killed and/ or consumed by 

wildfire since 1994 (Bailey et al. 2001). 

Butterfly Sampling in 1997 

All butterfly specimens captured in 1997 were included in the establishment of 

a permanent reference collection used for identification in later studies. Two person 

hours of sampling were conducted at each site every three weeks for five visits from 

May to August 1997. Butterflies were collected using a time-constrained area search 

during peak flight periods (0900 to 1500 hours), using standard 18" insect collecting 

nets. The same people collected data throughout the year to minimize variation in 

collection methods. We searched the pre-delineated stand, collecting as many butterflies as possible. All specimens were handled and mounted using conventional procedures, with butterflies identified using Pyle (1981) and Tilden and Smith (1986). 

When further taxonomic identification was necessary, wing venation was examined, 

as described by Borror et al. (197 6). External experts confirmed most species identification. 

Butterfly Sampling in 1998 

We conducted a time-constrained area search and counted all Lepidoptera observed and captured, using the same techniques and time constraints for capture as in 

the previous year (1997). All stands were visited once during the summer during 

peak butterfly activity (based on information from 1997 surveys). We only used one 
GRIFFIS ET AL. 129 

sampling visit based on when the largest numbers of lepidopterans were active 

during 1997. Butterfly populations fluctuate throughout the year based on species 

life history. Our goal was to assess using butterflies as a very rapid and simple 

indicator of the amount of disturbance in a stand; therefore, we made our sampling 

as simple as possible. At the end of the sampling period, we recorded the number 

and family of all butterflies observed and caught, and released all insects. Data were 

summarized at the family level because of identification ease in the field, and because 

others have shown responses of insects to changes in the environment at higher 

taxonomic and guild levels (Greenburg and McGrane 1996, Kevan 1999). 

Data Analysis 

We used data from 1997 to calculate Jaccard's similarity index to compare the 

similarity of butterfly species across treatments. Data from 1997 and 1998 were 

analyzed separately due to differences in sampling procedures. The butterfly numbers in 1997, by family, were averaged across repeated sampling over time to calculate 

one number per family per plot. We used Kruskal-Wallis rank tests to assess variation in number of individuals per family, by treatment type for both years. 

RESULTS 

The numbers of individuals caught in each treatment for each butterfly family in 

the analyses are listed in Table 1. A list of butterfly species caught during 1997 and 

verified to species is listed in Table 2. The total number of butterflies did not vary 

among treatment types for either 1997 (X2 = 0.622, df = 2, p = 0. 733) or 1998 (X2 = 

0.969, df= 3, p = 0.809). Neither species similarity nor abundance ofbutterflies by 

families was significandy different across the four experimental treatments. Butterfly 

similarity from Jaccard's similarity index, were statistically equal in 1997 across the 

experimental treatments (Table 3). These analyses were not repeated in 1998. Likewise, total abundance of butterflies distributed by treatment type was not statistically 

significant in either 1997 or 1998 (Table 4). 

Table 1. The numbers of individuals within families of Lepidoptera captured (1997) 

and captured and observed (1998) in ponderosa pine cover type under four 

experimental stand conditions (unmanaged, thinned, thinned and burned, and wildfire) 

on the Coconino National Forest. 

Family Unmanaged Thinned Thinned and Burned Wildfire 

97' 98' 97' 98' 97' 98' 97' 98' 

Lycanidae 6 81 - 59 3 79 1 43 

Peridae 26 9 4 26 20 12 27 

Nymphalidae 1 18 16 1 2 13 26 

Hesperidae 0 3 3 0 2 1 5 

Papilionidae 0 0 0 0 0 0 1 
130 BUTIERFLIES AS ECOSYS1EM CONDITION INDICATORS 

Table 2. Species of butterflies collected and verified in 1997 and 1998 in ponderosa 

pine cover type under four experimental stand conditions (unmanaged, thinned, 

thinned and burned, and wildfire) on the Coconino National Forest. 

Family Scientific Name Common Name 

PIERIDAE: Neophasia men apia ...................... Pine White 

Pontia protodice ............................ Checkered White 

Co/ias eurytheme .......................... Orange Sulphur 

Nathalis iole .................................. Dainty Sulphur 

LYCAENIDAE: Callophrys eryphou ....................... Western Pine Elfin 

Strymon melinus ........................... Gray Hairstreak 

Hemiargus isola ............................ Reakirt's Hairstreak 

Celastrina ladon ............................ Spring Azure 

Lycaeides melissa ........................ Melissa Blue 

lcaricia icariodes .......................... Boisduval's Blue 

lcaricia lupini ................................ Lupine Blue 

NYMPHALIDAE: Euptoieta claudia .......................... Variegated Fritillary 

Poladryas min uta .......................... Dotted Checkerspot 

Vanessa cardui ............................. Painted Lady 

Vanessa virginiensis .................... American Lady 

Phyciodes pratensis ..................... Field Crescent 

HESPERIDAE: Pyrgus communis ......................... Common Checkered-Skipper 

The butterfly community in this ponderosa pine system is dominated by three 

families: Lycaenidae, Pieridae, and Nymphalidae. The abundance of individuals 

within these families varied between years (Fig. 2). There appeared to be a trend of 

decreased abundance of lycanid butterflies across the treatment gradient in both 

years. There was an increase in perid butterflies across the same gradient in 1998, but 

a decrease in 1997. There may also be an increase in nymphalid species for both years 

across the disturbance gradient (Fig. 2). 

DISCUSSION 

We did not detect differences in butterfly abundance or diversity among forest 

treatments, but a similar study focused on forest restoration, including thinning and 

Table 3. Jaccard's similarity index calculated based on butterfly species similarity 

for 1997 sampling on the Coconino National Forest. 

Comparison 

Thinned and Prescribed Burned vs. Control 

Thinned and Burned vs. Wildfire 

Wildfire vs Control 

Jaccard's Similarity Index 

0.575 

0.650 

0.575 
GRIFFIS ET AL. 131 

Table 4. Results from Kruskai-Wallis rank tests on the distribution of butterfly families 

among treatments (unmanaged, thinned, thinned and prescribed burned, stand 

replacing wildfire) for 1997 and 1998 on the Coconino National Forest, northern 

Arizona. 

Family x2 df P-value 

1997 Lycaenidae 2.249 2 0.325 

Pieridae 1.689 2 0.430 

Nymphalidae 3.684 2 0.159 

Hesperidae 2.889 2 0.236 

1998 Lycaenidae 1.994 3 0.574 

Pieridae 2.051 3 0.562 

Nymphalidae 6.357 3 0.095 

Hesperidae 0.489 3 0.921 

Papilionidae 3.000 3 0.392 

burning treatments in northern Arizona, demonstrated increased species richness 

and abundance in treated areas of the ponderosa pine forest (Waltz and Covington 

1999). Their study concluded that there was an increase in butterfly abundance, 

correlated with an increase in flowering plants, in response to restoration treatments. 

However, their study compared a single transect in a thinned and burned stand with 

a single control-stand transect. Their lack of replication can decrease variance and 

artificially increase the chances of detecting significant relationships between treatments and butterflies. 

Other studies have detected changes in abundances of various insect taxa, including butterflies, in response to silvicultural treatments (Greenburg and McGrane 

1996, Swengel1998, Wood and Gillman 1998). Furthermore, some have suggested 

that sampling at the genus or family level of insects may give enough detailed information to permit evaluation of the health or sustainability of the system in question 

(Paoletti 1999). However, when the butterflies are lumped by family, it is likely that 

individual species effects are masked (Weaver 199 5). Species within a family vary in 

their life histories and, hence, may vary in their responses to environmental perturbation. It is often at the individual or population level that organisms respond to 

changes in the environment (11altby 1999). Future work should include a focus on 

individual species, as well as the family, and in particular species and families that are 

relatively abundant but specific in their ecological requirements (Thomas and Mallorie 

1985). 

We also want to point out that butterfly numbers varied gready between years. 

This variation could be an artifact of our small sample size. In addition, at high 

elevations climatic conditions (e.g., temperature, precipitation, wind) can vary erratically and cyclically between years (Gass and Lertzman 1980, Griffis 1999). Butterflies 
132 B uTrERFLI ES :IS EcosvsTE' ' CoNDITION I NDIC:ITORs 

-; "' 

"C = 

·;; 

...... 

"C 

.:l 

...... 

0 

'"' 

30 

25 

20 

15 

10 

5 

,.Q 

~ 

8 

0 

Lycaenidae 

I •~ = z 

-; 

0 80 

E-; 

70 

60 

50 

40 

30 

20 

10 

0 

Lycaenidae 

1997 

- Unmanaged 

Thinned 

- Thinned & Burned 

IIElllliiiZl Wildfire 

• • ~ 

Peridae Nymphalidae Hesperiidae Papilionidae 

1998 

Peridae Nymphalidae Hesperiidae Papilionidae 

Lepidopteran Families 

Figure 2. Total numbers of butterflies observed and captured; grouped by family 

during 1997 and 1998 in relation to forest stand treatments on the Coconino National 

Forest. Notice the difference in scales between graphs. 

may respond more directly to climatic conditions tlnn to stand conditions (Pollard 

and Yates 1993). This alone would make them very difficult to utilize as an indicator 

of stand condition. In addition, when surveying, we recorded adult butterfly diurnal 

behavior and did not measure butterfly fitness (i.e., survival or reproductive success) 

in relation to habitat patch. If butterflies disperse from a source population into 

marginal habitat, we may just be measuring density dependent population responses 

or dispersal events, and not responses to stand condition. 

Finally, the methods that we used in this study may be better used for assessing 
GRIFFIS ET AL. 133 

presence or absence of a species/family (e.g., Thomas and Harrison 1992). We 

suggest using transect counts, which can give quantitative estimates of abundance 

based on area, and may be better used to quantify butterfly abundance in heterogeneous forest stands (Pollard et al. 1975, Thomas 1983). 

We suggest that at the family level, butterflies may not be an indicator of ecosystem health that is both simply and rapidly measured. It is possible, with a sampling 

design based on achieving quantitative estimates of abundance and species identification, butterflies could be used as ecosystem indicators. However, in an arid environment such as northern Arizona, achieving a sample size large enough to account 

for climatic variation may be difficult. Also, the length of response time from treatment may influence butterfly presence. 

AcKNOWLEDGEMENTs 

This project was supported in part by the Rocky Mountain Forest and Range 

Experimental Station and in part by the Northern Arizona University Mcintire Stennis 

Research Program. 

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Stand Treatment Impacts 

on Forest Health (STIFH): 

Structural Responses Associated 

with Silvicultural Treatments 

John Duff Bailey 

Michael R. Wagner 

and 

Jonathan]. Smith 

College of Eco.rystem Science and Management 

School of Forestry, Northern Arizona Universiry 

POBox 15018 

Flagstqff,AZ 86011-5018 

Abstract: A major gap currently exists in our understanding of how landscape-level 

operational silviculture affects ponderosa pine (Pinus ponderosa) forest ecosystem health in 

northern Arizona. More than 70% of our forested landscape is in relatively young, evenaged stands resulting from a history of grazing, ftte exclusion, atypical climatic events, and 

large ('yellow') pine removal. This multi-year, multiple-investigator project specifically 

examined stands that have been thinned to improve forest ecosystem health, or similarly 

thinned and then treated with prescribed underburning. These two treatments were not 

different from one another with respect to any aboveground structural characteristic. 

However, every measure of living overstory density (trees and saplings) was lower in 

thinned treatments than in untreated stands, and mean tree size (stem and crown) was 

consistently greater in thinned treatments. Areas burned by stand-replacing wildftte in 

1996, now without an overstory, had zero seedlings and saplings as well as greater densities 

of standing dead trees than treated and untreated stands. The STIFH project as a whole 

is examining a range of species-specific and ecosystem responses to this spectrum of stand 

conditions, including fungi, insect, and understory plant composition. 

Key words: ponderosa pine, forest ecosystem health, silviculture, thinning, prescribed 

ftte. 

Email: John D. Bailey, John.Duff.Bailey@nau.edu 

137 
138 STci ND TREc\T,\ IENT L\iP:ICTS ON FOREST J-l Ec\LTI-1 

INTRODUCTION 

A major research gap currently exists in our understanding of how landscapelevel operational stand treatments, like thinning and prescribed fire, affect forest 

development and health in northern Arizona. In 1998, N orthern Arizona University's 

School of Forestry began to fill that gap with a multi-year, multiple-investigator 

research project in the ponderosa pine (Pinus ponderosa var. scopu!orum) forests of the 

Coconino and Mogollon Plateaus. We identified treatment specifics and stands that 

could potentially be used for a broad forest ecosystem health study that examines 

aboveground forest structure, soils, and insect and fungal populations (Harvey 1994). 

As many in northern Arizona are aware, a large percentage of our forested land 

is in relatively young, dense even-aged stands. This atypical condition has resulted 

from a management history that includes over-grazing, fire exclusion, uncommon 

climatic events, and major removal oflarge overstory pine (Pearson 1949, Swetnam 

and Betancourt 1990, Covington et al. 1994, Sampson and Adams 1994). The Stand 

Treatment Impacts on Forest Health (STIFH) project was designed specifically to 

examine stands that have been treated in tl1e last decade with either thinning (TH) or 

thinning with prescribed burning (TB) to reduce fuel accumulations and stimulate 

tree vigor, thus improving overall stand health (Sampson and Adams 1994). In 

addition, we examined untreated control stands (UN), with neither thinning nor fire 

in the last 20-30 years, and areas burned by stand-replacing wildfire in 1996 (WF). 

These four broad "treatments", defined below in detail, paint a wide spectrum of 

stand conditions available for management (Smith et al. 1996) and a broad range of 

disturbances both with and without fire (Fig. 1). In the future, other stand types/ 

treatments (e.g., pine/ oak mixes and large-scale ecological restoration treatments) 

will be available to include in the design to broaden its scope. 

STIFH was designed to examine large(> 40 ha), mechanically-thinned stands 

with and without prescribed low-intensity surface fire. These are typical silvicultural 

approaches used by land managers to improve forest health, reduce the risk of 

wildfire, and improve aesthetics (Smith et al. 1996). Such treatments will remain as 

likely objectives for much of the western landscape, particularly for reducing the risk 

Unburned Burned 

:ttl: ... •• ~~ .... •• 

Control Thinned Thinned & Burned Wildfire 

Disturbance Gradient 

Figure 1. Conceptual representation of the fou r study 'treatments' arranged along a 

disturbance gradient, both with/without fire and with/without mechanical treatment. 
BAILEY ET AL. 139 

of fire in the urban-wildland interface. Future treatment refinements likely will be 

spawned from these traditional silvicultural practices as well as new ecosystem-based 

management ideas and practices (including many aspects of ecological restoration). 

The initial four STIFH treatments represent our best first guess at two reasonable 

silvicultural options and two polar alternatives currendy available for comparison; 

they are the only treatments available in large pieces across this landscape. Large land 

areas are necessary for evaluation of many forest ecosystem health parameters (e.g., 

most wildlife habitat, plant dispersion, and watershed process issues). 

METHODS 

Stand Selection 

The first step in this project was to identify candidate stands of greater than 40 

ha on the Coconino or Mogollon Plateau (within two hours of Flagstaff) in each of 

the four "treatments" using the following selection criteria: 

Unmanaged (UN)- stands dominated by even-aged, smaller ( < 40 em diameter at breast height (DB H)) 'blackjack' ponderosa pine trees (a common name based 

on bark characteristics), with only a scattering of larger(> 60 em DBH) 'yellow' 

ponderosa pine, Gam bel oak (Quercus gambeliz), or other tree species. Specifically, the 

average density of yellow pine in this treatment does not exceed 10 trees/ha, with no 

stand exceeding 37 trees/ha, and oak/juniper stems comprising less than 10% of 

tree density. These stands had not received a density-altering treatment within the 

last 30 years, based on USDA Forest Service records and field observations, such that 

the stands have a Stand Density Index (Rieneke 1933) in excess of 270 and, thus, the 

trees are crowded and actively self-thinning (Smith et al. 1996). These stands serve as 

a control treatment for examining disturbance. 

Thinned stands (TH) - stands of mature, even-aged 'blackjack' ponderosa 

pine, similar to the unmanaged stands (i.e., a low density of 'yellow' pine, oak and/ 

or other species), but which have had greater than 30% of their basal area removed 

between 1988 and 1995. At least half of the volume removal came from diameter 

classes < 30 em ('pulpwood' size). Potential stands were identified from management records available from the USDA Forest Service. 

Thinned and prescribed burned stands (fB)-stands like the thinned stands 

that also received at least one prescribed broadcast surface burn treatment within 

three to four years of thinning (1989 to 1997). Overstory survival, following the 

broadcast burn, has been greater than 90%, indicating minimal fire disturbance to 

overstory trees. 

Wildfire areas (WF) - stands typically like the unmanaged stands prior to a 

stand-replacing wildfire during the summer of 1996, in which greater than 90% of 

the ponderosa pine basal area (blackjack and yellow pine) was killed and/ or consumed by the fire. This treatment serves as the maximum disturbance. 

Ten to twenty stands were identified in each of the first three treatments, from 

which ten stands were selected randomly for this study (Fig. 2). Due to the limited 

availability of wildfire stands on comparable terrain and soil, only seven wildfire areas 

were identified and included in the study. 
140 STci ND TREc\T,\ IENT L\iP:ICTS ON F O REST J-l Ec\LTI-1 

Figure 2. Location of STIFH research plots in ponderoda pine forests of northern 

Arizona. Circles on inset denote individual plots. 

Data Collection and Analysis 

Ten randomly-located (systematic following a random start), 20m by 20m 

square plots were established in each stand for sampling overstory trees, saplings 

(small trees between 0.1 and 7.4 em DBH) and seedlings (Table 1) using standard 

forest mensurational techniques (.Avery and Burkhart 1994). The intent was to augment and update existing data available for each stand, to establish permanent plot 

locations that can be re-measured over time, and to provide identifiable locations for 

sampling of other taxa (plant and animal) which may be related to tree vegetation 

and forest ecosystem health. Permanent plot centers were established with labeled 

iron pins inside painted PVC sleeves; neighboring trees were tagged with similar 

labels. Corners were pin-flagged for delimiting the plot and corner fuel transects. 

A systematic, random sample of ten plots within stands (along a grid with a 

random start) allowed calculation of stand means and variances for comparisons 

among stands, and will allow the exploration of correlations among different taxa 

within stands. Comparisons among treatments were based on the 7 or 10 randomly-selected stands within each treatment. Live overs tory and seedling density and 

structure were compared among the UN, TH, and TB treatments only; the WF 

treatment had no live trees. We used analysis of variance (.ANOV.A) to test for 

differences in structural characteristics among the treatments. Tukey's Honesdy Significant Difference (Tukey's HSD) was used as a multiple comparison test for means 

that had significant (P ~ 0.05) .AN OVA results (Zar 1996). 

RESULTS AND DISCUSSION 

Unmanaged (UN) stands had significandy higher total tree density (trees/ha) 

and Stand Density Index (SDI) (Rieneke 1933) than d1eir thinned (TH) and d1inned/ 
BAILEY ET AL. 141 

Table 1. Aboveground stand structure variables collected at each plot in each stand 

on STIFH, near Flagstaff, Arizona 

Trees greater than 7.6 em (3 in} in 20 m by 20 m square plot - tagged: 

tree number: 1 - x, 

o 1-letter species code: P = pipo, Y = yellow pipo, Q = quga, J = jude or jusc, 

o DBH: with d-tape just above the nail, 

total height and height to live crown: with clinometer, 

o crown radius in longest dimension and clockwise perpendicular to that radius: 

with distance tape, 

crown position (i.e., D =dominant, C = codominant, I= intermediate, or S = 

suppressed), 

o Hawksworth dwarf mistletoe rating (0-6), 

bark beetle rating (0-2, see below), and 

o Keen's crown classification. 

Saplings less than 7.6 em (3 in} within 20 m by 20 m square plot: 

direction and distance from point: with hand compass and distance tape, or 

laser, 

1-letter species code (as above), 

DBH: with d-tape at 1.4 m (4.5 ft), 

total height and height to live crown (as above), 

crown radius in longest dimension and perpendicular to that radius (as above), 

Hawksworth dwarf mistletoe rating (as above), and 

bark beetle rating (as above). 

Seedlings (saplings less than 1.4 m (4.5 ft} height} within 20 m by 20 m 

square plot: 

o direction and distance from point (as above), 

o 1-letter species code (as above), 

o total height with tape, and 

o severity of browse - number of past clippings/forks. 

Stumps within 20 m by 20 m square plot: 

o 1-letter species code, and 

o inside bark diameter: with tape. 

Snags, oak, and yellow pines within 50 m (164 ft) radius: 

o landscape density rating (0-3, see below). 

Bark Beetle Rating system (from USDA, FS Forest Insect and Diseases 

Field Guide): 

0- no attack 

1 - old attacks; pitch tubes on tree bole hard and pink to reddish. Needle color 

from green to yellowish-green or reddish to rusty brown. 

2 - fresh attack; green needles, but with soft pinkish-white pitch tubes on the 

bole. Dry reddish-brown boring dust in bark crevices and at the tree base. 

Snag, Oak, and Yellow Pine Landscape Density Rating system: 

0 - none visible within 50 m (164 ft) 

1 - low density; not in plot but less than 10 individuals within 50 m 

2 - medium density; 11-20 within 50 m with perhaps some in the plot 

3 - high density; greater than 20 individuals with 50 m with some in the plot. 
142 STci ND TREc\T,\ IENT L\IP:ICTS ON FOREST J-l Ec\LTI-1 

burned (TB) counterparts (Figs. 3a and 3b). This is logical given past management 

that removed overstory trees, and simply confirms that thinning treatments were 

effective. These data also show that the TB treatment, which includes prescribed 

surface fire, was not significantly different from the unburned, TH treatment in 

terms of overs tory density and, as shown below, individual tree characteristics. 

Differences in density across the three treatments were traceable to a higher 

density of small-diameter, blackjack pine trees in UN stands (Fig. 3c), which leads to 

notable differences in average tree stem and crown characteristics. Unmanaged stands 

had lower average stem diameters at breast height (DB H), which together with shading suppression and lower average live crown ratios (LCR), led to lower average 

Stand Type Comparisons Among the Three Treatments with Live Trees 

a. b. 

1200 500 F= 25.6 F =38.6 p < O.Oi p < O.tH 

·C 400 

~ 900 

~ 

~ 300 

t Q ,::: 600 "' 

~ c 

~ 

~ 200 :E 300 

100 

UN TH TB UN TH TB 

c. d. 

1200 40 

F= 21.8 F .. 9.9 

p < 0.01 p < O.O l 

~ 900 

·D ~ ~ 30 

~ 

~ 600 

= c 20 ~ 

:E 300 

~ 10 

UN TH TB UN TH T8 

Figure 3 a-d. a. Mean overstory tree density (trees/ha); b. mean Stand Density 

Index; c. mean density of small-diameter 'blackjack' pine trees (stems/ha); d. mean 

DBH (em). ANOVA F-statistic and observed significance (p) values are shown in the 

upper right corner of each graph. Significant differences among individual treatment 

means are denoted with letters to the left of each box plot. Each whisker represents 

range of values. Boxes represent interquartile range. Means are lines within boxes. 

Outliers (circles) represent values greater than 1.5 box lengths. 
RIILEY ET :IL. 143 

crown diameters (Figs. 3d-3f). Such differences in tree characteristics are predictable 

given differences in overs tory stand density (Smith et al. 1996). Unmanaged stands 

had a higher density of saplings (Fig. 3g). These saplings, however, were predominantly suppressed individuals from the same cohort as the overstory trees, rather 

than younger, vigorously-growing saplings that can contribute to future stand structure (Smith et al. 1996). There were no significant differences in seedling density 

among treatments (Fig. 3h). 

Comparisons were made across all four treatments with regard to the standing 

dead component. Wildfire stands had significandy higher densities of standing dead 

trees as a result of these stand-replacing events- an efficient way to kill trees (Fig. 4). 

Stand Type Comparisons Among the Three Treatments with Live Trees 

e. 

80 .--------------------------, 

~ 

" u 

...l 

70 

60 

; 50 

~ 

40 

F=7.4 

p < O.Ol 

30 L_ ________________________ ~ 

UN TH TB 

g. 

700 ,----------------------------, 

600 

~ 500 

! 

~ 400 

"' :!t 300 0: 

~ ~ 200 

100 

UN TH TB 

F= 18. 1 

r < 0.01 

f. 

UN 

h. 

TH TB 

F=5.7 

p < O.OI 

200,------------------------, 

UN TH TB 

F=0.5 

p :::0.6 

Figure 3 e-h. e. Average live crown ratio (percent); f. mean crown diameter (m); g. 

mean density of living saplings (stems/ha); h. mean density of living seedlings (stems/ 

ha). ANOVA F-statistic and observed significance (p) values are shown in the upper 

right corner of each graph. Significant differences among individual treatment means 

are denoted with letters to the left of each box plot. Each whisker represents range 

of values. Boxes represent interquartile range. Means are lines within boxes. Outliers 

(circles) represent values greater than 1.5 box lengths. 
144 STci ND TREc\T,\ IENT L\iP:ICTS ON F O REST J-l Ec\LTI-1 

1400 F ~ 63.0 

p < 0.01 

1200 

" 1000 -= "" s 

~ 800 "'0 

.. " Q 600 

= " .. 

::; 400 

200 

0 ~ a 

UN TH TB WF 

Figure 4. Mean density of standing dead trees (stems/ha). ANOVA F-statistic and 

observed significance (p) values are shown in the upper right corner of each graph. 

Significant differences among individual treatment means are denoted with letters to 

the left of each box plot. Each whisker represents range of values. Boxes represent 

interquartile range. Means are lines within boxes. Outliers (circles) represent values 

greater than 1.5 box lengths. 

Understory prescribed burning resulted in no tree mortality, which is consistent with 

the fire-adapted nature of ponderosa pine (Pearson 1949, Covington et al. 1994). 

MANAGEMENT IMPLICATIONS 

Knowledge of aboveground structural conditions in these four treatments will 

develop with additional data collection and analysis to support ongoing research on 

various taxa associated with assessing forest ecosystem health. These first results 

establish that the only difference between TH and TB treatments is the prescribed 

underburning, which should not affect aboveground tree structure. A possible 

exception to this could be the impact of prescribed fire on seedling density (Bailey 

and Covington, in press), though these results do not show a difference in seedling 

density between TH and TB treatments. Analysis of fuels transect data were not 

available to truly characterize the TB treatment. 

Unmanaged stands were high-density stands with an over-abundance of smaller 

diameter, suppressed ponderosa pine and associated ecosystem conditions identified by Covington et al. (1994). These stands represent a condition ripe for standreplacing wildfire during some impending drought year similar to 1996. Indeed, one 

of the UN treatments became a WF treatment during the 2000 fire season. We hope 

to have more results from the STIFH project that can provide conclusions about the 

overall ecosystem health implications of having a large percentage of our forested 

landscape in an unmanaged condition, heading for a wildfire condition. 
BAILEY ET AL. 145 

AcKNOWLEDGEMENTS 

We would like to acknowledge ftnancial support from the NAU School of 

Forestry, NAU Organized Research, and USDA Forest Service Rocky Mountain Research Station. Data collection could not have proceeded without the hard work of 

Noah Barstatis, Erik Brischler, Rebecca Davis, Kerry Griffis, David Huffman, Kathryn 

Smith, Marc Trenam and Bill Zipse. 

LITERATURE CITED 

Avery, T.E. and H.E. Burkhart. 1994. Forest Measurements, 4th Edition. McGrawHill, Inc. New York, NY 

Bailey, J.D. and WW Covington. 2001. Evaluating ponderosa pine regeneration rates 

following ecological restoration treatments in northern Arizona, U.S.A. Forest 

Ecology and Management. In press. 

Covington, W W, R. L. Everett, R. W Steele, L. I. Irwin, T. A. Daer, and A. N. D. 

AuClair. 1994. Historical and anticipated changes in forest ecosystems of the 

Inland West of the United States. Journal of Sustainable Forestry 2:13-63. 

Harvey, A.E. 1994. Integrated roles for insects, diseases and decomposers in ftte 

dominated forests of the inland western United States: Past, present and future 

forest health. Pages 211-220 in Samson and Adams, (eds.). Assessing Forest 

Ecosystem Health in the Inland West. The Haworth Press, Inc. Binghamton, NY 

Pearson, G.A. 1949. Management of Ponderosa Pine in the Southwest. U.S Department of Agriculture. Monograph No. 6, USDA Forest Service, Washington, DC. 

218 pp. 

Reineke, L.H. 1933. Perfecting a stand-density index for even-aged forests. Journal of 

Agriculture Res. 46: 627-638. 

Sampson, R.N. and D.L. Adams. 1994. Assessing Forest Ecosystem Health in the Inland 

West. Haworth Press, New York, NY 

Smith, D.M., B.C. Larson, M.J. Kelty, and P.M.S. Ashton. 1996. The Practice of 

Silviculture: Applied Forest Ecology. 9th Edition. John Wiley & Sons, New York, 

NY 

Swetnam, T. W and J. L. Betancourt. 1990. Fire - Southern oscillation relations in the 

southwestern United States. Science 249: 1017-1020. 

Zar, J.H. 1996. Biostatistical Analysis, 3rd Ed. Prentice-Hall, Inc. New Jersey. 

Post-fire Treatment of Noxious Weeds 

in Mesa Verde National Park, Colorado 

M. Lisa Floyd 

David D. Hanna 

Prescott College 

Environmental Studies Program 

Prescott, AZ 86301 

and 

Gary Salamacha 

ArchesfVa#onalPark 

Moab, UT 84532 

Abstract. Re-introduction of flre as a management strategy can be detrimental to conservation of native ecosystems by promoting "noxious weeds" within invasion-susceptible 

plant communities. This idea was central to treatments following flre in the pifion-juniper 

(Pinus edulis, Juniperus osteosperma) woodlands and mountain shrublands (Amelanchier utahensis, 

Quercus gambelii, Fend/era rupicola) of Mesa Verde National Park, southwestern Colorado. 

Fire is an integral ecological process in pifion- juniper woodlands and adjacent petran 

chaparral shrublands. However, wildfires in 1989 and 1996 created opportunities for the 

proliferation of noxious weeds, especially Carduus nutans, Cirsium arvense, and to a lesser 

extent, Bromus tectorum. Old-growth pifion-juniper woodlands were especially susceptible 

to non-native invasion and required aggressive management actions. In this study, we 

evaluated the effectiveness of three treatment strategies (mechanical, herbicide, and native 

grass seeding) in these high risk habitats. Introducing native perennial grasses, within three 

years of the flre, proved the most effective treatment in reducing non-native plant proliferation 

Key words: noxious weeds, aerial seeding, Mesa Verde, species of concern 

Email: Lisa Floyd, lfloyd-hanna@prescott.edu 

147 
148 PosT-FrRE TREATMENT OF No:xrous WEEDS 

INTRODUCTION 

In contrast to the early twentieth century, disturbed habitats today are increasingly targeted by non-native species (Heywood 1989, Mooney and Drake 1989, Soule 

1990, Westman 1990, Floyd-Hanna and Romme 1993, Burke and Grime 1996). Fire 

has been an important natural disturbance agent on the Colorado Plateau, but fire 

frequencies have increased in the past century (Covington et al. 1997, Grissino-Mayer 

and Swetnam 1997,Turner et al. 1998), thus changing fire-related ecological patterns. 

Native seed reserves and declines in native forb diversity are two such changes (Crawley 

1987, Hobbs and Huenneke 1992). Possibly as a result of these changes, post fire 

succession commonly includes non-native plant species. In Mesa Verde National 

Park (MVNP) in southwestern Colorado, large wildfires that occurred earlier in the 

twentieth century (1934, 1959, 1972) were not associated with weed invasion. However, following extensive fires in 1989 and 1996, recovery was characterized by significant non-native plant invasion. Based on this information, a series of mitigative 

treatments were tailored specifically for each burned community considered at risk for 

noxious weeds after the 1996 Chapin 5 fire in Mesa VerdeN ational Park. 

Vegetation recovery and treatment of noxious weeds were funded under the 

Burned Area Emergency Rehabilitation (BAER) program, 1996-1999. Mitigation 

treatments outlined in this paper involved mechanical and chemical controls and 

seeding treatments. Biological controls were also introduced as part of the BAER 

program and will be discussed elsewhere (Kendall, pers. comm.). In this paper, we 

focus specifically on the effectiveness of weed treatment strategies on high weed-risk 

areas following the 1996 fire. 

STUDY AREA 

Mesa Verde National Park (MVNP) is located in the extreme southwestern 

corner of Colorado (Fig. 1). The Park encompasses over one-half of a prominent 

cuesta, the top of which gently slopes from 2050 min the south to approximately 

2485 min the north. The southern end of the cuesta drops into the canyons of the 

Mancos River, while the northern end terminates in a steep, highly eroded escarpment. The cuesta is composed of Cretaceous sandstone and shale substrates: Mancos 

Shale, Point Lookout Sandstone, Menefee Shale, and Cliffhouse Sandstone (Griffits 

1990). The top of the cuesta consists of numerous north-south trending, relatively 

flat ridges or "mesas," separated by rugged canyons. 

Annual precipitation at MVNP averaged 45.8 em between 1923 and 1994. Most 

precipitation falls in winter months as snow, or during the summer monsoons as 

thundershowers. July (avg. 4.52 em) and August (avg. 5.2 em) are the highest precipitation months. Lightning from mid-July through mid-August is the cause of 94% 

of the fires at MVNP. 

Mesa Verde exhibits an elevation gradient in pre-fire vegetation communities. 

In the northern portions are several types of mountain shrublands, collectively called 

Petran chaparral (Fig. 1, light gray). These shrublands are of variable composition, 

often dominated by Quercus gambelii (gam bel oak), Amelanchier utahensis (Utah serviceberry), Cercocarpus montanus (mountain-mahogany), Fend/era rupicola (fendlerbush) 
MESA VERDE NATIONAL PARK 

MONTEZUMA COUNTY, COLORADO 

N 

~ 

F LOYD ET :\ L. 149 

2 3 Kilo meters 

15iiii'~iiiiiiiil""'l"""""'!~ 

Figure 1. Locations of Long Mesa (1989) and Chapin 5 (1996), Mesa Verde National 

Park, Colorado. 

and other perennial shrubs (Spence et al. 1995). Pinon-juniper woodlands (Fig. 1, 

dark gray) commonly interdigitate with these chaparral communities that share many 

of the same species, but have distinctly different structure. The woodlands are 

dominated by Pinus edu!is (Colorado pinon pine) and Juniperus osteosperma (Utah 

juniper ), and may have shrub understory dominated by gambel oak or Purshia 

tridentata (bitterbrush). 

Disturbance History 

Small, lightning-caused fires are frequent in MVNP; the annual average for fire 

starts between 1926-1969 was 5 per year, and between 1970-1997 was 18 per year. 

Most fires started in the piiion-juniper woodlands and burned less than 1 hectare. 

Large fires occurred in 1934, 1959, 1972, 1989, and 1996. The southern half of Mesa 

Verde is covered with dense, old-growth pinon-juniper woodlands that had not 
150 PosT-FrRE TREATMENT OF No:xrous WEEDS 

burned for several centuries. However, the 20th century has seen several spectacular 

wildfires that burned extensive portions of the pinon-juniper woodlands. The Chapin 

5 fire began with a lightning strike in the dense pifion-juniper/bitterbrush woodland 

on the archeologically-rich Chapin Mesa, and burned through Soda Canyon, Little 

Soda Canyon, and large portions of the research area Park Mesa, before it stopped at 

the Visitors Center and hotel complex in dense oak and serviceberry shrublands. 

The fire covered 1934 ha, including seven pre-fire vegetation communities (Fig. 1 ). 

Fire is the major disturbance factor in MVNP, but numerous smaller gaps also 

occur throughout the woodland canopy. Small gaps are often caused by pathogens, 

such as the Black stain root rot, Ophiostoma wagnerii Goheen and Cobb (= Verticicladiella 

wagnerii=Ceratorycsis wagnerit) which kills patches of up to 50 pifion trees, and has 

been present in southwestern Colorado since the 1930's (Harrington and Cobb 

1998). Also, roads and park facilities provide continual disturbances. The woodlands are also disturbed in narrow belts, surrounding housing and park buildings, by 

annual fuel reduction activities. Thus, small patches of noxious weeds have been 

present in MVNP in the last 3 decades (M. Colyer, pers. comm.). 

Weed Species of Special Concern in MVN"P 

Following the last two large fires, Cirsium aroense (Canada thistle) and Carduus 

nutans (musk thistle) aggressively invaded bare mineral soils. Musk thistle has an 

extensive native range from North Africa, Europe, Siberia, to Asia Minor. It has 

spread to New Zealand, Australia, and North America, where it is still expanding its 

range (Shea and Kelly 1998). In 1976, populations of musk thistle were located in 

eastern Colorado (Dunn 197 6), and since that time, it has spread at an alarming rate 

through the state. Musk thistle is usually a biennial, but it can also be annual or 

perennial, reproducing exclusively from seed. Treatments of herbicide (Colorado 

State University Extension Service), biocontrol agents, and limitations on grazing 

(Rees 1982, Shea and Kelly 1998) are used to control its local distribution. 

Canada thistle is more difficult to control because of horizontal adventitious 

roots that may extend 2m deep (Hodgson 1968, Rees 1990), from which it rapidly 

resprouts after fires. Canada thistle is an aggressive weed which can reproduce from 

seed or vegetative buds, expands 2-4m in one year, and significantly reduces forage in 

pastures throughout the western United States. The expansion of Canada thistle is 

controlled locally by herbicides, mowing, and biological controls (Colorado State 

University Extension Service). Urophora cardui and Ceutrolynus litura are commonly 

used biological control agents in Colorado (McCarty and Lamp 1982). 

Other invasive species, which have become persistent in disturbed sites within 

MVNP and the surrounding region, include Cirsium vulgare (Bull thistle), Sa/sola 

iberica (Russian thistle), Centaurea dehisa (knapweed), Centaurea repens (knapweed), 

Lactuca serriola (wild lettuce), Ranunculus testiculatus, Tamarix ramosissima (T. pentandra) 

(tamarix), A!Jssum minor, Linaria vulgare (butter and eggs), Lepidium latifolia 

(pepperweed), and the grasses Festuca pratense, Agropyron intermedium (intermediate 

wheatgrass), Bromus tectorum (cheatgrass), and Bromus inermis (smooth brome). 
METHODS 

Rehabilitation Treatments 

Aerial Seeding Treatments 

FLOYD ET AL. 151 

Although seeding treatments typically occur in a narrow window of opportunity within months of a fire, our treatments involved aerial seeding with assemblages of native grass seeds during three time periods: (1) immediately after the 

Chapin 5 fire, (2) on bare soils that remained one year after the fire, and (3) on bare 

soils two years after the fire. Perennial grass mixes simulated, as much as possible, 

the native grass community for the elevation, substrate, and pre-fire vegetation of 

each area (Appendix A). Seeds were primarily obtained from the Park itself or from 

local vendors who grow local seed varieties. In 1996, the extent of severely burned 

areas in the Chapin 5 burn exceeded the availability of native grass seeds (the demand 

for seeds was high because of numerous fires in the western United States). Therefore, we aerially seeded only 278 acres of the high-risk portion of the burn (Fig. 2). 

In 1997, additional areas of the fire were seeded. Again, seeds were obtained 

locally with every attempt made to ensure that local seed sources were used. The 

selected areas either encompassed a very high density of archeological sites, were 

particularly susceptible to erosion, or had very little regrowth and were, therefore, at 

high risk of weed invasion (Fig. 2). 

The 1996 and 1997 seeding treatments were quite successful in reducing weed 

invasion (see below). However, in the southern portion of the burn, up to 50% of 

the soils remained exposed in some areas that had not been treated. These soils 

could be targets for the incoming noxious weeds, native forb and grass species, or 

seeded grasses, if introduced. In 1998, we took the bold step of applying additional 

seeding treatments to six small areas that were deemed particularly vulnerable to 

weed invasion. 

During each year, seeds were applied with mechanical seeders from a Bell Jet 

Ranger helicopter (Mark Santee, pilot and Bob Greeno, seeder engineer). Seeding 

treatments took place in early October. 

Success of seeding was measured in a series of 80 m2 circular plots, placed at 

approximately 500 m intervals within treated areas (20 plots), and adjacent burned 

controls (20 plots), in the spring following each seeding treatment. In addition, a 

series of 20 plots were established within unburned portions of Park Mesa. Since 

the seeded species were bunch grasses, it was possible to identify individuals in the 

early stages of growth; therefore, the density, rather than the cover, of each species 

was recorded. The density of each weed species was also recorded. 

Herbicide Treatments 

Twenty-three Canada thistle patches were located (with GPS), photographed, 

and sprayed in June and again in August, 1998, with back-pack sprayer application of 

Curtail (3,6-dichloro-2-pyridinecarboxylic acid, monothanolamine salt 7.5 %, 2,4-

dichlorophenoxy acetic acid, tris-propanolmaine salt 38.4 %), mixed with Improved 

JLB oil plus and IFA- S-90 Surfactant. Each stand was revisited within two months 

of spraying treatment, post-application photographs taken, and the percent mortality was estimated visually. 
152 P OST-FIRE TREciT\IENT OF N OXIO US W EEDS 

500 0 500 1000 Meters 

s UntreatedThistle located in 1996 

"' Biologically controlled populations 

~ Herbicide treated Thistle populations located 1998 N 1996 fire perimeter 

·· Seeding treatments 

N 

t 

Figure 2. Weed treatments at Chapin 5 Fire site, Mesa Verde National Park. 

In 1999, each stand was revisited, and if the noxious weeds were still alive, an 

additional application of Curtail was used. Photographs were taken of each plot, 

and an ordinal variable was created whose values approximated the percent of Canada 

thistle mortality. 

Mechanical Treatments 

Particularly dense stands of musk thistle were removed by digging up the rosettes (hand-grubbing) in June-August 1977 and June 1998. Treatment areas in-
FLOYD ET AL. 153 

eluded canyon bottomlands, where native grasses were likely to resprout, and rocky 

canyon walls and mesa tops, where residual vegetation was lacking. A year after 

treatment, the number of musk thistle was counted in belt transects, each 33m long 

and 4 m (132 m~ wide, in each mechanical treatment stand. Fifteen transects were 

established in treatment areas, paired with an adjacent, non-treated "control" stand, 

and the density of musk thistle was statistically compared with a paired T-test. 

Data were analyzed using one-way analysis of variance or T-tests to compare 

means of each dependent variable across treatments (seeded or control). All data 

were analyzed with SPSS, Statistical Package for the Social Sciences, version 10. 

RESULTS 

Aerial Seeding Treatments 

One year after seeding treatment, grass density was significantly higher in the 

seeded areas than in nearby control plots (Table 1). This trend was also apparent in 

nearby plots the following two years (Floyd et al. unpub.). All species flowered and 

produced seeds in 1997 and 1998. Agropyron traci?Jcaulum (slender wheatgrass ), S itanion 

I?Jstrix (squirreltail grass), and Oryzopsis I?Jmenoides (Indian ricegrass) were especially 

conspicuous. 

The potential effect of the seeding treatment on the expansion of noxious 

weeds was analyzed in 1998 and 1999. We monitored the density of all non-native 

species; however, during the first 2 years, only musk thistle and Canada thistle had 

spread appreciably. Scattered patches of cheatgrass arrived later (in 1999). In the 1996 

treatments, there was a 7.5-times reduction in musk thistle in the seeded areas, 

compared with the control; there was a 4-times reduction in weed density following 

the 1997 seeding (Table 2). Weed invasion was absent from all unburned control 

plots; therefore, unburned controls were not shown in Table 2. During summer, 

1999, we monitored the germination of grass seed applied in fall, 1998. Germination was successful, resulting in significantly greater grass density than in control 

areas; however, the effect of the 1998 seeding on reduction of musk thistle cannot be 

evaluated until next year. 

In related studies (Floyd-Hanna et al. 1999) recovery by native species was tracked 

for over three years following the fire. Abundant native forbs included Pofygonum 

sawachensis (knotweed), Lupinus caudatus (lupine), Lupinus ammophila (lupine), and 

Penstemon linearoides (low penstemon). There has been no evidence to date that native 

forb diversity has declined due to seeding treatments compared with adjacent burned 

control plots. 

Herbicide Treatments 

Upland patches of Canada thistle were treated with herbicide applications. It 

should be noted that herbicide was not used in drainage systems near water. Herbicide treatments with Curtail varied in their effectiveness. In 75% of the herbicide 

applications, Curtail was locally effective, killing between 70-100% of the ramets of 

Canada thistle within two months of spraying, and maintaining an average of 80% 

kill the next year. In 25 % of the application, live plants persisted on the periphery of 
154 PosT-FrRE TREATMENT OF NoXIous WEEDS 

Table 1. The density of native perennial grasses one year after aerial treatment, 

Chapin 5 fire, Mesa Verde National Park. Each value is mean ± standard deviation. 

Sample sizes were n=20 per treatment. T-tests indicate significant differences 

between seeded and control (not seeded) burned treatments. (*denotes P<0.05). 

Grass Species Control Density Seeded Density Significance 

Poa fendleriana 1.0 ±2.1 12.0 ±17.4 T= 2.2* 

Sitanion hystrix 0.0 5.9 ±6.0 T= 3.5* 

Oryzopsis hymenoides 1.0 ±2.3 0.6 ±.9 T=0.6 

Agropyron trachycaulum 0 5.6 ±6.3 T= 3.2* 

the patch. In Soda Canyon, Canada thistle had been well-established before the fire, 

andre-sprouting was visible within a few weeks. These areas are strictly treated with 

biological controls, reported elsewhere (Kendall, pers. comm.). 

Mechanical Treatments 

Only extremely dense patches of musk thistle were chosen for mechanical treatments. Results varied considerably among the mechanically treated (hand-grubbed) 

areas. No significant difference in musk thistle density was detected one year after 

treatment in areas where grasses were lacking (pre-treatment average 32,400/ha, posttreatment average 37,600/ha). However, there was a three-fold (pre-treatment average 28,400, post-treatment 9,300/ha) and five-fold (pre-treatment average 33,900, 

post -treatment average 6650 ha) decrease in density in two treated areas where mechanical treatment was followed by "natural" grass invasion. While it appeared that 

mechanical reduction was an effective local treatment if followed by natural or artificial grass seeding, further long-term evaluation is needed. 

Table 2. The density of musk thistle, Carduus nutans, in seeded and control (not 

seeded) treatments, one year after treatment, Chapin 5 fire, Mesa Verde National 

Park. Each value is the mean ±standard deviation. Sample sizes were n=20 per 

treatment. Analysis of variance tests indicate significant differences between seeded 

and control (not seeded) treatments in 1996 treatments (F=1 0.9, P<0.05) and 1997 

treatments (F=8.1, P<0.05). 

Treatment 

1996 

1997 

Mesa top seeding 

Control, unseeded mesa top 

Canyon site seeding 

Control, unseeded canyon 

Density of musk thistle (#/80m2 ) 

10.4 ±8.3 

83.1 ±66.6 

28.0 ±53.4 

96.5 ±47.2 
FLOYD ET AL. 155 

DISCUSSION 

In the three years following the 1996 Chapin 5 wildfire in Mesa VerdeN ational 

Park, burned old-growth piiion-juniper woodlands supported the greatest diversity 

and density of non-native plant species relative to the six other vegetation types 

burned. In many of the other vegetation communities, residual vegetation, in the 

form of resprouting perennial shrubs and grasses, allowed rapid recovery and prevented noxious weed invasions (Floyd-Hanna et al. 1999). Post-ftte mitigation 

activities conducted under the Burned Area Emergency Rehabilitation (BAER) program, were designed to prevent noxious weed invasion and severe erosion, and to 

encourage native plant species. These were carried out most intensively in the oldgrowth piiion-juniper community. 

In all three treatments, we documented a significant reduction in weed densities. Of the treatments applied, seeding with native grass species has shown the most 

pronounced effects in reducing weed density. Furthermore, there has been no evidence that the diversity of native forbs has declined by introducing native perennial 

grasses. Herbicide and mechanical treatments were effective in the short-term, but 

whether they reduce population expansion, decreasing subsequent seedling germination and establishment, is not yet known. Mechanical treatment was only effective 

if followed by native grass invasion. Both mechanical and herbicide treatments can 

only be applied, realistically, in small patches; aerial seeding of native grasses can be 

applied over large areas. The effect of biological controls will not be evident for at 

least several more years (Kendall, pers. comm.). 

"While it cannot be known to what extent the noxious weeds would have spread 

had we not performed the treatments, it is reasonable to assume that the spatial 

extent has been reduced by at least the areas treated. BAER funding is available only 

to treat emergencies; therefore, we could only apply treatments to the most severely 

burned or threatened habitats (primarily old-growth piiion-juniper communities). 

Over one-half of untreated piiion-juniper communities were invaded by musk thistle 

within three years of the fire, as detected by helicopter survey (Floyd-Hanna et al. 

1999). Musk thistle is now the dominant species in these areas. Thus, we recommend that future fires be seeded with native species extensively in burned communities that lack residual vegetation, such as dense, old-growth piiion-juniper woodlands. Such treatments promote native, perennial grass growth and reduce the proliferation of non-native species. 

AcKNOWLEDGEMENTs 

Aerial seeding was accomplished with significant contributions by Mark Santee and 

Guy Keene. Field assistants included Charlotte Thompson, Anne DaVega, Dustin 

Hanna, Carianne Funicelli, Lyn Chenier, Lani Hanna,] oe Garcia, Rachel Garcia, and Paul 

McLaughlin. Funding was provided by the Burn Area Emergency Rehabilitation. 

LITERATURE CITED 

Burke, M.J. and J.P. Grime. 1996. An experimental study of plant community 

invasibility. Ecology 77: 776-789. 
156 PosT-FrRE TREATMENT OF No:xrous WEEDS 

Covington, WW, P.Z Fule, M.M. Moore, S.C. Hart, T.E. Kolb, J.N. Mast, S.S. Sackett 

and M.R. Wagner. 1997. Restoring ecosystem health in ponderosa pine forests of 

the southwest. Journal of Forestry 95: 23-29. 

Crawley, M.J. 1987. What makes a community invasible? Pages 429-454 in M. J. Crawley, 

P.J. Edwards and A.J. Gray (eds.). Colonisation, succession and stability. Blackwell 

Scientific, Oxford. 

Dunn, P.H. 1976. Distribution of Carduus nutans, C. acanthoides, C. pycnocephalus and C. 

crispus in the United States. Weed Science 24: 518-524. 

Floyd-Hanna, L. and W.H. Romme. 1993. Biological invasions after fire, Mesa Verde 

National Park. Park Science 29: 12-14. 

Floyd-Hanna, L., D. D. Hanna and W.H. Romme. 1999. Post-fire vegetation studies, 

BAER Final Report: Chapin 5 Fire. 

Griffits, M. 0. 1990. Guide to the geology of Mesa Verde National Park. Mesa Verde 

Museum Association, Lorraine Press, Utah. 

Grissino-Mayer, H.D. and T.W. Swetnam. 1997. Multi-century history of wildland fire 

in the ponderosa pine forests of El Malpais National Monument. New Mexico 

Bureau of Mines and Mineral Resources Bulletin 156: 163-171. 

Harrington, T.C. and F.W. Cobb. 1988. Leptographium Root Diseases on conifers. APS 

Press, the American Phytopathological Society, St. Paul, Minnesota. 

Heywood, V.H. 1989. Patterns, extents and modes of invasions by terrestrial plants. 

Pages 31-55 in J.A. Drake, H.A. Mooney, F. diCastri, R.H. Groves, F.J. Kruger, M. 

Rejmanek, and M. Williamson, (eds.). Biological invasions: a global perspective. 

SCOPE, Wiley & Sons. 

Hobbs, R.J., and L.F. Huenneke. 1992. Disturbance, diversity, and invasion: implications 

for conservation. Conservation Biology 6: 324-337. 

Hodgson, J.M 1968. The nature, ecology, and control of Canada thisde. USDA Technical 

Bulletin 1386. 

McCarty, M.K. and W.O. Lamp. 1982. Effect of a weevil, Rhinoryllus conicus on musk 

thisde seed production. Weed Science 30: 136-140. 

Mooney, H.A., and J.A. Drake. 1989. Biological invasions: a SCOPE program overview. 

Pages 491-510 in J.A. Drake, H.A. Mooney, F. diCastri, R.H. Groves, F.J. Kruger, 

M. Rejmanek, and M. Williamson, (eds.). Biological invasions: a global perspective. 

SCOPE, Wiley & Sons. 

Rees, N.E. 1982. Collecting, handling and releasing Rhinoryllus conicus, a biological 

control agent of musk thisde. USDA Agricultural Handbook No. 579. 

Rees, N.E. 1990. Establishment, dispersal, and influence of Ceuthorf?Jmchus litura on 

Canada thisde in the Gallatin Valley of Montana. Weed Science 38: 198-200. 

Shea, K. and D. Kelly. 1998. Estimating biocontrol agent impact with matirx models: 

Carduus nutans in New Zealand. Ecological Applications 8: 824-832. 

Spence, J.R., W.H. Romme, L. Floyd-Hanna, and P.G. Rowlands. 1995. A preliminary 

vegetation classification for the Colorado Plateau. Pages 193-213. in C. van Riper 

III, (ed.). Proceedings of the Second Biennial Conference on Research in Colorado 

Plateau National Parks. 25-28 October 1993. National Park Service Transactions 

and Proceedings Series NPS/NRNAU/NRTP-95/11. 

Soule, M.E. 1990. The onslaught of exotic species, and other challenges in the coming 

decades. Conservation Biology 4: 233-239. 

Turner, M.G., W.L. Baker, C.J. Peterson, and R.K. Peet. 1998. Factors influencing 

succession: lessons from large, infrequent disturbances. Ecosystems 1: 511-523. 

Westman, W.E. 1990. Park management and exotic plant species: problems and issues. 

Conservation Biology 4: 251-260. 
FLOYD ET AL. 157 

Appendix A. Community-specific native grass seed mixes used for rehabilitation of 

the Chapin 5 fire, Mesa Verde National Park. Locations of seeded areas are shown 

in Figure 2; approximate acreage which were seeded are shown in parentheses. 

Seeding Area A: (77 acres) 

Kohleria cristata, June Grass, 2 lb/acre 

Oryzopsis hymenoides, Indian Rice Grass, 6 lb/acre 

Agropyron trachycau/um , Slender Wheat Grass, 5 lb/acre 

Sitanion hystrix, Squirrel-tail Grass, 6 lb/acre 

Seeding Area B: (201 acres) 

Poa fendleriana, Mutton Grass, 2 lb/acre 

Oryzopsis hymenoides, Indian Rice Grass, 6 lb/acre 

Agropyron trachycaulum , Slender Wheat Grass, 5 lb/acre 

Sitanion hystrix, Squirrel-tail Grass, 6 lb/acre 

Seeding Area C.: (250 acres) 

Agropyron smithii, Western Wheat grass, Sibs/acre 

Poa fendleriana, Mutton Grass, 1.5 lb/acre 

Oryzopsis hymenoides, Indian Rice Grass, 6 lb/acre 

Agropyron trachycaulum , Slender Wheat Grass, 5.5 lb/acre 

Seeding Area D: (11 0 acres): 

Kohleria cristata, June Grass, 1.0 lb/acre 

Sitanion hystrix, Squirrel-tail Grass, 2.41b/acre 

Agropyron trachycau/um , Slender Wheat Grass, 5.5 lb/acre 

Stipa comata, Needle and thread grass, 3.5 lb/acre 

Seeding Area E: (125 acres) 

Oryzopsis hymenoides, Indian Rice Grass, 6 lb/acre 

Agropyron trachycaulum , Slender Wheat Grass, 5.5 lb/acre 

Sitanion hystrix, Squirrel-tail Grass, 2.41b/acre 

Poa fendleriana, Mutton Grass, 1.5 lb/acre 

Seeding Area F: (50 acres) 

Kohleria cristata, June Grass, 2 lb/acre, 

Oryzopsis hymenoides, Indian Rice Grass, 5 lb/acre 

Agropyron trachycaulum , Slender Wheat Grass, 5 lb/acre 

Sitanion hystrix, Squirrel-tail Grass, 2.5 lb/acre 

Seeding Area G: (125 acres) 

Poa fendleriana, Mutton Grass, 2 lb/acre 

Oryzopsis hymenoides, Indian Rice Grass, 6 lb/acre 

Agropyron trachycau/um , Slender Wheat Grass, 5 lb/acre 

Sitanion hystrix, Squirrel-tail Grass, 6 lb/acre 

Agropyron smithii, Western Wheat Grass, 5 lb/acre 

Seeding Area H: ( 35 acres) 

Poa fendleriana, Mutton Grass, 2 lb/acre 

Oryzopsis hymenoides, Indian Rice Grass, 6 lb/acre 

Agropyron trachycau/um , Slender Wheat Grass, 5 lb/acre 

Sitanion hystrix, Squirrel-tail Grass, 6 lb/acre 

Seeding Area 1: (60 acres) 

Kohleria cristata, June Grass, 2 lb/acre, 

Oryzopsis hymenoides, Indian Rice Grass, 6 lb/acre 

Agropyron trachycau/um , Slender Wheat Grass, 5 lb/acre 

Stanion hystrix, Squirrel-tail Grass, 6 lb/acre 

Agropyron smithii, Western Wheat Grass, 5 lb/acre 

Stipa comata, Needle and thread grass, 2 lb/acre 

Mapping Land Cover and Animal Species 

Distributions for Conservation Planning: 

An Overview of the Southwest Regional 

Gap Analysis Program in Arizona 

Sarah R. Jacobs 

Kathryn A. Thomas 

and 

Charles A. Drost 

USGS Forest and Rangeland Eco.rystem Science Center 

Colorado Plateau Field Station 

Northern Arizona University 

P.O. Box 5614 

Flagstciff,AZ 86011-5614 

Abstract. The Southwest Regional Gap Analysis Program will build upon previous gap 

analysis programs conducted in Arizona, Colorado, Nevada, New Mexico, and Utah to 

provide products that are consistent among areas of this large geographic region. The 

program will develop new land cover, vertebrate species distributions, and land stewardship data layers using a cooperative approach and similar methods across the five states. 

The three data layers will be seamless across the five state region, and detailed in resolution 

and content. The data layers will be used in a gap analysis to evaluate the conservation 

status of natural habitats and vertebrate species within and among all five states, and point 

out biotic elements needing further protection or management attention. The program will 

also provide region-wide digital map and database products that allow land managers, 

planners, scientists, and policy makers to make better informed land use decisions. 

Key words: gap analysis, biodiversity, Southwest, Arizona, remote sensing, vegetation, 

land cover, wildlife habitat relationship models, GIS. 

Email: Sarah Jacobs, Sarah.Jacobs@nau.edu 

159 
160 SouTHWEsT REGAP OVERVIEW 

BACKGROUND 

Loss of biological diversity is a serious ecological problem, with a major cause 

being human action in the form of altering land use (Freedman 1989). Humancaused changes have accelerated extinction (Wilcove et al. 1998), which threatens 

biodiversity. With foresight, people can minimize further biodiversity loss due to 

human activity. One important tool is biodiversity gap analysis, which has been 

developed by geographers and biologists to map distributions of vertebrate species 

and vegetation communities and identify gaps in their protection (McKendry and 

Machlis 1991, Scott et al. 1993). This coarse filter approach can be used for conservation planning at the ecosystem level (Noss 1987). 

Kepler and Scott (1985) found a gap in endangered Hawaiian honeycreeper 

protection on the island of Hawaii. They modeled the distribution of three honeycreeper species and compared maps of their distributions to determine areas of 

honeycreeper richness. Maps of existing nature reserves were then compared with 

the honeycreeper richness map to determine if reserves coincided with species-rich 

areas. As a result of their findings, the Hakalau Forest National Wildlife Refuge was 

established in one of the areas of highest honeycreeper richness, addressing the gap 

in protection revealed by their analysis (Scott et al. 1993). This study became the 

founding research for the National Gap Analysis Program. 

The National Gap Analysis Program (GAP), initially housed within the U.S. 

Fish and Wildlife Service and now managed by the U.S. Geological Survey, has guided 

the subsequent development and application of biodiversity gap analysis throughout the nation and internationally. The Gap Analysis Program maps distributions 

of land cover (vegetation communities) and vertebrate species. These maps are 

overlaid in a geographic information system with maps ofland stewardship (showing levels of biodiversity management) to identify biotic elements at potential risk of 

endangerment because of "gaps" in conservation management. A gap in conservation management is identified where a biotic element (vegetation community or 

animal species) is not present, or only occurs marginally in areas protected and managed primarily for biodiversity. One of the major goals of GAP is to provide 

consistent, periodic, regional assessments of the gaps in conservation management; 

in other words, to determine the conservation status of native vertebrate species and 

natural land cover types, and facilitate the application of this information to land 

acquisition, protection, and other management activities. 

In this paper, we describe the second generation gap analysis in the Southwest, 

which builds upon the successes and shortcomings of previous work on gap analysis 

in the region. This effort, the Southwest Regional Gap Analysis Program (SW 

ReGAP), is being conducted as a multi-state effort between Arizona, Colorado, 

Nevada, New Mexico, and Utah. This five state region covers almost 140 million 

hectares with 21% of that, or almost 30 million hectares, within the state of Arizona. 

The project is developing the operational model for the next phase of biodiversity 

gap analysis projects in the west (Prior-Magee, SW ReGAP Coordinator, pers. comm.). 

In addition to much-improved resolution and accuracy of map products, important 

refinements within the regional effort are consistent land cover classification throughout 
JACOBS ET AL. 161 

the area, and seamless coverage of maps across political and agency management 

boundaries. 

First Generation GAP- The State Model 

Gap analysis has traditionally been conducted on a state-by-state basis, with first 

generation biodiversity GAP completed in 10 states. In the Southwest, first generation gap analysis programs were initiated in the early 1990s. A complete gap analysis 

and accompanying map products were published in Utah (Edwards et al. 1995) and 

New Mexico (fhompson et al. 1996). Partial map products were produced in Arizona (USGS WERC 2001) and Nevada. Colorado is currently finishing the first 

generation GAP products for their state (Schrupp et al. 2000). 

The first generation Arizona project began in 1991. This effort, initially directed 

by Lee Graham of University of Arizona, Tucson, produced land cover, vertebrate 

distribution, and land stewardship maps in 1994. The USGS Sonoran Desert Field 

Station in Tucson, revised the initial Graham maps and plans on completing analysis 

for this first generation project. \Xlhen published, their report will represent the firstever detailed maps on a state-wide level of biotic elements and their conservation 

status. 

State boundaries rarely coincide with ecological units. The island ecosystem of 

Hawaii was convenient for mapping and conducting gap analysis because of the 

island's boundaries. However, a continental ecosystem such as the Colorado Plateau 

has fuzzy boundaries and may span several states, making mapping and conducting 

a gap analysis more difficult on a state-by-state basis. Gap analyses confined to a 

state's boundary tend to give incomplete or biased results when taken in the context 

of an extensive ecosystem. For example, a species may be rare in a state only because 

it is at the edge of its range. To recommend protection for this species in one state, 

when it is common in adjacent states, is not accounting for the regional nature of the 

distribution. For this reason, strategies to manage for the long-term maintenance of 

biodiversity are better focused on the characteristic biota of larger regions (N oss 

1983). 

Individual state GAP maps have proven difficult to merge into regional representations. State data layers typically have different classification systems, such that 

similar vegetation types are given different names in each state. This necessitates a 

cumbersome process to merge the types among the different classifications, followed 

by a cross-walk to the least detailed classification. 

In addition to classification problems, another edge-matching issue arises when 

the resolution of the data layer differs between states. The use of different primary 

data sources and methods to derive polygons can create maps that have different 

spatial properties across state lines. An ecoregionalland cover map was created for the 

Mojave Desert using portions of the first generation Arizona, California, Nevada, 

and Utah GAP maps. The resulting ecoregionalland cover map shows striking 

differences in map unit delineation across state lines (Fig. 1 ). The map unit inconsistencies could be due to source imagery resolution differing between the states because 

the Arizona, Nevada, and Utah projects used Thematic Mapper imagery with 30 

meter pixel resolution, while the California project used Multispectral Scanner imag-
Overview 

N 

+ \, 

100 0 1 00 Kilometers 

Figure 1. Mojave Desert Ecoregion Project land cover map (MDEP 1998) showing map unit inconsistencies across state boundaries. 
JACOBS ET AL. 163 

ery with 80 meter pixel resolution. The different resolutions of the base imagery layer 

and available technology produced land cover maps with different resolutions (1 00 

ha in California vs. 40 ha in Arizona). 

Differences in spatial properties ofland cover map units can introduce error in 

predictions of vertebrate species distributions, where distributions are modeled using the land cover map. Uneven map units can result in errors of omission or 

commission in predicted species' occurrence. 

Another motivation for second generation GAP studies is that gap analysis was 

designed to be repeated at approximately 10-year intervals, in order to provide periodic reassessment of the distribution of biota and their conservation management. 

Changes in distribution ofland cover and vertebrate species may occur due to naturally occurring disturbances (e.g., fire or flood), direct human disturbance (e.g., land 

clearing), or from the indirect effects of human activities (e.g., global warming). 

This second generation gap analysis in the Southwest will provide an updated 

view of current conditions, and is specifically designed to utilize a regional model. 

This will correct some of the problems that arose from the state model, such as the 

poor match across state boundaries of vegetation classification, map unit spatial 

discontinuity, and lack of a regional gap analysis. 

Second Generation GAP - The Regional Model 

The Southwest Regional Gap Analysis Program is multi-year, and will create 

land cover, vertebrate species distributions, and land stewardship data layers. This 

effort will conduct a gap analysis conservation assessment for each state and for the 

entire five-state region. Some remote sensing and animal modeling activities will be 

conducted by regional teams for the benefit of all participating states. State projects 

will collect distribution data, create models and map labels for their state, and coordinate with regional teams. A regional project coordinator will facilitate activities among 

regional teams, state projects, and federal agency offices. 

The project in Arizona will produce data layers that support a well-documented 

gap analysis conservation assessment throughout the state. These data and analyses 

will be readily available to land and resource managers, whether private, tribal, state, 

or federal. The project in Arizona is being coordinated by the USGS Forest and 

Rangeland Ecosystem Science Center, Colorado Plateau Field Station, in Flagstaff. 

"While Arizona, Colorado, Nevada, New Mexico, and Utah have previously conducted a gap analysis, vast improvements in technology and cooperative efforts will 

make this project more fruitful than first generation projects. The project will address 

the inconsistencies of methodology, information, classification, resolution, and expertise to produce seamless data layers across state boundaries for the Southwest. 

METHODS 

Mapping Land Cover 

A consistent approach to mapping land cover is essential for success of a regional gap analysis. A seamless land cover map for the region will contribute to 
164 SouTHWEsT REGAP OVERVIEW 

vertebrate species distributions and gap analyses that encourage an ecoregional approach to land management. 

National Vegetation Classification 

In previous GAP projects, each state had its own accepted standard of vegetation classification, which often did not correspond with neighboring states. The 

regional project will use a standardized classification system, the National Vegetation 

Classification (NVC; Grossman et al. 1998), to ensure classification consistency across 

the region and to retain an acceptable level of detail. In 1997, the Federal Geographic 

Data Committee recommended the that NVC become the standard for all federally 

funded vegetation mapping projects (FGDC 1997). Since that time, National GAP 

has sponsored the development of vegetation type (alliance) descriptions so as to 

provide a consistent, repeatable classification system across state and administrative 

boundaries. 

The NVC is regarded as a major step toward enhancing our ability to understand, protect, and manage the natural resources of the United States. It provides a 

hierarchical framework for describing vegetation, and a convention for identifying 

and naming additional vegetation types. The first five levels of the hierarchy are 

based on physiognomic characteristics of the vegetation, and the last two levels are 

floristic (Table 1 ). A set of 105 preliminary alliance descriptions have been developed 

for Arizona, but it is expected that the project will expand and further identify and 

define alliances for Arizona. As an example, 15 preliminary alliances have been deTable 1. Hierarchy of the National Vegetation Classification. 

Level Primary Basis for Classification Example 

Class Growth form and structure of vegetation Woodland 

Subclass Growth form characteristics, Evergreen woodland 

e.g., leaf phenology 

Group Leaf types, corresponding to climate Temperate or subpolar 

needle-leaved 

evergreen woodland 

Subgroup Relative human impact Natural/semi-natural 

(Natural/semi-natural, or cultural) 

Formation Additional physiognomic and Rounded-crowned 

environmental factors, including temperate or subpolar 

hydrology needle-leaved 

evergreen woodland 

Alliance Dominant/diagnostic species of Pinus ponderosa 

uppermost or dominant stratum woodland alliance 

Association Additional dominant/diagnostic Pinus ponderosa/ 

species from any strata Quercus gambelii 

woodland 
JACOBS ET AL. 165 

scribed for the relatively small region of Sunset Crater National Monument and 

environs (7 ,600 ha) in northern Arizona (Table 2). 

The target ofSWReGAP is to map land cover to the alliance level, at 5-hectare 

spatial resolution. This is a fine resolution and level of floristic detail that has not yet 

been accomplished in a land cover mapping project of this size. Where distinction 

between alliances is not possible, due to the ecological complexity or difficulty in 

remotely sensing or modeling the vegetation type, groups of alliances, known as 

ecological complexes or compositional groups, may be used for map labels. The 

Association ofBiodiversity Information, responsible for creation and maintenance 

of the NVC for the United States, will coordinate application of the NVC across the 

five states to promote the consistent development and application of map labels. 

Table 2. Preliminary NVC alliances for Sunset Crater National Monument and 

environs, Arizona (Thomas et al. in prep). 

Class 

Forest 

Forest 

Woodland 

Woodland 

Woodland 

Woodland 

Shrubland 

Herbaceous 

Herbaceous 

Herbaceous 

Herbaceous 

Herbaceous 

Sparse 

Sparse 

Sparse 

Alliance 

Pinus edulis Forest Alliance 

Pseudotsuga menziesii Forest Alliance 

Pinus edulis - (Juniperus spp.) Woodland Alliance 

Pinus flexilis Woodland Alliance 

Pinus ponderosa Woodland Alliance 

Populus tremuloides Woodland Alliance 

Ericameria nauseosa Shrubland Alliance 

Andropogon hallii Herbaceous Alliance 

Bouteloua gracilis Herbaceous Alliance 

Muh/enbergia montana Herbaceous Alliance 

Pascopyrum smithii Herbaceous Alliance 

Pinus ponderosa Wooded Herbaceous Alliance 

Eriogonum corymbosum Sparsely Vegetated Alliance 

Fal/ugia paradoxa Sparsely Vegetated Alliance 

Lava Bed Sparsely Vegetated Alliance 

Processing of Satellite Imagery 

Most GAP projects used Landsat Thematic Mapper satellite as the base imagery 

layer. Also, previous GAP projects have used only one date of imagery per scene to 

keep costs low. However, this limited the ability to distinguish between land cover 

types, resulting in more generalized land cover classes. Methods for delineating land 

cover classes from satellite imagery have included photo interpretive techniques, supervised and unsupervised clustering, and modeling using ancillary data sets (Eve 

and Merchant 1998). However, the application of various techniques inconsistently 

across the landscape has produced different map unit boundaries, and caused edgematching problems across state boundaries. 

This project will use three dates of imagery in 1999, 2000, or 2001 from the latest 
166 SouTHWEsT REGAP OVERVIEW 

earth-observing satellite, Landsat 7 Enhanced Thematic Mapper Plus. This imagery 

will be preprocessed (i.e., georectified and cleaned) and clustered by a regional remote 

sensing team. Preliminary cover types, using plot data supplied by each state and 

previous land cover maps, will then be assigned to the cluster map. Preliminary 

clusters will be given NVC vegetation type labels at the state level. The project in 

Arizona will use predictive modeling with ancillary data sources (e.g., elevation, slope 

aspect, precipitation, and soils) and focused field verification (i.e., labeling of individual polygons) for final cluster map labeling. The regional remote sensing team 

will then create a single land cover data layer for the entire Southwest region from 

individual state data layers. 

Mapping Zones 

Previous projects have mapped land cover on a satellite scene-by-scene basis 

(Eve and Merchant 1998). These scenes may contain a wide variety of ecological 

conditions and can cause confusion in delineating land cover types, leading to a land 

cover map that does not provide detailed floristic information. In this project, we 

will use vegetation-based mapping zones to maximize information extraction by 

separating imagery into more homogeneous areas prior to classification (Fig. 2). This 

will allow our classification to focus on a smaller set ofland cover types, which will 

reduce variation and improve classification results (Homer et al. 1997). Our proposed mapping zones have been delineated primarily based on elevation, latitude, 

and longitude, which are important factors in the regional zonation of vegetation 

throughout the Southwest. 

Each of the five states will be responsible for mapping zones that fall completely or partially within that state. %ere a zone overlaps state boundaries, one 

state will take primary responsibility for mapping that zone, with the other state(s) 

providing logistical support and information. Each state project will collect existing 

and new field data for mapping zones, model specific vegetation/ environmental 

parameters, work with the regional remote sensing team in developing cluster images, and provide ftnallabeling of the land cover types in their assigned mapping 

zones. 

The Arizona project has primary responsibility for classifying 11 of 73 regional 

mapping zones, an area of about 26 million hectares (19% of the region), and will 

contribute to land cover mapping in five shared zones. For each shared mapping 

zone, Arizona will coordinate with the adjoining state project(s) to map the overlapping areas. %en land management crosses state and mapping zone boundaries, 

such as the Navajo Nation in the Four-Comers area, one state project will take the 

lead to coordinate with involved land manager. 

The ecological labeling rules for vegetation types will be consistent within mapping zones. However, they may vary across mapping zones because of real differences in cover type distribution characteristics. For example, the elevation range of 

the "Pinyon Woodland Alliance" (Reid et al. 1999) will be higher in the Hualapai 

mapping zone in Arizona than in a mapping zone in Utah due to latitudinal changes. 

Farther north, the pinyon cover type occurs at lower elevations. We are ensuring 

consistency in applying map labels through periodic meetings with the state projects, 
N 

+ 

D State Boundary 

D Mapping Zone 

0 1 00 Kilometers 

Figure 2. Mapping zones for SW ReGAP. Zones are labeled with a descriptive name in Arizona. Elsewhere, they are labeled with the state 

responsible for mapping the zone. 
168 SouTHWEsT REGAP OVERVIEW 

and by overview of map label application by the Association of Biodiversity Information and regional remote sensing team. 

Vertebrate Species Distribution Maps 

The regional project will model predicted distributions of each vertebrate taxon 

that resides, breeds, or uses habitat in the five-state Southwest region for a substantial part of its life history, including winter range and important migration stopovers. 

In addition to native species, the project will model subspecies of particular interest 

and widespread non-native species. There are approximately 960 vertebrate taxa 

within the region, and the Arizona project will model the distribution of approximately 570 taxa. A regional animal modeling team will be responsible for resolving 

differences in models of taxa that cross state boundaries. 

Previous GAP projects developed vertebrate distribution models based on literature sources (USGS GAP 2000). Similarly, our project will construct wildlife habitat relationship models (WHRMs) from the best available literature on the distribution and habitat associations for each species, maintaining consistency with the traditional GAP approach to vertebrate distribution modeling. In addition, our project 

will use field information in a data-driven approach of distribution modeling for 

select groups of taxa. This process will be used for passerine birds and possibly other 

groups, depending on the availability of sufficient field inventory data. Primary data 

sources for birds will be breeding bird survey data (Sauer et al. 1999) and breeding 

bird adas data currendy being gathered for the state of Arizona (11cCarthey et al. 

1995). This data-driven approach will use correlation of georeferenced taxa location 

data (e.g., from census plots) with maps of habitat features (e.g., elevation and land 

cover type) to extract the \VHRM. 

The WHRMs will be applied to maps of habitat features to produce distribution maps indicating known, probable, and possible presence of each vertebrate 

taxon within its geographic range. Models produced from the data-driven approach 

will be compared with the traditional GAP approach to evaluate and then resolve any 

apparent differences. 

Land Stewardship Map 

Land ownership often does not cross state boundaries. However, in certain 

cases, such as tribal and federal lands, land ownership does cross state boundaries 

and will be mapped as such. In addition, the five states will cooperate to apply a 

consistent definition ofland stewardship across the region. 

Land stewardship of public and private land has traditionally been categorized 

by a four-level rating (Table 3). For the Arizona project, the first generation GAP land 

stewardship map will be used as a starting point; however, changes in land ownership and management are expected, and the land stewardship data layer will require 

an extensive update. Digital parcel boundaries will be obtained from each county 

within Arizona in order to refine the stewardship map resolution. Each tract will be 

attributed for land ownership, managing institution, and management status. A 
JACOBS ET AL. 169 

Table 3. Biodiversity management status categories used in the land stewardship 

map (USGS 2000). 

Status Description 

An area having permanent protection from conversion of natural land 

cover and a mandated management plan in operation to maintain a 

natural state within which disturbance events (of natural type, 

frequency, intensity, and legacy) are allowed to proceed without 

interference or are mimicked through management 

2 An area having permanent protection from conversion of natural land 

cover and a mandated management plan in operation to maintain a 

primarily natural state, but which may receive uses or management 

practices that degrade the quality of existing natural communities, 

including suppression of natural disturbance 

3 An area having permanent protection from conversion of natural land 

cover for the majority of the area, but subject to extractive uses of 

either a broad, low-intensity type (e.g., logging) or localized intense type 

(e.g., mining). It also confers protection to federally listed endangered 

and threatened species throughout the area 

4 There are no known public or private institutional mandates or legally 

recognized easements or deed restrictions held by the managing entity 

to prevent conversion of natural habitat types to anthropogenic habitat 

types. The area generally allows conversion to unnatural land cover 

throughout 

substantial effort will be made to identify and contact all known conservation land 

owners and/ or managers holding tracts at least as large as the minimum resolution 

size (16 ha) in order to verify stewardship status of that land tract. 

State and Regional Gap Analyses 

The conservation gap analysis consists of intersecting land cover and vertebrate 

distribution maps with the stewardship map, and calculating the amount of each 

vegetation type and vertebrate species distribution in each land stewardship category. 

This analysis will identify important gaps that have potential for mitigation by land 

stewards (USGS 2000). The analysis will consist of two steps: individual state 

analyses and a regional analysis. This will allow land stewards to better assess their 

role and responsibility for biota occurring on their lands, and in the greater context of 

the Southwest. 

ANTICIPATED REsuLTs 

The Southwest Regional Gap Analysis Program will provide detailed, spatially 

explicit information on the distribution and management status of each mapped 

vertebrate species and vegetation community within the greater Southwest region. 
170 SouTHWEsT REGAP OVERVIEW 

Map products and conservation analysis results will be released in the beginning of 

2005 (Table 4). Preliminary products are expected to be available in 2004 for examination and comment. 

Table 4. Products from Southwest Regional Gap Analysis Program in Arizona. 

Product Format Outlet 

Land Cover Map ArcGIS layer CD and website 

Terrestrial Vertebrate ArcGIS layers CD and website 

Species Distribution Maps 

Land Stewardship Map ArcGIS layer CD and website 

Wildlife Habitat Microsoft Access CD and website 

Relationship Models database and metadata 

Final Report for Arizona Report Technical Report 

Final Report for the Report Technical Report 

Southwest Region 

Important advances with this regional project for the Southwest include much 

fmer resolution (5 ha) land cover mapping, use of a consistent vegetation classification system (NVC) at a fine level of detail, and coordinated mapping to eliminate 

edge-matching problems across state boundaries. Vertebrate distribution models 

will be developed based on comprehensive syntheses of information on habitat and 

distribution, and recent inventory data. Development of vertebrate distribution 

models will make specific use of detailed accuracy assessment of earlier GAP distribution models. 

With the regional information base resulting from this project, decisions about 

human activities that affect biodiversity can be made with specific reference to scientific data on distribution of biota over entire landscapes and ecoregions. The application of a regional model will allow data users interested in locally-occurring plant 

communities or vertebrate species to evaluate species status in the context of a watershed, ecoregion, national range, or ultimately continental and global range (Crist and 

Jennings 1997). Cooperative planning among neighboring land managers (e.g., 

Bureau of Land Management, National Park Service, U.S. Forest Service, state lands, 

and private land owners) will benefit from the consistency afforded by the regional 

land cover and vertebrate distribution maps. In this way, products of this project will 

provide an important tool for management and conservation planning in the varied 

ecosystems of the Southwest. 
JACOBS ET AL. 171 

AcKNOWLEDGEMENTS 

The authors would like to thank J. Michael Scott and Tom Edwards for their 

thoughtful comments on the manuscript. 

LITERATURE CITED 

Crist, P. and M. Jennings. 1997. Regionalizing state-level data. Pages 15-16 in E. 

Brackney and M. Jennings, (eds.). Gap Analysis Bulletin, no. 6. USGS/BRD/GAP, 

Moscow, ID. 

Edwards, T. C., Jr., C. H. Homer, S. D. Bassett, A. Falconer, R. D. Ramsey, and D. W 

Wight. 1995. Utah Gap Analysis: An environmental information system. Final 

Project Report 95-1, Utah Cooperative Fish and Wildlife Research Unit, Utah 

State University, Logan, UT. 

Eve, M. and J. Merchant. 1998. A national survey of land cover mapping protocols used 

in the gap analysis program. Final Report. Internet WWW page, at URL: http:// 

www.calmit. unl.edu/ gap map/ report.html. 

FGDC (Federal Geographic Data Committee). 1997. Vegetation classification standard, 

FGDC-STD-005. Internet WWW page, at URL: http:/ /www.fgdc.gov /Standards/Documents/Standards/Vegetation. 

Freedman, B. 1989. Environmental ecology: The impacts of pollution and other stresses 

on ecosystem structure and function. Academic Press, San Diego, CA. 

Homer, C. G., R. D. Ramsey, T. C. Edwards, Jr., and A. Falconer. 1997. Landscape 

cover-type modeling using a multi-scene thematic mapper mosaic. Photogrammetric Engineering & Remote Sensing 63: 59-67. 

Grossman, D. H., D. Faber-Langendoen, A. S. Weakley, M. Anderson, P. Bourgeron, R. 

Crawford, K. Goodin, S. Landaal, K. Metzler, K. D. Patterson, M. Pyne, M. Reid, 

and L. Sneddon. 1998. International classification of ecological communities: 

terrestrial vegetation of the United States. Volume I. The National Vegetation 

Classification System: Development, status, and applications. The Nature 

Conservancy, Arlington, VA. 

Kepler, C. B., and J. M. Scott. 1985. Conservation of island ecosystems. Pages 255-271 

in P. 0. Moors, (ed.). Conservation of Island Birds. International Council of Bird 

Preservation, Technical Publication 3. 

McCarthey, T. D., T. E. Corman, and M. J. Latta. 1995. Arizona breeding bird atlas 

project: 1995 progress report. Arizona Game and Fish Department, Phoenix, AZ. 

McKendry,]. E. and G. E. Machlis. 1991. The role of geography in extending 

biodiversity gap analysis. Applied Geography 11: 135-152. 

MDEP (Mojave Desert Ecosystem Program). 1998. Mojave Desert Ecosystem Project 

CD-ROM. UBS4M 12/98 Department of Defense Legacy Program in cooperation 

with the Department of Interior. 

Noss, R. F. 1987. From plant communities to landscapes in conservation inventories: A 

look at The Nature Conservancy (USA). Biological Conservation 41: 1-37. 

N oss, R. F. 1983. A regional landscape approach to maintain diversity. BioScience 33: 

700-706. 
172 SouTHWEsT REGAP OVERVIEW 

Reid, M.S., K. A. Schulz, P.]. Comer, M. H. Schindel, D. R. Culver, D. A. Sarr, and M. 

C. Damm. 1999. An alliance level classification of vegetation of the coterminous 

western United States. Unpublished fmal report to the University of Idaho 

Cooperative Fish and Wildlife Research Unit and National Gap Analysis Program, 

in fulfillment of Cooperative Agreement 1434-HQ-97-AG-01779. The Nature 

Conservancy, Western Conservation Science Department, Boulder, CO. 

Sauer,]. R.,J. E. Hines, I. Thomas,]. Fallon, and G. Gough. 1999. The North American 

Breeding Bird Survey, Results and Analysis 1966- 1998. Version 98.1, USGS 

Patuxent Wildlife Research Center, Laurel, MD. 

Schrupp, D., L. O'Brien, and S. Russo. 2000. Colorado Gap Analysis Project. Pages 29-

30 in E. Brackney, R. Brannon, P. Crist, and K. Gergely, (eds.). Gap Analysis 

Bulletin, no. 9. USGS/BRD/GAP, Moscow, ID. 

Scott,]. M., F. Davis, B. Csuti, R. N ass, B. Butterfield, C. Groves, H. Anderson, S. 

Caicco, F. D'Erchia, T. Edwards, Jr.,]. Ulliman, and R. Wright. 1993. Gap analysis: 

A geographic approach to protection of biological diversity. Wildlife Monographs 

123: 1-41. 

Thompson, B. C., P.]. Crist, J. S. Prior-Magee, R. A. Deitner, D. L. Garber, and M. A. 

Hughes. 1996. Gap analysis of biological diversity conservation in New Mexico 

using Geographic Information Systems. Research Completion Report, New Mexico 

Cooperative Fish and Wildlife Research Unit, Las Cruces, NM. 968 pp. 

USGS GAP (Gap Analysis Program). 2000. A handbook for conducting Gap Analysis. 

Internet WWW page, at URL: http:/ /www.gap.uidaho.edu/handbook (version 

current at 24 February 2000). 

USGS WERC (Western Ecological Research Center), Sonoran Desert Field Station. 

2001. Arizona Gap Analysis Project. Final Draft 4/10/01. USGS WERC Sonoran 

Desert Field Station, Tucson, AZ. 

Wilcove, D., D. Rothstein, and E. Losos. 1998. Quantifying threats to imperiled species 

in the United States. BioScience 48: 607-612. 
Modeling Wildlife Habitat Corridors in the 

Greater Grand Staircase-Escalante Ecosystem 

Dawn Hartley 

Center for Landscape Anafysis 

The Wilderness S ociery 

1424 Fourth Avenue, Suite 816 

Seattle, WA 98101 

and 

Gregory H. Aplet 

Center for Landscape Anafysis 

The Wilderness S ociery 

7475 Dakin Street, Suite 410 

Denver, CO 80221 

Abstract. The Grand Staircase-Escalante National Monument was established, in 

part, to protect landscape connectors, a resource about which very little is known. In 

this paper we present a methodology that may be applied to identify potential wildlife 

movement corridors, in the absence of direct, scientific observation, but where 

something is known about habitat quality. Our analysis of the Monument landscape 

suggests that there are a number of places that deserve further scrutiny as potential 

movement corridors, including the heart of the Kaiparowits Plateau, the East Fork of 

the Virgin River east of Zion National Park, and the Dixie National Forest northeast of 

Bryce Canyon National Park. While we do not imply that these are movement corridors, 

we believe that our analysis provides new insights into potential habitat connectivity. 

Key words: movement corridors, GIS modeling, least-cost path analysis, landscape 

ecology, roads, national monuments, Bureau of Land Management, Gap Analysis 

Program, Utah. 

Email: Gregory Aplet, greg_aplet@tws.org 

173 
174 MODELING WILDLIFE HABITAT CORRIDORS 

INTRODUCTION 

For as long as principles of island biogeography have been applied to conservation, habitat connectivity has been understood to play an important role in the 

viability of species populations (Diamond 197 5, Wilson and Willis 197 5, Meffe and 

Carroll 1997). Habitat connectivity increases the likelihood of interaction among 

individuals within a population, which, in turn: (1) increases effective population 

size; (2) maintains gene flow; and (3) facilitates regular migration and dispersal. Each 

of these processes helps insure the viability and long-term persistence of a population (Primack 1993, Hunter 1996, Meffe and Carroll1997). 

The role of "corridors" in providing habitat connectivity is less well understood. Corridors, which are generally defined as strips of natural vegetation between 

protected blocks ofhabitat (Ben dey and Catterall1997, Beier and N oss 1998), have 

been proposed by some as crucial to the maintenance of healthy wildlife populations 

in otherwise degraded landscapes. Proponents of corridor protection note that 

wildlife seem to have preferred pathways through the land, as borne out by historical 

evidence, such as records of vehicle-wildlife collisions and the familiar "wildlife crossing" sign (Beier 1993). Protection of relatively good habitat strips cannot help but 

facilitate movement among patches (Noss 1987, Hobbs 1992, Noss and Cooperrider 

1994). 

Skeptics, on the other hand, argue that while wildlife certainly do not use all 

space uniformly, there is very litde evidence that natural vegetation strips left on an 

otherwise developed landscape will be used as migration routes (Mann and Plummer 

1995). They point to controlled experiments in which model species move more or 

less randomly about the landscape despite the provision of corridors (Ezzard 1992). 

Elsewhere, especially in sparsely vegetated desert settings, "corridors" may follow 

geological features not typically associated with habitat quality. Others have suggested 

that corridors might actually harm populations by facilitating the spread of disease or 

by concentrating prey species, making them easy targets for ambush predators 

(Simberloff and Cox 1987, Simberloff et al. 1992, Hess 1994). Some skeptics have 

argued that scarce conservation resources ought to be spent increasing the size of 

reserves rather than protecting movement corridors (Simberloff et al. 1992). 

In the midst of this debate, in September 1996, President Clinton designated 

the Grand Staircase-Escalante National Monument in southern Utah. In addition to 

saluting the remoteness and natural beauty of the area, the President recognized the 

important role that the monument plays as a landscape connector, specifically mentioning riparian corridors as an object of conservation under the Antiquities Act 

(Clinton 1996). Belnap (1997) noted, "The Monument contains several perennial 

streams that connect the high plateaus to the low desert, thus preserving these 

migration corridors and increasing the Monument's ability to conserve genetic and 

population diversity of plants and animals." Belnap's report states further that "the 

connection the Monument provides between Glen Canyon, Canyonlands, Grand 

Canyon, Capitol Reef, and Bryce Canyon National Park units increases the value of all 

these areas for protection of viability of plant and animal populations." 

This notion of a greater Grand Staircase-Escalante ecosystem, in which the 
HARTIEY AND APLET 175 

Monument helps sustain the health of a larger landscape, was explored by The 

Wilderness Society (1999) in "Crown of the Canyons: An atlas of the ecology, 

economy and future of the greater Grand Staircase-Escalante National Monument 

ecosystem." Despite the attention brought to the larger ecosystem by the President, 

scientists, and the conservation community, the management plan for the Monument (USDI Bureau ofLand Management 2000) is virtually silent on the role of the 

Monument as a landscape connector. Locations and sizes ofkey connectors remain 

unresolved, leaving managers unable to address one of the purposes for which the 

Monument was established. 

In this paper, we present a methodology for developing information about 

habitat connectivity in the absence of direct wildlife movement observations. To 

illustrate these methods, we modeled potential wildlife corridors between four established protected areas in the vicinity of the Grand Staircase-Escalante National 

Monument. While, there is much more work that needs to be done before we can 

rely on corridors to achieve conservation, we believe connectivity across landscapes 

should be maintained. We present herein one type of analysis that may help natural 

resource managers and researchers understand where to concentrate their future efforts. 

METHODS 

We employed methods derived from Walker and Craighead (1997, 1998) who 

modeled potential habitat corridors for grizzly bears, mountain lion, and elk in 

Montana. Walker and Craighead acknowledged the uncertainty surrounding the 

corridor issue, and rather than assert that they could identify transportation routes 

that animals would surely use, they suggest that it may be possible to identify habitat 

connectors that would likely increase the probability of animal survival. Thus, movement would be facilitated by these routes, whether animals actively followed them or 

not. Their approach was based on a set of four reasonable assumptions: 

1) Good corridors are primarily composed of good habitat. That is, good 

habitat makes a better connector than bad habitat. The question of what constitutes "good habitat" continues to occupy wildlife biologists. This evaluation 

assumes that habitat quality can be determined. 

2) Humans pose problems for successful wildlife transit. Specifically, roads 

and human developments create barriers to successful movement. Like habitat 

quality, the actual effect of roads on wildlife is a topic of intense scientific interest. This modeling approach assumes that habitat quality is diminished near 

roads. 

3) Current human developments are permanent. Walker and Craighead's 

model does not evaluate the possibility of removing barriers to facilitate movement. 

4) "Least-cost paths" constitute the best routes of transit. This key assumption allows that animals will follow an optimum route between two points that 

minimizes their exposure to low quality habitat. In reality, animals cannot know 

what lies beyond their sensory range and, so, cannot choose a truly optimum 
176 M ODELI NG WILDLI FE J-l:IIJIT.-IT CORRIDORS 

path. Instead, they select resources at a finer scale, which may not be "least-cost" 

across a broader landscape. Assuming a least cost path "balances habitat suitability, minimum Euclidean distance, and degree of 'connectivity' between the 

two endpoints" (Walker and Craighead 1997). Again, this is an assumption of 

the modeling process. The sensory range of wildlife varies with species, and 

some migrant wildlife species may respond to coarser-grained landscape cues 

(e.g., topographic gradients, riparian corridors) than are represented by our habitat grid. Actual behavior may vary with species, season, or time of day. 

The process that we used is illustrated graphically in Figure 1. Geographic 

Information Systems (GIS) software from Environmental Systems Research Institute (ESRI) was used to model the spatial relationship between roads and species 

habitat to derive potential travel corridors for a number of species. The species, 

whose conservation was recognized in the President's proclamation as a reason for 

establishment of the Monument, included black bear, mountain lion, desert bighorn sheep, bald eagle, and peregrine falcon. We obtained species habitat suitability 

data in a 90-meter resolution grid from the Utah Gap Analysis Project (GAP) of the 

United States Geological Survey (USGS). The Utah GAP vegetation cover-type modHabitat Data 

Species X 

Corridor XAB = Species 

X Between 

Sources A & B 

Input Data 

Layers 

Mask & Normalize 

CorridorxAn = 

Corridori 

Source Area A Source Area B 

Source Area B 

Grid 

Composite Corridor 

20 

= L Corridor i 

j = l 

Figure 1. Flowchart describing the modeling process used in this study (see text for 

more specifics). 
HARTIEY AND APLET 177 

eling consisted of two phases: (1) correlation of cover-type associations with spectral 

values from 30-meter TM imagery; and, (2) ecological modeling based on ancillary 

information, which included 3 arc-second digital elevation data, slope, aspect, and 

region-specific vegetation cover-type polygons. 

Classified pixel data were then aggregated to polygons (the GIS vector model) 

using a minimum mapping unit (MMU) of 100 ha. Riparian and wedands polygons 

were derived with a 40 ha MMU. Species distribution was then predicted for each of 

the polygons based on the mapped cover-types, elevation, and existing species ranges. 

A distance-to-water buffer was also added to the species distribution models to 

correct distributions of species closely linked with water. These habitat suitability 

models are fixed in time and do not reflect seasonal variation in habitat quality, nor 

have they been empirically tested. Road data were obtained from the USGS as 

1:100,000 digital line graphs (DLGs). 

The GRID module of ESRI's ARC/INFO software provided the modeling 

tools that we used to develop our corridor identification methodology. The GRID 

module provides a built-in "corridor" function, which identifies the least cost path 

between two source areas. In this study, sources are defined as federally protected 

areas in the vicinity of the Monument, including Zion National Park to the west, 

Bryce Canyon and Capitol Reef National Parks to the north, and Glen Canyon 

National Recreation Area to the southeast (other potential source areas exist in Arizona, including the Vermilion Cliffs National Monument and Grand Canyon National Park, but these were not evaluated in this modeling effort). Movement between any two of these sources occurs across a "cost surface" that is a representation 

of species-specific habitat. Cost surfaces are based on the notion that low value 

habitat "costs more" (in terms of exposure to mortality risks, energy balance, etc.) to 

cross than does high-quality habitat. 

A cost surface grid was derived for each species in the study based on GAP 

habitat suitability data. The GAP data identified habitat by five nominal classes 

(critical, high value, significant value, low value, and no habitat value), so we had to 

assign numerical values to these habitat classes in order to generate a cost surface. 

After conducting a sensitivity analysis in which we explored model behavior under a 

variety of scoring systems, we determined that a simple rating of 1 to 5 yielded the 

most acceptable model behavior. The sensitivity analysis involved varying the scores 

assigned to each nominal class (using constant, linear, and exponential increases) and 

the effect of roads as barriers. The model is extremely flexible and can be forced into 

a wide range of behaviors. Our sensitivity analysis led us to select parameter values 

that produced a reasonable wildlife movement behavior model. 

Once developed, each cost surface was then modified to increase costs (i.e., 

degrade habitat value) according to the influence of roads. The USGS road data were 

divided into major (high volume) and minor (low volume) road classes. We subjectively determined that major roads have a zone of influence that extends 1600 meters, 

while minor roads have an influence to 400 meters. We recognize that our buffers are 

mosdy arbitrary because different species respond differendy to roads. As more is 

learned about the response of individual species to roads, the road effect may be 
178 MODELING WILDLIFE HABITAT CORRIDORS 

tailored to fit particular species and road classes. For major roads, a road impact 

coefficient of 100 was applied at the surface of the road, with impact declining 

exponentially to a coefficient of one at 1600 meters. We assumed minor roads have 

1/3 the maximum impact of major roads and, therefore, have a coefficient of33 at 

the road, with influence decreasing exponentially to a coefficient of one at 400 meters. 

The final cost surface for each species was derived by multiplying the cell values from 

the respective habitat grids by the cell values from the road influence grid. The final 

cost grids represent the cost to move through a single grid cell. 

To account for the accumulated cost of dispersing away from a source area, we 

next developed "cost distance" grids in pairs for reciprocal source areas. For example, 

to ultimately identify a corridor between Bryce Canyon NP and Capitol Reef NP, two 

cost surface grids are required- one that represents the accumulated costs of a 

species dispersing from Bryce Canyon NP and a reciprocal grid that represents the 

same species as it disperses from Capitol Reef NP. For each species, cost distance 

grids were derived for four pairs of source areas (Zion to Bryce, Bryce to Capitol Reef, 

Bryce to Glen Canyon, and Zion to Glen Canyon). 

The pairs of cost distance grids were then combined using the GRID module's 

"corridor" function, resulting in a single grid that represented a continuum of values 

across the entire study area. Within this continuum, the corridor is represented by the 

lowest cell values, the "least-cost path." To isolate the corridor, a mask was applied to 

eliminate all but the lowest 1% of cell values. A total of 20 corridors were derived, 

one for each species between four pairs of sources (Fig. 2). To enable comparison, the 

cell values in each corridor were normalized to a scale of 1 to 50. A final, composite 

corridor (Fig. 3) was created by adding the normalized cell values from all20 corridors 

and rescaling the resulting range of values from 1 to 255 to facilitate final map 

shading. 

RESULTS AND DISCUSSION 

The variability among species in corridors identified by our model shows clearly 

that model results are affected by the distribution of habitat quality for each species 

(Fig. 2). Though corridors do tend to avoid roads, roads do not drive the model. 

High quality black bear habitat on the Aquarius Plateau to the north of the Monument would allow bears to travel from Bryce to Capitol Reef through the forested 

uplands. Conversely, the absence of good habitat on the Aquarius Plateau for desert 

bighorn sheep forces sheep to travel from Bryce to Capitol Reef across the Kaiparowits 

Plateau. Our results seem to make biological sense, suggesting that this model may 

have utility in predicting where species are likely to move across the southern Utah 

landscape. 

Despite these promising results, it is important to keep in mind that our model 

is speculative and represents only numerical manipulations. For example, while it 

may make sense for desert bighorn sheep traveling between Bryce and Capitol Reef 

to traverse the Kaiparowits Plateau, it may make no sense at all for bighorn sheep to 

even be found at Bryce Canyon. Desert bighorn sheep prefer open desert scrub, not 
Black Bear 

Glen Canyon NRA 

Bryce Canyon NP 

Bald Eagle 

Desert Bighorn 

Sheep Mountain Lion Peregrine Falcon 

A. 

B. 

c. 

D. 

Figure 2. Potential wildlife habitat corridors for the five species between four pairs of protected areas in the greater Grand Staircase-Escalante 

ecosystem: A. Bryce Canyon National Park to Capitol Reef National Park; B. Bryce Canyon National Park to Glen Canyon National Recreation Area; 

C. Zion National Park to Bryce Canyon National Park; and D. Zion National Park to Glen Canyon National Recreation Area. 
Bryce 

Canyon NP 

Glen Canyon NRA 

Figure 3. Composite wildlife habitat corridor map created by combining the 20 corridors in Figure 1. The darkest parts of the image indicate 

potential wildlife movement corridors. 

n 

0 

"' ~ 

0 

0 

c; 
HARTIEY AND APLET 181 

the subalpine forests of the Paunsaugunt Plateau. We believe the best use of these 

results is to direct the attention of scientists and land managers to particular places on 

the landscape that are worthy of further investigation as wildlife habitat connectors. 

Nevertheless, in the absence of ground-based observations or scientific research, 

models such as this can provide new insights to land managers and scientists. 

The compiled corridor map obscures information about individual species, but 

it does highlight some places that appear to be particularly important to landscape 

connectivity (Fig. 3). For example, the Kaiparowits Plateau in the center of the 

Monument is an obvious "hot spot." This is not surprising, given the area's legendary remoteness and unspoiled natural character. 

Less predictable is the apparently very important connector east from Zion to 

the Monument along the East Fork of the Virgin River. This mostly BLM land was 

left out of the Grand Staircase-Escalante National Monument but has been recommended for inclusion in the National Wilderness Preservation System by the Utah 

Wilderness Coalition because of its outstanding natural character. In addition to the 

importance of the corridor, our image indicates a crucial constriction at Mt. Carmel 

Junction, where roads and development threaten to cut off connection. Similarly, 

the national forest land east of Bryce Canyon National Park in the vicinity of Powell 

Point appears to be an important connector between Bryce Canyon and the Aquarius 

and Kaiparowits Plateaus. 

Managers of the Grand Staircase-Escalante National Monument will be making 

decisions implementing their management plan over the next several years. Among 

their decisions will be determinations of where to place developments and which 

roads to close and/ or rehabilitate. Clearly, in the absence of scientific research to the 

contrary, our model suggests that they should maintain the corridor integrity between the Aquarius Plateau and Glen Canyon National Recreation Area. Similarly, 

managers of the Dixie National Forest should seek to protect habitat connectivity 

between Bryce Canyon and Powell Point, and the BLM, Forest Service, and local 

authorities should be concerned about development in the vicinity of Mt. Carmel 

Junction. 

In presenting our model, we do not wish to assert that the corridors we have 

identified are the most important pathways for wildlife movement in the landscape. 

We are only suggesting that these may be productive places to focus further study. It 

is important to keep in mind that, ultimately, this work is only a mathematical 

model. However, in the absence of any other information on wildlife distribution 

and movement patterns, this represents the best available information, and management should take this information into account in conservation planning. Ultimately, we would prefer to see additional work done to examine the degree to which 

these apparentfy important corridors actually contribute to wildlife population viability. We hope that by presenting one feasible approach, we spur further work aimed at 

protecting wildlife habitat connectivity in this landscape and throughout the Colorado Plateau. 
182 MODELING WILDLIFE HABITAT CORRIDORS 

LITERATURE CITED 

Beier, P. 1993. Determining minimum habitat areas and habitat corridors for cougars. 

Conservation Biology 7: 94-108. 

Beier, P., and R.F. Noss. 1998. Do habitat corridors provide connectivity? Conservation Biology 12: 1241-1252. 

Belnap, J. 1997. Ecological resources of the Grand Staircase-Escalante National 

Monument. Pages 17-25 in L.M. Hill, (ed.) Learning from the land: Grand 

Staircase-Escalante National Monument Science Symposium proceedings. USDI 

Bureau of Land Management, Salt Lake City, UT. 

Bentley, J.M., and C.P. Catterall. 1997. The use of bushland, corridors, and linear 

remnants by birds in southeastern Queensland, Australia. Conservation Biology 11: 

1173-1189. 

Clinton, WJ. 1996. Establishment of the Grand Staircase-Escalante National Monument by the President of the United States of America, September 18, 1996: A 

proclamation. 

Diamond,J.M. 1975. The island dilemma: lessons of modern biogeographic studies for 

the design of natural preserves. Biological Conservation 7: 129-146. 

Ezzard. C. 1992. Wilderness corridors may not benefit all. Science News 8/29/92: 

135. 

Hess, G. 1994. Conservation corridors and contagious disease: a cautionary note. 

Conservation Biology 8: 256-262 

Hobbs, R.J. 1992. The role of corridors in conservation: solution or bandwagon? 

Trends in Ecology and Evolution 7: 389-392 

Hunter, M.L. 1996. Fundamentals of conservation biology. Blackwell Science, 

Cambridge, MA. 

Mann, C.C., and M.L. Plummer. 1995. Are wildlife corridors the right path? Science 

270: 1428-1430. 

Meffe, G.K., and C.R. Carroll. 1997. Principles of conservation biology, 2nd edition. 

Sinauer Associates, Sunderland, MA. 

Noss, R.F. 1987. Corridors in real landscapes: a reply to Simberloff and Cox. Conservation Biology 1: 159-164. 

Noss, R.F., and A. Cooperrider. 1994. Saving nature's legacy: protecting and restoring 

biodiversity. Defenders of Wildlife and Island Press, Washington, D.C. 

Primack, R.B. 1993. Introduction to conservation biology. Sinauer Associates, 

Sunderland, MA. 

Simberloff, D., and J. Cox. 1987. Consequences and costs of conservation corridors. 

Conservation Biology 1: 63-71. 

Simberloff, D., J.A. Parr, J. Cox and D. Mehlman. 1992. Movement corridors: conservation bargains or poor investments? Conservation Biology 6: 493-504. 

The Wilderness Society. 1999. Crown of the Canyons: an atlas of the ecology, 

economy, and future of the greater Grand Staircase-Escalante National Monument 

ecosystem. The Wilderness Society, Denver, CO. 

USDI Bureau of Land Management. 2000. Grand Staircase-Escalante National 

Monument Approved Management Plan and Record of Decision. USDI-BLM, 

Cedar City, UT. 
HARTIEY AND APLET 183 

Walker, R., and L. Craighead. 1997. Analyzing wildlife movement corridors in Montana 

using GIS. Presented at the 1997 ESRI Users Conference and published in the 

Proceedings on CD-ROM. Also available at http:/ /www.grizzlybear.org/ col/ 

lcpcor.html. 

Walker, R., and L. Craighead. 1998. Corridors: key to wildlife from Yellowstone to 

Yukon. Pages 113-121 in L. Willcox, B. Robinson and A. Harvey, (eds.) A sense 

of place: An adas of issues, attitudes and resources in the Yellowstone to Yukon 

ecoregion. Yellowstone to Yukon Conservation Initiative, Canmore, Alberta. 

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(Insert sketch: Physical Resources) 

Climate of the Central Colorado Plateau, 

Utah and Arizona: 

Characterization and Recent Trends 

John R. Spence 

National ParkS ervice 

Resource Management Division 

Glen Caf!Yon National Recreation Area 

POBox 1507 

Page,AZ 86040 

Abstract. The climate of the central portion of the Colorado Plateau is characterized 

using data from 27 climate stations. Mean annual temperature ranges from 16.9 o Cat Lee's 

Ferry (978 m) to 4.4 o Cat Bryce Canyon National Park (2412 m). Precipitation varies from 

138-405 millimeters, and is weakly bimodal, with a strong late summer-early fall peak and 

a weaker late winter-early spring peak. Annual Thornthwaite potential evapotranspiration 

rates vary from 993 to 474 mm, and at all stations, rates exceed annual precipitation. 

Temperature, precipitation and evapotranspiration are all strongly controlled by elevation. 

Latitude and longitude have some additional effects on some variables. A strong southeast 

to northwest decline in temperature occurs across the study region. An analysis of trends 

among nine stations with good records reveals that annual minimum temperatures have 

increased significantly in most areas since the 1960's. Those stations that fail to show this 

trend do show significant increases in winter minimum temperatures. Maximum temperatures have not responded in the same manner, and some high elevation stations document 

declines in maximum temperatures. Longer-term records at Escalante and Lee's Ferry 

confttm the warming trend back to 1925 and 1944, respectively. Precipitation amounts 

have changed relatively little, although there is a weak trend towards increasing winter 

season precipitation. There is no evidence in the data for a strengthening of the summer 

monsoon, which is a prediction of some global warming models. Potential impacts of 

global warming scenarios and changes in extent and timing of precipitation on the vegetation and rare species of the Colorado Plateau are discussed. 

Key words: central Colorado Plateau, climate, global warming, potential evapotranspiration, temperature, precipitation. 

Email: John Spence, John_Spence@nps.gov 

187 
188 CLIMA1E OF THE CENTRAL COLORADO PLATEAU 

INTRODUCTION 

The Colorado Plateau consists of a series of plateaus formed from sedimentary 

rocks, with scattered laccolithic ranges such as the Henry Mountains, San Francisco 

Peaks, and Navajo Mountain. Elevation of the Plateau averages between 1500-1800 

m, with several mountains exceeding 3300 m. The Colorado River has cut through 

the Plateau from northeast to southwest, and has carved a series of deep canyons 

ranging from 1400 m on the east edge in Colorado to 370m on the west edge along 

the Grand Wash Cliffs in Arizona. Relatively little is known about the climate of the 

Plateau, particularly in the central region where population is sparse. 

The central portion of the Colorado Plateau (Fig. 1) includes some of the lowest 

elevations on the Plateau. Along the Colorado River elevations range from 1219 m 

at Moab to 978 mat Lee's Ferry, a drop of only 241m in 450 km. Along the river 

corridor, extensive mesas range in elevation from 1400-1800 m. The high Wastach 

and Kaibab Plateaus to the north, west and southwest produce an extensive rain 

shadow effect on this portion of the Plateau. Average annual rainfall throughout 

much of the region is < 200 mm, and to the north and east of the Henry Mountains, 

in the San Rafael and Green River deserts, is < 150 mm. 

In this study, the climate of the central Colorado Plateau is characterized, using 

temperature, precipitation, and potential evapotranspiration, and their relationships 

with elevation. Because of the interest and speculation regarding the possible effects 

of global warming in the region, trends in temperature and precipitation are examined in detail for selected stations. The main objectives of this study are to (1) 

characterize the climate of the central Colorado Plateau, (2) provide regression equations for relationships between climate variables and elevation, and (3) document 

recent trends in climate. 

BACKGROUND AND SETTING 

The climate of the study area can be classified as temperate-arid (Walter 1985), 

with hot summers, extensive periods of frost in the winter, and low and variable 

precipitation. The average freeze-free season varies from > 200 days along the Colorado River to < 20 at the summit of the highest mountains (Ashcroft et al. 1992). 

Most of the study area experiences 120 or more frost-free days a year. During winter, 

the polar jet stream lies to the north of the study area, preventing most winter 

storms from reaching the Colorado Plateau (Mitchell1976, Petersen 1994). Occasionally, the winter high-pressure ridge over the western U.S. moves westward into 

the Pacific Ocean. This allows the development of a low-pressure trough between 

the Sierra-Cascade Mountains and Rocky Mountains, bringing winter storms into 

the region. As the region warms in the spring, the polar jet stream moves northward, 

replaced by high pressure. When this high pressure begins to move north in late 

June or July, warm, wet air from the Gulf of Mexico moves northwest into the 

region, bringing the late summer monsoons. The average position of the northern 

edge of these monsoons bisects the Colorado Plateau from northeast to southwest 

(Petersen 1994; Fig. 1). This position varies from year to year, producing highly-
SPENCE 189 

variable summer and early fall precipitation in the study area. In some years, late 

summer tropical hurricanes off Baja, California bring extensive rain into the region 

from the southwest up the Colorado River Valley (Petersen 1994). 

The study area comprises roughly the west-central portion of the Plateau, ranging from 36-38° latitude and 110-112° longitude (Fig. 1). The study area lies along the 

eastern edge of the Wastach Plateau, and is bounded on the north by the Book Cliffs, 

the east by the high plateaus of Canyonlands and the La Sal Mountains, the southeast by the Abajo and Chuska Mountains, and the south by the high rim of Black 

Mesa. Southeastern Utah, including towns like Moab, Monticello, and Blanding, are 

not included because preliminary inspection of climate data revealed a significant 

increase in summer moisture in that area; this may be an orographic effect produced 

by the high mountain masses of the La Sal, Abajo, and southern Rocky Mountains, 

or a closer proximity to the Gulf of Mexico. 

The vegetation of the study area consists primarily of a variety of arid and semiarid plant communities. Extensive areas below ca. 1500 mare dominated by either 

Co!eogyne ramosissima (blackbrush) shrubland on shallow soils, or mosaics of shrubland 

and grassland types in sandy soils. Clay barrens are common and generally vegetated 

by ephemeral annual forbs or dwarf shrubland that is dominated by species of 

Atrip!ex. Above ca. 1500 m, extensive areas are dominated by stands of Pinus edu!is 

(two-needled pinyon) and Juniperus osteosperma (Utah juniper). Pinus ponderosa (Ponderosa pine) woodlands occur at elevations above 2300 m on the higher mountains. 

Above ca. 2700 m, a mixed conifer forest can be found, dominated by Pseudotsuga 

menzjesii (douglas fir),Abies conco!or (white fir), and Populus tremu!oides (aspen). Patches 

of Abies bifo!ia (Rocky Mountain subalpine fir)-- Picea enge!mannii (Engelmann spruce) 

forest, subalpine meadows, and alpine tundra occur on the summits of the highest 

mountains above ca. 3000 m (Spence et al. 199 5). 

METHODS 

The data set consists of monthly, yearly, and total record means for minimum, 

maximum, and annual temperature (I), and precipitation (PCP). Climate stations 

and basic data are listed in Table 1, along with information on duration of record and 

elevation. Figure 1 shows the area under consideration and the station locations. In 

all, 27 stations with records of 10 years or greater were utilized. Data were taken from 

the World Wide Web site maintained by the Desert Research Institute at the University of Nevada, Reno (www.wrcc.dri.edu/ summary). Data was first inspected for 

gaps in records. If a particular monthly value was missing, the missing value was 

estimated as the mean monthly value of the previous year and following year. Data 

were then converted to metric values. Seasons were used for certain comparisons. 

Seasons are defmed as follows: winter (December-February), spring 0\{arch-May), 

summer Qune-August), and fall (September-November). Growing season (AprilSeptember) and winter season (October-March) were also compared. Latitude and 

longitude for each station were recorded, and two vectors were calculated using the 

pythagorean theorem, one from the southeast to northwest (135° to 315°), the 
190 CLIMA1E OF THE CENTRAL COLORADO PLATEAU 

Table 1. Climate data for selected stations on the central Colorado Plateau. 

PCP=precipitation, Tann=annual mean temperature, and POTE=Thornthwaite potential 

evapotranspiration. The recording period and duration in years for each station is 

also listed. 

Station Elevation1 PCP2 Tann3 POTE2 Record Yrs 

(1) Page 1372 164 14.5 853 1959-1998 39 

(2) Lee's Ferry 978 153 16.9 993 1916-1998 82 

(3) Wahweap 1136 158 15.7 911 1967-1998 31 

(4) Big Water 1250 171 14.7 859 1963-1998 35 

(5) Escalante 1773 278 9.4 622 1901-1998 97 

(6) Bullfrog 1165 152 15.2 899 1967-1998 31 

(7) Hite 1058 144 16.2 941 1949-1962 13 

(8) Hite Marina 137 136 16.0 942 1968-1978 10 

(9) Hite Ranger Station 1220 214 15.5 919 1978-1998 20 

(1 0) Hans Flat 2012 248 10.6 667 1981-1998 17 

(11) Mexican Hat 1265 159 13.6 810 1948-1998 50 

(12) Natural Bridges NM 1982 320 10.3 652 1965-1998 33 

(13) Hanksville 1313 138 11.8 745 1948-1998 50 

(14) Boulder 2034 272 9.3 614 1954-1998 44 

(15) Sandy Ranch 1615 193 10.1 660 1963-1988 25 

(16) Bryce Canyon Airport 2312 307 4.4 474 1948-1983 35 

(17) Bryce Canyon NP 2412 405 5.1 484 1959-1998 39 

(18) Henrieville 1832 264 9.2 605 1963-1979 16 

(19) Manu. Valley Mission 1616 188 13.4 783 1961-1989 18 

(20) Betatakin 2222 310 9.9 628 1948-1998 50 

(21) Navajo Mountain 1835 233 9.8 635 1956-1975 19 

(22) Capitol Reef NP 1679 193 12.1 731 1967-1998 31 

(23) Fruita 1677 174 11.7 711 1948-1967 19 

(24) CANY-Needles 1536 214 11.8 727 1965-1998 33 

(25) CANY-Neck 1808 231 11.4 713 1965-1998 33 

(26) Kayenta 1735 195 11.5 698 1915-1978 63 

(27) Green River 1241 159 11.4 773 1893-1998 105 

1 Elevation in meters 2 Precipitation and POTE in millimeters 

3Temperature in oc 

second from southwest to northeast (225° to 45°). The position of each station was 

determined along these two vectors, which roughly correspond to Gulf of California 

and Gulf of Mexico air masses and storm tracks. After the effects of elevation were 

removed, a series of regressions were run among latitude, longitude, two vectors 45° 

off the vertical-horizontal coordinates, and climate variable residuals. 

Potential evapotranspiration (P01E) rates were calculated for each station. P01E 

determines the potential annual loss of water for a region from evaporation and 

plant transpiration. Thornthwaite's POTE was used because it is widely applied in 

North America, and its weaknesses and strengths are well known. The principal 
NEVADA 

Salt Lake City • 

UTAH 

• Flagstaff 

ARIZONA 

Omi 

0 km 100 

Albers Equal Area Projection, 

Standard Parallels 29'30' and 45'30', 

Central Meridian -96' 

SPENCE 191 

WYOMING 

Albuquerque 

NEW MEXICO 

Figure 1. The Colorado Plateau , outlined in gray. Climate stations within the study 

area, encompassed in dark grey circle, are numbered (see Table 1 ). The heavy, 

dashed line represents the approximate boundary of regional air masses and the 

average northern limit of the summer monsoon season in the region (from Mitchell 

1976, Petersen 1994). 
192 CLIMA1E OF THE CENTRAL COLORADO PLATEAU 

weakness is that it tends to underestimate values in extremely arid regions. 

Thomthwaite's POTE (e) is calculated as: 

e = 1.8(1 Ott I) a ( 1) 

where: a= a constant, 

t = monthly mean temperature in ac, and 

I= annual temperature index obtained by summing the monthly values. 

An assumption was made that climate variable means and variances were stationary 

throughout the duration of each station's record. This may not be the case, particularly for stations with long term records (e.g., Escalante, 98 years). Generally, this is a 

reasonable assumption (Rowlands 1993), particularly given the relatively short period of time under consideration (e.g., 30-50 years). 

For analysis of trends in climate variables, a subset of nine of the 27 stations 

were selected. These were selected because of relatively complete records back to 1966, 

and because they represent an elevational gradient. This elevational gradient ranges 

from 978 mat Lee's Ferry to 2412 mat Bryce Canyon National Park. These nine 

stations were analyzed in relation to three T and three PCP variables. Trends were 

examined for the period 1966-1998 (33 years) using linear regression. For those 

stations with longer records, a second set of analyses was completed for the duration 

of each record. 

RESULTS 

Characterization 

Summary climate data can be found in Table 1. Mean annual temperature (I) 

varies from a high of 16.9°C at Lee's Ferry to 4.4°C at Bryce Canyon Airport. Precipitation (PCP) is generally low at most stations, ranging from 136 mm at the Hite 

Marina to 405 mm at Bryce Canyon National Park. Because of the generally low PCP 

and hot summers, POTE rates are relatively high, ranging from 993 mm at Lee's Ferry 

to 4 7 4 mm at Bryce Canyon Airport. POTE exceeds PCP for all stations. Even at the 

highest elevations around Bryce Canyon National Park (2300-2400 m), POTE exceeds PCP, on average, seven months of the year. For all stations, however, winter 

season (December-February) PCP exceeds POTE. 

There is strong seasonal control for all T and PCP variables. A breakdown of 

PCP by season shows that, for most stations, it peaks in fall (September-November). 

For all stations, 30% of summed yearly PCP occurs in fall, followed by 27% in 

summer. Winter and spring are somewhat lower, with 22% and 21%, respectively. A 

weakly bimodal pattern occurs for most stations, with a late summer-early fall peak, 

and a second smaller peak in late winter. The driest months of the year tend to be 

May and June, and the wettest months July and August. At intervals of every twothree years, September and October tend to have the heaviest PCP. 

Climate diagrams for three stations, Page (13 72 m), Escalante (1773 m), and 

Bryce Canyon National Park (2412 m) are depicted in Figure 2. 
SPENCE 193 

Page, AZ (1372 m) 

200 50 

180 45 

'E 160 40 

..§.. 140 35 §: 0.. 120 0 

0.. 100 ., 30 ~ 

25 .a 

r: c 80 "' 20 .. 

c. w 60 1- 15 E 

.. 0 

0.. 40 1- 10 

20 

10 11 12 

Month 

Escalante, UT (1773 m) 

140 50 

120 

'E ..§.. 100 

0.. 

0 80 0.. 

, 60 " 

w "' 40 1-

0 

0.. 20 

40 

30 §: 

"' 1i 20 "' (ij 

Q. 

10 E 

1-"' 

0 

0 -10 

2 3 4 5 6 7 8 9 10 11 12 

Month 

Bryce Canyon National Park, UT (2412 m) 

120 50 

'E 100 40 

30 §: 

"' 

E 

0.. 80 

E 20 "' (ij

Q. 

u 

0.. 

"0 60 

" "' 10 E 

"' 1-

w 40 

1-

0 

0.. 20 

-10 

10 11 12 

Month 

Figure 2. Simplified climate diagrams plotting mean monthly temperature (0C), mean 

precipitation (mm), and potential evapotranspiration rates for three climate stations 

on the central Colorado Plateau. The year starts in January (month 1) and goes to 

December (month 12). a. Page, Arizona (1372 m); b. Escalante, Utah (1773 m); c. 

Bryce Canyon National Park, Utah (2412 m). 
194 CLIMA1E OF THE CENTRAL COLORADO PLATEAU 

Elevational Relationships 

All climate variables are strongly controlled by elevation. The regression equations for PCP, annual T, and POTE are: 

Precipitation (mm) = 0.1498*elevation in meters- 25.7 (r2=0.802) (2) 

Annual T (

0C) = -0.007*elevation in meters+ 23.168 (r2=0.817) (3) 

POTE (mm) = -0.3148*elevation in meters+ 1244.5 (r2=0.847) (4) 

Figure 3 shows the relationship among elevation, PCP, and POTE. As elevation 

increases, PCP increases and POTE decreases in a linear manner. The point at which 

the two lines intersect is known as the arid-humid boundary, where the PCP /POTE 

ratio is one. On the central Colorado Plateau, this boundary, based on where the two 

lines intersect in Figure 3, is estimated to lie at ca. 2730 m. 

The relationship among annual maximum and minimum T, and elevation is 

similar, with T decreasing as elevation increases. All three curves are essentially identical, with the same slopes. The regression equations for maximum and minimum 

annual T are: 

Maximum annual T (0C) = -0.007*elevation in meters+ 30.817 (r2=0.935) (5) 

Minimum annual T (

0

C) = -0.007*elevation in meters+ 15.577 (r2=0.596) (6) 

The relationship between annual minimum T and elevation is much weaker 

than that for annual and maximum T. This may result from influences by local 

topographic factors, such as depressions or valleys, that can cause winter temperature 

inversions. The adiabatic lapse rate, using mean annual T, is -0.70° C for each 100-

meter increase in elevation. 

1200 

1000 

E" 

E. 800 

c 

0 

~ 600 :!: 

Q. 

·;::; 

Cll 400 n. 

200 

1200 

y =-0.3148x +1244.5 

R2 = 0.8472 

1000 

800 

600 

400 

200 

800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 

Elevation (m) 

E" 

E. 

c 

:8 

~ 

·c. 

Ill 

c 

~ 

0 

Q. 

t'C 

> w 

Figure 3. The relationship among precipitation (PCP), potential evapotranspiration 

(POTE), and elevation on the central Colorado Plateau. The regression curves, 

regression equations, and variance are noted for the two curves. 
SPENCE 195 

Summer PCP increases more rapidly with elevation than winter PCP, although 

the differences are rather slight (winter PCP regression slope= 0.038, summer PCP 

slope = 0.047). PCP increases at almost the same rate for both growing (AprilSeptember) and winter (October-March) seasons (growing season regression slope= 

0.076, winter season slope= 0.074). 

Geographic Relationships 

Regression analysis indicates that elevation shows the strongest relationship 

with climate variables (Table 2). Regressing the residuals against latitude shows a 

trend of increasing winter PCP at higher latitudes, as one would expect. Winter PCP 

declines along a southwest to northeast vector in the study area. Growing season 

PCP shows a weak relationship with longitude, decreasing from east to west. Spring 

PCP exhibits a weak southeast to northwest increase. 

All three T residuals show some relationships with geographic position, including both latitude and theSE to NW vector (Table 2). Both annual and maximum T 

Table 2. Summary of linear regressions of climate residuals after the effects of 

elevation are removed against latitude, longitude, and two 45° vectors, a southeast to 

northwest vector, and a southwest to northeast vector. The arrows indicate the 

direction of the trends. As the vector value increases northward (i) the value of the 

climate residual either increases (I) or decreases (-1.) in value. The results of the 

regression are also displayed (ns=not significant, P>0.15). 

Latitude Longitude SE---+NW SW---+NE 

Precipitation 

Annual ns ns ns ns 

Growing Season ns p=0.154 ns ns 

Vector/PCP i;-1. 

Winter Season ns ns ns ns 

Winter P=0.025 ns ns p=0.038 

Vector/PCP 1'/1 i;-1. 

Spring ns ns p=0.102 ns 

Vector/PCP j;j 

Summer ns ns ns ns 

Fall ns ns ns ns 

Temperature 

Annual p=0.034 ns p=0.029 ns 

Vector/T 1';-1. 1';-1. 

Maximum p=0.001 ns p=0.020 ns 

Vector/T 1';-1. 1';-1. 

Minimum ns ns p=O.O?O ns 

Vector/T i;-1. 

POTE p=0.022 ns ns ns 

Vector/POTE 1';-1. 
196 CLIMA1E OF THE CENTRAL COLORADO PLATEAU 

decline as latitude increases, as expected, but minimum T does not change along this 

gradient. All three T variables exhibit significant declines along the SE to NW vector. 

Climate Trends 

Regression analysis reveals numerous significant trends in T variables for most 

stations (fable 3). With few exceptions, T has increased over the last 33 years. Many 

of these increases are statistically significant. For minimum T, all stations show either 

an increase, or no trend. For those stations showing this trend, the change in minimum T varies from approximately 0.5 to 1.9°C, depending on the station. Minimum T at Wahweap, on Lake Powell, has not changed in the last 33 years. However, 

January mean minimum Tat Wahweap has shown strong increases (p=0.020). The 

largest increases in minimum T since 1966 have been at Page, the CANY-Needles, 

and Escalante. The smallest changes have been at Lee's Ferry, Wahweap, and Bryce 

Canyon National Park. Although there is only a weak, nonsignificant trend towards 

increasing minimum T since 1966 at Lee's Ferry, a significant increase has occurred 

(p=O.OSO) since 1944 (there are numerous gaps prior to this year). Only the two 

highest elevation stations, Bryce Canyon National Park and Betatakin, show weak 

increases or no trends in minimum T. Annual mean T parallels minimum T, with 

trends in the same direction. However, maximum T has decreased at two highelevation stations since 1966, Natural Bridges National Monument and Bryce Canyon National Park. At the same time, however, maximum T has increased at Betatakin. 

Overall, maximum T shows fewer defmite trends compared with annual and minimum T. 

PCP trends are more variable in the study area (fable 4). There is a weak trend 

towards increasing PCP among the nine stations. Five stations show increases in 

annual PCP, while the other four show no trend. Most of the increases in PCP are in 

the winter months rather than the summer months; few are significant. 

Table 3. Mean temperature trends in the last 33 years (since 1966) at selected 

climate stations on the central Colorado Plateau. Trends (p~0.25) are indicated as 

either increasing (t) or decreasing (,!.) using regression. Significance of each 

regression is shown (ns=not significant, p>0.25, no trend (~)). 

Annual Mean Annual Maximum Annual Minimum 

Station Trend Significance Trend Significance Trend Significance 

Lees Ferry t p=0.226 t p=0.174 t p=0.256 

Wahweap ~ ns t p=0.156 ~ ns 

Mexican Hat t p=0.017 t p=0.070 t p=0.033 

Page t p<0.001 t p=0.140 t p<0.001 

Needles t p=0.016 ~ ns t p=0.008 

Escalante t p=0.001 t p=0.054 t p<0.001 

Natural Bridges ~ ns -1- p<0.001 t p=0.002 

Betatakin t p=0.089 t p=0.008 ~ ns 

Bryce Canyon ~ ns -1- p=0.043 t p=0.118 
SPENCE 197 

Table 4. Mean precipitation trends in the last 33 years (since 1966) at selected 

climate stations on the central Colorado Plateau. Trends (p~0.25) are indicated as 

either increasing (t) or decreasing (,!.) using regression. Significance of each 

regression is shown (ns=not significant, p>0.25, no trend (~)). 

Annual Winter Season Growing Season 

Station Trend Significance Trend Significance Trend Significance 

Lees Ferry i p=0.124 i p=0.198 ~ ns 

Wahweap i p=0.111 i p=0.124 ~ ns 

Mexican Hat ~ ns ~ ns ~ ns 

Page i p=0.165 ~ ns i p=0.028 

Needles ~ ns ~ ns ~ ns 

Escalante ~ ns ~ ns ~ ns 

Natural Bridges ~ ns ~ ns ~ ns 

Betatakin i p=0.105 i p=0.174 ~ ns 

Bryce Canyon i p=0.028 i p=0.071 ~ ns 

Four stations have longer records that are fairly complete: Lee's Ferry, Escalante, 

Mexican Hat, and Betatakin. Table 5 displays the results of linear regression of 

climate variables over time for these four stations. For Lee's Ferry, the long-term 

record reveals a highly-significant increase in minimum T since 1944, but no changes 

in annual or maximum T. The trend towards increasing winter season PCP, since 

1966, becomes significant when extended back to 1944 (Fig. 4). For Mexican Hat, 

Table 5. Mean temperature and precipitation trends for four stations with long-term 

records on the central Colorado Plateau. Trends (~0.25) are indicated as either 

increasing (t) or decreasing (..!.) using regression. Significance of each regression 

is shown (ns=not significant, P>0.25, no trend ( ~ )). 

TEMPERATURE 

Annual Mean Annual Maximum Annual Minimum 

Station Trend Significance Trend Significance Trend Significance 

Lees Ferry1 ~ ns ~ ns i P=0.054 

Mexican Hat2 J, P=0.142 J, P=0.055 ~ ns 

Escalante3 i P<0.0001 i P<0.0001 i P<0.0001 

Betatakin4 ~ ns i P=0.174 J, P=0.099 

PRECIPITATION 

Annual Growing Season Winter Season 

Station Trend Significance Trend Significance Trend Significance 

Lees Ferry1 ~ ns ~ ns i P=0.072 

Mexican Hat2 i P=0.061 ~ ns i P=0.038 

Escalante3 J, P=0.009 J, P=O.OOS ~ ns 

Betatakin4 i P=0.065 ~ ns i P=0.079 

1 Years 1944-1998; 2 Years 1949-1998; 3 Years 1925-1998; 4 Years 1951-1998 
198 CLIMA1E OF THE CENTRAL COLORADO PLATEAU 

300 

250 

e 

E 200 

1:: 

0 

:;:; 150 C'I'S 

-·a. 

·u 

cu 100 ... 

a. 

50 

0 

1940 1950 1960 1970 1980 1990 2000 

Year 

Figure 4. Mean annual precipitation in millimeters for Lee's Ferry, Arizona, between 

1944-1998. The linear regression curve is also displayed (slope=0.65). 

extending the record back to 19 51 reveals a slight cooling trend overall and significant 

increases in winter PCP. The longest record, Escalante, reveals a highly-significant 

increase in all T variables since 1925 (Fig. 5), while at the same time PCP has decreased 

significandy during the growing season (April-September). Finally, T at Betatakin 

shows an unusual trend of decreasing minimum T and slighdy increasing maximum T. Winter season PCP has increased significandy since 1951. 

~ 1.5T-------~~r-r--+~----~~-----ft-ft--~~~~=h~--~ 

.a 

~ ~ 

~ 0.5+---~~~=---~~~~++~-4+4_.~~~~+-----------~ 

~ 

r 0+---+-~++---------4~-4+-~~~--------------------~ 

-1 +------,-----,------,------,------,-----,------,----~ 

1920 1930 1940 1950 1960 

Year 

1970 1980 1990 2000 

Figure 5. Mean annual minimum temperature in oc for Escalante, Utah, between 

1925-1998. The linear regression curve is also displayed (slope=0.02). 
SPENCE 199 

Changes in both T and PCP will affect POTE rates. Combining the general 

trend towards increasing temperature, with no or minor increases in PCP, POTE 

rates will increase in the study area. Figure 6 plots current POTE rate curves against 

elevation for the 27 stations, and future conditions based on a 3° C increase in annual 

T with no changes in PCP. The regression curves show that POTE + 3° C does not 

increase uniformly across the elevational gradient. The slope of the regression line 

steepens as T increases. POTE increases as much as 15% at some stations under the 

warming scenario; for example, POTE at Lee's Ferry increases from 993 mm to 1136 

mm. By comparison, the current POTE rate at Tucson in the Sonoran Desert is 1100 

mm. 

1200~------------------------------------------------~ 

• y = -0. 3835x + 1485.3 

R2 = 0.8771 1000+-~~-=~=-.-------------------------------------~ 

'E 8oo 

.§. 

w 600+-----------------------~~--~=--=--~~~~~--~ 

1- y = -0.3335x + 1281.5 

~ 400 +----R_2 = __ 0._88_2_3 ______________________________________ ----l 

200+---------------------------------------~ 

0+-----,------,-----,------,-----,-----,------,----~ 

1000 1200 1400 1600 1800 2000 2200 2400 2600 

Elevation (m) 

Figure 6. The relationship between current evapotranspiration rates (POTE) in 

millimeters and potential POTE rates with a 3°C increase in mean annual temperature 

is plotted against elevation for the central Colorado Plateau. The regression curves 

and equations for the two relationships are also displayed. 

DiscussiON AND CoNCLUSIONS 

Climate 

This study has presented an analysis of the climate of the central Colorado 

Plateau, and has examined recent trends in precipitation and temperature for selected 

climate stations. The study area comprises a relatively uniform climate region based 

on an analysis of geographic variables. Other than expected relationships between 

temperature and latitude, there are few changes in climate across the study area. The 

principal exception to this is a significant southeast to northwest decline in temperature. This may reflect the change across the boundary of the two air masses that 

separate the southern and eastern portions of the Colorado Plateau from the northem and western portions (Mitchell 1976). This boundary is much broader than 

implied by Figure 1, as its position shifts along this vector from year to year. 
200 CLIMA1E OF THE CENTRAL COLORADO PLATEAU 

Figures 4 and 5 illustrate, at single stations, the extremely high year to year 

variability in temperature and precipitation for all stations in the region. The Lee's 

Ferry precipitation record reveals a cycle of high precipitation episodes followed by 

low periods at ca. 1 0-year intervals since 1944. This pattern is similar at other stations, 

with changes between high and low precipitation at cycles of ca. every 8-12 years. The 

pattern for temperature is somewhat more variable, but again, a strong cyclic pattern 

can be discerned with climate records that have been analyzed in detail. This variability is typical of arid climates throughout the world (Evenari et al. 1985). Coefficients 

of variation are high for most stations, ranging from 20-50% for climate variables. 

Two principal global circulation models are the Goddard Institute for Space 

Science (GISS) and the Geophysical Fluid Dynamics Laboratory (GFDL). With a 

predicted doubling in CO2 content, the G ISS model predicts a 4. 7° C increase and an 

8% precipitation increase in the western U.S. (Hansen et al. 1988). For the GFDL 

model, the comparable predictions are 4.2° C and 30% increase (Manabe and Wetherald 

1987). Both are in agreement that much of the precipitation increase is likely to result 

from a strengthening of the summer monsoon rather than increases in winter precipitation. More recendy, two other models, the Hadley Centre and the Canadian 

Climate Centre models, have been analyzed with respect to the western U.S. (NAST 

2000). The Canadian model predicts larger increases in temperature (4.5-6.0° C), 

while the Hadley model is similar to the GFDL and GISS models. Both models also 

predict increases in precipitation of 25-50%, but differ from the GISS and GFDL 

models in that the increases are predicted to be in winter precipitation rather than 

summer precipitation. 

The climate data suggest two principal trends in the last 30-40 years in the study 

area: (1) significandy increasing minimum temperatures, and (2) slight increases in 

winter precipitation. The first trend is consistent with all GCM models, while the 

second is consistent with predictions of the Hadley and Canadian models, but not 

with predictions of the GISS and GFDL models. For the central Colorado Plateau, 

there is no evidence for a strengthening of the summer monsoon since 1966. 

Escalante, at the northwestern edge of the study area, has experienced a significant 

decline in summer precipitation (Table 3). Mexican Hat, at the southeastern edge of 

the study area, where the effects of a strengthened monsoon should become apparent first in the region, has had no significant changes in summer precipitation since 

1951 (Table 3). 

These trends may reflect one of two possible climate scenarios: global warming 

or short-term (decadal) climatic oscillations and variability. During the last 100 years, 

there have been several episodes of warming and cooling. For example, the 1930's 

and 1940's were relatively warm in the study area, and were followed by relatively cool 

conditions in the 1960's and 1970's. Hence, the high temperatures recorded in the last 

20 years may be part of this cyclic phenomenon. At present, the regional and global 

climate data are inadequate to clearly differentiate these two possibilities, at least at the 

regional level. Assuming that the trends presented in this study continue, and are 

early signs of global warming, a variety of hypotheses can be presented on the 

potential responses of vegetation as well as individual species. 
SPENCE 201 

Potential Effects on the Vegetation of the Colorado Plateau 

One effect of increasing temperatures, with little or no increase in precipitation, 

is increased evapotranspiration rates over time. Although the effects of this are 

probably not discernable at present, due to the high variability in climate, this trend 

could have consequences in both the short- and long-term future of the Colorado 

Plateau vegetation. Recent studies in the shortgrass steppe of the Great Plains have 

shown that increases in minimum temperatures can be linked with changes in abundance and productivity of herbaceous species. Alward et al. (1999) showed that the 

dominant native warm-season C4 grass, blue grama (Bouteloua gracilis), may have 

declined as a result of warming. Consequences of increased minimum temperatures 

include earlier spring growth of cool season C3 species, including exotics, that can 

then deplete soil moisture prior to the green-up or germination of warm season 

species like blue grama. Competition for available nutrients may also change with 

increased growth of cool season species. Given the current trend of increasing winter 

precipitation discernable on the central Colorado Plateau, the most likely short-term 

effect on arid vegetation would be increases in cool-season herbaceous and woody 

species, and declines in warm season species, most of which are grasses. Long-term 

predictions for all models are that grasslands and woodlands on the Colorado Plateau will increase, while arid scrub vegetation will decrease substantially. 

Effects in higher elevation, semi-arid and humid vegetation are less well understood. Increased evapotranspiration stress during the summer may cause declines in 

growth and recruitment at the lower limits of pinyon-juniper woodlands. Warmer 

temperatures, without an increase in the summer monsoonal precipitation, could 

reduce growth rates and seedling recruitment, and increase mortality in Ponderosa 

pine, a species that is closely tied to warm season precipitation. Over the long term, 

an increase in annual temperature of 3° C, without significant increases in precipitation, will raise the arid-humid boundary by approximately 90 m, from 2730 m to 

2820 m (estimate based on intersection ofPOTE and precipitation lines for a 3° C 

increase scenario, not illustrated). This could significantly reduce the extent of high 

elevation coniferous forests, subalpine meadows, and alpine tundra, on the Colorado Plateau. However, this scenario seems unlikely based on the predictions of 

increased precipitation found in all models. 

A combination of increases in temperature and precipitation is likely to have 

complex effects on the vegetation of the Colorado Plateau. Long-term shifts in 

vegetation boundaries and changes in temperature and precipitation may have significant impacts on populations of relict plant species and the many rare and often 

edaphically-restricted plant species that are characteristic of the Colorado Plateau (Spence 

1995, 1NC 1993). Also, the GCMs predict increased variability in precipitation, with 

dry and wet years alternating. This could change fire regimes, as fuel loads would 

build up during wet phases. If a drought period subsequently followed, there 

would be an increased chance of potentially destructive stand-replacing fires in the 

forest, woodland and grassland communities of the Colorado Plateau. 

Although all the GCM models discussed predict increases in precipitation in the 
202 CLIMA1E OF THE CENTRAL COLORADO PLATEAU 

western U.S., the differences between them are relatively large. The consequences of 

an 8% increase in precipitation, compared with a 50% increase, are likely to be very 

different for arid and semi-arid vegetation in the region. Also important is the 

timing of these increases, because many dominant plant species on the Colorado 

Plateau, and elsewhere in the Southwest, differentially utilize warm-season and coolseason moisture (Ehleringer et al. 1991, Comstock and Ehleringer 1992). Although 

it will be some time before we better understand the potential changes in timing and 

extent of precipitation in the region brought about by global warming, enough 

climate data are currendy available to model these potential future climates, based on 

global warming and different scenarios of precipitation changes. 

AcKNOWLEDGEMENTs 

Peter Rowlands kindly supplied the Excel spreadsheet for POTE calculations, 

and has stimulated my thoughts on climate change and vegetation through many 

interesting conversations. I am grateful to the Desert Research Institute and Western 

Regional Climate Center of the University of Nevada at Reno for making theregional climate data easily accessible. 

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