ECOSYSTEM MODELING OF THE PRYOR MOUNTAIN WILD HORSE RANGE

by

Michael B. Coughenour Natural Resource Ecology Laboratory Colorado State University Fort Collins, Colorado 80523

Final Report to: U.S. Geological Survey Biological Resources Division, U.S. National Park Service, and U.S. Bureau of Land Management

Natural Resource Ecology Laboratory Colorado State University Fort Collins, Colorado 80523

August, 1999 TABLE OF CONTENTS

Executive Summary...... 1 Introduction...... 1 Methods...... 3 Ecosystem Model Description...... 3 Model Parameterization...... 5 Results6 Model Tests...... 6 Dynamics...... 6 Spatial Distributions...... 7 Interactions with Bighorn Sheep...... 8 Comparisons to Previous Analyses...... 9 Conclusions...... 9 Introduction...... 1 3 History and Administration...... 1 5 Site Description...... 1 8 Climate...... 1 8 Landscape and Soils...... 1 9 Vegetation...... 2 0 Methods...... 2 1 Ecosystem Model...... 2 1 Weather Data ...... 2 5 Soils...... 2 6 Vegetation...... 2 6 Herbaceous Plant Biomass...... 2 7 Ecophysiological Parameters ...... 2 7 Shrub and Tree Cover...... 2 8 Shrub and Tree Morphometrics...... 2 8 Mountain Mahogany Morphometrics...... 2 8 Mountain Mahogany Current Annual Growth Production...... 2 9 Animal Habitats and Ranges...... 2 9 Wild Horses...... 2 9 Bighorn Sheep...... 3 1 Mule Deer...... 3 2 Animal Ecophysiology...... 3 2 Body Weights of Horses...... 3 2 Energy Requirements of Horses...... 3 3 Forage Intake Rates by Horses...... 3 5 Energy Requirements and Forage Intake of Bighorn Sheep and Mule Deer. . . . . 3 6 Animal Populations...... 3 6 Results...... 3 7 Site Level Herbaceous Biomass Dynamics...... 3 7 Temporal Dynamics 1950-1969...... 3 9 Temporal Dynamics 1970-1996...... 4 0 Spatial Model Output...... 4 2 Comparisons to Range-Site Forage Production Estimates, and Assumptions Implicit in Previous Carrying Capacity Estimates...... 4 5 Variations in Levels of Herbivory...... 4 6 Modeling Experiments...... 4 7 Responses to Fixed Numbers of Horses and Bighorn Sheep...... 4 7 Responses to Horse Culling...... 4 8 Responses to Stochastic Variation in Weather, and Horse Culling...... 4 9 Effect of Empirical vs. GIS-Modeled Sheep Range...... 5 0 Effect of Disallowing Horse Utilization of USFS Lands Outside the PMWHR. . 5 0 Discussion and Conclusions...... 5 1 Acknowledgements...... 5 8 References...... 5 9 Appendix...... 6 8 EXECUTIVE SUMMARY

Introduction

The US Bureau of Land Management (BLM) is responsible for the maintenance of a ?thriving ecological balance? on the Pryor Mountain Wild Horse Range (PMWHR) and has deemed it necessary to conduct periodic management removals of wild horses since 1970 to halt and prevent range degradation (USBLM 1997). The PMWHR includes lands which are administered by the National Park Service (NPS), specifically, portions of the Bighorn Canyon National Recreation Area (BCNRA), as well as lands administered by the BLM and the U.S. Forest Service (Hall 1972; US BLM 1984) . It has been federally mandated that wild horses have access to the NPS lands, by inclusion of the BCNRA in the designated PMWHR. However, in setting the appropriate numbers of wild horses, other NPS management objectives must be weighed, including protection of native plant communities and wildlife. In the past, horse carrying capacities have been calculated using a widely accepted method that sums up total forage biomass, applies a proper use factor of 50% utilization. The appropriate number of horses on the PMWHR must be explained in terms of measurable and predictable changes brought about by different horse densities in the abundance and composition of soils, plants and wildlife, and in the condition and welfare of the horses. These responses depend on plant species, the levels and patterns of herbivory, climate, soil properties, and their distributions on the landscape. Forage varies annually, and spatially over the landscape. Horse distributions vary seasonally, and parts of the landscape are used more heavily than others. Although it is unlikely that these wild horses will ever be allowed to self-regulate, there is a need to estimate the results if that were to occur. A general approach is necessary, which can assess the effects of herbivory on soils, plants, and animals in a dynamic and spatially heterogenous landscape. Here, a neutral, ecosystem modeling approach was used, which makes no specific assumptions about management objectives, levels of proper use, ideal range conditions, or equilibrial biotic communities.

The objectives of this study (Coughenour 1999) were to: (a) evaluate a general, ecosystem modeling approach to support more informed policies for managing large ungulates; and (b) assess the effects of different numbers of horses on ecosystem structure and function on the PMWHR. This is an alternative approach than traditional approaches to evaluating carrying capacity based solely on forage supply or animal population dynamics.

Methods

Ecosystem Model Description

An ecosystem simulation model called SAVANNA (Coughenour 1992, 1993) was used to represent ecosystem dynamics and interactions on the PMWHR landscape. SAVANNA is a spatially explicit, process-oriented model of grassland, shrubland, savanna, and forested ecosystems. The model simulates processes at landscape through regional spatial scales over annual to decadal time scales. The model is composed of site water balance, plant biomass

1 production, plant population dynamics, litter decomposition and nitrogen cycling, ungulate herbivory, ungulate spatial distribution, ungulate energy balance, and ungulate population dynamics submodels. The time-step of the model is one week. SAVANNA is a spatially explicit model (i.e., it is sensitive to spatial position). Typically, the landscape is typically divided 5,000- 10,000 grid-cells. The model simulates water and nutrient balance, plant growth, and herbivory on each grid-cell. Here, grid-cell size was 500x500m.

SAVANNA is driven by monthly weather data. The model spatially interpolates precipitation data using inverse distance weighting, correcting for elevation differences between the weather station and grid-cell. The water balance submodel simulates soil moisture dynamics and use on each patch type on each grid cell. Soils map data are used, in conjunction with soil properties for each soil type, to determine soil water-holding capacities of each subarea on each grid cell. The water budget includes terms for precipitation, interception, runoff, runon, infiltration, deep drainage, bare soil evaporation, and transpiration.

The net primary production (NPP) submodel simulates plant biomass production and dynamics. Plant biomass production is affected by light, water, temperature, nitrogen, and herbivory. This submodel is explicitly linked to the water budget submodel through transpiration and plant water use. Biomass is allocated to leaves, stems, and roots. Due to water or temperature stress or phenological stage, plant tissues die and turn over at a nominal rate that reflects their maximal longevities. The model represents plant nitrogen uptake and losses due to herbivory and tissue mortality. . A litter decomposition and nitrogen cycling submodel simulates the breakdown of dead plant materials and animal feces. Nitrogen is released during decomposition to mineral forms that can be taken up by the plants. Nitrogen enters the system through atmospheric wet and dry deposition and biotic fixation, and leaves the system through denitrification and urine volatilization.

Animal forage intake is determined by diet selection, forage abundance, forage quality, and snow cover. Forage intake rate is expressed as a function of forage biomass. As forage biomass increases from zero to a specified level, forage intake rate increases. Forage intake rate is also affected by snow depth. Animals choose among available plant types and tissues to achieve a preferred diet composition. Diet composition is affected by the relative availability of different forage types as well as preferences or avoidances.

The animal energy balance submodel simulates body weight of the average animal of each species, based on differences between energy intake and energy expenditure. Energy intake depends on forage biomass intake and forage digestibility. Expenditures depend on body weight and travel patterns.

The ungulate population dynamics submodel represents changes in the number of animals in each age classes, for each sex. Birth and death rates are affected by animal condition indices.

2 This is the way the model represents population responses to factors affecting forage availability, including plant growth rates, and snow depth, and forage removal by other animals.

The ungulate spatial distribution submodel simulates animal distributions over the simulated landscape or region on a weekly basis. Habitat suitability is affected by slope, temperature, forage biomass, forage intake rate, snow depth, distance to water, and tree cover.

Model Parameterization

Six groups of plants were simulated: grasses, forbs, shrubs, mountain mahogany (Cercocarpus), juniper, and coniferous trees. These groups were chosen to meet the objectives of this modeling analysis, without making the model overly complex.

A vegetation map existed for BCNRA (Knight et al. 1987), but none existed for the remainder of the PMWHR. Consequently, I developed a PMWHR vegetation map by merging the BCNRA vegetation map with a modeled vegetation map for the remaining area. The modeled vegetation map was based on a map of forest cover, from USGS quad sheets, the soils map, and qualitative relationships between major vegetation types, elevation, and soils observed on the Knight et al. (1987) map.

There are three relatively distinct herds of horses in the PMWHR, occupying habitats that are separated by distinct topographic barriers (Hall 1972; US BLM 1984, 1997; Singer et al. 1998).Each of the three herds was modeled separately and distributions were limited to their respective range areas. Seasonal movements were modeled as dynamic responses to changing forage and snow conditions, with a seasonal avoidance of areas below 1,500 m in summer The locations of known horse watering points within the horse range were digitized from information provided by BLM personnel (L. Padden, BLM, personal communication). A map of distance to water was calculated using the GIS. Bighorn sheep were kept within the seasonal ranges observed by Irby et al. (1994). Within these ranges, the model redistributed animals in relation to forage biomass and forage energy intake rate. Mule deer winter on the PMWHR, but during summer, most migrate to ranges north of the PMWHR (Irby et al. 1996). The model was parameterized so that the entire deer herd was on the PMWHR during December-April, and 10% were on the PMWHR during June-October.

In simulations using observed population data, the summarized horse data from USDI/BLM (1997) were used, based on data from Taylor (1990 memo), and Garrott and Taylor (1990). Sheep population data were obtained from Coates and Schemnitz (1989) and Kissell (1996). Mule deer population size was based on information from Kissell (1996). Horse culling data from the BLM (1997) were used in simulations using observed culling data. Deer were culled to maintain the population between 500 and 700. Bighorn sheep have never been culled or hunted.

Data from Detling and Gerhardt (1996) were used to parameterize the plant growth

3 model, and test its predictions. The objective was to maximize the model's skill in providing realistic simulations, by making maximal use of information contained in the field data and literature. Root biomass was estimated by fitting model aboveground predictions to the aboveground data. Biomass dynamics on plots inside grazing exclosures were verified first, followed by the dynamics of grazed plots. Grazing intensities in the grazed simulations were estimated by varying horse density to best simulate observed differences between exclosed and grazed plant biomass dynamics.

Results

Model Tests

The model performed well in simulating herbaceous plant growth with and without grazing. The model successfully simulated herbaceous biomass dynamics across a wide range of sites and weather years. The proportions of grasses and forbs, rates of transfers from live to dead, and rates of transfers of dead tissues to soil were adequately simulated. The model simulated reasonable population dynamics and distributions of horses, bighorn sheep, and mule deer. The rates of forage offtake per animal, and the compositions of the herbivore’s diets were realistic. Overall, the model provided estimates of plant production and animal behavior that were consistent with available data.

Dynamics

A simulation was run with horse numbers held at 270 animals (the number first counted by Feist and McCullough [1975]) during the period from 1950-1969. The model simulated markedly reduced herbaceous biomass compared to no horses. Forb biomass comprised a greater proportion of total peak standing crop, while grasses comprised a smaller proportion, compared to no horses. Root biomass was also diminished. Total aboveground plant growth, including transfers to dead and litter, decreased under grazing. In another simulation, horse populations were allowed to grow freely, to limits imposed by the environment. Plant responses were not markedly different from those observed with a fixed number (270) of horses. Horse numbers initially increased, leveled off at around 400, and then plunged dramatically in 1960-1961 in response to a drought in 1960. Thereafter, horse numbers gradually increased to 270.

In simulations of the period from 1970-1996 without horses, grass biomass generally increased over time. Forb biomass increased less, so the proportion of forbs in total herbaceous biomass decreased. With observed horse numbers over this period, grass biomass varied from year to year, but there was no long-term trend. The ratio of grass to forbs was similar throughout. When horses were not culled, biomass of grasses decreased over time.

With no grazing, root biomass of grasses fluctuated, but there was no trend. Grass roots eventually became more abundant than forb roots. With observed animal numbers, grass and forb roots varied within a range that was slightly less that with no animals, and there was no long-

4 term trend. With no culling, root biomasses of both plant groups were lower and decreased over time Herbaceous basal cover increased during the first 15 years of simulations with no animals. Grass and forb cover increased when grazed with observed animal numbers, but then leveled off at lower levels than with no animals. Grass and forb cover increased when grazed with observed animal numbers, but then leveled off at lower levels than with no animals.

Forage intake rate for horses varied seasonally and annually. Generally, most stressful years for forage intake were also years with deeper snow depths. Maximum intake rates for the Dryhead head were consistently lower than for the other two herds, as a result of the lower forage biomass on the Dryhead summer range. Intake rates were markedly reduced when horse herds were not culled. Maxima and minima were both reduced at higher horse densities, as a consequence of intraspecific competition for forage. Sheep intake rates were far less variable than horse intake rates, primarily because they could shift to browse species which continued to be abundant during the winter and were not used by horses.

Spatial Distributions

Maps of simulated total aboveground net primary production (ANPP, ie. total aboveground plant growth) with and without horses were similar, but there were certain areas where production was diminished with horse grazing. These included the area just south of the Sorenson Extension boundary, and areas in the south-central portions of the Sykes and Burnt Timber Ridge horse ranges. With observed horse numbers, grass ANPP was 60-80% lower in some areas than without horses. With no horse culling, there were areas where grass ANPP was reduced by 80-90% and forb ANPP was reduced by 50-70%. With observed numbers of horses, roots decreased by 25-75% in some areas, but these areas constituted a small fraction of the landscape. With no horse culling, areas of decreases rose to 50-90%, and a larger fraction of the landscape showed decreases in the 25-50% range.

Horse grazing shifted species composition towards a greater preponderance of forbs in most places, but the shifts were differentially distributed across the landscape. There was a greater species shift at the higher elevations, with forb proportions increasing to 40-70% at some high elevation areas. Litter and shoot biomass reductions cause an increase in bareground, with potential secondary effects on soil temperature, runoff, and soil erosion.

With the observed numbers of horses, grass herbivory levels reached 70-80% over significant portions of the landscape. However, much of the landscape was lightly (<20%) or moderately (20-60%) grazed. Grasses on the southern parts of the Sykes Ridge and Burnt Timber Ridge, and northern part of the Dryhead Range were heavily (>60%) or very heavily (>80%) grazed. Grazing intensity on the high elevation summer range was mostly moderate, High rates of grass offtake were far more prevalent when there was no horse culling. Grasses on 30% of the landscape were grazed in excess of 50%. Grasses on 8% of the landscape were utilized >90%.

Horse densities were heterogeneously distributed on the landscape. With observed horse

5 numbers, highest use areas at low elevations were 2-5 head per km2 year-long. Heavy use areas at the top of the mountain were higher, reaching 6-8 head per km2 year-long. When horses were not culled, densities reached higher levels everywhere, with low elevation high use areas reaching 5- 8 head per km22 and high elevation high use areas reaching 7-10 head per km year-long.

The fraction of the landscape receiving >80% herbivory varied markedly from year to year. During 1970-1996, 37-67% of the landscape received light (0-10%) herbivory with observed horse numbers and 13-38% received 10-50% herbivory. However, 3-14% experienced herbivory in the 50-80% range and 2-31% received >80% herbivory.

With no horse culling, the fraction of the landscape where grasses were grazed >80% rose to 21-44%. The fraction grazed 50-80% was 3-13 % with 100-150 head, 4-13% with 150-200 head, and 4-14% with 200-250 head, and 3-15% with no culling. The fraction where grasses were grazed >80% was 8-31% with 100-150 head, 12-34% with 150-200 head, 16-34% with 200-250 head, and 21-45% with no culling.

As horse numbers were increased, herbaceous ANPP on the primary horse range decreased by 10-13% for each additional 50 horses. There was approximately 75% as much herbaceous production with 200 horses as with 50 horses. With 350 horses, ANPP was about 60% of that with 50 horses. There was 60% as much herbaceous biomass with 200 horses as there was with 50 horses. With 350 horses there was <50% as much biomass as with 50 horses.

The effects of disallowing trespass use of USFS lands northeast of PMWHR was examined by running the model for the period 1970-1996 at seven different horse densities. There was a slight reduction in plant growth with no access to USFS lands compared to full access. The proportion of forbs was similar with or without USFS access, but there was a slight increase in forbs without USFS access. A similar response was simulated for root biomass. Horse condition was also slightly lower when USFS access was disallowed. Differences between runs with and without USFS access were neglible with less than 150 horses, but increased at higher numbers of horses.

Interactions with Bighorn Sheep

Neither sheep condition index nor sheep forage intake rate were affected by increasing horse numbers. One reason for this appeared to be a high degree of spatial segregation, where sheep ranges overlapped onto a small fraction of the total horse range. A second reason was dietary separation. A significant fraction of sheep diets were from evergreen shrubs, which were an insignificant component of horse diets (Kissell 1996). Finally, sheep appeared to be strongly influenced by their own density within their own range.

Increasing numbers of sheep had very little impact on horse condition or horse forage intake, but did have a substantial effect on sheep condition and sheep forage intake rate. This suggests that there was a considerable degree of intraspecific competition for food within the

6 prescribed sheep range. As sheep depleted forage within their prescribed range, horses had substantial options for foraging elsewhere.

In simulations using stochastic weather, horse numbers fluctuated within the ranges specified by culling targets at the 100-150 and 150-200 levels. When culling to 200-250, however, there was a drop in population sizes below the 200 level, as a result of horse interactions with vegetation and climate. When horses were not culled at all, there was marked variation among horse population trajectories. Horses reached at most 350 in the stochastic runs without culling. When horses were culled, or not present, the final median number of sheep was about 175, irrespective of horse numbers. When horses were not culled, the final median number of sheep was about 160.

Comparisons to Previous Analyses

Model predictions of forage biomass agreed with the actual forage productivity estimates used in the BLM’s 1984 carrying capacity assessment, but not with the 1984 potential productivity estimates. The model agreed reasonably well with the potential productivity estimates of the BLM’s 1992 carrying capacity assessment, but not with the 1992 actual productivity estimates.

It was difficult to make an exact comparison between the traditional range site-based carrying capacity estimates and the estimates of appropriate horse numbers using the ecosystem modeling approach, since the two methods measure carrying capacity differently. The ecosystem approach demonstrated that the proper use factor of 50% used in the range site approach is exceeded on portions of the landscape, even with 100 horses. Averaged over the entire landscape, offtake is actually much less than 50%, even with as many as 200 horses. The simple proper use factor is of limited utility when grazing is so heterogeneously distributed.

Conclusions

1. The model responded realistically to climate, soil properties, grazing, and their interactions. Model predictions were consistent with current knowledge about the underlying processes of ecosystem water and nutrient budgets, plant and animal ecophysiology, and animal population dynamics.

2. Horses increased to over 300 in many weather scenarios, and even to >450, if they were not culled. This could be regarded as the ecological carrying capacity of the population. The rate of increase of the simulated population was as observed, under the observed levels of forage availability per animal. It can be inferred from model results and other data that the horses have a capacity to increase to high densities relative to forage resource, and persist in environments with relatively low forage biomass. In other words at ecological carrying capacity, horse numbers

7 would be relatively high and plant biomass would be relatively low. The result is consistent with other studies which indicate that in natural grazing ecosystems, native equids tend to be predator rather that food limited.

3. As horse numbers increased, total forage production on the primary horse range decreased by 10-13% for each additional 50 horses. On average, there was approximately 75% as much forage production with 200 horses as with 50 horses.

4. Grazers decreased aboveground biomass, as expected . Model simulations and data indicated that most of the reduction was in standing dead rather than in live biomass. In the absence of grazing, dead biomass was more likely to accumulate.

5. The model simulated an increase in the relative abundance of forbs compared to grasses as herbivory pressure increased. There is only weak support for such a species shift in the data. However, changes in forb composition would be expected based on the lower relative abundances of forbs than grass in animal diets.

6. Basal cover and root biomass decreased under increased herbivory due decreases in above and belowground primary production relative to root turnover. This was consistent with the generally accepted theory that forage productive potential varies with range condition.

7. The model agreed reasonably well with the actual forage productivity estimates used in the BLM’s 1984 carrying capacity assessment and with the potential productivity estimates of the BLM’s 1992 assessment. However, there were discrepancies between the model and the 1984 potential productivity estimates (if range were in excellent condition), and between the model and the 1992 actual productivity estimates. These discrepancies suggested that there is uncertainty associated with previous estimates of maximum forage production and range condition.

8. The proper use factor of 50% that is used in the range site approach is exceeded on portions of the landscape, even with 100 horses. Averaged over the entire landscape, however, offtake is actually much less than 50%.

9. Grazing was heterogeneously distributed. With observed horse numbers, highest use areas at low elevations were 2-5 head per km2 year-long. Heavy use areas at the top of the mountain were higher, reaching 6-8 head per km2 year-long. During 1970-1996 with observed horse numbers, 40-70% of the landscape experienced light herbivory of grasses. However, 5-20% received >80% herbivory and 5-15% experienced herbivory in the 50-80% range. The fraction of the landscape receiving >80% herbivory varied markedly from year to year.

10. The percent of the landscape where grasses were grazed in excess of 80% increased from

8 16% at 125 horses to 25% at 225 horses, however in a given year the maximum percentage varied from 31% with 125 horses to 36% at 225 horses. The mean proportion of herbaceous plant growth consumed on the entire landscape varied from a mean of 15% at 125 horses to a mean of 23% at 225 horses.

11. A simple proper use factor is of limited utility when grazing is heterogeneously distributed. Instead, a proper use factor must be two-tiered, specifying both the spatial stratification of grazing intensities, and the acceptable grazing pressure within strata. For example, proper use could be specified as a maximum acceptable fraction of the landscape that is grazed in excess of 80%. Alternatively, a proper level of use should be applied to key areas of the landscape which typically most heavily grazed. It would be useful to reach a consensus about the level of acceptable use in these terms, which are more realistic than a simple proper use factor.

12. Horses had little effect on bighorn sheep populations due to a high degree of spatial segregation, dietary separation, and the fact that sheep are strongly influenced by their own density within their own range. Sheep ranges overlapped onto a small fraction of the total horse range. This result should be interpreted with caution since potential sheep habitat that is still unoccupied may be affected differently by horses. There is a need to assess potential horse-sheep interactions throughout the potential sheep range.

13. A GIS model of sheep habitat appeared to be too restrictive in the PMWHR, probably because it did not consider the effects of low forage availability, and the need for sheep to forage further from escape terrain. Although the GIS-based approach could be modified to incorporate effects of forage production and snow, an alternative, and probably better approach is to implement a less restrictive GIS habitat model in a spatial simulation model of forage production and snow cover, as was done here.

14. Continuous rather than threshold responses to increasing horse made the task of deciding upon an appropriate number of horses difficult. The analysis showed no optimal number of horses, as in a peak level of performance of ecosystem processes. With as few as 50 horses, there was some decrement of plant growth. Given this, the optimum number of horses may be the minimum number needed to safely ensure the population and genetic viabilities of the horses. This number would minimize decrements to vegetation productivity, while ensuring continued viability of the horse herd.

15. It would be desirable to spread out horse grazing impact so that the fraction of the landscape receiving heavy use is reduced. To achieve a more uniform distribution of percent offtake, it would be necessary to distribute horses in proportion to plant production, which varies markedly across the landscape.

16. At ecological carrying capacity, plant cover would be considerably lower than it is now. The range would appear to be extremely heavily utilized as grass offtake would climb to over 90% in many more areas. Some areas would be nearly barren. Herbaceous biomass above and

9 belowground would be reduced to less than 20% of potential in many areas. There would probably be secondary effects on wildlife. With greater exposure due to reduced plant and litter cover, soil erosion rates would likely be higher than at present. Horses would be in generally lower condition. Total annual horse mortality would be high, due to a larger total number of horses would be higher, and the fact that annual births would have to be balanced by deaths. Thus, to meet range management and animal welfare objectives, the population must be managed below its ecological carrying capacity.

17. The effects of curtailing recent horse trespass onto USFS lands northwest of the PMWHR boundary on forage production and horse energy balance would be negligible.

18. As part of an adaptive management process, the model should be revisited periodically, to check the consistency of it’s predictions with actual results. Ecosystem monitoring should be designed to test some of the key model predictions. The model should then be revised and a new assessment should be carried out.

19. Better data are needed for estimating plant production in the long-term absence of grazing. It would be best to monitor biomass at the 4 long-term exclosure sites and the 12 additional exclosure sites that were established in 1992 and 1994.

20. The combination of field research and ecosystem modeling that has been carried out in the PMWHR is an example for improving the scientific management and conservation of wild equids on rangelands in the western United States and other regions of the world.

10 INTRODUCTION

The concept of ecological carrying capacity has been applied throughout the last century to estimate the densities of native or domestic herbivores which can be supported by delineated land areas (Coughenour and Singer 1991, Bartels et al. 1993, Wagner et al. 1995). There are many definitions of carrying capacity, but one would be that at carrying capacity, herbivores should not diminish the capacity of soils, vegetation, and fauna to function together as an ecosystem. Defined this way, carrying capacity appears to be a straightforward concept. However, the task of defining what carrying capacity is exactly, and agreeing upon a method for calculating its value, is not so easy. Definitions vary with objectives. In the definition given above, it is not clear what constitutes an ecosystem. Different types of ecosystems could exist on the same land area, at different levels of herbivory. "Ecological" carrying capacity has also been defined as the herbivore population size that is in dynamic equilibrium with plants, brought about through food-limitation. "Economic" carrying capacity has been defined as the population size that sustainably maximizes animal productivity (Caughley 1979). The conceptual distinction between ecological and economic carrying capacity has been central to debates over ungulate management where the goal is to minimize human interference and preserve natural processes (Boyce 1991, Coughenour and Singer 1991). The concept of ecological carrying capacity might be appropriate for lands such as National Parks or Wilderness Areas which are designated natural areas. But problems arise in defining what is really natural and in determining whether such ecosystems can really be natural given human influence in the past and present. On lands designated for multiple uses, such as National Recreation Areas administred by the United States Department of Interior National Park Service (USDI-NPS), the preservation of natural processes is important but not paramount. The concept of ecological carrying capacity may be inappropriate, but the concept of economic carrying capacity is probably not appropriate either.

Problems also arise in the long accepted approach to determining appropriate stocking rates based upon range condition. Range condition is defined as the degree of departure from climax plant community composition (Dyksterhuis 1949, Lauenroth and Laycock 1989) , which is an equilbrium between vegetation, soils, and climate. However, an ecological equilibrium is unrealistic in many ecosystems, particularly in dry and unpredictable climatic regimes (Caughley et al. 1987, Ellis and Swift 1988, Westoby et al. 1989). Climatic variability keeps soils, plants, and herbivores from attaining equilibrium. Furthermore, the vegetation of a native grazing ecosystem can be expected to be different from that of an ecosystem on the same soil and experiencing the same climate, without herbivores.

Most definitions of carrying capacity are based upon mean levels of herbivory and plant growth and do not explicitly consider spatial heterogeneity (Bartels et al. 1993). However, herbivory is usually nonuniformly distributed on the landscape (Coughenour 1991). Herbivores are distributed across landscapes in relationships to forage, water, topography, and cover. Consequently, some parts of the landscape will be grazed more heavily than others. Although the mean level of herbivory might be quite low, small portions of the landscape could be heavily impacted. On topographically complex landscapes, plant growth environments vary markedly

11 with soils and climate, so the heterogenous distribution of herbivores cannot be interpreted without also considering the heterogenous distribution of plant growth.

Despite these conceptual and methodological difficulties, land management agencies such as the U.S. Bureau of Land Management, the National Park Service, and the U.S. Forest Service are charged with determining appropriate numbers of domestic and wild herbivores relative to land management objectives. It is an inescapable fact that in many landscapes, herbivore populations do have the capacity to grow to sizes which cause undesirable ecological outcomes, and herbivore populations must be managed accordingly.

The management of wild horses (Equus caballus) on the Pryor Mountain Wild Horse Range (PMWHR) in southern Montana is a classic example of the difficulties of estimating carrying capacity, and "appropriate" numbers of large herbivores. The UDSI Bureau of Land Management (BLM) is responsible for the maintenance of a “thriving ecological balance” on the PMWHR, and the BLM has deemed it necessary to conduct periodic management removals of wild horses from the PMWHR since 1970 to halt and prevent further range degradation (USDI/BLM 1997). This particular band of horses is not simply "feral", but are a unique genetic strain with ancestry going back to the first Spanish reintroductions. They have probably inhabited the PMWHR for at least a century. The basis for horse removals has been questioned, given interests in the genetic and population viability of the PMWHR horses. The horse population could argueably be managed to a larger size to reduce risks to its viability. At the extreme, some individuals might believe that the horses should not be interfered with at all, that they should left completely wild, and that they will self-regulate through natural processes.

The PMWHR includes lands which are administered by the National Park Service (NPS), specifically, portions of the Bighorn Canyon National Recreation Area, as well as lands administered by the BLM and the U.S. Forest Service (Hall 1972, USDI/BLM 1984) . It has been federally mandated that wild horses have access the NPS lands by inclusion in the designated PMWHR. However, in setting the appropriate numbers of wild horses, other NPS management objectives must be weighed, including protection of native plant communities and wildlife. The appropriate number of horses on the PMWHR must therefore be explained in terms of measurable and predictable changes brought about by different horse densities in the abundance and composition of plants and wildlife, and in the condition and welfare of the horses themselves. It must then be decided if these changes are acceptable, relative to the purposes of landuse designation.

The carrying capacity of the PMWHR has been set in the past using range condition criteria, and determining the forage production and utilization to achieve that range condition (USDI/BLM 1984, see Appendix). Forage production is calculated, and a proper level of utilization is assumed and not to be exceeded in order to prevent retrogressive succession, and for plants to maintain vigor. How many animals that amount of utilization corresponds to is based upon the forage requirement per individual animal. The range condition, forage-based approach has been used for years by the BLM and other land management agencies. It is generally

12 conservative, and it makes use of expert knowledge about the range and animal requirements. However, the range-condition, forage-based approach is vulnerable to criticism because: a) it is often based upon visual assessments of range condition and productivity in a single season and year; b) it employs a proper use factor that is rarely supported with data; c) it does not consider the effects of season and duration of grazing on plant responses; d) it does not address the way that grazing intensity is distributed over the landscape; e) it assumes that managers can rapidly adjust livestock numbers in response to precipitation and forage, which is not practical with wild populations; f) it does not consider interactions with other species of herbivores; g) the implicit assumption of an ungrazed climax plant community is questionable; h) it does not predict herbivore population responses to food, therefore it cannot be used to estimate ecological carrying capacity. Other authors have provided similar reasons why management for climax is not necessarily desirable nor achievable (Archer and Smeins 1991).

Herbivores affect numerous ecosystem processes through dynamic and interactive effects on plant growth, plant competition, nutrient cycling, organic matter decomposition, flows of water through plants and soil, competition or facilitation of other herbivores, and predation (Acher and Smeins 1991; McNaughton 1979, 1984, 1988; McNaughton et al 1989; Detling 1988; Holland et al. 1992, Frank 1998). Herbivory effects on plants elicit responses in nutrient and water flows, which feed-back onto the plants and herbivores, at ecosystem level of organization. Plant growth, nutrient cycling, and water balance can be diminished by herbivory, but in some cases they can be enhanced (McNaughton 1979, Milchunas et al. 1988). These responses depend upon plant species, level and pattern of herbivory, climate, soil properties, and their distributions on the landscape. Thus, a general ecosystem approach is necessary to assess the effects of herbivory in the contexts of dynamic and spatially heterogenous landscapes.

Understanding of the varied roles of ungulates in ecosystems relative to a wide array of land management goals has probably matured to the point where traditional concepts of carrying capacity are no longer sufficient bases for ungulate management (Coughenour and Singer 1991, Bartels et al. 1993, Wagner et al. 1995, Coughenour 1996). The role of ungulates can be better assessed relative to overarching management goals using a more generalized approach.that is based upon fundamental ecosystem processes..

The objectives here are to a) evaluate a general, ecosystem modeling approach to support more informed policies for managing large ungulates, and b) specifically, assess the effects of different numbers of horses on ecosystem structure and function on the PMWHR. An neutral, ecosystem modeling approach is used to avoid having to make specific assumptions about upon management objectives, ideal range condition, or equilibrial biotic communities.

HISTORY AND ADMINISTRATION

The Pryor Mountain wild horses are historically significant in that they are apparently a rare strain of the Andalusian Spanish mustangs that were brought to N. America as long ago as

13 the 16th century (Gregersen 1973, Hall 1972, Ryden 1977), and later acquired and extensively used by Native Americans. Spanish mustang were important in the Spanish explorations, and they were central to Native American and European American “cowboy” cultures in 1700's and 1800's. The Andalusian breed can be traced back to ninth century Spain, but is no longer found there. The Pryor Mountain horses appear to be genetically intact, and display true phenotypic and genotypic characteristics of the breed. This may be due to the fact that the population has been long isolated, and has experienced little outcrossing. Pryor and Spanish horses both have small body size, frequently stripes on legs and rump, long tails, and lack sixth vertebra or have fused fifth and sixth vertebrae. It is believed that this particular genetic strain has differentiated substantially while in N. America, has been subject to natural selection pressures of survival on the open range, and is therefore unique, if not “native” to N. America (Gregersen 1973).

It is not known how long the horses have inhabited the Pryor Mountains, but they could have been there as early as the 1700's (USDI/BLM 1984), according to some sources. The earliest record of their presence is a photograph of a 1910 horse roundup in the Pryors. The horses have been known to inhabit the Pryors since then, but were never counted until 1970. Extensive commercial mustanging took place in the Big Horn Basin in the 1920's and 1930's (Hall 1972). Horse traps were built by sheep and cattle ranchers in the 1930's in the Pryors and horses were undoubtably removed, but the numbers were not recorded or are unavailable.

In 1964 the BLM announced plans to remove all horses from the Pryors, to counter overgrazing and to protect soil and plant resources. Local ranchers and the chamber of commerce reacted adversely to these plans, while the Superintendent of the proposed Bighorn Canyon National Recreation Area supported the removals. Bighorn Canyon NRA was formally established in October of 1966. In the spring of 1968, a news correspondent named Hope Ryden researched the Pryor horses. Interestingly, she noted that the horses appeared to be in good shape, despite the poor appearance of the range. Equally interesting were comments by local ranchers that the range was not overgrazed, that horse birth rates were low, and that the horses were in no worse condition than they were 50 years earlier. The Ryden story about the planned removals was aired on ABC news television in July 1968, and there was an immediate public outcry. In August, the Humane Society filed suit. In September, Secretary of the Interior Stuart Udall designated the Pryor Mountain Wild Horse Range (PMWHR), later announced in the Congressional Record in September of 1969. This was the first designated wild horse range in the U.S.

The lands comprising the range of the Pryor Mountain wild horses consists of lands designated as the PMWHR in the 1968 enactment, and lands used by horses under agreement or lease with other government agencies or private landholders (Table 1, Figure 1). These include lands on the Bighorn Canyon National Recreation Area (NRA), and lands administered by the Bureau of Land Management. Additional lands used under prior agreement include the Lost Water Canyon allotment of the Custer National Forest (USFS), and the Mystic allotment comprised of private and BLM lands. The Sorenson Extension of the Bighorn Canyon NRA has been used by horses in the past under agreement. Additional lands are leased from the state of

14 Montana. The Crooked Creek Natural Area (1,488 acres) is a Federal protective withdrawal (BLM), and is not available to horses. For practical purposes, an area referred to as the Lower Pasture is fenced and unavailable (2,105 acres), although it lies within the designated PMWHR. The Lower Pasture includes a very small parcel of Bighorn Canyon NRA land (91 acres).

Grazing allotments north of PMWHR were heavily grazed historically (K. Reid, USFS Custer Nat. For., unpubl.). Severe overgrazing occurred on the USFS Pryor Spur Allotment (Lost Water Canyon area) between 1909-1928 4,000-7,300 sheep, 500 cattle, and 100 horses grazed the Pryor Spur allotment for a three month period. From 1929 to 1939 more that 2,400 sheep and 250 cattle grazed this allotment for two months each year. From 1939-1962 1,200-2,000 sheep grazed the allotment for 2 months per year. Grazing was halted in 1962. The BLM Mystic allotment was also apparently heavily grazed, as a 1967 range analysis indicated the range was in poor to bad condition. It is said that sheep and cattle ranchers removed wild horses from these allotments to prevent competition with livestock. Wild horses probably used these areas subsequent to livestock removal, despite a fence constructed at the north end of the Lost Water area in the early 1960's.

A census taken in 1970 totaled 270 horses (Feist and McCullough 1975). Forty horses were then excluded by completion of the Range boundary fence. Along with 5 deaths, this left 225 animals. Horse reductions began with roundups in 1971 and 1973, which reduced the herd to 120-130. Carrying capacity was first estimated by the BLM to be 130-140, based upon assessments of range site classes and conditions by experienced range scientists, apparently based on occular assesment. When the herd was completely rounded up and counted in 1971, there were 270 head, inclusive of the newest foal cohort (Feist and McCullough 1975, Garrot and Taylor 1990). In 1974, a joint land-use plan was developed for the PMWHR by the agencies involved (BLM, NPS, USFS, State of Montana). The Pryor Mountain Complex Land Use Decisions held to a horse carrying capacity of 140, in accordance with the earlier estimate. A Unit Resource Analysis conducted by the BLM (Hall 1972), stated that “range trend has been sharply downward for several years”, which suggested that “the 120-130 horses presently on the range are too many for the available forage”. Hall (1972) reported that a range survey had been conducted which indicated a carrying capacity of 85 adult horses. In 1980 an MOU was signed between the USFS, BLM and NPS to engage in a cooperative effort to analyze wild horse resource use, and to develop a joint management plan. In early 1981 it was apparent that the range was not recovering. The Sorenson Extension, was designated for 50% use to relieve grazing pressure. An ecological site inventory was conducted by the BLM and SCS, as part of an update to the SCS Soil Survey. In 1982, the BLM estimated carrying capacity at 121 head, based upon estimated forage productivities for range site and condition classes (see Appendix), and this estimate was subsequently published in the Billings Resource Management Plan of 1983. The estimate was based upon a classification of soil types and associated range sites, estimates of range condition for each range site, and estimates of potential production of climax vegetation for each range site (see Appendix) . In 1983 an MOU was signed between the BLM, NPS and USFS allocating responsibilites to the agencies to develop a comprehensive Habitat Management Area Plan (HMAP). The HMAP was released in 1984 (USDI/BLM 1984), holding to the 121 head

15 carrying capacity estimate. A range evaluation was conducted 1985 concluded that the range was still in poor condition despite 15 years of horse reductions.

There are no records of bighorn sheep (Ovis canadensis canadensis) in the PMWHR prior to 1975. A bighorn sheep population has become established from an initial band of 2-8 colonists that crossed Bighorn Canyon River ice in 1975. The colonists were dispersers from a 1973 reintroductin in the northern Bighorn Mountains by the Wyoming Game and Fish Department. By 1984 the population had grown to about 100 (Coates and Schemnitz 1989). Fecal analyses indicated significant dietary overlap of 68% between horses and bighorn sheep (Coates and Schemnitz 1989). An increase in death rate of horses on the Dryhead area during 1987-89 was felt to be due to increasing competition from sheep (Memo, BLM Horse Wrangler 1990). The sheep population grew to approximately 200 animals by 1993 (Irby et al. 1994 , Kissell 1996). There was a decline since 1993 due to unknown causes. In 1999 there has been a disease-incurred die-off of unknown proportions, as of this report.

The Sorenson extension has been used on and off by horses over the years. Before the establishment of Bighorn Canyon NRA in 1968, it was the privately held Sorenson Ranch, and wild horses were kept off of it. In fact, a fence existed well south of the current Sorenson area up until 1967 (Hall 1972). In 1974, an interagency agreement was signed allowing winter use (December-April) of the Sorenson by wild horses for a 5 year period. In 1979, the BLM requested summer-only horse use to alleviate grazing pressure elsewhere on the range. This was subsequently approved and grazing began in summer 1980. The NPS decided not to continue allowing use of the Sorenson extension in late 1989, since it was deemed prime bighorn habitat, and because range condition had not improved, which was the original intent of making the Sorenson available to horses. In 1991 BLM adjusted it’s horse carrying capacity estimate downwards to 95 head to account for the loss of the Sorenson (see Appendix). Although the Sorenson was to be closed to horse use in 1990, it was not actually closed until Fall, 1992.

In 1991 the National Biological Service scientist was called upon to initiate an analysis of the horse population and range carrying capacity. Field studies were begun in 1992 and 1993 on plants (J. Detling), horses (F. Singer), and bighorn sheep (R. Kissell). Carrying capacity modeling was initiated in 1995.

SITE DESCRIPTION

Climate

The climate of the PMWHR is temperate continental, with cold winters and warm summers. Mean annual precipitation at Lovell, Wyoming was 170mm 1948-1996, with a standard deviation of 37mm, which is 22% of the mean. Most precipitation falls during the spring and summer, and the rainiest months are May and June (Figure 2). Precipitation for any month is highly variable, with coefficients of variation of 70-90%. Average temperatures range

16 from -8.4 in December to 21.7 in July.

Precipitation increases with elevation (Figure 3), ranging from 200 mm at the lowest elevations to over 500 m at the highest elevations within the PMWHR. Regionally, the PMWHR lies in a rain shadow created by the Beartooth Mountains to the west. Temperature likely decreases with elevation according to a lapse rate of 0.5 oC per 100 m elevation.

Relative humidity during November-April at the Bighorn Canyon Ranger Station is 46- 49%, decreasing to a low of 28% in August. Humidity at the USFS remote area weather station (RAWS) on top of the mountain is 67-72% from October-May, decreasing to 54% in August.

Long-term climate records from Lovell and Powell Wyoming suggest that annual precipitation was less variable in the 1980's than in other decades (Figure 4). Droughts occurred in the Lovell record in 1960 and 1966. According to the Powell record, multiyear droughts occured in 1919-1921, and 1933-35. During the 1990's the driest year was 1994 in both Powell and Lovell.

Landscape and Soils

The PMWHR landscape is topographically diverse. Elevations range from 1200-2400m (Figure 5). A prominent feature of the landscape is the steep-walled esscarpment that runs north and south, rising dramatically above the lower elevation plains and bajadas below. The landscape has been characterized by many deep, steep-walled canyons, isolated plateaus, and foothill slopes. Steep slopes are pervasive (Figure 5), but are interspersed with gentle slopes particulary at the bottom and the top of the mountain. A thick layer of porous limestone underlies the majority of the horse range (Hall 1972). A complex and active geologic history has created a high diversity of geological substrates, including limestones, sandstones, shales, siltstones, and granites.

The landscape is dissected with many intermittent streams (Figure 6), as a result of a high degree of erosion that has occurred over geologic time. There are two perennial streams, Crooked Creek and Layout Creek, in addition to Bighorn Reservoir, lying at the bottom of Bighorn Canyon. The reservoir lies far below steep canyon walls is only accessible to horses at the mouth of Crooked Creek.

A soils map is shown in Figure 7. Soils have been described as follows (USDI/BLM 1984). “An intermixture of limestone, sandstone, and shale, weathered to form mostly very shallow to moderately deep, loamy soils that are calcareous and moderately alkaline. Most of the soils are lithic in nature and contain 35-70 percent coarse fragments. Rock outcrops comprise up to 35% of the landscapes in the mountains. The soils on the footslopes, fans, and terraces are forming in a mixture of alluvium from limestone, sandstone and shale. These soils are mainly steep, have a high content of coarse fragments, and are highly calcareous. Environmental influences, both human and natural, have contributed to much erosion and soil loss. This, along

17 with the high lime content, limits the productive capability of the soils.” Shallow soils are prevalent, as shown in Figure 8.

Vegetation

The vegetation of the PMWHR (Figure 9) is diverse, primarily due to the large elevation and associated climatic gradient, but also due to the wide variety of soils and substrates, and patterns of water redistribution on the landscape.

Desert shrubland occurs at the lowest elevations (<1200 m), and is comprised of saltbush (Atriplex confertifolia and A. gardneri), greasewood (Sarcobatus vermiculatus), and sagebrush (Artemesia tridentata) subtypes (Knight et al. 1987). Graminoid species include Indian ricegrass (Oryzopsis hymenoides), western wheatgrass (Pascopyrum smithii), Sandberg's bluegrass (Poa sandbergii), needleandthread (Stipa comata).

Sagebrush steppe occurs in the 1200-1600 m band, with junegrass (Koeleria macrantha), blue grama (Bouteloua gracilis) and needleandthread. Knight et al. recognized three different grassland types, two of which occur in the PMWHR. These are basin grasslands, and windswept plateaus. Basis grasslands occurr between 1150-1600 m with bluebunch wheatgrass (Pseudoroegneria spicata), blue grama, and needleandthread. Bluebunch wheatgrass is possibly the most common and widespread species in the BCNRA grasslands (Knight et al. 1987). Windswept plateaus occurs on the tops of mesas or ridges with low plant cover.

Juniper/mountain mahogany shrublands and woodlands occur on very shallow soils at 1100-1550 m elevation. Utah juniper (Juniperus osteosperma) and curleaf mountain mahogany (Cercocarpus ledifolius) are dominants. Black sagebrush (A. nova) greasewood occur in the shrub layer, while graminoids include bluebunch wheatgrass and bluegrama, and Fendler threeawn (Aristida fendleriana). Mountain mahogany and juniper are also widespread in pure stands, but no clear correlation could be found between the occurance of either type of pure stand and soil or elevation (Knight et al. 1987).

Coniferous forests occur at higher elevations, above 1600 m. Douglas fir (Pseudotsuga meziezii) occurs below 2000m, particularly in ravines and on limestone. Limber pine and Douglas fir often occur together, and primarily limber pine stands are common. Spruce-fir woodlands occur at the higehst elevations (>2000m), dominated by Engelmann spruce (Picea engelmanii) and subalpine fir (Abies lasciocarpa). Coniferous forests are interspersed with high elevation grasslands dominated by Idaho fescue (Festuca idahoensis), wheatgrass (Agropyron dasystachym), sandbergs bluegrass, sedge (Carex filifolia), junegrass, and numerous forbs (Detling 1995, Detling and Gerhardt 1996). Mid-elevation grassland is dominated by bluebunch wheatgrass, junegrass, and Sandberg's bluegrass (Detling 1995, Detling and Gerhardt 1996).

18 METHODS

Ecosystem Model

SAVANNA is a spatially explicit, process-oriented model of grassland, shrubland, savanna, and forested ecosystems (Coughenour 1992, 1993). The Savanna model simulates processes at landscape through regional spatial scales over annual to decadal time scales. The model is composed of site water balance, plant biomass production, plant population dynamics, litter decompostion and nitrogen cycling, ungulate herbivory, ungulate spatial distribution, ungulate energy balance, and ungulate population dynamics submodels (Figure 10). The time- step of the model is one week.

Savanna has a hierarchical spatial structure (Figure 10). It is spatially explicit (ie. it is sensitive to spatial position) at the landscape scale and it is spatially inexplicit at patch scales. Grid-cells cover landscapes or regional-scale ecosystems. Grid-cell size is scaled to the spatial extent of the simulated ecosystem.

Within grid-cells the model simulates vegetation patch types or "facets". These are defined by covers of herbaceous plants, shrubs and trees. Therefore facet cover is a dynamic outcome of vegetation growth and mortality. Facet locations are not modeled, only the fractions of subareas that are covered by the facets. Within each facet, the model simulates plant growth and soil water budgets. The results are scaled-up to the grid-cell level by multiplying by the fractions of the grid-cell area covered by each facet.

The area of land covered by trees or shrubs varies in response to changes in tree and shrub numbers and sizes. As a consequence, direct competitive or facilitative interactions among established plants of these three life forms are limited to patches where established plants are rooted. Trees and herbs, for example, interact directly on tree-dominated patches but do not interact on herbaceous-dominated patches except during the establishment phase. Tree and shrub vegetation covers are defined in terms of rooted area; ie. the area of ground which corresponds to the exploited soil volume. Cover is defined in terms of rooted area mainly because soil water and nutrient budgets are computed on a soil volume basis.

The vertical spatial structure of the model is defined by soil and plant canopy layers. The soil is divided into three layers. The top soil layer is a zone of potential bare soil evaporation. The second layer is generally the deepest layer that is exploited by herbaceous roots. The bottom layer is generally occupied only by tree roots. Plant canopies are organized into herb, shrub and tree strata. In turn, each of these are divided into three substrata to compute light intensity.

Savanna is driven by monthly weather data. The model spatially interpolates precipitation data using inverse distance weighting. Each month, a regression is performed between

19 precipitation and elevation using data from all weather stations. If the regression is significant (r2>0.2), the rate of increase in precipitation with elevation is used to estimate precipitation at each point in the study area, using the elevation difference between the point and each weather station. Temperature for each point on the landscape is estimated from the temperature at the main base station by applying lapse rates to the maximum and minimum daily temperatures.

Atmospheric water vapor content is calculated from relative humidity and temperature of the main base station. Relative humidity is calculated from the water vapor concentration, and the temperature at each landscape location. Solar radiation is calculated from monthly cloud cover, latitude, and day of year, correcting for slope and aspect using the methods descibed by Nikolov and Zeller (1992). Cloud cover is estimated from montly precipitation and water vapor content.

The water budget submodel simulates soil moisture dynamics and use on each patch type on each grid cell. Soils map data are used, in conjunction with soil properties for each soil type, to determine soil water holding capacities of each subarea on each grid cell. The water budget includes terms for precipitation, interception, runoff, runon, infiltration, deep drainage, bare soil evaporation, and transpiration.

The runoff curve number method of the U.S. Soil Conservation Service is used to calculate runoff. Runoff depends on daily rainfall, the quantity and distribution of water in the soil relative to water holding capacity, and the condition curve number for the soil. Bare soil evaporation is simulated using the Ritchie (1972) model, which is also used in the USDA SPUR model (Wight and Skiles 1987).

The snow submodel adds water to the snowpack if temperature is below freezing. The rate of snowmelt depends on temperature. Melting during night and day are modeled individually. Water may also be lost from the snowpack through sublimation.

The light submodel simulates shading within and among plant canopies. On tree covered facets, incident radiation first passes through the tree canopy, then the shrub understory and finally the herbaceous understory. Light extinction follows an exponential decay function, dependent on leaf area indices.

The net primary production (NPP) submodel simulates plant biomass flows and dynamics. Plant biomass production is affected by light, water, temperature, nitrogen and herbivory. The NPP submodel is explicitly linked to the water budget submodel through transpiration and plant water use. Biomass is allocated to leaves, stems and roots. Plant tissues die due to water or temperature stress or phenological stage, and they turn over at a nominal rate that reflects their maximal longevities. Simulated plants maintain a pool of labile carbon, largely comprised of carbohydrates. Early regrowth in the spring or following grazing is supported by this pool, until needs can be met by photosynthesis. Plant nitrogen uptake and losses due to herbivory and tissue mortality are simulated.

20 Potential net photosynthesis is calculated as

Ps = Psmx×min(Efwp(Awpet),Eflp(Rad)]×Eftp(Tday)×Efnp(Pnb)

where Psmx is maximal net photosynthesis rate. The 0-1 function Efnp describes how Ps decreases as plant nitrogen concentration (Pnb) decreases. The 0-1 function Efwp reduces Ps due to water stress. Awpet is the ratio of available water (mm d-1) to potential evapotranspiration (mm d-1). Eflp is a 0-1 function of light intensity.

After potential photosynthesis is calculated, stomatal conductance is calculated based upon the equation of Ball et al. (1997), in which stomates open wider as potential photosynthesis and humidity (H) increase, and as atmospheric CO2 (C) decreases.

gs01 = e + e ×(Ps×H/C)

From stomatal conductance, atmospheric water vapor pressure deficit, windspeed, and radiation, the model uses the Penman-Monteith approach to calculate transpiration. From transpiration and net photosynthesis, the model calculates water use efficiency.

The water demands for competing plants are summed, and if total demand exceeds supply, water is allocated among species in proportion to root biomass. Photosynthesis is recalculated from water use using the water use efficiency calculated above.

Respiration is separated into growth and maintenance components. Maintenance respiration depends upon nitrogen concentration (Ryan 1991), and temperature. Due to high biomass, maintenance respiration in woody plants can outstrip carbon supply, so must be limited to 50% of available photosynthate.

Plant tissues senesce at rates affected by water stress, temperature, and phenology. Shoot tissues (leaves and stems of herbs and shrubs, leaves of trees) are transferred to "standing dead" upon senescence. Then the standing dead falls to "litter" on the soil surface

Plant population submodels simulate plant establishment, size, and mortality. The woody plant population models represent six size classes of plants, and plants are promoted to larger size classes based upon biomass production. Woody plant establishment and mortality can be modeled, but for purposes here, this part of the model was turned off. Instead of population size, the model represents an index of basal cover for herbaceous plants, measured in units of potential total shoot biomass. This index of basal cover increases if simulated root biomass exceeds that expected for the current basal cover. The index decreases as root biomass falls further below the root mass expected at the current basal cover. Shoot production in a given year cannot exceed the amount expected for the current basal cover. In this way, the model can simulate degradation, or a loss of potential production, due to overgrazing or climatic stress. Likewise, it can simulate a gradual recovery of potential production.

21 A litter decomposition and nitrogen cycling submodel simulates the breakdown of dead plant materials and animal feces. Nitrogen is released during decomposition to mineral forms that can be taken up by the plants. The decomposition and nitrogen submodel is based upon the Century model (Parton et al. 1987, 1993). Dead plant tissues and animal feces are divided into fast (metabolic) and slowly (structural) decomposing fractions. The fractionation depends upon dead tissue lignin and nitrogen concentrations. As lignin increases and nitrogen decreases, the fraction of structural increases. The two litter fractions are decomposed at rates that depend upon soil moisture and temperature, with faster decompostion rates under warm, wet conditions. A fraction of the decomposed carbon is respired by microbes, and another fraction is stabilized to an active soil organic matter pool. Slow and resistant pools of soil organic matter are not simulated, but are assumed to be at steady-state. Mineral nitrogen (ammonium and nitrate) is released upon decomposition if substrate nitrogen concentration is higher than N concentation of microbial biomass . Mineral N is taken up from the soil if substrate nitrogen concentration is lower than microbial N concentration. Nitrogen enters the system through atmospheric wet and dry deposition and biotic fixation, and it leaves the system through denitrification and urine volatilization.

Animal forage intake is determined by diet selection, forage abundance, forage quality, and snow cover. Forage intake rate is expressed as a function of forage biomass. As forage biomass increases from zero to a specified level, forage intake rate increases. Above the saturating forage biomass, forage intake rate remains the same. Forage intake rate is also affected by snow depth. Animals choose among available plant types and tissues to achieve a preferred diet composition. Diet composition is determined by using preference weightings, similar to Ellis et al. (1976). In this approach, diet composition is affected by the relative availabilities of different forage types as well as by preferences or avoidances.

The ungulate energy balance submodel simulates body weight of the mean animal of each species, based on differences between energy intake and energy expenditure. Energy intake depends on forage biomass intake and forage digestibility. Expenditures depend on body weight and travel patterns. The body weight of the mean animal is used to derive an animal condition index, which affects ungulate population dynamics. Metabolizable energy intake from forage consumption is the product of kg total forage intake per animal per day, the mean digestibility of the forage, the gross energy content of digestible plant matter, and metabolizability.

The energy balance model is partly based upon the models of Coppock et al. (1986), and Hobbs (1989). Energy requirements consist of a "base cost" metabolic energy demand per unit body weight per day and added energetic costs of activity, growth, thermoregulation, gestation, and lactation. A minimum and maximum energy requirement is specified, with the minimum corresponding to a resting metabolic rate. The maximum includes energy for activities. The model lets energy use vary between the minimum and maximum in relationship to condition index. If the condition index is high, animals will use more energy on activity. If condition index is low, animals will conserve energy by reducing activity. If energy intake exceeds requirements, then the animals convert the excess energy to fat. If energy intake is less than requirements, the

22 deficit is drawn from fat reserves.

Animal condition index is a number that varies between 0.0 and 1.1, and is calculated as the ratio of kg's below maximum body weight to the difference between maximum and minimum body weights (kg). At condition index 0.0 animals are at the minimum weight while at condition index of 1.0 animals are at maximum weight. Condition index can increase slightly above 1.0 to allow forage intake to continue above maximum body weight. However, above 100% of maximum body weight, forage intake rate is considerably reduced.

The ungulate population dynamics submodel is a age-sex class model with one age class for each year, for each sex. Birth rates and death rates are affected by animal condition indices. As condition index increases, birth rates increase, and death rates decline. The effect of condition index on birth rate is multiplicative, for example a condition index of 0.2 might reduce birth rate to 35% of the maximum. The effect of condition on death rate is additive - if animals are in poor condition, an additional death rate is added to the nominal death rate due to ageing. The effects of condition index represent population responses to ecological conditions governing forage availability (eg. forage production, snow depth) and intra- and interspecific competition. As competing animals can reduce forage supply, forage intake rate, and thus body condition and population growth rate.

Animals may be culled from their respective populations in a prescribed or rule-based manner, where culling occurs at a specified “trigger” population size, and culling then occurs to bring the population down to a specified “target” population size. Culling can also be prescribed in terms of numbers of animals per year. This option can be used to impose an observed culling rate.

The ungulate spatial distribution submodel dynamically simulates animals distributions s over the simulated landscape or region. The suitability of habitat in a grid-cell is affected by slope, forage biomass, forage intake rate, snow depth, distance to water, shrub and tree cover. An elevation effect can be specified by month, to cause an seasonal migration up and down an elevation gradient. Animals are distributed across their range in response to the distribution of habitat suitability indices among grid-cells. This redistribution occurs on a weekly basis.

Weather Data

Daily precipitation, temperature, windspeed, and humidity data were obtained from the Bighorn Canyon N.R.A. Sorenson Ranch weather station, elevation 1375m, for October, 1988 - December, 1996. Hourly precipitation, and temperature, windspeed and humidity data were obtained from the USFS for the remote area weather station (RAWS), elevation (2877 m), located at the top of East Pryor mountain (Figure 6). Daily precipitation data from weather stations at Lovell, Powell, Cody, Burgess Junction, and Grey Bull in Wyoming, and from stations at Pryor, Billings, Red Lodge, Crow Agency, Bridger, Wyola, Hardin in Montana, for as many years as available through 1996, were obtained from a CDROM data-base (EarthInfo Inc.,

23 National Climate Data Center Summary of the Day). All daily and hourly data were summarized to monthly values.

Soils

A level 2 soil survey of the PMWHR and adhacent lands was carried out by BLM soil scientist (Robert Mitchell, Miles City District Office). Soil type, acreages, and range site classifications are given in Table 2. The survey was based upon the level 3 Carbon County survey conducted by the USDA Soil Conservation Service (Parker et al 1975). The original classification was differentiated at a higher level of precision based upon additional field reconnasance and soil pedon descriptions (soil pits) made at the PMWHR. For purposes of GIS and simulation modeling, the polygon data were rasterized to 100m and 500m grid-cells (Figure 10). A small portion of land inside the PMWHR in the far southeast corner was missing from the BLM map, so soil types were inferred from BLM soil type on adjacent land and the Knight et al. vegetation map.

Soil properties were obtained from the Carbon County Soil Survey (Parker et al. 1975). Soil depth (to bedrock), field capacity, and wilting point are used to determine water holding capacity in the model. Observations of roots by soil layer in the soil survey descriptions are used to estimate herbaceous rooting depth in the model. The BLM range site classification (Table 2) was used to estimate parameters for runoff and bare soil evaporation models, using information provided in the SPUR model documentation (Wight et al. 1987).

Vegetation

A vegetation map for Bighorn Canyon National Recreation Area (BCNRA) was developed by Knight et al. (1987), but none exists for the remainder of the PMWHR. I developed a PMWHR vegetation map by merging the BCNRA vegetation map with a modeled vegetation map for the remaining area. The Knight et al. (1987) map was reclassified as in Table 3. The modeled vegetation map is based upon a map of forest cover, from USGS quad sheets, the soils map, and qualitative relationships between major vegetation types, elevation and soils observed on the Knight et al. map (Table 4).

A forest cover map was developed from the USGS quad sheets, where forest and woodland areas are depicted in green shading. The quad sheets were scanned on a color scanner, and the bitmap images were imported into IDRISI.. A computer program was written to extract green (forested) and non-green pixels (non-forested) from the scanned image. The green/non- green image was FILTERED in IDRISI. The digital maps were resampled to UTM, concatenated, and contracted to 100m resolution. The soils map, DEM, and forest cover map were used to derive 10 vegetation types as in Table 3. The rules were derived by an evaluation of the cross- tabulations of vegetation types for each combination of soil and elevation on the BCNRA vegetation map. The modeled map and the Knight et al. map were merged, with good agreement along their common border (Figure 9). The combined map was generalized to 500 m resolution

24 for use in the simulation model (Figure 12).

Herbaceous Plant Biomass

Herbaceous biomass was sampled 1992-96 by Detling et al (1995), Detling and Gerhardt (1996), and Gerhardt and Detling (1998), at a total of 13 sites (Figure 13, Table 5). Each of the sites had a grazing exclosure, and biomass was sampled inside and outside of the exclosures to estimate the effects of grazing . Biomass was separated by plant functional group (grasses and forbs), and into live and dead tissues. Biomass was sampled monthly at 5 sites in 1992-94, and once at time of peak biomass at the remaining sites in 1994-96. Peak live plus dead biomass values are shown in Table 5.

The biomass data of Detling and Gerhardt were used to test model predictions. Model verification involved comparing model results to data to identify problems in model formulation and parameterization. The model formulation and parameterizations were refined to improve the degree of agreement with the data. The objective was to make maximal use of the information contained in the data, and improve the model's predictive capabilites to the fullest extent afforded by the data. Sites varied markedly in standing biomass and plant species composition. Thus, for each sample site, initial conditions for the model were estimated to improve fits to the data. Herbaceous plants were assumed to be perennial, so root biomass is the initial condition that determines subsequent shoot production. Biomass dynamics on plots inside grazing exclosures were verified first, followed by unexclosed plots. Grazing intensity on the unexclosed plots was calibrated by varying horse density to best simulate the observed differences between exclosed and unexclosed biomass dynamics.

Ecophysiological Parameters

Six functional groups of plants were simulated; grasses, forbs, shrubs, Mountain Mahogany (Cercocarpus), Juniper, and Coniferous tree. These groups were chosen to meet the objectives of this modeling analysis, without making the model overly complex. Grasses and forbs represent the herb layer, and are different with respect to morphology, root:shoot allocation, leaf thickness, leaf nitrogen concentration, phenology, and most importantly, with respect the ungulate dietary preference. Dietary data indicate a relatively low amount of forbs in horse diets (Kissell 1996) relative to their abundance (Gerhardt and Detling 1998).

Key ecophysiological parameter values are shown in Table 6. Photosynthesis and stomatal conductance values were based upon field measurements by Fahnstock (1998). Leaf death rates and rates of transfer of standing dead to litter were calibrated to fit the biomass dynamics data of Detling and Gerhardt. Leaf nitrogen to biomass ratios were also calibrated to data of Detling and Gerhardt. Some parameters were based upon previous modeling of N. American temperate grasses (Coughenour and Chen 1997). Shrub ecophysiology was parameterized largely based upon information for sagebrush (Doeschler et al. 1990, DeLucia and

25 Heckathorn 1989, DeLucia and Schlesinger 1991, Branson et al. 1976, Fetcher 1981). Ecophysiological data for Cercocarpus were lacking. The photosynthetic rate was based on generic values for cool desert shrubs (Woodward and Smith1994) and adjusted upwards to improve fit to biomass data (Detling and Gerhardt 1996, Peterson et al. 1997). Values for N:B and digestibility are from Peterson et al. (1997). The leaf death rate was based on the estimate of Duncan (1975) of a 1.5-2 year lifespan. Juniper values are taken from DeLucia and Schlesinger (1991), Kaufman et al. (1982), Debano et al. (1987), Levernz (1995). Conifer parameter values were based upon Day et al. (1989) , DeLucia and Schlesinger (1991), Levernz (1995), Ryan et al (1996), Pearson et al. (1987), Prescott et al. (1989), and Fahey et al. (1985).

Shrub and Tree Cover

Canopy cover in Juniper, Mountain Mahogany, and shrub communities was initialized based upon the estimates given in Knight et al. (1987). Canopy cover of the three Juniper/Mountain Mahogony mixtures is given in Table 7 .

Knight et al. reported that canopy covers of low elevation desert shrub communities were 14% (saltbush), 11% (sagebrush), 26% (greasewood), and 11% (mixed). Sagebrush steppe canopy covers were reported as 34% in black sage, and 18% in big sage. Based on this information desert shrub vegetation type was initialized with 12% shrub cover, and sagebrush vegetation type was initialized with 18% shrub cover. Based roughly on "other species" in Table 7, Mountain mahogany and juniper types were given 3% shrub cover. For lack of better information, grasslands were estimated to have 2% shrub cover and coniferous forested grid-cells were initialized at 5% shrub cover.

Shrub and Tree Morphometrics

The model represents woody plant morphometrics (dimensions and biomass) for six size classes of plants. These include dimensions for crown diameter, stem diameter, height, and rooting zone area or the root biomass density per square meter of soil (Table 8). Biomass values are specified for leaves, fine branches, coarse branches, coarse roots, and fine roots for each of the size classes.

Morphometric parmeters for shrubs were based upon sagebrush data reported by Sturges and Trlica (1978), Branson et al. (1976), and Fetcher (1981). Cercocarpus crown diameters were taken from Duncan (1975). Other morphometric parameters for Cercocarpus are derived from values for chapparal shrubs reported in Miller et al. (1981). Juniper was parameterized largely based upon Meewuwig and Budy (1980). A computer program was written to convert their morpometric equations into discrete values for the 6 size classes. Coniferous trees were parameterized from information in Brown (1978), Keane et al. (1989), Ker and Smith (1955), Dale et al. (1986), Knight (1991), and Vogt (1991).

Mountain Mahogany Morphometrics

26 Duncan (1975) studied 22 stands dominated by Cercocarpus ledifolius in southwestern Montana. The diameters, heights, and densities of the shrubs were measured in each stand. Stand densities varied from 472-8105 C. ledifolius per ha, and 4343-14407 total shrubs per ha. Based upon these data, the following relationship was found between density and crown diameter

density (#/ha) = 15961 - 8666diameter(m) r2=0.53, p=0.0001 .

The inverse of density is ground area per plant. The relationship

area/plant (m22) = -1.35 + 3.59diameter(m) r =0.70, p<0.0001 was found. The area occupied per plant is reflective of the root area occupied exclusively by each plant. A diameter of this rooting area can be found from equation for a circle. The relationship between rooting diameter and crown diameter was better than the above relationships,

rooting diameter(m) = 0.34 + 1.22 crown diameter(m) r2=0.73, p<0.0001

Canopy area (m2) per m2 ground can be calculated from the plant density per ha and the canopy diameter per plant. Resultant canopy cover in stands was 0.41 + 0.15 (sd), minimum 0.18, maximum 0.71.

Based upon Duncan’s(1975) data, the following regression equation was found between canopy height and diameter,

crown diameter (m) = 0.0667 + 0.897height(m) r2 = 0.955, p<.0001, n=22

Duncan (1975) reported that leaves comprised 40% of CAG, the other part being twigs.

Mountain Mahogany Current Annual Growth Production

Cercocarpus current annual growth (CAG) was 0-168 kg ha-1 in Cercocarpus stands, as reported by Duncan(1975). A mean of 16.3+18.7 kg ha-1 was found in 1973 and 45.9+43.3 in 1974. Mean shrub density was 4470 plants ha-1, so mean CAG per shrub was 3.65 and 10.2 g/plant in 1973 and 1974. Peterson et al. (1997) found means of 1.6, 14.6, and 89.6 g CAG/plant over 5 sites. Mean CAG over 4 sites was 1.07, 4.1, and 27.2 g/plant in small (<10cm), medium (10-40cm), and large (>40cm) shrubs (Peterson et al. 1997). Total CAG varied from <100 to >400 kg ha-1 in Cercocarpus stands (Peterson et al. 1997) and 38-414 kg ha-1 with a mean of 143 kg ha-1 (Peterson et al. 1997).

Animal Habitats and Ranges

Wild Horses

27 There have been not been any thorough studies done of horse distributions using air surveys. Radio location studies have not been feasible due to the problems of fitting horses with radio collars, and the objections raised by the public. Hall (1972) delineated earlier horse use patterns based upon ground observations. Irby et al. (1994) delineated horse summer and winter ranges on a map, but did indicate how these ranges were drawn. Apparently 2 helicopter surveys were flown. More recently, horse observations have been noted through non-winter months for 3 years by BLM field crew (Figure 14).

It has been established that there are three relatively distinct herds of horses in the PMWHR, occupying habitats that are separated by distinct topographic barriers (Hall 1972, USDI/BLM 1984, Singer et al. 1998, USDI/BLM 1997). The Dryhead herd spends the entire year at low elevations below the Sykes Ridge escarpment. The Sykes Ridge herd moves up and down Sykes Ridge, to high elevations in summer, and low elevations in winter. The Burnt Timber Ridge herd moves similarly up and down that ridge. Sykes Ridge and Burnt Timber summer ranges overlap. Sykes Ridge and Burnt Timber Ridge are separated by a steep, deep canyon (Big Coulee).

Each of the three herds was modeled separately, and limited to the areas depicted in Figure 15. Seasonal movements were modeled as dynamic responses to changing forage and snow conditions, with a seasonal avoidance of areas below 1500 m in summer. Forage increased habitat suitability linearly from .01-1 between 5 and 50 g m-2 while forage energy intake rate effect increased from 0 to 1 between 0 and 5 MJ kg -1 d -1. Habitat preference was assumed to decrease from 1 to 0.1 as tree cover increased from 5% to 35%. Horses were assumed to prefer areas with slopes <8oo and to avoid slopes >15 .

Water is a major determinant of horse distributions during the spring, summer and fall, while snow is available to horses during winter, allowing horses to use virtually all of the horse range (Hall 1972). Kissell (1996) observed 60-80% of horse were observed within 1.5km of a water source, with more being closer to water in spring and summer. The model was parameterized so that there is a high preference for water less than 1.5 km distant, increasing to maximum preference within 1 km of water. Preference declines at greater distances, so that areas beyond 6 km of water are considered unuseable. The importance of distance to water as an effect on habit suitability was set to be diminished during months when snow is likely to be present.

The locations of known watering points within the horse range were digitized from information provided by the BLM (L. Padden, BLM, pers. comm.). Only watering points located within the horse range were considered. These include 3 permanent springs, water piped from a spring on the Sorenson Extension, 2 artificial catchments, 2 reservoirs on top of the mountain, and an access point to Crooked Creek. A map of distance to water was calculated using the GIS (Figure 16a).

The range of the Dryhead herd has varied with the availability of the Sorenson Extension. The Sorenson Extension is available to these horses in simulations for the period 1970-96, during

28 1975-1979 in winters, and during 1980-1992 during summers.

The simulated ranges of the Sykes Ridge and Burnt Timber Ridge horses will include all of the Lost Water and Mystic Allotments through 1970-1996, and additionally, the portion on USFS land outside the PMWHR/Lost Water boundary at the top of the mountain, during 1990- 1997. The Lower Pasture area, containing the Crooked Creek Natural Area, will be assumed unavailable throughout 1970-1996.

In simulations for the period 1950-1969, the horses were combined (Figure 17), effectively making the population a single herd. It has been stated that before the mid 1970's, horses migrated to the Dryhead during winter, and went back up over Sykes Ridge during summer (Hall 1972). It will be assumed that the northern part of the Dryhead area was also unavailable, since a fence existed there up until 1967 (Hall 1972). Although not officially part of the PMWHR, and not agreed to use until 1974, horses unofficially used areas on the Lost Water and Mystic allotments (Hall 1972), and probably outside these allotments, particularly after domestic sheep and cattle were excluded in 1962.

Use patterns prior to the early 1970's were different than at present due to water development. The Sorenson Ranch water source was constructed in 1971, while the two artificial catchments at Mystery Cave and Tillet Ridge were built in 1970 and 1971. These sources were not freely used until 1972 (Hall 1972). The water map used in 1950-1969 simulations is shown in Figure 16b.

Bighorn Sheep

Bighorn sheep ranges expanded markedly as the population increased from several animals to approximately 75 animals between 1975 and 1988 (Coates and Schemnitz (1989). In early 1987 they occupied a 22 m2 area, but they expanded their range to a 67 km2 area by 1987 (Figure 18). Ewes occupied a strip of habitat along the rim of Bighorn Canyon, with lambing occurring near the lakeshore. The range indicated by Coates and Schemnitz is clearly an approximation, probably drawn by triangulating from southmost and northmost points to the westmost point of observation.

Bighorn sheep were radiocollared and monitored during 1993-1994 in Bighorn Canyon N.R.A. and the PMWHR (Irby et al. 1994). Maps of their distributions in summer and winter (Irby et al. 1994) were digitized and converted to the format used by the ecosystem model (Figure 19). Observed ranges were within those observed earlier by Coates and Shemnitz (Figure 18), except for the rams summer range and a small band of animals that migrated to a small patch of habitat near Crooked Creek. Rams and ewe/lamb groups were extremely segregated spatially during the summer (Irby et al. 1994), with ewes staying near the primary lambing areas, in steep topographic relief and close to water in Bighorn Canyon. In contrast, rams occupied an area of little topographic relief at considerable distance (>3km) from permanent water. Rams returned to lower elevations in late fall and early winter to the areas where ewes had been spending summer

29 and fall. Thus, separate maps were developed for modeling summer distributions of rams and ewes (Figure 19).

Ecosystem model runs were conducted with a forcing of the sheep into the seasonal ranges observed by Irby et al. (1994). Within these ranges, the model then redistributed animals in relation to forage biomass and forage energy intake rate. As forage biomass increased from 0 to 50 g m-2, habitat suitability index (HSI) increased from 0.1 to 1.0. The multiplicative effect of metabolic energy intake rate on HSI increased from 0 at 0 MJ kg-1 d -1 to 1.0 at 0.5 MJ kg -1 d -1. Snow was assumed to negatively affect HSI, decreasing from a multiplier effect of 1 at 5cm, to 0.01 at 30cm depth for ewes and 35cm for rams. The model was parameterized to prevent animals from occurring at densities less than 1 km-2, based upon the data of Coates and Schemnitz (1989) and Irby et al. (1994), and densities > 30 km-2 as suggested by references reported in Smith et al. (1991).

Potential sheep habitats have been modeled using GIS by Smith et al. (1991), Johnson and Swift (1995) in Utah and throughout the western U.S, and by Gudorf et al (1996) for Bighhorn Canyon N.R.A. This procedure was used here to estimate winter and summer sheep habitats for ecosystem model experimentation. A slope map was used to delineate escape terrain having slopes of 27-85o (Figure 20). Although the Smith et al. model assumed areas within 300 m of escape terrain were suitable, Kissell (1996) observed 50% of animals less than 400 m from escape terrain, and >20% at distances >500m from escape terrain, with 95% within 750 m of escape terrain. Horizontal visibility was presumed to be too low for sheep in coniferous forests, so these areas were removed. Areas greater than 3.2 km from permanent water sources were eliminated next. Winter habitat was limted to areas with aspects between 135-225o. Summer habitat was limited to slopes <27o (60%). The final winter and summer ranges are shown in Figure 21. When the maps were generalized to the 500 m resolution used in model runs (Figure 21), areas of less than 25 nearly contiguous hectares were effectively eliminated. The effects of snow depth and forage within these suitable habitats were modeled as described above.

Mule Deer

Mule deer winter on the PMWHR, but during summer, most migrate to ranges north of the PMWHR (Irby et al. 1996). The model was parameterized so that the entire deer herd is on the PMWHR during December-April, and 10% are on the PMWHR during June-October. While on the PMWHR, deer have access to the entire landscape, but will avoid slope greater than 45o and not use slopes greater than 60o. Forage increases habitat suitability linearly from 0.01-1 between 5 and 50 g m-2 while the energy intake rate effect increases from 0 to 1 as intake increases from 0 to 5 MJ kg -1 d -1. Snow reduces habitat suitability linearly from 1 to 0.01 between 10 cm and 40 cm depths.

Animal Ecophysiology

Body Weights of Horses

30 Berger (1986 p. 110) reported mean body weights of wild horses as follows. Stallions - 453 kg; bachelors 435 kg; all adult males 444 kg; adult females 413 kg. Berger (p. 111) reported a growth curve which results in 30% of adult weight in 1 year olds, and 80% in 2 year olds, which corresponds to approximately 128 kg and 342 kg.

Pryor horses are smaller, the smaller size being more typical of the orginal mustang type horse. Weights for Pryor Horses were given in the BLM’s (Hall 1972) report as being 272-340 kg for females and 364-386 kg for males. A filly colt from the herd was raised on a ranch with free access to high quality forage, and reached 238 kg as a yearling.

Berger (1986) observed seasonal fluctuations in body weights, which as the condition of the animals varied from poor to good. Except for one, adult females in poorest condition were 375 kg, while those in best condition were 525 kg. In that sample, the mean body weight was ~430 kg, so the ratio of actual to mean body weight varied from 0.87 to 1.22. Based on limited data, horses from the PMWHR typically vary by as much as 100 lbs in a year (L. Coates-Markel, pers. comm.). If a mean weight is 850lbs, then the range of variation would be 850+50 lbs, or 0.95-1.05 of mean weight. This would likely be an underestimate of maximum fluctuation. For the model, I estimate the range as 0.9 to 1.18 of mean weight.

Energy Requirements of Horses

Energy requirements can be expressed in terms of the digestible energy (DE), metabolizable energy (ME), or net energy (NE). Energy requirements of horses are usually expressed in terms of DE rather than ME. The fraction of the gross energy content of forage that is undigestible is excreted as feces. The DE fraction is the energy that is actually digested. Of the amount digested, some is lost to urine and, in ruminants, to fermentation gases (methane) produced by rumen microbes. The energy left after these losses is the metabolizable energy. The fraction of energy lost to fermentation is considerable in ruminants, and metabolizabilty may vary from 0.8 to 0.88, depending on the digestibility of diet (Moe ant Tyrrell 1976, cited in Demment 197 ). However horses are ceacal digestors, and fermentation losses are considered to be neglible (Demment 197 ). In fact, metabolizability values for horses are not reported in the literature, and estimates of fermentation loses were not reported by the NRC (1978). The amount of energy lost to urine is also unreported, however, an estimate of energy needed for methane production in ruminants is about 8% of gross energy or 12% of digestible energy (McDonald et al. 1988). If metabolizability is 80%, then about 8% out of the 20% total loss must be to urine. However, estimates of urinary energy loss in horses are unavailable, so I assume that they are about 5%, and that metabolizability is 0.95.

Maintenance costs refer to the energy costs needed by a resting animal, that is not gaining or losing weight, gestating, or lactating. The Kleiber equation (Kleiber 1947, Robbins 1983) M(kcal) = 0.29W0.75 yields 26 MJ per day for a 400 kg animal, or 0.065 MJ kg-1 d -1. This is a fasting metabolic rate. Most studies of wild ungulates have reported higher values for maintenance energy requirements than given by the Klieber equation (eg. Brockway and Maloiy

31 1968, Renecker and Hudson 1985). The following estimates are digestible energy (DE) requirements. An NRC (1973) recommendation is 0.64W0.75 MJ, or 0.145 MJ kg-1 d -1 for a 400 kg animal. Pilliner (1992) indicated that 0.125 MJ kg-1 d -1 was a good generalization of horse maintenance requirement, but also suggested that M(MJ) = 18.0 + 0.1W(kg), which for a 400 kg animal yields a requirement of 0.145 MJ kg-1 d-1. Another equation often used is for horse under 600kg is M(Mcal) = 1.4 + 0.03W (Lewis 1995, NRC 1982), which is equivalent to M(MJ) = 5.9 + 0.125W(kg). This yields 0.14 MJ kg-1 d-1 for a 400kg animal.

Rittenhouse et al. (1982) used dietary markers to measure 0.01-0.015 kg digestible dry matter intake per kg bodyweight per day in mares. Assuming a gross energy content of forage of 18.5 MJ kg (McDonald et al 1988) and a metabolizability of 0.82 (Robbins 1983), the metabolic energy intake would have been 0.127-0.228 MJ kg-1 d-1.

Additional costs of walking and other activities must be added to the maintenance costs. Pilliner (1992) reported estimates of energy costs to horses for different activities. For example, an hour of walking adds 0.002 MJ kg-1 d-1 while an hour of fast trotting adds 0.53 MJ kg -1 d -1 for a 400 kg animal. An activity budget of 4 hrs walking and ½ hour of fast trotting adds 0.031 MJ kg -1 d -1, resulting in a total of 0.145 + 0.031 = 0.176 MJ kg -1 d -1 total requirement. She estimated that a moderate activity budget adds 30-60 MJ d-1 for a 500 kg horse, or 0.06-0.12 MJ kg -1 d -1. The energy requirements for light, medium, and intense work have also been estimated at 1.25, 1.5 and 2.0 times maintenance (Lewis 1995). Berger (1986) calculated the additional costs incurred by the activity of herding and harem defense by males by assuming as cost of 0.096MJ kg-1 hr-1 for cantering or jumping (NRC 1978), multiplied by the hours spent in those activities. For a 453 kg stallion, extra costs could amount to 0.17-0.25 MJ kg -1 d -1 (Berger 1986, Fig. 7.8). This would represent an approximate two-fold increase over a basic maintenance and activity requirements.

Pilliner (1992) reported that the extra costs associated with gestation are insignificant during the first 8 months, and only rise to 6% of maintenance requirements in the last 3 months. Lewis (1995) and NRC (1982) indicate that energy costs rise to 1.11, 1.13, and 1.2 times maintenance during months 9-11 of pregnancy (or equivalently 11-20% of maintenance).

Costs of lactation amount to 0.1 MJ kg -1 d -1, at a peak milk production of 5% bodyweight per day (Pilliner 1992). If a foal is weaned at 6 months, the added cost is about 0.05 MJ kg -1 d -1 over the course of a year. Lewis (1995) gives energy needs of 1.5-1.8 times maintenance during lactation. In turn, lactating mares may eat as much as ate twice as much as pregnant mares (Boulot1987, cited in Duncan 1990). The energy of lactation, passed to foals, supports maintenance, and the increase in body weight of foals until they are weaned. It also reduces or eliminates the need for foals to obtain through grazing. Berger (1986) noted that while evidence for the importance of milk vs. forage for preweaning weight gain is sparse, the existing evidence suggests it is vital. In accounting for herd level forage intake, it would be appropriate to model lactation costs, thereby accounting for foal body growth and maintenance. However, forage intake of foals should probably be assumed to be much less than that based on their body weight

32 and growth rate, because the energy is derived from milk rather than forage. I therefore reduce foal forage intake by half that predicted based on body weight.

Cost of growth can be calculated using the formula G(Mcal) = (4.81 + 1.17A - 0.023A2)ADG where A is age in months and ADG is average daily gain (kg) (NRC 1982). Lewis (1995) reports that postweaning animals (4mo) gain 0.9 kg d-1, decreasing to 0.25 kg d-1 by 18mo. For a 10mo colt, weighing 60% of mature weight of 400 kg (240 kg), gaining 0.5 kg d-1, the cost would be 9.4 Mcal or 0.04 Mcal kg -1 d -1 or 0.16 MJ kg -1 d -1. This is a total of 38 MJ d-1. A 6 month old at 50% of adult weight gaining 0.8 kg d-1 would require 0.21MJ kg -1 d -1.

Based upon this information, maintenance plus basic activity energy requirements could range from 0.13-0.23 MJ kg -1 d -1 of DE. Assuming an metabolizability of 0.95, the DE costs are converted to 0.125-0.22 MJ kg -1 d -1 of ME. Gestation and lactation costs are added using the multipliers given in Lewis (1995). Growth costs of foals and immature animals are assumed to be 0.09 and 0.16 MJ kg -1 d -1 respectively, based on Lewis (1995) and NRC (1982).

Forage Intake Rates by Horses

Generally it is said that a horse will eat about 2.5% of body weight per day, although lactating mares and growing foals may eat more (Pilliner 1992). This is considered to be the “appetite” of the animal, or the intake rate which satiates. In agreement with this value, Rittenhouse et al. (1982) observed dry matter intake rates of 2.0-2.4% of body weight per day on good pasture.

Duncan (1992) summarized data for wild and domestic equid intake rates (Table 6, p. 62). Non productive domestic and wild species under captive conditions had similar intake rates. He noted that animals typically used in the feeding trials reported in agricultural literature are geldings with voluntary intake rates on the order of 75-115 g W-0.75 d-1 (W in kg), or 1.6-2.5% of body weight per day for a 400 kg horse. Five wild species studied by Foose (1982) had intake rates of 96-104 g W--0.75 d-1. In contrast, intake rates of domestic animals that were producing (lactating, growing, gestating) were much higher, at 165-193 g W-0.75 d-1, and zebra that had been wild-caught and recently tamed had intake rates of 161-195 g W-0.75 d-1 (Gakahu 1982). The median value of 178 g W-0.75 d-1, results in a maximal intake rate of 4% per day for a 400 kg animal. Duncan argues that breeding or growing equids are capable of much higher intake rates than typically reported in standard feeding trials.

Working backwards, and computing the intake necessary to meet energetic demands, the intake rate (I) required to meet an energy requirement at dry matter digestibility (DMD) can be estimated from the equation Ein = DMD × Intk × 0.95 × 18.5, where 0.95 is a fraction of digestible intake that is metabolized (see above), and 18.5 gross energy content of forage (MJ kg- 1 ). Thus Intk = Ein/7.93 if DMD is 0.45, and Intk would need to be 2.1% per day to meet a requirement of 0.17 MJ kg -1 d -1. For a lactating mare with an energy requirement of 0.29 MJ kg-1 d -1 (1.5x) the intake would need to be 3.6 % per day. The intake rate of a stallion engaged in

33 active harem defense would need to be even higher to meet requirements, based on a two-fold increase estimated by Berger (1986). Intake rates of over 3.5% per day necessary to meet these energy demands are consistent with conclusion of Duncan (1992) that agricultural-based intake rates underestimate the maximal intake rates of wild equids in-situ. Consistent with this information a maximum intake rate of 4% per day was used in the model.

Forage intake rates of 1.43-3.6% of body weight per day were observed by Chappel and Hudson (1978a) for bighorn sheep. Intake rates of 1.9% and 3.2% were garnered by Skiles et al. (1981) from the literature (Chappel and Hudson 1978a, unpublished memo). For mule deer, Skiles et al. (1981) found values of 1.7-2.1 reported by Walmo et al. (1977). Hobbs (1989) used a value of 1.7 based on Aldredge et al. (1974), but considered the range could span 1.4-2.3.

Energy Requirements and Forage Intake of Bighorn Sheep and Mule Deer

Sheep bodyweights are set at 60 kg for ewes and 75 kg for rams after data in Chappel and Hudson (1978a,1978b). Mule deer body weights are set at 65 kg for does and 90 kg for bucks (Hobbs 1989).

Minimum and maximum metabolizable energy requirement vary between 0.16 and 0.22 MJ kg -1 d -1 based upon information in Chappen and Hudson 1978a,1978b. Chappel and Hudson (1978a) measured metabolic rates of 0.12-0.2 MJ kg -1 d -1 after fasting animals overnight. This may not be long enough to reach a fasting met rate, however, as ruminants must be fasted for at least 4 days to reach a true fasting metabolic rate (McDonald et al 1988). A fasting metabolic rate of 0.0865 MJ kg -1 d -1 has been reported for domestic sheep (McDonald et al. 1988). Energy costs for lactation and for body growth of newborns and immatures are based upon information given in McDonald et al. (1988). The lower critical temperatures for thermoregulation was assumed to b -20C (Chappel and Hudson 1978a). Energy costs of thermoregulation for bedded and active animals are taken from data in Parker and Robbins (1984).

Mule deer minimimum and maximum energy requirements are set at 0.14-0.21 MJ kg -1d- 1. Hobbs (1989) used 0.16 MJ kg -1 d -1 (for a 65 kg doe). Values reported in Robbins(1983) and Hobbs (1989) were used to estimate the maximum value of 0.21. Parameters describing thermoregulatory costs were taken from Hobbs (1989). For purposes here, mule deer gestation and lactation costs were not simulated.

Animal Populations

In simulations using observed population data, the summarized horse data from USDI/BLM (1997) were used, based on data from L. Taylor (1990 memo), and Garrot and Taylor (1990). Sheep population data were obtained from Coates and Schemnitz (1989) and

34 Kissell (1996). Mule deer population size was 600 animals in 1992, 800 animals in 1993-1994, and 125 animals in 1996 (Kissell 1996). Due to a lack of data, the deer population was set at 600 prior to 1992. Horse culling data from USDI/BLM (1997) were used in simulations using observed culling data. Deer were culled according to a rule that maintained the population between 500 and 700. Bighorn sheep have never been culled or hunted.

Maximum foaling rate of 3-5yr old horses was 0.65, and rates up to 83% and 68% were observed in 5-10 year olds and >10 year olds (Garrott and Taylor 1990). Values in the 3 best years ranges 71-83% and 57-68% for 5-10 year olds and >10 year olds. A maximum rate of 64% was observed by Singer et al. (1997), while the mean was 45%. A mean value of 67% was observed in 4-11yr olds (Singer et al.1998). Based on this information, maximum foaling rates were set as shown in Table 9. Sex ratio at birth was assumed to be 50:50.

Maximum annual survival rate was set at 98%, for 1-16 year olds, 75% in 17-22 year olds, 50% in 23-24 year olds, 0 in 25 year olds based upon information in Singer et al. (1997,1998). These rates result in the lifetime survivorships shown in Table 9. The effect of condition index (CI) on birth rate was calibrated to match the range of foaling rates observed historically (Garrot and Taylor 1990, Singer et al. 1997, 1998), and to minimize differences between observed and simulated population dynamics 1971-1996. Foal rate deceased from maximum value at CI of 1.0, to 75% of maximum at CI of 0.6, to 45% of maximum at CI of 0.2, to 0% at CI of 0. The added death rate due to low CI was parameterized to be 50% per year at CI of 0, 20% per year at CI of 0.5, and 0 at a CI of 1.

Bighorn sheep are often assumed to begin reproduction at age 3, but some birthing occurs at age 2. Thus, a birth rate of 0.5 for two-year olds is assumed (Leslie and Douglas 1986) .Maximum birth rates were set at 0.91 for 3 year old and older animals (Leslie and Douglas 1986). The sex ratio at birth is assumed to be 50:50. Maximum survival rates were set at 0.9, declining to 0.5 between ages 8 and 14. These numbers are based upon information in Hansen (1980) and Leslie and Douglas (1986). The effects of condition index on birth rates were calibrated to achieve lamb:ewe ratios similar to those observed (Singer et al. 1997,1998, Coates and Schemnitz 1989, Kissell 1996), and the observed population growth rate (Kissel 1996). The birth rate declines from the maximum at condition index (CI) of 1.0, to 60% of maximum at CI of 0.6, to 35% of maximum at CI 0.2, to 0 at a CI of 0. The additive death rate due to low CI was set be 0 at CI of 1, increasing to 20% per year at a CI of 0.15, to 25% per year at a CI of 0.

Mule deer population parameters were based upon information given in Bartalow (1988).

RESULTS

Site-Level Herbaceous Biomass Dynamics

The capability of the model to predict herbaceous shoot biomass and leaf nitrogen

35 dynamics was tested through comparisons of simulated results with the data of Detling et al. (1995,1996), and Gerhardt et al. (1998). Initial conditions for roots, and horse densities necessary for best fits to data are given in Table 10.

The model simulated more live forb than grass biomass at North Dryhead, in agreement with data (Figure 22). However, there was less dead forb than grass biomass inside exclosures, indicating that dead forb biomass was transferred to soil surface litter at a much higher rate that dead grass biomass. Total standing biomass is likely to be less than net aboveground primary production due to the continual transfer of dead to litter. Biomass outside exclosures was also reasonably simulated (Figure 23). Live biomass inside exclosures was only slightly larger than outside, while there were more marked differences in dead biomass inside compared to outside. Dead biomass of exclosed grasses was underestimated in 1994, however the total biomass was reasonably simulated, indicating an unexplained effect on leaf death rate, but not total production.

At the South Dryhead site there was a higher proportion of grass (Figure 24) than at North Dryhead. Live grass biomass was well simulated. Although live forb biomass was overestimated while dead grass biomass was understimated in 1994, total biomass was reasonably simulated. Simulations of grazed biomass at South Dryhead were less problematic (Figure 25). The balance between live and dead grass biomass was well represented. Two outlying data points for live forb biomass were not simulated by the model. Grazing had little effect on any biomass component, according to the data or the model.

Simulated leaf nitrogen concentration compared well to observed data at both Dryhead sites. Decreases in nitrogen concentration through the growing season observed in data were represented by the model.

At the Sorenson sites, there were considerably more forbs than grasses, irrespective of grazing treatment (Figures 26-29). As at the Dryhead sites, dead forb biomass remained low despite the high amount of live biomass. Dead grass biomass was undersimulated inside exclosures in 1993, but total biomass was well simulated. Simulated nitrogen concentrations were in agreement with data.

The mid-elevation site on Sykes Ridge produced comparable amounts of biomass to the low elevation sites on the Dryhead range (Figures 30,31). However, there was a higher proportion of grass relative to forb. There appeared to be problem in simulating dead biomass inside exclosures. However, total biomass was reasonably well simulated. Simulations of grazed biomass were better than ungrazed biomass, with the exception of two outlying live forb data points.

The high elevation Penn's Cabin site produced more biomass than low and mid elevation sites (Figure 32). Forbs were preponderant. Dead forb biomass remained low even inside exclosures. Total biomass inside exclosures was well simulated. Leaf nitrogen concentration

36 reached higher levels than at low elevation sites, and this was represented by the model.There was slightly less live and dead biomass outside the exclosure (Figure 33), and total biomass outside was approximately 15% less than inside the exclosure.

The Forest Service site was also productive (Figures 34,35). The model simulated more live forbs than grasses, but forb abundance was more similar to grass abundance than at Penns Cabin. Dead and total biomass amounts were underestimated by about 10-20% inside exclosures. The proportion of live forbs relative to grass was higher outside exclosures than in (Figure 35). Dead biomass was markedly less outside than inside exclosures, but simulated total biomass was reduced little by grazing.

At the remaining sites, only total biomass was measured (Figures 36-38). The model represented biomass dynamics reasonably well, except at the Peninsula site, where biomass was overestimated. Very small quantities of biomass were produced at the North Bay, Bat Cave, and Yellowhill sites, and the model represented the responsible limitations on growth quite well. The model also did well in simulating biomass dynamics at the more high elevation RAWS and Subalpine sites.

Overall, the model simulated herbaceous biomass dynamics well across a wide range of sites ranging from peak standing crops of just a few grams to peak standing crops of over 250 grams per meter square. The proportions of grasses and forbs, transfers from live to dead tissues, and from dead tissues to soil were adequately simulated.

Temporal Dynamics 1950-1969

Temporal dynamics reported here are mean values averaged over the whole of the simulated landscape.

Model-derived estimates of mean annual precipitation varied from 175-460 mm yr-1 (Figure 39) during 1950-1969. Simulated snow depth varied considerably among years, from <10 cm to 60 cm at peak depth.

Grass biomass reached annual highs of 20-35 g m-2 with no horse grazing, except in 1960 and 1961 (Figure 40). Forb biomass was usually about 50-75% of grass biomass, at 10-20 g m-2. The low amount of production in 1960 resulted in a low quantity of root biomass to begin regrowth in 1961 (Figure 41). This reduced production in 1961 despite adequate precipitation. Aboveground net primary production (ANPP) with no horse grazing varied from 18-35 g m-2 of grass and 10-25 g m-2 forbs, except in 1960 and 1961 (Figure 42). ANPP usually exceeded peak standing crop, but by variable amounts, as a result of the transfer of standing dead to litter during the growing season. Total ANPP in 1960 was very low in both grasses and forbs Forb ANPP recovered well in 1961, but grass biomass did not, resulting in a predominance of forb relative to grass ANPP in 1961.

37 With horse numbers held at 270 animals throughout (the number first counted by Feist and McCullough 1975) there were markedly smaller peak standing crops than without horses (Figure 40). Peak grass biomass varied from 14-24 g m-2, except in 1960-61, while forb biomass varied from 8-18 g m-2. With horses, forb biomass comprised a greater proportion of total peak standing crop, while grasses comprised a smaller proportion. Root biomass was also diminished by horse grazing (Figure 41). Maximum grass root biomass was about 35 g m-2, in contrast to the 50 g m-2 with no horses. ANPP was similarly decreased by grazing (Figure 42). There were no marked differences in plant responses between the scenarios with fixed numbers of horses and with the simulated population dynamics (Figures 40-42).

With simulated horse populations and no culling, horse numbers initially increased, leveled off at around 400, and then plunged dramatically in 1960-61 (Figure 43). Numbers gradually increased to the 270 level that was observed in the first count (Feist and McCullough 1975).

With horses fixed at 270, the horse condition index mainly varied between 0.5 and 1.0. However in 1960-61, and again in 1967-69 the indices fell to lower values. Peaks were reached at the ends of summers, while troughs occurred at the ends of winters. The low values in winters of 1967/1968 and 1968/1969 were probably a result of the heavier snow depths in those years (Figure 39). With fixed horse numbers, condition indices of the Dryhead herd were often higher than the other two herds. In contrast, with simulated horse populations, condition indices (CI's) of all 3 herds were similar. There were other notable differences in CI's in the two scenarios (Figure 44). The greater CI's of Dryhead animals in the fixed number scenario were a consequence of the lack of a population adjustments to forage biomass, which did occur when the populations were dynamically simulated rather than held fixed. The Dryhead population was set at 30, while the Burnt Timber and Sykes Ridge herds were both set at 120 in the fixed scenario, which apparently resulted in more forage per animal on the Dryhead range.

Temporal Dynamics 1970-1996

Annual precipitation averaged over the PMWHR varied between 250-475 mm (Figure 45). There were no drought years such as the one that occurred during 1950-1969. Mean snow depth was variable among winters, with an exceptionally severe winter in 1978/1979 (Figure 45). There were series of mild winters in the early 1980's and early 1990's. Winters were milder since 1980, compared to pre-1980.

Grass aboveground biomass varied from 15-35 g m-2 and forb biomass varied from 15-25 g m-2 without horse grazing (Figure 46). Grass biomass generally increased over time. Forb biomass increased less, so the proportion of forbs in total herbaceous biomass decreased. Grass biomass varied between 12-27 g m-2 with the observed levels of horse grazing, and forb biomass was similar to grass biomass. The ratio of grass to forbs was similar throughout. With no horse culling, biomass of grasses decreased over time.

38 ANPP of grasses slightly exceeded grass peak standing crop, but ANPP of forbs was much higher than forb peak standing crop (Figure 46) as a result of the higher transfer rates of dead forb biomass to litter. While forb ANPP exceeded grass ANPP early, with no animal grazing grass ANPP came to exceed forb ANPP. With observed animal numbers, forb ANPP varied between being higher and lower than grass ANPP, while with no culling, forb ANPP mainly exceeded grass ANPP. ANPP appeared to be increasing over time with no animal grazing, and decreasing over time with no culling.

With no grazing, root biomass of grasses fluctuated between 20-60 g m-2, while that of forbs varied between 16-30 g m-2 (Figure 48). Grass roots eventually became more abundant than forb roots. With observed animal numbers, grass and forb roots varied between 15-45 g m-2 and 14-31 g m-2, respectively, and there was no long-term trend in either. With no culling, root biomass of both plant groups was lower, and appeared to be decreasing over the long term.

Herbaceous basal cover increased during the first 15 years of the simulations with no animals, in both grasses and forbs (Figure 49). Grass cover was markedly higher than forb cover by the end of the simulation, and was still trending upwards. Grass and forb cover increased when grazed with observed animal numbers, but then leveled off at lower levels than with no animals. Forb cover stabilized at a slightly lower level than without animals. With no horse culling, grass and forb covers both trended downwards before appearing to level off.

Averaged over the entire PMWHR, current annual growth (CAG) of shrubs varied between 4-14 g m-2 (Figure 50). There was little effect of herbivory. CAG of Cercocarpus was about 3 g m-2 without herbivory, decreasing to about 2.5 g m-2 with observed animal numbers and 2 g m-2 with no horse culling. Juniper CAG stabilized at 0.5-1 g m-2, and was not affected by herbivory. Aboveground net primary production (ANPP), which includes total leaf and stem growth, exceeded CAG by approximately two-fold (Figure 51). ANPP of shrubs was not affected by herbivory, but ANPP of Cercocarpus declined as herbivory level increased.

Litter pools fluctuated seasonally as a consequence of differential rates of litter inputs and decomposition at different times of year (Figure 52). Metabolic litter pools varied between 5-31 g m-2 without herbivory, between 5-28 g m-2 with observed animal densities, and between 4-25 g m- 2 with no horse culling. Structural litter pools were less dynamic, and not signicantly affected by herbivory. The active soil organic matter pool varied around 5 g m-2 with no animals and observed numbers of animals, and around 4 g m-2 with no horse culling.

Forage intake rate by horses varied seasonally from <0.5 kg kg-1 d-1 to the maximum of 3.5 kg kg-1 d-1 (Figure 53). Minimum values varied more among years than maximum values, with especially low minimal intake rates being predicted for 1978 and 1979. Generally, most stressful years for forage intake were also years with deeper snow depths (Figure 45). Maximum intake rates for the Dryhead head were consistently lower than for the other two herds, as a result of the lower forage biomass on the Dryhead summer range. Intake rates were markedly reduced when horse herds were not culled (Figure 53). Maxima and minima were both reduced at higher

39 horse densities, as a consequence of intraspecific competition for forage. Sheep intake rates were far less variable than horse intake rates (Figure 53), primarily because they could shift to browse species which continued to be abundant during the winter.

Simulated horse diets were 70-85% grass, 15-30% forb, and 5-10% browse with the observed numbers of animals (Figure 54). Diets shifted when horse herds were not culled, towards higher proportions of forbs and browse, indicative of competition for the more desired grass forage at higher horse densities. Sheep diets began with 60-70% grass, but shifted to higher percentages of shrubs, Cercocarpus, and forbs. The shift was likely a response to the increase in sheep, and increased increased intraspecific competition for grass on the limited areas of suitable habitat. There were more forbs and Cercocarpus in sheep diets when horses were not culled, but only during 1980-1990. Deer diets were predominantly forbs (Figure 55), with less than 10% grass. The effects of not culling horses were very minor.

Horse condition indices varied primarily between 0.5 and 1.0 (Figure 56). Years with high minimum forage intake rates (Figure 53) were years with high minimum condition indices. Condition indices on the Sykes Ridge and Burnt Timber Ridge ranges often reached lower minimum values than on the Dryhead range, most likely a result of deeper snow cover at the higher elevations. When horse herds were not culled, horse condition indices decreased (Figure 56). Both maximal and minimal values decreased on the Dryhead range, while on the higher elevation ranges, only the minimum values decreased. Sheep condition indices (Figure 56) varied seasonally, more than forage intake rates (Figure 53), as a result of seasonal changes in forage quality (digestible energy concentration).

Observed horse population dynamics are displayed in Figure 57, along with the predicted horse population dynamics using observed rates of culling. The mean number of horses over the period was simulated adequately, however there were discrepencies in some of the detailed dynamics. The observed die-off in winter of 1977/78 was not as severe as observed. The model overpredicted numbers of animals in the Dryhead herd as compared to data. With no horse culling, the total population rose to 400-500 animals before leveling off. The Dryhead herd leveled off at half the values of the Sykes and Burnt Timber herds.

Simulated sheep populations grew at rates comparable to data (Figure 58). The die-back that occurred in 1995-96 was not simulated however. Without culling of horses, the sheep population grew at a slower rate, reaching approximately 60% of the values with observed horse culling.

Spatial Model Output

The simulated distribution of mean annual precipitation over the PMWHR (Figure 59) was similar in magnitude and spatial pattern to that estimated by a hydrologist (Figure 3). Conditions were slightly drier during 1950-59 than during 1970-96. Mean annual precipitation varied from 200 mm at the lowest elevations to over 500 mm at the highest elevations.

40 Modeled herbaceous aboveground net primary production (ANPP) did not correspond exactly to precipitation gradients, due to soil differences accross the landscape (Figure 60). ANPP varied from <175 kg ha-1 to over 220 kg ha-1. Lands within the Sorenson Extension, and just south of it were more productive for a similar amount of precipitation than areas further south on the Dryhead horse range. The difference was clearly related to the distribution of deeper and shallower soils (Figure 8), and their associated water holding capacities. Production was lower during the 1950-69 period, even in areas where there were no horses such as the Sorenson Extension and northern Dryhead (see Figure 17).

ANPP maps with and without horses (Figures 60 A,B) were similar, but there were certain areas where production was diminished with horse grazing. These included the area just south of the Sorenson Extension boundary, and areas in the south central portions of the Sykes and Burnt Timber Ridge horse ranges. These areas can be better seen using difference maps (Figure 61). There were smaller decreases in ANPP on portions of the landscape, and there were no differences, or even increases in ANPP in some areas. Grass ANPP was diminished more than forb ANPP. With observed horse numbers, grass ANPP was 60-80% lower in some areas than without horses. With no horse culling, there were areas where grass ANPP was reduced by 80- 90% and forb ANPP was reduced by 50-70%. There was a clear difference in ANPP reduction at the Sorenson boundary. The Sorenson was available to horses for some, but not all of the time period simulated (see History and Administration section).

Herbaceous root biomass distributions paralleled ANPP distributions (Figure 62). There was less root biomass at lower elevations, and on shallower soils than deeper soils at the same elevation. There were fewer roots during 1950-1969 than during 1970-1996. Root biomass was diminished by grazing in some areas. Decreases in shoot and root biomass resulting from horse grazing are shown in Figure 63. Shoot and root responses were comparable. With observed numbers of horses, roots decreased by 25-75% in some areas, but these areas constituted a small fraction of the landscape. With no horse culling, areas of decreases rose to 50-90%, and a larger fraction of the landscape showed decreases in the 25-50% range.

With no horse grazing 1970-1996, proportion forbs was mostly in the 20-30% range at higher elevations, and the 50-60% range at low elevations (Figure 64a-c). Horse grazing shifted species composition towards a greater preponderance of forbs in most places, but the shifts were differentially distributed accross the landscape (Figure 65a,b). There was a greater species shift at the higher elevations, with forb proportions increasing to 40-70% at some high elevation areas. The changes were more pronounced with no culling than with observed horse numbers.

The pattern of forb abundance was different in 1950-1969 (Figure 64d). There were fewer forbs on the Sorenson and north Dryhead with no culling 1950-1969, than in 1970-1996, even without horses in 1970-1996. This area was not grazed by horses in 1950-1969. There were more forbs at high elevations in 1950-1969 compared to any horse level in 1970-1996.

Plant litter on the soil surface was reduced by grazing on portions of the landscape

41 (Figure 65c,d. The litter response paralleled the herbaceous shoot response, as would be expected. Litter and shoot biomass reductions cause an increase in bare ground, with potential secondary effects on soil temperature, runoff, and soil erosion.

Herbivory levels on grasses and forbs were heterogeneously distributed over the landscape (Figures 66,67). With observed numbers of horses, grass herbivory levels reached 70- 80% over significant portions of the landscape (Figure 66a). However, much of the landscape was grazed lightly or moderately. Grasses on the southern parts of the Sykes Ridge and Burnt Timber Ridge, and northern part of the Dryhead Range were grazed heavily. Areas that were most heavily grazed were not necessarily lowest in ANPP (see Figure 60). In fact, some of the lowest offtake rates were on areas having lowest ANPP. Grasses on the summer range on top of Sykes and Burnt Timber Ridge ranges was mostly moderately grazed, but a small portion was heavily grazed. Forbs were generally more lightly grazed than grasses (Figure 67a) when grazed by observed horse numbers. A much smaller fraction was heavily grazed. Areas with highest rates of forb offtake were not closely related to areas of highest grass offtake.

High rates of grass offtake were far more prevalent with no culling of horses 1970-1996 than with observed horse numbers (Figure 66b). Grasses on a majority of the landscape were grazed in excess of 50%. Grasses on a large fraction of the landscape were completely utilized (>90% offtake). Forb offtake was still less than grass offtake, but herbivory levels on forbs were mostly light to moderate (Figure 67b). Herbivory pressure was quite intense on a larger portion of the landscape 1950-1969 (Figure 66c), even though horse numbers were lower than in the 1970-1996 no culling scenario. However, ANPP values were lower in 1950-1969 than in 1970- 1996 (Figure 60d).

Horse densities were very heterogeneously distributed on the landscape (Figure 68). With observed horse numbers, highest use areas at low elevation were 2-5 head per km2 yearlong. Heavy use areas at the top of the mountain were higher, reaching 6-8 head per km2 yearlong. When horses were not culled, densities reached higher levels everywhere, with low elevation high use areas reaching 5-8 head per km2 and high elevation high use areas reaching 7-10 head per km2 yearlong (Figure 68b). Densities reached higher levels during 1950-69 (Figure 68c) than during 1970-1996. Much of the northern Dryhead and Sorenson was unavailable, and mid and higher elevations were futher from water during the dry seasons, so horses were more compressed. Densities on top of the mountain were lower than in the nocull scenario for 1970- 96, probably due to lack of water and a resultant shorter period of use.

Seasonal distributions were as expected (Figure 69). During winter months, horses tended to use lower elevations on Sykes and Burnt Timber Ridges, reaching densities of 2-4 head per km2 under observed horse numbers (Figure 69a). On the Dryhead during winter, horses were widely distributed on both the northern and southern parts of the range. During summer (Figure 69c), Sykes and Burnt Timber horses were mainly concentrated on the top of the mountain, but there was also some use of lower elevations. Dryhead horses were most concentrated on the northern part of the Dryhead range. When horses were not culled, spatial distributions were

42 similar, but with higher densities (Figure 69b,c). Winter range densities reached 7-8 head per km2 and high elevation summer ranges densities reached 13-15 head per km2. A larger area of the Dryhead experienced moderate to high horse densities in summer (Figure 69d).

Sheep distributions were modeled (Figure 70) based on empirical range extents (Figure 19). Within these ranges, highest concentrations of ewes during summer were located in the center of the Dryhead horse range, and in the Trail Creek Extension. Ewe winter densities were highest on the western Sorenson. Ram summer distributions were bimodal, with high densities in a few grid-cells and low densities in most of the others. Ram winter distributions were more uniform within the specified range.

Using the GIS modeled sheep habitats (Figure 71), rams and ewes were similarly distributed, but over ranges which were much different than those found empirically. Much of the area that the GIS model determined as being suitable summer range (Figure 21) received very little use in the simulation model (Figure 71). Areas at lower elevation, in particular on the Dryhead, were little used, while scattered higher elevation sites were used more heavily. The simulated summer distribution probably reflects the distribution of forage. Most of the areas specified as being suitable winter range (Figure 21) were used in the simulation model (Figure 71).

Current annual growth (CAG) of shrubs was a reflection of the distribution of shrub cover among different vegetation types, as well as potential effects of precipitation and herbivory (Figure 72). Shrub CAG in desert shrublands was 50-100 kg ha-1 during 1950-1969 (Figure 72d), but decreased slightly by 1970-1996 (Figure 72a). There was very little shrub CAG through the Juniper and Mountain Mahogany vegetation types. Higher shrub CAGs were predicted at higher elevations, in coniferous woodlands and sagebrush steppe where values of 150-500 kg ha-1 were common. Shrub CAG was depressed slightly by observed numbers of horses, and markedly with no culling of horses (Figures 72b,c).

Cercocarpus CAG was limited to Mountain Mahogany Juniper/Mountain Mahogany vegetation types (Figure 73). Values of 100-150 kg ha-1 were most common, however a few grid cells produced up to 250-300 kg ha-1. There was very little difference between no horses and observed horse numbers, and no horse culling, which is not surprising given the negligble amount of Cercocarpus in horse diets. .

Juniper CAG varied from 5-70 kg ha-1, and appeared to be largely insensitive to horse density (Figure 74). Conifer CAG was mostly in the 20-30 kg ha-1 range, and was also insensitive to horse density (Figure 75).

Comparisons to Range-Site Forage Production Estimates, and Assumptions Implicit in Previous Carrying Capacity Estimates

Modeled herbaceous biomass was comparable to estimates based on range sites in the soil

43 survey (Parker 1975, Appendix), except for estimates for excellent range condition. The model predicted a range of approximately 30-65 g m-2 grass plus forb biomass with no grazing and 25- 35 g m-2 with 270 horses 1950-1969, and 25-55 g m-2 with observed numbers of horses 1970- 1996. In comparison, biomass based upon range-sites (Appendix Figure 2), averaged over the entire landscape, yields 22 g m-2 if all in poor condition, 45 g m-2 if all in fair condition, 68 g m-2 if all in good condition, and 90 g m-2 if all in excellent condition. Thus, the model would predict that production would never achieve excellent condition range site estimates, even if the best of years. Herbaceous ANPP on ungrazed simulations (Figure 60a) seemed to be comparable to levels estimated for excellent range condition (Appendix Figure 2a) on a subset of the higher production sites, at high elevations, or in the Sorenson Extension. The model generally estimated lower production amounts for less productive sites, especially on thin soils and at lower elevations.

The forage production values implicit in the BLM 1984 carrying capacity estimate were within the ranges simulated by the model (Appendix Table 1). Given the AUMs per acre used, forage productivity would average 431 kg ha-1, or 43 g m-2, over the landscape, with a range of 6- 111 g m-2 among different range sites and precipitation zones (Appendix). The corresponding assumed production would be 92 g m-2 if in excellent condition, which would not be attained in the model even under no grazing. The BLM 1992 carrying capacity estimate was apparently based upon lower forage production values, which averaged 25 g m-2 with a range of 6-107 g m-2 among range sites. In excellent condition, forage production would average 54 g m-2 over the landscape. Thus, the model agrees reasonably well with the actual forage productivity estimates used in the 1984 assessment, but not with the 1984 potential productivity estimates. The model agrees reasonably well with the potential productivity estimates of the 1992 assessment, but not with the 1992 actual productivity estimates.

Variations in Levels of Herbivory

Variation in herbivory accross landscape can be portrayed as a bar graph which shows fractions of the landscape receiving different levels of herbivory. This distribution can change over time as herbivore numbers, herbivore distribution, and plant growth change in response to climate and other factors. An often used rule of thumb in estimating proper use is a 50% offtake rate, or "take half, leave half". As the following results illustrate portions of the landscape experience >50% offtake even when horse numbers are diminished to relatively low levels.

During 1950-1969, with no culling of horses, 40-65% of the landscape experienced very light (<10% offtake) herbivory of grasses (Figure 76). However, 15-40% of the landscape experienced heavy (80-100%) herbivory, 10-30% received 10-50% herbivory, and 5-10% received 50-80% herbivory. Forb herbivory was less intense. Heavy forb herbivory occurred on 1-30% of the landscape, and light herbivory occurred on 60-85% of the landscape. When horse numbers were culled to 125-175, the amount of heavy grass herbivory dropped slightly while the amounts of light and moderate herbivory rose slightly. Herbivory on forbs dropped even more slightly than herbivory on grasses, as a result of reducing horse numbers.

44 During 1970-1996, 40-70% of the landscape received light herbivory with observed horse numbers (Figure 77). However, 5-20% received heavy herbivory and 5-15% experienced herbivory in the 50-80% range. The fraction of the landscape receiving heavy herbivory varied markedly from year to year. Herbivory on forbs was lighter, with only 1-10% of the landscape having either 50-80% or 80-100% offtake. With no horse culling, the fraction of grasses grazed >80% rose to 20-50% and the fraction of forbs grazed 10-50% rose to 20-50% (Figure 78).

Focus is now shifted to the primary portion of the horse range, where the model predicted there was a mean of >1 horse per km2 using observed horse numbers for 1970-1996. This area comprised 4875 ha, or about 25% of the simulated area inclusive of the Sorenson and the USFS area outside the PMWHR.

When horses were culled to 100-150 head, the fraction of the primary horse range where grass was grazed >80% varied between 10-30% (Figure 78, top row). If horses were culled to 150-200 the fraction increased slightly to 15-30% (Figure 78, bottom row). If horses were culled to 200-250 the fraction increased to 18-40% (Figure 79, top row), and if there was no culling, the fraction rose to 18-45% (Figure 79, bottom row). The fraction where grasses were grazed 50- 80% varied from 5-12% with 100-150 horses, to 7-12% with 150-200 horses, to 6-13% with 200- 250 horses, and 4-13% with no culling (Figures 78,79).

Forb herbivory on the primary horse range was slightly less intense than grass herbivory. Between 8-20% of the landscape was grazed >80% with 100-150 or 150-200 horses, 9-25% was heavily grazed with 200-250 horses, and 10-30% was heavily grazed with no culling (Figures 78- 79). The fraction grazed 50-80% varied between 2-8% with 100-150, 150-200, or 200-250 horses, and between 2-11% with no culling.

Modeling Experiments

Responses to Fixed Numbers of Horses and Bighorn Sheep

A simulation experiment was designed to investigate the effects of horse and bighorn sheep numbers. Horses or sheep numbers were maintained at a constant level throughout the period 1970-1996. In the first set of runs horses were set at seven levels between 50-350 head, with sheep numbers held at 300. In the second set of runs sheep were set at seven levels between 75-525, with horses held at 150. Weather was actual data from 1970-1996, and was the same in all runs. Horses were limited to areas inside the PMWHR, exclusive of the Sorenson Extension. Sheep were limited to the ranges observed by Irby et al. (1997). Plant responses reported are means for the primary portions of the horse range only (where there was >1 horse km2 with observed horse numbers1970-1996).

As horse numbers were increased, herbaceous aboveground net primary production (ANPP) on the primary horse range decreased by 10-13% for each additional 50 horses (Figure 80). There was approximately 75% as much herbaceous production with 200 horses as with 50

45 horses. With 350 horses, ANPP was about 60% of that with 50 horses. In contrast, as sheep were increased from 75 to 525, ANPP on the horse range decreased by less than 10%. A much smaller effect of sheep compared to horses was not unexpected, since sheep body weights are approximately 15% of horse body weights, and since the sheep range covers a small fraction of the primary horse range.

Decreases in herbaceous aboveground biomass were slightly more pronounced than decreases in ANPP (Figure 80). There was 60% as much biomass with 200 horses as there was with 50 horses. With 350 horses there was <50% as much biomass as with 50 horses. Increasing sheep from 75 to 525 resulted in a <10% decrease in biomass. Horses reduced herbaceous root biomass, and there was about 65% as much root biomass with 200 horses as with 50 horses, and 55% as much with 350 horses as with 50 horses (Figure 81). Sheep reduced roots by about 5% at 525 compared to 75 animals. The proportion of forbs increased with horse number (Figure 81). There was approximately 40% higher percentage of forbs in total biomass with 200 as compared to 50 horses. Increasing horses further had no additional effect on forb proportion. Forb proportion was unaffected by sheep number.

Mean horse condition index declined with increases in horse numbers, indicating increased competition for food (Figure 82). Indeed, forage intake rate declined with increasing horse number. Neither sheep condition index nor sheep forage intake rate were affected by horse numbers. Increasing numbers of sheep had very little impact on horse condition or horse forage intake, but it did have a substantial effect on sheep condition and sheep forage intake rate (Figure 82). This suggests that there was a considerable degree of intraspecific competition for food within the prescribed sheep range. The small effect of sheep on horses is logical, as sheep depleted forage within their prescribed range, horses had substantial options for foraging elsewhere. Furthermore, the sheep range was quite a small portion of the total horse range.

Responses to Horse Culling

In this experiment, horse numbers were controlled through culling to a specified range. The objective was to determine if populations managed through culling would have the same effects as populations held to exactly a fixed number. Horses were culled to 7 different levels between 50-225, when their numbers reached 50 more than the target number. Culling was allowed once per year. Horses were also culled to 0, to simulate a complete removal. Finally, horses were not culled at all. Sheep numbers were free to fluctuate according to their population model. Weather data from 1970-1996 was used in all runs.

Responses of herbaceous ANPP, biomass, and proportion of forbs were similar to those with fixed numbers of horses, however the most of responses tended to be less curvilinearly to horse number (Figure 83). Forb proportion was linearly related until 225 target horse number, then reached an asymptote. The differences between having 50-100 horses and 0 horse were similar in magnitude to the difference between having 50-100 and having 125-150 horses.

46 Horse condition declined with increasing horse number, steeply at first, then more gradually, then steeply again (Figure 84). There was no consistent effect of horse number on sheep condition.

The effects of different horse numbers on sheep population dynamics are shown in Figure 85. The sheep population increased in size to over 300 animals when horses were culled to 100- 150, then declined to 150-180. Very similar dynamics resulted when horses were culled to 150- 200, or 200-250. However, when horses were not culled, sheep did not increase to the 300 level and then crash, but rose to the 200 before then declining slightly to the 150-180 range.

Responses to Stochastic Variation in Weather, and Horse Culling

The experiments above were deterministic, in the sense that the same weather sequence was used in all runs. In this experiment, weather is varied randomly among runs at the same level of horse culling. The purpose is to demonstrate the range of variability that can be expected in possible outcomes. Random weather is generated based upon the montly data from 1970-96. Six runs are conducted at each culling level. The set of 6 random weather scenarios is the same in all culling treatments, to prevent confounding the effects of culling with the effects of random weather.

Horse numbers fluctuated within the range specified by culling targets, at the 100-150 and 150-200 levels (Figure 86). When culling to 200-250, however, there was a drop in population sizes below the 200 level, as a result of horse interactions with vegetation and climate. When horses were not culled at all, there was marked variation among horse population trajectories. In contrast to the horse population climbing to a 450 level as it did in the standard run (Figure 57), horses reached at most 350 in the stochastic runs. In some runs, lower maxima were reached. Populations declined to 200-230 in half of the runs, and in the other half declined to to 275-300. These results suggest that the weather sequence of 1970-1996 would have supported above average numbers of horses, if they had not been culled.

Sheep population trajectories were also affected by weather scenarios (Figure 87), particularly when there were no horses. With no horses, some of the sheep population trajectories rose to 350 or more before declining. In other runs with no horses, sheep rose to 200-250. Sheep populations declined in all runs, to as low as 100 animals, or as high as 300 animals. When horses were culled to 100-150, sheep populations rose to about 325 at most, and 225 minimally. The final median was about 175. With horse culling, there was a smaller difference in sheep population trajectories among runs than there was with no horses. With horse culling, there were no declines to low levels of 100 or less sheep. With horses culled to 200-250, sheep numbers rose to about 300 at most, and 175 at least. The final median level was about 175. When horses were not culled, sheep populations still rose to 300 maximally, and 225 minimally. The range of difference among runs was reduced compared to culling at lower levels. The final median was about 160.

47 Herbaceous ANPP declined at a similar rate with increased horse number as in fixed numbers and culling experiments (Figure 88). Interestingly, there was a wider range of variation in ANPP and biomass with no horses than with any level of horses. This suggests than horses have a homogenizing effect on plant growth, even at relatively low densities. Despite a wide range in horse numbers and weather among runs, the range of variability in ANPP and plant biomass was relatively small.

Horse condition index also declined with increasing horse number (Figure 89). There was considerable variation in the index among runs, induced by combined variations in weather and animal densities. Sheep condition index varied, but there was no systematic relationship to horse number.

Effect of Empirical vs. GIS-Modeled Sheep Range

The model was run for 47 years using weather data 1950-1996, with no horses, horses culled to 150-200, and not culled. Sheep populations were simulated with ranges restricted to either the empirical sheep ranges of Irby et al. (1997) or the GIS-modeled sheep ranges based on the methods of Smith et al. (1991), Johnson and Swift (1995). The objective was to compare sheep population dynamics in the two habitats.

The difference in sheep population dynamics when using empirical and GIS-modeled sheep ranges was large (Figure 90). While sheep populations climbed to 250-300 and fluctuated within a range of 125-300 on the empirical range, they dropped to <50, then rose to a maximum of 75-85 on the GIS-modeled sheep range. There was a consistent, but small, decline in sheep numbers with increased horse numbers when sheep were using the empirical range. The effect of horses on sheep was less consistent when using the GIS-modeled sheep range. There was no effect of sheep range on horse population dynamics.

Effect of Disallowing Horse Utilization of USFS Lands Outside the PMWHR

The land outside the PMWHR at the top of Burnt Timber and Sykes Ridge Horse ranges has been used by horses which trespass through a broken fence. The effects of disallowing this useage is examined here at varying horse densities. The model was run for the period 1970-1996, with and without access to USFS land, at 7 different horse densities. The effects of these treatments on plants was focused on the area inside the PMWHR boundary on the high elevation summer range.

Aboveground ANPP declined at very similar rates with increases in horse number, irrespective of USFS access (Figure 91). There was a slight reduction brought about by no access to USFS lands, especially at medium horse densities. For example, at a horse number of 200, ANPP on the summer range was about 94 g m-2 with USFS access, and 92 g m-2 without USFS access. The proportion of forbs was also similar with or without USFS access, but there was a

48 slight increase in forbs without USFS access. A similar response was simulated for root biomass (Figure 92). Horse condition was also slightly lower when USFS access was disallowed.

DISCUSSION AND CONCLUSIONS

The model performed in a realistic way to soil properties, climate, grazing pressure, and their interactions. Predictions of plant biomass dynamics were consitent with data over a wide range of soils and climatic conditions. Plant production and biomass generally declined under increasing levels of herbivory, as is usually expected. Horse distributions were generally consistent with available data, as they responded to topography and changes in forage density and snow cover. Herbivore forage intake rates and energy use rates were consistent with data. Animal condition varied in response to the balance between forage intake and energy expenditure, as would be expected. Simulated horse population dynamics, while not fitting yearly dynamics exactly, fit the observed population trajectory over the 27-year period. Herbivore populations reached food-limited, or ecological carrying capacities due to expected limitations of density- dependent competition. More fundamentally, the model was consistent with current knowledge of underlying ecosystem processes of water and nutrient budgets, plant and animal ecophysiology and population dynamics. Photosynthesis rates were consistent with available data, and this process resulted in realistic levels of primary production. Nitrogen limited plant growth, but was conserved and recycled only through nutrient cycling and decomposition. The water budget simulated infiltration, interception, transpiration, and other flows, yet the rates of water use by the plants were consistent with ecophysiologically based estimates of photosynthetic water use efficiency. Changes to these process brought about by soils, climate, and grazing were both direct responses and indirect consequences of interacting processes. Simulated results were emergent properties of system-level dynamics - an important characteristic of ecosystems.

Ecological changes were predicted to increase as horse numbers increase. The changes were continuous, rather than discontinuous, so the ecosystem will be changed to some extent by horses, even with low horse densities.

Decrease in total aboveground biomass. A decrease in aboveground biomass is to be expected in the presence of grazers, due to removal by herbivory. Model simulations and data at the site level (Figures 22-32) suggest a greater difference in standing dead than in live biomass inside vs. outside exclosures. This suggests that in the absence of grazing, dead biomass is more likely to accumulate. Biomass differences inside vs. outside exclosures were not entirely consistent, but there were more cases of reduced biomass outside exclosures than inside (Detling et al. 1996, Detling and Gerhardt 1996, Gerhart and Detling 1998). Notably, there were cases of greater biomass outside exclosures, indicating either stimulation of growth by grazing, or significant spatial variability at the scale of the exclosure.

49 A decrease in peak biomass does not necessarily equate to a decrease in net primary production, especially on frequently and intensively grazed sites. Although herbivores may remove biomass, this does not necessarily imply that plant production is diminished. The difference between biomass and production would be greatest where herbivores are removing biomass at a rapid rate up until the time of peak biomass.

Decrease in aboveground NPP. Aboveground net primary production (ANPP) decreased as horse numbers increased. There are no measurements of aboveground primary production in the PMWHR to support or refute this prediction. However, under conditions of relatively low herbivory during the growing season, ANPP responses should be similar to biomass responses. ANPP would decrease as a result of a reduction in leaf area, when there are no mechanisms for compensatory regrowth or they have been impaired. The conservation of soil moisture through reduction of leaf area, for example, would support compensatory regrowth (McNaughton 1979). Other processes could offset this effect, such as a loss of nutrients, or an increase in runoff, or the onset of temperature limitations on growth. It is difficult to predict the net outcome of these processes without a model. The model here predicted that the net outcome would be incomplete compensation under these growing conditions.

Increase in forb relative abundance. The model simulated an increase in the relative abundance of forbs compared to grasses as herbivory pressure increased. This result is still a hypothesis at this point, as there is little support for such a species shift in the data collected by Detling et al. (Detling 1995, Detling et al. 1995, Detling and Gerhardt 1996, Gerhardt and Detling 1998). There was a suggestion of an increase in forb relative cover outside exclosures at one long term site (Penns Cabin), and at 8 low elevation sites (Gerhardt and Detling 1998), but these were not statistically significant. Changes in forb composition would be expected based upon the relative abundances of grass and forbs in diets vs. the plant community. Diet compositions of horses and bighorn sheep are relatively low in forb percentages, with forbs being at most 15% of horse diets and 25% of sheep diets in any season (Kissell 1996). In contrast, forbs represent 20-70% of herbaceous production (Figure 64) and half or more of total peak biomass (Figures 22-32). The rapid transfer of dead forb biomass to litter could account for some of the difference between intake and production. However, avoidance of forbs compared to grasses is suggested by the data. If herbivores are avoiding forbs, then theory would suggest that they would have an increasing competitive advantage under elevated herbivory. This is a principle mechanism for changes in species composition which are indicative of retrogressive succession relative to an ungrazed climax. This mechanism is embodied in the model formulation and parameterization for this site. Although data do not lend much support for this modeled hypothesis, data sampling may lack sufficient power to detect such a change. The large difference in standing dead dynamics between grasses and forbs would impair the inability of any methods used to detect a difference in production. At present, however, these model results are mainly useful for indicating the potential for changes which are supported by theory.

Decrease in basal cover and root biomass. These are slower variables to change than either aboveground biomass or annual primary production. They reflect a sustained decrease in

50 above and belowground primary production rates which then fail to keep pace with perennial root turnover. This is a principle mechanism of rangeland degradation under heavy grazing. It expresses a loss of productive potential, and precipitation use efficiency, in contrast to the annual response expected per mm of precipitation in many models. Recovery of basal cover will likewise be a slow process, occurring under periods of elevated production relative to turnover. These responses are consistent with theory, provided that grazing does in fact diminish above and belowground primary production.

The model predicted that horses would grow in size to in excess of 300 head, in many scenarios, and even to >400 head, if they are not culled. This was a higher than expected number, given the current numbers of horses that have been maintained, or the number first found on the range (270). On retrospect, however, this could be realistic. There is no evidence of density dependent population regulation in the population data since 1970 (Singer et al. 1998). Neither foaling nor mortality are correlated to population size at least as long as the population has remained in the 86-200 range. Thus, ecological carrying capacity is likely well above 200. Secondly, the model here was fit to observed population dynamics and levels of intraspecific competition for forage. The rate of increase of the simulated population was as observed, under the observed levels of forage availability per animal. Thirdly, it can be inferred from model results and other data that these horses have a high capacity to persist in an environment with relatively low forage biomass. In particular, the Dryhead herd, which summers primarily at low elevations, is remarkable. The other two herds, which summer in more productive ranges at higher elevations may seem to have a disproportionate advantage with respect to forage, but there is considerable competition for forage on a relatively small area of suitable habitat. Wild equids can persist in unproductive environments (Berger 1986), suggesting that their "ecological carrying capacity" would be relatively high, and corresponding plant biomass would be relatively low. The model results here support that general assertion, but additional work needs to be done to solidify conclusions. Finally, the ecological carrying capacity of the PMWHR has likely been increased by water development and increased access to land,compared to the situation in 1970 or before when there were at least 270 horses. Water has been developed at the top of the mountain, prolonging potential use of summer ranges there. The northern Dryhead range and Sorenson extension have been made available for much of the period since 1970, and the provision of water there has facilitated their use.

The BLM has defined that a maximum appropriate management level (AML) would be "the number of adult animals that can be sustained before deterioration of the thriving natural ecological balance begins"(National Program Office, Reno). This terminology probably originates with the Wild Free-Roaming Horses and Burros Act (16 U.S.C. 133 et seq.) that directs the BLM to manage wild horses to achieve and maintain a thriving natural ecological balance on public lands. The problem with this definition is that a thriving ecological balance cannot be clearly defined. The ecosystem could thrive, but in a different configuration, when there are 200 horses as compared to no horses. "Thriving" is a matter of degrees, and qualities.

With respect to horse impacts on vegetation, the model suggests that with historical horse

51 numbers, some parts of the landscape will experience significant decreases in herbaceous biomass (Figure 63). With no horse culling the fraction of the landscape so affected rises markedly. There appears to be some consensus that the level of change that has occurred with past horse numbers is acceptable, particularly relative to perceived range condititions when the PMWHR was first established. There is a perception on the part of persons with long experience, that range condition has gradually improved (Singer et al. 1998). Thus, an acceptable level of change must lie somewhere between no culling and historic levels of horse numbers.

According to the enabling legislation for the Pryor Mountain Wild Horse Range, the PMWHR " shall be in all respects subordinate to the Bighorn Canyon National Recreation Area .... ". From the point of view of the managers of BCRNA, horses could be perceived to be a recently reintroduced species, while bighorn sheep were more likely to have been native to the area but extirpated sometime in the 1800's. A goal of horse management, therefore, would be to minimize impacts of an uncertainly native species on the reestablishment of a certainly native bighorn sheep population.

It is somewhat contrary to expectations that horses did not have more of an effect on sheep populations. Sheep probably limited their own densities more than did horses, because of their very restricted habitats. This was supported by model experiments which showed that as sheep numbers increased, their condition index decreased substantially, while the same response did not occur to increased horse numbers. Furthermore, sheep did reach a food-limited ecological ]carrying capacities, in many cases following the scenario of an eruption followed by a decline. This brings into question assumptions about limited range size that were used in the model. Standard model runs used the empirical sheep range extents observed by Irby et al. (1996). However, it is not known whether these ranges could expand as sheep densities increase on the existing range. The only basis we have for predicting potential range expansion at this point is based upon GIS habitat modeling.

The difference between sheep population dynamics on empirical compared to GIS- modeled ranges suggests that the GIS model may be too restrictive. Sheep populations did increase to >200, which was more that could be supported on GIS modeled habitats. Furthermore, there was not a good agreement between the sheep distributions observed by Irby et al. and the habitats predicted as suitable by the GIS model. The GIS model does not consider the effects of forage production in any fashion, but focuses instead on predator avoidance through escape terrain and visibility. Slope/aspect positions and distance from water are also considered. Predator avoidance is important for bighorn sheep, but so is forage supply. Clearly, the GIS- based approach should be modified to incorporate effects of forage availability. Limits imposed by needs for escape terrain may also be too restrictive, particularly where forage is more limiting. A trade-off the benefits of predator avoidance and forage intake could be involved

Underlyng simplifying assumptions are made in the AUM-based, or range-site methodology that weaken its credibility under circumstances such as those encountered here. The method is sound in attempting to calculate forage production in relationship to animal forage

52 requirements. It is laudable that this approach has been used in way that accounts for variations in forage production across the diverse and complex PMWHR landscape. The method fall short in several areas, however. First, the basis for estimating potential and actual forage production by range site depends upon the accuracy of estimates of production under excellent condition range, upon the the assessment of current deviation from excellent condition range, and upon the more fundamental assumption that excellent condition range should be expected while there are horses present. While there is some correspondence between such estimates (Appendix Tables 1,2), the model, and field data, the level of accuracy is low. The data base for estimating production in the long-term absence of grazing (ie. if in excellent condition) is weak, if not lacking altogether. There are only two long-term grazing exclosures on the PMWHR (Penn's Cabin and Forest Service), and in one biomass is as high inside as outside (Detling et al.. 1995, Gerhardt and Detling 1998, Table 5). In two exclosures that were 10-11 years old, there were no differences between inside and outside (Detling et al. 1995). Other problems include providing a basis for justifying a 50% proper use factor, predicting spatially heterogenous grazing intensity, accounting for temporal variations in forage production and use, and estimating spatial and dietary overlaps with other herbivore species.

A simple rule of thumb approach to calculating carrying capacity is difficult to apply in cases where parts of the landscape are grazed in excess of single threshold such as 50%, and other areas are grazed less than that threshold. Instead, a more complex criteria must be used, which specifies what percentage of the landscape, or what parts of the landscape, should be grazed no more than the threshold. Further complicating the problem, some parts of the landscape will be heavily grazed even at relatively low herbivore densities. The response to herbivore density could therfore be non-linear.

A minimum AML would be the "lowest number of animals allowed to graze, considering the genetic viability of the herd" (National Program Office, Reno). A goal of the BLM and other agencies is to preserve a self-sustaining population of wild horses that retains its unique genetic and phenotypic characteristics (Singer et al. 1997). Determining the appropriate population size to achieve these objectives is beyond the scope of the modeling reported here, and is covered elsewhere (Singer et al. 1997, 1998).

The question of whether horse populations in the past were food limited in this area can never be answered with complete certainty. However, we might speculate using the best knowledge available. If this band of horses established itself in the Pryors sometime in the late 1800's, they would have been subject to mustanging, as well as harassment and other control - measures by early livestock-based agriculturalists, and competition with domestic sheep and cattle. The Pryors were heavily used by sheep and cattle ranchers since at least 1909. Records indicate that 4,000-7,300 AUMS of sheep, cattle, and horses used grazing allotments in this area for 3 months per year during 1909-1928, and 700-1,700 AUMS used the area for a 2 month per year period 1929-1939, and 500,700 AUMS of sheep used the area for two months per year 1939-1962 (Reid, 1997). A second question concerns the possible rates of dispersal that could have occurred in earlier times when boundaries were less controlled, and land use in the

53 surrounding area was less intense. It is logical to surmise that some sort of dynamic balance existed where the rate of dispersal would be impeded to some extent by threats from humans in the surrounding lower lying areas. This would be counteracted by a tendency of horses to disperse in response to growing competition for forage in the Pryors. The strength of this dispersal tendency, and the strength of the counteracting force which impairs dispersal, are unknown quantities.

Although horses are a native genera to N. America over a geological time frame, it is uncertain if the Pryor horses are ecologically similar to pre-Pleistocene equids, or for that matter, if current vegetation is similar either. The degree of ecophysiological and morphological divergence within the Equus genus is high. Current ecosystems certainly lack the predator components of Miocene or Pleistocene fauna. Whether humans had any effect on controlling or eliminating Pleistocene fauna is open to speculation and debate (Martin 1967, Kay 1990). And, if horses were abundant at one time, what were their spatial distributions, especially at landscape and subregional scales?

Ultimately, as Wagner et al. (1995) suggest, the determination of acceptable levels of ungulates in National Parks and on public lands in general, involves an assessment of ecological changes relative to management goals for the lands in question. Sound scientific information is required to be able to predict what those changes will be at different ungulate densities. Once such information is provided, management decisions must be made. For public lands, public input on the pros and cons of ecological changes and different ungulate densities must be considered.

The results of these modeling excercises and the results from field studies must be evaluated by resource managers to determine what level of change is acceptable relative to land management guidelines. If the guidelines are vage (eg. "thriving ecological balance"), then they could be revised based upon the improved information base and informed discussions.

The task of developing an ecological basis for the management of wild horses in the Western United States is not yet accomplished. However, the databases and analyses that have been generated for PMWHR are good compared to those of many other wild horse ranges. In a highly critical report, the U.S. General Accounting Office found that "BLM's decisions on the how many horses to remove from federal rangelands have not been based on direct evidence that existing wild populations exceed what the range can support" (GAO 1990). The report found that wild horse removals are not linked to rangeland conditions, that there was little evidence that carrying capacity had been determined using scientific data, and that the BLM lacks adequate data to make informed wild horse decisions. Furthermore, the report alledges that the BLM's basis for wild horse removals is inappropriate, and that wild horse removals have not significantly improved range conditions in any event. The BLM's specific efforts in the PMWHR are not accurately portrayed in the GAO assessment, but the conclusion highlights a broader problem that could be addressed using ecological assessments. The BLM has made efforts to assess PMWHR carrying capacity based upon estimates of rangeland productivity, traditional

54 methods for assessing rangeland condition, and animal forage requirements. Although shortcomings of the traditional carrying capacity approach have been highlighted here, ways to improve upon it have been demonstrated here and in the other recent studies that have been conducted on rangeland production, and horse and bighorn sheep ecology and population dynamics. The opportunity to conduct an ecosystem modeling assessement of a wild horse range appears to be unprecedented. The Pryor Mountain horses have a remarkable historical, and ecological attributes which merit special attention. However, these recent studies provide examples for ways to better conduct appropriate management wild horses on rangelands throughout the western U.S, and to promote the conservation of wild equids world-wide.

55 ACKNOWLEDGMENTS

Mary Harrison of the BLM's Billings State Office provided much of the necessary GIS data necessary for modeling. L. Coates-Markel of the BLM's Billings District Office, provided valuable insights and information pertaining to horse ecology. J.K. Detling and T. Gerhardt and J. Peterson from CSU shared results prior to publication and were excellent sources of information for plant and plant-herbivore ecology. T. Peters, former Resource Manager of the Bighorn Canyon National Recreation Area, was a key facilitator. L. Padden of the BLM's Billings District Office was quite helpful in providing access to BLM range monitoring results. R. Mitchell, of the BLM Miles City Office was helpful in providing his soil survey, and a bit of explanatory time in the field. Dora Medellin of USGS provided excellent editorial assistance. Francis Singer of USGS and CSU facilitated the initiation of the project, and provided helpful support throughout.

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