United States Department of Agriculture

Forest Service Rocky Mountain Research Station Proceedings RMRS-P-77 February 2020

Symposium Proceedings on

Piñon-Juniper Habitats: Status

and Management for Wildlife–2016

October 12–14, 2016 

Albuquerque, New Mexico
Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. 

Symposium Proceedings on Piñon-Juniper Habitats: Status and Management for Wildlife–2016. 

Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 128 p.

Abstract

Piñon-juniper vegetation types, including juniper woodland and savannah, piñon-juniper, and piñon woodland, 

cover approximately 40 million ha in the western United States, where they provide ecosystem services, wildlife 

habitat, and cultural and aesthetic value (Romme et al. 2009). These ecosystems are also the sites of oil and 

gas activities, grazing, and urban development and are impacted by changing climate and wildfire. The realization that piñon-juniper ecosystems are being lost and degraded by human activities and changing climate 

(Cole et al. 2008, Williams et al. 2010, Clifford et al. 2011, McDowell et al. 2016) has stimulated interest in 

management of these habitats for wildlife. The goal of the 2016 symposium, Piñon-juniper Habitats: Status and 

Management for Wildlife, was to bring together information on the management of piñon-juniper ecosystems for 

the wildlife that depend on them. 

Keywords: adaptation, climate change, drought, fire, forage, invasive species, precipitation, restoration, 

thinning, vulnerability

Pesticide Precautionary Statement

This publication reports research involving pesticides. It does 

not contain recommendations for their use, nor does it imply 

that the uses discussed here have been registered. All uses 

of pesticides must be registered by appropriate State and/or 

Federal agencies before they can be recommended.

CAUTION: Pesticides can be injurious to humans, domestic 

animals, desirable plants, and fish or other wildlife if they are 

not handled or applied properly. Use all pesticides selectively 

and carefully. Follow recommended practices for the disposal 

of surplus pesticides and pesticide containers.

Papers published in these proceedings were submitted by authors in electronic media. Editing was 

done for readability and to ensure consistent format and style. Authors are responsible for content and 

accuracy of their individual papers and the quality of illustrative materials. Opinions expressed may not 

necessarily reflect the position of the U.S. Department of Agriculture.

The use of trade or firm names in this publication is for reader information and does not imply 

endorsement by the U.S. Department of Agriculture of any product or service.

All Rocky Mountain Research Station publications are published by U.S. Forest 

Service employees and are in the public domain and available at no cost. Even 

though U.S. Forest Service publications are not copyrighted, they are formatted 

according to U.S. Department of Agriculture standards and research findings and 

formatting cannot be altered in reprints. Altering content or formatting, including the 

cover and title page, is strictly prohibited.
Editors

Karl Malcolm, Wildlife Ecologist, USDA Forest Service, Southwestern Region, Albuquerque, New Mexico

Brian Dykstra, Wildlife Program Leader (retired), USDA Forest Service, Southwestern Region, Albuquerque, 

New Mexico

Kristine Johnson, Zoology Coordinator, Natural Heritage New Mexico, University of New Mexico, 

Albuquerque, New Mexico

David Lightfoot, Senior Collection Manager, Museum of Southwestern Biology, University of New Mexico, 

Albuquerque, New Mexico

Esteban Muldavin, Division Leader, Natural Heritage New Mexico, University of New Mexico, Albuquerque, 

New Mexico

Marikay Ramsey, Wildlife Biologist, USDOI Bureau of Land Management, Santa Fe, New Mexico

Acknowledgments

The idea for this symposium was suggested in 2015 by Leland Pierce of the New Mexico Department of 

Game and Fish (NMDGF), in a conversation with Peggy Darr, NMDGF ornithologist, and Kristine Johnson, 

zoology coordinator at Natural Heritage New Mexico, University of New Mexico (NHNM). The symposium 

was organized by the steering committee, Bryan Dykstra and Karl Malcolm (U.S. Forest Service), Casey 

Hendricks (NM State Land Office), Kristine Johnson (NHNM), David Kreuper and Kammie Kruse (U.S. Fish 

and Wildlife Service), and Marikay Ramsey and Zoe Davidson (NM BLM). Marikay Ramsey provided funding from NM BLM. Kristine Johnson organized the scientific program. Jacqueline Smith (NHNM) organized 

additional logistics, including communications, check-in, and the field trip. Rebecca Keeshen (NHNM) took 

care of accounting and payment. Brian Dykstra, David Kreuper, Karl Malcolm, Esther Nelson (U.S. Forest 

Service), and Marikay Ramsey led discussion groups. Craig Allen (U.S. Geological Survey), Esteban 

Muldavin (NHNM), and Bill Romme (Colorado State University) led the field trip. Corrie Reasner (NHNM) 

and Zoe Davidson managed the slide presentations. NHNM made the symposium poster and posted it on 

their web site.

Karl Malcolm served as technical editor-in-chief for this volume. Other technical editors were: Brian Dykstra, 

Kristine Johnson, David Lightfoot (Museum of Southwestern Biology, University of New Mexico), Esteban 

Muldavin, and Marikay Ramsey. Deborah Finch and Patricia Cohn (U.S. Forest Service) edited the volume.

We especially thank the speakers for presenting their work and manuscripts. The organizing committee 

apologizes if we have failed to acknowledge anyone who contributed to this effort. We appreciate all who attended and participated in discussion groups. 

Introduction

This volume includes both full papers based on the symposium talks and abstracts of talks, in the case of 

papers submitted or in press elsewhere. The symposium program included several talks not represented 

here; only talks by those authors who provided full manuscripts or abstracts are included in this volume. 

The first group of papers focuses on piñon-juniper vegetation. William Romme gave two talks, one addressing the different types of piñon-juniper vegetation and their historical disturbance regimes, a second 

on tailoring management to the variability in these ecosystems. For purposes of these proceedings, these 

two talks were combined into a single contribution. Robert Parmenter and co-authors talked about the 

environmental drivers of mast production in piñon-juniper-oak ecosystems in the Southwest. With their talk 

on root-associated microbes and implications for management, Lela Andrews and Catherine Gehring added 

an often unappreciated perspective on the underground ecology of piñon trees and its importance for management. Sam Flake and Peter Weisberg reported on the influence of stand structure and aridity on piñon 
ii

mortality and implications for climate resilience. Jack Triepke presented models of piñon-juniper vulnerability 

to climate change in the Southwest. Ellis Margolis discussed the role of low-severity fire in a piñon-juniper 

savanna. Together, these chapters provide valuable background information on piñon-juniper natural history, 

ecosystem dynamics, disturbance regimes, and current threats.

The second group of talks focused on piñon-juniper habitat use and wildlife management. Three chapters 

address habitat needs and management for individual piñon-juniper wildlife species. Kristine Johnson and 

co-authors presented models of Pinyon Jay habitat use at home range, nesting colony, and nest scales 

at three study sites in New Mexico and discussed management implications for this declining bird. Lynn 

Wickersham and co-authors presented multi-scale models of Gray Vireo habitat use at four study sites in 

New Mexico and presented management recommendations. Lou Bender discussed habitat needs of deer 

and elk and assessed management practices in piñon-juniper woodlands.

The final group of talks discussed management actions and their effects on wildlife. Ryan Luna and coauthors reported on changes in habitat use of Montezuma Quail in response to tree canopy reduction in the 

Capitan Mountains, New Mexico. Pat Magee discussed the responses of avian communities to piñon-juniper 

woodland thinning in the Arkansas River Valley, Colorado. In a related talk from the same study, Jonathan 

Coop presented results of an assessment of the impacts of fuels treatments on woodland vegetation, fuels, 

birds, and modeled fire behavior. He suggested ways that fuels treatments could be optimized to maintain 

valued ecosystem components and still reduce fire hazards. David Lightfoot and co-authors discussed a 

study of the effects of thinning treatments on rodents, birds, and large mammals in piñon-juniper and ponderosa pine habitats in the Manzano Mountains, New Mexico. Alex Laurence-Traynor and co-authors presented 

the Bureau of Land Management’s Assessment, Inventory, and Monitoring strategy (AIM, used in the Taos, 

NM BLM Field Office) as a method of monitoring treatment effects on vegetation.

These talks and the associated papers and abstracts in this volume provide a picture of piñon-juniper woodland condition and threats, wildlife use, and potential effects of woodland management practices on wildlife. 

Presenters emphasized the need for ongoing monitoring of wildlife populations and continued research on 

the effects of woodland treatments on wildlife habitats. The organizers hope that this volume will stimulate 

land and wildlife managers to carefully consider the habitat needs of piñon-juniper wildlife when planning 

thinning, burning, and other piñon-juniper management and to incorporate research and monitoring as a 

regular component of all management actions.

References

Clifford, M.J.; Cobb, N.S; Buenemann, M. 2011. Long-term tree cover dynamics in a pinyon-juniper 

woodland: Climate-change-type drought resets successional clock. Ecosystems. 14: 949–962.

Cole, K.L.; Ironside, K.E.; Arundel, S.T.; Duffy, P.; Shaw, J. 2008. Modeling future plant distributions on the 

Colorado Plateau: An example using Pinus edulis. In: The Colorado Plateau III; integrating research and 

resources management for effective conservation. Van Riper III, C., Sogge, M. (eds.). Tucson, AZ: University 

of Arizona Press. 319-330.

McDowell, N.G.; Williams, A.P.; Xu, C.; [et al.]. 2016. Multi-scale predictions of massive conifer mortality due 

to chronic temperature rise. Nature Climate Change. 6: 295–300.

Romme, W.H., Allen, C.D., Bailey, J.D.; [et al.]. 2009. Historical and modern disturbance regimes, stand 

structures, and landscape dynamics in piñon–juniper vegetation of the western United States. Rangeland 

Ecology and Management. 62: 203–222.

Williams, A.P., C.D. Allen, C.I. Millar, [et al.]. 2010. Forest responses to increasing aridity and warmth in 

the southwestern United States. Proceedings of the National Academy of Sciences of the United States of 

America. 107:21289-21294.
iii

A Focus on Piñon-Juniper Vegetation...............................................................................................................1

Piñon-Juniper Is Not Just One Vegetation Type: Key Variation in Composition, Structure, And 

Disturbance Dynamics, With Implications for Wildlife and Ecosystem Management ...........................1

William H. Romme

Environmental Drivers of Tree Mast Production in Piñon-Juniper-Oak Woodlands of Central

New Mexico ..........................................................................................................................................5

Robert R. Parmenter, Roman I. Zlotin, Orrin B. Myers, and Douglas I. Moore

Root-associated Microbes in Pinyon Pine: Implications for Management ...............................................6

Lela V. Andrews and Catherine Gehring

Stand Structure and Drought Alter Tree Mortality Risk in Nevada’s Pinyon-Juniper Woodlands ...........20

Samuel W. Flake and Peter J. Weisberg

Vulnerability of Piñon-Juniper Habitats to Climate Change in the Southwestern USA...........................23

F. Jack Triepke

Fire Exclusion Linked to Increased Forest Density in a New Mexico Piñon-Juniper

Savanna Landscape............................................................................................................................26

Ellis Q. Margolis

Piñon-Juniper Habitat Use by Wildlife ............................................................................................................28

Pinyon Jay Habitat Use and Management Recommendations in New Mexico Piñon-Juniper

Woodlands...........................................................................................................................................28

Kristine Johnson, Jacqueline Smith, Giancarlo Sadoti, and Teri Neville

Habitat Use at Multiple Scales by Nesting Gray Vireos in New Mexico .................................................31

Lynn E. Wickersham, Kristine Johnson, Giancarlo Sadoti, Teri Neville,

and John Wickersham

Elk, Deer, and Pinyon-Juniper in New Mexico: Needs, What Works, and What Doesn’t.......................34

Louis C. Bender

Management Actions and Wildlife Responses ...............................................................................................59

Changes in Habitat Use of Montezuma Quail in Response to Pinyon-Juniper Canopy

Reduction in the Capitan Mountains of New Mexico ...........................................................................59

Ryan S. Luna, Elizabeth A. Oaster, Karlee D. Cork, and Ryan O’Shaughnessy

Effects of Piñon-Juniper Woodland Thinning on Avian Communities in the Arkansas

River Valley, Colorado .........................................................................................................................68

Patrick A. Magee and Jonathan D. Coop

Contents
iv

Identifying and Mitigating Social-Ecological Tradeoffs: Vegetation, Birds, Fuels, and Modeled

Fire Behavior in Piñon-Juniper Fuel Reduction Treatments ................................................................72

Jonathan D. Coop and Patrick A. Magee

Wildlife Responses to Small-Scale Tree Thinning in Central New Mexico Pinyon-Juniper and 

Ponderosa Pine Woodlands ................................................................................................................74

David Lightfoot, Anne Russell, Victoria Amato, Cody Stropki, Conor Flynn, Ian Dolly,

and Joanna Franks

Using the Assessment, Inventory, and Monitoring Strategy to Measure Treatment Effectiveness

in Pinyon-Juniper Woodlands............................................................................................................119

Alexander C. Laurence-Traynor, Jason Karl, Zoe M. Davidson, and Jessica C. Davis

Field Trip to Sandia and Manzanita Mountains ............................................................................................124

Conference Agenda ........................................................................................................................................127
USDA Forest Service RMRS-P-77. 2020. 1

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Piñon-Juniper Is Not Just One Vegetation Type: Key Variation 

in Composition, Structure, and Disturbance Dynamics, With 

Implications for Wildlife and Ecosystem Management 

A FOCUS ON PIÑON-JUNIPER VEGETATION

KEYWORDS—piñon-juniper savannas, persistent piñon-juniper woodlands, wooded shrublands, wildlife, 

fire, restoration, ecosystem management

Piñon-juniper vegetation covers 100 million acres 

across the western United States, in areas of diverse 

topography, climate, and soils. Consequently, piñonjuniper vegetation varies greatly in structure and 

disturbance processes at both the stand and landscape 

scales. Until recently, however, this variation was 

not widely recognized, and piñon-juniper vegetation 

was interpreted and managed in much the same way 

throughout its range (Jacobs 2008a). Early studies 

were conducted primarily in the Great Basin and 

Mexican borderlands, where juniper expansion into 

grasslands and shrublands was a major ecological 

process and management concern. Results of these 

studies were then applied uncritically to piñon-juniper 

vegetation elsewhere, notably on the Colorado Plateau, 

where more recent research has revealed very different 

stand and landscape dynamics. 

This later research resulted in a workshop in August, 

2006, that brought together 15 scientists with piñonjuniper expertise from across the range of piñon and 

juniper in the western USA. These researchers summarized available data and understanding of these varied 

ecosystems and published their synthesis in three 

venues: an academically oriented, invited review paper 

in the journal Rangeland Ecology and Management

(Romme et al. 2009), and two shorter articles geared 

toward managers and policy-makers (Romme et al. 

2007, 2008). Key findings from these three articles and 

recent related literature are summarized below.

Variation in Tree Species Composition

Two piñon species and five juniper species are common components of piñon-juniper woodlands across 

the western USA (names from USDA Plants database, 

https://plants.usda.gov/java/). Local composition varies with geographic location, elevation, and synoptic 

climate (Jacobs 2008a). Utah juniper (Juniperus osteosperma) and two-needle pinyon, also called Colorado 

piñon (Pinus edulis), dominate woodlands in regions 

where precipitation is winter-dominated or bimodal, 

e.g., on the northern Colorado Plateau. Oneseed juniper (J. monosperma), alligator juniper (J. deppeana), 

and two-needle piñon dominate summer monsoonal 

regions, e.g., in central and southern Arizona and New 

Mexico. Rocky Mountain juniper (J. scopulorum) 

is common at higher elevations throughout most of 

the Rocky Mountain cordillera. Singleleaf pinyon 

(P. monophylla) and western juniper (J. occidentalis) 

William H. Romme

Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado
2 USDA Forest Service RMRS-P-77. 2020.

dominate the summer-dry country of the Great Basin 

and eastern Sierra Nevada. Two additional piñons and 

four additional junipers are found along the southern 

and eastern boundaries of the American Southwest 

(Jacobs 2008a). 

Variation in Stand Structure and Dynamics

The scientists at the 2006 workshop identified three 

fundamentally different kinds of piñon-juniper vegetation. This classification is based not on species composition, but on functional characteristics including 

typical stand structures, historical disturbance regimes, 

and temporal dynamics. 

Piñon-juniper savannas are composed of trees within 

a grassland matrix, and are found in areas of summerdominated precipitation, e.g., in southern and eastern 

Arizona and New Mexico, and southeastern Colorado. 

Tree densities probably were limited historically by 

periodic fires, but in the last century junipers have expanded extensively into former grasslands, especially 

on concave or depositional landforms, converting 

those grasslands to young savannas and woodlands in 

many places (e.g., Fuchs 2002, Jacobs 2008b, Poulos 

et al. 2009, Parker 2009, Margolis 2014). 

Wooded shrublands are composed of trees within a 

shrub matrix, e.g., sagebrush (Artemisia spp.), Gambel 

or wavyleaf oak (Quercus gambelii or Q. undulata), 

mountain-mahogany (Cercocarpus montanus), and 

Utah serviceberry (Amelanchier utahensis). Tree density naturally waxes and wanes in response to decadalscale variations in climate, fire, and insect activity. 

These woodlands are probably most prevalent in areas 

of winter-dominated or bimodal precipitation, e.g., 

in eastern Utah, western Colorado, and northwestern 

New Mexico. 

Persistent piñon-juniper woodlands are found throughout the distribution of piñon-juniper woodlands in 

places that burn very infrequently, such as dry rocky 

upland sites, but especially on the northern Colorado 

Plateau where precipitation is winter-dominated or 

bimodal. Notably, these are not young woodlands created by recently expanding junipers and piñons, but 

are often ancient stands with centuries-old trees, snags, 

and fallen boles, and little or no evidence of fire. The 

paucity of fire in persistent piñon-juniper woodlands 

apparently is due to a generally discontinuous fuel 

structure (Romme et al. 2009). Heavy grazing has 

reduced fine fuels in many piñon-juniper woodlands, 

but fuels tend to be discontinuous and fires infrequent 

in persistent woodlands even where grazing has been 

absent or minimal for nearly a century, e.g., Mesa 

Verde National Park (Floyd 2003). 

This classification, recognizing three broad kinds of 

piñon-juniper woodlands, is obviously a huge simplification of an extensive and varied vegetation type. 

Local floristic composition can vary greatly within 

each of these three functional piñon-juniper types. For 

example, Romme and Jacobs (2015; table 4) identified 

three floristic plant associations within the persistent 

piñon-juniper type and one within the piñon-juniper 

savanna type at El Malpais National Monument, New 

Mexico. Nevertheless, a functional classification of 

this kind highlights some of the important differences 

in composition, structure, and ecological dynamics to 

be found within piñon-juniper woodlands across the 

West and provides a first step in characterizing any 

specific woodland stand or landscape.

Some Applications to Wildlife and Ecosystem 

Management

Management opportunities and constraints are greatly 

influenced by the kind of piñon-juniper (PJ) ecosystem 

being considered. For example, the greatest departure 

from historical conditions is likely to be seen in PJ savannas, especially where oneseed juniper has converted 

former grasslands into new savannas and woodlands 

during the past century. Persistent PJ woodlands, on the 

other hand, generally have changed the least from historical conditions. Persistent PJ woodlands are also most 

likely to support a unique suite of wildlife species that 

are closely linked with and dependent upon this particular kind of habitat, e.g., Pinyon Jay (Gymnorhinus cyanocephalus) and Juniper Titmouse (Parus inornatus). 

Thinning or burning these woodlands is likely to reduce 

habitat quality for these PJ specialists. 

Reducing tree density to improve forage production for 

livestock, or habitat for grassland-associated wildlife 

species, will often be most successful in PJ savannas, 

where soils and climate are inherently favorable for 

grasses, and least successful in persistent PJ woodlands 

where rocky soils favor trees and shrubs over grasses. 
USDA Forest Service RMRS-P-77. 2020. 3

Prescribed fire can be applied effectively in many PJ 

savannas for a number of objectives, but prescribed fires 

in persistent PJ woodlands notoriously fail to spread 

through surface fuels, or spread uncontrollably into the 

canopy. In wooded shrublands dominated by shrub species that resprout after fire, e.g., Gambel oak (Quercus 

gambelii) or mountain-mahogany (Cercocarpus montanus), prescribed fire can be an effective way to improve 

forage for deer (Odocoileus hemionus) and elk (Cervus 

elaphus). However, if the dominant shrubs tend to be 

killed by fire without resprouting, e.g., big sagebrush 

(Artemisia tridentata), then prescribed burning may 

produce little improvement in forage but promote an unwanted increase in nonnative plants such as cheatgrass 

(Bromus tectorum). 

Piñon-juniper vegetation is managed for a wide range 

of objectives, and in some cases the same kind of treatment can achieve several different objectives. But this 

is not always true. For example, the term restoration is 

sometimes used loosely to bundle multiple objectives, 

but restoration has a precise definition as “... an intentional activity that initiates or accelerates the recovery 

of an ecosystem with respect to its health, integrity 

and sustainability ... attempts to return an ecosystem 

to its historic trajectory,” (Society for Ecological 

Restoration, http://www.ser.org/). Genuine restoration 

often does not achieve some of the other objectives 

we have for PJ vegetation, and accomplishing some 

of those other objectives may actually move an ecosystem farther from its historical condition.

Floyd and Romme (2012) explore some of the 

conceptual issues in restoration of PJ ecosystems. 

Restoration may focus on structural parameters, e.g., 

canopy density and understory composition, or on 

functional aspects, e.g., fire frequency and effects. A 

structural restoration can return a site to its condition 

at a particular time in the past, but that may be only 

one point within the natural range of variability on that 

site, and it may not persist without repeated treatment. 

Therefore, a functional or process-oriented restoration 

may be more sustainable, by allowing natural dynamics to continually reshape the ecosystem within historical bounds. Restoration may be passive or active. 

Passive restoration merely removes any chronic stressors from an ecosystem that is generally in good condition, e.g., minimizing impacts of grazing or recreation, 

without directly altering structure or process by actions 

like thinning or planting. Active restoration directly alters the ecosystem to restore parameters that have been 

lost and cannot be recovered without intervention. 

The specific type of restoration needed depends on 

the current condition and history of a PJ ecosystem. 

Floyd and Romme (2012) suggest some very general 

guidelines and review specific examples of restoration 

experience in different kinds of PJ. If a PJ ecosystem 

remains within or very close to its historical range of 

variability (Weins et al. 2012), then passive restoration 

may be all that is needed to maintain that condition; 

active treatments like canopy thinning could actually 

degrade stands by disturbing the soil and encouraging 

non-native plant establishment. An example requiring 

only passive restoration is the old-growth PJ woodlands on Mesa Verde (Floyd 2003). In other cases the 

overstory may be in good shape—within the historical 

range of tree density, composition, and cover—but the 

understory has been degraded by excessive grazing or 

recreational impacts. 

Piñon-juniper woodlands on the Kaiparowitts Plateau 

fit this description: native graminoids and herbs were 

greatly reduced by more than a century of summer 

grazing, and nonnative cheatgrass has proliferated in 

place of the natives, even though the overstory remains 

intact (Floyd et al. 2008). Restoration of this ecosystem requires both passive and active approaches: 

passive to reduce grazing intensity and active to 

reduce the cheatgrass and possibly to also plant seed 

of species native to the site, but the overstory needs no 

treatment. 

In other places both the overstory and understory have 

been degraded by past land use, and active treatment 

is needed in both components of the ecosystem. Such 

a stand in Bandelier National Monument was restored 

by mechanically thinning the overly dense overstory 

and scattering the slash over the bare ground between 

retained tree canopies. Within 3-5 years, understory 

plant cover increased several-fold and run-off and 

sediment production were reduced by an order of magnitude (Jacobs 2015). The treated stand also was more 

resilient in the face of a severe drought, a wildfire, and 

a bark beetle outbreak that occurred a few years after 

the treatment.
4 USDA Forest Service RMRS-P-77. 2020.

A final class of PJ ecosystems that may be in special 

need of restoration is found in areas that have recently 

burned in high-severity wildfires. A large portion 

of the old-growth woodland on Mesa Verde burned 

between 1989 and 2008 (Floyd et al. 2004). Severe 

fires in this kind of ecosystem are natural, although 

infrequent, but postfire vegetation dynamics have been 

anything but natural. Nonnative annual and biennial 

plant species such as cheatgrass, muskthistle (Carduus 

nutans), and Canada thistle (Cirsium arvense) have 

expanded rapidly into the burned areas and are the 

dominant species in many places. Notably, the stands 

having understories of resprouting shrubs, e.g., 

Gambel oak or Utah serviceberry, have been more 

resistant to invasion by nonnative plants than have the 

stands with few sprouting shrubs. This may be because 

the sprouting shrubs compete strongly with the nonnatives. Active restoration by means of planting native 

grass seeds in the burned areas has substantially reduced the abundance of the nonnatives, although it has 

not eliminated them altogether (Floyd et al. 2006). 

REFERENCES

Floyd, M.L. (ed.). 2003. Ancient piñon-juniper woodlands: 

A natural history of Mesa Verde country. Boulder, CO: 

University Press of Colorado. 389 p.

Floyd, M.L.; Romme. W.H. 2012. Ecological restoration 

priorities and opportunities in piñon-juniper woodlands. 

Ecological Restoration. 30(1):37-49.

Floyd, M.L.; Hanna, D.D.; Romme, W.H. 2004. Historical 

and recent fire regimes in piñon-juniper woodlands 

on Mesa Verde, Colorado, USA. Forest Ecology and 

Management. 198:269-289.

Floyd, M.L., Hanna, D.; Romme, W.H.; Crews, T. 2006. 

Predicting and mitigating weed invasions to restore 

natural post-fire succession in Mesa Verde National Park, 

Colorado, USA. International Journal of Wildland. Fire 

15:247-259

Floyd, M.L., W.H. Romme, D.D. Hanna, M. Winterowd, D. 

Hanna, and J. Spence. 2008. Fire history of piñon-juniper 

woodlands on Navajo Point, Glen Canyon National 

Recreation Area. Natural Areas Journal 28(1):26-36.

Fuchs, E.H. 2002. Historic increases in woody vegetation 

in Lincoln County, New Mexico. VanGuard Printing 

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and Natural Range of Variability. In: Gottfried, Gerald 

J.; Shaw, John D.; Ford, Paulette L., compilers. 2008. 

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and ponderosa pine ecosystems: combined proceedings 

of the 2005 St. George, Utah and 2006 Albuquerque, 

New Mexico workshops. Proceedings RMRS-P-51. Fort 

Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station.

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(piñon–juniper) woodland sites improves ecohydrologic 

condition and primes understory resilience to subsequent 

disturbance. Ecohydrology: published online in Wiley 

Online Library (wileyonlinelibrary.com) DOI: 10.1002/

eco.1591.

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Mapping “old” vs. “young” piñon-juniper stands with a 

predictive topo-climatic model. Ecological Applications 

18:1627-1641.

Margolis, E.Q. 2014. Fire regime shift linked to increased 

forest density in a piñon-juniper savanna landscape. 

International Journal of Wildland Fire 23:234-245.

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from 1936-1997 around Wupatki using repeat aerial 

photography. Thesis, Northern Arizona University.

Poulos, H.M., R.G. Gatewood, and A.E. Camp. 2009. 

Fire regimes of the piñon-juniper woodlands of Big 

Bend National Park and the Davis Mountains, west 

Texas, USA. Canadian Journal of Forest Research 

39:1236-1246.

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P. Brown, K. Eisenhart, L. Floyd-Hanna, D. Huffman, 

B. Jacobs, R. Miller, E. Muldavin, T. Swetnam, R. 

Tausch, and P. Weisberg. 2007. Historical and modern 

disturbance regimes of piñon-juniper vegetation in the 

western U.S. Published by The Nature Conservancy.

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P. Brown, K. Eisenhart, L. Floyd-Hanna, D. Huffman, 

B. Jacobs, R. Miller, E. Muldavin, T. Swetnam, R. 

Tausch, and P. Weisberg. 2008. Historical and modern 

disturbance regimes of piñon-juniper vegetation in the 

western U.S. Published by Colorado Forest Restoration 

Institute, Colorado State University (www.cfri.colostate.

edu).

Romme, W, C. Allen, J. Bailey, W. Baker, B. Bestelmeyer, 

P. Brown, K. Eisenhart, L. Floyd-Hanna, D. Huffman, 

B. Jacobs, R. Miller, E. Muldavin, T. Swetnam, R. 

Tausch, and P. Weisberg. 2009. Historical and modern 

disturbance regimes of piñon-juniper vegetation in 

the western United States. Rangeland Ecology and 

Management 62:203-222.

Romme, W. H., and B. Jacobs. 2015. Assessment of the 

Ecological Condition of Piñon-Juniper and Ponderosa 

Pine Vegetation at El Malpais and El Morro National 

Monuments, New Mexico. Unpublished report, Natural 

Resource Ecology Laboratory, Colorado State University, 

Fort Collins, CO 80523. (available upon request to W.H. 

Romme).

Weins, J.A., G.D. Hayward, H.D. Safford, and C.M. Giffen 

(editors). 2012. Historical environmental variation in 

conservation and natural resource management. WileyBlackwell, John Wiley and Sons, West Sussex, UK.
USDA Forest Service RMRS-P-77. 2020. 5

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Robert R. Parmenter

Valles Caldera National Preserve, National Park Service, Jemez Springs, New Mexico and Department of Biology,

University of New Mexico, Albuquerque, New Mexico

Roman I. Zlotin

Department of Geography, Indiana University, Bloomington, Indiana

Orrin B. Myers

School of Internal Medicine, Division of Epidemiology, University of New Mexico, Albuquerque, New Mexico

Douglas I. Moore

Department of Biology, University of New Mexico, Albuquerque, New Mexico

Environmental Drivers of Tree Mast Production in Piñon-Juniper-Oak 

Woodlands of Central New Mexico

ABSTRACT—The mast dynamics of two-needle piñon pine (Pinaceae: Pinus edulis), oneseed juniper 

(Cupressaceae: Juniperus monosperma), and Sonoran scrub oak (Fagaceae: Quercus turbinella), were 

analyzed in relation to weather variables (precipitation, air/soil temperatures, relative humidity, vapor pressure deficit, wind) over different time frames (current year, 1- and 2-yr lags) at 6 sites over 15-19 years 

(1997–2015) within the Sevilleta National Wildlife Refuge, New Mexico, USA. Juniper mast production 

was positively correlated to current year winter-spring precipitation, combined with a negative correlation 

with current-year summer temperatures. Oak mast production was positively correlated only with current 

year winter/spring precipitation and was not affected by subsequent summer temperatures. Piñon pine 

mast production was positively correlated with 1-year-lagged total annual precipitation, and negatively correlated with both summer temperatures and 2-year lagged autumn temperatures. Increased precipitation 

led to a greater proportion of trees producing mast, as well as greater mast production per tree. A series 

of hypotheses concerning mast dynamics—resource matching, resource switching, cycling and deltatemperature—were evaluated. The observed relationships between weather variables and mast production 

indicate that future climate warming will likely reduce mast production in extant Southwestern woodland 

ecosystems and will contribute to geographic shifts in future woodland distributions.

KEYWORDS—climate change, mast, precipitation, temperature, weather
6 USDA Forest Service RMRS-P-77. 2020.

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Root-associated Microbes in Pinyon Pine: Implications for 

Management

INTRODUCTION

Plants and fungi frequently form intimate associations 

with one another that can be mutually beneficial, neutral, or parasitic. Among the most widespread of these 

associations are the mycorrhizas, mutualistic associations between soil fungi and the fine roots of plants 

in which plants exchange carbon fixed through photosynthesis for soil resources (Smith and Read 2008). 

In addition to promoting plant survival and growth, 

mycorrhizal fungi can influence larger scale processes 

such as plant species richness (Teste et al. 2017; van 

der Heijden et al. 1998), the abundance and species 

composition of soil organisms (Smith and Read 2008), 

nutrient cycling (Phillips et al. 2013), and soil stability 

ABSTRACT—Colorado pinyon pine (Pinus edulis) forms mutualistic associations with root-associated 

ectomycorrhizal (EM) fungi which confer improved fitness under stressful conditions and thus may be 

critical to the success of this species in response to global climate change. We review studies of the EM 

fungal associations of P. edulis with an emphasis on the literature focused on drought and associated 

P. edulis mortality. We also describe the importance of other soil microbes including nonmycorrhizal fungi 

and bacteria, and present data describing dominant soil microbial taxa associated with P. edulis. Finally, 

we discuss the relevance of the patterns observed in these studies to the management of pinyon-juniper 

(Pinus spp.-Juniperus spp.) ecosystems. We conclude that P. edulis reestablishment or restoration following drought-induced mortality may be limited in some areas because of reduced availability of EM fungal 

inoculum. Genetically based differences in P. edulis drought tolerance are also associated with differences 

in EM fungal species composition. Given that the diversity of mycorrhizal fungi can be directly tied to host 

plant performance, loss of EM fungal diversity after differential P. edulis mortality may negatively affect 

recruitment in natural populations or in restoration efforts. Yet drought-tolerant P. edulis genotypes and 

their associated EM fungi may be critical to survival of both host and fungus during ongoing drought.

KEYWORDS—climate change, drought, ectomycorrhizal fungi, Pinus edulis, pinyon pine, rhizosphere

(Wilson et al. 2009). There are several types of mycorrhizal associations (Johnson and Gehring 2007). The 

most widespread are the arbuscular mycorrhizas (AM), 

in which the fungi involved are limited in diversity 

(~300 species in a single phylum), and the plants are 

highly diverse (~85 percent of land plants) and widespread (Wang and Qiu 2006). Arbuscular mycorrhizas 

generally produce hyphae without septa. They also 

produce characteristic exchange structures (arbuscules) 

and storage structures (vesicles) that occupy the cortex 

of host roots and must be stained to be observed. 

Ectomycorrhizal (EM) fungi form a diagnostic morphological structure within roots, the Hartig net, but 

Lela V. Andrews

Northern Arizona University, Department of Biological Sciences, Environmental Genetics and Genomics Laboratory, 

Flagstaff, Arizona

Catherine Gehring

Northern Arizona University, Department of Biological Sciences, Merriam-Powell Center for Environmental Research, 

Flagstaff, Arizona
7

also envelope root tips within a sheath or mantle of 

fungal hyphae. Ectomycorrhizas are also widespread 

because they dominate on plants that occur in many 

temperate and boreal forest ecosystems. In this case, 

fewer plant taxa are involved with thousands of species of fungi from two major phyla, the Basidiomycota 

and the Ascomycota (Tedersoo et al. 2008, 2011). 

Prominent EM host trees include pine (Pinus spp.) and 

spruce (Picea spp.) (Wang and Qiu 2006), which have 

broad distributions across the forest ecosystems of the 

northern hemisphere and are functionally obligately 

dependent on EM fungal associations (Molina et al. 

1992). Importantly, members of only a few plant families are able to form associations with both AM and 

EM fungi; this specificity can reduce mycorrhiza formation on plants in some settings (Chen et al. 2000).

Numerous studies demonstrate the importance of 

mycorrhizal fungi to their host plants, but there also 

is significant evidence of functional variation among 

fungal species. For example, the species composition of EM fungi associated with plants varies with 

environmental variables such as precipitation (Karst 

et al. 2014), soil nitrogen (N) availability (Lilleskov 

et al. 2011), and disturbance such as fire (Hewitt et 

al. 2016; Kipfer et al. 2011). Similarly, species of EM 

fungi vary in their ability to access different forms of 

N (Smith and Read 2008) and to promote drought tolerance in their host plants (Peay et al. 2007). The AM 

fungi also vary in distribution and function (Kivlin et 

al. 2011); recent studies suggest that many AM fungal 

species are distributed across six continents (Davison 

et al. 2015). However, for both AM and EM fungi, it 

is important to understand not only whether inoculum 

is available at a site, but also whether that inoculum 

consists of suitable fungal species for a given environment. Associated fungal species composition can 

vary among both species (Molina et al. 1992) and 

genotypes of plant hosts (Lamit et al. 2016), and some 

fungal taxa demonstrate considerable host specificity 

(Molina et al. 1992), such as between members of 

the EM fungal family Suillaceae and members of the 

Pinaceae (Bruns et al. 2002). 

Although many forested ecosystems contain plant 

species that form either AM or EM associations, 

pinyon-juniper (Pinus spp.-Juniperus spp.) woodlands 

are unusual in that the codominant trees that characterize the ecosystem form different types of mycorrhizal 

associations, AM for juniper and EM for pinyon. In 

addition, the grasses and shrubs that form most of the 

remainder of the vegetation in pinyon-juniper woodlands form AM associations (Haskins and Gehring 

2005). This distribution means that pinyons are often 

the only host for EM fungi across a substantial portion of their geographic range (Gehring et al. 2016). 

Colorado pinyon pine (Pinus edulis) has experienced 

significant mortality across the southwestern United 

States, with models projecting further contraction 

of its range (Cole et al. 2008; Rehfeldt et al. 2006). 

In this paper, we discuss what is known about the 

mycorrhizal associations of P. edulis, with particular 

emphasis on applications for management given its 

sensitivity to drought and its recent drought-induced 

mortality across much of the Southwest (Breshears et 

al. 2005; Garrity et al. 2013; Mueller et al. 2005a). We 

also describe research on other microbes associated 

with P. edulis and their potential importance to pinyon 

ecology.

PINYONS, EM FUNGI, AND PLANT 

NEIGHBORS

Given that across much of the range of pinyon-juniper 

woodlands, pinyons are often the sole host for EM 

fungi, available EM inoculum and successful pinyon 

reestablishment could be limited following droughtinduced mortality. Gehring et al. (2016) estimated that 

P. edulis is the only host for EM fungi across nearly 60 

percent of its geographic range. At sites where junipers 

dominate, pinyons show reduced overall abundance 

of EM fungi, and seedlings are less likely to have 

any EM fungal associations when grown in soil from 

juniper-dominated areas (Haskins and Gehring 2005). 

Pinyons experimentally isolated from neighboring 

junipers respond with a rapid doubling of fine-root 

biomass; EM fungi respond similarly, also doubling in 

abundance (Haskins and Gehring 2004). Allen et al. 

(2010) observed that the EM associations of P. edulis

were more sensitive to N deposition than the AM 

associations of juniper. These results indicate that junipers and their associated soil microbial communities 

(including AM fungi) may influence the environment 

through competitive or antagonistic effects (or both) 

that render P. edulis less able to establish and survive.

A similar competitive release was seen when AMassociated shrubs, principally Fallugia paradoxa, were 
8 USDA Forest Service RMRS-P-77. 2020.

removed from beneath mature pinyons in the field, 

resulting in increases in root biomass, needle length, 

and EM abundance (McHugh and Gehring 2006). 

Associations with this species of shrub help juvenile 

pinyons to survive and grow in stressful environments 

(Sthultz et al. 2007). At low-stress sites, this relationship becomes more competitive, with both aboveground and belowground factors contributing to the 

variation, though additional research is necessary to 

clarify mechanisms (Sthultz et al. 2007). The dynamics 

of these relationships are likely to change as climate 

continues to warm and dry, with increased likelihood 

that relationships between shrubs and pinyons will be 

beneficial when trees are young and detrimental as 

they age.

Research from other forested ecosystems also demonstrates the importance of EM plants to one another. 

A large-scale study of northern temperate forest trees 

showed that species that form EM associations are 

generally positively influenced by soils in which 

conspecifics had grown previously and negatively 

influenced by soil in which heterospecifics had grown 

previously. The formation of mycorrhizas in EMdependent trees was more efficient when seedlings 

were grown in soil collected beneath a conspecific 

tree rather than beneath a heterospecific tree, and 

this difference was further reflected in the overall 

performance of the seedling as a function of biomass 

(Bennett et al. 2017). This result could be due to decreased or less-compatible EM fungal inoculum from 

heterospecific soil, or an increase in pathogen load, 

or both. Combined with previous studies of P. edulis, 

these results suggest that EM seedlings establishing in 

areas lacking conspecifics will perform poorly, potentially due to lack of EM inoculum or other complex 

differences in local soil communities. Inoculation with 

live soil or other sources of EM inoculum may remedy 

inoculum limitation. 

An experiment conducted in Pinus patula demonstrated that soil collected from a P. patula plantation 

offered an effective source of EM fungal inoculum, 

as did mixed cultures of certain EM fungal species 

(Restrepo-Llano et al. 2014). Similarly, de Oliveira 

et al. (2006) isolated and cultured EM fungi from a 

loblolly pine (P. taeda) plantation which was then 

successfully used to inoculate greenhouse seedlings. 

It therefore may be possible to supplement soil with 

ectomycorrhizal inoculum where pine restoration is 

desired following high-mortality events. Because culturing techniques for most EM fungal taxa associated 

with P. edulis have yet to be developed, inoculation 

with soil from nearby areas with living pinyons is 

more likely to provide inoculum-limited pinyons with 

beneficial EM fungal taxa than inoculation with soil 

fungal cultures or commercially produced generalist 

EM fungi that may not be native to the area (Schwartz 

et al. 2006). 

In cases where P. edulis is extirpated from an area 

due to drought or other disturbance, reestablishment 

may occur with EM fungal inoculum that persists in 

the soil. However, reestablishment requires that EM 

fungal communities persist in the absence of a host 

plant. Little is known about how long existing EM 

fungal inoculum remains viable or which taxa persist 

after disturbance events. Long-term experimental 

data on the ability of dormant EM fungal spores to 

colonize bishop pine (P. muricata) yield mixed results. 

Several Rhizopogon species (Basidiomycota) show an 

improved potential to colonize roots over time (Bruns 

et al. 2009), whereas other EM fungal taxa were undetectable after several years (Nguyen et al. 2012). 

Though these data indicate that at least some EM 

fungal propagules will persist and facilitate growth of 

pines over time, they also imply an eventual loss of 

EM fungal diversity in the absence of the EM host. 

This may help to explain the result of Haskins and 

Gehring (2005), who observed decreased colonization 

by EM fungi among pinyon seedlings grown in soil 

collected from juniper-dominated stands. This observation implies a density-dependent relationship as 

stands previously shared by pinyons and junipers shift 

toward a juniper-only woodland following droughtinduced pinyon mortality events. Further, the effect of 

persistent juniper dominance may result in an altered 

soil microbial community that specifically favors the 

growth of junipers over pinyons as soil communities 

become locally adapted to the juniper-dominated regime (Revillini et al. 2016).

Besides examining roots directly to assess mycorrhizal 

colonization and EM fungal species composition, another approach to describing EM fungal communities 

(or other microbes) found in pinyon-juniper woodlands is the use of next-generation DNA sequencing 
USDA Forest Service RMRS-P-77. 2020. 9

technologies. These techniques can provide data on 

the relative abundance of all fungi or bacteria present 

in a soil sample (e.g., Caporaso et al. [2012]). In a 

preliminary survey, we used this technology at Sunset 

Crater National Monument (SCNM) in northern 

Arizona, which revealed differences in EM and AM 

fungal composition between root-associated soil 

collected beneath live, adult P. edulis (n = 31), and 

interspace zones devoid of vegetation for a diameter 

of 6 m (n = 26, collected in four transects within the 

population of adult trees). Taxa were identified from 

polymerase chain reaction amplicon sequencing of 

the ITS2 rDNA gene for each sample using fungalspecific primers (Taylor et al. 2016) on an Illumina®

MiSeq Desktop Sequencer (Illumina Inc., San Diego, 

California). Paired t-tests in which the data were 

matched by sample were used for comparing relative 

abundances of AM and EM taxa within each group 

(interspace or adult trees). These tests revealed no 

difference between AM and EM abundances in the 

interspace soil (t = 1.4488, p = 0.1536), and a strong 

difference between AM and EM abundances in soil 

from adult trees (t = 14.4659, p < 0.0001). 

Unpaired t-tests were used to compare AM or EM taxa 

abundances separately between each group, revealing that differences exist for both AM (t = 4.3331, 

p < 0.0001) and EM (t = 3.8931, P = 0.0003) fungal 

relative abundances. Pinyon-associated soil was rich 

with EM fungal symbionts at this site (12.1 percent 

relative abundance), consisting mostly of the genera 

Geopora and Rhizopogon, while there was a marked 

absence of AM fungi (0.8 percent relative abundance) 

(fig. 1). These EM taxa have been observed consistently at this site in conjunction with pinyon roots 

(Gehring et al. 1998, 2014b; Gordon and Gehring 

2011; Sthultz et al. 2009b). EM taxa were also 

abundant in the interspace soils (8.1 percent relative 

Figure 1—Average relative abundances of total ectomyccorhizal (EM) or arbuscular myccorhizal (AM) fungal taxa 

observed by DNA sequencing of root-associated soil associated with adult Pinus edulis (n = 31) or from vegetationfree interspaces (n = 26) at Sunset Crater National Monument, Arizona. Error bars represent +/- one standard 

deviation. Pinus edulis hosted a significantly larger fraction of EM than AM fungi (p < 0.0001), in contrast with interspaces (p = 0.1536). Pinus edulis hosted a larger fraction of EM fungi than observed in interspaces (p = 0.0003) 

while the opposite was true for AM fungi (p < 0.0001).
10 USDA Forest Service RMRS-P-77. 2020.

abundance), locations where it would not have been 

possible to observe them in the absence of pinyon 

roots without the use of this DNA sequencing technique. We note the presence of both AM (12.4 percent 

relative abundance) and EM fungal taxa in the interspace soils, which implies that mycorrhizal symbionts 

are readily available for establishing seedlings of AMand EM-associated plant species in this ecosystem. 

Though these data represent just a single time point 

for a single site, they serve as a useful illustration of 

the challenges that EM hosts may face during seedling 

recruitment in heterospecific soils.

Across approximately 40 percent of its geographic 

range, P. edulis occurs with other hosts for EM fungi, 

such as ponderosa pine (P. ponderosa) and a variety of 

species of oaks (Quercus spp.) (Gehring et al. 2016). 

Pinus edulis and P. ponderosa habitats overlap near the 

upper elevational limit of P. edulis. When the rhizospheres of P. edulis and P. ponderosa overlapped, they 

shared 88 percent of the fungal taxa observed (Hubert 

and Gehring 2008) (fig. 2). The EM taxa shared 

between P. edulis and P. ponderosa were dominated 

by what were then defined as unknown Pezizalean 

(Ascomycete) fungi (Hubert and Gehring 2008), and 

are probably what we now recognize as a group of EM 

fungi in the genus Geopora of particular significance 

to arid P. edulis ecosystems (Flores-Renteria et al. 

2014; Gordon and Gehring 2011). Such zones of hostspecies overlap may provide important refugia for EM 

fungal taxa important to P. edulis. 

Less is known about overlap with other EM hosts 

such as oaks. To our knowledge, the EM community 

composition of P. edulis and Quercus species has 

not been examined in areas where their distributions overlap. However, data from other systems 

demonstrate the importance of host-overlap zones for 

maintenance of EM taxonomic diversity. For example, 

Horton et al. (1999) observed that in mixed stands of 

Douglas-fir (Pseudotsuga menziesii) and manzanita 

(Arctostaphylos spp.), most trees were colonized by 

Figure 2—Average relative abundance of ectomyccorhizal fungal taxa observed on roots of P. edulis and P. ponderosa

when the rooting zones of the two species overlapped. The two pine species shared six of seven EM fungal taxa with 

similar relative abundance (redrawn from Hubert and Gehring [2008]).
USDA Forest Service RMRS-P-77. 2020. 11

EM fungal taxa which regularly colonize the roots of 

either host, illustrating an ecological linkage between 

gymnosperm and angiosperm populations. Likewise, 

Cullings et al. (2000) examined a mixed forest of 

Engelmann spruce (Picea engelmannii) and lodgepole 

pine (P. contorta), and discovered that 95 percent of 

the observed EM fungi colonized both hosts. These results demonstrate little evidence for host specificity in 

their respective systems and imply that the presence of 

one host tree species may have benefits for maintaining EM fungal diversity that could benefit other tree 

species dependent on EM fungi.

HOST GENETICS, DROUGHT 

TOLERANCE, AND EM FUNGI

Pinus edulis is especially vulnerable to drought stress 

relative to other regional forest tree species, and 

experiences high rates of mortality under prolonged 

periods of drought (Gitlin et al. 2006). This vulnerability can be magnified by the effects of climate change, 

which are predicted to include a continued increase 

in mean average temperature for the present range of 

P. edulis (Adams et al. 2009) and a greater likelihood 

of more frequent and severe drought events across 

the southwestern United States (Seager et al. 2007). 

Indeed, projections of future plant distribution based 

on climate modeling data suggest the current range of 

P. edulis may no longer be suitable habitat for these 

trees by the end of this century (Rehfeldt et al. 2006). 

An extreme drought event from 2002 to 2003 resulted 

in high rates of pinyon mortality across the range of 

the species (Breshears et al. 2005). Later observations 

revealed that survival or death of P. edulis during this 

event was correlated with lower precipitation and 

vapor pressure levels (Clifford et al. 2013). 

Pinus edulis mortality can be highly variable among 

stands; dead and surviving trees may occur side by 

side, suggesting individual variation in drought tolerance (Mueller et al. 2005a; Ogle et al. 2000). Sthultz 

et al. (2009a) showed that at several sites near SCNM, 

genetic differences across the population correlated 

with survival or demise during the 2002–2003 drought, 

indicative of a genetic basis for drought tolerance or 

intolerance. At these sites, drought-intolerant trees, 

initially present as a high proportion of the population, were reduced to a similar proportion to that of 

drought-tolerant trees, even as the overall population 

density contracted (Sthultz et al. 2009a). 

The genetic basis of drought tolerance and intolerance 

in these trees was distinguishable because of variation 

in susceptibility to a stem-boring moth whose chronic 

herbivory on mature trees alters their architecture. 

Susceptible trees chronically attacked by the moth 

have a prostrate, shrub-like architecture in contrast to 

the normal upright architecture of moth-resistant trees 

(Whitham and Mopper 1985). Subsequent studies 

demonstrated a genetic basis to moth resistance or susceptibility (Mopper et al. 1991). Sthultz et al. (2009a) 

observed that moth-resistant trees were approximately 

three times more likely than moth-susceptible trees to 

perish under drought stress, indicating that this phenotypic differentiation is indicative of drought tolerance 

in the moth-susceptible group. Seedlings of mothsusceptible trees also survived longer under drought 

conditions experimentally imposed in a greenhouse, 

demonstrating the genetic basis of drought tolerance 

(Sthultz et al. 2009a). Such phenotypically dimorphic 

populations present important opportunities for study 

as not every population will provide such a simple diagnostic tool for examining genetic effects on ecological interactions, and resources for studying the genetic 

composition of pinyon pine populations remain limited 

(Krohn et al. 2013; Lesser et al. 2012).

Sthultz et al. (2009b) observed that the differential 

phenotypes of the pinyon population previously 

described associate with specific communities of 

EM fungi. Specifically, the drought-tolerant group 

hosted more Pezizalean EM fungi, including the genus 

Geopora (Ascomycota), and this pattern held whether 

data were from a relatively wet or dry year. Gehring 

et al. (2014a) explored the EM fungal communities 

of drought-tolerant and -intolerant trees over time and 

observed that drought-tolerant trees hosted a consistent 

community as the regional climate became more 

arid, whereas the drought-intolerant trees exhibited a 

significant shift in EM community during the same 

period. In removing shrubs from beneath mature P. 

edulis, a competitive release was observed in which 

the focal pinyon exhibited increased growth (Gehring 

et al. 2014a). This competitive release was more 

significant in the drought-tolerant trees, which maintained a consistent EM fungal community, than in the 

drought-intolerant trees, whose EM fungal community 
12 USDA Forest Service RMRS-P-77. 2020.

also shifted following shrub removal. The improved 

performance of the drought-tolerant trees after shrub 

removal is most likely due to added benefits provided 

from the associated EM community, implying that the 

EM community commonly observed among droughttolerant trees is composed of mutualists superior to those 

observed among drought-intolerant trees (Gehring et 

al. 2014b, 2017). These superior mutualists may confer 

upon drought-tolerant trees the opportunity to perform 

more consistently under variable climatic conditions, 

though members of the genus Geopora have increased 

in abundance with drought both within and among sites 

(Gehring et al. 2014b; Gordon and Gehring 2011), possibly benefiting drought-intolerant trees as well.

Given the evolutionary patterns of P. edulis mortality 

following recent drought, in which climatic pressure 

selected against drought-intolerant genotypes (Sthultz 

et al. 2009a), it is possible that drought-intolerant genotypes will be extirpated during subsequent drought 

events. A potential consequence of this differential 

mortality is the loss of EM fungal diversity across 

local or regional scales due to the differences in EM 

fungal communities among the different genotypes. It 

is unclear how the loss of P. edulis genetic diversity 

and EM fungal biodiversity may affect recruitment of 

seedlings in natural settings or in restoration efforts. 

The idea that it may be important to maintain intraspecific diversity of either the host plant or the fungal 

symbiont is not a new one (Johnson et al. 2012), and 

may be particularly relevant to the P. edulis system. 

While exploring the effect of AM fungal diversity 

on plant communities, van der Heijden et al. (1998) 

observed that increased AM fungal species richness 

yielded greater hyphal mass in the soil, increased plant 

biomass, and increased plant species richness. These 

effects corresponded with a depletion of available 

phosphorus in the soil, implying that the increase in 

AM fungal hyphae provided an improved mechanism 

for foraging nutrients from the environment (van der 

Heijden et al. 1998). More recently, Teste et al. (2017) 

demonstrated the importance of diverse soil biota 

to maintaining taxonomic and functional diversity 

in Australian shrublands. While experimental data 

showed that different resource acquisition strategies by 

plants (e.g., EM-dependent or AM-dependent) resulted 

in variability in plant response to soil microbial content, simulations indicated that the presence of these 

different strategies combined with their associated 

soil microbial communities contributed strongly to 

maintenance of plant species and functional diversity 

across the environment (Teste et al. 2017). Although 

comparable data for pinyon-juniper woodlands are 

not available, these studies speak to the importance of 

maintaining soil microbial diversity in order to benefit 

plant communities. Because P. edulis is considered 

a foundation species, negative environmental effects 

on this species may exert a disproportionate effect on 

many dependent organisms (Whitham et al. 2003).

OTHER ROOT AND SOIL MICROBES

Many organisms besides mycorrhizal fungi inhabit the 

soil among plant roots, but our overall understanding of their species composition and interactions 

remains limited (Ingleby and Molina 1991). In addition to pine-associated EM fungal communities, 

non-EM fungi and bacteria are present and participate 

in ecological processes in the soil. Bacteria are 

most abundant by cell count, regardless of host type 

(Neal et al. 1964; Rouatt and Katznelson 1961), and 

represent the bulk of soil genetic diversity, which 

may contain thousands of genomes per gram of soil 

(Torsvik and Øvreås 2002). The contribution of soil 

bacteria to host tree performance was first observed 

by comparing the growth of P. radiata in fumigated 

and nonfumigated soils, though it was unclear at the 

time which microbial group was mediating this effect 

(Ridge and Theodorou 1972). Later, it was recognized 

that various bacteria may either benefit the host plant 

directly as plant growth-promoting rhizobacteria 

(PGPR) (Kloepper and Schroth 1978) or indirectly by 

promoting the interaction between the host plant and 

potential mycorrhizal associates as mycorrhizal helper 

bacteria (MHB) (Garbaye 1994). Archaea are less well 

studied than bacteria due to their relative scarcity and 

a lack of available tools for examining these organisms. However, recent work indicates that although 

they represent just a small percentage of soil microbial 

communities, they may exert a powerful influence on 

nutrient-cycling processes such as ammonia oxidation 

(Leininger et al. 2006).

Nonmycorrhizal fungi represent a class of potent 

decomposers which release the bulk of present but 

unavailable nutrients into the ecosystem (Schlesinger 

and Bernhardt 2013). Much of the matter consumed 
USDA Forest Service RMRS-P-77. 2020. 13

Figure 3—Average relative abundance of nonmycorrhizal fungal taxa observed by DNA sequencing of soil from roots 

of P. edulis. Data are summarized to the deepest confident taxonomic designation for each observation. Hierarchical 

taxonomic depth for each classification is indicated in bold parentheses (k = kingdom, p = phylum, c = class, o = order, 

f = family, g = genus). Error bars represent +/- one standard deviation.

during decomposition consists of various plant tissues, 

so these taxa rely on the plant community even as they 

make nutrients available to that community. The apparent relationship between plants and microbial taxa gets 

even more complicated when we consider that, despite 

the well-known symbiotic mutualisms such as mycorrhizal associations in such communities, microbes and 

plants effectively compete for available nutrients (Harte 

and Kinzig 1993), implying a relationship that is simultaneously competitive and mutualistic. It is likely that 

the relationships among plant and microbial community 

members, combined with the abiotic characteristics of 

the surrounding soil, are the main influences that define 

the community structure in plant ecosystems.

Besides EM fungi, heterotrophic fungi and bacteria 

have been examined in pinyon-associated soils (Kuske 

et al. 2003). Patterns were observed with differences 

in host genetics, tree age, and soil type for droughttolerant and -intolerant P. edulis. Tree age consistently 

affected bacterial content with older (ca. 150 years 

old) trees hosting about three times that of younger 

(ca. 60 years old) trees, whereas fungi do not correlate 

with age (Kuske et al. 2003). These results demonstrate that bacterial communities may become denser 

as the trees age, which could improve survival beyond 

a certain age if those communities possess adequate 

levels of PGPR or MHB taxa. Conversely, because 

some soil bacteria may be pathogens (Revillini et al. 

2016), the host may instead require more consistent 

associations with EM fungi to better survive increased 

antagonistic pressure through the protective effects of 

EM (Laliberté et al. 2015).

DNA sequencing data that we have collected for the 

microbial content of pinyon-associated soils describe 

the nonmycorrhizal fungi (based on ITS2 rDNA gene 

sequencing) (fig. 3) and bacteria (based on 16S v4 

rDNA gene sequencing) (fig. 4) in finer detail than 

previously possible. Importantly, the largest observed 
14 USDA Forest Service RMRS-P-77. 2020.

component of the nonmycorrhizal fungal community 

includes undescribed taxa in the phylum Ascomycota, 

highlighting the need for continued basic research describing the biodiversity of fungi in the environment. 

High proportions of fungi from the genus Penicillium

indicate ample presence of saprotrophs (Kirk et al. 

2008), as well as potential pathogen pressure as various species of Penicillium may also cause disease in 

plants (Ballester et al. 2015; Louw and Korsten 2013). 

Intriguing is the considerable proportion of fungi from 

the order Pleosporales, taxa that generally belong to 

the less well understood class of plant-symbiotic fungi 

known as dark septate endophytes (DSE) (Jumpponen 

and Trappe 1998). These fungi may turn out to be 

important players, as a meta-analysis revealed that associations by a variety of plant species with DSE generally confer an increased growth response in a host 

plant (Kivlin et al. 2013). Identification of soil bacteria 

using short DNA sequences generally fails to provide 

taxonomic information deeper than family, and we 

present only phylum-level classifications here (fig. 4). 

In terms of the dominant phyla, these data are generally 

consistent with previous sequencing-based analyses 

from other forest soil bacteria (Roesch et al. 2007).

Numerous studies have implicated specific nonmycorrhizal taxa as either direct or indirect facilitators, 

including in various Pinus systems (e.g., Kataoka et al. 

[2009]). While studying the grass Brassica rapa, Lau 

and Lennon (2011) observed that more complex soil 

microbial communities facilitated an increase in plant 

biomass, leaf number, and flower count, due in part 

to the bacterial community composition. They later 

found that soil microbial communities adapted to environmental conditions, offering an alternative adaptive 

strategy to the host plant, which can utilize the benefits 

of a well-adapted soil microbial community as a proxy 

for evolutionary adaptation in order to better survive 

under stressful conditions (Lau and Lennon 2012). Such 

effects emphasize that beneficial soil microbes include 

those besides mycorrhizal fungi, and that these microbes 

can also have a profound impact on their plant hosts.

Figure 4—Average relative abundance of bacterial taxa observed by DNA sequencing of soil from roots of P. edulis. 

Data are summarized to the phylum level for all observations. Error bars represent +/- one standard deviation.
USDA Forest Service RMRS-P-77. 2020. 15

MANAGEMENT IMPLICATIONS

At sites where P. edulis has been lost due to drought 

and EM fungal inoculum may be limited, provision of 

EM fungal inoculum during restoration may be necessary. Although there are few data on the direct effect of 

fire on pinyon EM fungi, a recent review showed that 

fire and drought have similar consequences for EM 

fungal populations and communities, particularly when 

they represent a loss of pines from the landscape (Karst 

et al. 2014). Because pines may exhibit specificity for 

certain EM taxa and vice versa (Bruns et al. 2002), 

commercially available EM fungal inoculum may be 

ineffective. A better option may be to grow plants in 

soil inoculated with live soil from surviving pinyons. 

Data on variation in drought tolerance among P. edulis

genotypes (Sthultz et al. 2009a) indicate that it may also 

be important to manage pinyon ecosystems to maximize 

diversity of individual trees, which would be expected 

to translate into improved genetic diversity. In turn, 

greater genetic diversity of the P. edulis host is likely to 

contribute to maintenance of a more complete resource 

of belowground EM fungal propagules which will 

facilitate recruitment of new seedlings in the future as 

well as improve survival during restoration or conservation projects. 

A feasible strategy for restoration of P. edulis in 

drought-stressed areas could make use of drought-tolerant genotypes, at least initially. These trees would better 

survive additional drought stress (Sthultz et al. 2009a), 

and may help to establish an adequate reservoir of EM 

fungal inoculum to eventually expand the genetic diversity of the host in order to further improve the resident 

EM inoculum potential. However, this strategy may 

have additional challenges. For instance, the droughttolerant genotypes at SCNM often produce significantly 

fewer female cones (Mueller et al. 2005b), implying 

that collection of seeds from the desired genotypes 

could be more difficult. However, annual application of 

the systemic insecticide Cygon (Drexel Chemical Co., 

Memphis, Tennessee) is sufficient to control insect herbivory such that insect damage on drought-tolerant trees 

becomes equivalent to that observed on drought-intolerant trees, and cone production is similarly restored after 

just 4 years of treatment (Mueller et al. 2005b). Not all 

drought-tolerant genotypes will share the same ecological pressures as those observed at SCNM that result in 

easily identifiable drought-tolerant genotypes. Adequate 

study of individual populations at different sites will be 

required to identify trees that possess drought-tolerant 

traits, and additional tools will need to be developed to 

better understand the genetics behind such traits.

When managing land for wildlife, land management 

agencies are often concerned with maintaining healthy 

plant populations that serve as food and shelter for game 

and nongame animals. The studies and data presented 

here suggest that soil micro-organisms contribute in 

important ways to plant and soil health. Of particular 

importance are the mycorrhizal fungal communities on 

which plant communities and their dependent organisms 

rely for efficient nutrient extraction from the soil (Smith 

and Read 2008) and for efficient deposition and storage 

of belowground carbon (Treseder and Holden 2013). 

For a recent, comprehensive review of how management practices relate to the management of mycorrhizal 

fungi, see Tomao et al. (2017). It is important to recognize that this review, which focuses on silvicultural 

practice, relates more directly to forest stands of limited 

composition and relatively uniform age. The authors 

specifically highlight the need for more research into 

the growth of mushrooms (which generally constitute 

available EM fungi in forest ecosystems) under forest 

conditions more typical of pinyon-juniper woodlands. 

Another review by Karst et al. (2014) describes findings directly relevant to EM fungi and pines in general 

with respect to fire, drought, and herbivory, but again 

concludes by highlighting the many gaps in our understanding of the dynamics of EM fungal communities 

and their hosts. Based on the data available, we suggest 

that land managers regard effects of stand-reducing 

events (e.g., fire, drought, insect infestation) in pinyon 

pine and EM fungi similarly. Such reductions in host 

populations alter EM abundance, species richness, or 

community composition, thus decreasing the resiliency of the landscape to recover, at least temporarily. 

However, though fire will have an indiscriminate effect 

across a population, drought or insect infestation is 

likely to target certain host plant genotypes with greater 

consequences than others, reducing available genetic 

diversity of forest trees, and in turn, altering the composition of available EM fungi across an affected area. 

Including soil micro-organisms, such as mycorrhizal 

fungi that link aboveground and belowground portions 

of ecosystems, in future research and management of 

pinyon-juniper woodlands could improve outcomes for 

host plants, soils, and associated wildlife. Given these 
16 USDA Forest Service RMRS-P-77. 2020.

challenges, we implore forest management agencies to 

direct available funding specifically to exploration of 

EM fungal communities in both healthy and affected P. 

edulis ecosystems.

CONCLUSIONS

Associations between plants and microbes are increasingly recognized as key to plant performance and ecosystem function. The studies we have described here 

demonstrate the importance and complexity of microbial associations in pinyon-juniper woodlands, with an 

emphasis on mycorrhizal fungi. The mycorrhizal associations of pinyon-juniper woodlands are unique in 

ways that may affect their management, including the 

potential for EM fungal inoculum limitation following 

P. edulis mortality and the tight association between 

pinyon genotype and a genus of poorly described EM 

fungi. As studies of microbial communities remain 

limited spatially and temporally, much remains to be 

learned, though several recent studies have begun to 

explore the geographic distribution of mycorrhizal 

fungi on broad scales (Davison et al. 2015; Kivlin 

et al. 2011; Opik et al. 2010; Swaty et al. 2016; 

Tedersoo et al. 2011). Newer, culture-independent 

DNA sequencing methods are making characterization of microbial communities more rigorous and less 

costly (e.g., Caporaso et al. [2012]). A host of emerging methods are also elucidating the complexities of 

microbial function. These techniques may improve our 

understanding of the drivers of pinyon-juniper ecosystem function and better inform management of these 

ecosystems in an era of rapid environmental change.

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20 USDA Forest Service RMRS-P-77. 2020.

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Stand Structure and Drought Alter Tree Mortality Risk in Nevada’s 

Pinyon-Juniper Woodlands

KEYWORDS—piñon, stand density, Great Basin, forest mortality, forest inventory, semi-arid woodlands, 

aridity, competition

Severe droughts in the southwestern USA in 2002-

2003 revealed that pinyon pine and juniper trees are 

susceptible to widespread mortality caused by severe 

water deficits and drought-facilitated insect outbreaks 

(Breshears et al., 2005; Clifford et al., 2008). Despite 

increased interest in forest susceptibility to drought, 

the influence of stand structure on the drought resilience of pinyon-juniper woodlands remains uncertain 

(Meddens et al., 2015). Stand structure is of particular 

interest because it is readily manipulated by silvicultural practices such as thinning. Page (2008) suggests, 

for example, thinning pinyon-juniper woodlands to 

between 5 and 25 percent of maximum stand density 

index (SDI) in order to reduce fuel loads and increase 

understory vegetation. If trees are more susceptible to 

drought in denser stands, thinning may also be a viable 

strategy to increase resilience to drought. 

We measured 98 plots in central Nevada pinyonjuniper woodlands before and after a severe multi-year 

drought in order to assess the effect of stand density 

on drought-related tree mortality and canopy dieback 

(Flake, 2016). The stands are dominated by singleleaf 

pinyon pine (Pinus monophylla) and Utah juniper 

(Juniperus osteosperma) with a small component of 

curl-leaf mountain mahogany (Cercocarpus ledifolius). 

Each plot measured 0.1 ha in area, and the plot network spanned a wide range of elevations (1820 m.a.s.l 

to 2360 m.a.s.l), mean annual precipitation (230 mm 

to 414 mm), and soil properties (soil depth 7.4 to 43 

cm). Plots were inventoried in 2005 (Greenwood and 

Weisberg, 2008, 2009) and resampled in 2015. For 

each tree, we estimated the amount of its canopy that 

was dead (dead branches, brown needles, or defoliated) in both 2005 and 2015, allowing us to calculate 

canopy loss over the study period. 

We modeled tree canopy loss and the probability of 

stem mortality using generalized linear mixed-effects 

models. We first estimated the effects of environmental 

variables such as temperature, precipitation, vapor 

pressure deficit, climatic water deficit (Stephenson, 

1998), Forest Drought Stress Index (Williams et al., 

2013), soil depth, and soil available water capacity. We 

then added variables reflecting stand structure and tree 

attributes, including tree diameter and height; the basal 

area of neighboring trees within 4-m of the focal tree 

(4-m BA), the distance which was most strongly correlated with canopy dieback; the distance to the nearest 

neighboring tree (ENN Dist); and the ratio of the basal 

area of the nearest neighbor to that of the focal tree 

(Neighbor Ratio). We fit separate models for pinyon 

canopy dieback, juniper canopy dieback, and pinyon 

stem mortality. 

On average, pinyons lost 23.7 ± 0.58 percent of 

their canopy and junipers only 10.8 ± 0.46 percent. 

In our study area, 10.9 percent of the pinyon pines 

but only 0.6 percent of junipers had died over the 

past decade. This mortality was driven largely by 

Samuel W. Flake and Peter J. Weisberg

Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada
USDA Forest Service RMRS-P-77. 2020. 21

soil characteristics, with greater mortality on deeper 

soils, and by site aridity, with the greatest mortality 

at sites with the greatest climatic water deficit and 

vapor pressure deficits, but was mediated by stand 

structure. We found that the basal area of neighboring 

trees had a large effect on tree dieback and mortality 

after accounting for climate and soil effects. In pinyon 

pine, the effect of stand structure depended upon the 

annual water deficit of the site. At the wettest sites 

(10th percentile of climatic water deficit (CWD)), an 

increase in neighborhood basal area was associated 

with decreased canopy loss, while the opposite pattern 

was seen at the driest sites (90th percentile of CWD), 

where trees were predicted to lose about 10 percent 

of their canopy as neighborhood basal area increased 

from its minimum (0 m2) to maximum (1.4 m2). 

Increased neighborhood basal area was also associated with greater pinyon mortality, with an increase of 

mortality risk of 3.8 times from minimum to maximum 

basal area. For juniper, the effect was weaker and not 

statistically significant. Pinyon canopy health was 

negatively associated with proximity to adjacent trees. 

For each 1-m reduction in distance between a tree with 

its nearest neighbor, canopy health was predicted to 

decline by 1.6 percent. Proximity to neighboring trees 

was not important for juniper dieback or for pinyon 

stem mortality, which were driven more by abiotic factors such as soil and climate. 

These results indicate that on drier sites, competition 

among trees reduces the resilience of pinyon-juniper 

woodlands during drought. This finding implies that 

risk of drought-induced mortality may change during 

stand development. Drought may precipitate self-thinning in denser, intermediate-aged stands, while sparse 

young stands or old-growth may be less susceptible to 

drought. Severe drought is not uncommon in the Great 

Basin (fig. 1), and it may play an important role in the 

long-term dynamics of pinyon-juniper woodlands by 

introducing structural and habitat heterogeneity. 

Silvicultural treatments in pinyon-juniper woodlands 

may be a useful strategy in the management of denser 

stands. Moreover, because the effect of competition 

depends upon site conditions, with more arid sites 

more prone to competition-induced drought sensitivity, thinning may be more useful in sites with higher 

stocking relative to the maximum stand density index. 

We encourage the collection of baseline data before 

and after thinning treatments, as well as in comparable 

Figure 1—Forest drought stress index (FDSI; Williams et al., 2013) over the entire PRISM climate 

time series (PRISM Climate Group) for our central Nevada plot network. Lower values indicate greater 

drought stress, and values of FDSI below the horizontal line at FDSI = -1.41 are linked historically with 

decreased tree vigor.
22 USDA Forest Service RMRS-P-77. 2020.

control sites, in order to create datasets required to 

more precisely quantify stand density influences 

on drought resilience. Further study of stand-level 

processes in pinyon-juniper woodlands (e.g. selfthinning) is also needed to develop basic silivicultural 

guidelines (e.g. density management) appropriate for 

highly heterogeneous, topographically and edaphically complex woodland systems. It is probable that 

the thinning of dense patches of trees, particularly on 

dry sites likely to have low maximum pinyon-juniper 

stocking, will enhance drought resilience. However, 

additional controlled experiments manipulating densities and stand structure are needed to provide a critical 

test of the role of stand density in pinyon-juniper resilience to drought, and for development of silvicultural 

prescriptions aimed at maintaining healthy woodlands 

given drought conditions and climate change processes. 

REFERENCES

Breshears, D.D.; Cobb, N.S.; Rich, P.M.; [et al.]. 2005. 

Regional vegetation die-off in response to global-changetype drought. PNAS. 102: 15144–15148.

Clifford, M.J.; Rocca, M.E.; Delph, R.; [eta al.]. 2008. 

Drought induced tree mortality and ensuing bark beetle 

outbreaks in southwestern pinyon-juniper woodlands. In: 

Gottfried, Gerald J.; Shaw, John D.; Ford, Paulette L., 

compilers. 2008. Ecology, management, and restoration 

of pinon-juniper and ponderosa pine ecosystems: 

combined proceedings of the 2005 St. George, Utah and 

2006 Albuquerque, New Mexico workshops. Proceedings 

RMRS-P-51. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research 

Station: 39-51

Flake, S.W. (2016). Stand dynamics during drought: 

Responses of adult trees, tree regeneration, and 

understory vegetation to multiyear drought in pinyonjuniper woodlands. Reno, NV: University of Nevada. 

Thesis. 109 p.

Greenwood, D.L.; Weisberg, P.J. 2008. Density-dependent 

tree mortality in pinyon-juniper woodlands. Forest 

Ecology and Management. 255: 2129–2137.

Greenwood, D.L.; Weisberg, P.J. 2009. GIS-based modeling 

of pinyon-juniper woodland structure in the Great Basin. 

Forest Science. 55, 1–12.

Meddens, A.J.H.; Hicke, J.A.; Macalady, A.K.; [et al.]. 

2015. Patterns and causes of observed piñon pine 

mortality in the southwestern United States. New 

Phytology. 206, 91–97.

Page, D.H. 2008. Preliminary thinning guidelines using 

stand density index for the maintenance of unevenaged pinyon-juniper ecosystems. In: Gottfried, Gerald 

J.; Shaw, John D.; Ford, Paulette L., compilers. 2008. 

Ecology, management, and restoration of pinon-juniper 

and ponderosa pine ecosystems: combined proceedings 

of the 2005 St. George, Utah and 2006 Albuquerque, 

New Mexico workshops. Proceedings RMRS-P-51. Fort 

Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station: 104-112

Stephenson, N.L. 1998. Actual evapotranspiration 

and deficit: Biologically meaningful correlates of 

vegetation distribution across spatial scales. Journal of 

Biogeography. 25: 855–870.

Williams, A.P.; Allen, C.D.; Macalady, A.K.; [et al.]. 2013. 

Temperature as a potent driver of regional forest drought 

stress and tree mortality. Nature Climate Change. 3: 

292–297.
USDA Forest Service RMRS-P-77. 2020. 23

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Vulnerability of Piñon-Juniper Habitats to Climate Change in the 

Southwestern USA

KEYWORDS—adaptation, climate envelope, climate vulnerability, fire severity, Global Circulation Models, 

piñon-juniper, realignment

Wildlife managers are concerned about the impacts of 

climate change on piñon-juniper habitats and on the 

ecosystem services that they provide (Friggens et al. 

2013). Contrary to common perception, this extensive 

ecosystem type is greatly varied in structure, composition, ecology, and habitat value (Comer et al. 2003; 

Moir and Carlton 1986; Romme et al. 2009), along 

with the expected manifestations of climate change 

vulnerability (Comer et al. 2012; Rehfeldt et al. 2006). 

This work resulted in a geospatial climate vulnerability 

surface that included five types of piñon-juniper across 

Arizona and New Mexico.

To predict vulnerability, the landscape was spatially 

stratified into recognizable ecosystem types that repeat 

across the landscape. Then, base-level polygons were 

generated for the analysis area with each segment 

representing similar site potential at the scale of individual plant communities. Segments were attributed 

with biophysical, contemporary climate, and projected 

climate for multiple Global Circulation Models 

(GCMs). Climate envelopes were developed for each 

type of piñon-juniper based on pre-1990 climate data 

and the most discriminating climate variables. The discriminant analysis of 21 climate variables resulted in 

the selection of five key variables for building climate 

envelopes for each piñon-juniper type. Finally, each 

segment was assigned a vulnerability score based on 

the projected departure in future climate from the characteristic climate—an inference of probable change in 

site potential climate envelopes. In the context of this 

analysis, vulnerability ratings are a consequence of 

the breadth of the envelope for a given type of piñonjuniper, the current climate status of a given location 

relative to its envelope, and the magnitude of projected 

change in climate at that location.

Of the five major types of piñon-juniper in the southwestern USA, the Piñon-Juniper Sagebrush system appears to be the most vulnerable with nearly 90 percent 

of its area occurring as high vulnerability (table 1). 

The percentage of high vulnerability in the remaining 

types varies considerably, between 22-64 percent.

In all cases, by the late 21st-century, the majority 

of area currently classified as piñon-juniper will 

have at least exceeded historic envelopes, suggesting important changes ahead for dependent wildlife. 

The efficacy of the vulnerability surface was tested 

by assessing contemporary fire severity patterns 

(Eidenshink et al. 2007) against the predicted vulnerability for all piñon-juniper types collectively, and 

found that low vulnerability sites were over 38 percent 

more likely to experience stand replacement fire than 

the background levels suggested in the vulnerability 

surface. Conversely, high vulnerability areas were 

nearly 33 percent less likely to experience stand 

replacement fire, with vulnerability playing a hypothetical role on plant productivity (Parks et al. 2016; 

Rocca et al. 2014). A follow-up study on the Lincoln 

F. Jack Triepke

Regional Ecologist and Air Program Manager, USDA Forest Service, Southwestern Region, Albuquerque, New Mexico
24 USDA Forest Service RMRS-P-77. 2020.

National Forest in south-central New Mexico is being 

conducted to evaluate the relationship of vulnerability 

and plant productivity, while also considering the 

respective influences of vulnerability and uncharacteristic fire on tree regeneration patterns. Finally, the vulnerability surface was focused on vegetation and not 

wildlife habitat specifically; but, because vegetation 

serves as the principle structural component of habitat, 

the surface can offer an inference of habitat vulnerability and a means for underpinning analyses for specific 

species of wildlife that utilize piñon-juniper.

In applying the vulnerability surface for a given type 

of piñon-juniper, biologists could focus adaptation 

management for habitat in those landscapes with the 

highest vulnerability. An adaptive capacity process of 

analysis, planning, and management can link outputs 

from the vulnerability surface to practitioners and decisionmakers by integrating vulnerability predictions 

with knowledge of resilience-resistance characteristics 

and plant functional traits. For example, prescribed 

burning and tree thinning, particularly in the fireadapted types (Juniper Grass and Piñon-Juniper 

Grass) that have been substantially altered from 

reference conditions, may improve adaptive capacity 

and facilitate realignment, while reducing the risk of 

high-severity disturbance to piñon-juniper (Bradford 

and Bell 2017). For all vulnerable piñon-juniper types, 

managers can plan and apply treatments adaptively 

with particular emphasis on ecosystem function as 

a useful operational framework for responding to 

climate change for piñon-juniper habitat in the southwestern United States.

REFERENCES

Bradford, J.B.; Bell, D.M. 2017. A window of opportunity 

for climate-change adaptation: easing tree mortality by 

reducing forest basal area. Frontiers in Ecology and the 

Environment. 15(1): 11-17.

Comer, P.; Faber-Langendoen, D.; Evans, R.; Gawler, 

S.; Josse, C.; Kittel, G.; Menard, S.; Pyne, M.; Reid, 

M.; Schulz, K.; Snow, K.; Teague, J. 2003. Ecological 

Systems of the United States: A working classification 

of US Terrestrial Systems. NatureServe technical guide 

available online: http://www.natureserve.org. Home 

Office, Arlington, VA. 83 pp.

Eidenshink, J.; Schwind, B.; Brewer, K.; Zhu, Z.; Quayle, 

B.; Howard, S. 2007. A project for monitoring trends in 

burn severity. Fire Ecology. 3(1): 3-21.

Friggens, M.M.; Bagne, K.E.; Finch, D.M.; Falk. 

D.; Triepke, F.J.; Lynch, A. 2013. Review and 

recommendations for climate change vulnerability 

assessment approaches with examples from the 

Southwest. Gen. Tech. Rep. RMRS-GTR-309. Fort 

Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station: 106 p.

Table 1—Vulnerability of the five major types of piñon-juniper to climate 

change in the southwestern USA.

Piñon-Juniper ERU

Vulnerability

category Percent

PJ Evergreen Shrub Low 28

15,908 km2 Moderate 50

High+ 22

PJ Woodland (persistent) Low 8

22,199 km2 Moderate 42

High+ 50

PJ Grass Low 7

24,607 km2 Moderate 30

High+ 64

Juniper Grass Low 3

37,488 km2 Moderate 43

High+ 55

PJ Sagebrush Low 0

9,173 km2 Moderate 12

High+ 88

All Piñon-Juniper Low 8

109,375 km2 Moderate 38

High+ 54
USDA Forest Service RMRS-P-77. 2020. 25

Moir, W.H.; Carlton, J.O. 1986. Classification of pinyonjuniper (P-J) sites on National Forests in the Southwest. 

In: Everett, R.L., compiler, Proceedings--Pinyon-Juniper 

Conference, January 13-16, 1986. Gen. Tech. Rep. INT215. Ogden, UT: U.S. Department of Agriculture, Forest 

Service Intermountain Research Station: 216-226.

Parks, S.A.; Miller, C.; Abatzoglou, J.T.; Holsinger, L.M.; 

Parisien, M.A.; Dobrowski, S.Z. 2016. How will climate 

change affect wildland fire severity in the western US? 

Environmental Research Letters. 11(3): 1-10.

Rehfeldt, G.E.; Crookston, N.L.; Warwell, M.V.; Evans, J.S. 

2006. Empirical analyses of plant-climate relationships 

for the western United States. International Journal of 

Plant Sciences. 167(6): 1123-1150.

Rocca, M.E.; Brown, P.M.; MacDonald, L.H.; Carrico, C.M. 

2014. Climate change impacts on fire regimes and key 

ecosystem services in Rocky Mountain forests. Forest 

Ecology and Management. 327(2014): 290-305.
26 USDA Forest Service RMRS-P-77. 2020.

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Fire Exclusion Linked to Increased Forest Density in a New Mexico 

Piñon-Juniper Savanna Landscape

KEYWORDS—fire scar, tree ring, Pinus edulis, Juniperus scopulorum, Piñon-Juniper savanna

Piñon-juniper (PJ) ecosystems cover approximately 30 

million ha in the western United States (West 1999). 

High variability in species assemblages and structure 

exists across this large geographic range, driven in 

part by varying climate. Among the three functional 

PJ types (persistent PJ woodlands, PJ savannas, and 

PJ shrublands), the least is known about ecosystem 

processes and vegetation dynamics in PJ ecosystems 

containing a substantial grass component - PJ savannas 

(Romme et al. 2009). The best supported model for 

fire regimes in PJ is one of infrequent, high-severity 

fire (e.g., Floyd et al. 2004). However, it is logical that: 

(1) the grass component of PJ savannas could support frequent, low-severity fire, and (2) exclusion of 

frequent fire is a plausible mechanism for the observed 

increases in PJ tree density in PJ savannas. To assess 

these hypotheses I used dendroecological methods 

to reconstruct the history of fire, forest age structure, 

and composition on a ~ 30,000 ha PJ-dominated mesa 

landscape (Rowe Mesa, New Mexico), located at the 

PJ (two-needle pinyon (Pinus edulis)-Rocky Mountain 

juniper (Juniperus scopulorum), grassland, ponderosa 

pine (Pinus ponderosa) (PIPO) ecotone (Margolis 

2014). At seven sites located on a 7-km grid I collected 

fire scars, aged trees, and measured forest composition 

and density. I cross-dated 112 fire-scarred trees (8% 

juniper, 17% pinyon, and 74% ponderosa) containing 

630 fire scars that burned during 87 unique fire years 

(1547-1899). Fires recorded synchronously at multiple 

sites (spreading fires) occurred every 7.8 years on 

average. Fires recorded at over half of the sites (widespread fires) occurred every 23.7 years on average. 

Evidence of high-severity fire (e.g., even-aged stands 

or groups of fire-killed snags or logs) was not observed. Instead, multi-decadal variability in tree establishment was negatively correlated with fire frequency 

(i.e., frequent fire reduced tree establishment). Peak PJ 

establishment was synchronous with the collapse of 

the frequent fire regime and dry conditions in the late 

19th century. I conclude that frequent, low-severity 

fires burning grass fuels historically maintained low 

tree densities across what was a PJ–PIPO savanna 

landscape. Further, late 19th century fire exclusion 

associated with the removal of fine fuels from intensive livestock grazing was the primary driver of 

current high tree densities on Rowe Mesa dominated 

by PJ species (mean density = 881 live trees ha-1, 

94 percent of which were < 130 years old and likely 

would not have been present when the frequent fire 

regime was active). Wildlife species that favor a more 

open, grassy environment would not be favored by the 

documented changes, whereas other species that prefer 

a dense woodland environment could benefit from the 

changes.

REFERENCES

Floyd, M.L.; Hanna, D.D.; Romme, W.H. 2004. Historical 

and recent fire regimes in Piñon-Juniper woodlands on 

Mesa Verde, Colorado, USA. 198:269–289.

Ellis Q. Margolis

USGS Fort Collins Science Center, New Mexico Landscapes Field Station, Santa Fe, New Mexico
USDA Forest Service RMRS-P-77. 2020. 27

Margolis, E.Q. 2014. Fire regime shift linked to increased 

forest density in a piñon-juniper savanna landscape. 

International Journal of Wildland Fire. 23:234–245.

Romme, W.H.; Allen, C.D.; Balley, J.D.; [et al]. 2009. 

Historical and modern disturbance regimes, stand 

structures, and landscape dynamics in piñon-juniper 

vegetation of the western United States. Rangeland 

Ecology and Management. 62:203–222.

West, N.E. 1999. Distribution, composition, and 

classification of current juniper-pinyon woodlands and 

savannas across western North America. In Ecology and 

management of pinyon–juniper communities within the 

Interior West, 15–18 September 1997, Provo, UT. (Eds 

SB Monsen, R Stevens). Fort Collins, CO: USDA Forest 

Service, Rocky Mountain Research Station, Proceedings 

RMRS-P-9, p. 20–33.
28 USDA Forest Service RMRS-P-77. 2020.

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Kristine Johnson

University of New Mexico, Natural Heritage New Mexico, Biology Department, Albuquerque, New Mexico

Jacqueline Smith

University of New Mexico, Natural Heritage New Mexico, Biology Department, Albuquerque, New Mexico

Giancarlo Sadoti

Department of Geography, University of Nevada, Reno, Nevada

Teri Neville

University of New Mexico, Natural Heritage New Mexico, Biology Department, Albuquerque, NM

Pinyon Jay Habitat Use and Management Recommendations in

New Mexico Piñon-Juniper Woodlands

PIÑON-JUNIPER HABITAT USE BY WILDLIFE

Pinyon Jays and piñon pines have a mutualism 

whereby the trees provide mast crops of highly nutritional seeds that enhance the jays’ population viability 

(Marzluff and Balda 1992), and the jays cache the 

seeds, serving as the tree’s main long-distance seed 

disperser (Ligon 1978). Breeding Bird Survey data 

indicate that Pinyon Jay populations have been declining for 50 years (Sauer et al. 2014), and they are currently the fastest-declining piñon-juniper bird species 

(Boone et al. in press). Ongoing decline of the piñon 

tree’s main seed disperser will limit the potential of the 

trees to reestablish in areas of high mortality, colonize 

higher elevations, or shift distributions northward in 

response to climate change. 

We modeled Pinyon Jay habitat use to inform management and benefit both the bird and tree species. To 

understand Pinyon Jay habitat needs, we modeled 

habitat use at the home range, nesting colony, and 

nest scales at three study sites across New Mexico, 

USA. At the landscape scale (Ferrari and Ferrarini 

KEYWORDS—Gymnorhinus cyanocephalus, habitat, management, piñon-juniper, Pinyon Jay, spatial scale

2008), the home range of a Pinyon Jay flock can range 

from 3000–5000 ha. At the ecosystem scale (Ferrari 

and Ferrarini 2008), a nesting colony can range from 

1–50 ha. The nest scale includes the nest tree and its 

immediate surroundings, an area of about 1 ha. Study 

areas were the Oscura Mountains, White Sands Missile 

Range, NM; the Manzanita Mountains, Kirtland Air 

Force Base, NM; and the Bureau of Land Management 

Farmington, NM Resource Area.

At the landscape scale, we created habitat maps of the 

three study areas. For the Oscura Mountains, we used 

an existing vegetation map (Muldavin et al. 2000) in 

combination with 1-m natural color aerial photography 

taken in 2009. For the Manzanita Mountains, we created a vegetation classification based on 6-inch color 

digital ortho-imagery taken in 2008 (Johnson et al. 

2014, 2016). For the Farmington Resource Area, we 

used National Agriculture Imagery Program 1-m visible and near-infrared digital aerial photography from 

2014 and Landsat 8 satellite imagery to create a land 
USDA Forest Service RMRS-P-77. 2020. 29

cover classification (Johnson et al. 2017). Map units 

were defined based on Pinyon Jay habitat use. Yearround home ranges of two Pinyon Jay flocks in central 

NM averaged 4,779 ha (Johnson et al. 2014). The summer range of one flock in northwestern NM was 4,034 

ha (Johnson et al. 2015). Jays used mainly Juniper 

Woodland and Savanna, Piñon-Juniper Woodland, and 

Piñon Woodland habitats (Johnson et al. 2014, 2017). 

Higher elevation Piñon-Juniper Woodland and Piñon 

Woodland were used for nesting, while lower elevation 

Juniper Woodland and Savanna was used more often 

in the nonbreeding season (Johnson et al. 2016). 

We created a predictive habitat model of colonyscale habitat using the landscape-scale habitat model 

(above) land cover data combined with geospatial data 

such as slope, aspect, elevation, solar radiation, and 

NDVI (greenness). We first classified colony sites, 

then created a supervised classification of the entire 

study area based on the geospatial measures. For the 

models, we retained areas conforming to the geospatial 

and vegetation profiles of known nesting colonies. 

A comparison of the predictive model to validation 

colonies at the Oscura Mountains and Manzanita 

Mountains study sites showed 100 percent and 76 

percent overlap of the validation colonies with the 

predictive model, indicating good predictability of the 

colony-scale model (Johnson et al. 2016).

Using conditional logistic regression, we modeled 

nest-scale habitat use at nine nesting colonies, three at 

each of the three study sites. We collected data on 5-m 

and 11.3-m radius BBird (Martin et al. 1997) plots at 

each nest and a paired, random plot 100 m away from 

the nest plot. Data collected included diameter and 

height of nest tree and trees on plot, nest height, nest 

aspect, canopy cover, ground cover, number of trees 

and shrubs, and so on. Tree density at nests averaged 

965 trees/ha at seven colony sites in central NM and 

436 trees/ha at two colonies in northwestern NM. 

Pinyon Jays nested on plots with higher canopy cover, 

larger trees, and higher litter cover in central NM 

(Johnson et al. 2014). In northwestern NM, they nested 

in larger-diameter trees (measured as root crown 

diameter, RCD) and taller trees, compared to random 

plots, while avoiding the tallest, most emergent trees 

(Johnson et al. 2015). Combining nest-scale data 

from all sites for a case-controlled conditional logistic 

regression, we found that nest plots were located on 

cooler, northeast facing slopes and had higher total 

canopy cover than random plots. Nest trees were larger 

diameter, taller, more asymmetrical, and were surrounded by smaller trees, than corresponding trees on 

random plots.

In New Mexico piñon-juniper woodlands, management for Pinyon Jays should include adequate area for 

a flock’s home range, at least 5,000 ha. Multiple land 

cover types such as Juniper Woodland and Savanna, 

Piñon-Juniper Woodland, and Piñon Woodland should 

be included to account for variation in use across 

seasons and life history stages. Sagebrush shrubland 

or grassland components may also be included, 

especially for wintering habitat. Home ranges should 

include multiple areas containing 1) many large (>15 

cm RCD), mast-producing trees, 2) multiple colonysized patches (50 ha) of nesting habitat, and 3) water 

sources, especially near colonies.

Management for Pinyon Jays at the nesting colonyscale should include Piñon-Juniper Woodland and/

or Piñon Woodland and have relatively high canopy 

cover. Colony sites should 1) be at least 50 ha in area, 

2) contain large nest trees, 3) include > 20 dense 

clumps containing potential nest trees, 4) be within 1 

km of a water source, 5) have minimal fragmentation 

by roads, well pads, and so on, and 6) have minimal 

noise and foot traffic from March—June. 

Management for Pinyon Jays at the nest-scale should 

include: 1) tall piñon or Utah juniper trees, 2) large 

diameter piñon or Utah juniper trees (mean for this 

study 35.4 cm, range 7 – 100 cm), 3) high tree density 

(400–1200+ trees /ha), 4) high canopy cover (in this 

study, 40% measured from the ground; mean aerial 

30%), and 5) healthy trees with dense foliage. We believe these general recommendations are applicable to 

areas other than these three sites (e.g., nesting in larger 

trees, higher canopy cover), but specific numerical 

recommendations (e.g., number of trees/ha, mean tree 

diameter) will vary depending on the characteristics 

of each site. In general, retaining large piñon trees 

and high canopy cover would also benefit mule deer, 

Juniper Titmouse, Gray Flycatcher, Bewick’s Wren, 

and Black-throated Gray Warbler (Pavlacky and 

Anderson 2001).
30 USDA Forest Service RMRS-P-77. 2020.

ACKNOWLEDGMENTS

The research reported here was funded by grant #09-

425 from the Department of Defense Legacy Resource 

Management Program and agreement #L12AC20119 

Supplement No. 0005 from New Mexico Bureau of 

Land Management. We thank Kirtland Air Force Base 

and White Sands Missile Range for providing access 

to study sites and logistical support. John Hansen 

and John Kendall of the Farmington, NM BLM Field 

Office provided logistical support on BLM lands. Lisa 

Arnold created the solar radiation layer. Paul Neville 

helped create the KAFB and BLM vegetation maps. 

Matthew Baumann, Jason Kitting, Nathan Petersen, 

Eric Smith, and Cole Wolf collected Pinyon Jay field 

data. Penny Frazier provided P. edulis seeds. 

REFERENCES

Boone, John D.; Ammon, Elisabeth; Johnson, Kristine. 

2018. Long-term declines in the pinyon jay 

(Gymnorhinus cyanocephalus) and management 

implications for piñon-juniper woodlands. In: Shuford, 

W.D.; Gill Jr., R.E.; Handel, C.M. (eds). Avifaunal 

change in western North America. Studies of Western 

Birds 2. Western Field Ornithologists. Camarillo, CA: 

Western Field Ornithologists. p. 190-197. doi 10.21199/

SWB3.10

Ferrari, Ireneo; Ferrarini, Alesandro. 2008. From ecosystem 

ecology to landscape ecology: A progression calling for 

a well-founded research and appropriate disillusions. 

Landscape Online 6:1-12. doi:10.3097/LO.200806.

Johnson, Kristine; Horner, Mark; Neville, Paul; Neville, 

Teri; Petersen, Nathan; Smith, Jacqueline; and 

Wickersham, Lynn. 2017. Landscape-scale habitat map 

for Pinyon Jay and Gray Vireo at Farmington BLM 

Resource Area. Natural Heritage New Mexico Report # 

401. Biology Department, University of New Mexico, 

Albuquerque, NM. 36 p. http://nhnm.unm.edu/sites/

default/files/nonsensitive/publications//landscape%20

2015%20BLM%20FINAL.pdf

Johnson, Kristine; Neville, Teri B.; Smith, Jacqueline 

W.; Horner, Mark W. 2016. Home range- and colonyscale habitat models for pinyon jays in piñon-juniper 

woodlands of New Mexico, USA. Avian Conservation 

and Ecology 11:6. http://www.ace-eco.org/vol11/iss2/

art6/.

Johnson, Kristine, Wickersham, Lynn; Smith, Jacqueline; 

Petersen, Nathan; Wickersham, John. 2015. Nest-scale 

habitat use by pinyon jay and gray vireo in the BLM 

Farmington Resource Area 2013-2014, final report. 

Natural Heritage New Mexico Report GTR-15-386. 

Albuquerque, NM: University of New Mexico Biology 

Department. 42 pp. http://nhnm.unm.edu/sites/default/

files/nonsensitive/publications//FINAL%202014%20

BLM%20P-J%20%20report.pdf

Johnson, Kristine; Wickersham, Lynn; Smith, Jacqueline; 

Sadoti, Giancarlo; Neville, Teri; Wickersham, John; 

Finley, Carol. 2014. Habitat use at multiple scales 

by pinyon-juniper birds on Department of Defense 

lands III: Landscape, territory/colony, and nest scale. 

Natural Heritage New Mexico Report 14-GTR-381, 

Albuquerque, NM: University of New Mexico Biology 

Department. 128 pp. http://nhnm.unm.edu/sites/

default/files/nonsensitive/publications//Year%203%20

Legacy%20FINAL2.pdf

Ligon, James D. 1978. Reproductive interdependence of 

piñon jays and piñon pines. Ecological Monographs. 

48(2):111-126.

Martin, Thomas E; Paine, Charles; Conway, Courtney J.; 

Hochachka, Wesley M.; Allen, Paul; and Jenkins, Wajid. 

1997. BBIRD Field Protocol. Missoula, MT: Montana 

Cooperative Wildlife Research Unit, University of 

Montana.

Marzluff, John M.; Balda, Russell P. 1992. The pinyon jay: 

The behavioral ecology of a colonial and cooperative 

corvid. London: T & AD Poyser. 344 p.

Muldavin, Esteban; Harper, Glenn; Neville, Paul; and 

Chauvin, Yvonne. 2000. The vegetation of White Sands 

Missile Range, New Mexico Volume II: Vegetation map. 

Natural Heritage New Mexico Publ. No. 00-GTR-299. 

Albuquerque, NM: Natural Heritage New Mexico, 

University of New Mexico. 100 pp.

Pavlacky, David C. Jr.; Anderson, Stanley H. 2001. 

Habitat preferences of pinyon-juniper bird specialists 

near the limit of their geographic range. The Condor. 

103(2):322-331.

Sauer, J. R., J. E. Hines, J. E. Fallon, K. L. Pardieck, D. J. 

Ziolkowski, Jr.; Link, W.A. 2014. The North American 

Breeding Bird Survey, results and analysis 1966–2013, 

Version 01.30.2015. Laurel, MD: USGS Patuxent 

Wildlife Research Center. 
USDA Forest Service RMRS-P-77. 2020. 31

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Habitat Use at Multiple Scales by Nesting Gray Vireos in New Mexico

KEYWORDS—Gray Vireo, piñon-juniper, habitat, New Mexico, Vireo vicinior

Piñon-juniper (Pinus edulis–Juniperus spp.) woodlands cover approximately 40 million hectares of 

the western United States (Romme et al. 2009) and 

provide habitat for a diverse suite of avian species. 

Several U.S. Fish and Wildlife Service (FWS) Birds of 

Conservation Concern (BCC) depend on piñon-juniper 

habitats for breeding, including the Gray Vireo (Vireo 

vicinior). Gray Vireos breed within a limited range 

only in the hot, arid southwestern USA (Barlow et al. 

1999). In New Mexico, they occupy the piñon-juniper 

habitats of foothills, canyons, mesas, and rolling hills. 

Their populations are disjunct, however, with most 

occupied sites containing few, often less than 10, territories (DeLong and Williams 2006). The New Mexico 

Department of Game and Fish (NMDGF) currently 

lists the Gray Vireo as a threatened species (NMDGF 

2016). 

We evaluated habitat use by Gray Vireos at landscape, 

territory, and nest scales on U.S. Department of 

Defense (DOD) and Bureau of Land Management 

(BLM) lands in New Mexico. We collected data 

between 2009 and 2012 on three DOD sites: White 

Sands Missile Range (WSMR; Sierra County), 

Kirtland Air Force Base (KAFB; Bernalillo County), 

and Camel Tracks Training Area (CTTA; training 

grounds on BLM land in Santa Fe County). We 

expanded the study to BLM lands in the Farmington 

Field Office from 2013 to 2016, including Crow Mesa 

(Sandoval County), Pump Canyon and Pump Mesa 

(San Juan County), and several canyons and rolling 

hills north and west of the City of Aztec (San Juan 

County). To date, we have completed landscape- and 

territory-scale analyses only at DOD sites and nestscale analyses at both DOD and BLM sites.

At the landscape scale, the vast majority (≥75 percent) of Gray Vireo detections and nests at DOD 

sites occurred in juniper-dominated woodlands or 

savanna habitat types containing less than 25 percent 

piñon trees. At KAFB and WSMR, 10 percent or 

fewer detections/nests also occurred in piñon-juniper 

woodlands (25–50 percent piñons). Additionally at 

WSMR, 10 percent or fewer detections/nests occurred 

Lynn E. Wickersham

Animas Biological Studies, Durango, Colorado 

Kristine Johnson

University of New Mexico, Natural Heritage New Mexico, Biology Department, Albuquerque, New Mexico 

Giancarlo Sadoti

Department of Geography, University of Nevada, Reno, Nevada 

Teri Neville

University of New Mexico, Natural Heritage New Mexico, Biology Department, Albuquerque, New Mexico

John Wickersham

Animas Biological Studies, Durango, Colorado
32 USDA Forest Service RMRS-P-77. 2020.

in shrubland and ≤4 percent in arroyo riparian habitat 

types dominated by shrub species such as wavyleaf 

oak (Quercus x pauciloba), mountain mahogany 

(Cercocarpus montanus), acacia (Acacia spp.), catclaw 

mimosa (Mimosa aculeaticarpa var. biuncifera), featherplume (Dalea Formosa), common sotol (Dasylirion 

wheeleri), Apache plume (Fallugia paradoxa), 

resinbush (Viguiera stenoloba), and desert willow 

(Chilopsis linearis; see Johnson et al. 2014). 

Mean territory size at DOD sites was 0.9 ha at 

WSMR (n=44), 2.8 ha at CTTA (n=24), and 3.1 ha at 

KAFB (n=49); overall mean territory size was 2.3 ha. 

Important predictor variables for the territory-scale 

analysis included slope, aspect, elevation, curvature, 

solar radiation, and greenness; however, the importance and/or relationship of these predictors varied 

across sites. At CTTA, territories occurred on terrain 

with more north-facing aspects, higher elevations, 

intermediate slopes, and lower overall solar radiation 

compared with non-territories. At KAFB, territories 

occurred at more intermediate aspects (e.g., east- or 

west-facing), lower elevations, and in areas with more 

intermediate evergreen greenness than non-territories. 

Finally, at WSMR, territories were situated in areas 

with more southerly aspects, negative (bowl-shaped) 

curvature, intermediate elevations, lower slopes, and 

lower solar radiation than non-territories. Differences 

in the relationship of some territory predictor variables 

occupancy—specifically slope, aspect, and elevation—

probably reflect inherent differences in topography and 

elevation across the three study sites rather than specific preferences by vireos (see Johnson et al. 2014). 

We found 89 Gray Vireo nests across DOD sites, all 

in juniper trees. Of 65 nests at BLM sites, 82 percent 

were in junipers, 15 percent in piñon trees, and 3 

percent in big sagebrush (Artemisia tridentata). All 

but one of the piñon tree nests were from a single site, 

Crow Mesa, where the majority (75 percent) of vireo 

nests were in piñon trees. At that site, the ratio of piñon to juniper trees in the area immediate surrounding 

vireo nests was considerably higher (0.7) compared 

with the other BLM and DOD sites (≤0.2). 

Nest-scale habitat use models at both DOD and BLM 

sites indicated that Gray Vireos selected nest sites in areas with more and taller trees relative to random points 

within their territories; however, actual tree density 

and height varied across sites. Mean tree density on 

BLM sites was almost three times greater (316.1 trees/

ha) than on DOD sites (113.0 trees/ha). Conversely, 

mean tree height surrounding nests on DOD lands 

averaged higher (3.3–4.0 m across sites) than BLM 

lands (2.5–3.4 m). At DOD sites, Gray Vireos also 

selected nest locations with more south-facing aspects 

and negative (bowl-shaped) curvature compared with 

randomly-selected locations within their territories. Our 

aspect data contradict an earlier study by DeLong and 

Cox (2005) in Socorro and Santa Fe, NM, who reported 

that Gray Vireos tend to nest on west-facing aspects. At 

BLM sites, vireos also exhibited a weak preference for 

nest trees with smaller, on average, tree foliage width 

than random trees within their territories.

Our data indicate that Gray Vireos mainly occupy 

juniper-dominated woodlands and savannas but 

can be flexible in choosing nest substrates. Likely, 

preferences for territory characteristics differ across 

landscapes based on varying topographic features and 

variation in woodland species composition. While vireos may occupy woodlands of varying density, height, 

and composition, they prefer nesting around taller 

and denser trees within their territories. Thus, piñon 

-juniper management activities should strive to retain 

older, more mature stands with similar tree densities as 

documented in our study.

ACKNOWLEDGMENTS

This research was funded by grants from the U.S. 

Department of Defense, New Mexico Bureau of Land 

Management, and New Mexico Department of Game 

and Fish. We thank our contacts at the military installations for their financial and/or logistical support: 

Steve Latimer (CTTA), Dustin Akins and Gregg Dunn 

(KAFB), and Trish Cutler (WSMR). John Kendall 

of the Farmington, NM BLM Field Office provided 

logistical support on BLM lands. Lisa Arnold created 

the solar radiation layer. Paul Neville helped create 

the KAFB and BLM vegetation maps. Steve Cox provided information on Gray Vireo locations at KAFB. 

Doug Burkett provided Gray Vireo survey data and 

logistical support at WSMR. Thanks also to Larisa 

Crippen-Chavez, Rachel Grey, Michael Hilchey, Jessa 

Hutchins, Jason Kitting, Cristin Salaz, Pete Skartvedt, 

Raymond Van Buskirk, James Wickersham, and 

Cassandra Wilson for their work in the field. 
USDA Forest Service RMRS-P-77. 2020. 33

REFERENCES

Barlow, J.C., S.N. Leckie, and C.T. Baril. 1999. Gray Vireo 

(Vireo vicinior), The Birds of North America Online (A. 

Poole, Ed.). Ithaca, NY: Cornell Lab of Ornithology; 

Retrieved from the Birds of North America Online. 

doi:10.2173/bna.447. http://bna.birds.cornell.edu/bna/

species/447

Delong, J.P.; Cox, N.S. 2005. Nesting ecology of gray 

vireos in central New Mexico: 2005 results. Unpublished 

report prepared for the Bureau of Land Management 

and the New Mexico Department of Game and Fish by 

SORA and Eagle Environmental, Inc. 15 pp.

Delong, J.P., and S.O. Williams. 2006. Status report and 

biological review of the Gray Vireo in New Mexico. 

Unpublished report prepared for the New Mexico 

Department of Game and Fish, Santa Fe, NM. 

Johnson, K.; Wickersham, L.; Smith, J; [et al.]. 2014. 

Habitat use at multiple scales by pinyon-juniper birds on 

Department of Defense lands III: Landscape, territory/

colony, and nest scale. Natural Heritage New Mexico 

Publication 14-GTR-381. Albuquerque, NM: Natural 

Heritage New Mexico, University of New Mexico. 128 

pp.

New Mexico Department of Game and Fish (NMDGF). 

2016. Threatened and endangered species of New 

Mexico, 2014 biennial review. Santa Fe, NM: Wildlife 

Management and Fisheries Management Divisions.

Romme, W.H.; Allen, C.D.; Bailey, J.D.; [et al.]. 2009. 

Historical and modern disturbance regimes, stand 

structures, and landscape dynamics in piñon-juniper 

vegetation of the western United States. Rangeland 

Ecology and Management 62:203–222.
34 USDA Forest Service RMRS-P-77. 2020.

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Elk, Deer, and Piñon-Juniper in New Mexico: Needs, What Works, and 

What Doesn’t

INTRODUCTION

Habitat quality is the main determinant of individual 

and population performance of mule deer (Odocoileus 

hemionus) and elk (Cervus elaphus), primarily through 

the influence of nutrition on individual body condition 

and consequent effects on demographic vigor (Cook 

2002; Hanks 1981; Wakeling and Bender 2003). 

Because availability and nutrient content of forage 

largely determine the condition of individuals (Cook 

2002; Verme and Ullrey 1984; Wakeling and Bender 

2003), a direct link exists between habitat and population performance (Bender 2012; Cook et al. 2004; 

Gaillard et al. 2000; Hanks 1981; Hoenes and Bender 

2012). Thus, attributes of habitat, including plant 

community composition, phenological development, 

and availability of cover, influence the condition of individuals and hence population performance (Hoenes 

ABSTRACT—Elk (Cervus elaphus) and mule deer (Odocoileus hemionus) differ in nutritional and structural habitat needs; consequently, management of pinyon-juniper (Pinus spp.-Juniperus spp.) woodlands 

to provide optimal habitat differs for these herbivores. Diet quality requirements are higher for deer, and 

are highest for both species during late gestation, lactation, and antler growth. Properly timed treatments, 

especially prescribed burning, can enhance forage quality and alter forage species composition, providing dietary benefits during these times. Late-winter burns provide a nutrient flush during these critical 

periods, and burn intervals can be manipulated to selectively benefit deer or elk by favoring browse or 

herbaceous species in the understory. Burning at other times can increase forage biomass, but has fewer 

beneficial effects on forage quality. Overstory thinning can also increase forage biomass given proper 

slash treatment, but excessive thinning can degrade cover attributes. For example, deer prefer higher 

levels of security cover, and most movements are near denser woodland patches, which ideally compose 

approximately 25 percent of each home range on the landscape mosaic. Elk seek thermal cover when 

average daily temperatures exceed their thermal neutral zone, but this can be provided by single trees or 

clumps. Other treatments, such as chipping or shredding, may decrease forage and reduce habitat quality 

if residual biomass is not treated properly.

KEYWORDS—elk, fire, forage, mule deer, New Mexico, nutrition, pinyon-juniper, thinning

and Bender 2012; Wakeling and Bender 2003), 

although the mechanisms that drive these habitatperformance relationships are often poorly appreciated 

by managers.

Expansion and increasing density of pinyon-juniper 

(Pinus spp.-Juniperus spp.) woodlands, particularly 

juniper, is a contentious issue for management of mule 

deer and elk habitat in the southwestern United States 

including New Mexico (Lutz et al. 2003; Short and 

McCullough 1977; Short et al. 1977). Pinyon-juniper 

(PJ) woodlands are one of the most extensive vegetation types in North America (Romme et al. 2009), 

and include nearly 3 million ha in New Mexico (Van 

Hooser et al. 1993), almost 10 percent of the total area 

of the state. Most of this woodland is actual or potential habitat for mule deer and elk, making PJ woodlands the most abundant habitat type for mule deer and 

Louis C. Bender

Extension Animal Sciences and Natural Resources, New Mexico State University, Las Cruces, New Mexico
USDA Forest Service RMRS-P-77. 2020. 35

elk in New Mexico. However, a number of attributes 

associated with these woodlands can limit their value 

to deer and elk, including:

1. High concentrations of secondary plant compounds, especially terpenes, in foliage. The 

antimicrobial properties of these limit fermentation and thus rumen function (Schwartz et al. 

1980a,b). Consequently, forage value of juniper is 

limited despite high digestible energy.

2. Presence of other secondary plant compounds 

such as phenolics, which may have communitylevel effects on the herbaceous and woody shrub 

understory, such as inhibiting growth due to 

leachate (Ramsey 1989).

3. Limited understory development due to competition for light, water, and other nutrients (Hoenes 

and Bender 2012; Lutz et al. 2003; Pieper 1990; 

Short and McCullough 1977; Short et al. 1977). 

This limits the availability of ungulate forage in 

denser PJ stands. 

The lack of understory development in particular has 

driven most management efforts intended to improve 

PJ woodlands as deer and elk habitat. Most treatments 

attempt to increase understory production through 

partial or complete removal of the PJ canopy, because 

an inverse relationship between overstory PJ cover 

and understory production is frequently seen (e.g., 

Brockway et al. [2002]; Halbritter and Bender [2011b]; 

Hoenes and Bender [2012]; Howard et al. [1987]; 

Pieper [1990]; Short et al. [1977]). Understory species 

include potentially important forages such as forbs and 

woody browse (Hoenes and Bender 2012). 

An implicit assumption behind overstory reduction 

treatments is that understory biomass equals food, 

which ignores differing foraging strategies and forage 

quality requirements of large herbivores (Hofmann 

1985) as well as differing nutritional quality and palatability of resultant understory communities. Moreover, 

the magnitude and type of understory response can be 

influenced by a variety of factors, including the type, 

timing, and size of treatments, and the composition 

of extant understory. (Bender 2011, 2012; Bergman 

et al. 2014). Consequently, the habitat-performance 

relationship is far more complex than the simple 

generalization that less PJ equals more food. Because 

of this, the anticipated benefits of treatments are often 

not realized. In a recent review of published material 

that assessed population responses of wildlife to PJ 

treatments, Bombaci and Pejchar (2016) found that 

less than 10 percent of mechanical removal treatments 

(i.e., chaining, bulldozing) and less than 20 percent of 

thinning treatments resulted in any positive response 

by ungulates. 

The lack of positive responses raises the issue of 

what role PJ actually plays in deer and elk habitatperformance relationships; in other words, does the 

presence of untreated PJ negatively affect performance 

of elk and deer or not? Further, inconclusive findings 

may not solely result from a simplistic “less PJ equals 

more food” assumption. Habitat-herbivore interactions 

and resultant individual and population dynamics are 

highly complex, and require understanding of how 

habitat attributes fundamentally affect individuals 

in a population, and how individual responses may 

(or may not) be reflected in population responses. 

Understanding these fundamental relationships is 

necessary for developing informed management treatments to benefit deer and elk. 

Moreover, mule deer and elk are very different herbivores, with very different forage quality and cover 

requirements (Cook 2002; Hofmann 1985; Verme 

and Ullrey 1984; Wakeling and Bender 2003). Hence, 

there is no single prescription for optimal management 

of PJ woodlands or other habitats for elk and deer or 

large herbivores, as often presented in management 

guidelines that tend to emphasize thermal cover (e.g., 

Miller et al. [2005]; Thomas et al. [1979]; Wittmer et 

al. [1985]). Other issues may also cloud the results of 

herbivore-PJ studies, including the use of evaluation 

metrics (e.g., population size, survival, pellet group, or 

telemetry location density) that are either problematic 

indices of habitat quality or far removed from fundamental responses to habitat; a poor understanding of 

treatment effects on elk and deer habitat needs; and 

use of management treatments ostensibly intended for 

deer or elk but actually intended to achieve other, often 

unstated, management goals. A common example 

is the continued emphasis on winter thermal cover 

(Thomas et al. 1979) in Federal lands management 

for elk and deer, despite the demonstration that winter 

thermal cover provides no energetic benefit (Cook et 
36 USDA Forest Service RMRS-P-77. 2020.

al. 1998; Freddy et al. 1986) and that security cover 

can be provided by multiple seral states other than 

large sawtimber or old growth.

These issues contribute to the challenge of identifying successful treatments of PJ for elk and deer. 

To illustrate, Bombaci and Pechjar (2016) recently 

reviewed more than 40 primary papers regarding PJ 

treatments and elk or deer. A January 2017 search of 

Web of Knowledge (Biological Abstracts) revealed at 

least 67 papers on mule deer and PJ, and at least 26 on 

elk and PJ, and this search did not include literature 

from sources such as USDA Forest Service publications. Given all of these publications and knowledge, 

it would be a reasonable expectation that any number 

of management treatments that consistently improve 

PJ woodlands as elk and deer habitat would have 

been identified. Instead, the actual efficacy of most 

treatments is uncertain (Bombaci and Pechjar 2016). 

Reasons for this uncertainty include the limitations of 

many of these studies, particularly the lack of relating 

habitat management practices to increases in individual performance, which must be present to realize 

population-level responses.

Consequently, understanding fundamental relationships to habitat (i.e., habitat-individual performance), 

how these differ between mule deer and elk, and how 

these are (or are not) met by PJ habitats, and why, is 

necessary to develop informed actions to benefit these 

large herbivores. Herein, I attempt to clarify these issues in order to guide effective habitat management. 

My goals included:

1. Clarifying the fundamental role of habitat for elk 

and deer 

2. Illustrating how this fundamental relationship affects individual and population performance 

3. Evaluating the influence of PJ on performance of 

deer and elk, and

4. Evaluating preliminary results from selected 

ongoing long-term studies of management treatments in terms of providing for elk and deer 

needs for forage and cover. 

To do this I review the role of habitat in individual and population productivity (i.e., the 

habitat-condition-productivity link), and existing 

literature on fundamental relationships between mule 

deer, elk, and PJ in New Mexico. I further use existing 

and new datasets from multiple populations and study 

areas in New Mexico to define needs of mule deer and 

elk relative to PJ woodlands, and to identify treatments 

that provide for these needs in the hotter, more arid 

woodlands of New Mexico.

What is Elk and Deer Habitat?

Understanding how habitat fundamentally benefits 

individuals, and how or why populations then may (or 

may not) respond to treatments, is key to designing effective habitat management strategies for deer and elk. 

The fundamental role of habitat is to maximize the energy balance of individuals, because every life process 

of homeothermic organisms is fundamentally influenced by nutrition and individual condition (e.g., Cook 

2002; Cook et al. 2004; Hanks 1981; Mautz 1978; 

Verme and Ullrey 1984; Wakeling and Bender 2003). 

Habitat affects individual energetics of wildlife both 

directly and indirectly. The primary direct influence is 

providing nutrients, that is, foraging habitat, especially 

forage quality. The indirect influences include:

1. Minimizing disturbance, that is, hiding or security 

cover

2. Acquiring food, that is, ambush or stalking cover

3. Life security, that is, hiding or security cover, and

4. Moderating environmental effects, that is, thermal 

cover.

Each of these ultimately focuses on optimizing 

individual body condition through acquiring energy, 

minimizing loss of energy, or preventing the individual 

from being converted to a different individual’s energy. 

To maximize individual energy balance, individuals 

must balance food vs. cover needs. With respect to 

PJ woodlands, the relevant questions thus are: How 

does PJ affect individual energetics, why, and how 

can management treatments affect these fundamental 

relationships?

Evaluating the influences of PJ on deer and elk, and 

the efficacy of management treatments to enhance 
USDA Forest Service RMRS-P-77. 2020. 37

individual and population performance, requires 

understanding of how individuals and populations respond to resource availability. Individuals (and populations) respond to resource stress in a hierarchical and 

predictable manner (Gaillard et al. 2000; Hanks 1981), 

including:

1. Declines in individual body condition

2. Declines in juvenile conception and fecundity

3. Declines in juvenile survival 

4. Declines in adult fecundity, and

5. Declines in adult survival.

Thus, resource stress is first evident in declining nutritional condition of adults, which results in delayed 

sexual maturation of yearlings and decreased litter 

size of both primiparous and multiparous females 

(Bender and Hoenes 2017a; Bishop et al. 2009; Cook 

et al. 2004; Gaillard et al. 2000; Johnstone-Yellin 

et al. 2009). Decreased condition further results in 

later birth dates and lighter birth weights of neonates, 

which decreases preweaning survival (Bender and 

Hoenes 2017a; Cook et al. 2004; Lomas and Bender 

2007). Only under extreme stress may adult survival 

be affected (Gaillard et al. 2000). Thus, individual and 

population productivity are most sensitive to stress, 

adult survival least. 

Performance metrics that assess individual condition 

and juvenile growth and fecundity are much closer to 

indexing the fundamental role of habitat on deer and 

elk, and thus capturing real relationships between habitat attributes and deer and elk performance (Bergman 

et al. 2014; Hanks 1981). Conversely, metrics such as 

adult survival or population rate-of-increase may not 

be affected until extreme levels of resource stress are 

reached because of compensatory responses in survival 

dynamics of juveniles and adults, both within and 

among age classes (Bender 2008). Additionally, survival and population size, being further removed from 

the fundamental influence of habitat, can be affected 

by many extraneous variables, including harvest, 

accidents, severe density-independent events (e.g., 

weather, some diseases), and population age-structure, 

particularly in relatively small geographic areas such 

as most treatment test sites or study areas. Hence, the 

distance of these metrics from the fundamental effect 

can lead to incorrect or inconclusive evaluations of 

habitat treatments. They are poor evaluation metrics, 

and their use should be avoided (Van Horne 1983).

Nutritional requirements of individuals vary throughout the annual cycle of deer and elk (fig. 1), and these 

differences must also be understood if management 

treatments are to benefit individual energetics. For 

both deer and elk, the greatest nutritional requirement 

occurs during the last trimester of gestation and during 

primary lactation (Cook 2002; Wakeling and Bender 

2003), equivalent to early spring through mid-summer. 

The magnitude of this effect is apparent by the overwhelming negative effect of lactation on individual 

condition under all circumstances below optimal 

nutrition (Bender and Hoenes 2017b; Cook et al. 2004; 

Piasecke and Bender 2011). Because free-ranging 

populations rarely if ever attain optimal nutrition, 

lactation status has a greater effect on individual condition than most habitat or environmental influences 

in free-ranging mule deer and elk populations in New 

Mexico (Bender and Hoenes 2017b; Piasecke and 

Bender 2011). This impact, however, can be reduced 

as habitat quality increases (Piasecke and Bender 

2011). The importance of the individual energetic 

cycle with regard to habitat management of PJ is that 

management treatments must provide increased nutrition or decreased disturbance when most needed. This 

means, for deer and elk, during late gestation and early 

lactation. Consequently, treatments should be designed 

to provide high quality forage or a nutrient flush during spring and early summer (Bender 2011, 2012). 

Cover requirements also vary. Security cover is 

important throughout the annual cycle of deer and 

elk, both to minimize disturbance and to keep the 

individual alive. Mule deer are more vulnerable than 

elk to predation, and thus their preferences for security 

cover are much stronger (e.g., Bender et al. [2007b]; 

Heffelfinger et al. [2006]; Hoenes [2008]; Lomas and 

Bender [2007]; Lutz et al. [2003]; Short et al. [1977]). 

Minimizing disturbance is more critical during periods 

of high energetic needs or low resource availability; 

for example, Shively et al. (2005) documented decreased productivity of elk in response to increased 

disturbance during the calving season. 
38 USDA Forest Service RMRS-P-77. 2020.

The need of elk and mule deer for winter thermal 

cover (Cook et al. 1998; Freddy et al. 1986) has been 

discounted, although the thermal tolerance of both 

species may be challenged in hotter climates (Parker 

and Gillingham 1990; Parker and Robbins 1984). In 

particular, relatively recent colonization (or recolonization) of hotter, more arid PJ woodlands by elk in New 

Mexico (e.g., Chaco Canyon area, Oscura and San 

Andres Mountains) makes summer thermal tolerance 

of elk a relevant issue in PJ management. The upper 

thermal neutral zone of elk is approximately 26.5 °C 

(Parker and Robbins 1984); above this, they need 

to actively cool themselves. Consequently, elk shift 

activity patterns when mean monthly high temperature 

exceeds their thermal tolerance, including avoiding 

activity during midday hours and seeking bedding sites 

with high overhead cover, particularly overstory trees 

(Bender et al. 2012a). Conversely, summer thermal 

cover needs of mule deer can be satisfied with understory shrubs (Tull et al. 2001). 

Last, mule deer and elk are not the same animal, despite being frequently treated as such, and both differ 

from cattle in their dietary needs. Forage quality requirements of elk and mule deer are much higher than 

for cattle; for example, good diet quality for elk is 60 

to 64 percent digestibility (or about 2.75–2.90 kcal/g 

of forage) (Cook et al. 2004). Needs of mule deer 

are even higher (Hanley 1997; Wakeling and Bender 

2003), and thus mule deer are more sensitive to the 

specific characteristics and species composition of foraging environments. Consequently, cattle—not deer or 

elk—are often the primary beneficiary of management 

treatments that are intended to benefit wild ungulates 

but are poorly planned with respect to timing and type 

of forage responses. 

These diet quality differences are reflected in the food 

habits of mule deer and elk. Woody browse and forbs 

are the main components of mule deer diets (Boeker 

et al. 1972; Krausman et al. 1997; Short et al. 1977; 

Wakeling and Bender 2003), whereas elk include 

much higher proportions of grass in their diets (Cook 

2002; Short et al. 1977). Mule deer require higher 

quality forages (i.e., higher cell soluble:cell wall ratio) 

because their smaller rumeno-reticulum limits their 

ability to digest forages higher in cellulose (such as 

grasses) as compared to elk (Hanley 1997; Hofmann 

1985; Putnam 1988). Thus, mule deer are far more 

selective of higher quality forage, spend more time 

Figure 1—Energetic requirements (E) of elk relative to key periods in their life 

history; the larger the E, the greater the requirements. Energetic requirements 

are greatest during late gestation and early lactation, corresponding to late April 

through June in most of New Mexico. Mule deer show the same general pattern, 

but later parturition periods “twist” the figure clockwise approximately 0.5 to 1 

month depending on latitude.
USDA Forest Service RMRS-P-77. 2020. 39

foraging, and are more sensitive to species composition and structure of plant communities (Hoenes and 

Bender 2012; Wakeling and Bender 2003). Because of 

this environmental sensitivity, woody browse is generally the predominant year-long component of deer 

diets (Boeker et al. 1972; Krausman et al. 1997), as its 

availability compared to forbs is less influenced by environmental factors such as precipitation (Hoenes and 

Bender 2012). But many forbs are better sources of nitrogen (N), digestible energy (DE), and other nutrients 

(Boeker et al. 1972; Urness et al. 1971; Wallmo et al. 

1977). Optimal forage for mule deer is thus perfectly 

suitable for elk. However, elk can be highly productive 

on ranges dominated by lower quality foods such as 

grasses in which mule deer could not be productive.

Understanding the fundamental role of habitat helps 

clarify the current dynamics of cervids in New 

Mexico, and the importance of habitat quality is 

clearly seen in population demographics. Survival and 

performance of multiple elk and especially mule deer 

populations throughout New Mexico were limited 

by poor habitat quality through effects on individual 

body condition (Bender et al. 2007a, 2011, 2012b; 

Bender and Hoenes 2017b; Bender and Piasecke 2010; 

Halbritter and Bender 2011a Hoenes 2008; Lomas 

and Bender 2007; Piasecke and Bender 2011), which 

strongly affected adult survival (Bender et al. 2007a, 

2011; Halbritter and Bender 2011a), production and 

survival of juveniles (Bender and Piasecke 2010; 

Hoenes 2008; Lomas and Bender 2007), and predisposition to mortality (Bender et al. 2007a, 2011, 2012b; 

Bender and Rosas-Rosas 2016; Hoenes 2008; Lomas 

and Bender 2007). Consequently, the leading cause of 

mortality among mule deer populations was malnutrition (Bender et al. 2007a, 2011, 2012b; Hoenes 2008; 

Lomas and Bender 2007). 

In response, mule deer populations have significantly 

declined throughout much of New Mexico (Bender et 

al. 2007a, 2011, 2012b), mostly because of declines 

in the quantity and quality of food and also because of 

seasonal drought (particularly from conception through 

parturition, approximately January–June) (Bender et 

al. 2007a, 2011, 2012b; Hoenes 2008). Conversely, elk 

are increasingly colonizing (or recolonizing) new habitats, including hotter, arid habitats such as PJ, which 

is typically associated with environmental conditions 

that challenge both thermoregulatory and water needs 

of elk (Bender et al. 2010, 2012a). However, population productivity of elk is similarly strongly related to 

individual condition and hence forage quality in New 

Mexico (Bender and Piasecke 2010; Halbritter and 

Bender 2011a, 2015). Thus, while both cervids share 

the same fundamental relationships with habitat, trends 

of each species in response to recent past and current 

changes in habitats, including PJ expansion and related 

natural and anthropogenic changes, are dissimilar, 

highlighting the fundamental differences between 

these large herbivores. 

How Does PJ Function as Elk and Deer 

Habitat?

In this section, I review studies from New Mexico that 

assessed the fundamental quality of PJ as mule deer 

and elk habitat, in other words, the influence of PJ 

on accrual of condition (e.g., body fat). I also review 

the influence of PJ on home range size, an index of 

habitat quality (Bender 2012) that is likely to reflect 

cover requirements more so than nutritional needs. Last, 

I review the role of PJ as cover for mule deer and elk. I 

include only data for females, since their dynamics drive 

population rate-of-increase (White and Bartmann 1998).

Mule deer

On the Corona Range and Livestock Research Center 

in east-central New Mexico, Bender et al. (2013) 

found that body fat of mule deer was not significantly 

related to either the proportion of PJ in spring-summer-autumn (SSA) home ranges or to the proportion of 

deer locations in PJ during the SSA period. However, 

body fat was positively related to the proportion 

of locations in mechanically thinned PJ during the 

SSA. Size of SSA home ranges was also not related 

to the proportion of PJ in SSA home ranges, but was 

negatively related to the number of locations of deer 

in PJ during the SSA period, as well as the number of 

locations in mechanically thinned PJ; the latter two 

indicate that habitat quality for deer apparently increased as more PJ was included in their home ranges. 

Additionally, more than 88 percent of locations of 

mule deer were less than 200 m from unmanaged PJ 

woodlands or savannas (Bender et al. 2013), indicating 

that deer required PJ woodlands for security cover in 

this site (Bender et al. 2013). 
40 USDA Forest Service RMRS-P-77. 2020.

Similarly, body fat was unaffected by proportion of 

PJ in SSA home ranges or use of PJ during SSA in the 

San Andres Mountains in south-central New Mexico 

(Hoenes 2008; L. Bender, unpublished data). Size of 

SSA home ranges was negatively related to the proportion and use of PJ during the SSA. Deer were also located in PJ in SSA from 4 to 16 times more often than 

the availability of PJ in SSA ranges would predict, and 

1.2 to 24 times more often than PJ availability in annual home ranges would predict. 

In contrast, accrual of body fat was negatively impacted by increasing proportion of PJ in SSA home 

ranges and increasing use of PJ during the SSA period 

in north-central New Mexico (Bender et al. 2007b). 

Size of SSA ranges was not related to any PJ measure. 

Piñon-juniper was overrepresented by 12.4 percent in 

SSA ranges and 13.0 percent in annual home ranges as 

compared to its availability on the landscape, and deer 

were located in PJ at least 1.1 times more frequently 

than predicted by landscape availability (Bender et al. 

2007b). 

Results from these individual sites suggest a mixed 

response to PJ by mule deer, similar to the inconclusive results reviewed by Bombaci and Pejchar (2016). 

Condition appears to be unaffected or negatively 

affected by untreated PJ, yet many metrics of distribution suggest positive effects, likely driven by cover 

needs. Consequently, gains in mule deer habitat can be 

attained by management of PJ woodlands to increase 

forage quantity and quality (e.g., Bergman et al. 

[2014]), but care must be taken to preserve adequate 

structure for cover needs of mule deer (Bender 2012). 

Elk

Results from elk studies were also variable. In the 

Chaco Canyon area of northwestern New Mexico, 

Bender et al. (2012a) found accrual of body fat to 

be unrelated to the proportion of PJ in SSA home 

ranges, while size of both annual and SSA home 

ranges was negatively related to the proportion of PJ 

in home ranges. Elk home ranges included PJ at 1.8 

to 2.4 times its availability on the landscape, and elk 

were found in PJ 1.4 to 1.5 times more frequently 

than availability on the landscape would predict. 

Additionally, PJ woodlands were an important microsite element of elk habitat. Twenty-seven percent of 

elk bedding sites were located under pinyon or juniper 

trees (second only to rock ledges and caves, at 35 

percent), and bedding sites averaged 46 to 56 percent 

overhead cover and 55 to 63 percent horizontal cover 

(Bender et al. 2012a). 

In the Lincoln National Forest of south-central New 

Mexico, Halbritter and Bender (2011b) found body fat 

to be negatively related to the proportion of PJ in SSA 

home ranges. In contrast to the results from the Chaco 

Canyon area, elk included PJ in SSA home ranges at 

only 0.05 to 0.16 times its availability on the landscape. 

The Lincoln study area was characterized by predominant availability of more mesic forest types such as aspen 

(Populus tremuloides) and mixed conifer (Halbritter and 

Bender 2011b), which were not present on the Chaco 

site. Availability of these more mesic elk habitats very 

likely decreased the use of PJ woodlands for cover.

Similar to mule deer, results from these individual sites 

suggest a mixed response to PJ by elk. Unmanaged PJ 

woodlands appear to have no positive impact on body 

condition, but metrics of distribution suggest positive 

effects, at least in hotter, more arid sites such as Chaco 

Canyon. Also similar to mule deer, gains in elk habitat 

potentially could be attained by management of the PJ 

woodlands to increase forage quantity and quality. In 

hotter, more arid sites, adequate amounts of thermal 

cover need to be maintained for elk (Bender et al. 

2012a). This management practice may be particularly 

relevant given that elk are increasingly using these 

hotter, lower elevation PJ woodlands (e.g., Chaco 

Canyon, the Oscura and San Andres Mountains), and 

the thermal benefits of PJ may be a key element to 

maintain elk in such areas given their thermal tolerance limitations (Bender et al. 2012a). 

METHODS

Study Areas

Deer and Elk Condition and Distribution 

My study areas for mule deer condition and distribution included past projects on the Corona Range and 

Livestock Research Center (CRLRC), the San Andres 

Mountains (SAM), and Colfax County (Northcentral). 

Elk study areas included past projects on Chaco 

Culture National Historic Park (Chaco) and Lincoln 
USDA Forest Service RMRS-P-77. 2020. 41

National Forest (LNF), and ongoing work in the Oscura 

Mountains and Chupadera Mesa (Oscura). Herein, I 

restricted individuals from past and current studies to 

those with home ranges that included only untreated 

PJ woodlands, and eliminated individuals with home 

ranges that included areas of treated PJ regardless of 

treatment type. Brief descriptions of these sites follow; 

see respective citations for additional details. 

The CRLRC is a 113-km2 working research ranch 

located in east-central New Mexico. Average high temperature in July was 29 oC and average low temperature in January was –6 oC. Mean annual precipitation 

was 40 cm, most of which occurs in July and August 

as high intensity, short duration convectional thunderstorms. During this study, annual precipitation averaged 0.93 times the normal (range = 0.72–1.22 times). 

Pinyon-juniper (J. monosperma) woodlands made up 

approximately 20 percent of the study site (Bender et 

al. 2011, 2013).

The SAM study area encompassed approximately 

11,000 km2 primarily within White Sands Missile 

Range (WSMR), south-central New Mexico. Average 

high temperature in July was 35 °C and average 

low temperature in January was –3 °C at WSMR. 

Precipitation averaged 20 to 35 cm annually, with the 

bulk of moisture occurring as short, intense rainstorms 

from July through September. During this study, annual precipitation averaged 1.05 times the normal 

(range = 0.50–1.46 times). Pinyon-juniper (primarily J. monosperma with occasional J. deppeana) 

woodlands made up about 7 percent of the study site 

(Bender et al. 2012b; Hoenes and Bender 2012).

The Northcentral study area encompassed approximately 4,860 km2 in Colfax County, north-central New 

Mexico. Average high temperature in July was 28 °C 

and average low temperature in January was –8 °C at 

Raton, New Mexico. Mean annual precipitation averaged 44 cm, with 62 percent falling from May through 

August. During this study, annual precipitation averaged 1.03 times the normal (range = 0.72–1.56 times). 

Pinyon-juniper (primarily J. monosperma) woodlands 

made up approximately 16 percent of the study site 

(Bender et al. 2007a,b).

The Chaco study area covered approximately 308 km2

on and adjacent to Chaco Culture National Historical 

Park in northwestern New Mexico. Average high temperature in July was 32 oC and average low temperature 

in January was –11 oC. Average annual precipitation 

was 23 cm, with 52 percent falling from July through 

October. During this study, annual precipitation averaged 0.92 times normal (range = 0.84–1.02 times). 

Pinyon-juniper (primarily J. monosperma) woodlands 

made up approximately 15 percent of the study site 

(Bender and Piasecke 2010; Bender et al. 2012a). 

The LNF study area encompassed much of the 1,823-

km2 Sacramento Ranger District in the southern 

Sacramento Mountains of south-central New Mexico. 

Average high temperature in July was 22 °C and average low January temperature was –7 °C in Cloudcroft, 

New Mexico. Annual precipitation averaged 67 cm 

with more than 50 percent falling from June through 

September. During this study, annual precipitation 

averaged 0.82 times the normal (range = 0.66–0.96 

times). Pinyon-juniper (primarily J. monosperma

with occasional J. deppeana) woodlands made up approximately 10 percent of the study site (Halbritter and 

Bender 2011a,b).

The Oscura study area encompassed approximately 

870 km2 of the Oscura Mountains and adjacent 

Chupadera Mesa in south-central New Mexico. 

Average high temperature in July was 33 °C and average low January temperature was –6 °C in Bingham, 

New Mexico. Annual precipitation averaged 27 cm 

with 66 percent falling from July through October, 

although this average underestimates actual precipitation received in the higher elevation PJ zone. During 

this study, annual precipitation averaged 0.97 times the 

normal (range = 0.74–1.07 times). Pinyon-juniper (J. 

monosperma and scattered J. deppeana) woodlands 

made up approximately 48 percent of the study site.

Pinyon-juniper treatment sites

I also include preliminary results from a subset of longterm studies assessing responses of preferred mule deer 

and elk forages to differing treatments of PJ woodlands 

on private ranches across the Southwest. Because these 
42 USDA Forest Service RMRS-P-77. 2020.

studies are ongoing and several landowners preferred 

not to have their property identified, I refer to these 

ranches by general geographic location only.

Pinyon-juniper treatment areas were located in southcentral (SCNM1 and SCNM2), north-central (NCNM), 

and central (CNM) New Mexico. Climatic and PJ 

relationships for the SCNM sites were similar to those 

listed for the Oscura site. Research on SCNM1 evaluated the influence of light long-term (i.e., decades) 

seasonal suitability and best pasture grazing on understory composition; this was the only site on which 

livestock grazing occurred. Research on the SCNM2 

site has evaluated the effects of broadcast burning (i.e., 

understory burning of the entire ranch) on understory 

species composition since 2011. During this study, 

annual precipitation measured at the site averaged 0.86 

times (range = 0.50–1.27 times) the approximate norm 

of 32 cm recorded at the nearest Western Regional 

Climate Center (wrcc.dri.edu) station. This site was 

previously thinned approximately 10 years before 

implementation of the burning trials. 

Conditions for the NCNM site were similar to those 

listed for the Northcentral site, except that PJ woodland was more varied, ranging from savanna with less 

than 10 stems/ha to denser woodland with more than 

200 stems/ha. During this study, annual precipitation at 

the site averaged 0.94 times (range = 0.67–1.17 times) 

the approximate norm of 41 cm recorded at the nearest 

Western Regional Climate Center station. Described 

treatments were limited to the PJ savanna on this site, 

where the effect of broadcast burning on understory 

composition has been evaluated since 2008.

Conditions for the CNM site were similar to those for 

the CRLRC. During this study, annual precipitation at 

the site averaged 1.00 times (range = 0.89–1.18 times) 

the approximate norm of 40 cm recorded at the nearest 

Western Regional Climate Center station. This site has 

been used to evaluate the effects of overstory thinning 

and chipping or mastication since 2013. 

Deer and Elk Condition and Distribution

I captured adult female elk and mule deer by darting 

or net-gunning from a Bell 206B Jet Ranger/OH58 

helicopter using carfentanil citrate and xylazine hydrochloride in late fall at the seasonal peak of condition 

on each site (Bender et al. 2007a, 2011, 2012b; Bender 

and Piasecke 2010; Halbritter and Bender 2011a; L. 

Bender, unpublished data). I blindfolded individuals 

to minimize stress during handling, and administered penicillin, vitamin B, vitamin E and selenium 

(MU-SE), and an eight-way Clostridium bacterin to 

alleviate capture stress. I radio-collared each individual with a very high frequency collar (Advanced 

Telemetry Systems, Asanti, Minnesota) and attached 

unique numbered ear tags (relative to site).

Individuals were located from the ground at least once 

per week, and locations were recorded with a handheld 

GPS unit and plotted using the geographic information 

system ArcGIS 10.0 (ESRI, Redlands, California). 

I calculated 95-percent minimum convex polygons 

(MCPs) (Mohr 1947) for each individual annually 

and for SSA (April–November) locations only, and 

a composite 100-percent MCP with a 2.5-km buffer 

from all locations pooled to define the overall herbivore use area. I used annual and SSA home ranges of 

each individual as a clip coverage in ArcGIS 10.0 to 

clip 30-m raster vegetation covertype coverages to 

determine the vegetation composition of annual and 

SSA home ranges. I delineated vegetation covertypes 

from the U.S. Geological Survey’s Southwest ReGAP 

land cover classification (https://www.usgs.gov/corescience-systems/science-analytics-and-synthesis/gap/

science/land-cover-data-overview?qt-science_center_

objects=0#qt-science_center_objects). 

I used a SonoVet 2000 ultrasound (Medison, Seoul, 

South Korea) with a 5 mHz probe to measure subcutaneous fat thickness along a straight line midway 

between the spine, at its closest point to the coxal tuber 

(hip bone), and the ischial tuber (pin bone) (MAXFAT) 

(Cook et al. 2001; Stephenson et al. 2002). I also 

determined a rump body condition score (rBCS) by 

palpation of the soft tissue of the rump along the sacrosciatic (ischial) ligament and scored results of rBCS 

from standards which ranged from 1 (emaciated) to 5 

(obese) in intervals of 0.25 (Bender et al. 2007a; Cook 

et al. 2001). I estimated body fat of elk using rBCS 

and MAXFAT following Cook et al. (2001), and of 

mule deer following Bender and Hoenes (2016).

As noted earlier, I limited this analysis to individuals 

that had only untreated PJ woodlands in their annual 

and SSA home ranges in order to assess the influence 
USDA Forest Service RMRS-P-77. 2020. 43

of PJ woodlands on condition in the absence of woodland treatments. I used a linear mixed model (PROC 

GLIMMIX; SAS Institute Inc., Cary, North Carolina) 

to model the effect of proportion of PJ in annual and 

SSA home ranges of mule deer and elk on accrual of 

body fat and size of annual and SSA home ranges. I 

normalized data within populations to account for differences in magnitude of home ranges and fat levels 

among populations. Population was treated as a random 

effect in all models, and I used a hierarchical model 

for body fat to account for the effect of lactation status 

on condition of mule deer and elk (Bender and Hoenes 

2017b; Piasecke and Bender 2011). If PJ had a significant effect on body fat, I further modeled the lactation 

× pinyon-juniper interaction to see if PJ cover had differing effects on condition of lactating and dry females. 

Forage Responses

I used line-point intercept transects to determine plant 

cover and species composition during mid- to late 

August or September annually depending on location, 

near the end of the growing season. I randomly placed 

a randomly oriented 100-m transect in each of 15 to 

20 patch or stand replicates for landscape treatments 

(or 10–15 transects within each stand for stand-level 

treatments), and recorded ground cover as bare, rock, 

litter, or vegetation at every 1-m interval along transects, 

with vegetation being further identified to species. I 

segregated plants into highly palatable, high quality food 

for deer or elk, or both (hereafter, preferred), or food for 

other species. I used diet studies from New Mexico (e.g., 

Boeker et al. 1972] Hoenes 2008; Short et al. 1977; L. 

Bender, unpublished data) and diets and associated diet 

quality data collected from many of the previously mentioned study areas used for elk and deer condition as well 

as ongoing studies to identify preferred forages at each 

site (L. Bender, unpublished data).

I estimated woody species densities using the pointcenter quarter method (Wyoming Game and Fish 

1982). Center points were established every 10 m 

along transects, with the four quadrants defined by the 

transect line and a line perpendicular to it. The nearest 

shrub (<5.1 cm diameter at breast height [d.b.h.]) and 

tree (>5.1 cm d.b.h.) located within each quadrant was 

identified to species and the distance from the center 

point to each tree or shrub measured. Canopy coverage was also estimated at each 10-m interval along 

transects by using a spherical densiometer (Lemmon 

1956), taking four separate estimates (one in each cardinal direction) and using the average of all readings 

as the interval estimate. I also determined the volume 

(m3) of preferred browse species by measuring canopy 

height (at the center) and diameter (mean of two 

perpendicular measurements) and using the formula 

for the shape of a solid that best approximated growth 

form of the shrub following Ludwig et al. (1975). Not 

all vegetation measures were collected at all sites. 

Last, I used historical USDA Soil Conservation 

Service and Natural Resources Conservation Service 

Range Site Descriptions (RSDs) and Ecological Site 

Descriptions (ESDs) (https://www.nrcs.usda.gov/wps/

portal/nrcs/main/national/technical/ecoscience/desc/) 

to infer historical species cover for ecological sites that 

occurred on the study areas.

Pinyon-Juniper Treatments

Burn treatments were conducted in late winter or early 

spring (March–early April) depending on site following 

Bender (2011, 2012). Burn areas ranged from approximately 2 to 10 sections in the SCNM2 and NCNM 

sites. I assessed fire intensity using ground char and 

flame length classes following Ryan and Noste (1985).

Thinning and chipping or masticating treatments were 

performed throughout the summer on six 0.1-acre 

(0.04 ha) plots in the CNM site. An adjacent untreated 

plot was blocked with one thinned and one chipped 

plot into three groups. Thinned plots had slash lopped 

and scattered during treatment. Chip treatment (i.e., 

chip, and leave or remove) plots were untreated for the 

following two springs, then chips were manually raked 

and removed in the fall after the second spring because 

of a lack of understory growth. 

I used a linear mixed model (PROC GLIMMIX; 

PROC GLM; SAS Institute Inc.) to analyze landscapelevel data: time since burn and precipitation effects 

on plant cover on the NCNM site. I used patch or 

stand as the random effect. I used MANOVA (PROC 

GLM; SAS Institute Inc.) to analyze the following 

within-stand data: cover of forage classes preburn, 

1-year postburn, and 4-years postburn in the SCNM2 

site and among untreated, thinned, and chip treatments 

in the CNM site. I also compared preburn and 1-year 
44 USDA Forest Service RMRS-P-77. 2020.

postburn cover at the NCNM site using randomization 

tests (Efron and Tibshirani 1993). Where appropriate, 

I contrasted candidate models using Akaike’s information criteria (Burnham and Anderson 1998) and 

assessed contributions of covariates using 86-percent 

confidence intervals (CIs) (Arnold 2010). 

RESULTS

Deer and Elk condition and Distribution 

Overall, proportion of PJ in both annual (F1,86 = 

4.8; P = 0.025) and SSA home ranges (F1,86 = 5.2; 

P = 0.031) was negatively related to the amount of 

body fat that mule deer were able to accrue (table 1), 

although lactating and dry females were differentially 

affected. Pinyon-juniper cover had a negative effect 

on body fat accrual of lactating females (annual: 

P = 0.008; SSA: P = 0.011), but no effect on dry females (annual: P = 0.209; SSA: P = 0.194). Proportion 

of PJ in annual (F1,69 = 2.5; P = 0.122) or SSA 

(F1,69 = 2.0; P = 0.166) home ranges did not affect accrual of body fat in elk (table 1). 

Size of annual home ranges of mule deer was 

negatively related to proportion of PJ in annual 

(F1,120 = 13.8; P < 0.001) and SSA (F1,201 = 12.7; P

< 0.001) home ranges. Similarly, size of SSA home 

ranges of mule deer was negatively related to proportion of PJ in annual (F1,120 = 17.9; P < 0.001) and SSA 

(F1,201 = 20.9; P < 0.001) home ranges. Proportion 

of PJ in either annual or SSA home ranges of elk was 

not related to size of either annual (annual: F1,90 = 

0.3; P = 0.610; SSA: F1,172 = 0.5; P = 0.475) or SSA 

(annual: F1,90 = 0.01; P = 0.936; SSA: F1,172 = 0.9; 

P = 0.343) home ranges.

Vegetation Composition

Understory vegetation cover of mule deer and elk 

study sites reflected the lack of understory development in denser untreated PJ woodlands (table 2). The 

same pattern was present on treatment areas (table 3). 

Preferred forage was only a small proportion of the 

extant understory on all sites (tables 2 and 3). Among 

untreated sites, preferred grasses accounted for 65 percent (90-percent CIBOOTSTRAP = 51–79 percent) of total grass cover, preferred forbs 30 percent (90-percent 

CIBOOTSTRAP = 21–39 percent) of total forb cover, and 

preferred shrubs 37 percent (90-percent CIBOOTSTRAP

= 21–53 percent) of total shrub cover (tables 2 and 3). 

Availability of nutritious herbaceous forage during 

late gestation and early lactation was even rarer; coolseason grasses composed only 12 percent (90-percent 

CIBOOTSTRAP = 8–20 percent) of total grass cover.

Among the three treatment sites, treatments generally increased cover of preferred grasses (+6 percent 

[90-percent CIBOOTSTRAP = 3–9 percent]), forbs (+11 

percent [90-percent CIBOOTSTRAP = 7–15 percent]), 

and shrubs (+5 percent [90-percent CIBOOTSTRAP = 

0–10 percent]), but not cool-season grasses (+2 percent 

[90-percent CIBOOTSTRAP = –1–4 percent]). The grazing treatment (SCNM) was not included as it had only 

1 year of data without a pretreatment or other control.

Preferred forage was generally comparable between 

present and historical plant communities (table 4), with 

the exception of cool-season grasses, which were less 

abundant in current communities in six of nine comparisons. I am uncertain how relevant these data are, 

however. Development of RSDs began in the 1930s 

and 1940s and ESDs were developed mostly starting 

in the late 1980s, after decades of generally static management of dynamic rangelands. Because herbivory impacts associated with overuse by large herbivores can 

occur rapidly (Irwin et al. 1994; Noy-Meir 1981) and 

eliminate preferred forages (Irwin et al. 1994; Klein 

1981; Severson and Urness 1994), past herbivory may 

have eliminated preferred forages or altered understory 

compositions prior to development of ESDs.

Prescribed Burning

Fire intensity on the SCNM2 site was light (mean char 

class = 1.3 [standard error (SE) = 0.08]; mean flame 

length class = 1.6 [SE = 0.09]), corresponding to light 

ground char and flame lengths of less than 1 m (Ryan 

and Noste 1983). Burning did not significantly alter 

PJ density (F2,42 = 0.2; P = 0.861). Preburn, 1-year 

postburn, and 4-year postburn vegetation cover differed (F4,40 = 42.8; P < 0.001) (table 5). Grass (F2,42 = 

5.5; P = 0.008) and forb (F2,42 = 3.3; P = 0.048) cover 

increased significantly after burning, then declined 

(table 5). Shrub cover (F2,42 = 3.3; P = 0.047) declined 

initially, then recovered by 4 years postburn. Burning 

decreased successional status of preferred browse species (mountain mahogany [Cercocarpus montanus]: 
USDA Forest Service RMRS-P-77. 2020. 45

Table 2—Proportion of mule deer and elk study areas in pinyon-juniper (PJ) cover, percent understory 

grass, cool-season grass (CSG), forb, and woody shrub cover; percent preferred (P) grass, forb, and 

shrub cover; shrub and tree density; and canopy cover (CC) of untreated pinyon-juniper woodlands in 

mule deer and elk study areasa, New Mexico. Values in parentheses are standard errors.

Mule deer Elk

CRLRC SAM Northcentral Chaco LNF Oscura

Proportion PJ 0.20 0.07 0.16 0.15 0.10 0.48

Grass 15 (7) 12 (2) 26 (13) 23 (8) --- 27 (5)

Grass (P) 9 (6) 7 (2) 12 (6) 12 (5) --- 17 (6)

Grass - CSG 2 (2) 2 (1) 3 (2) 11 (5) --- 1 (<1)

Forb 2 (2) 3 (1) 0.3 (0.9) 5 (3) --- ---

Forb (P) <1 (<1) 1 (1) 0 (0) 2 (2) --- ---

Shrub 4 (4) 9 (1) 6 (7) 27 (9) --- ---

Shrub (P) 2 (1) 3 (3) 6 (7) 10 (6) --- ---

Shrub/ha --- 541 (40) --- --- --- 642 (179)

Shrub/ha (P) --- 274 (66) --- --- --- 257 (77)

Tree/ha --- 223 (18) 188 (41) --- --- 106 (30)

Tree (% CC) 62 (3) 25 (3) --- 33 (12) --- ---

aCRLRC: Corona Range and Livestock Research Center; SAM: San Andres Mountains; Northcentral: Colfax County;

 Chaco: Chaco Culture National Historic Park; LNF: Lincoln National Forest; Oscura: Oscura Mountains and

 Chupadera Mesa. 

Table 1—Relationships between the proportion of pinyon-juniper woodlands in annual (annual) and spring–autumn (SSA) 

home ranges of mule deer and elk, and the size of annual home range (annual HR), spring–autumn home range (SSA HR), 

and percent body fat among study areas in New Mexico.

Proportion of pinyon-juniper woodlands in home range

Annual home range SSA home range

Species Variable P(rand)a ßb SEc P Nd P(rand) a ßb SEc P Nd

Mule deer Size (annual HR) 0.922 –0.32 0.09 <0.001 122 0.939 –0.30 0.08 <0.001 205

Size (SSA HR) 0.900 –0.39 0.09 <0.001 122 0.977 –0.31 0.07 <0.001 205

Body fat 0.414 –0.25 0.11 0.025 91 0.311 –0.22 0.10 0.031 91

Elk Size (annual HR) 0.554 0.46 0.89 0.610 93 0.675 0.47 0.65 0.475 176

Size (SSA HR) 0.556 –0.05 0.58 0.936 93 0.471 0.29 0.31 0.343 176

Body fat 0.319 –0.19 0.12 0.122 74 0.116 –0.15 0.10 0.166 74

aProbability of random effect in model.

bCoefficient.

c

Standard error.

dNumber of individuals sampled.
46 USDA Forest Service RMRS-P-77. 2020.

Table 4—Percent cover of all grasses, cool-season grasses (CSG), forbs, and woody browse species (shrub), and preferred forage (P)

within these classes, in current (Ext) understory and in historical (Hist) plant communities in mule deer and elk study areas, New 

Mexicoa

. Extant cover that is lower than historical is in bold.

No specific treatment Treated

CRLRC SAM Northcentral Chaco Oscura SCNM1 SCNM2 NCNM CNM

Ext Hist Ext Hist Ext Hist Ext Hist Ext Hist Ext Hist Ext Hist Ext Hist Ext Hist

Grass 15 20 12 13 26 21 23 24 27 20 16 12 15 13 19 16 2 20

G (P) 9 12 7 9 12 14 12 12 17 15 12 9 12 9 16 14 <1 12

CSG 2 3 2 1 3 3 11 11 1 2 <1 2 <1 1 1 4 0 3

Forb 2 2 3 1 <1 3 5 2 --- 2 <1 1 4 1 1 1 1 2

Forb (P) <1 1 1 <1 0 1 2 1 --- 1 <1 <1 1 <1 --- 1 1 1

Shrub 4 3 9 8 6 13 27 10 --- 12 <1 3 22 8 <1 2 4 3

Shrub (P) 2 1 3 3 6 11 10 6 --- 4 0 2 6 3 0 1 2 1

aCRLRC: Corona Range and Livestock Research Center; SAM: San Andres Mountains; Northcentral: Colfax County; Chaco: Chaco Culture National

 Historic Park; Oscura: Oscura Mountains and Chupadera Mesa; SCNM1 and SCNM2: south—central New Mexico; NCNM: north-central New 

 Mexico; CNM: central New Mexico. 

Table 3—Percent understory grass, cool-season grass (CSG), forb, and woody shrub 

cover; percent preferred (P) grass, forb, and shrub cover; shrub and tree density; and 

canopy cover (CC) of pinyon-juniper treatment areasa, New Mexico. Values in 

parentheses are standard errors.

SCNM1 SCNM2 NCNM CNM

Grass 16 (3) 15 (2) 19 (2) 2 (2)

Grass (P) 12 (3) 12 (2) 16 (2) <1 (<1)

CSG <1 (1) 1 (<1) 1 (<1) 0 (---)

Forb <1 (<1) 4 (1) 1 (<1) 6 (2)

Forb (P) <1 (<1) 1 (1) 0 (---) 1 (1)

Shrub <1 (<1) 22 (3) <1 (<1) 4 (1)

Shrub (P) 0 (---) 6 (4) 0 (---) 2 (1)

Tree/ha 29 (9) 131 (37) 5 (3) ---

Tree (% CC) --- --- --- 76 (6)

aSCNM1 and SCNM2: south-central New Mexico; NCNM: north-central New Mexico; CNM:

 central New Mexico. 
USDA Forest Service RMRS-P-77. 2020. 47

preburn volume = 0.63 [SE = 0.11]); postburn volume 

= 0.30 [SE = 0.06]; PRAND < 0.001). This decrease in 

plant volume was reflected in the drop in shrub cover 

1 year postburn.

Fire intensity was similarly light on the NCNM site 

(mean char class = 1.6 [SE = 0.07]; mean flame length 

class = 1.9 [SE = 0.09]), corresponding to lightmoderate ground char and flame lengths of less than 1 

m (Ryan and Noste 1983). Burning increased cover of 

all grasses (+21 percent; PRAND = 0.007) and warmseason grasses (+19 percent; PRAND = 0.014) by 1 year 

postburn, but not cool-season grasses, forbs, or shrubs 

(PRAND < 0.166). Among candidate models including 

time since burn, annual precipitation, growing season 

precipitation, precipitation during the previous winter, 

and their additive and interaction effects, only time 

since burn (F1,34 = 23.0; P < 0.001) and time since 

burn (F1,33 = 25.4; P < 0.001) + winter precipitation 

(F1,33 = 4.7; P = 0.037) were potentially informative (i.e., ΔAkaike’s information criterion [AICc] 

< 3). Time since burn (summed AIC ω = 1.00) was 

the predominant variable influencing trends in grass 

cover following burning for the first 7 years postburn 

(table 6); grass cover declined approximately 3 percent 

annually each year. 

Table 5—Percent cover of forage classes and preferred forage (P), and tree 

density, prior to burning, 1 year after burning, and 4 years after burning, 

south-central New Mexico. Values in parentheses are standard errors. 

Values followed by different letters differ at the P < 0.001 significance level. 

Preburn Postburn (1 year) Postburn (4

years)

Grass 15 (2)A 31 (4)B 23 (5)AB

Grass (P) 12 25 24

Cool-season grass <1 1 1

Forb 4 (1)A 11 (3)B 5 (2)A

Forb (P) 1 4 2

Shrub 22 (3)A 14 (2)B 23 (4)A

Shrub (P) 6 6 5

Tree (trees/ha) 131 (37) 105 (30) 115 (33)

Table 6—Candidate models of grass cover as influenced by time since burning (Time), 

precipitation (Precip), rm (Int) in north-central New Mexico. Values of variables with 86-

percent confidence intervals that do not include 0 are in bold. P(w) = winter precipitation.

Model ΔAICc AIC ω Time SE Precip SE

Time 0 0.81 -0.027 0.006 --- ---

Time, P(w) 2.9 0.19 -0.027 0.005 0.023 0.011
48 USDA Forest Service RMRS-P-77. 2020.

Precipitation received during the previous winter 

(summed AIC ω = 0.19) was the only other variable 

that had 86-percent CIs that excluded 0 in some models (table 6). Warm-season grass and all grass cover 

showed nearly identical relationships, whereas cover 

of cool-season grasses, forbs, and shrubs was not 

related to time since burning (P > 0.185) or winter or 

growing season precipitation (P > 0.693), very likely 

because of scarcity at this site (fig. 2).

Similar results were seen following burning in 2015. 

Burning increased cover of all grasses (+15 percent; 

PRAND = 0.013) and warm-season grasses (+13 percent; PRAND = 0.025) by 1 year postburn, but not coolseason grasses, forbs, or shrubs (PRAND < 0.209). 

Thinning and Chipping 

Overstory canopy cover was reduced from more than 

70 percent to less than 20 percent with each treatment 

(table 7). Vegetation responses differed among treatment combinations (F3,5 = 36.2; P < 0.001). Three 

years following treatment, the chip treatment did not 

differ (P ≥ 0.266) from the control in any forage class 

(table 7). The thinning treatment had greater cover of 

all forage classes (P ≤ 0.032) as compared to the chip 

treatment and control, with the exception of forb cover, 

which was similar between the thinning treatment and 

control (P = 0.166). 

Figure 2—Trends in percent cover of all grasses, warm-season grasses, cool-season 

grasses, forbs, and shrubs following prescribed burning in late winter in the north-central 

New Mexico (NCNM) site. Also shown is the regression line for total grass cover, indicating 

an approximate 3-percent decline each year postburn.
USDA Forest Service RMRS-P-77. 2020. 49

Table 7—Percent cover (SE) of forage classes and preferred forage (P), and tree 

canopy cover, after 3 growing seasons on pinyon-juniper control sites, chip treatment

sites, and thinned sites, central New Mexico. Values in parentheses are standard 

errors. Values followed by different letters differ at the P < 0.001 significance level.

Control Chip and leave or 

remove

Thin

Grass 2 (2)A 6 (3)A 27 (7)B

Grass (P) <1 <1 8

Cool-season grass 0 0 1

Forb 6 (2)AB 4 (1)A 9 (2)B

Forb (P) 1 1 2

Shrub 4 (1)A 4 (1)A 12 (3)B

Shrub (P) 2 2 6

Tree (% CC) 76 (6) 19 (3) 17 (2)

Grazing

The SCNM1 site was included as an example of a site 

representative of light multipasture seasonal suitability 

and best pasture grazing. Differences between current 

and historical plant composition on this site showed 

patterns similar to those on the other, ungrazed sites 

(table 4). Cool-season grasses and preferred shrubs 

were particularly lacking relative to historical plant 

community composition. 

DISCUSSION

The lack of understory associated with untreated PJ 

woodlands was reflected in the nutritional condition of deer and elk using these sites, as inclusion of 

untreated PJ woodlands in home ranges negatively 

affected nutritional condition of mule deer, and had 

no positive effect on elk (table 1). These results were 

similar to individual site results, which showed variable responses within species, although condition was 

never positively associated with untreated PJ in New 

Mexico (Bender et al. 2007b, 2012a, 2013; Halbritter 

and Bender 2011b; Hoenes 2008; see Bergman et al. 

2014 for similar results in Colorado). Particularly 

rare in untreated PJ stands were cool-season grasses 

(12 percent of grass cover) and preferred browse (37 

percent of shrub cover), forages that would be palatable and nutritious during the critical period of late 

gestation through early lactation (Short et al. 1977). 

Cool-season grasses were also often underrepresented 

in current understory communities compared to their 

historical presence (table 4). Because cool-season 

grass was the only forage class that did not increase 

among treatments, preferred cool-season herbaceous 

native forages (or substitutes) may have to be reestablished in many PJ woodlands if forage attributes are to 

be enhanced significantly. This approach would particularly aid mule deer given their higher (relative to 

elk) diet quality requirements during the pre-monsoon 

late-gestation period. The generally positive responses 

of preferred warm-season grasses to a variety of treatments indicate that forage preferred by elk (Cook 

2002; Short et al. 1977) can be significantly enhanced 

by many treatment options (tables 5 and 7).

Because of the current composition of most understory communities, enhancement of woody browse 

has the highest potential to increase the quality of PJ 

woodlands, especially for mule deer. Woody browse 

is the predominant item in mule deer diets (Boeker 

et al. 1972; Krausman et al. 1997; Short et al. 1977; 

Wakeling and Bender 2003) because it is available 

year-round with at least moderate dietary quality. 

Woody browse is also less susceptible to drought 

than herbaceous species, especially forbs, which is an 

important consideration for forage management in arid 

Southwestern woodlands (Bender 2012; Hoenes and 

Bender 2011). Although seasonal drought will always 

decrease productivity of cervids (Bender et al. 2007a, 

2011; Hoenes 2008), the presence of sufficient relatively drought-tolerant browse can maintain survival of 

adults (Bender et al. 2012b; Hoenes and Bender 2011). 
50 USDA Forest Service RMRS-P-77. 2020.

Similar to herbaceous forages (excluding preferred 

warm-season grasses, which are primarily an important forage component for elk) (Cook 2002; Short et 

al. 1977), preferred browse was a limited component 

of the overall shrub understory (table 4) and may not 

necessarily increase in cover in the short term with 

treatment of PJ woodlands (among treatments, effect = 

+5 percent [90-percent CIBOOTSTRAP = 0–10 percent]). 

Many treatments, particularly mechanical and herbicidal, have been used to increase browse in PJ woodlands (e.g., Bender et al. [2013]; Howard et al. [1987]; 

Miller et al. [2005]; Short et al. [1977]). However, 

Bender (2012) showed that mechanical removal of 

overstory PJ (to 11 percent CC) did not increase the 

cover of preferred browse as compared to unthinned 

PJ (62 percent CC) or PJ savanna (17 percent CC) on 

the CRLRC site. Despite this, body fat of mule deer 

was positively affected by increasing use of mechanically treated PJ sites during SSA (Bender et al. 2013). 

Although the mechanism behind this response was not 

identified, it is likely to have included both increased 

nutritional quality of individual plants (because of increased solar and other resource capture) and decreased 

successional status of individual plants (because of 

damage, i.e., crushing) associated with mechanical 

treatments (Cook and Harris 1950; Heffelfinger et al. 

2006). On other sites, thinning increased protein levels 

of preferred shrubs by 1.2 to 1.5 times that of unthinned 

sites (L. Bender, unpublished data). Similarly, decreasing individual plant height and volume can increase 

browse availability and nutritional quality because of 

increased younger, less lignified, growth (Cook and 

Harris 1950; Heffelfinger et al. 2006; Hoenes and 

Bender 2011; Severson and Urness 1994). 

Despite the positive relationship between mechanically 

treated PJ and body fat of mule deer, body fat levels 

of deer on the CRLRC were low (≤5.7 percent annually for lactating females, < 1/3 of what mule deer can 

accrue under optimal nutrition) (Bender et al. 2013). 

Thus, while mechanical clearing or thinning treatments 

may increase forage quality, increases in quantity of 

preferred forages are also needed to significantly enhance the quality of foraging environments, at least for 

mule deer. An increase in preferred browse was seen in 

other thinned sites such as the CNM site (table 7). But 

the magnitude of response (to 6 percent cover from 2 

percent) was relatively low, and the total cover lower 

than that seen on the CRLRC site (≤6 percent vs. 11 

percent), although the species composition differed. It is 

likely that cover of preferred browse will increase with 

additional time since treatment, however, and further 

monitoring is needed to see the level ultimately attained.

Overstory reduction of PJ by herbiciding is not recommended as a management strategy for elk or deer. 

Broadcast application of tebuthiuron decreased live PJ 

canopy cover from more than 60 percent to less than 

30 percent on CRLRC, but also resulted in a decrease 

in cover of preferred mule deer browse from 11 to 18 

percent down to less than 3 percent (Bender 2012; 

Bender et al. 2013). Individual tree treatments also 

decreased preferred browse to less than 10 percent 

cover, because browse often is established adjacent to 

or under the canopy of PJ trees (Bender 2012; Bender 

et al. 2013).

Overstory thinning in combination with broadcast 

burning, or broadcast burning under previously 

thinned stands, has shown the most potential to 

enhance forage in PJ, more than doubling grass and 

forb cover in the first year following burning in the 

SCNM2 (table 5) and NCNM sites as well as several 

other sites in the Southwest (L. Bender, unpublished 

data). Although shrub cover was initially decreased, it 

typically recovered by the third year postburn. Further, 

some of the decrease in shrub cover was desirable, 

because it reflected loss of older, decadent canopies, 

as shrub volume decreased with burning. The younger 

growth from resprouting increased the forage quality 

of these shrubs significantly despite the lessened cover 

(see later discussion). Preferred browse followed these 

trends, but remained a small proportion of overall 

woody browse cover (table 5).

Vegetative protein levels were not assessed on the 

SCNM2 and NCNM sites, but burning increased 

protein levels of preferred grasses more than 1.6 times 

and shrubs more than 2 times that of unburned sites 

in early spring and summer following late-winter 

burns, although this effect was lessened by year 2 

and absent by year 3 (L. Bender, unpublished data). 

Similar responses have been seen elsewhere (e.g., 

DeWitt and Derby 1955; Dills 1970; Einarsen 1946; 

Grelen and Epps 1967; Hobbs and Spowart 1984; 

Miller et al. 2005), and result from the freeing of 

N (Covington et al. 1991; Rau et al. 2009). Burned 
USDA Forest Service RMRS-P-77. 2020. 51

sites also greened-up on average 14 days earlier than 

unburned across several southwestern sites (L. Bender, 

unpublished data). Fire speeds spring green-up, at least 

in the first year, because ash warms in the sun, creating 

a warmer microclimate for plants to germinate or initiate growth (Hobbs and Spowart 1984). Both of these 

responses increase diet quality of cervids during the 

critical late-gestation and early-lactation periods. 

Burning also has nutritional benefits for elk and deer 

beyond those seen with mechanical treatments (which 

can also increase protein levels, as discussed earlier). 

Properly timed burns increase the quality of diets 

by removing dead plant material, allowing access to 

newly emergent forages in early phenological states 

(Hobbs and Spowart 1984; Severson and Urness 

1994). Frequent fire can cause species shifts away 

from perennial warm-season grasses to forbs (Ford and 

McPherson 1996), which can also be preferentially 

increased in abundance by spring fires (Brewer and 

Platt 1994). Fire also tends to decrease crude fiber of 

plants, increasing digestibility. Fire therefore results in 

increased energy available, both within plants (because 

of increased digestibility) and overall (because of 

increased plant biomass) (Edwards et al. 2004). Many 

of these additional effects are more long-lived than the 

1- to 2-year protein flush (Bender 2011), and benefit 

both deer and elk.

Similar to protein responses, live understory cover 

may be lost with increasing time since burning (fig. 2). 

Herbaceous responses were lost each year following 

burning, and lasted approximately 4 to 5 years in arid 

New Mexico woodlands (fig. 2, table 5) regardless of 

precipitation received during the winter or growing 

season. Thus, not only should burns be conducted 

at appropriate times to provide needed nutritional 

benefits (i.e., nutrient flush, earlier green-up, increased 

preferred forage cover) (Bender 2011) to cervids, but 

fire intervals should be timed to maintain the desired 

understory response(s) as well. Bender (2011, 2012) 

recommended that sites with moderate soil productivity be burned every 5 to 7 years, whereas sites should 

be burned on 10- to 15-year intervals if low productivity soils dominate or if increased woody browse is a 

management goal. Burning must be done with care, 

however, as data from several areas indicate that even 

light- to moderate-intensity (i.e., flame lengths ≤ 1 m) 

burns in thinned PJ can result in substantial mortality 

of remaining trees, decreasing residual cover below 

desired levels. Even when the goal is increased woody 

browse, periodic burning is necessary to alter successional status of shrubs, increasing palatability and 

availability.

Additional treatments beyond properly timed prescribed burning may be needed to achieve high coverage of preferred herbaceous forage in understories. 

In such cases, forage seedings are frequently used 

to establish palatable forages (Bender 2012). For 

example, dryland alfalfa (Medicago sativa var. Ladak) 

was seeded in a thinned (to about 30 percent canopy 

cover) PJ stand at 5 to 10 lb/acre (6 to 12 kg/ha) in 

mid-summer following a late-winter burn near the 

SCNM2 site. By fall, cover of alfalfa was 12 percent 

(SE = 4) on the site, and increased to 20 percent (SE = 

6) the next spring. Dryland alfalfa is a highly palatable 

legume of great nutritional quality, and hence is highly 

preferred by mule deer and elk. Because it is a coolseason species, it provides needed nutrition during 

late gestation and throughout lactation, as it maintains 

quality throughout the warm season. Dryland alfalfa 

plantings frequently fail because seeds are planted too 

deep (Bender 2012), and relatively shallow-rooted 

seedlings are susceptible to freeze-out during their first 

winter (L. Bender, unpublished data). However, once 

it or similar seedings are established, they can maintain high cover of extremely high quality forage that 

responds well to appropriate prescribed burning timing 

and intervals (Bender 2012).

In areas including livestock grazing, warm-season 

grazing is a tool that may favor increased cover of 

cool-season grasses and forbs (Chris Allison, New 

Mexico State University, Las Cruces, New Mexico, 

personal communication). Warm-season grazing may 

also increase the foraging efficiency of mule deer and 

elk due to the removal of coarse herbaceous cover 

(Severson and Urness 1994). Understory composition 

data from the SCNM1 site (table 4), however, suggested that even lightly grazed systems that include 

cool-season use may not maintain cover of cool-season 

grasses. More frequent burning of understories may 

decrease cover of warm-season grasses in favor of 

greater coverage by forbs, including cool-season 

species (Bender 2011; Ford and McPherson 1996; 

Launchbaugh 1964; Wright and Bailey 1980). Very 

short fire intervals (<3 years), however, can eliminate 
52 USDA Forest Service RMRS-P-77. 2020.

most preferred woody shrubs (Bender 2011; Ford and 

McPherson 1996). Thus, this strategy should be limited 

to areas such as perennial grasslands that are proximate 

to PJ woodlands, and not the woodlands themselves, if 

mule deer habitat is a management goal. Burns should 

also be in late winter, as late-spring burns may decrease 

cover of cool-season grasses (Hart 1990).

Other treatments, such as chipping and other mastication treatments, may show little understory response 

if biomass is left on the ground, particularly in hotter 

and more arid PJ woodlands (table 7). Juniper wood 

(240:1) and leaves (55:1) have high carbon (C):N 

ratios (Bates 1996; Miller et al. 2005), and Bates 

(1996) found that C:N ratios in soil increased by 32 

to 43 percent where western juniper (J. occidentalis) 

slash was deposited. High C:N ratio litter can cause 

soil microbes to immobilize available N (Schimel 

and Firestone 1989), slowing decomposition (Miller 

et al. 1979). Consequently, chip treatments may have 

lower total nitrogen, phosphorus, organic phosphorus, 

calcium, and potassium concentrations than untreated, 

lop-and-scatter, or broadcast burn treatments (Waldrop 

et al. 2003). 

Chipping can also result in lower forest floor enzyme 

activity, which may slow decomposition rates up to 

60 percent (Waldrop et al. 2003). Other work suggests 

that though chipping and mastication treatments may 

not be detrimental to N mineralization in the short 

term, slow decomposition of masticated material and 

widening C:N ratios may be a concern in the long term 

(Gottfried et al. 2009). Because decomposition can 

be influenced by temperature and moisture (Klopatek 

et al. 1995), these dynamics may be a particular challenge in the hotter, more arid PJ woodlands of New 

Mexico, west Texas, and northern Mexico. Studies 

showing positive understory responses to chipping or 

mastication were in cooler, moister sites, or regions 

with different precipitation patterns than the CNM site, 

which has a high (>50 percent) monsoonal index (e.g., 

Klopatek et al. 1995; Ross et al. 2012). 

Leaching of secondary plant compounds from residual 

chip biomass may also inhibit understory responses. 

Ramsey (1989) found that phenolic leachates did not 

affect emergence, but generally inhibited growth, such 

as root and shoot biomass and tiller numbers, of several 

grass species (Ramsey 1989). In contrast, leaving 

biomass may promote plant growth by increasing soil 

moisture because of the mulch effect (Benson 2006; 

Bergman et al. 2014; Brockway et al. 2002). However, 

this also makes follow-up burning more difficult, particularly in late winter or early spring, because lower chip 

layers and adjacent soil remain moist. For example, I 

was unable to burn chipped areas in the CNM site 1 

year and 2 years post-chipping during late winter because of saturation of chips in contact with the ground. 

A further concern with this treatment is that chipping 

or mastication treatments may decrease soil temperature in spring (Brockway et al. 2002; Waldrop et al. 

2003). This effect is particularly relevant to the annual 

energetic cycle of mule deer and elk, because it would 

result in later green-up, delaying availability of early 

phenology forage during late gestation when nutritional needs increase dramatically (Cook et al. 2004; 

Verme and Ullrey 1984; Wakeling and Bender 2003). 

Most documented responses of preferred forages 

(other than warm-season grasses) to treatments have 

been relatively small (tables 5, 7). However, even a 

small change in plant community composition may 

result in a significant change in elk or deer habitat 

quality. For example, it takes only an approximately 

10-percent change in diet quality to significantly 

increase elk productivity (Cook et al. 2004). Diet and 

diet quality data from pronghorn (Antilocapra americana) suggest that even a relatively small increase—5 

to 7 percent—in preferred forages may be able to 

produce such a change in diet quality (L. Bender, unpublished data). Similar changes were seen with cattle 

diet quality (Holechek and Vavra 1983; McGinty et 

al. 1983). However, further work is needed to identify 

what level of change in preferred forage can significantly increase diet quality. 

For treatments that remove woodland overstory and 

horizontal cover, care must be taken to maintain sufficient canopy cover or stem density to meet security 

and thermal requirements. Inclusion of untreated PJ 

woodlands was negatively related to home range size 

of mule deer, indicating that presence of denser PJ increased habitat quality. This beneficial effect was probably because of cover attributes. Previously, Bender et 

al. (2013) found that the majority of mule deer activity 

was near unthinned PJ woodlands on the CRLRC. Elk 

decreased diurnal activities in the Chaco area when 
USDA Forest Service RMRS-P-77. 2020. 53

mean high temperatures exceeded their thermal tolerance, with juniper trees being an important diurnal 

bedding site (Bender et al. 2012a). For example, radiocollared elk in the Oscura site significantly decreased 

use of portions of home ranges on Chupadera Mesa 

following prescribed burning that resulted in canopy 

loss of 70 to more than 90 percent and near elimination of shrubs (at least in the near term). Observations 

of mule deer in these areas also decreased by more 

than 90 percent after these burns (L. Bender, personal 

observation). Additionally, some cool-season grasses 

are positively correlated with overstory PJ canopy cover 

(Schott and Pieper 1985), so too much canopy reduction 

may negatively impact some key foods as well.

In PJ-dominated sites with few other cover options, 

Bender (2012) recommended a “Rule of 4s” to maintain adequate cover for mule deer when managers are 

treating PJ stands. This strategy recommends that for 

each section of PJ (which approximates the average 

home range size of female mule deer in New Mexico) 

(Bender et al. 2007a, 2011; Hoenes 2008), one-fourth 

should remain in untreated (i.e., PJ canopy cover > 60 

percent; if existing canopy cover is <60 percent, these 

areas should be allowed to develop to >60 percent 

canopy cover) patches of 40 acres (16 ha) or more, 

one-fourth should be thinned to no less than 30 percent 

PJ canopy cover, and the remaining two-fourths should 

be thinned to no less than 10 to 15 percent canopy 

cover. This would create a mosaic in which one-fourth 

of each home range is in untreated PJ ideal for security 

cover, one-fourth is in a structural state that provides 

both minimal cover requirements and increased forage (30 percent canopy cover), and one-half is in a 

structural state that provides scattered thermal cover 

and optimal foraging attributes (10–15 percent canopy 

cover). At a relative fine scale (i.e., ≤40-acre treatments), these treatment allocations would result in 100 

percent of the landscape being suitable for mule deer 

(Bender 2012). 

While designed for the security preferences and needs 

of mule deer, this strategy also provides abundant 

summer thermal cover for elk, and probably does not 

decrease the nutritional carrying capacity of elk range 

significantly. In areas of high disturbance, the extra 

security cover beyond the likely requirements of elk 

may be useful to avoid unnecessary movements and 

thus help maintain energy balance and productivity 

(Shively et al. 2005). Further, distribution of patches or 

stands should be allowed to shift across the landscape 

over time as succession, disturbance, or management 

treatments change the overstory PJ cover, thus maintaining a dynamic landscape.

The potential for enhancing extant PJ woodlands for 

mule deer and elk is a viable and relevant opportunity 

for managers of public or private land. These woodlands include almost 10 percent of the total area of 

New Mexico, making PJ woodlands the most abundant 

habitat type capable of meeting all forage and cover requirements for mule deer and elk in the State. Though 

current habitat quality of many woodlands may be 

relatively low, this may not have been true historically. 

In several of my study areas, agency and landowner 

survey data indicate much higher mule deer densities 

in PJ woodlands historically. For example, mule deer 

densities in the San Andres and Oscura Mountains were 

approximately 5 to 20 times greater during the 1970s 

and 1980s as compared to the early 2000s (Bender et 

al. 2012b, 2017; White Sands Missile Range: Bender, 

unpublished data). Similar trends have occurred in the 

Corona and NCNM sites (Bender et al. 2007). 

Density can be a misleading index of habitat quality, 

as mentioned earlier (Van Horne 1983), but differences 

of these magnitudes probably at least partially reflect 

significant changes in habitat quality. While many 

site-specific factors can affect local deer and elk populations (e.g., disease, drought, overharvest) (Bender et 

al. 2007), one constant among these sites has been a 

lack of disturbance appropriate for rejuvenating woody 

forage, especially fire. Currently, deer and elk are 

productive in PJ habitats in New Mexico, as reviewed 

earlier, but not as productive as they can be. Public 

and private land managers who desire to increase the 

quality of mule deer and elk habitat in PJ woodlands 

in New Mexico thus have historical context indicating that habitat quality can very likely be improved 

substantially.
54 USDA Forest Service RMRS-P-77. 2020.

MANAGEMENT IMPLICATIONS

1. Needs and opportunity exist to improve the quality 

of PJ woodlands as foraging habitat for elk and 

mule deer. 

• Increases in understory, however, do not necessarily equal increases in food.

• It is likely that relatively small increases in preferred forages can have a significant nutritional 

benefit.

2. Increased nutrients (protein, digestible energy) need 

to be provided during late gestation, or beginning in 

approximately mid-April. 

• Broadcast burning during late winter (March) 

appears to be the single most effective tool to 

achieve this goal. Burns can be done following 

thinning with lop-and-scatter of slash, in previously thinned stands, or in savannas.

• Agency emphasis on “natural” lightning-driven 

fire regimes cannot provide a nutrient flush at 

appropriate times. 

• Piling and burning of slash should be avoided 

unless piling sites are to be seeded to a highly 

preferred cool-season forage (i.e., a food plot). 

3. Cool-season forages should be encouraged because 

they provide critical early-phenology forage during 

late gestation through early lactation. 

• Late-winter burning and warm-season livestock 

grazing may help increase this component, but 

more information is needed. 

• Plantings are a further alternative.

4. “Xeriscaping” foraging habitats by providing an 

abundant and diverse browse component can mitigate the effects of drought in many cases. 

• This is particularly important for habitat for mule 

deer because their greater diet selectivity emphasizes forbs from herbaceous communities, which 

are more susceptible to drought.

5. Methods to assess responses of mule deer and elk to 

management treatments must be closely related to 

the fundamental influence of habitat. 

• Individual condition, juvenile growth, and juvenile fecundity are appropriate direct metrics. 

• Measures of forage quality of preferred forages 

and diet quality of mule deer and elk are appropriate indirect methods. 

• Removed metrics (i.e., adult survival, population rate of increase) that can be confounded 

by numerous environmental and human-caused 

influences should not be used.

• Remotely sensed metrics often used to infer 

forage quality such as the normalized difference vegetation index are not strongly related to 

nutritional condition of deer or elk and should 

be avoided unless better indices are developed 

(Caltrider 2012).

6. Mule deer and elk are different herbivores with differing nutritional and cover requirements. 

• High-quality mule deer habitat will benefit elk. 

• High-quality elk habitat may not benefit mule deer. 

7. Management treatments should retain adequate 

cover for mule deer and elk.

• Mule deer are likely to require significant areas of 

high density, high canopy cover for security.

• Elk require much less cover, primarily for summer thermal constraints.

• Landscape prescriptions that balance forage and 

cover such as the “Rule of 4’s” can result in up 

to 100 percent of the landscape being suitable for 

mule deer and elk. 

• Cover and forage patches should be allowed to 

shift across the landscape over time in response 

to planned or unplanned perturbations, thereby 

maintaining a dynamic landscape. This subjects 

all forage to periodic quality-enhancing disturbance over time.

ACKNOWLEDGMENTS

Numerous individuals reviewed and contributed to 

this work through discussion, on-the-ground treatments, or other field work, including Dave Anderson, 

Chris Allison, Allen Darrow, Cervidnut, Jon Boren, 

Heather Halbritter, Brock Hoenes, James Lucero, 

Jessica Piasecke, Pat Mathis, Patrick Morrow, Mike 

Rearden, Cristina Rodden, Kevin Rodden, Mara 

Weisenberger, and the many other owners and managers of ranches in New Mexico, Texas, and Mexico. 
USDA Forest Service RMRS-P-77. 2020. 55

Funding was provided by the Santa Fe Trail Mule Deer 

Adaptive Management Partners; ranching partners 

in New Mexico, Texas, and Mexico; U.S. Fish and 

Wildlife Service; U.S. Army; U.S. Bureau of Land 

Management; USDA Forest Service; National Park 

Service; New Mexico Department of Game and Fish; 

Rocky Mountain Elk Foundation; NMSU Cooperative 

Extension Service; and NMSU Department of Animal 

and Range Sciences. Activities for this project were in 

accordance with New Mexico State University IACUC 

permits 2001-022, 2002-027, 2003-013, 2003-023, 

2005-023, and 2009-025. 

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Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Changes in Habitat use of Montezuma Quail in Response to 

Pinyon-Juniper Canopy Reduction in the Capitan Mountains

of New Mexico

MANAGEMENT ACTIONS AND WILDLIFE RESPONSES

INTRODUCTION

Montezuma quail (Cyrtonyx montezumae), also 

known as Mearns quail, fools quail, or crazy quail, 

are a cryptic species and little is known about their 

ecology, roosting behaviors, or behavioral changes to 

adjust to habitat modifications. As a result, this quail 

ABSTRACT—Montezuma quail (Cyrtonyx montezumae) are unique among quail with respect to clutch 

size, diet, covey dynamics, and habitat use. With the exception of a few notable early studies, there is 

relatively little information on the ecology of Montezuma quail. Previous research has indicated that one 

of the primary habitats utilized by Montezuma quail is pinyon-juniper woodlands. Throughout many areas 

of the southwestern United States, pinyon-juniper woodlands are often targeted for thinning projects. 

Many studies have been conducted on the amount of tree canopy cover needed by other quail species, 

however, no data is currently published on Montezuma quail tree canopy cover needs and their response 

to thinning projects. The goal of this research was to evaluate Montezuma quail responses to common 

silviculture practices, specifically pinon juniper thinning in the Capitan Mountains of New Mexico. Results of 

our research indicated that Montezuma quail selected sites thinned to reduce tree canopy cover to 30-40 

percent. Selection for this habitat was much higher than selection for the surrounding area which consisted 

of ≥70 percent tree canopy cover (Manly-Chesson Selectivity Index=1.68). Overall, this study provides 

important information for managers planning pinyon-juniper thinning projects in Montezuma quail habitat.

KEYWORDS—Cyrtonyx montezumae, habitat management, Montezuma quail, population characteristics, 

tree canopy cover, thinning

species is one of the least understood upland game 

birds in North America. A possible factor contributing 

to the paucity of literature pertaining to this species 

can be attributed to difficulties in locating coveys and 

abnormal methods required for trapping. Montezuma 

quail are secretive in nature (crouch and freeze instead 

of flushing) which can make detection of coveys 

Ryan S. Luna

Department of Natural Resource Management/Borderlands Research Institute, Sul Ross State University, Alpine,Texas.

Elizabeth A. Oaster

Department of Natural Resource Management, Sul Ross State University, Alpine, Texas

Karlee D. Cork

Department of Natural Resource Management, Sul Ross State University, Alpine, Texas

Ryan O’Shaughnessy

Department of Natural Resource Management/Borderlands Research Institute, Sul Ross State University, Alpine, Texas
60 USDA Forest Service RMRS-P-77. 2020.

difficult (Hernandez et al. 2006; Oberholser 1974; 

O’Connor 1936). Additionally, Montezuma quail differ 

from other quail species in that they primarily dig for 

subterranean plants such as soft bulbs, sedge tubers, 

and seeds (Bishop and Hungerford 1965, Harveson et 

al. 2007, Stromberg 2000). As a result of their different foraging behavior, methods of trapping that work 

well for other quail species (e.g., funnel traps) typically don’t have a high success rate with Montezuma 

quail (Hernandez et al. 2006, Stromberg 1990). Due 

to difficulties trapping and locating this species, little 

is known about their preferred habitat and how they 

respond to various management practices such as thinning juniper (Juniperus spp.). 

One way to assess Montezuma quail response to thinning juniper is through radio-telemetry. Aside from 

Stromberg (1990), Hernandez et al. (2006), and Greene 

(2013) little data have been collected on Montezuma 

quail with the aid of radio-telemetry. Montezuma quail 

are most frequently associated with habitat consisting 

of pine-juniper woodlands (Pinus spp. and Juniperus

spp.), montane meadows, and desert grasslands 

(Brown 1982; Hernandez 2004; Holdermann 1992; 

Holdermann and Holdermann 1998). 

It is known that even though Montezuma quail are 

found in a variety of habitats, there is one factor 

that all Montezuma quail habitats have common. 

Montezuma quail habitats require adequate grass cover 

(e.g. bunchgrasses) and forbs and grasses that support 

subterranean foods. It is known that Montezuma quail 

need bunchgrasses, but little is known about how 

tree canopy cover influences their habitat selection. 

Many studies have addressed the amount of tree or 

herbaceous canopy cover needed by other quail species (Bidwell et al. 1991; Guthery et al. 2000; Johnson 

and Guthery 1988; Kopp et al. 1998; Rice et al. 1993; 

Schroeder 1985). However, there is currently no data 

on Montezuma quail tree canopy cover preferences or 

how they respond to tree thinning projects. Therefore, 

studies are warranted to fill in this missing data, which 

will help to increase our knowledge on the habitat 

requirements of Montezuma quail and allow us to 

make informed decisions on thinning projects in areas 

occupied by Montezuma quail.

The goal of this research is to assess Montezuma 

quail responses to common silviculture and rangeland 

practices in the Capitan Mountains of New Mexico. The 

primary objective of this research was to assess habitat 

use of Montezuma quail before and after thinning a pinyon-juniper woodland resulting in a tree canopy cover 

reduction from 70-80 percent down to 30-40 percent.

METHODS

Study Area

The Capitan Mountains represent the northeastern 

range of Montezuma quail in the greater Sacramento 

Mountain complex in Lincoln and Otero counties 

of south-central New Mexico. The 30-year average 

temperature in the Capitan Mountains range from -5 

to 29 °C and annual average precipitation is 6.69 cm 

(NOAA, 2017). Elevations of the Capitan Mountains 

range from 1,524m to over 3,353m. The study site is 

located in the Madrean Encinal and Madrean Pine-Oak 

Forest and Woodland area of the Apache Highlands 

and AZ-NM Mountains Ecoregion (TNC Ecoregions). 

The study site consists of approximately 500 ha within 

the Capitan Mountains 2 km west of Fort Stanton, 

New Mexico. Dominant trees on the study site were 

oneseed juniper (Juniperus monosperma), pinyon 

pine (Pinus edulis), alligator juniper (J. deppeana) in 

the higher elevations, and gray oak (Quercus grisea) 

along the rocky hillsides and canyons (USDA Plants). 

Woody plants in the pinyon-juniper zone include 

fragrant mimosa (Mimosa borealis), skunkbush sumac 

(Rhus trilobata), and at least two species of rabbit 

brush (Chrysothamnus spp.) are noted along draws 

and dry hillsides. Grass species include sandhill muhly 

(Muhlenbergia pungens), bush muhly (Muhlenbergia 

porteri), mountain muhly (Muhlenbergia montana), 

slender muhly (Muhlenbergia monticola), scratchgrass (Sporobolus asperifolius), sacaton (Sporobolus 

wrightii), alkali sacaton (Sporobolus airoides), Fendler 

threeawn (Aristida longiseta), Arizona three-awn 

(Aristida arizonica), common wolfstail (Lycurus 

phleoides), James’ galleta (Hilaria jamesii), and 

tobosagrass (Hilaria mutica) (USDA Plants). Also 

common are tumblegrass (Schedonnardus paniculatus), sideoats grama (Bouteloua curtipendula), 

sixweeks grama (Bouteloua barbata), black grama 

(Bouteloua eriopoda), Mexican lovegrass (Eragrostis 

mexicana), western wheatgrass (Agropyron smithii), 

foxtail barley (Hordeum jubatum), Canada wild rye 

(Elymus canadensis), and blue grama (Bouteloua 
USDA Forest Service RMRS-P-77. 2020. 61

gracilis) (USDA Plants). Common forbs in the study 

area include Carruth's sagewort (Artemisia carruthii), 

ragweed (Ambrosia spp.), Rocky Mountain zinnia 

(Zinnia grandiflora), common mullein (Verbascum 

thapsus), fleabane (Eriogeron spp.), scarlet globemallow (Sphaeralcea coccinea), flaxflowered ipomopsis 

(Ipomopsis longiflora), scarlet gilia (Ipomopsis aggregata), yellow nutsedge (Cyperus esculentus), and 

silverleaf nightshade (Solanum elaeagnifolium).

Tree Canopy Cover Reduction

Within the 500 ha study area, an area consisting of 151 

ha was selected for pinyon juniper thinning (fig. 1). 

The area selected for thinning treatment had a tree 

canopy cover of approximately 70-80 percent. The 

rest of the study site had tree canopy cover that ranged 

from 20-80 percent. Tree canopy cover was assessed 

using NDVI (Normalized Difference Vegetation Index, 

Earth Explorer, June and September 2015-2016). 

Some of the study site had steep slopes and canyons. 

These areas were not included to undergo the tree 

canopy reduction due to them having too great a slope 

to be prime Montezuma quail habitat. Within the area 

selected for thinning, trees were chosen at random to 

be harvested by chainsaw. A 5-acre plot was thinned 

(August 2015) as an example of what was to be completed across the entire area designated for thinning. A 

commercial tree crew was hired to complete the thinning treatment. During the thinning process, slash was 

collected and piled, each pile with an estimated size of 

around 1x2x1m in width, length, height respectively. 

The slash was burned after it was piled. The thinning 

was completed in December 2015 and resulted in a 

tree canopy cover that varied from 30-40 percent. 

Trapping and Handling

To assess how Montezuma quail respond to pinyonjuniper thinning projects, we collected pre-thinning 

(2014, 2015) and post-thinning data (2015, 2016) from 

November through May. To adequately survey the 500 

ha study site, we used ArcGIS to generate a stratified 

random sampling pattern consisting of 10 transects. 

Each of these transects were 500m in length. We traversed each transect with trained pointing dogs every 

other week in November and again in February (2014 

and 2015) to facilitate detection of Montezuma quail. 

We employed 2 methods of capture for Montezuma 

quail. The first method was a modified version of the 

capture technique described by Brown (1978) using 

trained pointing dogs and large O-shaped dip nets. 

The majority of transects were searched within 4 

hours of sunset. Once coveys were located during the 

day, a global positioning system (GPS) was used to 

mark the covey location. When a dog went on point, a 

researcher equipped with a large O-shaped dip nets net 

would try to locate the covey and place the net over 

them prior to covey flush.

The second and main trapping technique that we 

used for capturing Montezuma quail involved trained 

dogs, large hoop nets, and captures at night similar to 

methods described in Hernandez et al. 2006. Transect 

searches were conducted in the evenings (2 hours prior 

to sunset) using the dogs. Once a dog located a covey, 

the covey was marked on a GPS, and the research 

team backed out of the area until nightfall. Capture 

crews revisited the GPS covey locations ≥1hour after 

sunset accompanied by a bird dog. A lighted collar 

and tracking device (Garmin Alpha TT15) was used 

for monitoring the dog at night. Search efforts at 

night were focused in the general area of the original 

detection site but expanded to approximately a 200-m 

radius. If the dog was able to relocate the covey and 

went on point, we used headlamps to locate the exact 

location of the roost. The researcher in closest proximity to the covey would then attempt to maneuver a 

hoop net on top of a covey prior to covey flush. 

Captured birds were carefully removed from the net 

and placed individually in 18x28cm soil sampling 

bags. Each individual bird was weighed, aged and 

sexed. Captured birds were then fitted with either 

a VHF (2014, 2015) or GPS (2015) backpack style 

radio transmitter (4-6 g, GPS: Telemetry Solutions 

Quail_5219, Lotek Pinpoint 120, VHF: Advanced 

Telemetry Systems quail, Frequency range 148-

151Mhz), and banded with an individually numbered 

aluminum leg band. After all data were obtained 

from the captured birds, we released them at the site 

of their capture. During 2014, VHF backpacks were 

attached to record Montezuma quail movements. In 

2015, technology advanced so that store on board GPS 

units were available for quail. Therefore, in 2015, 

we primarily deployed GPS backpacks. These GPS 

backpacks were programed to obtain a location every 

4 hours between the hours of 06:00 and 22:00. The 
62 USDA Forest Service RMRS-P-77. 2020.

Figure 1—Montezuma quail habitat use on the study area prior to commencing the pinyon-juniper thinning project (November 2014 through May 2015).
USDA Forest Service RMRS-P-77. 2020. 63

goal was to have a minimum of 2 birds from each 

covey fitted with the GPS backpacks, while remaining 

birds were fitted with VHF backpacks. We determined 

VHF locations 1 time per day, 5 days a week, every 

other week. The GPS backpacks were used to assess 

finer scale daily movements. Once a covey had ≥1 

transmitted bird, the covey was relocated at night 

using a night-netting technique initially described by 

Labisky (1959, 1968). Researchers located the radiomarked birds at night with the use of a radio receiver 

and a yagi antenna (ATS, R4000). When a covey was 

located, an O-shaped dip net was placed over as many 

birds in the covey as possible. Previously captured 

birds were examined and weighed, while newly captured individuals were aged, sexed, and had backpacks 

and leg bands attached. All trapping activities were 

conducted in accordance with state (New Mexico 

Department of Game and Fish Scientific Authorization 

Permit #3595) and university policy (SRSU IACUC 

#SPR-0592-525).

Spatial Patterns and Habitat Use

Monitoring of radio-marked birds was carried out with 

the use of a receiver (ATS R4000, Isanti, MN) and a 

yagi antenna. Locations of each individual were determined 2-5 times every other week from November 

2014 through May 2015. Locations were imported 

into ArcGIS (ESRI, Redlands, California, USA) where 

95 percent and 50 percent MCPs (Minimum convex 

polygons) were determined for each individual and 

covey. MCPs were determined to ensure that covey 

breakup had not been initiated. If the covey was still 

intact, MCPs were used to assess overall covey habitat 

use instead of individual habitat use.

We developed habitat use maps for the study site and 

evaluated habitat selection pre and post thinning, with specific interest in post thinning data (use of unthinned versus 

areas where thinning projects reduced tree canopy cover 

to 30-40 percent). A Manly-Chesson Selectivity Index was 

used to assess selection of the thinned area versus those 

areas with greater tree canopy cover.

RESULTS

From the pre-thinning sample period, a total of 292 

Montezuma quail locations were obtained between 

November 2014 and May 2015. The locations were 

acquired from 40 Montezuma quail that we captured 

from 5 different coveys (fig. 2). Of the 292 locations, 

60 percent were within the area designated to undergo 

the pinyon-juniper canopy reduction. Assessing the 

covey movements, the 95 percent MCP was 50.65ha 

±14.2ha, and ranged from 2 to 103 ha over the 7 

month study period. The 50 percent core use area for 

the coveys was 8.65ha ±4.68ha. After the tree canopy 

reduction had taken place, we were able to capture 

53 individuals and equip them with radio-telemetry 

(GPS) backpacks. The 95 percent MCP was slightly 

larger than the previous year/pre-thin period. The 

post-thin 95 percent MCP was 70.67ha ± 36.80ha with 

a range of 3.2ha to 94.6ha. However, the 50 percent 

MCP core use area was less than the pretreatment. The 

post-thinning 50 percent MCP was 5.36ha ± 3.38ha. 

Overall, the area that had undergone the pinyonjuniper canopy reduction accounted for 85 percent of 

the total locations obtained from Montezuma quail. 

Given the assumption of equal use between thinned 

and non-thinned areas, our Manly-Chesson Selectivity 

Index was 1.68. This selectivity index centered at 1, 

which would indicate use at the level of availability. 

Any number below 1 would indicate less use or avoidance, and any number above 1 would indicate that 

it is selected for, or used more than available. Our 

selectivity index indicates Montezuma quail on our 

study site were selecting for the 30-40 percent tree 

canopy cover over the ≥70 percent tree canopy cover 

surrounding the thinning project (fig. 1). An unintentional finding of this study was that Montezuma quail 

also utilized the slash piles created on the thinning 

site. We recorded coveys as well as individuals using 

these slash piles for cover during loafing and roosting 

periods. However, use of slash piles could not be fully 

determined due to the piles being burned as part of the 

thinning contract.
64 USDA Forest Service RMRS-P-77. 2020.

Figure 2—Montezuma quail locations from November 2015 through May 2016. Locations indicate 

habitat use with respect to utilization of a thinning project that reduced the tree canopy cover from 

≥70% to 30-40% in the Capitan Mountains near Ft. Stanton, New Mexico.
USDA Forest Service RMRS-P-77. 2020. 65

Each of our coveys monitored during the post-thinning 

treatment had what appeared to have been exploratory 

movements of up to 2.4 km, thereby inflating overall 

home range estimates. However, each covey returned 

to their core range after these movements. One of the 

coveys had a very small home range (3.21 ha, SD = 

0.01). If this covey is excluded from the overall average, the average home range size for coveys on the 

study site would increase to 70.68 ha (SD =24.33). 

DISCUSSION

Our study indicated that by reducing the tree 

canopy cover of pinyon-juniper woodlands from 70 

percent down to around 30-40 percent, use of the 

area by Montezuma quail increased by 25 percent. 

Additionally, our selectivity index indicated that areas 

that underwent thinning were selected for compared 

to other available habitat. Future studies should incorporate factors to assess Montezuma quail use of slash 

piles post-thinning.

During the pre-thinning data collection period, the 

Montezuma quail coveys that we were able to locate 

on the study site seemed to be relatively dispersed 

throughout the 500 ha study area. After the thinning 

was completed, transects that were close to or in 

thinned areas of the study site were much more productive than those that were in the non-treated portions 

of the study area. This observation was supported by 

the radio-telemetry data we received from the coveys. 

Overall, the majority of the movements were associated with the area that had undergone tree canopy 

reduction. Each covey did have what appeared to be 

exploratory movements. During these movements, the 

covey ventured off of the area where tree canopy reduction had taken place and made a large loop, only to 

return back to the area that had undergone tree canopy 

reduction. These exploratory movements increased 

the 95 percent MCP and resulted in a post-thinning 

95 percent MCP (70.67ha) that was greater than the 

pre-thinning 95 percent MCP (50.65ha). However, the 

50 percent core range MCP was smaller post-thinning 

(5.36ha versus 8.65ha), which could indicate that 

habitat requirements were met on a smaller scale in 

the treatment area. Further experiments are warranted 

to determine if this is actually the case. Overall, our 

home range estimates during the study period were 

similar to those reported in other Montezuma quail 

literature (Chavarria et al. 2017; Stromberg 1990).

Stromberg (1990) was the first to describe home range 

and movements of Montezuma quail. However, his 

sample size (15 radio-marked birds) and the number 

of relocations (<25) could be a limiting factor to 

determine true Montezuma quail home range size. 

Because of our greater sample size, the results of this 

study should be more indicative of true habitat use 

during the study period. Leopold and McCabe (1957) 

suggested 4–10 ha for general home range estimates, 

but these estimates were based on observations of nonmarked coveys in Mexico. One key component that 

previous studies did not account for was the difference 

between total home range size and that of core areas. 

Other studies, namely Greene et al. (2013), recorded 

movements up to 12.7 km in the Davis Mountains of 

Texas. The much smaller movements observed during our study compared to those reported in Greene 

et al. (2013) are likely the result of our study being 

conducted prior to the breeding season while Greene 

et al. (2013) incorporated breeding season movements. 

Another factor that could be influencing movements 

is the differences in habitat between study sites in 

the literature. Differences in the Montezuma quail 

habitats in the Davis Mountains in Texas or study sites 

in Arizona could influence how much individuals are 

able to travel in attempts to locate mates. Home range 

estimates recorded during this study were much closer 

to those reported by Stromberg (1990) which indicated 

home ranges of 50.65 ha and core use areas of 1.69 

ha. Home range estimates for Montezuma quail are 

thought to be generally less than scaled quail, but 

similar to Gambel’s quail. Zornes and Bishop (2009) 

estimated home range sizes for Gambel's quail that 

were similar to those of Montezuma quail (8–38 ha), 

while scaled quail home ranges estimates were generally much larger and ranged from 10-882ha.

The small core areas recorded in our study indicate 

that these coveys are likely showing feeding site 

fidelity. Leopold and McCabe (1957) suggested 

Montezuma quail showed feeding site fidelity which 

could result in limited daily movements. Brown (1978) 

observed that coveys normally have home ranges <6 

ha. Before pairing season, coveys generally do not 

make as large of movements as they do in the breeding 
66 USDA Forest Service RMRS-P-77. 2020.

season; however, movements observed in our study 

were still larger than the 6 ha suggested by Brown 

(1978). During our post-thinning study period, home 

ranges spanned from 3.21 ha to 94.63 ha. Although 

this is a wide margin between individual covey home 

range sizes, there appeared to not be major differences 

attributed to the type of habitat selected between each 

covey since all were primarily utilizing the area that 

underwent thinning. Overall, our study indicated a 

positive response with habitat selection and use associated with reducing tree canopy cover from ≥70 percent 

down to 30-40 percent.

MANAGEMENT IMPLICATIONS

Montezuma quail habitat use can be influenced by 

tree canopy cover. This could be associated with 

escape cover. Thick tree canopies likely restrict flight 

movements, and might influence a Montezuma quail’s 

ability to evade predators. Therefore in some areas, 

tree canopy reductions may be warranted. Our research 

provides a baseline for future thinning projects in pinyon-juniper woodlands that contain Montezuma quail. 

This research indicated that quail spent approximately 

85 percent of their time in areas that have tree canopy 

cover in the 30-40 percent range. Within our study 

area, habitat that contained tree canopy cover that exceeded 40 percent received relatively little use. The 50 

percent MCP core use area resided within the thinning 

project boundary for all 5 coveys that we tracked during this study. This study yields vital information for 

managers considering implementing thinning projects 

in pinyon-juniper woodlands that contain Montezuma 

quail. By reducing tree canopy cover to 30-40 percent, 

one can greatly increase habitat that is selected for by 

this enigmatic quail species.

ACKNOWLEDGMENTS

This project was funded by the National Wild Turkey 

Federation and Bureau of Land Management. We 

would also like to thank the Quail Coalition and Quail 

Forever for their contributions toward the project. We 

thank our past and present graduate students and technicians for their hard work throughout the study.

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selection of Arizona Mearns quail. Journal of Wildlife 

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Arizona. National Quail Symposium Proceedings 

8:359-368. 

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Davis Mountains of Texas. Alpine, TX: Sul Ross State 

University. Thesis. 60 p. https://tpwd.texas.gov/huntwild/

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23-29.

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populations and habitats at Elephant Mountain Wildlife 

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of our native quail. Field and Stream. March (24, 25): 

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bobwhite. Biological Report 82. (10:104). Washington, 

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68 USDA Forest Service RMRS-P-77. 2020.

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Effects of Piñon-Juniper Woodland Thinning on Avian Communities 

in the Arkansas River Valley, Colorado

KEYWORDS—piñon pine, juniper, piñon-juniper woodlands, avian occupancy, mastication, woodland 

thinning

Piñon-juniper woodlands, the third largest vegetation 

type in the United States, encompass 40 million ha of 

western North America, and occur in 10 western U.S. 

States (Tausch and Hood 2007, Romme et al. 2009, 

West 1984, Laylock 1999). Piñon-juniper thinning 

and mastication treatments have been used by public 

lands managers and private landowners throughout 

the western United States to mitigate fire risk, reduce 

encroachment, enhance wildlife and livestock forage 

production, and improve wildlife habitat. The Bureau 

of Land Management along with private and public 

partners have treated approximately 10,000 ha of 

piñon-juniper woodlands in the Royal Gorge Field 

Office area of Colorado primarily to reduce fire risk. 

The influence of tree removal on piñon-juniper bird 

communities is not well documented, however, and 

leads to varied, species-specific responses (Bombaci 

and Pejchar 2016). Piñon-juniper woodlands provide 

breeding habitat for more than 70 bird species, the 

highest of any terrestrial ecosystem in the western 

USA (Balda and Masters 1980), including several 

at-risk species experiencing long-term population declines (Sauer et al. 2014). The piñon-juniper bird community is unique compared to other ecosystems and 

includes many obligate species that differ substantially 

from those of other ecosystems (Paulin et al. 1999, 

Francis et al. 2011). The purpose of our study was to 

determine the effects of thinning efforts on bird populations and communities in piñon-juniper woodlands in 

southcentral Colorado. We measured multiscale avian 

occupancy on 29 paired (control/treatment) sites in 

piñon-juniper woodlands in the Arkansas River valley 

of central Colorado. 

The treatments consisted of 24 masticated (with hydroax) and 5 hand-thinned (using chainsaws) sites. Our 

study allowed for a broad evaluation of bird response 

to thinning across a variable piñon-juniper landscape. 

Treatments varied in size and shape and significantly 

reduced canopy cover from over 30 percent in control 

sites to less than 15 percent in treatment sites. Most 

treatment configurations included patchy cuts with 

clustered trees remaining within the treatment boundary. Some treatments occurred in dense piñon-juniper 

woodlands, whereas others were in more open sites. 

Some were located at higher elevations mixing with 

ponderosa pine and other treatments were prescribed 

at lower elevation grassland/woodland ecotones. 

Controls were located adjacent to treatments to reduce 

site variation between controls and treatments. 

We documented avian occupancy (presence or absence) using 10-minute surveys at each of 232 point 

count stations three times each season in 2014 and 

Patrick A. Magee

Department of Natural and Environmental Sciences, Western State Colorado University, Gunnison, Colorado 

Jonathan D. Coop

Center for Environment and Sustainability, Western State Colorado University, Gunnison, Colorado 
USDA Forest Service RMRS-P-77. 2020. 69

2015. We used hierarchical multiscale modeling 

(Program Mark; Pavlacky et al. 2012) to obtain unbiased estimates of landscape (Ψ) and local occupancy 

(θ; i.e., probability of use) in treated and untreated 

sites for 31 bird species identified in the study. We also 

measured seven landscape scale covariates to gain insight into the mechanism of treatment effects on avian 

occupancy. These included mean annual temperature 

(MAT), mean annual precipitation (MAP), Heat Load 

Index (HLI; incident radiation integrated across slope, 

aspect and latitude), elevation, forest cover at 10-ha 

and 100-ha scales, and time elapsed (years) since treatment was initiated (time elapsed = 0 for control sites; 

oldest treatments occurred 11 years prior to sampling). 

At the local scale we measured 11 covariates including percent cover of bare ground, herbs, and shrubs; 

density and basal area of all live trees, live piñon pines 

only, and live junipers only; mean tree height; and the 

standard deviation of tree height (a measure of woodland structural complexity). 

We used a three-step modeling procedure to analyze 

effects of thinning and mastication treatments on local 

and landscape scale avian occupancy. For each species 

we first identified a best-fitting structure for modeling 

detection probability to provide unbiased occupancy 

estimates. Secondly, we used the best-fitting detection 

model as we modeled treatment effects on landscape 

scale (Ψ) or local scale (θ) occupancy, or both. Finally, 

we fixed both detection and treatment structures and 

modeled effects of habitat-specific covariates representing mechanisms that drove treatment effects on 

occupancy.

We identified 77 bird species in the study area during 

surveys. The spotted towhee was the most numerous 

and ubiquitous species present in the study area. We 

also documented the presence of five piñon-juniper 

obligates including the black-throated gray warbler, 

Virginia’s warbler, gray flycatcher, pinyon jay, and 

juniper titmouse. 

Thirteen species had lower occupancy on treated sites, 

whereas six species showed increased occupancy 

on treated sites (table 1). At the landscape scale six 

species had strong negative treatment effects. These 

included two piñon-juniper obligates, Virginia’s warbler and gray flycatcher. The mature conifer specialist, 

mountain chickadee, along with a forest generalist, 

white-breasted nuthatch, and two open-conifer associated birds, Steller’s jay and Clark’s nutcracker 

showed strong negative treatment effects. In addition, 

three more species (Townsend’s solitaire of open 

conifer, western tanager of open conifer, and northern 

flicker, a forest generalist) showed moderate negative 

treatment effects at the landscape scale. In contrast, 

two species showed positive treatment effects at the 

landscape scale including the mountain bluebird (edge 

species) and the pinyon jay (piñon-juniper obligate). 

At the local spatial scale, the black-headed grosbeak, Table 1—Significant responses of bird species occupancy in treated (masticated or hand-thinned) and 

control sites in piñon-juniper woodlands in south-central Colorado during 2014 and 2015. Occupancy 

effect determined by beta estimate plus or minus 95 percent confidence interval for Ψ (landscape 

occupancy) and θ (local scale occupancy). Where the 95percent confidence interval overlapped 0 by 

less than 10 % the effect was deemed strong, whereas moderate treatment effects included betas with 

confidence intervals overlapping zero by 10-20 percent. 

Occupancy parameter Negative treatment effect Positive treatment effect

Landscape Occupancy (Ψ)

Strong effect Virginia’s warbler Mountain bluebird

Gray flycatcher

Mountain chickadee

Steller’s jay

Clark’s nutcracker

White-breasted nuthatch

Moderate effect Townsend’s solitaire Pinyon jay 

Western tanager

Northern flicker

Local Scale Occupancy (θ)

Strong effect Black-headed grosbeak Lark sparrow

Pinyon jay American robin

Moderate effect Broad-tailed hummingbird Western bluebird

Ash-throated flycatcher Blue-gray gnatcatcher
70 USDA Forest Service RMRS-P-77. 2020.

broad-tailed hummingbird, ash-throated flycatcher, 

and pinyon jay showed significant negative treatment 

effects. The lark sparrow, American robin, western 

bluebird, and blue-gray gnatcatcher, in contrast, had 

elevated local occupancy in treatments. 

In a covariate analysis, we found that bird occupancy 

not only varied by treatment, it was also influenced 

by landscape and local scale habitat, topographic, climatic, and temporal factors. Each species had a unique 

set of covariates that were associated with occupancy, 

but generally, reduced occupancy of forest species was 

associated with declines in tree density, basal area, and 

forest cover, whereas occupancy of species associated 

with more open habitats increased with less forest 

cover.

Piñon-juniper woodlands are rich in bird species and 

provide habitat for a suite of high profile conservation 

species, many of which are experiencing long-term 

population declines. Our data suggest that mastication 

and hand-thinning treatments may reduce local and 

landscape occupancy of several of these species while 

simultaneously increasing occupancy for species associated with more open habitats and characterized by 

less urgent conservation needs. Woodland and piñonjuniper obligate species showed the strongest negative 

responses to treatments. According to Bombaci and 

Pejchar (2016), woodland birds are “losers” when 

considering wildlife functional group responses to tree 

removal; however, this negative response is buffered 

following less extensive tree thinning. As a group, 

birds in this study did not respond uniformly to mastication and hand-thinning treatments. Similarly, avian 

taxa and functional group, as well as treatment type 

led to divergent responses among birds across numerous studies (Bombaci and Pejchar 2016). Further, our 

results highlighted the differential treatment effects on 

occupancy across spatial scales and among and even 

within species. Pinyon jays, for example, had lower 

occupancy on treated sites at the local scale; however, 

occupancy was greater in treatments at the landscape 

scale. pinyon jays may find treated landscapes suitable 

for occupancy as long as they contain fairly dense 

patches, but within treated forest stands, fragmentation 

may exceed a threshold that pinyon jays can tolerate 

for nest and roost habitat selection.

Mastication and hand-thinning treatments may not 

be needed for fire mitigation in the future as climate 

driven piñon-juniper tree mortality has increased in the 

21st century (Breshears et al. 2005). In the wildland 

urban-interface, protection of property and life are 

priorities, and fire mitigation treatments will continue 

in these areas. Managers may be able to alter treatment 

prescriptions that favor forest bird habitat such as leaving more live trees and further clustering treatments 

rather than clearcutting or leaving evenly dispersed 

savannas (Bombaci et al. 2016, Francis et al. 2011, 

Pavlacky and Anderson 2001). In our study, canopy 

cover was reduced beyond what was needed to moderate fire risk. Species such as the pinyon jay and blackheaded grosbeak were positively associated with tree 

density and would benefit if fewer trees were removed 

in mastication treatments. 

ACKNOWLEDGEMENTS

We thank Matt Rustand, wildlife biologist at BLMRGFO, who generally conceived of the project and 

provided funding and support. We thank our field crew 

as well as additional research technicians and collaborators including Paige Colburn, Marcel Such, Kyle 

Gordon, Jake Powell, Erin Twaddell, Ryan Walker, 

Connor Jandreau, Jessie Dodge, Liz Moore, Glenda 

Torres, Caitlin Bernier, and Shannon Sprott. We gratefully acknowledge the invitation from Kris Johnson to 

present these results at the piñon-juniper symposium 

in October 2016 in Albuquerque, NM. Finally we 

thank local landowners for providing permission to 

access their land during the study: Curt Sorenson, 

Aaron Tezak and Jeremy Cole. We thank the BLMRGFO, the Joint Fire Science Program (13-1-04-45), 

and Western State Colorado University, including the 

Natural and Environmental Sciences Department, the 

Center for Environment and Sustainability, and the 

Thornton Biology Undergraduate Research Program 

for funding. 
USDA Forest Service RMRS-P-77. 2020. 71

REFERENCES

Balda, R.P; Masters, N.L. 1980. Avian communities in 

the pinyon-juniper woodlands: A descriptive analysis. 

In DeGRaaf, R.M., technical coordinator. Workshop 

Proceedings: Managing western forests and grasslands 

for non-game birds. Gen. Tech. Rep. INT-GTR-86. 

Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment 

Station. 146-169. 

Bombaci, S.; Pejchar, L. 2016. Consequences of pinyon 

and juniper woodland reduction for wildlife in North 

America. Forest Ecology and Management. 356:34-50.

Breshears, D.D.; Cobb,N.S.; Rich, P.M.; [et al.]. 2005. 

Regional vegetation die-off in response to global-changetype drought. Proceedings of the National Academy of 

Sciences. 102:15144-15148.

Francis, C.D.; Ortega, C.P.; Hansen, J. 2011. Importance 

of juniper to birds nesting in piñon-juniper woodlands 

in Northwest New Mexico. The Journal of Wildlife 

Management. 75:1574-1580.

Laylock, W.A. 1999. Ecology and management of pinyonjuniper communities within the Interior West: Overview 

of the “Ecological Session” of the symposium. In 

Monsen, S.B.; Stevens, R.; tech. coords. Proceedings: 

Ecology and management of pinyon-juniper communities 

within the Interior West. Sustaining and restoring a 

diverse ecosystem. Proceedings RMRS-P-9 Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station: 7-11.

Paulin, K.M.; Cook, J.J.; Dewey, S.R. 1999. Pinyon-juniper 

woodlands as sources of avian diversity. In Proceedings: 

Ecology and Management of Pinyon-Juniper 

Communities within the Interior West (S. B. Monsen and 

R. Stevens, technical coordinators). In Monsen, S.B.; 

Stevens, R.; technical coords. Proceedings: Ecology 

and management of pinyon-juniper communities within 

the Interior West. Sustaining and restoring a diverse 

ecosystem. Proceedings RMRS-P-9 Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Rocky 

Mountain Research Station: 240-243.

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management of the major ecosystems of southern Utah. 

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U.S. Department of Agriculture, Forest Service. 110 p. 

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In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

Jonathan D. Coop

Center for Environment and Sustainability, Western State Colorado University, Gunnison, Colorado 

Patrick A. Magee

Department of Natural and Environmental Sciences, Western State Colorado University, Gunnison, Colorado

Identifying and Mitigating Social-Ecological Tradeoffs: Vegetation, 

Birds, Fuels, and Modeled Fire Behavior in Piñon-Juniper Fuel 

Reduction Treatments

KEYWORDS—crown fire, invasive species, hydro-ax, Juniperus monosperma, Juniperus scopulorum, 

Pinus edulis

Mastication and hand-thinning treatments are increasingly used by land managers as a means of reducing 

tree cover for fire hazard mitigation and other habitat 

objectives in piñon-juniper (P-J) woodlands. (Battaglia 

et al. 2010; Redmond et al. 2013). However, the 

effects of these treatments on ecological processes 

including fire, and on a wide range of species, particularly vulnerable P-J obligate birds, are incompletely 

understood. To address these knowledge gaps, we 

measured vegetation and fuels, and conducted bird 

point counts at 232 sites in 29 pairs of 1 to 11-yearold treatments and untreated adjacent controls in P-J 

woodlands of the Arkansas River Valley, Colorado. We 

used a suite of statistical approaches including paired 

t-tests, mixed-effects models, and a principal component analysis to assess treatment impacts on vegetation 

composition and structure, bird abundance, fuels. We 

also used BehavePlus (Andrews et al. 2005) to develop 

fire behavior models to examine expected treatment 

impacts on fire behavior along gradients of fuel loads 

and under varying fuel moisture scenarios. 

Treatments drove major, persistent ecological shifts 

relative to controls. Tree cover and canopy fuels were 

reduced; concomitantly, down woody surface fuels, 

forbs, and grass cover increased (Coop et al. 2017). 

Treatments exhibited rapid, large, and persistent 

increases in the frequency, richness, and cover of 20 

non-native plant species including cheatgrass (Bromus 

tectorum). Exotic plant expansion was associated with 

reductions in tree canopy, alterations to ground cover 

(distribution of mastication debris and bare soil), and 

other treatment activities (Coop et al. 2017). Effective 

mitigation of nonnative species may necessitate additional pre- and posttreatment control measures. 

Decreased cover by piñon and juniper in treatments 

was also closely associated with reduced frequency of 

observations of a suite of P-J obligate bird species.

Reduced canopy fuels and increased herbaceous 

surface fuels, including exotic annuals, are expected 

to alter potential fire behavior through reduced active 

crown fire probability, but also increased surface fire 

intensity, flame length, and rate of spread. Modeled 

fire behavior suggests that under most conditions, 

treatments will be highly effective at reducing active 

crown fire risk, but also that treatments generally 

removed more trees than necessary to mitigate this 

risk. For example, average canopy cover was 6 percent 

in treatments but only 29 percent in untreated control 

units. However, even under fairly extreme 97th percentile conditions, models indicated that sustained 
USDA Forest Service RMRS-P-77. 2020. 73

active crown fire required greater than 30 to 50 percent 

canopy cover. Retention of more trees within treated 

sites will likely benefit P-J obligate birds. Fire behavior models also indicated that residual trees within 

treatment sites, particularly in masticated sites, were 

still highly susceptible to passive crown fire (torching), 

due to abundant surface fuels, increased flame lengths, 

and low tree crown base heights. Models, including 

simulated treatment modifications, suggest that this 

remaining crown fire risk could mostly be eliminated 

through follow-up surface fuel reductions (e.g., prescribed fire) and/or crown base height increases (e.g., 

pruning). We propose that managers consider additional posttreatment fuels interventions to increase the 

fire resistance of residual trees, which is anticipated to 

yield both social and ecological benefit. We encourage 

managers carrying out P-J fuels reductions projects 

to explicitly consider potential trade-offs between 

desired treatment outcomes and unintended ecological 

impacts, and how these might be mitigated. In areas 

distant from the wildland-urban interface, and in 

persistent P-J woodlands without imminent restoration 

needs, we also question whether or not tree removal 

treatments may be useful given anticipated climate 

change impacts to these woodlands (e.g., McDowell et 

al. 2016). 

ACKNOWLEDGEMENTS

Funding was provided by the Joint Fire Science 

Program (13-1-04-45) and the Western State Colorado 

University Thornton Biology Research Fund.

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2010. Surface fuel loadings within mulching treatments 

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Redmond, M.D.; Cobb, N.S.; Miller, M.E.; Barger, N.N. 

2013. Long-term effects of chaining treatments on 

vegetation structure in piñon–juniper woodlands of the 

Colorado Plateau. Forest Ecology and Management. 305: 

120-128.
74 USDA Forest Service RMRS-P-77. 2020.

In: Malcolm, Karl; Dykstra, Brian; Johnson, Kristine; Lightfoot, David; Muldavin, Esteban; Ramsey, Marikay. 2020. Piñon-juniper habitats: 

Status and management for wildlife. Proceedings RMRS-P-77. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountian Research Station. 128 p.

Papers published in these proceedings were submitted by authors in electronic media. Editing was done for readability and to ensure 

consistent format and style. Authors are responsible for content and accuracy of their individual papers and the quality of illustrative materials. 

Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

David Lightfoot

SWCA Environmental Consultants, Albuquerque, New Mexico; Biology Department, Museum of Southwestern Biology, 

University of New Mexico, Albuquerque, New Mexico

Anne Russell

SWCA Environmental Consultants, Albuquerque, New Mexico

Victoria Amato

SWCA Environmental Consultants, Albuquerque, New Mexico

Cody Stropki

SWCA Environmental Consultants, Albuquerque, New Mexico

Conor Flynn

SWCA Environmental Consultants, Albuquerque, New Mexico; Arizona Public Service Company, Prescott, AZ

(current affiliation)

Ian Dolly

SWCA Environmental Consultants, Albuquerque, New Mexico

Joanna Frank

SWCA Environmental Consultants, Albuquerque, New Mexico

Wildlife Responses to Small-Scale Tree Thinning in Central New 

Mexico Pinyon-Juniper and Ponderosa Pine Woodlands

ABSTRACT—Small-scale (<5 ha) tree thinning treatments were implemented at two pinyon-juniper (Pinus 

edulis-Juniperus monosperma) and two ponderosa pine (Pinus ponderosa) wildland-urban interface 

woodland sites in central New Mexico. We used paired watershed, Before/After/Control/Impact sampling 

designs with 3 years of prethinning measurements followed by 5 years of posttreatment measurements. 

Characteristics of soils, vegetation, and wildlife were measured. Manual thinning treatments reduced small 

trees, removed firewood, and chipped slash. The treatments reduced wildfire fuel loads, changed the 

woodlands to more open stands of proportionately larger trees, increased herbaceous understory vegetation canopy cover, and caused some shifts in bird, rodent, and larger wildlife species assemblages. Animal 

species that prefer tree cover either declined slightly or showed no response to thinning, while species that 

prefer open habitats increased slightly. Birds showed primarily neutral responses, but several avian species showed positive responses. Rodents showed primarily neutral or negative responses, but dominant 

pinyon mice (Peromyscus truei) declined. Larger native mammals and wild turkeys (Meleagris gallopavo) 

showed negative responses while domestic livestock increased on thinned locations.

KEYWORDS—Southwest, pinyon, birds, rodents, mammals, ecology, restoration, climate change
USDA Forest Service RMRS-P-77. 2020. 75

INTRODUCTION

Pinyon-juniper (Pinus spp.-Juniperus spp.) woodlands 

are one of the most widespread woodland vegetation 

types across western North America (Romme et al. 

2009), and they support a great diversity of wildlife 

species (Bombaci and Pejchar 2016), including approximately 70 species of breeding birds (Balda and 

Masters 1980; Paulin et al. 1999), about 60 species 

of mammals (Chung-MacCoubrey 2005; Finch and 

Ruggiero 1993), and hundreds of insects and other 

arthropod species (Frischknecht 1975; Furniss and 

Carolin 1977; Higgins et al. 2014; Lightfoot et al. 

2008; Nyoka 2010). Amphibian and reptile associations are poorly documented (Bombaci and Pejchar 

2016), but are probably represented by about 20 species in New Mexico (Degenhardt et al. 2005). Pinyon 

and juniper provide not only physical habitats for 

animals, but also food, especially when these plants 

produce cones and berries. The physical structure of 

pinyon-juniper woodlands supports a mix of animal 

species that prefer patchy to open woodlands, and species that require some dense tree structure (Bombaci 

and Pejchar 2016; Gottfried et al. 1995). 

Pinyon-juniper woodlands are widespread at midelevations across the West and are composed of three 

primary ecological stand types: 1) relatively long-term 

stable and persistent pinyon or pinyon-juniper woodlands on steeper, rocky environments; 2) dynamic 

pinyon-juniper savannas on well-developed loamy 

soils; and 3) dynamic wooded shrublands in environments that favor shrubs, but also support pinyon trees 

(Romme et al. 2009). Pinyon pine and juniper are represented by several species in North America (Romme 

et al. 2009). The species in central New Mexico are 

twoneedle pinyon pine (also called Colorado pinyon 

or in Spanish, piñon; Pinus edulis) and oneseed juniper (Juniperus monosperma) (U.S. Department of 

Agriculture 2016). 

The temporally dynamic pinyon-juniper savannas of 

central New Mexico are transitional between grasslands 

and conifer forests, and consist largely of pinyonjuniper savannas mixed with persistent pinyon stands. 

The pinyon-juniper savannas have expanded into the 

lower elevation grasslands over the past 150 years, primarily due to livestock overgrazing of perennial grasses, 

wildfire suppression, and climate change (Dick-Peddie 

1993; Miller and Wigand 1994; Romme et al. 2009). 

Despite the great extent of pinyon-juniper woodlands 

across the Southwest, the management of both vegetation and wildlife is still poorly developed due to the 

regional or landscape-scale variability of pinyon-juniper 

stand types, the many wildlife species that utilize 

pinyon-juniper habitats, and the different management 

approaches used to reduce pinyon-juniper stand densities (Bombaci and Pejchar 2016; Romme et al. 2009). 

The historical within-stand overgrowth and landscape 

expansion of pinyon-juniper, followed by the recent 

declines in pinyon-juniper stands in response to 

climate change as atmospheric temperature warming 

intensifies drought (Gutzler 2013; Gutzler and Robbins 

2011; Llewellyn and Vaddey 2013; Petrie et al. 2014; 

Seager et al. 2008), have created a natural resource 

management challenge; the overgrown stands are now 

at risk of drought-induced tree mortality (Breshears 

et al. 2005, 2015; Clifford et al. 2008; Ganey and 

Vojta 2011; Gaylord 2014; Gaylord et al. 2013, 2015; 

Hicke and Zeppel 2013; McDowell and Allen 2015; 

McDowell et al. 2016; Williams et al. 2013) and 

increasing high-severity wildfire (Allen et al. 2010; 

Savage et al. 2013). These conditions all interact to 

create a critical need for natural resource managers to 

employ the most effective methods for managing the 

natural resources of pinyon-juniper and ponderosa pine 

(Pinus ponderosa) woodlands across the West, including their associated wildlife species. 

Tree thinning management practices (Agee and 

Skinner 2005) and associated research about the effects of tree thinning on forest and watershed health 

and function across the Southwest have focused 

on commercially valuable ponderosa pine and 

mixed-conifer stands (e.g., Abella 2009; Abella and 

Covington 2004; Allen et al. 2002; Friederici 2003; 

Fulé et al. 2001, 2007; Gaylord 2014; Griffis et al. 

2001; Moore et al. 2006; Noss et al. 2006; Overby 

et al. 2015; Robles et al. 2014; Stoddard et al. 2011; 

Thomas and Waring 2015). In contrast, relatively little 

research and information are available on the effects 

of thinning in pinyon-juniper woodlands (e.g., Baker 

and Shinnerman 2004; Gottfried et al. 2005; Ross et al. 

2012). Tree thinning in ponderosa pine stands has been 

demonstrated to increase the health and productivity of remaining trees (Keane et al. 2002; Kolb et al. 
76 USDA Forest Service RMRS-P-77. 2020.

1998; Ronco et al. 1985; Skov et al. 2005; Wallin et 

al. 2004; Zausen et al. 2005) and to increase the cover 

and production of understory vegetation (Abella 2009; 

Abella and Covington 2004; Matchett et al. 2010; 

Moore et al. 2006; Ramirez et al. 2008; Stoddard et al. 

2011; Thomas and Waring 2015), but thinning effects 

on plant species composition and species diversity 

are less clear and generally minor (Abella 2009). Tree 

thinning also has been found to reduce soil erosion, 

while often increasing surface water yields from 

watersheds (Baker 1986, 2003; Keane et al. 2002; 

Moghaddas 2013; Robles et al. 2014; Stednick 1996; 

Troendle et al. 2010). How soils and hydrology are 

affected depends on the method of thinning and the 

weather conditions during and after thinning (Elliott et 

al. 1996; Moghaddas 2013; Wyatt 2013).

Tree thinning affects wildlife by changing the 

physical structure, microclimates and patch pattern of 

pinyon-juniper and ponderosa pine forest/woodland 

habitats, and understory vegetation composition and 

productivity (Allen et al. 2002; Bombaci and Pejchar 

2016; Kalies et al. 2009; Noss et al. 2006; Pilliod et al. 

2006; Stephens et al. 2012; Thomas and Waring 2015). 

Reducing tree densities creates open stands of habitat 

for animal species that prefer less tree canopy cover 

and more productive understory vegetation (Verschuyl 

et al. 2011), such as mule deer (Odocoileus hemionus), 

North American elk (Cervus canadensis), and others 

(Thomas and Waring 2015). 

Birds tend to be diverse in Southwestern forests and 

woodlands, and because of their species richness, 

species-specific habitat preferences, and relatively 

high abundances, birds are good indicators of the effects of tree thinning treatments on native animal communities (Hutto 1998; Hutto et al. 2014). Tree thinning 

treatments in Southwestern conifer woodlands have 

been found to have neutral to positive effects on bird 

assemblages (Bombaci and Pejchar 2016; Gaines et 

al. 2010; Hurteau et al. 2008; Hutto et al. 2014; Kalies 

et al. 2009; Pilliod et al. 2006; Stephens et al. 2012; 

Verschuyl et al. 2011; White et al. 2013). However, 

bird responses to thinning treatments tend to be 

specific to the species or foraging guild, and variable 

over time relative to weather conditions and vegetation 

changes (Hutto et al. 2014; Kalies et al. 2009; Pilliod 

et al. 2006; Stephens et al. 2012; Yegorova 2013). 

Small mammal or rodent assemblages also have been 

found to have positive or neutral responses to tree thinning treatments (Bombaci and Pejchar 2016; McIver 

et al. 2013; Pilliod et al. 2006; Stephens et al. 2012). 

Ground-dwelling rodent species tend to respond positively to small-diameter tree removal (Kalies 2010; 

Zwolak 2009). The presence of downed woody slash 

following treatments has been found to increase habitat 

and abundance of several ground-dwelling rodent species (Bagne and Finch 2010; Converse et al. 2006a,b,c; 

Severson 1986; Stephens et al. 2012). However, the 

effects of tree thinning projects on wildlife are variable 

and depend on characteristics of the local environment, 

the type of tree reduction method used (e.g., mechanical, manual, variable prescriptions), the ecology of each 

animal species, and time since thinning treatments were 

imposed (Hutto et al. 2014; Kalies et al. 2009; Pilliod 

et al. 2006; Stephens et al. 2012).

Tree thinning treatment effects on wildlife across the 

Southwest have largely focused on ponderosa pine 

forests (Converse et al. 2006a,b,c; Kalies et al. 2009; 

Kennedy and Fontaine 2009, Pilliod et al. 2006; 

Stephens et al. 2012), and less is known about tree 

thinning effects on wildlife in pinyon-juniper woodlands (O’Meara et al. 1981; Severson 1986). Bombaci 

and Peichar (2016) provide a comprehensive literature 

review of studies addressing the effects of pinyonjuniper woodland tree reduction on wildlife and have 

found that 69 percent of studies showed no change in 

overall wildlife abundance. However, they found both 

positive and negative responses to be more frequent 

when single species or guilds were evaluated. As tree 

thinning becomes a common management tool in 

pinyon-juniper woodlands, our need to understand the 

environmental effects of thinning on wildlife becomes 

more urgent.

The research reported in this article is part of a larger 

research project on effectiveness of tree thinning 

in the Estancia Basin, New Mexico. The Estancia 

Basin Watershed Health, Restoration and Monitoring 

Program (Watershed Restoration Program) is a cooperative consortium of local government agencies that 

acquired funding to conduct forest thinning projects 

on private lands in the Manzano Mountains of central 

New Mexico. The goals of the Watershed Restoration 

Program are to reduce the threat of catastrophic 
USDA Forest Service RMRS-P-77. 2020. 77

high-severity wildfires and to improve forest and 

watershed environmental health and function. The tree 

thinning treatments follow prescriptions developed by 

New Mexico State Forestry that emphasize hand thinning practices with low environmental impact. Largediameter wood is removed and used for firewood, and 

small-diameter wood or slash is chipped and spread 

over the soil surface to reduce soil erosion potential. 

The individual project areas tend to be relatively small 

in size, ranging from around 2 to 12 ha, typical for 

wildland-urban interface (WUI) tree thinning treatments (Martinuzzi et al. 2015; Matchett et al. 2010).

Under this program, we developed and implemented 

an experimental monitoring study to evaluate the effects of small-scale (<5 ha) tree thinning, chipping, 

and wood chip distribution, in pinyon-juniper and 

ponderosa pine woodlands in the Manzano Mountains. 

Effectiveness monitoring for the experimental tree 

thinning provides data appropriate to evaluate trends 

in the management goals relative to forest thinning 

treatments. The objectives of the restoration effectiveness monitoring were (1) to evaluate the success of 

tree thinning treatments relative to decreasing excess 

small trees and fuels; (2) to reduce high severity 

wildfire and enhance woodland watershed function, 

health and growth of remaining trees, and understory 

herbaceous vegetation productivity; and (3) to improve 

wildlife habitat and animal diversity and abundance. 

Here, we report on wildlife-specific research questions 

developed for the project: (1) How do small-scale tree 

thinning treatments change wildlife habitat (overall 

physical structure and vegetation species composition), and (2) how does small-scale tree thinning affect 

wildlife species composition and abundance? Wildlife 

addressed in this study includes diurnal birds, nocturnal rodents, and medium-sized to large mammals. 

METHODS

Study Sites

The study area was on the eastern slopes of the 

Manzano Mountains 10 to 40 km northwest of 

Mountainair, New Mexico (fig. 1). We used a Before/

After/Control/Impact, or BACI, experimental monitoring design, where thinning treatment impact was 

evaluated by comparing results for paired control 

and treated catchments or watersheds (Green 1979; 

Neary 2016; Quinn and Keough 2007). We installed 

two paired watershed sites in ponderosa pine forest 

and two paired watershed sites in pinyon-juniper 

woodland in the study area in fall 2007. This study 

was conducted over an 8-year period, with 2.5 years of 

pretreatment measurements from 2008 through 2010, 

and 5 years of posttreatment measurements from 2011 

through 2015. Two ponderosa pine study sites, PP1 

and PP2, were located in the Arizona/New Mexico 

Mountains, Montane Conifer Forests ecoregion 

(U.S. Environmental Protection Agency Ecoregions) 

(Griffith et al. 2006) and within the Southern Rocky 

Mountain Ponderosa Pine Woodland (Southwest 

Regional Gap Analysis Project Land Cover) (Lowry et 

al. 2007), or Lower Montane, Ponderosa Pine-Gambel 

Oak Series vegetation type (Dick-Peddie 1993: 66), 

at about 2,300 m in elevation. Two pinyon-juniper 

sites, PJ1 and PJ2, were located in the Southwestern 

Tablelands, Central New Mexico Plains ecoregion 

(Griffith et al. 2006) and within the Southern Rocky 

Mountain Pinyon-Juniper Woodland (Lowry et al. 

Figure 1—Southwest Regional Gap Analysis Project land 

cover map of the study region and location of study sites 

on the eastern slopes of the Manzano Mountains, Torrance 

County, New Mexico.
78 USDA Forest Service RMRS-P-77. 2020.

2007) or Pinyon-Juniper Woodland—Pinus edulis-Juniperus monosperma/S/Bouteloua gracilis—vegetation 

type (Dick-Peddie 1993:89), and both sites were at 

about 2,100 m in elevation. Pinyon-juniper woodlands 

of the region and both pinyon-juniper study sites were 

within the pinyon-juniper savanna type described by 

Romme et al. (2009). Refer to Dick-Peddie (1993) for 

details on floristic composition, climates, and ecological succession within those vegetation types. 

Domestic livestock, primarily cattle but also some 

horses, were present at three of the study sites, but 

not at one ponderosa pine site (PP1). All sites except 

PP1 were lightly to moderately grazed by livestock 

(sensu Holechek et al. 1999); the two pinyon-juniper 

sites had similar levels of moderate grazing (~40% 

utilization), and the PP2 ponderosa pine site had light 

grazing (<30% utilization). All control and treatment 

watersheds per site were exposed to the same amount 

of grazing by the same animals. 

The selection of paired watershed study sites was subjectively based on (1) landowner permission from a set 

of available landowners involved in the private lands 

tree thinning program, and (2) by selecting adjacent 

paired watershed locations representing similar topography, soils, and vegetation (using topography and 

soils maps and aerial images), ranging in size from 2 

to 3 ha each. A mini-meteorological (mini-met) station, 

a Parshall flume, a soils and vegetation measurement 

plot, and a wildlife and vegetation measurement plot 

were located near the center of each of the eight paired 

watersheds, and are described later. One set of ponderosa pine and pinyon-juniper study sites (PP1 and PJ1) 

was located within the same larger scale watershed, 

and the other set of sites (PP2 and PJ2) was located 

within another larger scale watershed. Each site, PP1, 

PP2, PJ1, and PJ2, consisted of two adjacent paired 

watersheds, one randomly selected for the thinning 

treatment and one left alone as a control, for a total of 

eight study watersheds. 

The original PP1 site that was established in 2007 

burned in the high-severity Trigo wildfire that burned 

the eastern slopes of the Manzano Mountains in May 

2008, and data from that site are not addressed here. 

The new PP1 site was established in fall 2008, farther 

to the north in a different watershed. Below we refer 

to the four primary study locations with paired watersheds as sites, and we refer to each of the eight small 

paired study watersheds as watersheds (entire paired 

hydrologic catchment drainages), treatments (control 

or tree reduction treatments applied to each paired 

watershed). With each treatment there are smaller study 

plots that are specific measurement plot locations where 

various data for plants and animals were collected.

Regional Climate

The project area is located within the Arizona/New 

Mexico Mountains Ecoregion (Griffith et al. 2006). 

The climate across the project area is semiarid temperate and characterized by winter and summer rain patterns, a wide range of diurnal and annual temperatures, 

and low relative humidity. Most precipitation tends to 

occur in July and August from summer convectional 

storms. The mean annual precipitation (including 

water from snow) for Mountainair (1902–2015) is 

36.6 cm, average total snowfall is 69.6 cm, average 

maximum annual temperature is 19.7 °C, and average 

minimum air temperature is 2.0 °C (Western Regional 

Climate Center 2016). Mountainair is at an elevation 

similar to the pinyon-juniper sites and there were no 

long-term meteorological stations at elevations similar 

to the ponderosa pine sites. 

Measurement Plots

Three different environmental measurement study plot 

designs were installed on each of the eight watersheds. 

The study plots were subjectively located within the 

center of each watershed, such that measurement 

plots were at least 100 m apart, and located on similar 

topography, soils, and vegetation structure and composition. Tree measurements were made following the 

Forest Service, U.S. Department of Agriculture (Forest 

Service) Forest Inventory and Analysis (FIA) plots and 

protocols (Forest Service 2005). Rangeland monitoring 

plots and protocols (Herrick et al. 2005) were installed 

and used for measuring understory vegetation. The 

rangeland monitoring plots were approximately the 

same overall size and dimensions as the FIA plots, and 

both plot types were overlaid on each other to provide 

combined tree and understory vegetation measurement plots. Soil variables also were measured from 

sampling locations based on the overlaid plot design, 
USDA Forest Service RMRS-P-77. 2020. 79

and those overlaid plots are hereafter referred to as 

“vegetation/soils plots.” Tree data presented here were 

measured on the vegetation/soils plots.

A separate wildlife and vegetation (wildlife habitat) 

measurement plot (hereafter, “wildlife plots”) was also 

installed within 20 m of each of the eight vegetation/

soils plots. The wildlife plots had the same general 

topography, soils, and vegetation as the adjacent vegetation/soils plots. Each wildlife plot was 50 m × 50 

m and composed of a 6-station × 6-station grid of 36 

permanently marked rodent trap stations and thirty-six 

1-m2 vegetation measurement quadrats at 10-m intervals (fig. 2). 

Variables Measured

Weather

An automated mini-met station (WatchDog model 

2425, Spectrum Technologies, Aurora, Illinois) was 

installed in an open location adjacent to the vegetation/

soils plot at each of the eight watersheds (i.e., four 

BACI sites) and programmed to record data at 1-hour 

intervals continuously year-round. Each of the eight 

mini-met stations was enclosed in a barbed-wire fence 

to prevent livestock from damaging the equipment. 

Solar-powered electric wires were attached to the 

fences at the ponderosa pine sites to prevent American 

black bears (Ursus americanus) from damaging the 

equipment (damage occurred without protection). Each 

mini-met station was equipped with a tipping bucket 

rain gauge, an ambient temperature recorder, and a 

soil moisture probe and temperature probe at 10 cm 

below the soil surface. Graduated cylinder rain gauges 

also were installed at each mini-met station to provide 

backup rainfall data. Graduated cylinder rain gauges 

contained small amounts of mineral oil to prevent 

evaporation of precipitation. Cumulative precipitation amounts in the rain gauges were recorded every 

month, and all mini-met station data were offloaded 

monthly year-round. 

Soils, Vegetation, and Hydrology

Detailed measurements on soils (texture, chemistry, 

water content, surface stability, and erosion), vegetation (plant species composition, canopy cover, and 

standard stand metrics), and hydrology (surface 

runoff) were taken, but soils and hydrology data are 

not reported in this article. Some summary data on 

vegetation are presented here, because vegetation is a 

primary component of wildlife habitat. 

Vegetation—Trees

All trees on both the pinyon-juniper and ponderosa 

pine plots were tallied on each of three 14.6-m-diameter tree subplots of each individual study plot. All 

trees were identified to species and tagged for future 

reference. All tree measurement protocols followed 

the standard methods in the FIA guidelines (2005). 

Tree diameter was measured by using a diameter tape 

at diameter at breast height (d.b.h.) for ponderosa pine 

trees or diameter at root collar (d.r.c.) for pinyon and 

juniper. Along with diameter, tree stand structure, species composition, and tree health were measured on all 

four FIA tree plots, on each of the eight watersheds. 

Tree basal area was measured by point sampling techniques (Avery and Burkhart 2002) with a basal area 

factor of 20. These measurements were taken on all 

plots in 2008 prior to thinning treatments, and again in 

2011 and 2015 following treatments. Measures of tree 

health, including crown dieback (mortality percentage 

Figure 2—Diagram of a wildlife and vegetation measurement plot. Numbers 1 through 36 are 1-m2 vegetation 

quadrat numbers. Components of the diagram are not to 

scale for ease of viewing.
80 USDA Forest Service RMRS-P-77. 2020.

of the canopy of each tree), and tree mortality were 

recorded for all trees across all watersheds once each 

year in September from 2008 to 2015. 

Vegetation Vertical Canopy Cover

The amount of vertical vegetation (herbaceous and 

tree) above the ground surface to a height of 2 m was 

measured with a canopy structure pole as described by 

Herrick et al. (2005) and made from 2.5-cm-diameter 

white polyvinylchloride (PVC) pipe. The 2-m pole was 

partitioned into four consecutive 0.5-m-tall subsections, and each of the four subsections was further 

divided into five 10-dm subsegments alternating black 

(black vinyl tape) and white, for a total of twenty 1-dm 

subsegments in vertical sequence above the ground 

surface. One biologist held the pole in a vertical position at 10 permanently established measurement points 

that were located at 10-m intervals on each wildlife 

plot. The measurement points were at the rodent 

trap and vegetation quadrat locations 10, 15, 22, 27, 

34, 14, 15, 16, 17, and 18 on each wildlife plot (see 

fig. 2). Measurements were taken once each year from 

2010 through 2015 at the end of the summer growing 

season. The hollow PVC pipe pole was always placed 

over the permanent rebar marker stake at each point. 

A second observer stood 10 m away, at points 3, 10, 

15, 22, and 27 (north to south view), and at points 13, 

14, 15, 16, and 17 (west to east view) (see fig. 2) and 

recorded whether each of the 1-dm subsegments was 

obscured 50 percent or greater by vegetation or tree foliage, stems, and branches. An overall visual obstruction 

average score was then calculated for each 0.5-m segment of the pole over all 20 sampling points per plot. 

Tree Crown Canopy Cover

Tree crown canopy densities were measured with a 

spherical densiometer (Cook et al. 1995) at the same 

11 measurement/observation points where vertical 

canopy cover was measured. Given that densiometer 

readings often differ among observers (Cook et al. 

1995), the same observer took all densiometer measurements from 2010 through 2015 to reduce observer 

error. The spherical densiometer also is known to 

overestimate canopy density due to the shape of the 

mirror (Cook et al. 1995), but the primary objective 

of measuring the tree canopy densities in this study 

was to directly compare the canopy densities of paired 

plots over time, so that error was consistent. Reducing 

observer bias and taking 11 measurements from each 

plot provided us with a useful tool to compare changes 

in tree canopy densities over the study plots and over 

time, despite known measurement problems with the 

spherical densiometer. 

Pinyon Cone and Seed Production

Pinyon is a masting plant species where individual 

trees collectively produce large seed crops infrequently 

(every several years) on an irregular basis (Janetski 

1999). Given the uncertainly of cone production, we 

did not initially design sampling protocols to measure 

cone crop production. From 2008 to 2015, pinyon 

trees produced cone crops at our study sites in 2009 

and again in 2015. In 2015 we attempted to measure 

cone production by visually estimating the percentage of each tree’s canopy containing cones from all 

trees in each of twenty-five 10-m × 10-m plot cells 

of each wildlife plot (see fig. 2). The same observer 

recorded cone production from all eight study plots in 

September 2015. 

Understory Vegetation and Ground Surface Cover

Vegetation growing below tree canopies and cover 

of soil surface features (e.g., litter and duff, rocks, 

biotic crusts) were measured in late September each 

year from 2010 through 2015 from the permanent 

1-m2 quadrats located at each of the 36 permanently 

marked rodent trapping stations on each wildlife plot 

(see fig. 2). All herbaceous plant species, cacti, and 

woody shrubs were measured on each quadrat. The 

total canopy cover and maximum height (cm) of each 

species was measured per quadrat. Vegetation quadrat 

data were also categorized by growth form (i.e., grass 

[graminoid], forb, succulent, and woody shrubs and 

subtrees such as oaks [Quercus spp.]) and life history 

(annual or perennial). We followed the taxonomic 

classification and common names of plants provided 

by the U.S. Department of Agriculture (2016) PLANTS 

Database. Voucher plant specimens were photographed, 

collected, and identified in the laboratory as needed to 

provide accurate species-level identifications. 

In addition to vegetation, soil surface cover categories 

were measured on the quadrats, including the percent 

cover of bare soil, leaf litter (and dead and downed 
USDA Forest Service RMRS-P-77. 2020. 81

woody material <2 cm diameter), dead and downed 

wood (>2 cm diameter), rock, and biotic soil surface 

crusts (blue-green algae, algae, lichens, and moss 

growing on the soil surface). Measures of wood chip 

ground coverage resulting from tree thinning and chipping of small branches and foliage were added in 2011 

following the tree thinning treatments.

Wildlife

Birds, rodents, and medium-sized to large mammals 

were measured on the study sites to evaluate the effects of tree thinning on the species composition and 

relative abundances of all species over time. Birds and 

rodents were sampled from each site twice each year, 

once in the spring (May) and once in the late summer 

(September) from 2008 through 2015. Automated 

wildlife cameras or camera traps were installed on the 

wildlife study plots and run continuously year-round, 

from 2012 through 2015. 

Birds were sampled by fixed-center point counts (see 

Ralph et al. 1995) from one sample point located 

at the center of each wildlife plot (see fig. 2). The 

woodland trees blocked the view of observers, so no 

distance limits (e.g., fixed-distance circular plots) were 

imposed on observations, and many observations were 

by call rather than sight, without a center point-to-bird 

distance measured or estimated. An experienced bird 

observer recorded all birds visually sighted or heard 

calling (or both) during a 20-minute period during the 

early morning hours for three consecutive mornings at 

each site, once in May and once in September of each 

year. Efforts were made to record each bird only once 

during each 20-minute observation period, but because 

individuals were not marked, a single individual may 

have been recounted on each of the 3 days. 

Bird counts by species over each 3-day seasonal 

sampling period were averaged to provide a count 

per day per species for each period to account for 

potential multiple daily recounts of individuals. Some 

bird species were routinely detected calling from great 

distances or flying over at high altitudes and were not 

utilizing the local study site habitats. Those species are 

reported in the Results and were excluded from data 

analysis because they were not present on study plots. 

The May observations were intended to capture territorial breeding and nesting birds, whereas the September 

observations were meant to capture migrating and 

local resident birds. Bird species common names, 

Latin names, and taxonomic classification followed 

the American Ornithological Society’s seventhedition checklist of North American birds (American 

Ornithological Society 2016).

Rodents were sampled using 23-cm aluminum livetraps (H.B. Sherman Traps, Inc., Tallahassee, Florida). 

Each wildlife sampling plot consisted of 36 permanent 

rodent trap stations at 10-m intervals along the grid 

pattern (see Measurements and fig. 2). Each rodent 

trap was fitted with a 30.5-cm section of white PVC 

roofing gutter, placed upside down over the top of 

each trap to provide protection from rainfall and the 

sun. Each trap was baited with oatmeal and also contained cotton to provide captured animals with nesting 

material. Rodent trapping was conducted twice each 

year in May and in September at the same time that 

bird point counts were conducted. Rodent traps were 

placed on each wildlife plot for 3 consecutive nights 

(days) and checked early each morning for the presence of animals. 

All animals captured were identified to species, 

gender, and age (juvenile or adult) and weighed to the 

nearest gram with a spring scale (Pesola, Schindellegi, 

Switzerland). Each captured animal was temporarily 

marked with a Sharpie® permanent marker on the upper chest area; blue was used on control plots and red 

on treatment plots. The color markings wore off over 

the 3- to 6-month intervals between trapping sessions. 

All recaptured animals were recorded as recaptures 

so they could be differentiated from new animals captured on subsequent nights. Persons handling the traps 

and rodents took safety precautions to avoid exposure 

to the Sin Nombre virus (Centers for Disease Control 

and Prevention 2016) and hanta virus from deer mice 

(Peromyscus maniculatus). 

Some mice (Peromyscus spp.) were difficult to identify. In such cases, key body measurements (ear length, 

hind foot length, body length, and tail length) were 

measured and recorded to the nearest millimeter, and 

photographs were taken to provide dorsal and lateral 

views for fur colors and patterns. Those data were later 

evaluated to verify questionable field identifications. 

Traps and covers were removed from study plots 

for the remainder of each year. Since animals were 
82 USDA Forest Service RMRS-P-77. 2020.

marked, only data for newly captured individuals over 

each 3-night trapping period were used for analysis. 

Recaptured animal counts were omitted to provide 

nonredundant count data for each individual per each 

3-night seasonal sampling period. Rodent names and 

taxonomic classification followed Findley et al. (1975).

Measurements of other larger mammals were not 

originally included in the study design, but were 

added in 2012, 1 year following thinning treatments. 

Medium-sized to large animals (mostly mammals) 

were sampled from each wildlife plot by permanently 

mounted, automated wildlife cameras or camera traps 

(Truth® Cam 35, model 63010, Primos Hunting, 

Flora, Mississippi). One camera was mounted on a 

2-m-tall steel T fence post, approximately 1.5 m above 

the ground surface, facing a relatively open area on 

each wildlife plot. Cameras were left on year-round, 

with settings for low sensitivity and a 1-minute pause 

between photographs (to avoid many photographs of 

wind-blown vegetation). Images stored on secure digital camera cards were offloaded once each month from 

each camera. Qualified wildlife biologists reviewed 

images and recorded all animals photographed from 

each plot for each month. Each discernible individual 

was recorded only once if multiple photographs of the 

same individual were taken during a given time period. 

All animals, including domestic livestock, were recorded. Data were summed for each individual of each 

species from each camera over each calendar year. 

Mammal names and taxonomic classification followed 

Findley et al. (1975).

Tree Thinning Treatments

One watershed of each pair was randomly selected to 

be treated by thinning trees and the other watershed 

of the pair was left intact as a control for comparison. 

New Mexico State Forestry provided the standard tree 

thinning prescriptions for the region and forest stand 

types. A summary of the thinning prescription (in 

English units) for the pinyon-juniper watersheds was 

a reduction in conifer tree basal area to an average of 

40 to 60 ft2/acre (9.2–13.7 m2 /ha), leaving a variety 

of tree size classes, groups of trees, and random open 

areas. The thinning prescription for the ponderosa pine 

watersheds was a reduction in tree basal area to an 

average of 60 ft2/acre (5.6 m2/ha) and removal of all 

small trees under the driplines of remaining large trees, 

leaving a variety of size classes in groups of two to 

seven trees, and random openings. Both prescriptions 

additionally called for selectively removing diseased 

and insect-damaged trees, but leaving one to five dead 

standing trees and one to five logs at least 12 feet (3.6 

m) long, per acre for wildlife habitat, and remaining 

larger diameter wood to be removed for firewood. 

Small-diameter wood or slash was chipped or masticated and the chips spread to an average depth of 2 

inches (5 cm), but no greater than 6 inches (15 cm), 

with no chips under the driplines of remaining trees.

The tree thinning crew was composed of workers 

who also performed the same thinning treatments at 

other locations in the area as part of the Watershed 

Restoration Program. The same crew thinned all four 

treatment watersheds for this study, and all four thinning treatments were inspected and approved by New 

Mexico State Forestry for compliance with prescriptions. Thinning treatments for all four watersheds were 

completed between November 2010 and March 2011. 

Data Analysis

Measurement data from environmental variables were 

used to compare variable values from each of the four 

paired control and treatment watersheds on a per site 

basis (i.e., each watershed pair per site was analyzed 

and compared separately from the other sites). There 

was not enough site replication to use sites as statistical 

replicates. Data were analyzed in different ways depending on the variables measured, sampling designs, 

and purposes for analysis. Statistical testing for differences in variables between paired control and treatment 

watersheds was performed with both parametric twotailed paired t-tests for mean values of variables measured with suitable within-plot sample replication (e.g., 

understory vegetation canopy cover and ground surface 

cover variables), and nonparametric Chi-Square goodness of fit tests were used for nonnormally distributed 

animal count data. The similarity of plant and animal 

species compositions among all of the study plots since 

2010 was evaluated with nonparametric multivariate 

hierarchical group-average cluster analysis (McCune et 

al. 2002), where the relationships among the eight watersheds were based on the similarity of species counts 

(animals) or canopy cover (plants). SAS 9.4 statistical 

software (SAS Institute Inc., Cary, North Carolina) was 

used for those analyses. 
USDA Forest Service RMRS-P-77. 2020. 83

All statistical testing for differences in mean values of 

variables between control and treatment watersheds 

was set at a standard p-value of 0.05 or less (≤5-percent 

probability of data values supporting the null hypothesis of no difference between treatments). We consider 

a test criterion of p ≤ 0.05 to be excessively precise for 

field-measured data and therefore highly conservative. 

Given such conservative testing, we did not apply further conservative Bonferroni adjustments for cases of 

multiple testing (e.g., multiple species tested separately 

for the same control versus treatment differences). 

Only data pertinent to the effects of tree thinning on 

wildlife are addressed here, and in some cases only 

data for key periods of time relative to the before- and 

after-treatment dates, such as the years 2010 (1 year 

pretreatment), 2011 (1 year posttreatment), and 2015 (5 

years posttreatment), are presented here. 

RESULTS

Weather Conditions

An extreme drought event occurred across the region 

and most of the Southwest during the 8-year period 

during which this study was conducted (U.S. Drought 

Monitor 2016). The study area was not in drought 

in 2008, 2009, and 2010, but then went into extreme 

drought in 2011, severe drought in 2012 and 2013, abnormally dry in 2014, and no drought in 2015. Annual 

precipitation amounts for 2009 through 2015 averaged 

over each year from hourly data from each of the four 

mini-met stations at the two ponderosa pine sites and 

from each of the four mini-met stations at the two 

pinyon-juniper sites are presented in fig. 3. American 

black bears damaged the weather stations several times 

at both ponderosa pine sites in 2008, creating gaps in 

weather data for that year, so we chose not to report 

annual precipitation and ambient temperature averages 

for 2008. The solar-powered electric fences placed 

around the stations in 2009 reduced bear damage in 

later years. 

Annual precipitation averaged from hourly recordings 

for each year, over both mini-met stations (control 

and treatment watersheds) at each of the four study 

sites, was slightly above the long-term average for 

the region in 2009 through 2010; then drought conditions occurred from 2011 and 2012, with near-average 

precipitation in 2013 through 2015. Annual average 

ambient temperatures for 2009 through 2015 averaged 

over each year from hourly data from all four weather 

stations at the two ponderosa pine sites and from all 

four weather stations at the two pinyon-juniper sites 

are presented in fig. 4. Ambient temperatures generally 

increased steadily from 2009 to 2015, with a slight 

decrease in 2013. 

Trees

Basal Area

In 2010 prior to the thinning treatments, tree basal area 

averaged around 200 ft2/acre (46 m2 /ha) at the ponderosa pine sites and around 140 ft2/acre (32 m2/ha) at 

the pinyon-juniper sites with no differences between 

control and treatment watersheds. Most of the trees 

on all watersheds were smaller diameter trees located 

close together. In 2011, tree basal area was reduced to 

85 ft2/acre (19.5 m2/ha) on the treated ponderosa pine 

watersheds, while at the pinyon-juniper sites basal area 

was reduced to around 40 ft2/acre. 

Figure 3—Total annual precipitation values for (a) the two 

pinyon-juniper (PJ1 and PJ2) and (b) the two ponderosa 

pine (PP1 and PP2) sites, 2008–2015. Data gaps were due 

to rain gauge outages, primarily from American black bear 

damage.
84 USDA Forest Service RMRS-P-77. 2020.

Stand Structure

Measurements of tree diameters (DBH and DRC) were 

taken in 2010 prior to thinning treatments and again in 

2012 following treatments to assess the impacts of the 

treatments on stand structure. Most trees across both 

the pinyon-juniper and the ponderosa pine watersheds 

were within the smaller size classes (>5 inches [12.7 

cm], 5–9 inches [12.7–22.9 cm]) with the majority of 

the trees smaller than 5 inches in diameter, before the 

thinning treatments. The thinning treatments then created more of a uniform distribution of trees across all 

size classes with the number of trees less than 8 inches 

(20.3 cm) in diameter greatly reduced. 

Historical forestry and land management practices on 

both the control and treated plots had resulted in very 

few if any large-diameter old-growth trees (unpublished data). The remaining trees on the treated pinyon-juniper plots were more evenly distributed in the 

5- to 9-inch and 9- to 12-inch (22.9–30.5 cm) diameter 

classes with only a few juniper trees in the 12- to 

18-inch (30.5–45.7 cm) and 18- to 24-inch (45.7–61.0 

cm) size classes. There were no trees in the <5-inch 

class left on any of the pinyon-juniper measurement 

plots. The remaining trees on the ponderosa pine plots 

were more evenly distributed in the 5- to 9-inch and 9- 

to 12-inch diameter classes with a small proportion of 

trees less than 5 inches and greater than 12 inches.

Tree Mortality

Tree mortality measurements taken across both the 

ponderosa and pinyon-juniper plots showed less than 

5 percent mortality, with one exception, from 2008 

through 2015 with no significant differences between 

the control and treatment plots. During the height 

of the regional drought in 2012, however, there was 

an extensive pinyon ips (Ips confusus) bark beetle 

outbreak across the region that killed large stands of 

pinyon trees. Pinyon trees on both the pinyon-juniper 

treatment and control plots were impacted by this 

outbreak but not at a significant level. One of the 

pinyon-juniper plots had more than 5 percent mortality in 2012; beetles killed a cluster of five pinyon 

trees on a treatment plot. Even though the area had 

been thinned to reduce competition between trees, 

the number of beetles inhabiting the surrounding area 

overwhelmed this cluster of trees. Other pinyon trees 

within the treatment and control plots were impacted 

by the beetles as well, but they were resilient enough 

to survive the attacks. In contrast to the pinyon-juniper 

sites, the ponderosa pine sites on average had less 

than 2 percent individual tree mortality over the study 

period, with no significant differences between control 

and treatment plots.

Lower Tree Vertical Canopy Cover 

Lower tree vertical canopy cover from ground level to 

a height of 2 m showed a general reduction in canopy 

structure on the plots that were thinned in 2011 as 

compared to control plots (fig. 5). In 2010, before tree 

thinning, pretreatment plots at each of the two ponderosa pine sites had significantly lower (paired t-test, n = 

12 for all tests) canopy structure; PP1 had significantly 

greater cover structure on the control plot (p = 0.02), 

while PP2 had significantly greater cover structure on 

the pretreatment plot (p = 0.006). Pretreatment and 

control plots at the two pinyon-juniper sites were not 

different. In 2011 after tree thinning, lower tree canopy 

cover structure was significantly lower on treated 

plots at both ponderosa pine sites (PP1 site, p = 0.001; 

Figure 4—Average annual temperatures for (a) the two 

pinyon-juniper (PJ1 and PJ2) and (b) the two ponderosa pine 

(PP1 and PP2) sites, 2008–2015. Data gaps were due to 

temperature gauge outages, primarily from American black 

bear damage. 
USDA Forest Service RMRS-P-77. 2020. 85

PP2 site, p < 0.0001) and at one of the pinyon-juniper 

sites (PJ2, p < 0.0001), but not significantly different 

at the PJ1 site (fig. 5). In 2015, vertical canopy cover 

structure was significantly lower on treatment plots 

than on control plots at the PP2 site (p < 0.0001) and at 

both pinyon-juniper sites (PJ1 site, p = 0.026; PJ2 site, 

p < 0.0001), but not at the PP1 site (fig. 5). 

Tree Crown Canopy Cover

Tree crown horizontal canopy cover as measured by a 

spherical densiometer showed a significant reduction 

in tree upper canopy cover on all of the treatment plots 

in 2011 compared to the control plots following tree 

thinning in late 2010 (fig. 6). Before tree thinning in 

Figure 5—Average (averaged over 11 points per plot) vertical vegetation canopy cover 

structure 0 to 3 m above the ground surface for the two pinyon-juniper (PJ1 and PJ2) 

and the two ponderosa pine (PP1 and PP2) sites. C = control; T = treatment.

Figure 6—Average densiometer score (averaged over 11 points per plot) for horizontal tree 

crown canopy. Error bars represent ± one standard error of the mean.
86 USDA Forest Service RMRS-P-77. 2020.

2010, there were no significant differences (paired 

t-test, n = 11 for all tests) in the tree canopy cover 

between the control and pretreatment plots at both 

pinyon-juniper sites and at one ponderosa pine site 

(PP2). But at the PP1 site, the control plot had greater 

prethinning cover than the pretreatment plot. In 2015, 

tree canopy cover within plots was not significantly 

different from 2011. In 2015, control plots remained 

significantly different from the treated plots at the PP1 

(p < 0.0001) and PP2 (p = 0.01) sites, but not at either 

of the pinyon-juniper sites.

Pinyon Cone and Seed Production

Results of testing for differences between the percentages of tree canopies with cones between control and 

treatment plots using the Wilcoxon test revealed that 

trees at the PJ2 site on the treatment plot had a significantly greater percentage of tree canopies bearing 

cones (57 percent vs. 85 percent) (p = 0.02, n = 25). 

At the PJ1 site, the difference between treatment and 

control was not significant. 

Understory Vegetation Foliage Canopy Cover

Results for herbaceous understory vegetation canopy 

cover measured from the thirty-six 1-m2 quadrats from 

2010 through 2015 are presented in fig. 7. Within-year 

paired t-test results for differences between control 

and treatment watersheds are presented in table 1. 

Total herbaceous canopy cover was not significantly 

different between control and treatment plots at any 

site in 2010 before tree thinning treatments, but total 

herbaceous cover was significantly greater on treated 

plots at all sites except PP2 in 2011 through 2015 following thinning treatments. The amount of herbaceous 

vegetation cover on treatment plots tended to increase 

even more with each successive year relative to 

control plots at the three sites other than PP2 through 

2015, except for a leveling off and decline in total 

canopy cover at the PJ1 site (fig. 7, table 1). The PJ2 

site demonstrated the greatest increase in herbaceous 

vegetation cover on the treatment plot compared to 

the control plot over time. Total herbaceous vegetation 

cover was consistently about twice as high at the two 

pinyon-juniper sites compared to the two ponderosa 

pine sites over time. 

Figure 7—Mean herbaceous plant canopy cover values across all vegetation quadrats 

on (a,b) the two pinyon-juniper (PJ1 and PJ2) and (c,d) the two ponderosa pine (PP1 

and PP2) sites, 2010–2015. C = control; T= treatment. Thinning treatments occurred 

on the treatment plots between 2010 and 2011. Error bars represent ± one standard 

error of the mean.
USDA Forest Service RMRS-P-77. 2020. 87 Table 1—Test results for paired t-tests of no difference between mean values of vegetation and ground cover types measured from vegetation quadrats on each study plot pair at the two pinyon-juniper (PJ1 and PJ 2) and the two ponderosa pine (PP1 and PP2) study sites prior to thinning treatments in 2010, and in 2011 and 2015 following thinning treatments. Year Site Ground cover Control mean Treatment mean P-value (significance) 2010 PJ1 All herbs 24.3 27.2 0.6573 Wood chips 0.0 0.0 na PJ2 All herbs 21.5 19.5 0.6952 Wood chips 0.0 0 na PP1 All herbs 13.1 10.7 0.5888 Wood chips 0.0 0.0 na PP2 All herbs 4.4 5.9 0.6092 Wood chips 0.0 0.0 na 2011 PJ1 All herbs 11.4 13.1 0.7227 Wood chips 34.11 0.0 <0.0001 PJ2 All herbs 6.4 11.1 0.2121 Wood chips 41.6 0.0 <0.0001 PP1 All herbs 2.1 7.0 0.0016 Wood chips 33.9 0.0 <0.0001 PP2 All herbs 3.3 3.3 0.9980 Wood chips 40.2 0.0 <0.0001 2015 PJ1 All herbs 19.8 33.0 0.0103 Wood chips 0.0 22.8 <0.0001 PJ2 All herbs 28.6 68.0 0.0002 Wood chips 0.0 20.1 <0.0001 PP1 All herbs 12.2 32.0 <0.0001 Wood chips 0.0 18.0 <0.0001 PP2 All herbs 3.4 2.9 0.8953 Wood chips 0.0 15.6 <0.0001 Note: Results in rows that are presented in bold were significantly different (P < 0.05) between control and treatment plots. All tests were with sample sizes of 36; P-values of less than 0.05 represent significant differences. 
88 USDA Forest Service RMRS-P-77. 2020.

The majority of forbs were native species such as 

Chenopodium species that grew on disturbed soils and 

wood chips. The exotic invasive weed species redstem 

stork’s bill (Erodium cicutarium), prickly Russian 

thistle (Salsola tragus), puncture vine (Tribulus terrestris), and common mullein (Verbascum thapsus) were 

found only at one of the four sites (PJ2, treated) and 

in very low numbers in 2014 and in 2015. Dominant 

grasses at the two pinyon-juniper sites that responded 

positively to tree thinning were perennial species such 

as blue grama (Bouteloua gracilis) and James’ galleta 

(Pleuraphis jamesii). Those perennial grasses grew 

through the wood chips from existing individual plants 

that were in place prior to thinning treatments, unlike 

annual forbs that colonized the disturbed soils and 

wood chips from seeds. One species of invasive exotic 

grass, cheatgrass (Bromus tectorum), was found only 

at one of the four sites (PJ2, treated) and in very low 

numbers in 2015.

Understory Plant Species Composition

Cluster analysis dendrograms (trees) for all paired 

plots within sites in 2010, 2011, and 2015 are presented in fig. 8. Before the tree thinning treatments 

in 2010 and 2011, the paired plots at both ponderosa 

pine sites and at both pinyon-juniper sites shared more 

similar understory plant species compositions within 

each site location than between site locations. There 

were no groupings of treatment plots or groupings of 

control plots; all groupings or similarities in plant species compositions were based on location (fig. 8a). 

Within 1 year following the tree thinning treatments, 

the location-based site and plot cluster groupings observed in 2010 were less pronounced, and the treated 

plots at the two ponderosa pine sites grouped together, 

not by location as before thinning (fig. 8b). In 2011, 

the PJ2 control plot grouped with the ponderosa pine 

sites, while the PJ2 treatment plot grouped with both 

PJ1 plots. The ponderosa pine plots were most similar 

in understory plant species compositions by location 

in 2010 before treatment, then were most similar by 

treatment in 2011; but they became more similar by 

location again by 2015 (fig. 8). The pinyon-juniper 

control plots from the two different pinyon-juniper 

sites grouped together based on treatment, while the 

PJ2 treatment plot had plant species compositions distinctly different from any of the other pinyon-juniper 

or ponderosa pine sites or plots (fig. 8c). Overall, 

cluster analysis results showed that the tree thinning 

treatments resulted in large changes in the plant 

species compositions of the treated plots at both 

pinyon-juniper sites, but tree thinning did not change 

the understory vegetation species compositions of the 

ponderosa pine sites (fig. 8).

Wood Chips

Wood chips were not present before the thinning 

treatments in late 2010, and they were applied only to 

the treated watersheds and plots in late 2010. Wood 

Figure 8—Cluster analysis results showing the similarity of 

sites and paired plots based on similarity of the herbaceous 

plant community species compositions in (a) 2010, (b) 2011, 

and (c) 2015.
USDA Forest Service RMRS-P-77. 2020. 89

chip cover on the treated plots declined by about 20 

percent at all sites between 2011 and 2012, indicating 

some decomposition or redistribution, or increased 

herbaceous plant canopy cover over wood chips (or 

a combination thereof) (fig. 9). From 2012 through 

2015, wood chip cover remained fairly constant at the 

two pinyon-juniper sites. In 2015, wood chip cover 

increased to about 20 percent cover again at the two 

ponderosa pine sites.

Wildlife

Birds 

Lists of bird species encountered across all four study 

sites, along with their daily average counts averaged 

over 3 years before tree thinning and averaged over 5 

years after tree thinning, are presented in table 2 for 

the pinyon-juniper sites and table 3 for the ponderosa 

pine sites. In total, 1,359 birds representing 66 species 

were recorded from the pinyon-juniper sites, and 1,064 

birds representing 61 species were recorded from the 

ponderosa pine sites from 2008 through 2015. Birds 

that could not be identified to species were recorded 

to the most precise taxonomic level possible (e.g., 

flycatcher, sparrow, passerine, hummingbird), and 

those records accounted for a small percentage of the 

total records (tables 2 and 3). Bird species that were 

recorded from point counts on the study plots, but 

were typically observed or heard some distance away 

from the sites and were not actually utilizing the local study plot habitats, included American crow (see 

tables 2 and 3 for scientific names of all bird species 

mentioned in this section), common raven, great blue 

heron, northern harrier, red-tailed hawk, Swainson’s 

hawk, and turkey vulture. Those species data were 

excluded from the analysis. 

The numbers of birds recorded across the two pinyonjuniper study sites during the spring breeding season 

from 2009 (no spring counts in 2008) through 2015 

and during the fall migratory season from fall 2008 

through fall 2015 were generally more abundant on 

the treatment plots, even before the tree thinning 

Figure 9—Mean wood chip cover values across all vegetation quadrats on (a,b) the two 

pinyon-juniper (PJ1 and PJ2) and (c,d) the two ponderosa pine (PP1 and PP2) sites, 

2010–2015. Thinning treatments occurred on the treatment (T) plots between 2010 and 

2011. Error bars represent ± one standard error of the mean.
90 USDA Forest Service RMRS-P-77. 2020. Table 2—Pinyon-juniper bird species and mean counts of individuals per species averaged over counts per 3-day sample period, averaged over the seasons and years prior to tree thinning from fall 2008 through fall 2010, and averaged over the seasons and years after tree thinning from spring 2011 through fall 2015. Bird species on pinyon-juniper (PJ) sites Before thinning, 2008–2010 After thinning, 2011–2015 Common name Scientific name AOU1 code PJ1 PJ2 PJ1 PJ2 Row average Control Treated Control Treated Control Treated Control Treated American crow Corvus brachyrhynchos AMCR 0.5 0.7 0.9 0.7 0.9 0.7 0.5 0.6 0.7 American goldfinch Spinus tristis AMGO 0.0 0.3 0.0 0.0 0.0 0.0 0.0 2.0 0.3 American kestrel Falco sparverius AMKE 0.0 0.0 0.3 0.0 0.0 0.3 0.0 0.3 0.1 American robin Turdus migratorius AMRO 0.3 0.3 0.5 0.5 0.7 0.5 0.8 0.4 0.5 Ash-throated flycatcher Myiarchus cinerascens ATFL 0.3 0.3 0.5 0.3 0.4 0.3 0.5 0.3 0.4 Audubon's warbler Setophaga coronata auduboni AUWA 0.3 0.3 0.3 0.8 0.3 0.3 0.3 0.4 0.4 Barn swallow Hirundo rustica BARS 0.3 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.1 Black-capped chickadee Poecile atricapillus BCCH 0.3 0.3 0.3 0.3 0.0 1.0 0.0 0.0 0.3 Black-chinned hummingbird Archilochus alexandri BCHU 0.0 0.0 0.0 0.0 0.0 0.3 0.5 0.5 0.2 Bell's vireo Vireo bellii BEVI 0.0 0.0 0.0 0.0 0.3 0.0 0.3 0.0 0.1 Bewick's wren Thryomanes bewickii BEWR 0.3 0.4 0.7 0.6 0.3 0.7 0.3 0.3 0.4 Blue-gray gnatcatcher Polioptila caerulea BGGN 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.0 0.1 Broad-tailed hummingbird Selasphorus platycercus BTAH 0.3 0.0 0.0 0.3 0.3 0.3 0.0 0.4 0.2 Black-throated gray warbler Setophaga nigrescens BTYW 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 Bushtit Psaltriparus minimus BUSH 0.0 0.3 0.0 0.0 0.6 0.5 0.0 0.0 0.2 Canyon towhee Melozone fusca CANT 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.1 Chipping sparrow Spizella passerina CHSP 0.3 0.4 0.7 0.8 1.3 0.5 0.7 0.6 0.7 Cordilleran flycatcher Empidonax occidentalis COFL 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.1 Cooper's hawk Accipiter cooperii COHA 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.7 0.1 Common raven Corvus corax CORA 0.3 0.4 0.8 0.6 0.7 0.8 0.6 1.0 0.6 Dark-eyed junco Junco hyemalis DEJU 0.0 1.0 1.0 0.3 0.0 0.0 0.0 0.0 0.3 Great blue heron Ardea herodias GBHE 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 Gray flycatcher Empidonax wrightii GRFL 0.0 0.0 0.0 0.3 0.0 0.3 0.3 0.0 0.1 Gray vireo Vireo vicinior GRVI 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.5 0.1

(CONT.)
USDA Forest Service RMRS-P-77. 2020. 91 Bird species on pinyon-juniper (PJ) sites Before thinning, 2008–2010 After thinning, 2011–2015 Common name Scientific name AOU1 code PJ1 PJ2 PJ1 PJ2 Row average Control Treated Control Treated Control Treated Control Treated Hairy woodpecker Picoides villosus HAWO 0.0 0.3 0.0 0.3 0.0 0.0 0.0 0.3 0.1 Hermit thrush Catharus guttatus HETH 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 House finch Haemorhous mexicanus HOFI 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.1 Juniper titmouse Baeolophus ridgwayi JUTI 0.3 0.6 0.7 0.5 0.4 0.6 0.6 0.6 0.5 Lark sparrow Chondestes grammacus LASP 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.9 0.2 Ladder-backed woodpecker Picoides scalaris LBWO 0.0 0.0 0.0 0.3 0.3 0.3 0.0 0.0 0.1 Lesser goldfinch Spinus psaltria LEGO 0.0 0.0 0.0 0.0 0.3 0.0 0.3 0.5 0.1 Mountain bluebird Sialia currucoides MOBL 0.0 0.0 1.7 5.0 0.0 0.4 1.0 0.5 1.1 Mountain chickadee Poecile gambeli MOCH 0.3 0.3 0.5 0.3 0.5 0.4 0.0 0.4 0.4 Mourning dove Zenaida macroura MODO 0.7 0.9 0.5 0.5 0.3 0.8 0.4 0.9 0.6 Northern flicker Colaptes auratus NOFL 0.4 0.4 0.5 0.6 0.4 0.4 0.5 0.5 0.5 Northern harrier Circus cyaneus NOHA 0.3 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.1 Northern mockingbird Mimus polyglottos NOMO 0.0 0.0 0.3 0.0 0.0 0.0 0.4 0.5 0.1 Northern rough-winged swallow Stelgidopteryx serripennis NRWS 0.0 0.0 0.0 0.0 1.7 0.5 0.5 0.5 0.4 Orange-crowned warbler Oreothlypis celata OCWA 0.7 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.1 Pinyon jay Gymnorhinus cyanocephalus PIJA 0.3 0.3 0.5 0.0 1.7 2.3 2.8 2.3 1.3 Plumbeous vireo Vireo plumbeus PLVI 0.0 0.0 0.0 0.3 0.3 0.0 0.3 0.4 0.2 Red-breasted nuthatch Sitta canadensis RBNU 0.0 0.0 0.0 0.3 0.3 0.0 0.0 0.3 0.1 Ruby-crowned kinglet Regulus calendula RCKI 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 Red crossbill Loxia curvirostra RECR 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.1 Red-tailed hawk Buteo jamaicensis RTHA 0.0 0.0 0.0 0.0 0.3 0.3 0.0 0.9 0.2 Rufous hummingbird Selasphorus rufus RUHU 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Say's phoebe Sayornis saya SAPH 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.4 0.1 Savannah sparrow Passerculus sandwichensis SAVS 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.1 Spotted towhee Pipilo maculatus SPTO 0.3 0.3 0.6 0.6 0.3 0.3 0.3 0.4 0.4 Sharp-shinned hawk Accipiter striatus SSHA 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0

(CONT.)
92 USDA Forest Service RMRS-P-77. 2020. Bird species on pinyon-juniper (PJ) sites Before thinning, 2008–2010 After thinning, 2011–2015 Common name Scientific name AOU1 code PJ1 PJ2 PJ1 PJ2 Row average Control Treated Control Treated Control Treated Control Treated Steller's jay Cyanocitta stelleri STJA 0.0 1.0 0.0 0.0 0.0 0.0 0.5 0.5 0.3 Swainson's hawk Buteo swainsoni SWHA 0.0 0.0 0.0 0.0 0.3 0.0 0.3 0.0 0.1 Swainson's thrush Catharus ustulatus SWTH 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 Townsend's solitaire Myadestes townsendi TOSO 0.3 0.8 0.3 0.3 1.0 0.7 0.3 0.4 0.5 Tree swallow Tachycineta bicolor TRES 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 Turkey vulture Cathartes aura TUVU 2.2 0.3 0.7 0.5 0.7 0.5 0.4 0.7 0.7 Vesper sparrow Pooecetes gramineus VESP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 Violet-green swallow Tachycineta thalassina VGSW 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.1 White-breasted nuthatch Sitta carolinensis WBNU 0.3 0.3 0.5 0.0 0.3 0.3 0.3 0.5 0.3 Western bluebird Sialia mexicana WEBL 0.0 0.0 0.0 1.0 1.6 0.8 1.2 1.0 0.7 Western kingbird Tyrannus verticalis WEKI 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.5 0.1 Western meadowlark Sturnella neglecta WEME 0.3 0.3 0.0 0.0 0.4 0.3 0.3 0.8 0.3 Western tanager Piranga ludoviciana WETA 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 Williamson's sapsucker Sphyrapicus thyroideus WISA 0.3 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.1 Wild turkey Meleagris gallopavo WITU 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.1 Woodhouse's scrub-jay Aphelocoma woodhouseii WOSJ 0.7 0.9 1.0 1.3 0.4 0.4 0.6 0.5 0.7 White-winged Dove Zenaida asiatica WWDO 0.0 0.0 0.0 0.0 0.0 2.7 0.0 0.0 0.3 UNKNOWN BIRD none UNKNOWN 0.7 0.7 3.7 0.3 2.0 0.0 0.7 0.0 1.0 UNKNOWN HUMMINGBIRD none UKHUMM 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 UNKNOWN PASSERINE none UKPASS 0.0 0.0 0.0 0.0 0.4 0.6 0.6 0.3 0.2 UNKNOWN SPARROW none UKSPAR 0.0 0.0 0.0 0.0 0.0 0.3 0.0 1.7 0.3 UNKNOWN VIREO none UKVIRE 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 UNKNOWN WOODPECKER none UKWOOD 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.0 0.1 Column average 0.2 0.2 0.3 0.3 0.3 0.4 0.3 0.4 0.3 1American Ornithological Union.
USDA Forest Service RMRS-P-77. 2020. 93 Table 3–Ponderosa pine bird species and mean counts of individuals per species averaged over counts per 3-day sample period, averaged over the seasons and years prior to tree thinning from fall 2008 through fall 2010, and averaged over the seasons and years after tree thinning from spring 2011 through fall 2015. Birds on ponderosa pine (PP) sites Before thinning, 2008–2010 After thinning, 2011–2015 Common name Scientific name AOU1 code PP1 PP2 PP1 PP2 Row average Control Treated Control Treated Control Treated Control Treated American crow Corvus brachyrhynchos AMCR 0.3 0.3 0.0 0.0 0.7 0.3 0.0 0.0 0.2 American robin Turdus migratorius AMRO 0.4 0.4 0.5 0.3 0.3 0.4 0.5 0.6 0.4 Ash-throated flycatcher Myiarchus cinerascens ATFL 0.0 0.0 0.4 0.3 0.0 0.0 0.5 0.7 0.2 Audubon's warbler Setophaga coronata auduboni AUWA 0.0 0.4 0.0 0.0 0.5 0.9 0.4 0.6 0.4 Black-capped chickadee Poecile atricapillus BCCH 0.3 0.9 0.6 0.6 0.3 0.0 0.0 0.0 0.3 Black-chinned hummingbird Archilochus alexandri BCHU 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.1 Blue-gray gnatcatcher Polioptila caerulea BGGN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 Black-headed grosbeak Pheucticus melanocephalus BHGR 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 Brown creeper Certhia americana BRCR 0.0 0.7 0.0 0.0 0.3 0.3 0.3 0.0 0.2 Broad-tailed hummingbird Selasphorus platycercus BTAH 0.0 0.0 0.3 0.4 0.4 0.3 0.0 0.3 0.2 Bushtit Psaltriparus minimus BUSH 0.0 0.0 0.3 1.3 0.0 2.3 2.0 1.7 1.0 Chipping sparrow Spizella passerina CHSP 0.0 0.0 0.0 1.7 0.9 0.6 0.3 0.5 0.5 Cordilleran flycatcher Empidonax occidentalis COFL 0.0 0.0 0.0 0.0 0.3 0.3 0.0 0.0 0.1 Cooper's hawk Accipiter cooperii COHA 0.0 0.0 0.3 0.0 0.0 0.0 0.3 0.0 0.1 Common raven Corvus corax CORA 0.5 0.5 0.5 0.5 0.4 0.3 0.6 0.5 0.5 Dark-eyed junco Junco hyemalis DEJU 0.3 0.3 1.1 0.4 0.7 0.9 1.0 0.4 0.6 Downy woodpecker Picoides pubescens DOWO 0.0 0.0 0.0 0.0 0.3 0.0 0.3 0.5 0.1 Evening grosbeak Coccothraustes vespertinus EVGR 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.1 Great horned owl Bubo virginianus GHOW 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 Gray flycatcher Empidonax wrightii GRFL 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.1 Grace's warbler Setophaga graciae GRWA 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 Hairy woodpecker Picoides villosus HAWO 0.4 0.4 0.5 0.3 0.5 0.4 0.3 0.0 0.3 Hermit thrush Catharus guttatus HETH 0.0 0.0 0.3 0.4 0.3 0.7 0.0 0.0 0.2 Juniper titmouse Baeolophus ridgwayi JUTI 0.0 0.8 0.0 0.0 0.0 1.4 1.0 0.5 0.5 Ladder-backed woodpecker Picoides scalaris LBWO 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 Lesser goldfinch Spinus psaltria LEGO 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.1

(CONT.)
94 USDA Forest Service RMRS-P-77. 2020. Birds on ponderosa pine (PP) sites Before thinning, 2008–2010 After thinning, 2011–2015 Common name Scientific name AOU1 code PP1 PP2 PP1 PP2 Row average Control Treated Control Treated Control Treated Control Treated Lucy's warbler Oreothlypis luciae LUWA 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.1 Mountain bluebird Sialia currucoides MOBL 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.7 0.2 Mountain chickadee Poecile gambeli MOCH 0.4 0.3 0.6 0.7 1.1 0.7 0.8 0.8 0.7 Mourning dove Zenaida macroura MODO 0.5 0.3 0.3 0.3 0.0 0.0 0.0 0.7 0.3 Northern flicker Colaptes auratus NOFL 0.3 0.5 0.4 0.5 0.4 0.4 0.3 0.4 0.4 Northern mockingbird Mimus polyglottos NOMO 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 Orange-crowned warbler Oreothlypis celata OCWA 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 Pinyon jay Gymnorhinus cyanocephalus PIJA 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.1 Pine siskin Spinus pinus PISI 0.5 0.5 0.0 0.0 0.0 0.7 0.9 0.7 0.4 Plumbeous vireo Vireo plumbeus PLVI 0.4 0.5 0.4 0.5 0.4 0.5 0.5 0.4 0.4 Pygmy nuthatch Sitta pygmaea PYNU 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.1 Red-breasted nuthatch Sitta canadensis RBNU 0.3 0.7 0.0 0.3 0.3 0.3 0.3 0.5 0.3 Ruby-crowned kinglet Regulus calendula RCKI 0.3 0.4 0.0 0.7 0.7 0.7 0.0 0.3 0.4 Red crossbill Loxia curvirostra RECR 0.8 1.2 1.4 1.2 0.7 0.0 0.0 5.0 1.3 Red-tailed hawk Buteo jamaicensis RTHA 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 Rufous hummingbird Selasphorus rufus RUHU 0.0 0.0 0.3 0.3 0.0 0.0 0.0 0.0 0.1 Spotted towhee Pipilo maculatus SPTO 0.0 0.3 0.3 0.3 0.0 0.3 0.0 0.0 0.2 Sharp-shinned hawk Accipiter striatus SSHA 0.0 0.0 0.0 0.0 0.3 0.3 0.0 0.3 0.1 Steller's jay Cyanocitta stelleri STJA 0.4 0.3 0.4 0.4 0.5 0.7 0.5 0.5 0.4 Swainson's hawk Buteo swainsoni SWHA 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 Townsend's solitaire Myadestes townsendi TOSO 0.5 0.0 0.0 0.4 0.0 0.3 0.0 0.3 0.2 Tree swallow Tachycineta bicolor TRES 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.1 Turkey vulture Cathartes aura TUVU 0.3 0.7 0.3 0.0 1.7 0.3 0.3 0.0 0.5 Violet-green swallow Tachycineta thalassina VGSW 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.6 0.2 White-breasted nuthatch Sitta carolinensis WBNU 0.4 0.6 0.5 0.4 0.4 0.4 0.5 0.4 0.4 White-crowned sparrow Zonotrichia leucophrys WCSP 0.0 0.0 0.3 0.3 0.0 0.0 0.0 0.0 0.1 Western bluebird Sialia mexicana WEBL 0.0 0.0 0.0 0.3 0.8 0.8 1.4 0.7 0.5 Western kingbird Tyrannus verticalis WEKI 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 Western tanager Piranga ludoviciana WETA 0.0 0.0 0.0 0.0 0.0 0.8 0.3 0.0 0.1 Western wood-pewee Contopus sordidulus WEWP 0.0 0.5 0.0 0.3 0.0 0.0 0.0 0.0 0.1

(CONT.)
USDA Forest Service RMRS-P-77. 2020. 95 Birds on ponderosa pine (PP) sites Before thinning, 2008–2010 After thinning, 2011–2015 Common name Scientific name AOU1 code PP1 PP2 PP1 PP2 Row average Control Treated Control Treated Control Treated Control Treated Williamson's sapsucker Sphyrapicus thyroideus WISA 0.0 0.0 0.5 0.0 0.3 0.0 0.0 0.3 0.1 Wild turkey Meleagris gallopavo WITU 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.0 0.1 Wilson's warbler Cardellina pusilla WIWA 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 Woodhouse's scrub-jay Aphelocoma woodhouseii WOSJ 0.5 0.8 0.0 0.7 0.3 0.4 0.4 0.5 0.4 UNKNOWN FLYCATCHER none UKFLYC 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 UNKNOWN HUMMINGBIRD none UKHUMM 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.1 UNKNOWN PASSERINE none UKPASS 0.0 0.0 0.0 0.0 0.5 0.5 0.8 1.0 0.4 UNKNOWN SPARROW none UNKSPAR 0.4 0.9 0.3 1.2 0.0 0.0 0.0 0.0 0.3 UNKNOWN VIREO none UKVIREO 0.0 0.0 0.0 0.0 0.5 0.5 0.8 1.0 0.4 Column average 0.1 0.2 0.2 0.3 0.2 0.3 0.3 0.4 0.3 1American Ornithological Union.
96 USDA Forest Service RMRS-P-77. 2020.

treatments during the winter of 2010–2011 (figs. 10a, 

10b). Bird abundance on the control plot versus the 

treatment plot of the PJ1 site was higher during fall 

2008, fall 2011, and fall 2015, and at the PJ2 site during fall 2009, spring 2010, fall 2011, and fall 2015. A 

similar pattern of more birds on treatment plots even 

before tree thinning also occurred at the two ponderosa 

pine sites (fig. 10c, 10d). Overall bird abundance at 

both the pinyon-juniper and the ponderosa pine sites 

was generally greater in the fall than during the spring 

breeding season, except for spring 2015, which had 

the highest bird counts across the four study sites and 

considerably more birds on the treatment plots than on 

the control plots (fig. 10). 

Abundant birds found at the pinyon-juniper sites over 

the duration of the study included the American robin, 

ash-throated flycatcher, black-chinned hummingbird, 

Bewick’s wren, chipping sparrow, juniper titmouse, 

mountain bluebird, mountain chickadee, mourning 

dove, northern flicker, pinyon jay, spotted towhee, 

Townsend’s solitaire, western bluebird, western kingbird, western meadowlark, and Woodhouse’s scrub-jay 

(table 2). The abundance of most bird species was 

not significantly different between the pinyon-juniper 

control and treatment plots over the 3 years prior to 

thinning treatments nor over the 5 years following 

treatments, based on Chi-Square test results. Birds at 

the pinyon-juniper sites that did increase significantly 

on treatment plots relative to control plots after tree 

thinning, but not before tree thinning, included the 

black-chinned hummingbird (PJ1 site only, p = 0.02), 

lark sparrow (PJ1 site only, p = 0.02), northern flicker 

(PJ1 site only, p = 0.007), western bluebird (PJ2 site 

only, p = 0.0006), and western kingbird (PJ2 site only, 

p = 0.03). No birds at the pinyon-juniper sites declined 

significantly in response to tree thinning. 

Abundant birds found at the ponderosa pine sites 

over the duration of the study included the American 

robin, Audubon’s warbler, black-capped chickadee, 

dark-eyed junco, Grace’s warbler, hairy woodpecker, 

mountain chickadee, northern flicker, plumbeous 

vireo, red-breasted nuthatch, red crossbill, Steller’s 

jay, white-breasted nuthatch, western bluebird, and 

Woodhouse’s scrub-jay (table 3). As at the pinyonjuniper sites, abundance of most bird species was not 

significantly affected by the thinning treatments. Bird 

species that were significantly more abundant on the 

treated plots following thinning treatments, but not 

before, included the chipping sparrow (PP1 site only, 

p = 0.003), Steller’s jay (PP2 site only, p = 0.03), and 

western bluebird (both PP1 and PP2 sites, p = 0.003). 

The brown creeper was the only bird species that 

showed a significant decrease in abundance after tree 

thinning treatments at the PP2 site (p = 0.04). 

Figure 10—Average number of birds per day from 3-day point counts in the spring 

and fall on (a,b) the two pinyon-juniper (PJ1 and PJ2) and (c,d) the two ponderosa 

pine (PP1 and PP2) sites, 2010–2015.
USDA Forest Service RMRS-P-77. 2020. 97

Cluster analysis results of similarities in bird species 

compositions between site locations and between 

control vs. treatment study plots during the spring 

breeding seasons showed that in 2010, prior to thinning treatments, study plot bird assemblages grouped 

together by site location only (fig. 11a). During the 

fall 2010 migration period, the bird species compositions at the PJ1 and PP1 sites were more similar to 

each other than to the PJ2 or PP2 sites. In spring 2011 

immediately following tree thinning, both spring and 

fall bird assemblages across study plots at both the 

pinyon-juniper sites and ponderosa pine sites again 

grouped together by location, not by treatment or 

control. But in fall 2011 the two pinyon-juniper control 

sites grouped together, while both pinyon-juniper 

treatment plots were very different from all others, 

including ponderosa pine plots, and the ponderosa 

pine plots grouped by location, not treatment (fig. 

11d). In 2015, 5 years after tree thinning treatments, 

both spring and fall bird assemblages of control plots 

at both ponderosa pine sites grouped together, while 

ponderosa pine treatment plot assemblages were very 

different; the pinyon-juniper site plots grouped more 

by site location, not by treatment (fig. 11e,f). These 

findings suggest that the tree thinning treatments did 

cause the bird species compositions of ponderosa pine 

sites to shift from similar species compositions based 

on location to similar species compositions based on 

tree thinning treatments and that breeding season bird 

species assemblages in ponderosa pine responded to 

thinning more so than did fall migration bird species 

assemblages. In contrast, bird species compositions 

of the pinyon-juniper sites showed little consistent response to the thinning treatments in the spring or fall. 

Rodents

Lists of all nocturnal rodent species encountered across 

all four study sites before tree thinning and after tree 

thinning, along with their summed counts per 3-night 

sample period, are presented in table 4 for the pinyonjuniper sites and in table 5 for the ponderosa pine 

sites. In total, 461 rodents represented by 9 species 

were recorded from the pinyon-juniper sites and 156 

rodents representing 7 species were recorded from the 

ponderosa pine sites from 2008 through 2015. Diurnal 

rodents such as chipmunks and squirrels were present 

at all study sites and several were captured in traps, 

but were not appropriately sampled with nocturnal live 

traps, and are not included in the nocturnal rodent data 

records. 

Numbers of all individual nocturnal rodents over all 

species recorded across the study sites in both the 

spring and fall of each year from 2008 through 2015 

tended to be considerably higher at the pinyon-juniper 

sites (about 10 individuals per plot per 3-night sampling period) compared to the ponderosa pine sites 

(fewer than 5 individuals per plot per 3-night sampling 

period) (fig. 12). Overall rodent abundance at the 

pinyon-juniper sites was highest in spring 2009, then 

declined through 2015, with peaks in abundance in 

2011 and again in 2013. At both pinyon-juniper sites, 

in 2008, 2009, and 2010, rodent abundance fluctuated between being lower and higher on the treatment 

plots than on the control plots prior to tree thinning. 

But from 2011 on, rodent abundance was primarily 

higher on the control plots following tree thinning, 

especially at the PJ1 site (fig. 12). Rodent abundance 

at the ponderosa pine sites was similar between control 

and treatment plots over time, with shifts back and 

forth between greater abundance at control or treatment plots, but there was no distinct change in rodent 

abundance that might be attributed to tree thinning 

treatment effects (fig. 12). 

The most abundant and relatively consistent rodent 

species found at the pinyon-juniper sites was the 

pinyon mouse (see tables 4 and 5 for scientific names 

of all rodents mentioned in this section). Other common rodents found at the pinyon-juniper sites included 

the deer mouse, white-footed mouse, white-throated 

woodrat, and silky pocket mouse. Rare rodent species 

found at the pinyon-juniper sites included the Mexican 

vole, northern grasshopper mouse, plains pocket 

mouse, and plains woodrat (table 4). The most common rodent species typically found at the ponderosa 

pine sites was the deer mouse, along with pinyon 

mouse; brush mouse, Mexican vole, western harvest 

mouse, white-footed mouse, and white-throated woodrat were recorded rarely (table 5). 

The pinyon mouse was not only the most abundant 

rodent species at the pinyon-juniper sites, but it was 

also the only species to significantly decline on the 

treated plots following thinning treatments at both 

pinyon-juniper sites based on Chi-Square tests (PJ1 

site, p = 0 .002; PJ2 site, p < 0.0001). The deer mouse 
98 USDA Forest Service RMRS-P-77. 2020. Figure 11—Cluster analysis results showing the similarity of sites and paired plots based on similarity of the bird species compositions in (a) spring 2010, (b) fall 2010, (c) spring 2011, (d) fall 2011, (e) spring 2015, and (f) fall 2015.
USDA Forest Service RMRS-P-77. 2020. 99 Table 4—Pinyon-juniper rodent species and mean counts of individuals per species averaged over counts per 3-day sample period, averaged over the seasons and years prior to tree thinning from fall 2008 through fall 2010, and averaged over the seasons and years after tree thinning from spring 2011 through fall 2015. Nocturnal rodent species on pinyon-juniper sites Before thinning, 2008–2010 After thinning, 2011–2015 Common name Scientific name Species code PJ1 PJ2 PJ1 PJ2 Row averag e Contro l Treate d Contro l Treate d Contro l Treate d Contro l Treate d Deer mouse Peromyscus maniculatus PEMA 2.0 1.0 5.3 3.0 2.0 1.0 1.6 4.0 2.5 Mexican vole Micropus mexicanus MIME 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 Northern grasshopper mouse Onychomys leucogaster ONLE 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 Pinyon mouse Peromyscus truei PETR 6.0 8.5 4.4 6.4 7.4 4.0 6.1 2.8 5.7 Plains pocket mouse Perognathus flavescens PEFL2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.1 Plains woodrat Neotoma micropus NEMI 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.3 Silky pocket mouse Perognathus flavus PEFL 0.0 0.0 1.0 1.5 1.0 0.0 1.0 1.0 0.7 White-footed mouse Peromyscus leucopus PELE 5.0 5.5 2.0 2.0 1.0 1.0 1.7 1.0 2.4 White-throated woodrat Neotoma albigula NEAL 0.0 0.0 0.0 1.0 1.5 2.0 1.0 1.0 0.8 Column average 1.4 1.8 1.5 1.5 1.4 0.9 1.3 1.4 1.4 Table 5—Ponderosa pine rodent species and mean counts of individuals per species averaged over counts per 3-day sample period, averaged over the seasons and years prior to tree thinning from fall 2008 through fall 2010, and averaged over the seasons and years after tree thinning from spring 2011 through fall 2015. Nocturnal rodent species on ponderosa pine (PP) sites Before thinning, 2008–2010 After thinning, 2011–2015 Common name Scientific name Species code PP1 PP2 PP1 PP2 Row averag e Contro l Treate d Contro l Treate d Contro l Treate d Contro l Treate d Brush mouse Peromyscus boylei PEBO 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.4 Deer mouse Peromyscus maniculatus PEMA 5.5 1.3 3.0 2.7 1.5 1.8 2.5 2.4 2.6 Mexican vole Micropus mexicanus MIME 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.3 Pinyon mouse Peromyscus truei PETR 2.5 6.5 4.0 1.5 1.4 1.4 2.0 0.0 2.4 Western harvest mouse Reithrodontomys megalotis REME 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.3 White-footed mouse Peromyscus leucopus PELE 2.0 1.0 0.0 2.0 1.0 0.0 1.0 0.0 0.9 White-throated woodrat Neotoma albigula NEAL 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.3 Column average 1.4 1.3 1.0 0.9 0.7 0.8 1.2 0.8 1.0
100 USDA Forest Service RMRS-P-77. 2020.

also significantly declined at the PJ1 site (p = 0.04) 

after thinning treatments, but deer mice were significantly more abundant on the treated plot at the PJ2 site 

(p = 0.02) following thinning treatments. Otherwise, 

there were no significant differences in rodent numbers 

between control and treated plots at the pinyon-juniper 

sites over the pretreatment, or over the posttreatment, 

periods. Deer mice numbers were significantly lower 

on the PP2 site treatment plot (p = 0.006) compared 

to the control plot, but only following thinning treatments, and there were no significant differences in 

rodent numbers between control and treated plots at 

the ponderosa pine sites over the pretreatment, or over 

the posttreatment, periods.

Cluster analysis of similarities or differences in rodent 

species compositions between site locations and 

between control vs. treatment plots in 2010, 2011, and 

2015, showed that in 2010 prior to thinning treatments, 

rodent assemblages across the pinyon-juniper and the 

ponderosa pine sites had similar species compositions 

and that there were no clear groupings by forest type 

or treatments (fig. 13). In spring 2011 immediately 

after tree thinning, rodent assemblages at the two pinyon-juniper sites were most similar based on treatment 

type, not site location, while all ponderosa pine site 

control and treatment plots were more similar by forest 

type (fig. 13). In fall 2011, the treatment plots from the 

two pinyon-juniper sites still were similar in rodent 

compositions, while the pinyon-juniper control plots 

were very different from each other and the treatment 

plots, and all of the ponderosa pine sites still grouped 

together (fig. 13). Five years after tree thinning treatments, rodent assemblages no longer grouped by location or by treatment. The PJ1 site control and treatment 

plots grouped together and with the PP1 control plot, 

while the PJ2 site plots grouped with the PP2 and 

PP1 plots in both the spring and fall (fig. 13). These 

findings show that the thinning treatments changed the 

species compositions at the pinyon-juniper sites 1 year 

after thinning, but that pattern had disappeared by 5 

years since thinning treatments.

Medium-sized to Large Animals

Over 2,000 animals were recorded from the wildlife 

camera traps that were operated year-round from 2012 

through 2015. However, many of the animals photographed on study plots at the pinyon-juniper sites were 

domestic livestock, primarily cattle and some horses. 

Many small animal species also were captured on the 

wildlife cameras, but were often difficult to identify, 

so we excluded analysis of small animals such as 

chipmunks, squirrels, and small birds. We did include 

wild turkeys (see table 6 for scientific names of wildlife species mentioned in this section) because of their 

large size and ease of identification in photographs. 

Cameras at the ponderosa pine sites recorded more 

native animals (284 individuals) than cameras at the 

Figure 12—Total number of rodents over 3-night live trapping periods in the spring and 

fall on (a,b) the two pinyon-juniper (PJ1 and PJ2) and (c,d) the two ponderosa pine 

(PP1 and PP2) sites, 2010–2015.
USDA Forest Service RMRS-P-77. 2020. 101 Figure 13—Cluster analysis results showing the similarity of sites and paired plots based on similarity of the rodent community species compositions in (a) spring 2010, (b) fall 2010, (c) spring 2011, (d) fall 2011, (e) spring 2015, and (f) fall 2015.
102 USDA Forest Service RMRS-P-77. 2020. Table 6—Wildlife species and counts summed over the 4-year period 2012 through 2015 from camera traps at pinyon-juniper (PJ) and ponderosa pine (PP) sites following tree thinning treatments in winter 2010–2011. Native wildlife species Pinyon-juniper sites Ponderosa pine sites Row sum Common name Scientific name PJ1 PJ2 PP1 PP2 Control Treated Control Treated Control Treated Control Treated American black bear Ursus americanus 0 0 1 0 4 2 4 0 11 Black-tailed jackrabbit Lepus californicus 9 7 17 0 3 0 2 0 38 Bobcat Lynx rufus 0 0 0 0 3 0 0 0 3 Common raccoon Procyon lotor 0 0 0 0 1 1 0 0 2 Coyote Canis latrans 31 6 19 1 4 7 4 0 72 Desert cottontail Sylvilagus auduboni 0 1 3 0 22 1 2 0 29 Gray fox Urocyon cinereoargenteus 1 0 0 0 1 0 7 2 11 Mule deer Odocoileus hemionus 1 3 5 5 13 7 89 40 163 North American elk Cervus canadensis 3 0 0 0 0 0 7 2 12 Wild turkey Meleagris gallopavo 0 13 60 33 32 22 1 1 162 Column sum 45 30 105 39 83 40 116 45 503
USDA Forest Service RMRS-P-77. 2020. 103

pinyon-juniper sites (219 individuals), and many more 

native animals were recorded on the control plots than 

on the treatment plots across both the pinyon-juniper 

and the ponderosa pine sites (fig. 14). 

The most abundant animal recorded at the two pinyonjuniper sites was wild turkey, which were recorded 

only from the treatment plot at the PJ1 site, but were 

abundant on the control plot at the PJ2 site (table 6). 

Chi-Square tests for differences in animal numbers 

between control and treatment plots within each site 

(table 6) showed that over the posttreatment period 

from 2012 to 2015, wild turkeys were significantly 

more abundant on the treated plot than the control 

plot at the PJ1 site (p = 0.0003), but were significantly 

more abundant on the control plot at the PJ2 site 

(p = 0.005). The coyote was the next most abundant 

animal species at the pinyon-juniper sites, and coyotes 

were significantly more abundant on the control plots 

at both sites compared to the treated plots (both PJ1 

and PJ2 sites, p < 0.0001). Black-tailed jackrabbits 

were significantly more abundant on the control plot at 

the PJ2 site (p < 0.0001), but not significantly different 

between control and treatment plots at the PJ1 site. 

Otherwise, there were no significant differences in 

the numbers of large animals between the control 

and treatment plots from 2012 through 2015 at either 

pinyon-juniper site. 

Mule deer were the most abundant animal photographed at the ponderosa pine sites, and they were 

significantly more abundant on the control plot than 

on the treated plot at the PP2 site (p < 0.0001), but not 

significantly different at the PP1 site. Wild turkeys 

were common at the PP1 site and rare at the PP2 site, 

but numbers were not significantly different between 

control and treatment plots at either site. Coyotes and 

black bears were significantly more abundant on the 

control plot at the PP2 site (both p = 0.04), but not significantly different between plots at the PP1 site, and 

desert cottontails were significantly more abundant 

on the control plot at the PP1 site (p < 0.0001), but 

not different between plots at the PP2 site. Otherwise, 

there were no significant differences in the numbers of 

large animals between the control and treatment plots 

from 2012 through 2015 at either ponderosa pine site. 

Figure 14—Total number of native medium to large mammals and wild turkeys recorded 

from camera traps on the control and treatment plots at the two pinyon-juniper (PJ1 and 

PJ2) and the two ponderosa pine (PP1 and PP2) sites following tree thinning treatments, 

2012–2015.
104 USDA Forest Service RMRS-P-77. 2020.

Domestic livestock were considerably more abundant 

at the pinyon-juniper sites from 2012 through 2015 

(511 animals) than at the ponderosa pine sites (145 

animals); at all sites except the PP1 site where grazing 

was supposed to be excluded (60 trespass animals), 

livestock were much more abundant on the treated 

study plots (fig. 15). Chi-Square tests revealed that 

significantly more livestock occurred on the treatment 

plots than on the control plots from 2012 through 2015 

at both pinyon-juniper sites (p < 0.0001, both PJ1 and 

PJ2 sites), and at one ponderosa pine site (p = 0.0001, 

PP1 site), but not at the other ponderosa pine site 

(p = 0.74, PP2 site). 

DISCUSSION

The tree thinning treatments accomplished the primary 

forest restoration objectives of reducing tree stand and 

fire fuel densities; changing stand structures from a 

dominance of small and young trees to stands dominated by fewer, older, and larger trees with some small 

patches of dense trees; and creating more open habitats 

and increased understory vegetation foliage cover. 

Responses of wildlife to the thinning treatments were 

generally neutral or positive for birds and neutral or 

negative for rodents, and generally negative for large 

mammals and wild turkey. Additionally, we found 

that domestic livestock were much more abundant on 

treated study plots, apparently in response to increased 

production of understory vegetation. Three years of 

prethinning baseline data followed by 5 years of posttreatment data allowed us to understand the short-term 

(i.e., 5 years) temporal dynamics of tree thinning effects on vegetation and wildlife in both pinyon-juniper 

and ponderosa pine woodlands. 

Wildlife Habitat

Trees

The New Mexico State Forestry treatment prescriptions applied in our study were similar to tree thinning prescriptions used across the Southwest in both 

pinyon-juniper and ponderosa pine stands (Allen et 

al. 2002, 2008; Fulé et al. 1997; Savage et al. 2008) 

and were designed to renew ecosystem structure and 

function and reverse unhealthy forest characteristics. 

The treatments were successful in reducing stand 

basal area on both pinyon-juniper and ponderosa pine 

sites, and in creating small clustered groups of trees of 

variable diameter, with small-diameter trees removed 

within the dripline of larger trees. The prescription 

created more open stands with reduced canopy cover 

and increased distance between groups of trees in 

both woodland types. This “thin from below” treatment approach is a common method employed by 

land managers for restoration (Fulé et al. 2002); by 

removing smaller understory trees, both old-growth 

Figure 15—Total number of domestic livestock (cattle and horses) recorded from camera 

traps on the control and treatment plots at the two pinyon-juniper (PJ1 and PJ2) and the 

two ponderosa pine (PP1 and PP2) sites following tree thinning treatments, 2012–2015. 
USDA Forest Service RMRS-P-77. 2020. 105

trees and appropriate diameter class distribution are 

retained (Kaufman et al. 1998). The resulting stands 

had more mature individuals and variable openings, a 

common outcome for most Southwestern forest restoration prescriptions, particularly for ponderosa pinedominated stands (Wightman and Rosenstock 2008).

The more open crown structure impacts the subcanopy 

environment by removing the canopy’s filtering effect 

on sunlight and precipitation (Naumburg and DeWald 

1999), which alters habitat for some animals and understory plant species. The greater proportion of larger, 

healthier trees is expected to increase tree productivity 

in the long term as a result of reduced competition, 

thus improving resiliency to wildfire, drought, insects, 

and disease (Allen et al. 2010, 2015).

The fuel reduction aspects of the treatment prescriptions were designed to reduce the potential for severe 

wildfire (Feeney et al. 1998; Keane et al. 2002; Sala 

et al. 2005). When crown spacing is increased, density 

is reduced in the lower canopies, and ladder fuels are 

reduced, a wildfire is likely to burn with lower intensity and thereby cause lower mortality to overstory 

trees (Agee and Skinner 2005), which in turn reduces 

loss of habitat for wildlife. Restoration treatments in 

Southwestern forest types can increase individual tree 

growth (Feeney et al. 1998; Fulé et al. 2007; Ronco 

et al. 1985; Skov et al. 2005), decrease tree water 

stress (Kolb et al. 1998; Skov et al. 2005; Wallin et 

al. 2004), increase tree defense against bark beetles 

through increased resin production (Kolb et al. 1998), 

and increase leaf nitrogen concentration and hence 

photosynthetic capacity in some cases (Feeney et al. 

1998; Wallin et al. 2004; Zausen et al. 2005). These 

improvements in forest health are expected to have 

positive effects on wildlife habitat.

Tree thinning also may potentially affect wildlife food 

sources. Twoneedle pinyon is a masting plant species 

that produces large crops of cones and seeds sporadically about every 5 to 7 years (Janetski 1999; Zlotin 

and Parmenter 2008), and juniper berries are produced 

almost every year but in variable densities (Chambers 

et al.1999). Our finding of greater cone production 

(and possibly larger seeds) on the pinyon trees that 

remained after treatment, compared to pinyon trees 

on control plots at one of two sites, indicates that tree 

thinning may have reduced competition for some 

remaining larger trees, allowing for greater reproductive output. We are not aware of any other studies that 

have addressed tree thinning effects on pinyon cone 

and seed production, and we recommend that others 

conduct such studies. If tree thinning does increase 

pinyon and perhaps ponderosa pine cone and seed 

production, the implications are important to animal 

species such as the many birds, rodents, and larger 

mammals, including bears, coyotes, and foxes, that 

utilize pine seeds as a food resource. 

Understory Vegetation

The strong positive response of herbaceous understory 

vegetation to tree thinning in our study probably 

resulted from increased plant growth due to increased 

soil moisture from reduced tree canopy interception 

and evaporation of precipitation, less water uptake 

by tree roots, and perhaps the mulching effects of 

wood chips. Perennial grasses and forbs that were in 

place before tree thinning and annual forbs sprouting 

from seeds after thinning accounted for the increase 

in herbaceous plant cover following tree removals. 

Understory vegetation responses to tree thinning 

treatments in dry forests are thought to be driven by 

changes in availability of limiting resources, primarily nitrogen and water (Ares et al. 2010; Coomes and 

Grubb 2000; Miller and Seastedt 2009). Abella et al. 

(2015) concluded that increased species richness and 

cover following tree thinning are variable and depend 

on soil parent material, the specifics of thinning implementation, and the presence or exclusion of livestock 

grazing. 

Other research in pinyon-juniper woodlands has 

found the same response patterns (Albert et al. 2004; 

Brockway et al. 2002; Huffman et al. 2013; Jacobs 

2015; Matchett et al. 2010; Owen et al. 2009; Ramirez 

et al. 2008; Stephens et al. 2016). Stephens et al. 

(2016) concluded that understory vegetation responses 

to pinyon-juniper thinning depend on the type of 

treatment used, but that all treatments that reduce tree 

cover increase understory vegetation cover. Brockway 

et al. (2002) studied the effects of juniper removal by 

mechanical mastication (not chipping) on understory 

vegetation in pinyon-juniper savanna near our study 

area and compared the effects of removing, piling, 

and spreading slash. They found that herbaceous 
106 USDA Forest Service RMRS-P-77. 2020.

vegetation cover and diversity significantly increased 

when all three methods were used, but spreading 

the slash had the most beneficial effect; they recommended mulching with slash over the other treatments. 

Jacobs (2015) found total understory cover increased 

several-fold 3 to 5 years posttreatment. Matchett et al. 

(2010) evaluated short-term effects of thinning methods on southwestern pinyon-juniper woodlands and 

found that thinning treatments with slash pile/burn and 

mastication increased the abundance of herbaceous 

vegetation; pretreatment tree dominance dictated 

the strength of the increase. Matchett et al. (2010) 

concluded that thinning-induced increases in perennial grass cover in areas of high tree dominance were 

mainly due to an increase in growth of individuals 

present before the treatment, as opposed to an increase 

due to the recruitment of new individuals, which corresponds to our findings.

The same findings apply to ponderosa pine woodlands 

(Abella 2009; Fulé et al. 2001; Jacobs 2015; Laughlin 

and Fulé 2008; Miller and Seastedt 2009; Moore et al. 

2006; Stoddard and McGlone 2008; Stoddard et al. 

2011; Thomas and Waring 2015). Korb and Springer 

(2003) concluded that understory productivity in 

ponderosa pine forests is inversely related to the 

density of the overstory trees. Thinning treatments to 

reduce overstory density have repeatedly been shown 

to increase understory productivity, particularly when 

pretreatment stands are dense (Bedunah et al. 1988; 

Metlen and Fiedler 2006; Moore and Deiter 1992). 

Invasion and colonization of treated sites by exotic 

weeds is a potential negative consequence of tree 

thinning. As discussed earlier, minimizing soil surface 

disturbance during treatments and mulching with wood 

chips or scattered slash are effective ways to reduce 

exotic weed invasion (Miller and Seastedt 2009; Owen 

et al. 2009). The tree thinning treatments imposed in 

our study did not lead to increases in exotic invasive 

weed species. Many exotic invasive weed species occurred along roads and other disturbed locations across 

our study area, including prickly Russian thistle, 

kochia (burning bush) (Bassia scoparius), white 

horehound (Marrubium vulgare), and cheatgrass. Over 

the 8-year study, we found only four exotic invasive 

weed species at only one treatment plot following tree 

removals: prickly Russian thistle, common mullein, 

redstem stork's bill, and cheatgrass. We attribute the 

lack of exotic invasive weed increases to very little 

soil surface disturbance from the manual thinning 

methods used, along with the mulching effects of 

wood chips. 

The exceptional drought that occurred over our region 

for 4 years after the removal of trees probably dampened the increases in herbaceous vegetation establishment and production compared to levels expected 

under average or wetter than average conditions. The 

study sites also were grazed by livestock, and the foliage canopies of herbaceous plants were reduced by 

an estimated 20 to 40 percent of what remained to be 

measured at the end of each annual growing season 

(based on livestock stocking rates and our observations). Therefore, the significant increase in understory 

herbaceous cover that we measured was likely to be 

an underestimate of the plant growth and standing biomass that would have been in place during nondrought 

conditions and without livestock grazing. 

Smith (2011) found that precipitation is a strong determinant of understory response following thinning, 

concluding that long-term drought can compromise 

the ability of vegetation to respond to management. 

Climate influences on understory response have been 

discussed in many studies, with a general finding of a 

strong positive correlation between annual precipitation and understory productivity and diversity (Abella 

and Covington 2004; Bataineh et al. 2006; Fulé et al. 

2002; Moore et al. 2006; Sabo et al. 2008). Climate 

change projections call for continued warming temperatures and intensified future droughts in the region 

(Llewellyn and Vaddey 2013), so we expect our findings to be indicative of tree thinning effects on vegetation for projected future conditions.

Wildlife

Birds

The neutral to positive responses of overall bird 

abundance to treatments at both the pinyon-juniper 

sites indicate that the small-scale manual tree thinning treatments had little effect on birds collectively. 

Some species that prefer more open woodland habitats 

such as the western bluebird, lark sparrow, northern 

flicker, and western kingbird increased significantly on 
USDA Forest Service RMRS-P-77. 2020. 107

pinyon-juniper thinned locations compared to control 

locations. Despite severe drought conditions, tree 

thinning did not negatively affect bird abundance in 

either the pinyon-juniper or the ponderosa pine woodlands. Only one species, the brown creeper, declined 

on thinned locations at one ponderosa pine site. No 

bird species declined in response to thinning at either 

pinyon-juniper site. 

These findings indicate that small-scale WUI-type 

thinning treatments do not have negative impacts on 

bird communities in the region, but probably increase 

bird diversity by adding open and edge habitats. These 

results are consistent with Bombaci and Pejchar’s 

(2016) findings across a number of thinning studies in 

pinyon-juniper woodlands. In contrast, studies of bird 

responses to large-scale mechanical (e.g., chaining, 

blading, extraction) pinyon-juniper reduction treatments consistently have resulted in bird declines in 

treated areas (Bombaci and Pejchar 2016). Stephens et 

al. (2012) found that nonmechanical thinning and low 

severity fire both had positive effects on many more 

bird species than did mechanical thinning. Greater bird 

numbers during the fall migration period when many 

nonterritorial migrants were moving through the study 

areas also are consistent with results of other research 

on tree thinning effects on birds in pinyon-juniper 

woodlands (Bombaci and Pejchar 2016). Our findings 

of greater bird abundances and species richness at 

the pinyon-juniper sites compared to the ponderosa 

pine sites are consistent with Paulin et al. (1999) in 

Utah. These results indicate that the more diverse 

pinyon-juniper habitats support more species of birds 

than ponderosa pine and emphasize the importance of 

pinyon-juniper habitat to birds in central New Mexico. 

Many studies have found that the removal of smalldiameter trees typical of fuel reduction treatments has 

a neutral to positive effect on bird species (Gaines 

et al. 2010; Hurteau et al. 2008; Kalies et al. 2009; 

Manley et al. 2015; Stephens et al. 2012; Verschuyl et 

al. 2011; White et al. 2013). But studies have revealed 

that responses are generally species- or guild-specific 

or vary over time, attributable to the pace of vegetation response of the understory and overstory strata 

(Yegorova 2013). Kalies et al. (2009) found that, at the 

guild level, aerial foraging birds benefited from smalldiameter tree removal, but had negative responses to 

large-diameter tree overstory removal, while ground 

shrub-foraging birds responded positively to overstory 

removal. Woodpeckers, however, declined following 

overstory removals. Gaylord (2014) found that the 

occurrence probability of bark foragers and seed eaters 

was more closely associated with annual variability of 

food resources such as bark beetles, seed production, 

and composition of tree species. Foliage insectivores, 

which glean invertebrates from foliage of trees and 

shrubs, were associated with higher tree cover and fuel 

reduction that reduced cover of these species. 

Hurteau et al. (2008) concluded that treatments to 

reduce forest fuels had little effect on avian diversity 

over 4 years, but did affect some aspects of species 

composition and abundance. Their results suggest that 

although the small-scale forest treatments they studied 

may have influenced the avian species present, natural 

annual variation in bird abundance was a stronger 

source of variation. Similarly, Szaro and Balda (1986) 

found that various intensities of forest thinning treatments influenced bird density and species richness, 

but treatments had a greater influence on community 

composition. White et al. (2013) used computer 

simulations to evaluate avian response to conifer forest fuel reduction treatments and found that treatment 

methods that increased stand structure complexity 

also increased overall avian species richness. Kalies 

et al. (2009) similarly found that a mosaic of forest 

conditions may be the most appropriate technique for 

providing suitable habitat for a wide range of forest 

passerines. They suggest that landscape-level forest 

treatments applied by land managers will have only 

modest effects on avian species. 

The spring breeding season bird species compositions 

of pinyon-juniper woodlands are known to differ from 

those of breeding birds of ponderosa pine woodlands 

(Laudenslayer and Balda 1976; Paulin et al. 1999). 

Our cluster analysis results of spring breeding season 

bird assemblages confirmed that our pinyon-juniper 

and ponderosa pine sites had different bird species 

assemblages. In addition, bird assemblages at the ponderosa pine sites were affected more by the thinning 

treatments than those at the pinyon-juniper sites. Why 

bird species compositions of our pinyon-juniper sites 

were little affected by tree thinning is not clear, but is 

indicative of a greater change to the ponderosa pine 

habitats than to the pinyon-juniper habitats, relative 

to the different assemblages of birds that use those 
108 USDA Forest Service RMRS-P-77. 2020.

respective habitat types. Kalies et al. (2009) reported 

that tree canopy reduction in ponderosa pine forests 

negatively affected bird communities, while Bombaci 

and Pejchar (2016) reported that tree canopy reductions in pinyon-juniper woodlands had little to positive 

effects on birds. 

Rodents

The weak response of rodent abundances to tree thinning at one pinyon-juniper site and both ponderosa 

pine sites, along with the significant declines of pinyon 

mice at both pinyon-juniper sites, and significant 

declines of deer mice at one pinyon-juniper and one 

ponderosa pine site, is consistent with previous research on the effects of tree thinning in pinyon-juniper 

woodlands. Bombaci and Pejchar (2016) reported 

that most of the studies they reviewed concluded that 

tree thinning in pinyon-juniper woodlands had largely 

neutral responses of rodents to tree reductions; the 

exception was when wood slash was left on the ground 

after thinning treatments, which caused increases in 

rodents. Pinyon-juniper woodland rodent species such 

as pinyon mice, deer mice, and woodrats (Neotoma

spp.) tended to decline when trees were completely 

removed, but increased when slash or other wood 

was left on the ground following thinning treatments 

(Baker and Frischknecht 1973; Turkowski and Watkins 

1976), especially when trees were thinned rather than 

completely removed (Severson 1986). In contrast, 

grassland specialist rodent species in pinyon-juniper 

savanna areas have been found to increase only when 

trees were completely removed (Severson 1986). 

Deer mice were the primary species found to increase 

among those studies when pinyon-juniper was thinned, 

but only if wood slash was left on the ground (Albert 

et al. 1995; Sedwick and Ryder 1987; Severson 1986). 

Studies of rodent responses to tree thinning in 

ponderosa pine woodlands also have yielded results 

similar to ours. Several studies found early and positive responses of small forest-floor mammals to thinning (Converse et al. 2006a,b,c; Muzika et al. 2004; 

Sullivan et al. 2005; Suzuki and Hayes 2003; Wilson 

and Carey 2000; Wilson and Forsman 2013; Zwolak 

2009). Some researchers suggest that increases in herbaceous and shrub cover following thinning enhance 

habitat for some rodents (Bagne and Finch 2010; 

Block et al. 2005; Carey and Johnson 1995; Converse 

et al. 2006b). Positive responses have generally been 

strongest in forests that originally lacked understory 

cover and shrub components (Wilson and Forsman 

2013). 

Bagne and Finch (2010) studied rodent responses 

to tree thinning in ponderosa pine woodlands of the 

Santa Fe watershed, and deer mice increased in some 

habitats where wood slash was left on the ground following tree reductions and also increased in response 

to greater precipitation. Other studies also have attributed positive effects of ponderosa pine tree thinning 

for rodents to increases in downed woody debris left 

on the ground after thinning (Converse et al. 2006a,b; 

Manning and Edge 2004; Suzuki and Hayes 2003) and 

to increases in herbaceous understory plants following 

tree reductions (Converse et al. 2006a; Manning and 

Edge 2004). 

Increases in overall rodent habitat heterogeneity 

resulting from tree reductions have also been reported (Carey and Wilson 2001; Muzika et al. 2004). 

Stephens et al. (2012) found that rodents tended to respond positively to mechanical thinning and negatively 

to nonmechanical thinning and low severity fire across 

a variety of forest types, probably due to residual slash 

left in place after mechanical thinning. In contrast, 

McIver et al. (2013) suggested that across a broad 

spectrum of woodland and forest ecosystems, treatment 

responses by rodents tended to be subtle or nonexistent, 

and any changes in rodent abundance that did occur 

were subtle and transient, lasting only 1 to 3 years.

The Northern Arizona University Ecological 

Restoration Institute (2010) reviewed literature 

and examined posttreatment time period effects on 

rodents in ponderosa pine woodlands and concluded 

that vegetation density was an important factor for 

four key rodent species, and that species associated 

with dense plant cover were the only ones to increase 

concurrently with increased vegetation density. The 

presence of slash piles and longer duration of the slash 

pile presence produced positive occupancy responses 

from all rodent species. The presence of downed 

wood or slash is especially important for some species 

(Chambers 2002; Converse et al. 2006a), particularly 

deer mice, because the animals use slash habitats 

for cover, nesting, and food (e.g., more insects). The 

Northern Arizona University study acknowledged 
USDA Forest Service RMRS-P-77. 2020. 109

the importance of downed wood as a habitat feature for 

some members of the small mammal community, but 

concluded that the presence of downed wood is less 

important than overstory and understory vegetation 

composition and structure. Thinning operations also may 

open forests, increasing the success of predators hunting 

small mammals (Gese et al. 1996). The reduction in 

trees along with the removal of downed woody materials 

apparently had little to no effect on woodland rodents in 

our study, especially at the ponderosa pine sites. 

The one negative response at the PJ1 site was primarily due to a decline in pinyon mice following tree 

reduction, as Severson (1986) and Albert et al. (1995) 

found in southwestern and northwestern New Mexico, 

respectively. Pinyon mice are specialists of pinyonjuniper woodlands and primarily use the trees as habitat (Albert et al. 1995; Findley et al. 1975; Holbrook 

1978). Severson (1986) found that pinyon mice 

declined when trees were removed, even if slash was 

left on the ground, while pinyon mice increased where 

some trees were left and slash remained on the ground. 

Most other rodents increased in response to slash piles. 

Our PJ1 site had a higher density of pinyon trees than 

the PJ2 site, and the proportionately greater loss of 

trees at the PJ1 site apparently had a proportionately 

greater impact on pinyon mice than at the PJ2 site. 

Another important difference between the two pinyonjuniper sites was that the landowner at the PJ2 site also 

left some slash in a small drainage on the treated study 

plot. The slash probably provided habitat for generalist deer mice, which did increase slightly on that plot 

following the thinning treatment and were captured 

in traps near the slash. Grassland rodent species such 

as white-footed mice, silky pocket mice, western 

harvest mice, and plains woodrats were present in low 

numbers at both pinyon-juniper sites, and we had more 

captures of those species, especially at the PJ2 site, 

following tree thinning treatments. That response of 

grassland rodent species is consistent with the findings of others (O’Meara et al. 1981; Rodhouse et al. 

2010; Severson 1986). Pinyon mice, which are largely 

arboreal foragers on pinyon trees (Albert et al. 1995; 

Findley et al. 1975; Holbrook 1978), declined following reductions of standing pinyon trees, while deer 

mice, which dominated at the ponderosa pine sites, did 

not respond to reductions in standing ponderosa pine 

trees or to the spreading of wood chips on the ground.

Higher numbers of rodents at both pinyon-juniper sites 

and at one ponderosa pine site at the onset of the study, 

and again toward the end of the study, in contrast 

with low numbers during the extreme drought period 

in 2011 through 2013, were probably in response to 

increased precipitation and plant production (i.e., food 

resources). Rodents in the Southwest are known to respond positively to the bottom-up effects of increased 

rainfall and plant production, and populations typically 

increase fairly rapidly within 1 year of increased plant 

production (Ernest et al. 2000; Lightfoot et al. 2012). 

Bagne and Finch (2009) concluded that precipitation interacted with tree thinning treatments to affect 

deer mice abundance. Little change in rodent species 

compositions following thinning treatments in cluster 

analysis corresponds with the associated lack of 

change in rodent numbers relative to the thinning treatments. Our pinyon-juniper sites were dominated by 

pinyon mice and the ponderosa sites were dominated 

by deer mice; the other rodent species were probably 

at low enough numbers to have little effect on the species similarity values in cluster analysis. Overall, the 

tree thinning treatments in our study had little effect on 

rodent abundances or on rodent species compositions, 

but there is evidence for individual species responses 

based on an increase or a decrease in their individually 

preferred habitat resources, especially the decline in 

pinyon mice after tree thinning. 

Medium-sized to Large Animals

The medium-sized to large mammals and wild turkeys 

that declined in abundance (based on camera trap photograph frequency) following tree thinning treatments 

may have reacted to a change in habitat structure that 

created more open stands of trees with fewer trees 

for protective cover. Mule deer, coyotes, black-tailed 

jackrabbits, and wild turkey all have preferences for 

mixes of relatively open habitat, along with vegetation 

cover for visual protection from predators (Bender et 

al. 2013; Findley 1987; Findley et al. 1975; Kennedy 

and Fontaine 2009; New Mexico Department of Game 

and Fish 2013). Turkeys additionally require surface 

water sources, trees for roosting, and summer brood 

areas (New Mexico Department of Game and Fish 

2013). Given that camera installation and data collection on control and treatment plots did not begin 

until 2012, we cannot be certain that the differences 

in animal abundances between treatment and control 
110 USDA Forest Service RMRS-P-77. 2020.

plots did not exist before the treatments, but the high 

degree of control vs. treatment differences and the 

consistency of the pattern across all four study sites indicate that lower numbers of native animals on thinned 

study plots were a response to tree thinning. Another 

consideration relative to the results of our study was 

the unaccounted-for effects of the landscape habitats 

surrounding our study sites. Large wildlife species 

tend to have larger territories and movement patterns 

relative to our small study sites, and the surrounding 

landscape habitats probably affected the behavior of 

large animals using the smaller study site patches. We 

could not control for surrounding landscape effects, so 

those remain unknown but likely factors affecting the 

animals that did move through our study sites. 

Relatively little research has been conducted on 

tree thinning effects on larger mammals and wild 

turkey. Albert et al. (1995) found that tree thinning 

in pinyon-juniper woodlands in northwestern New 

Mexico increased mule deer use based on fecal pellet 

counts. Kramer et al. (2014) found that tree thinning 

and livestock exclusion from savanna, pinyon-juniper, 

and ponderosa pine study sites in northeastern New 

Mexico caused an increase in mule deer forage species similar to our findings and concluded that tree 

thinning was beneficial for short-term improvements 

in mule deer forage. Watkins et al. (2007) emphasize 

the importance of woody shrubs as browse forage for 

mule deer, along with trees and forest edges for cover. 

Understory vegetation increased following thinning 

treatments in our study, yet mule deer abundance declined. In contrast, other research has shown increased 

mule deer abundance in larger-landscape-scale thinning studies in ponderosa pine woodlands (Thomas 

and Waring 2015). Because our study sites were on 

private lands with occasional human activity, tree 

cover may have been especially important to mule deer 

for visual cover, as Watkins et al. (2007) suggested. 

The relatively uniform open habitats created by the 

removal of trees in this study may have resulted in 

lower numbers of mule deer, coyotes, and other native 

animals. Watkins et al. (2007) also conclude that domestic livestock damage mule deer habitat by consuming vegetation and altering the vegetation composition 

and structure. Perhaps the great increase in livestock 

on our treated plots also had a negative effect on mule 

deer and other native wildlife. 

Implications of Pinyon-Juniper Tree Thinning 

Treatments to Forest Ecology and Management 

for Wildlife

One of the objectives of tree thinning treatments for 

the Watershed Restoration Program was to improve 

habitat for wildlife. Based on the findings of this study, 

that objective was partially fulfilled. Animal species 

that prefer more open habitats became more abundant 

(e.g., western bluebirds and others), while species that 

prefer more tree canopy and vertical structure cover 

declined (e.g., brown creepers, pinyon mice, large 

mammals, and turkeys). Our findings of larger wildlife 

species declining in relation to tree thinning indicates 

that more small dense stands of trees should be left in 

place to provide cover for such species (Bender et al. 

2013; Patton 2011). Animal diversity is well known to 

be a function of habitat or environmental diversity or 

heterogeneity (Patton 2011); the more types of habitat 

present in a given area, the more types of animal 

species that area will support. Edge effects also are 

important to many species, so creating edges of dense 

and sparse trees also increases habitat heterogeneity 

and species richness (Patton 2011). 

Stephens et al. (2012) concluded that there is no single 

fuel reduction treatment method that will enhance 

habitat for all wildlife species, and that wildfire fuel 

reduction projects should aim to create a variety of 

vegetation successional stages to accommodate a 

range of habitat specialist wildlife species. In order to 

increase the abundance and diversity of wildlife relative to this program, we recommend that tree thinning 

treatments should maximize habitat diversity by retaining even more small patches of dense trees within the 

project areas than the thinning prescription called for, 

perhaps of about 0.25 to 0.5 ha in size for species that 

require some denser vegetation. Tree thinning project 

plans should consider what types of wildlife species 

are targeted for management, and then the tree thinning treatments should be planned to create habitat for 

those particular types of species, along with achieving 

the needs for wildfire fuel reductions. 

In addition to habitat structure and cover, increasing 

understory vegetation and tree production increases 

food resources to animals. Understory vegetation cover and production increased significantly 
USDA Forest Service RMRS-P-77. 2020. 111

in response to tree thinning treatments, apparently 

resulting in increases in wildlife that prefer greater 

understory vegetation and clearly increasing use by 

domestic livestock. If producing more productive 

understory vegetation for wildlife is part of a thinning 

project management plan, then the plan should have 

provisions for livestock grazing and management 

of livestock as one of the planned objectives. Our 

findings showed that livestock use increased dramatically on thinned pinyon-juniper plots. Such a finding 

is beneficial to livestock producers, but may not be 

appropriate for supporting wildlife species that also 

utilize productive understory vegetation. 

Field research addressing vegetation and wildlife is 

affected considerably by weather conditions, before 

and during the study and for the short term (<10 years) 

and long term (>10 years). The entire New Mexico 

region experienced not only an extreme drought and 

increasing annual temperatures beginning at the onset 

of this study in 2011 and persisting through 2014 (U.S. 

Drought Monitor 2016), but also an extreme freeze 

event in the winter of 2011 (Western Regional Climate 

Center 2016). In that context, both vegetation and 

animals that we studied were under an umbrella of environmental stress conditions that probably dampened, 

or perhaps in some cases enhanced, potential responses 

of some species and overall biotic diversity and productivity relative to tree thinning treatments. Given 

that recent human-caused climate change is already 

affecting vegetation and wildlife (Chen et al. 2011; 

Fettig et al. 2013; Root et al. 2003), research on forest 

thinning activities should interpret data in the context 

of more variable weather conditions along with gradually increasing temperatures and their cumulative 

effects on ecosystem processes. 

This study was conducted on only a subset of greater 

landscape topography, soils, and woodland types that 

are found in the region. The findings of this study may 

not apply to other landscapes, soils, and woodland or 

forest vegetation types in the region. Additionally, the 

small number of study sites and paired plots did not 

provide us with site replication needed for powerful 

statistical analyses and experimental testing on a broad 

landscape scale. Bombaci and Pejchar (2016) reviewed 

what is currently known about tree thinning effects 

on wildlife in pinyon-juniper woodlands, including 

deficiencies in and needs for studies of wildlife responses to tree reductions. They recommend long-term 

studies (>10 years), large spatial scale studies (>100-

ha plots), equal research effort among different treatment types, equal levels of research effort in different 

geographic regions, and more studies of amphibians, 

reptiles, and arthropods. However, there also is a need 

for more studies on smaller scale tree thinning projects 

such as WUI projects, along with larger scale studies 

for thinning projects that cover large areas of landscapes. We believe that this type of paired watershed 

study design is a good approach, as Neary (2016) 

concluded, but that more such studies are needed with 

greater replication of sampling locations over the long 

term (i.e., ≥10 years), and broader coverage of landscape and forest or woodland types and tree thinning 

methods. Such an expansion of research will be expensive, but is the best way to understand the effects of 

forest tree thinning on wildlife across the Southwest. 

ACKNOWLEDGMENTS

We thank the New Mexico Water Trust Board for 

providing the majority of funding for this project. 

Dierdre Tarr of the Claunch-Pinto Soil and Water 

Conservation District (SWCD) has been critical to 

the implementation and success of this project, along 

with the help of Brenda Smythe and Cheri Lujan from 

the Edgewood and East Torrance SWCDs, Kent Reid 

and Joe Zebrowski from the New Mexico Forest and 

Watershed Restoration Institute, Lawrence Crane 

from New Mexico State Forestry, and Mike Matush 

from the New Mexico Environment Department. 

Special thanks to the Kelly, Vigil, and Wester families 

and to Juan Sanchez of the Chilili Land Grant for 

allowing us to conduct this research on their properties. Special thanks to the SWCA field biologists 

and editor Justin Elza who assisted with this project. 

Thank you, Dr. Kris Johnson of Natural Heritage New 

Mexico and Marikay Ramsey of the Bureau of Land 

Management, and Karl Malcolm of the Forest Service, 

Rocky Mountain Research Station, for organizing and 

producing this symposium volume on pinyon-juniper 

management and wildlife. We appreciate the thoughtful reviews and suggestions by Malcolm, Esteban 

Muldavin, and Marikay Ramsey that improved the 

quality of this manuscript. 
112 USDA Forest Service RMRS-P-77. 2020.

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Using the Assessment, Inventory, and Monitoring Strategy to 

Measure Treatment Effectiveness in Pinyon-Juniper Woodlands 

KEYWORDS—AIM, restoration, monitoring, treatment, pinyon juniper, prescribed fire, Bureau of Land 

Management

It is increasingly important to understand how different management actions affect public lands and renewable resources. In response to a lack of standardized 

monitoring, the Bureau of Land Management (BLM) 

developed the Assessment, Inventory, and Monitoring 

(AIM) strategy (Toevs et al. 2011). The AIM approach 

is based on five key elements: 1) a standardized set 

of core and contingent indicators, 2) a statistically 

valid sampling design, 3) a structured implementation 

process, 4) electronic data capture, and 5) integration 

with remote sensing. This strategy can help to more 

efficiently meet national, regional, and local resource 

information needs. The Taos Field Office (TFO) of the 

BLM has used the AIM protocol for 3 successive years 

to monitor vegetation treatment effectiveness and to 

inventory wildlife habitat. Here we discuss the implementation of AIM protocols and use AIM data to describe five, two-needle pinyon pine (Pinus edulis) and 

oneseed juniper (Juniperus monosperma) treatment 

areas in and around Taos, New Mexico. The use of 

AIM data shows us the complex consequences of these 

management actions and illustrates the advantages of 

ongoing monitoring at the field office level. 

The BLM’s AIM protocol includes both core and 

supplementary methods of data collection; core methods are used on every AIM plot and supplementary 

methods may be added at the project level to suit any 

additional data needs. More detailed information on 

Alexander C. Laurence-Traynor

Chicago Botanic Gardens, Chicago, Illinois

Jason Karl

U.S. Department of Agriculture, Agricultural Research Service, Jornada Experimental Range, Las Cruces, New Mexico

Zoe M. Davidson

U.S. Department of the Interior, Bureau of Land Management, Santa Fe, New Mexico

Jessa C. Davis

U.S. Department of the Interior, Bureau of Land Management

AIM methods and implementation can be found in 

Herrick et al. (2009) and Taylor et al. (2014). Core 

methods include: plot characterization, line point 

intercept (LPI), vegetation heights, species inventory, 

canopy gap intercept, and soil stability. Core methods 

were executed in the present study in addition to belt 

vegetation measurement transects and tree density 

measurement (AIM supplemental methods) to characterize ecological trends and treatment effectiveness. 

Data were collected at a total of 19 treatment plots 

from five individual treatment areas named A-E (four 

thinning plus prescribed burning and one with thinning only, table 1). Each treatment area varied in the 

date of treatment, method of burning/thinning and 

size. These 19 plots were then compared to 24 AIM 

plots from untreated sites within respective Ecological 

Sites (ES). Plots within the same ES share similar soil, 

vegetation, topography, and climate thus representing 

areas within the landscape that are likely to respond 

similarly to disturbances. Both treated and untreated 

plots consisted of a sample area of 2,827.4 square meters and were distributed across the landscape using a 

stratified, spatially balanced random sample design as 

described by Stevens and Olden (2004). The ES were 

used as strata for both the treated and untreated sample 

designs. Data collection from treated and untreated 

areas occurred over a span of three years from 2014-

2016 during the peak plant growth seasons for each 

plot area.
120 USDA Forest Service RMRS-P-77. 2020.

In order to evaluate treated areas, data were reduced 

to the subset of indicators that best represented the 

management objectives of treatments. Data were then 

summarized within treatment areas and within the 

ES. These data included: tree density, number and 

size of canopy gaps, desirable species cover, and soil 

aggregate stability. Desirable species were defined 

as those species which are desired and preferred 

forage for wildlife species and cattle within each 

relevant Natural Resources Conservation Service 

(NRCS) Ecological Site Description (ESD). These 

ESDs included: F048AY015NM (Mixed Conifer), 

R048AY005NM (Mountain Malpais), R036XA004NM 

(Gravelly slopes), R036XB007NM (Malpais), and 

F036XA001NM) (PJ-Oak Woodland) (Sylvester et 

al. 2003a,b; Wright and Bishop 2003). Since there 

is no published ESD for the PJ-Oak Woodland site, 

desirable species were defined from the similar ES 

Mountain Malpais. Data were analyzed using standard 

statistical methods without adjustment; these include 

calculating the mean values and standard deviation 

for tree density, canopy gaps, species cover, and soil 

stability values. 

With the exception of the thinning only area (D) and 

the thinning and jackpot burn area (C) all other treated 

areas had a lower mean tree density than non-treatment 

areas within the same ES (table 2). Tree foliar cover 

from LPI shows a similar pattern.

Desirable species cover was the indicator that varied 

the most among ecological sites and treatment areas. 

Within area A across all ESs, the treated plots had a 

similar or higher average cover of desirable species 

when compared to untreated plots within respective 

ESs (table 3). Similarly, treatment areas B and E 

had slightly higher average values within the Mixed 

Conifer site compared to untreated plots. Conversely, 

area C had significantly lower average cover of 

desirable species within the Mountain Malpais site 

when compared to untreated areas (5.1% compared to 

23.3%). 

When evaluating canopy gap size classes, almost all 

treatment areas across all ESs had significantly more 

large gaps (greater than 200cm) when compared to 

non-treated areas (table 4). The exception to this was 

treatment A in the Malpais and Mountain Malpais ESs, 

which had a similar proportion of large gaps compared 

to non-treated plots in the same ES. No significant 

differences in soil aggregate stability were found when 

comparing untreated and treated areas.

When comparing among treatment areas, area A, 

treated with thinning, pile, broadcast, and jackpot 

burning, was the most successful across all AIM indicators. This area achieved a reduction in tree density, 

had similar or greater average cover of desirable species compared to untreated areas, had similar amounts 

and distribution of large canopy gaps compared to 

untreated sites, and showed no signs of reduced soil 

stability. Despite similar precipitation, soils, and treatment methods this treatment has responded better than 

the four other treatment areas and provides better wildlife habitat and a more hydrologically stable site. The 

success of this treatment is most likely a product of 

the mosaic nature of the multiple treatment methods (a 

combination of thinning, pile, jackpot, and broadcast 

burning), the size of the treatment area, and the longer 

time to recover from treatment as compared to the 

other areas. Treatment area A underwent a combination 

of thinning and burning treatments spread out over 646 

acres (table 1). This approach allowed a mosaic structure of treated and untreated patches providing cover 

and forage for wildlife species as well as a sufficient 

seed bank for recovery. 

Table 1—Description of each treatment area including methods of treatment, year of treatment, and size 

of treatment area. Each treatment area, A-E, is a geographically distinct area within the Rio Grande 

del Norte National Monument, within the BLM Taos Field Office, New Mexico.

Treatment 

area Thinning Pile burn

Broadcast 

burn Jackpot burn

Size

(acres)

A 2005 2011 2011 2014 646

B 2013 -- -- 2015 191

C 2013 -- -- 2014 39

D 2015 -- -- -- 236

E 2005 2008-2104 -- -- 231
USDA Forest Service RMRS-P-77. 2020. 121 Table 2—Average tree density and standard deviations for each treatment area A-E (Table 1) and non-treated areas summarized by ecological site. An X represents an ecological site that was not found in the respective treatment area. Data were collected between May 2014 and October 2016. Non- treatment data were not collected from the Gravelly ecological site. Number of plots (n) within each treatment area and ecological site are shown below the average values. Ecological Site Tree Density (adult trees/hectare) A B C D E Non-Treatment Average Std Dev Average Std Dev Average Std Dev Average Std Dev Average Std Dev Average Std Dev Mixed Conifer 28.29 (n=2) 0.00 0.00 (n=1) n/a X X X X 36.78 (n=5) 34.16 114.36 (n=3) 68.61 Gravelly 0.00 (n=1) n/a X X X X X X X X X X Malpais 0.00 (n=2) 0.00 X X X X X X X X 2.72 (n=13) 7.93 Mountain Malpais 10.61 (n=2) 5.00 19.45 (n=2) 7.50 73.09 (n=3) 20.72 X X X X 60.13 (n=3) 104.14 PJ-Oak X X X X X X 74.27 (n=1) n/a X X 38.91 (n=5) 23.46
122 USDA Forest Service RMRS-P-77. 2020.

Table 3—Average desirable species cover for each treatment area A-E (Table 1) and non-treated areas 

summarized by ecological site. An X represents an ecological site that was not found in the respective 

treatment area. Data were collected between May 2014 and October 2016. No non-treatment data 

were collected from the Gravelly ecological site. Number of plots (n) within each treatment area and 

ecological site are shown below the average values.

Ecological Site Average of Desirable Species Cover

A B C D E Untreated

Mixed Conifer 8.7%

(n=2)

4.7%

(n=1) X X 3.9%

(n=5)

1.6%

(n=3)

Gravelly 4.7%

(n=1) X X X X X

Malpais 20.0%

(n=2) X X X X 14.2%

(n=13)

Mountain Malpais 21.7%

(n=2)

12.7%

(n=2)

5.1%

(n=3) X X 23.3%

(n=3)

PJ-Oak Woodland X X X 4.7%

(n=1) X 0.46%(n=5)

Table 4—Average proportion of plots with large canopy gaps (greater than 200cm) for each treatment area 

A-E (Table 1) and non-treated areas summarized by ecological site. An X represents an ecological site 

that was not found in the respective treatment area. Data were collected between May 2014 and 

October 2016. No non-treatment data were collected from the Gravelly ecological site. Number of plots 

(n) within each treatment area and ecological site are shown below the average values.

Ecological Site

% area with canopy gaps >200cm

A B C D E Non-Treatment

Mixed Conifer 32%

(n=2)

40%

(n=1) X X 33%

(n=5)

21%

(n=3)

Gravelly 0%

(n=1) X X X X X

Malpais 5%

(n=2) X X X X 6%

(n=13)

Mountain Malpais 12%

(n=2)

20%

(n=2)

33%

(n=3) X X 13%

(n=3)

PJ-Oak Woodland X X X 40%

(n=1) X 23.7%

(n=5)
USDA Forest Service RMRS-P-77. 2020. 123

Continued monitoring with the AIM protocols is important because it will allow land managers to understand the recovery of these treatments over time. The 

utility of these data could be improved by increasing 

sample sizes within treatment areas to add more power 

to conclusions drawn between different treatment 

methods. This is particularly important to BLM management as different treatment methods vary in terms 

of cost, practicality, and complexity. 

These AIM data also support the idea that similar management actions will have varied results across different 

ecological sites. This is particularly evident in the treatment that was jackpot burned two years post-thinning, 

[treatment B] in which the same method of thinning and 

burning produced an increase in desirable species in one 

ES (Mixed Conifer) and a decrease in desirable species 

cover in another site (Mountain Malpais).

However, it is assumed that the untreated data provide 

a reliable proxy for the pretreatment condition. While 

this is the best available option when evaluating treatment effectiveness, there is still a large range of variability within the data from untreated sites. Moreover, 

the untreated data set did not cover all the relevant 

ecological sites that occurred in the treatment areas—

the Gravelly site (R036XA004NM) did not have any 

untreated data. Continued monitoring of sites following treatment has the potential to better inform management in an adaptive framework. Ideally, it would 

also be possible to collect data using the standard AIM 

protocols before any management action occurs. Thus, 

AIM indicators could be used to directly assess pre 

and post treatment. 

AIM provides an effective and standardized approach 

to assessing outcomes of various management actions 

within Pinyon-Juniper habitat types. In particular, AIM 

data provide a thorough assessment of ecosystem attributes that can inform evaluations of wildlife habitat 

and forage. Wildlife biologists can use the detailed 

data on understory vegetation cover and heights, species composition, soil surface cover, canopy gaps, and 

soil stability to accurately evaluate habitat suitability 

for many wildlife species, including game birds, 

pinyon-juniper obligate bird species, mule deer, and 

elk. Furthermore, supplemental indicators can be 

added to provide more specific data relevant to each 

wildlife species.

REFERENCES

Herrick, Jeffrey E. 2005. Monitoring manual for grassland, 

shrubland, and savanna ecosystems. Las Cruces, N.M: 

U. S. Department of Agriculture, Agricultural Research 

Service Jornada Experimental Range.

Stevens, Don Jr. L.; Olden, Anthony R. 2004. Spatially 

balanced sampling of natural resources. Journal of the 

American Statistical Association. 99: 262-278.

Sylvester, Don; Wright, Elizabeth; Tunberg, John; 

Carpinelli, Michael. 2003a. Ecological Site Description 

– R036XA004NM (Gravelly slopes). United States 

Department of Agriculture, Natural Resources 

Conservation Service. https://esis.sc.egov.usda.gov/

ESDReport/fsReport.aspx?approved=yes&repType=regu

lar&id=R036XA004NM [Accessed on 31August 2017]. 

Sylvester, Don; Wright, Elizabeth; Tunberg, John; 

Carpinelli, Michael. 2003b. Ecological Site Description 

– R036XB007NM (Malpais). United States Department 

of Agriculture, Natural Resources Conservation Service. 

https://esis.sc.egov.usda.gov/ESDReport/fsReport.aspx

?approved=yes&repType=regular&id=R036XB007NM

[Accessed 31 August 2017].

Toevs, Gordon R.; Taylor, Jason J.; Spurrier, Carol S.; 

MacKinnon, W. Craig; Bobo, Mathew R. 2011. Bureau 

of Land Management Assessment, Inventory, and 

Monitoring Strategy: For integrated renewable resources 

management. Denver, CO: Bureau of Land Management, 

National Operations Center.

Taylor, Jason J.; Kachergis, Emily J.; Toevs, Gordon R.; 

Karl, Jason W.; Bobo, Mathew R.; Karl, Michael; 

Miller, Scott; Spurrier, Carol. 2014. AIM-Monitoring: 

A Component of the BLM Assessment, Inventory, 

and Monitoring Strategy. Technical Note 445. Denver, 

CO: U.S. Department of the Interior, Bureau of Land 

Management, National Operations Center.

Wright, Elizabeth; Bishop, Christine. 2003. Ecological Site 

Identification and Concept: R0A8AY005NM (Mountain 

Malpais). United States Department of Agriculture, 

Natural Resources Conservation Service. https://esis.

sc.egov.usda.gov/ESDReport/fsReportPrt.aspx?id=R048

AY005NM&rptLevel=all&approved=yes [Accessed on 

August 31 2017].
124 USDA Forest Service RMRS-P-77. 2020.

Field Trip to Sandia and Manzanita Mountains

FIELD TRIP

On Friday, 14 October, the symposium sponsored a 

field trip to the Sandia and Manzanita Mountains. 

Craig Allen, Esteban Muldavin, and Bill Romme led 

the trip to the Elena Gallegos Picnic Ground, Otero 

Canyon, and Chamisoso Canyon. The trip focused on 

the local application of descriptions of broad piñonjuniper (Pinus edulis, Juniperus spp.) types in relation 

to historical fire regimes, restoration, and wildlife 

management (Fig. 1, Romme et al. 2009). 

At Elena Gallegos, in the western foothills of the 

Sandia Mountains, the vegetation exemplified piñonjuniper savanna at the lower end of the elevationmoisture gradient where junipers are dominant 

(Fig. 2). Trip leaders discussed current and preferred 

management practices and the role of fire in this 

summer-precipitation-dominated ecosystem. They 

noted that because this site had been protected from 

grazing for several decades, the reintroduction of low 

intensity surface fire would be desirable to maintain 

ecosystem processes, limit unnatural juniper establishment, and enhance wildlife habitat. However, management options were limited because of the location in a 

wildland-urban interface. 

Otero Canyon, at higher elevation on the east side of 

the Manzanitas, exemplified persistent piñon-juniper 

woodland toward the upper end of a moistureelevation gradient where tall piñons dominated over 

junipers (Fig. 3). Leaders discussed the evidence that 

natural fire frequency was considerably lower in this 

type, given limited fine fuels. Accordingly, wildlife 

habitat management practices would be very different 

here from that in a savanna, with focus on maintaining 

(rather than simplifying) complex stand structures and 

avoiding habitat fragmentation. 

 The final stop was a treated area in Chamisoso 

Canyon, at mid-elevation on the east side of the 

Manzanitas. Based on an adjacent untreated stand, 

Figure 1—A general framework for the distribution of three broad types 

of piñon and juniper woodlands (Romme et al. 2009).
USDA Forest Service RMRS-P-77. 2020. 125

Figure 2—Piñon-juniper savanna at Elena Gallegos Picnic Ground, Sandia Mountain foothills. Photo: Jacqueline Smith.

Figure 3—Persistent piñon-juniper woodland, Otero Canyon, east side of Manzanita Mountains. Photo: Jacqueline Smith.
126 USDA Forest Service RMRS-P-77. 2020.

the site had likely been co-dominated by piñons 

and junipers forming a moderately dense woodland 

(Fig. 4). At this site, trip leaders discussed the efficacy 

of the treatment for wildfire reduction and the state of 

the remaining woodland. Participants suggested that 

the lack of quantitative monitoring of post-treatment 

wildlife populations and vegetation made understanding the impacts of the treatment on wildlife uncertain. 

Potential effects of the treatment on wildlife were 

discussed, in light of talks presented at the symposium.

REFERENCES

Romme, W. H., C. D. Allen, J. D. Bailey, W. L. Baker, 

B. T. Bestelmeyer, P. M. Brown, K. S. Eisenhart, M. 

L. Floyd, D. W. Huffman, B. F. Jacobs, R. F. Miller, 

E. H. Muldavin, T. W. Swetnam, R. J. Tausch, and P. 

J. Weisberg. 2009. Historical and modern disturbance 

regimes, stand structures, and landscape dynamics in 

piñon–juniper vegetation of the western United States. 

Rangeland Ecology and Management 62:203-222.

Figure 4—Treated piñon-juniper, Chamisoso Canyon, east side of Manzanita Mountains. Photo: Jacqueline Smith.
USDA Forest Service RMRS-P-77. 2020. 127

Piñon-Juniper Habitats: Status and Management for Wildlife

12-14 October 2016

Sheraton Airport Hotel, Albuquerque, NM

CONFERENCE AGENDA

Wednesday, October 12

Session 1: Current and Predicted State of Piñon-juniper Habitats, 8:30-12:25 (25 min./talk)

8:00-8:30 AM: Coffee 

8:30-8:40 AM: Welcome: Why a symposium on piñon-juniper? – Intro and session chair – Marikay Ramsey, BLM 

NM 

8:40-9:05 AM: P-J is not just one vegetation type: key variation in structure and disturbance dynamics – Bill 

Romme 

9:10-9:35 AM: Piñon-juniper woodlands as dynamic ecosystems: Past, present, and future – Craig D. Allen 

9:40-10:05 AM: Vulnerability of piñon-juniper habitats to climate change in the Southwest – Jack Triepke 

10:05-10:25 AM: Break 

10:30-10:55 AM: Using remote sensing data to map juniper response to drought induced mortality – Raul 

Campos-Marquetti and Daniel Ginter 

11:00-11:25 AM: Fire regime shift linked to increased forest density in a piñon-juniper savanna landscape – Ellis 

Margolis 

11:30-11:55 AM: Stand structural dynamics and density-dependent tree mortality in drought-affected Great Basin 

Pinus monophylla – Juniperus osteosperma woodlands – Samuel W. Flake and Peter J. Weisberg 

12:00-12:25 PM: Environmental drivers of tree mast production in piñon-juniper-oak woodlands of central New 

Mexico – Robert R. Parmenter, Roman I. Zlotin, Orrin B. Myers, and Douglas I. Moore. 

12:25-2:00 PM: Lunch 

2:00-2:25 PM: The odd couple: mutual facilitation by piñon pines and ectomycorrhizal fungi - Catherine Gehring, 

Andrew Krohn, Chris Sthultz, and Tom Whitham 

Session 2: Habitat Needs of Piñon-Juniper Wildlife, 2:00-5:45 (25 min./talk)

2:30-2:35 PM: Intro and session chair – Chuck Hayes, NM Department of Game and Fish 

2:35-3:25 PM: Elk, deer, and P-J: Needs, what works, and what doesn’t – Lou Bender (50 minutes) 

3:30-3:55 PM: The importance of P-J to black bears in New Mexico – Rick Winslow 

3:55-4:15 PM: Break 

4:20-4:45 PM: Pinyon Jay habitat use in New Mexico piñon-juniper woodlands – Kristine Johnson, Jacqueline 

Smith, Giancarlo Sadoti, and Teri Neville 

4:50-5:15 PM: Habitat use at multiple scales by nesting Gray Vireos in New Mexico – Lynn Wickersham, Kristine 

Johnson, Giancarlo Sadoti, Teri Neville, and John Wickersham 

5:20-5:45 PM: Ecology of Montezuma Quail in the Capitan Mountains of New Mexico - Ryan S. Luna, Elizabeth 

A. Oaster, Karlee D. Cork, Ryan O’Shaughnessy, Randy L. Howard, Scott P. Lerich, and Louis A. Harveson 

5:45-7:15 PM: Social with cash bar 
128 USDA Forest Service RMRS-P-77. 2020.

Thursday, October 13

Session 3: Current management activities and relevance for wildlife: goals, practices, and 

outcomes, 8:30-12:25 (25 min./talk)

8:00-8:30 AM: Coffee 

8:30-8:35 AM: Intro and session chair – Kristine Johnson, Natural Heritage NM, UNM Biology 

8:35-9:00 AM: Effects of piñon-juniper woodland thinning on avian communities in the Arkansas River Valley, 

Colorado – Pat Magee 

9:05-9:30 AM: Identifying and mitigating social-ecological tradeoffs: Vegetation, birds, fuels, and modeled fire 

behavior in piñon-juniper fuel treatments – Jonathan Coop 

9:35-10:00 AM: Restore NM: Creating partnerships for landscape restoration – Jeremy Kruger 

10:00-10:20 AM: Break 

10:30-10:55 AM: Using the Assessment, Inventory, and Monitoring strategy to measure treatment effectiveness in 

the Taos Field Office – Alexander Laurence-Traynor, Jessa Davis, Jason Karl, and Zoe Davidson 

11:00-11:25 AM: The effects of tree thinning on wildlife in piñon-juniper and ponderosa pine woodlands in the 

Manzano Mountains, New Mexico – David Lightfoot, Cody Stropiki Victoria Amato, Conor Flynn, and Anne 

Russell 

11:30-11:55 AM: Managing piñon-juniper for multiple values and goals – Rebecca McLain 

12:00-12:25 PM: Tailoring management to the inherent variability of P-J vegetation – Bill Romme 

12:25-2:30 PM: Lunch 

Session 4: Breakout Discussion Groups 

2:30-4:30 PM: Sign up for a discussion group at the registration table. 

4:30 PM: Wrap-up

Posters: 

Contraction and expansion zones in western North America piñon-juniper woodlands under projected 21st century climate change – Jacob R. Gibson, Thomas C. Edwards, Jr., Gretchen G. Moisen, Tracey Frescino, 

Niklaus Zimmermann, and Achilleas Psomos 

Effects of prescribed fire on Gray Vireo nesting success on the Sevilleta NWR - G. Manakitivipart, D.C. Barton, K. 

Granillo, and M.E. Persche 

Surveying and nest monitoring of the Gray Vireo (Vireo vicinior) in the Sevilleta National Wildlife Refuge - Ben 

Vizzachero and Sze Wing Yu

Friday, October 14

8:30–3:30 PM: Field trip to Sandia and Manzano Mountains 
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