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Research Note RMRS-RN-45. 2011

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Rocky Mountain Research Station

Research Note

Citation:

Abella, Scott R.; Denton, Charles W.; Brewer, David G.; Robbie, 

Wayne A.; Steinke, Rory W.; Covington, W. Wallace. 2011. Using 

a terrestrial ecosystem survey to estimate the historical density of 

ponderosa pine trees. Res. Note. RMRS-RN-45. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 9 p.

Authors:

Scott R. Abella, Associate Research Professor, School of 

Environmental and Public Affairs, University of Nevada Las 

Vegas.

Charles W. Denton, Program Coordinator, Ecological Restoration 

Institute, Northern Arizona University, Flagstaff.

David G. Brewer, Program Coordinator, Ecological Restoration 

Institute, Northern Arizona University, Flagstaff.

Wayne A. Robbie, Supervisory Soil Scientist, U.S. Forest Service, 

Southwestern Region, Albuquerque, New Mexico.

Rory W. Steinke, Watershed Program Manager, Coconino National 

Forest, Flagstaff, Arizona.

W. Wallace Covington, Regents’ Professor and Executive Director, 

School of Forestry and Ecological Restoration Institute, 

Northern Arizona University, Flagstaff.

United States Department of Agriculture

Forest Service

Rocky Mountain Research Station

Research Note RMRS-RN-45

June 2011

Using a Terrestrial Ecosystem Survey to Estimate the 

Historical Density of Ponderosa Pine Trees

Scott R. Abella, Charles W. Denton, David G. Brewer, Wayne A. Robbie, 

Rory W. Steinke, and W. Wallace Covington

Abstract: Maps of historical tree densities for project areas and landscapes may be useful for a variety of management purposes such as 

determining site capabilities and planning forest thinning treatments. 

We used the U.S. Forest Service Region 3 terrestrial ecosystem survey in a novel way to determine if the ecosystem classification is a 

useful a guide for estimating historical (1880) ponderosa pine (Pinus 

ponderosa) tree densities on a northern Arizona landscape. Based on 

sampling 53 sites spanning 9 ecosystem types, we grouped the types 

into low and high density categories. Tree density was less than 24/

acre on 91 percent (21 of 23) of sites in cinder, dry limestone, and clay 

basalt ecosystems. In contrast, 70 percent (21 of 30) of sites that contained densities exceeding 24 trees/acre were in basalt, mixed igneous, 

and moist limestone ecosystems.

Introduction

The study of historical forest structure has revealed the dramatic changes that have occurred in ponderosa pine (Pinus 

ponderosa) forests since the late 1800s following fire exclusion 

and irruptions of tree density (Covington and Moore 1994). 

In northern Arizona, for instance, Fulé and others (1997) used 

forest reconstruction methods to determine that ponderosa 

pine density rose more than 11-fold from 26 trees/acre in 

1883 (the year of the last surface fire prior to fire exclusion) to 

291 trees/acre in 1995. The persistence of dead wood (stumps, 

fallen logs, and snags) in the semi-arid climate of ponderosa pine forests, combined with fire exclusion preventing the 

wood from burning, facilitates reconstructing where historical 

trees were located (fig. 1). To complete the reconstruction of 

historical density, currently living old trees also are recorded 

(Harrod and others 1999). The accuracy of this reconstruction 

method has been assessed by re-measuring 15 stem-mapped 

plots established by the U.S. Forest Service’s Southwestern 

Region (Region 3) from 1909 to 1913 (Moore and others 

2004). This assessment confirmed that dead wood that was 

mapped in 1909 was still present in 1999. The reconstructed 

density was within 9 percent of the densities recorded in the 

early 1900s.

As a result of the known deviance of present forest conditions from historical conditions, there is interest in decreasing 

the probability of severe wildfires by thinning ponderosa 
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Research Note RMRS-RN-45. 2011

pine forests. The goal of thinning is not necessarily to replicate historical conditions (prior to late 1800s Euro-American 

settlement), but rather to reduce tree densities to levels that 

support surface fires instead of crown fires (Covington 2003). 

As a result, knowledge of the historical forest structure can 

help with the planning of thinning projects by providing estimates of tree densities that were sustainable through at least 

several generations of trees (Allen and others 2002).

A map of historical tree density for project areas and landscapes could be valuable for several management purposes 

(Harrod and others 1999). First, it would help managers understand spatial variation in tree structure and factors (such as 

soils) that may have been associated with the variation, thereby helping managers understand long-term site capability for 

supporting trees. Second, historical tree densities could be 

compared to present forest inventories to identify areas of the 

landscape that have experienced the greatest or least change 

in tree density, thus helping prioritize areas for management 

(Bell and others 2009). Third, the historical densities, in combination with other management considerations, can provide 

rough density targets for forest thinning projects when considering past reference conditions in management planning.

The terrestrial ecosystem survey (TES) in the Southwestern 

Region maps ecosystems across landscapes based on climate, 

geology, soils, and late-successional vegetation (U.S. Forest 

Service 1986). This system is similar to other ecological land 

classification and mapping initiatives such as the terrestrial ecological unit inventory (also a Forest Service program), USDA 

Natural Resources Conservation Service ecological site types, 

and ecosystem classifications developed in individual research 

projects (Abella and Covington 2006). The major concept underpinning these systems is that climate, geology, soils, and 

vegetation are interrelated and form repeating combinations 

across the landscape that can be classified and mapped (Robbie 

1992). We used the TES in a novel way to determine if it could 

help estimate historical ponderosa pine tree densities across the 

landscape. We hypothesized that dry ecosystems, where pine 

establishment may be limited, contained lower densities than 

moist ecosystems. We anticipated that the TES would capture 

this variation.

Figure 1. Examples of stumps (foreground, and to the left of the large tree), indicative of historical tree locations, and a live tree of 

pre-settlement origin (left-center). Photo by S.R. Abella in 2009, Forest Road 3E, Coconino National Forest, northern Arizona (UTM: 

440,918 m E, 3,885,850 m N, North American Datum 1983).
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Research Note RMRS-RN-45. 2011

Methods

Study Area

We conducted this study within a 250,000-acre area in 

northern Arizona on the northern half of the Coconino 

National Forest and on the Northern Arizona University 

Centennial Forest surrounding the city of Flagstaff (fig. 2). 

Study sites ranged in elevation from 6300 to 8410 ft and were 

located in ponderosa pine forests. Half of the annual precipitation occurs in winter as snow and much of the rest occurs 

in July through September as monsoonal storms. Slope gradients are typically less than 10 percent, but cinder cones, 

ravines, and undulating hills of greater slope gradient are 

present. These forests historically experienced frequent surface fires, with fire intervals of two to five years based on five 

fire-history studies (Van Horne and Fulé 2006). Understories 

were and are dominated by grasses and sedges such as blue 

grama (Bouteloua gracilis), White Mountain sedge (Carex 

geophila), squirreltail (Elymus elymoides), Arizona fescue 

(Festuca arizonica), and mountain muhly (Muhlenbergia montana) (Pearson 1942).

The study area is covered by the Coconino National Forest 

TES (http://alic.arid.arizona.edu/tes/tes.html) that maps 

ecosystem types at a 1:24,000 scale with a minimum mapping unit of 40 acres (Miller and others 1995). Ecosystem 

Figure 2. Location of 53 sample 

sites on a northern Arizona 

landscape. Sizes of points 

for sites are scaled to reflect 

ponderosa pine tree density 

reconstructed for 1880 forests. 

Shading depicts gradients in 

annual precipitation estimated 

from sample site values using 

the PRISM model (Daly and 

others 2008). Coordinates are 

UTM, North American Datum 

1983.
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Research Note RMRS-RN-45. 2011

types were primarily classified and delineated based on soil 

parent material, landform, climate, and secondarily by dominant overstory trees and understory species (Robbie 1992). 

The TES identified ecosystem types by a numerical code. 

In this paper, we name the ecosystems based on soil parent 

material and moisture status and provide the corresponding 

numerical codes in table 1.

Site Selection and Sampling

With a digital map of the TES for the study area (Miller 

and others 1995), we randomly selected a Universal 

Transverse Mercator (UTM) coordinate for a sample location within each of five to seven mapping units of nine 

ecosystem types. This resulted in 53 independent sites replicated across the landscape (fig. 2). The slight variation in 

the number of sites among ecosystems resulted from rejecting 

mapping units for sampling if a post-settlement fire or other 

disturbance destroyed evidence of historical trees. The nine 

ecosystem types encompass a range of soil parent materials, 

including basalt, benmorite, limestone, and volcanic cinders 

(Miller and others 1995). Four soil orders are represented 

in the ecosystems, including the predominant subgroups of 

Typic and Udic Argiborolls, Typic and Mollic Eutroboralfs, 

Typic Ustorthents, and Vitrandic Ustochrepts. Soils ranged 

from sandy, dry, nitrogen-poor volcanic cinders in the black 

cinder ecosystem to silt loam and nitrogen-rich in the moist 

basalt ecosystem. Soil, climate, and other characteristics of all 

nine ecosystems are summarized in table 1.

We established a 328-ft × 328-ft, 2.5-acre plot at the 

randomly selected UTM coordinate at each site. Using the 

methods of Fulé and others (1997), we counted the number 

of evidences of pre-settlement trees based on the presence of 

dead wood and living, old trees. We selected the year 1880 

as the forest reconstruction year because the start of fire exclusion associated with Euro-American settlement has been 

dated between 1876 and 1883 in the study area (Van Horne 

and Fulé 2006). We identified pre-settlement-origin live trees 

based on the observation that ponderosa pine develops orange, platy bark by about 100 years of age (White 1985). To 

ensure the accuracy of this observation, we cored trees of uncertain status (typically 10 percent of the total live trees) and 

counted rings in the field to classify these trees as pre- or postsettlement. We did not attempt to reconstruct stem diameters 

that evidence would have had in 1880. Reconstruction of stem 

diameters is more complicated than that of density, requiring 

information on year of death and decomposition models for 

the dead wood and cross-dated tree cores, and bark thickness 

for currently live trees (Fulé and others 1997). Tree size and 

basal area data could be useful to obtain in future research 

for understanding potential variation in tree growth rates and 

biomass among ecosystems.

Analysis

We compared the mean tree density among TES types 

with a one-way analysis of variance followed by Fisher’s protected least significant difference for multiple comparisons 

(SAS Institute 1999). Data were analyzed on a transformed 

scale (square root) to improve equality of variance and normality. Medians, with confidence intervals at the 95 percent 

level determined through bootstrapping (1000 samples), 

Table 1. Characteristics of terrestrial ecosystem survey types sampled in ponderosa pine forests of northern Arizona. Ecosystem types are 

arranged in order of increasing tree density classes according to table 2.

Ecosystem typea Mean ± SDb Median (CI)c Precipitationd Descriptione

Clay basalt (523) 2 ± 3 d 1 (0 to 4) 22 Basalt, clay loam soils with large cracks and clay subsoils

Red cinder (513) 23 ± 25 bcf 15 (10 to 16) 19 Dry, red volcanic cinders, sandy loam

Dry limestone (500) 16 ± 7 c 16 (12 to 21) 20 Climatically dry, sandy loam limestone

Black cinder (558) 22 ± 5 bc 19 (18 to 26) 18 Dry, nitrogen-poor, sand-gravel, black volcanic cinders

Benmorite (570) 34 ± 19 ab 27 (19 to 48) 23 Benmoritic, loam, sometimes rocky, clay subsoils

Mixed igneous (551) 23 ± 9 abc 25 (15 to 29) 24 Mixed igenous, loam soils around the San Francisco Peaks

Rocky basalt (585) 29 ± 8 abc 28 (25 to 35) 27 Rocky basalt, loam surface soils with clay loam subsoils

Moist limestone (536) 28 ± 4 abc 27 (25 to 31) 25 Moist, sandy loam limestone, productive understories

Moist basalt (582) 40 ± 19 a 46 (27 to 53) 25 Basalt, silt loam, widespread type, productive understories

a Numbers in parentheses are the ecosystem unit identification number in the ecosystem survey manual (Miller and others 1995).

b Historical tree density (stems/acre) reconstructed for the year 1880. Values are mean ± standard deviation. Means without shared letters differ 

at P<0.05. c Median historical tree density (stems/acre) with lower and upper bounds of 95 percent confidence intervals for medians in parentheses.

d Average precipitation in inches/year estimated from sample site values using the PRISM model (Daly and others 2008).

e Soil parent material and 0- to 6-inch soil texture, unless specified as corresponding to 6- to 20-inch subsoils. Data from Miller and others 

(1995) and Abella and Covington (2006). f The red cinder ecosystem had a site with an unusually high density of 74 trees/acre, which was 16 trees/acre greater than any of the other 52 

sites and 4.4-fold greater than the next highest density (17 trees/acre) within this ecosystem. If the site with 74 trees/acre is excluded, the 

mean tree density of the red cinder ecosystem falls to 13 ± 4 trees/acre and the pattern of significant differences among ecosystems is (by 

ecosystem unit number): 523d, 513c, 500c, 558bc, 570ab, 551bc, 585ab, 536ab, and 582a.
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Research Note RMRS-RN-45. 2011

were also calculated. We further grouped densities into five 

categories ranging from low to high (<10, 10 to 17, 18 to 

23, 24 to 29, and ≥30 trees/acre) and assessed the number 

of sites of each ecosystem type that fell into the categories. 

These density categories were based on natural breaks in the 

data and contained approximately equal numbers of total 

sites. These categories were also anticipated to have ecological 

significance by representing a gradient from extremely low to 

high densities.

To help formulate hypotheses about possible causes of 

variation in density within and among ecosystems, we used 

data on understory vegetation and soil collected in 2003 during previous research at the study sites (Abella and Covington 

2006). We examined Pearson correlation coefficients for associations between tree density and understory grass cover; 

percent sand, silt, and clay; volumetric nitrogen and organic 

carbon content of the 0- to 6-inch soil layer; and average annual precipitation estimated for each site using the PRISM 

model (Daly and others 2008). For grass cover, we are not 

implying that understory vegetation is necessarily the same in 

contemporary forests as in historical forests, but rather that 

variation in current understories across the landscape corresponds to variation in the productivity of ecosystems reported 

in the TES (Miller and others 1995; Abella and Covington 

2006). We further subjected the environmental variables 

(soil and climate only) to a principal components analysis 

(cross-products matrix derived from correlation coefficients) 

using the software PC-ORD (McCune and Mefford 1999) to 

evaluate whether tree density was correlated with multivariate 

combinations of variables as synthesized by principal components. Using Pearson coefficients, we related tree density to 

plot scores of the first principal component (no additional 

component accounted for ≥18 percent of the variance).

Results

The density of historical trees differed significantly among 

ecosystem types (F8,44 statistic = 8.14; P<0.0001). Means 

varied 20-fold, ranging from 2 trees/acre in a clay basalt to 

40 trees/acre in a moist basalt ecosystem (table 1). Medians 

similarly ranged from 1 to 46 trees/acre across ecosystems. 

Patterns also emerged when partitioning density into classes, 

with the clay basalt, red cinder, and dry limestone ecosystems 

containing the most sites in the low and low-medium density 

classes (table  2). The black cinder ecosystem had the most 

medium density sites. Mixed igneous, basalt, benmorite, and 

moist limestone ecosystems possessed the most medium-high 

and high density sites. Four of the six sample sites of the 

moist basalt ecosystem were in the highest density class (≥30 

trees/acre)—the largest proportion of any ecosystem.

Variation in density among ecosystems became even more 

pronounced when simply dividing ecosystems into low (<24 

trees/acre) and high (≥24 trees/acre) density categories (table 3). This classification accurately portrayed 42 of the 53 

(79 percent) sample sites by ecosystem type.

In examining continuous variation, density was not 

strongly correlated with any single variable or principal 

component. Correlations (r) of density with understory and 

environmental variables were as follows: -0.25 (grass cover), 

-0.17 (sand), 0.35 (silt), -0.14 (clay), -0.23 (nitrogen), 0.07 

(organic carbon), and 0.24 (average annual precipitation). In 

principal components analysis, the first principal component 

accounted for 60 percent of the variation in the environmental variables. This component had a positive loading (0.92) 

for sand and negative loadings (-0.68 to -0.84) for the other 

variables. However, density was not strongly correlated (r = 

0.05) with this component.

Table 2. Distribution of density classes of ponderosa pine trees reconstructed 

for historical, 1880 forests on 53 sites of 9 terrestrial ecosystem survey 

types in northern Arizona. Ecosystems are arranged in general order of 

low to high tree density.

Trees/acre

Ecosystem type <10 10 to 17 18 to 23 24 to 29 ≥30 Total

––––––––––––––– Number of sites ––––––––––––––––

Clay basalt 6 6

Red cinder 1 4 1 6

Dry limestone 1 3 2 6

Black cinder 1 3 1 5

Benmorite 1 1 1 2 5

Mixed igneous 1 1 3 1 6

Rocky basalt 2 4 1 7

Moist limestone 1 4 1 6

Moist basalt 1 1 4 6

Total 9 10 11 13 10 53

Table 3. Grouping of terrestrial ecosystem survey types into low and 

high density classes of ponderosa pine trees reconstructed for 

historical, 1880 forests on 53 sites in northern Arizona.

Trees/acre

Ecosystem types <24 ≥24 Total

–––– Number of sites ––––

Clay basalt, red cinder, dry 21 2 23

limestone, black cinder

Benmorite, mixed igneous, 9 21 30

rocky basalt, moist 

limestone, moist basalt

Total 30 23 53
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Research Note RMRS-RN-45. 2011

Discussion

Variation in overall historical density of ponderosa pine 

trees across the landscape was well-captured by the TES framework. The TES was especially useful for identifying where low 

and high density sites occurred (table 3). Since the TES already 

is in map form, managers could use the TES to anticipate density ranges for mapping units in project areas. These expected 

densities should be checked by site-specific field surveys, since 

21 percent of sites were exceptions that did not fit the typical 

low or high density classification of ecosystem types.

Our hypothesis that dry ecosystems contained the lowest 

tree densities was only partially supported by the data. The 

four ecosystems that received the least precipitation did have 

the lowest densities (table 1). However, within this group, the 

black cinder ecosystem had 18 percent less precipitation, but 

possessed 11-fold greater mean density, than the clay basalt 

ecosystem. These findings were consistent with the relatively 

weak correlation (0.24) of density with precipitation. Density 

was likely influenced across the landscape by multivariate interactions with moisture and other factors such as soil clay 

content affecting frost-heaving (Haasis 1923), species-specific 

competition with perennial grasses and sedges (Pearson 1942), 

fire regimes shaping availability of safe sites for seedling establishment and survival (White 1985), and possibly rock cover 

providing protection for seedlings in some ecosystems. The 

TES may have captured variation in these factors, explaining 

why specific ecosystem types within the TES were more closely 

related to density than was moisture alone.

Variability within the broad dry and moist ecosystem type 

groupings and within specific ecosystem types could be related 

to several factors. For example, the TES mapping units contain 

variability in soils and vegetation, which is inherent in any ecological mapping effort that breaks continuous variation into 

discrete mapping units (Miller and others 1995). Competition 

with understory vegetation could have influenced tree densities 

within ecosystems (Pearson 1942). The two sites, for instance, 

in the moist basalt ecosystem that contained the lowest densities (table 2) exhibited 25 percent more plant species/10.8 ft2

in the understory at the sites in 2003 (Abella and Covington 

2006). Whether or not understory vegetation has changed 

since historical times, we can say that contemporary sites with 

especially species-rich and productive understories did not support the highest historical tree densities. While no quantitative 

data are available on variation of historical understory vegetation across the landscape, future research that examines effects 

of soil influences mediated through understory vegetation on 

tree densities in contemporary forests could help assess understory effects on trees within the TES framework. Fire regimes 

were not part of TES development, and while it is unclear how 

variable fire regimes were across the landscape given that five 

investigations in the study area have reported a tight range of 

two- to five-year fire return intervals (Van Horne and Fulé 

2006), the possibility that fire regimes influenced tree densities 

within ecosystem types cannot be dismissed. Other factors that 

were not part of the TES could have influenced density, such as 

wildlife, insects/disease, local disturbances, or patterns of pine 

seed dispersal.

In the following sections, we describe density patterns within each ecosystem type.

Clay Basalt

None of the six sample sites of this ecosystem had greater 

than 8 ponderosa pine trees/acre and four of six sites contained 

less than 1 tree/acre, making this the least dense among the 

nine ecosystem types examined. Study sites were situated on 

Anderson Mesa and southwest of Slate Mountain, at an average 

elevation (7167 ft) identical to the average of all 53 sites, and 

with medium modeled precipitation (table 1). Based on the 

high average 0- to 6-inch soil clay concentration (31 percent), 

we suspect that clayey soils limited density in this ecosystem. 

Large cracks in the soil, sometimes a foot wide and several feet 

deep, suggest that soil expansion/contraction (frost-heaving or 

drought-related) could have reduced seedling survival (Haasis 

1923). Ponding of water on the clay surface in spring also may 

have influenced survival. The few ponderosa pine trees occurring on sample sites were often associated with rocky outcrops. 

A study by Gascho Landis and Bailey (2005) on Anderson 

Mesa in this ecosystem indicated that densities of Utah juniper 

(Juniperus osteosperma) and two-needle pinyon pine (Pinus edulis) were much greater than ponderosa pine. These data suggest 

that this ecosystem historically was a juniper-pinyon woodland 

with only scattered ponderosa pine.

Red Cinder

Density of five of the six sites was in the range of 7 to 

17 trees/acre, among the lowest of all ecosystems. Low precipitation combined with gravelly, sandy textured soils low in 

moisture probably limited seedling establishment. However, 

the sixth site of this ecosystem contained 74 trees/acre, 4.4 

times greater than the next densest site of this ecosystem and 16 

trees/acre greater than the second densest site (58 trees/acre) of 

all 53 sample sites. This unusually dense site in this ecosystem 

is perplexing because the site’s soil textural class (sandy loam) 

is the same as the average for this ecosystem, and there were 

no readily apparent soil or topographic differences with sites 

that possessed lower densities. There was a high incidence of 
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Research Note RMRS-RN-45. 2011

fire-scarred trees at this site (Abella 2008), although this could 

simply have been related to the high density of trees available 

for recording fires.

Dry Limestone

Density was 14 to 24 trees/acre at five of the six sites, placing 

this type in the low half among ecosystems. Tree establishment 

was likely limited by dry climates and sandy soils low in available water. The site with an unusually low density (6 trees/acre) 

for this ecosystem had sandy loam-loamy sand surface soils like 

the other sites. Precipitation and topography also were comparable. However, similar to the clay basalt ecosystem, more 

juniper (Juniperus spp.) trees were present at this site, possibly 

indicating a marginal site for ponderosa pine or greater interspecific competition.

Black Cinder

This ecosystem contained the least understory vegetation of 

all sampled ecosystems, with soil patches of 0.025 acres commonly having no plants at all. Therefore, competition with 

understory plants likely minimally affected pine establishment in this ecosystem. The same dry, surficial layer of gravelly 

cinders limiting understory vegetation would be expected to reduce tree establishment (Hanks and others 1983). Apparently, 

however, pine had the ability to root through the cinders to 

finer textured bands below (Abella and Covington 2006) because density was medium in this ecosystem. The site with the 

highest density of trees in this ecosystem (30 trees/acre, 8 trees/

acre greater than the next highest site) had a slight increase in 

silt in the surface layer making it a loamy sand (rather than 

a sand). We hypothesize that tree density increases when the 

thickness of the surficial layer of cinders declines or as the silt 

content increases.

Benmorite

This ecosystem could benefit from additional sampling as 

three sites contained densities of 16 to 27 trees/acre, while the 

remaining two sites had particularly high densities of 52 and 

57 trees/acre. All sites exhibited loam textures for the 0- to 

6-inch layer and 23 to 24 inches of precipitation/year. The two 

sites with elevated densities contained 1.7 times more total nitrogen on average than the three low density sites.

Mixed Igneous

Density was between 24 and 29 trees/acre at four of the 

six sites, but it was comparatively low at 6 and 20 trees/acre at 

the other two sites. These two sites were northwest and north 

of the San Francisco Peaks and had the highest precipitation, 

soil total nitrogen, and cover of understory grasses and sedges 

within this ecosystem. Arizona fescue, the dominant grass, is 

considered the strongest competitor with pine among perennial grasses (Pearson 1942). We hypothesize that understory 

competition especially limited pine establishment on these 

sites that otherwise had climates and soils favorable for supporting high pine densities.

Rocky Basalt

Six of the seven sites had a tight range of 21 to 29 trees/

acre, while the seventh site contained 46 trees/acre. This site 

had loam surface textures typical of the other sites within this 

ecosystem, only 54 percent of the average soil nitrogen, slope 

gradients less than 8 percent like the other sites, and within an 

inch of the ecosystem’s average precipitation. Further work is 

needed to resolve why these types of rare exceptions occurred 

to the typical densities in this ecosystem.

Moist Limestone

This ecosystem exhibited the most consistent density among 

the TES types, ranging from 22 to 34 trees/acre with four of 

six sites differing by fewer than 3 trees/acre. Although soils are 

derived from limestone, pH from a 0- to 20-inch depth averaged 6.1 to 6.4, similar to the basalt and benmorite ecosystems 

(Abella and Covington 2006). Precipitation also was similar. 

Soil texture from 0 to 6 inches was sandier (sandy loam) in this 

ecosystem than in the basalt-benmorite types (loam-silt loam). 

This sandier texture may have constrained the ability of this 

ecosystem to support the particularly high densities that some 

sites of the basalt-benmorite ecosystems did. On the other 

hand, this ecosystem did not have the low density sites that the 

basalt-benmorite ecosystems contained.

Moist Basalt

This ecosystem had four (39 to 58 trees/acre) of the seven 

densest sites among the 53 sample sites. The other two sites in 

this ecosystem, however, contained only 16 and 18 trees/acre. 

Surface textures on these sites were loams rather than silt loams 

as in the other sites of this ecosystem. Still, silt + clay content 

differed little (62 percent on the low density versus 69 percent on the high density sites). Precipitation actually averaged 

3 inches greater on the low density than on the high density 

sites, and all plots had similar topography (slope gradients <5 

percent). These sites illustrate that chance or factors (for example, fire regimes) other than those that we measured may be 

important in explaining exceptions to typical density patterns.

Collectively, patterns of tree density among the individual 

ecosystems suggest that the TES effectively corresponded with 
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Research Note RMRS-RN-45. 2011

generalized variation in tree density classes across the landscape. Managers should be aware, however, that exceptions 

to the typical density patterns within an ecosystem did occur. 

Reasons for these exceptions are poorly understood and could 

be related to variation within the TES (such as soil inclusions 

that are not typical of the rest of the mapping units), factors 

like fire regimes that are not part of the TES, or continuous 

variation in factors (for example, rock cover) or combinations 

of factors constraining tree densities. Further research on the 

potential relationships of these factors with historical density 

could be useful to advance our understanding of the use of the 

TES for estimating tree density. 

Management Implications

•	 The TES is useful for understanding variation in the 

historical density of ponderosa pine trees across the 

landscape. The TES correctly categorized 79 percent of 53 

sample sites as low (<24 trees/acre) or high (≥24 trees/acre) 

density.

•	 The TES is available as a map (Miller and others 1995); 

therefore, managers can identify ranges of anticipated 

historical densities for TES mapping units within project 

areas.

•	 Having these density estimates facilitates an understanding 

of the tree densities that different ecosystems supported in 

the past. These densities may be useful for assessing site 

capability, evaluating the degree to which forest structure 

has changed, and preparing tree thinning prescriptions.

•	 Future research could refine anticipated densities among 

the TES types through further sampling and could seek to 

understand exceptions within ecosystems to their typical 

densities.

Acknowledgments

Funding was provided by the U.S. Forest Service and 

the Ecological Restoration Institute for fieldwork and the 

Ecological Restoration Institute for the preparation of the 

manuscript. We thank the Coconino National Forest and 

the Northern Arizona University Centennial Forest for access 

to study sites; Matt Tuten, Mike Stoddard, Dave Passovoy, 

Kristen Pearson, and students with the Ecological Restoration 

Institute for help mapping six of the sites; Judy Springer for 

providing logistical support for fieldwork; Brian Zimmer for 

performing soil carbon and nitrogen analyses; Joe Crouse for 

conducting PRISM GIS analyses and preparing fig. 1; Sharon 

Altman for formatting the figures; and David Bell (Duke 

University) and Gerald Gottfried, L. Scott Baggett, and Lindy 

Myers of the Rocky Mountain Research Station for reviewing 

the manuscript.

References

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