United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

Research Paper RMRS-RP-80WWW

June 2010

Coarse Woody Debris Assay 

in Northern Arizona Mixed-Conifer and 

Ponderosa Pine Forests

Joseph L. Ganey and Scott C. Vojta
Ganey, Joseph L.; Vojta, Scott C. 2010. Coarse woody debris assay in 

northern Arizona mixed-conifer and ponderosa pine forests. Res. Pap. 

RMRS-RP-80WWW. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 19 p.

Abstract

Coarse woody debris (CWD) provides important ecosystem services in forests 

and affects fire behavior, yet information on amounts and types of CWD 

typically is limited. To provide such information, we sampled logs and stumps 

in mixed-conifer and ponderosa pine (Pinus ponderosa) forests in north-central 

Arizona. Spatial variability was prominent for all CWD parameters. Correlations 

between amounts of CWD and current forest structure (tree density and basal 

area) were relatively weak. Most plots in mixed-conifer forest exceeded current 

USFS guidelines for retention of large logs. In contrast, 50% of ponderosa 

pine plots contained no large logs, and only 37% met current guidelines for 

log retention. Biomass of CWD in mixed-conifer forest typically fell within or 

above recommended levels for this forest type, whereas biomass of CWD in 

ponderosa pine forest typically fell within or below recommended levels. These 

results provide empirical data on amounts and types of CWD in this area and 

establish a baseline for monitoring CWD. The pronounced spatial variability in 

CWD parameters argues for managing CWD at broad spatial scales, rather than 

attempting to maintain average characteristics on every piece of ground.

Keywords: coarse woody debris, fuels, logs, mixed-conifer forest, ponderosa 

pine forest, stumps

Authors

Joseph L. Ganey and Scott C. Vojta, U.S. Forest Service, Rocky Mountain 

Research Station, 2500 S. Pine Knoll, Flagstaff, AZ.

This is a web only publication available at http://www.fs.fed.us/rm/pubs/rmrs_rp080.html.

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USDA Forest Service RMRS-RP-80WWW. 2010. 1

Introduction

Logs and stumps provide important sources of coarse woody debris (CWD) 

(Harmon and others 1986, McComb and Lindenmayer 1999). These structures help 

sustain populations of numerous fungi, lichens, vascular plants, and animals (e.g., 

Maser and Trappe 1984, Butts and McComb 2000, Bull 2002, Lee and Sturgess 2002, 

Torgersen 2002) and can affect fire behavior (Sackett 1979, Brewer 2008). Despite 

CWD’s important contributions to ecological processes, information on amounts and 

especially types of CWD typically is limited. Existing information frequently results 

from fuels transects (Brown 1974) that provide only limited information on specific 

types of CWD present (Brewer 2008). Further, many CWD inventories do not consider standing stumps (USDA 2004: 3). Stumps can contribute importantly to overall 

amount and distribution of CWD and can provide important habitat components such 

as burrow, foraging, and perch sites for wildlife (fig. 1).

(B)

Figure 1. Examples of 

wildlife stump use. (A) 

Foraging sign at base 

of high stump; (B) 

Burrow located under 

stump; (C) Stump used 

as foraging perch by 

a Mexican spotted 

owl (Strix occidentalis 

lucida; photo by Todd 

A. Rawlinson).

(A)

(C)
2 USDA Forest Service RMRS-RP-80WWW. 2010.

Awareness of the general importance of CWD in forest planning and management has increased greatly in recent years (e.g., Bull and others 1997, Harmon 2001, 

Laudenslayer and others 2002, Torgersen 2002, Brewer 2008). Incorporating CWD in 

forest management will become more critical in southwestern mixed-conifer and ponderosa pine (Pinus ponderosa) forests in future years. Management emphasis in these 

fire-adapted forests focuses on reducing fuels and restoring more open forest structure through thinning and prescribed fire (Lynch and others 2000, Allen and others 

2002, Peterson and others 2005). Prescribed fires typically burn many existing logs in 

these forests (Gordon 1996, Randall-Parker and Miller 2002, Innes and others 2006), 

although they also can create snags and logs (Horton and Mannan 1988, Fulé and 

Laughlin 2007, Roccaforte and others 2009). In addition, there is growing interest in 

using woody biomass from these forests to generate energy and produce wood products 

(Levan-Green and Livingston 2001, Neary and Zieroth 2007), which could reduce both 

current amounts and sources of future CWD. Finally, the amount and types of CWD 

strongly influence fire risk and behavior (Sackett 1979). For all of these reasons, empirical data on amounts and types of CWD are needed.

To provide such information, we sampled logs and stumps in mixed-conifer and 

ponderosa pine forests in north-central Arizona. We also sampled live trees to characterize stand structure in plots sampled. Here we (1) describe log and stump populations 

in these forest types, (2) evaluate potential relationships between stand structure and 

plot factors and CWD, (3) compare existing densities of large logs to standards for 

retention of large logs in these forest types (Reynolds and others 1992, USDA 1996), 

and (4) compare existing biomass of CWD to recommendations for biomass of CWD 

in these forest types (USDA 1999, Brown and others 2003). These data provide an 

initial assay of amounts and types of CWD in these forest types, provide preliminary 

information on factors potentially affecting amounts and types of CWD, and establish a 

baseline for monitoring changes in CWD.

Study Area

The study area encompassed approximately 73,000 ha in two National Forests in 

north-central Arizona (fig. 2). Within this area, study plots were randomly located in 

mixed-conifer and ponderosa pine forest (see Ganey 1999 for details on plot selection, 

location, and establishment). Mixed-conifer forests were dominated by Douglas-fir 

(Pseudotsuga menziesii), white fir (Abies concolor), and ponderosa pine. Other common species included limber pine (P. flexilis), Gambel oak (Quercus gambelii), and 

quaking aspen (Populus tremuloides). Ponderosa pine forest was dominated by ponderosa pine, but Gambel oak also was common and alligator juniper (Juniperus deppeana), 

Douglas-fir, quaking aspen, limber pine, pinyon pine (P. edulis), and Utah (J. osteosperma) and one-seed (J. monosperma) juniper were present in small numbers in some 

stands.

The study area included a wide range of topography and ecological conditions. The 

entire elevational range of these forest types within this area was represented, from 

the transition zone between pinyon-juniper woodland and ponderosa pine at lower 

elevations to the ecotone between mixed-conifer and Engelmann spruce (Picea engelmanni)–corkbark fir (Abies lasiocarpa var. arizonica) forests at higher elevations. In 

addition, plots ranged from intensively managed forests to administratively reserved 

lands such as wilderness and other roadless areas. We did not intentionally select plots 

in severely burned areas, but several plots were severely burned in recent wildfires. 

Consequently, these plots represent the range of variability well in these forest types.
USDA Forest Service RMRS-RP-80WWW. 2010. 3

Figure 2. Location of the study area (black box, top) in northern Arizona, and locations of sampled plots 

within the study area (bottom). Plots were located in the Kaibab (left) and Coconino (right) National 

Forests. Plots in ponderosa pine forest (n = 60) are indicated by red circles, and plots in mixed-conifer 

forest (n = 53) are indicated by blue triangles.
4 USDA Forest Service RMRS-RP-80WWW. 2010.

Methods

Field Sampling

This study opportunistically utilized a series of permanent plots established in 

1997 to monitor snag dynamics (n = 53 and 60 plots in mixed-conifer and ponderosa 

pine forest, respectively). The original plots were 1 ha each in area (100 by 100 m), 

but we sampled logs, stumps, and live trees in a 0.09-ha subplot (30 by 30 m) within 

each plot. We reduced plot size because logs, stumps, and trees were more abundant 

than snags, and time constraints precluded sampling these features on the entire 1-ha 

plot. Logs, stumps, and trees were sampled in 2004.

Plot-level characteristics recorded included forest type, timber status (logged or 

thinned versus unlogged), road access to plot (yes or no), and terrain (flat [≤10% 

slope], moderate [11-25% slope], or steep [>25% slope]).Within each plot, we sampled all logs >20 cm in large-end diameter and ≥2 m in length; all stumps ≥20 cm top 

diameter and <2 m in height; and all live trees >20 cm in diameter at breast height 

(dbh) and ≥2 m in height. The 20-cm minimum diameter was selected for consistency 

with the original snag monitoring study, which ignored smaller snags because they 

were suspected to be relatively unimportant to native wildlife. Thus, all logs and 

stumps sampled correspond to >1,000-hr fuels as defined by fuels managers (Maser et 

al 1979: table 25), but not all 1,000-hr fuels were sampled (i.e., pieces with large-end 

diameter >7.6 cm and <20 cm or with length <2 m were not sampled).

We uniquely marked all logs and stumps with numbered metal tags to facilitate 

tracking of individual structures in future re-inventories. For all logs, we recorded 

origination class (cut versus broken), large- and small-end diameter (nearest cm), 

length (nearest 0.1 m), and decay class. Parameters for length and diameter of logs 

referred to the portion of the log contained within plot boundaries, and only that portion of the log was sampled. Decay classes for logs (fig. 3) followed Parks and others 

(1997: 34-35). Class 1 logs retained most bark and branches, had little decay in the 

wood, and rested largely above ground, held up by existing branches. Class 2 logs 

were in contact with the ground, had lost some of their bark and branches, and had 

some decay in the wood. Class 3 represented logs that were no longer intact and had 

begun decomposing into the forest floor. These logs were extensively decayed and 

lacked both bark and limbs. Assignment to decay classes was subjective, but all sampling was done by the authors, and we cross-checked classification between ourselves 

to minimize variability between observers.

For all stumps, we recorded origination class (cut or broken), diameter (nearest 

cm), stump height (nearest 0.1 m), and decay class. We measured diameter across 

the top of the stump; for stumps that were markedly non-round, we took two perpendicular measures and averaged them. We recognized five decay classes for stumps, 

with higher numbers indicating older stumps (fig. 4). Class 1 stumps were freshly cut 

or broken, with sap still present, bark intact, and original wood color retained. Class 

2 stumps retained bark and had exposed surfaces that were still flat and smooth, but 

the exposed wood had turned gray. Class 3 stumps were older, with loose bark, exposed surfaces beginning to round, and gray exposed wood. Class 4 stumps generally 

lacked bark, with wood decaying and few or no flat portions remaining in the exposed 

surface. Class 5 stumps were decomposing, with rounded surfaces.

We recorded species and dbh (nearest cm) of all live trees.
USDA Forest Service RMRS-RP-80WWW. 2010. 5

Figure 3. Decay classes recognized 

for logs. (A) Decay class 1; 

(B) Decay class 2; (C) Decay 

class 3. Decay classes are 

described in the text.

(A)

(B)

(C)
6 USDA Forest Service RMRS-RP-80WWW. 2010.

Figure 4. Decay classes recognized for stumps. (A) Decay 

class 1; (B) Decay class 2; (C) Decay class 3; (D) Decay 

class 4; (E) Decay class 5. Decay classes are described in 

the text.

(C) (D)

(A) (B)

(E)
USDA Forest Service RMRS-RP-80WWW. 2010. 7

Data Analysis

We estimated log and stump volume based on mean diameter (calculated for logs 

as: [large-end diameter + small-end diameter]/2) and length, and assuming cylindrical 

shape. We estimated basal area of live trees from dbh measurements.

Because species composition, stand density, disturbance regimes (Kaufmann and 

others 2007), and management guidelines for CWD (USDA 1996, 1999, Brown and 

others 2003) all differ between mixed-conifer and ponderosa pine forest, we expected 

amounts and types of CWD to differ between these forest types. Consequently, we 

were not interested in formally comparing estimates of CWD between forest types. We 

present some general comparisons between forest types for informational purposes, but 

summarized data are presented within forest type.

We present data for a number of different CWD parameters, including density, volume, area covered, total log length, and biomass, because managers have used all of 

these parameters in various contexts (Bull and others 1997). We focused most statistical 

analyses on estimates of volume because such estimates incorporate both number and 

size of CWD pieces and, therefore, provide more information than other parameters. 

We used number of logs or stumps rather than volume in summarizing size-class and 

decay-class distributions, however.

Because plots were selected randomly rather than stratified by levels of plot factors, 

sample sizes were small for some levels of main plot factors (table 1) and very small for 

some combinations of plot factors (e.g., unlogged ponderosa pine forest lacking road 

access in steep terrain). This greatly limited our ability to model CWD as a function 

of plot factors. Consequently, we conducted simple exploratory analyses within forest 

type by comparing log and stump volume across levels of main plot factors, using either Mann-Whitney or Kruskal-Wallis tests (Conover 1980), depending on the number 

of factor levels. We evaluated the strength of potential relationships between forest 

structure (tree density and basal area) and log and stump volume using Pearson’s correlation coefficient. All measures of variability around means presented in the text are 

standard errors. We also present ranges for parameter estimates because variability in 

CWD parameters likely is as or more important than central tendency (e.g., Stephens 

2004, Stephens and others 2007).

Table 1. Number and percent (in parentheses) of sample plots by forest type and levels of 

plot factors, northern Arizona, 2004.

Forest type

Factor Level Mixed-conifer forest Ponderosa pine forest

Timber status

Logged 29.(54.7) 50.(83.3)

Unlogged 24.(45.3) 10.(16.7)

Road access

Yes 16.(30.2) 33.(55.0)

No 37.(69.8) 27.(45.0)

Terrain

Flat 3.(5.7) 33.(55.0)

Moderate 24.(45.3) 24.(40.0)

Steep 26.(49.0) 3.(5.0)
8 USDA Forest Service RMRS-RP-80WWW. 2010.

We estimated density of large logs (defined as ≥30.5 cm midpoint diameter and 

≥2.44 m in length) by forest type and compared those estimates to U.S. Forest Service 

(USFS) management guidelines for retention of large logs in the Southwestern Region 

(Reynolds and others 1992, USDA 1996). We estimated biomass of logs and stumps 

using estimates of wood density from Brown and See (1981). We used their estimate 

for sound material (25 lbs/ft3) for logs in decay class 1 and stumps in decay classes 1 

and 2, their estimate for rotten material (19 lbs/ft3) for logs in decay class 3 and stumps 

in decay classes 4 and 5, and an intermediate value (22 lbs/ft3

) for logs in decay class 2 

and stumps in decay class 3. We compared the resulting estimates to recommendations 

for biomass of CWD in these forest types (USDA 1999, Brown and others 2003).

Results

Plot Characteristics

Relative to ponderosa pine plots, mixed-conifer plots were less likely to show evidence of recent logging or thinning, less likely to have road access to or adjacent to the 

plot, less likely to occur in flat terrain, and more likely to occur in steep terrain (table 1). 

Tree density ranged from 78 to 489 (mean = 274.8 ± 13.1) trees/ha in mixed-conifer 

forest and from 11 to 689 (mean = 237.6 ± 18.1) trees/ha in ponderosa pine forest; and 

basal area ranged from 7 to 52 (mean = 25.8 ± 1.4) and from 1 to 44 (mean = 20.8 ± 1.2) 

m2

/ha in mixed-conifer and ponderosa pine forest, respectively, illustrating the wide 

range in forest structural conditions sampled.

Mixed-Conifer Forest

Logs (n = 638) and stumps (n = 287) were present on 100 and 85%, respectively, of 

mixed-conifer plots. Most logs (93%) were classified as broken in origin, whereas 64% 

of stumps resulted from harvest activities in mixed-conifer forest. On average, CWD 

(logs and stumps combined) covered 261.2 ± 23.8 m2/ha of ground area in mixedconifer forest (or approximately 2.6% of total area), with area covered varying more 

than 30-fold among plots (range = 21.8–740 m2/ha). Total length of logs averaged 

895.6 ± 83.6 m/ha in mixed-conifer forest, and varied more than 20-fold across plots 

(range = 96.7–2583.3 m/ha).

Both log (mean = 68.5 ± 7.1, range = 1.0–191.4 m3/ha) and stump (4.8 ± 0.7, range 

= 0–21.2 m3

/ha) volume also varied enormously across plots in mixed-conifer forest. 

Neither log nor stump volume was significantly correlated with either tree density or 

basal area in mixed-conifer forest (all P-values > 0.482).

Log volume did not differ between logged and unlogged plots in mixed-conifer forest (P = 0.480, see table 1 for sample sizes). In contrast, stump volume was significantly 

greater (P = 0.015) in logged plots (6.1 ± 1.0, range = 0–21.2 m3

/ha) than in unlogged 

plots (3.2 ± 0.7, range = 0–10.7 m3/ha). This finding may seem self-evident, but recall 

that almost 36% of stumps were natural in origin in this forest type, and some of these 

stumps were large. Neither log nor stump volumes differed significantly between plots 

with and without road access in mixed-conifer forest (both P-values > 0.076). Stump 

volume declined significantly (P = 0.020) from flat to moderate terrain (6.6 ± 1.1, 

range = 0–21.2 m3

/ha) to steep terrain (2.9 ± 3.0, range = 0–10.7 m3/ha), whereas log 

volume did not differ across terrain type in mixed-conifer forest (P = 0.712).
USDA Forest Service RMRS-RP-80WWW. 2010. 9

Figure 5. Diameter distributions of live trees, logs, and stumps in mixed-conifer (left) and ponderosa 

pine (right) forest, northern Arizona, 2004, based on samples of 53 and 60 plots in mixed-conifer and 

ponderosa pine forest, respectively. Percentages of trees, logs, and stumps by size class are shown. 

Size classes (diameter) are: 1 = <30 cm, 2 = 30–39.5 cm, 3 = 40–49.5 cm, 4 = 50–59.5 cm, and 

5 = >59.5 cm. Structures were assigned to size classes based on diameter at breast height (trees), 

large-end diameter (logs), or top diameter (stumps). Sample sizes are: 1,327 trees, 638 logs, and 

287 stumps in mixed-conifer forest and 1,267 trees, 224 logs, and 411 stumps in ponderosa pine 

forest.

Density of both logs (133.8 ± 10.2, range = 11.1–311.1 logs/ha) and stumps 

(60.2 ± 9.1, range = 0–355.6 stumps/ha) also was highly variable across plots in 

mixed-conifer forest. Logs greatly outnumbered stumps in most plots and contributed 

approximately 69% of CWD density and 93% of CWD volume in this forest type.

Diameter distributions of logs and stumps differed from tree populations in mixedconifer forest (fig. 5). Logs and especially stumps tended to be less common than 

trees in the smallest diameter classes and more common than trees in the largest 

diameter classes. Logs in later decay classes dominated log populations in mixedconifer forest (fig. 6). Stump populations also were dominated by later decay classes 

(fig. 7), and class 1 stumps were absent in this forest type.

Density of large logs also was highly variable in mixed-conifer forest (range = 

0–133.3 large logs/ha). Both mean (35.2 ± 4.5 logs/ha) and median (22.2 logs/ha) density of large logs exceeded current guidelines for retention of large logs (12.4 logs/ha) 

in mixed-conifer forest. Most plots (79.3%) in this forest type contained large logs, 

and 69.7% of all mixed-conifer plots met or exceeded the guideline.

Biomass of CWD averaged 25.8 ± 2.5 tonnes/ha in mixed-conifer forest and also 

was highly variable spatially (range = 1.7–71.1 tonnes/ha), with 20% of plots providing 

43% of total biomass. Only 30.2% of mixed-conifer plots fell within the range for CWD 

biomass recommended for this region by USDA (1999; ~18 to 36 tonnes/ha or 8 to 

16 tons/ac), with 39.6% falling below these levels and 30.2% exceeding recommended levels. In contrast, 58.5% of ponderosa pine plots fell within “optimum” levels of 

CWD recommended for warm, dry forest types in general (~11 to 45 tonnes/ha or 5 to 

20 tons/ac; Brown and others 2003: figure 2); 26.4% fell below these recommended 

levels; and only 15.1% exceeded these recommended levels.
10 USDA Forest Service RMRS-RP-80WWW. 2010.

Figure 7. Decay-class composition (%) 

of stumps in mixed-conifer (n = 287 

stumps) and ponderosa pine (n = 

411 stumps) forest, northern Arizona, 

2004, based on samples of 53 and 60 

plots in mixed-conifer and ponderosa 

pine forest, respectively. Decay 

classes are described in text.

Figure 6. Decay-class composition (%) 

of logs in mixed-conifer (n = 638 

logs) and ponderosa pine (n = 224 

logs) forest, northern Arizona, 2004, 

based on samples of 53 and 60 plots 

in mixed-conifer and ponderosa pine 

forest, respectively. Decay classes 

are described in text.
USDA Forest Service RMRS-RP-80WWW. 2010. 11

Ponderosa Pine Forest

Logs (n = 224) and stumps (n = 411) were each present on 81.7% of ponderosa pine 

plots. Only two ponderosa pine plots (3.3%) lacked both logs and stumps. As in mixedconifer forest, most logs (81%) were classified as broken in origin. In contrast, 91.5% 

of stumps in ponderosa pine forest resulted from harvest activities. Amount of ground 

area covered by CWD varied widely among plots (74.5 ± 11.1, range = 0–438.9 m2

/ha; 

or approximately 0.7% of total area), as did total length of logs (241.1 ± 36.3, range = 

0–1326.7 m/ha).

As in mixed-conifer forest, both log (52. 2 ± 21.1, range = 1.0–191. 4 m3/ha) and 

stump (3.1 ± 0.4, range = 0–11.4 m3/ha) volume varied widely across plots in mixedconifer forest. Log volume was weakly correlated with tree density in ponderosa pine 

forest (Pearson’s r = 0.287, P = 0.026), but was not correlated with basal area (P = 

0.108). Stump volume was not correlated with either tree density or basal area in ponderosa pine forest (both P-values > 0.222).

Log volume did not differ between logged and unlogged plots or between plots 

with and without road access in ponderosa pine forest (all P-values > 0.090, see table 1 for sample sizes). In contrast, stump volume was significantly greater on logged 

(3.5 ± 0.4, range = 0–11.4 m3

/ha) than on unlogged plots (1.1 ± 0.5, range = 0–4.2 m3/

ha; P = 0.003) and on plots with road access (4.0 ± 0.5, range = 0–11.4 m3/ha versus 

2.0 ± 0.4, range = 0-6.5 m3/ha in plots lacking road access; P = 0.005). Stump volume 

did not differ across terrain levels (P = 0.198), but log volume increased with terrain 

steepness (9.4 ± 2.9, 24.1 ± 5.8, and 42.4 ± 25.9 m3/ha in flat, moderate, and steep 

terrain, respectively; P = 0.002).

Density of logs (41.5 ± 5.7, range = 0–222.2 logs/ha) and stumps (76.1 ± 12.4, 

range = 0–444.4 stumps/ha) also varied widely across plots in ponderosa pine forest. 

Unlike mixed-conifer forest, however, stumps frequently outnumbered logs in ponderosa pine forest and contributed approximately 65 and 15% of total CWD density 

and volume in ponderosa pine forest.

As in mixed-conifer forest, diameter distributions of logs and stumps differed from 

tree populations in ponderosa pine forest (fig. 5). Again, logs and especially stumps 

tended to be less common than trees in the smallest diameter classes and more common than trees in the largest diameter classes, although this trend was less pronounced 

in this forest type. Logs in later decay classes dominated log populations in ponderosa 

pine forest (fig. 6). Stump populations in ponderosa pine forest also contained relatively high proportions of stumps in the two latest decay classes, but class 1 stumps 

also were well represented in ponderosa pine forest (fig. 7).

Large logs were sparsely distributed in ponderosa pine forest. Mean density of 

large logs (9.8 ± 2.7, range = 0–111.1 logs/ha) exceeded the target for retention 

(7.4 logs/ha) in this forest type, but this parameter was greatly influenced by high 

numbers of large logs in only four plots. Median density of large logs in this forest 

type was zero, and only 36.7% of ponderosa pine plots met or exceeded the guideline 

for retention.

CWD biomass averaged 7.0 ± 1.2 tonnes/ha in ponderosa pine forest, and spatial 

variability again was relatively pronounced (range = 0–45.0 tonnes/ha), with only 

20% of plots providing 63% of total biomass. Ninety percent of ponderosa pine plots 

fell below levels for CWD recommended by USDA (1999), and only one plot exceeded the recommended levels. Most plots (78.7%) also fell below “optimum” levels of 

coarse woody debris recommended for warm, dry forest types in general (Brown and 

others 2003: fig. 2); and no plots exceeded these levels.
12 USDA Forest Service RMRS-RP-80WWW. 2010.

Discussion

Coarse woody debris was well distributed across the landscape in both forest 

types. Spatial variability in amount of CWD was considerable but still lower than 

variability in Jeffrey pine (P. jeffreyi)–mixed-conifer forests studied in the Sierra San 

Pedro Martir of Baja California, Mexico (Stephens 2004, Stephens and others 2007). 

In that area, 37% of plots had no 1,000 hr fuels, versus only 1.8% of our plots, and 

75% of the 1,000-hr fuels occurred on only 20% of the plots (versus 43 and 63% of 

CWD in this study by forest type; see above). Stephens (2004, see also Stephens and 

others 2007) hypothesized that the large spatial variability in the Sierra San Pedro 

Martir resulted from an intact, frequent surface fire regime that maintained a patchy 

distribution of fuels. We suspect that disruption of surface fires in our study area has 

resulted in a more continuous distribution of forest fuels than occurred under historical conditions (see also Skinner 2002).

Amounts of CWD were not strongly linked to current stand structure in these forest types. However, road access, terrain, and past harvest activities appeared to affect 

CWD volume, especially stump volume. Differences between areas with and without 

recent thinning are primarily related to effects of those management treatments. In 

contrast, lower stump volumes in areas lacking road access and/or areas of steep 

terrain likely reflect lower levels of human access. For example, Wisdom and Bate 

(2008) documented significant reductions in densities of snags, which serve as sources of CWD, related to both human access and harvest intensity.

Relative contributions of logs and stumps to CWD varied between forest types. 

Stumps contributed over 65% of CWD density and 15% of CWD volume in ponderosa pine forest. Stumps appeared less important in mixed-conifer forest but still 

contributed 31% of CWD density. This suggests that inventories that do not include 

standing stumps are ignoring an important source of CWD in these forest types, particularly in managed stands.

Comparative data on CWD in these forest types are sparse. Area covered by CWD 

in this study was relatively low in both forest types, but generally bracketed reported values from Jeffrey pine–mixed-conifer forest in the Sierra San Pedro Martir, 

Mexico (1.5 ± 0.2%; Stephens and others 2007: table 2) and two ponderosa pine 

sites in Oregon and northern California (1.5 ± 0.2 and 2.2 ± 0.4%; Youngblood and 

others 2004: table 2). Observed log densities in Arizona were considerably lower 

than mean densities (approximately 219–230 logs/ha in mixed-conifer forest and 

100–154 logs/ha in ponderosa pine forest, respectively) observed in similar forest 

types in northeastern Oregon (from Bull and Holthausen 1993, Torgersen and Bull 

1995, and Torgersen 1997 as cited in Bull and others 1997:38; Youngblood and others 2004). Observed stump densities also were considerably lower than mean stump 

densities in ponderosa pine forests at Grand Canyon, Arizona (141.2 ± 34.8 stumps/

ha, Fulé and others 2002). Many of the data on log density from Oregon represent 

late-successional or old-growth stands, however, which typically (but not always; see 

Kaufmann and others 2007) contain relatively large numbers of logs. In contrast, our 

data represent a wide range of successional stages. Further, sampling methodology 

and spatial scale likely differed among studies. For example, Fulé and others (2002) 

sampled smaller stumps, and this likely explains much of their increased stump density. Direct comparisons between regions and studies are difficult due to the paucity 

of comparative data, which reinforces the need for empirical data on amounts and 

types of CWD.
USDA Forest Service RMRS-RP-80WWW. 2010. 13

Tree populations were more skewed toward smaller size classes than either log or 

stump populations in these forest types (fig. 5). This could reflect differences in residence times for logs and stumps of different sizes (presumably large logs and stumps 

last longer than small logs and stumps; see Ganey and Vojta [2005] for similar results 

relative to snags in these same plots), discrepancies between past and current stand 

structure (relatively greater proportions of large trees historically, due to frequent fires; 

Covington and others 1994, Fulé and others 2003, Stephens and others 2009), past 

harvest patterns (especially for stumps), or combinations of these factors. Future inventories (planned at 5-yr intervals) will provide data on residence times useful in modeling 

these relationships.

Decay class distributions were dominated by logs and stumps in the later decay 

classes in both forest types (figs. 6 and 7), and Youngblood and others (2004: fig. 11) 

reported similar results from two ponderosa pine sites in central Oregon. These forest 

types historically experienced relatively frequent surface fire (Covington and others 

1994, Skinner 2002, Fulé and others 2003, Stephens and Fulé 2005, Kaufmann and 

others 2007). These fires presumably burned many logs and stumps when they occurred 

(e.g., Gordon 1996, Randall-Parker and Miller 2002, Skinner 2002, Kaufmann and 

others 2007) but also created snags that provided a source of future logs (Horton and 

Mannan 1988, Fulé and Laughlin 2007, Roccaforte and others 2009). Thus, dynamics 

of log and stump populations in these forest types likely was driven more by fire than 

by decay dynamics (Skinner 2002, Kaufmann and others 2007, Stephens and others 

2007, Brewer 2008), and large logs may decay slowly over long time periods in these 

forest types in the absence of fire. However, Stephens (2004, see also Stephens and 

others 2007) reported that 81% of fuels in a Jeffrey pine/mixed-conifer forest subject to 

frequent surface fires also were from rotten material. Thus, the role of fire in structuring 

decay distributions of CWD remains unclear.

Regardless of the reasons underlying observed decay-class distributions, those 

distributions have implications for fuels managers. Rotten woody material is easily 

consumed by fire under dry conditions, readily produces fire brands that contribute to 

torching and spotting activity in fires, and provides receptive ignition sites for brands 

produced elsewhere (Sackett 1979, Stephens and others 2007). Thus, the large numbers 

of rotten logs and stumps will pose challenges for fuels managers charged with reintroducing prescribed fire in these forest types.

Most mixed-conifer plots met or exceeded USFS guidelines for retention of large 

logs. In contrast, large logs were sparse and patchily distributed in ponderosa pine forest, with 50% of plots lacking large logs entirely. This suggests a need to emphasize 

retention of large trees and snags in ponderosa pine forest to provide future sources of 

large logs.

Estimated values for fuel loads in these forest types generally fell within the range of 

values reported previously for mixed-conifer (21.5–62 tonnes/ha; Sackett 1979, Brown 

and See 1981, Knapp and others 2005, Stephens and Moghaddas 2005) and ponderosa pine forest (3–23.2 tonnes/ha; Sackett 1979, Brown and See 1981, Robertson and 

Bower 1999, Stephens 2004, Stephens and others 2007). Many mixed-conifer plots 

fell within recommended ranges for biomass of CWD (USDA 1999, Brown and others 

2003), but a substantial proportion fell above recommended levels. In contrast, most 

ponderosa pine plots fell below recommended levels. Again, direct comparisons are 

difficult here due to differences in sampling methods and size and types of CWD measured. Nevertheless, these data suggest that, despite years of fire suppression and many 

missed fire cycles in ponderosa pine forests, fuel loads frequently remain within or 
14 USDA Forest Service RMRS-RP-80WWW. 2010.

below recommended limits in this forest type. Passovoy and Fulé (2006: fig. 6) also 

reported that observed fuel loads generally remained within recommended limits even 

in severely burned ponderosa pine forests. In contrast, fuel loads in many mixed-conifer 

plots already exceed recommended levels, despite the fact that fewer fire cycles have 

been missed in this forest type. Stephens (2004, see also Stephens and others 2007) 

attributed this to the higher productivity of mixed-conifer forests and the resulting potential for rapid structural change.

We offer three caveats relative to “acceptable” fuel loads, however. First, Brewer 

(2008) noted that recommended levels of fuels are acceptable only where stand structure is consistent with structure that existed when natural fire regimes operated. In many 

cases, current stands have greater tree densities and more homogeneous and continuous canopies than historical stands in these forest types (Covington and others 1994, 

Fulé and others 2003, Stephens and Gill 2005), and CWD may be more continuously 

distributed as well (Stephens 2004, Stephens and others 2007). Under these conditions, fire can be difficult to control even when fuels are within recommended ranges. 

Second, we are witnessing a large pulse of drought-mediated tree mortality in these 

forests (Ganey and Vojta 2005, USDA 2008, Negron and others 2009). As the snags 

created break and/or fall, this will add considerable CWD to these forests, which may 

push CWD biomass above recommended levels. Third, this elevated tree mortality may 

not be a short-term phenomenon. Considerable climate-related mortality is occurring 

in and beyond western forests (e.g., van Mantgem and Stephenson 2007, van Mantgem 

and others 2009, Allen and others 2010). Coupled with model predictions suggesting 

increasingly warmer and drier climate in the southwest (e.g. Seager and others 2007), 

this suggests a possibility for rapid and potentially large increases in fuel loads in southwestern forests. All of these factors suggest a need for monitoring trend in amounts and 

kinds of CWD in these forests.

Conclusions

The data presented here provide both an assay of current amounts and types of CWD 

and a baseline for future monitoring of CWD dynamics. Clearly, however, much additional information will be required to effectively manage CWD in the face of changing 

climates, disturbance regimes, and management paradigms (e.g., Millar and others 

2007). Information on residence time of various species, sizes, and decay classes of 

logs and stumps will be required even to understand current patterns and their causes, 

let alone to predict future patterns. Present targets for management of CWD typically 

are based either on perceived wildlife habitat requirements or on acceptable fuel loads. 

These different types of targets may not always be in agreement or directly compatible 

with each other. For example, fuel loads typically are estimated by fuel inventories that 

may not provide information on the types and spatial distribution of CWD structures 

necessary to evaluate wildlife habitat (Bull and others 1997), and meeting wildlife targets may require retaining levels of CWD that fuels managers view as unacceptable. 

Further, there are, at present, numerous parameters estimated to describe CWD, including (at least) density, volume, biomass, log length, and percent area covered (Bull and 

others 1997). Each of these parameters has distinct advantages in particular situations, 

and there is no general consensus on which parameter or parameters have the greatest general applicability. For example, general wildlife targets may rely on estimates 

of density by structure type, whereas fuels managers may rely on biomass estimates.
USDA Forest Service RMRS-RP-80WWW. 2010. 15

Standardizing methods, targets, and parameters estimated would have obvious benefits 

in terms of communication across disciplines, but it may prove difficult because of the 

different objectives involved.

It also would be desirable to begin evaluating empirical data on CWD at larger spatial 

scales. Data from the Forest Inventory and Analysis National Program (http://fia.fs.fed.

us/) may provide opportunities to conduct landscape-scale assessments of amounts and 

types of CWD present. Such large-scale assessments should become more important 

as managers increasingly begin planning at broader spatial scales. Finally, planners 

should recognize the inherent spatial variability in CWD (Stephens 2004, Stephens and 

others 2007, this study) and incorporate such variability in targets and/or planning efforts. This variability again argues for large-scale assessments, as well as for focusing 

management guidelines at broad scales rather than attempting to manage for average 

characteristics on every acre (Stephens 2004, Stephens and others 2007).

Acknowledgments

We thank J. Jenness, G. Martinez, M. Stoddard, B. Strohmeyer, R. White, and 

especially A. and J. Iníguez for their assistance in establishing plots, and D. and N. 

Ganey for assistance with sampling plots. For assistance with initial plot selection, 

we thank J. Ellenwood, B. Higgins, K. Menasco, C. Nelson, G. Sheppard (Kaibab 

National Forest) C. Beyerhelm, A. Brown, H. Green, T. Randall-Parker, C. Taylor, 

and M. Whitney (Coconino National Forest). R. M. King and D. L. Turner provided 

advice on statistical analyses, and D. Brewer, P. Z. Fulé, and S. L. Stephens reviewed 

earlier versions of this paper.

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