Rapid Increase in Log Populations in Drought-Stressed Mixed-Conifer and Ponderosa Pine Forests in Northern Arizona

doi:10.4236/ojf.2012.22008

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Open Journal of Forestry

2012. Vol.2, No.2, 59-64

Published Online April 2012 in SciRes (http://www.SciRP.org/journal/ojf) http://dx.doi.org/10.4236/ojf.2012.22008

Rapid Increase in Log Populations in Drought-Stressed

Mixed-Conifer and Ponderosa Pine Forests in

Northern Arizona

Joseph L. Ganey, Scott C. Vojta

US Forest Service, Rocky Mountain Research Station, Flagstaff, USA

Email: [email protected]

Received December 15th, 2011; revised February 23rd, 2012; accepted February 29th, 2012

Down logs provide important ecosystem services in forests and affect surface fuel loads and fire behavior.

Amounts and kinds of logs are influenced by factors such as forest type, disturbance regime, forest man-

agement, and climate. To quantify potential short-term changes in log populations during a recent global-

climate-change type drought, we sampled logs in mixed-conifer and ponderosa pine (Pinus ponderosa)

forests in northern Arizona in 2004 and 2009 (n = 53 and 60 1-ha plots in mixed-conifer and ponderosa

pine forests, respectively). Over this short time interval, density of logs, log volume, area covered by logs,

and total length of logs increased significantly in both forest types. Increases in all log parameters were

greater in mixed-conifer than in ponderosa pine forest, and spatial variability was pronounced in both

forest types. These results document rapid increases in log populations in mixed-conifer forest, with

smaller changes observed in ponderosa pine forest. These increases were driven by climate-mediated tree

mortality which created a pulse in log input, rather than by active forest management. The observed in-

creases will affect wildlife habitat, surface fuel loads, and other ecosystem processes. These changes are

likely to continue if climate change results in increased warmth and aridity as predicted, and may require

shifts in management emphasis.

Keywords: Climate Change; Fuels; Logs; Mixed-Conifer Forest; Ponderosa Pine Forest; Spatial

Variability

Introduction

Coarse woody debris (CWD), defined as the large-size com-

ponent of downed woody material (Harmon et al., 1986), pro-

vides important ecosystem services in forest systems (McComb

& Lindenmayer, 1999; Butler et al., 2002; Woldendorp &

Keenan, 2005), and amounts and types of CWD can affect sur-

face fuel loads and fire behavior in these systems (Brown et al.,

2003; Brewer, 2008). Much of the volume of CWD in south-

western mixed-conifer and ponderosa pine forests consists of

logs (defined here as down woody material >20 cm in large-end

diameter and ≥2 m in length). For example, logs provided 93%

and 85% of total CWD sampled by Ganey and Vojta (2010) in

mixed-conifer and ponderosa pine forest, respectively. Logs

perform various ecological roles, but are particularly important

in providing foraging sites and shelter for many species of

wildlife (Bull et al., 1997). Primarily because of their impor-

tance to native wildlife, specific guidelines for retention of logs

were developed for a number of forest types in the Southwest-

ern Region of the US Forest Service (USFS [USDA Forest

Service, 1996: pp. 92-93]). Information on abundance of logs in

these forest types, as wells as trends in log populations, typi-

cally is sparse, however.

To quantify potential changes occurring in log populations in

two important forest types in northern Arizona, we sampled

logs in mixed-conifer and ponderosa pine (Pinus ponderosa)

forest in 2004 and 2009. Our specific objectives were to: 1)

Estimate trends in various parameters used to describe log

populations, including log density, log volume, area covered by

logs, and total log length; and 2) evaluate trends in decay-class,

size-class, and species composition of log populations. These

data document trends in log populations in mixed-conifer and

ponderosa pine forest during a period of rapid forest change

mediated by climate (Breshears et al., 2005; Allen et al., 2010;

Ganey & Vojta, 2011). Results thus may provide a glimpse of

trends in log populations likely under future climate in the

southwestern United States, which is predicted to be both

warmer and drier (Seager et al., 2007; Seager & Vecchi, 2010),

and may aid managers in planning for and mitigating the effects

of climate change.

Study Area

The study area encompassed approximately 73,000 ha in two

National Forests in north-central Arizona (Figure 1). Within

this area, study plots were randomly located in mixed-conifer (n

= 53 plots) and ponderosa pine (n = 60 plots) forest. Mixed-

conifer forest was dominated by Douglas-fir (Pseudotsuga

menziesii), white fir (Abies concolor), and ponderosa pine.

Other common species included limber pine (P. flexilis), Gam-

bel oak (Quercus gambelii), and quaking aspen (Populus

tremuloides). Ponderosa pine forest was dominated by ponder-

osa pine, which comprised >90% of trees across all plots. Other

relatively common species included Gambel oak and alligator

juniper (Juniperus deppeana), with Douglas-fir, quaking aspen,

limber pine, pinyon pine (P. edulis), and Utah (J. osteosperma)

J. L. GANEY ET AL.

0 40 Kilometers

W E

Figure 1.

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 circles and plots in

mixed-conifer forest (n = 53) are indicated by triangles.

and one-seed (J. monosperma) juniper present in small numbers

in some stands.

The study area included a wide range of topography and

ecological conditions. Ponderosa pine plots ranged from 1778

to 2561 m in elevation, and mixed-conifer plots ranged from

1886 to 2720 m. This represented the entire elevational range of

these forest types within this area, from the transition zone

between pinyon-juniper woodland and ponderosa pine at lower

elevations to the ecotone between mixed-conifer and Engel-

mann spruce (Picea engelmanni)—corkbark fir (Abies lasio-

carpa var. arizonica) forests at higher elevations. In addition,

plots included both commercial forest lands and administra-

tively-reserved lands such as wilderness and other roadless

areas. As a result, we sampled a wide range in forest structural

conditions in each forest type. For example, density of trees

≥20 cm diameter at breast height in 2004 ranged from 78 to 489

(mean = 274.8 ± 13.1 [SE]) trees·ha–1 in mixed-conifer forest

and from 11 to 689 (mean = 237.6 ± 18.1) trees·ha–1 in ponder-

osa 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–1 in

mixed-conifer and ponderosa pine forest, respectively (Ganey

& Vojta, 2011).

No plots underwent commercial timber harvest between

2004 and 2009. One ponderosa pine plot (1.7% of plots) un-

derwent thinning of smaller trees during this period, three pon-

derosa pine plots (5.0%) underwent prescribed fire, and two

plots experienced intense wildfire (one each in mixed-conifer

[1.9%] and ponderosa pine [1.7%] forests).

Methods

Field Sampling

This study opportunistically utilized a series of permanent

plots established in 1997 to monitor snag dynamics. We estab-

lished plots using a stratified random sampling procedure (see

Ganey and Vojta 2011 for further details). The original plots

were 1 ha each in area (100 by 100 m), but we sampled logs in

a 0.09-ha subplot (30 by 30 m) within each plot, because time

constraints precluded sampling these features on the entire 1-ha

plot. The subplot was established starting at the first corner of

the larger plot and following the same compass bearings used to

establish the larger plot. Because both plot locations and com-

pass bearings were selected randomly, subplots were located

randomly with respect to forest structure.

Within each plot, we sampled all logs ≥ 20 cm in large-end

diameter and ≥2 m in length in 2004 and 2009. The 20-cm

minimum diameter was selected for consistency with the origi-

nal snag monitoring study, which ignored smaller snags be-

cause they were suspected to be relatively unimportant to native

wildlife. Thus, all logs sampled correspond to >1000-hr fuels as

defined by fuels managers (Maser et al., 1979: table 25), but not

all 1000-hr fuels were sampled (i.e., pieces with large-end di-

ameter > 7.6 cm and <20 cm or with length < 2 m were not

sampled).

We uniquely marked all logs with numbered metal tags to

facilitate tracking of individual structures in future re-invento-

ries. For all logs we recorded origination class (cut versus bro-

ken), large- and small-end diameter (nearest cm), length (near-

est 0.1 m), species, 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 followed Bull et al. (1997: figs.

62 and 63). 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.

Analysis

Our primary focus was on assessing changes in log parame-

ters between 2004 and 2009 within each forest type. Therefore,

log parameters were estimated separately by forest type and

year. We included all plots in analyses, including recently-

disturbed areas, because our objective was to quantify log pa-

rameters across the landscape.

We present estimates for a number of different parameters,

because managers have used all of these parameters in various

contexts (Bull et al., 1997). Parameters estimated include log

density, log volume, total log length, and ground area covered

by logs. We estimated log volume based on mean diameter

(calculated as: [large-end diameter + small-end diameter]/2)

and length, and assuming cylindrical shape.

J. L. GANEY ET AL.

We compared these parameters between years within forest

types. Distributions for many log parameters were highly

skewed, especially in ponderosa pine forest. As a result, we

were not able to use paired t-tests to compare parameters be-

tween years because distributions deviated grossly from normal

(Zar, 2009), and could not use Wilcoxon signed-ranks tests due

to violations of the symmetry assumption and the presence of

many ties in the data (Conover, 1999). Consequently, we used

the asymptotic uniformly most powerful nonrandomized (ANU)

test described in Coakley and Heise (1996: p. 1244). This test

was implemented using R (version 2.13.1; R Foundation for

Statistical Computing, 2011).

We compared decay-class distributions and species composi-

tion of log populations between years using chi-square tests

(Conover, 1999). We compared diameter distributions of logs

between years using Kolmogorov-Smirnov (hereafter referred

to as K-S) tests (Conover, 1999).

Because of the highly skewed distributions for many log pa-

rameters, neither the mean nor the median always described

central tendency adequately. Therefore, we report both pa-

rameters. We also report ranges to quantify spatial variability in

log parameters, which may be as important as measures of cen-

tral tendency (Stephens, 2004).

Results

Mixed-Conifer Forest

We sampled 638 logs in mixed-conifer forest in 2004. Of

these, 91.1% were relocated in 2009, 6.0% had decayed to the

point where they no longer functioned as a log, 1.4% burned,

and another 1.6% suffered unknown fate (most likely burned or

decayed). We sampled 818 logs in mixed-conifer forest in 2009,

including 237 logs that were recruited after 2004. Almost 99%

of newly recruited logs were classified as natural in origin,

versus 93% of logs existing in 2004.

Logs were present on 100% of mixed-conifer plots in both

years. Median log density increased by 36.4%, median log

volume by 33.6%, median area covered by logs by 53.1%, and

median total length of logs by 35.8% in mixed-conifer forest

from 2004 to 2009 (Table 1). All parameters describing log

populations were highly variable among plots in both years

(Table 1).

Log populations in mixed-conifer forest were dominated by

logs in decay classes 2 and 3 in both years, but the distribution

of log decay classes differed between years (chi-square test, P <

0.001). The main difference was an increase in the proportion

of logs in decay class 1 (from 9.0% to 17.0%) and a decrease in

logs in decay class 3 (from 57.9% to 50.2%). Proportions of

logs in decay class 2 changed little.

Diameter distribution of logs did not differ significantly be-

tween years in mixed-conifer forest (K-S test, P = 0.947). Log

populations were heavily dominated by logs in the smallest size

classes in both years, with >73% of all logs <30 cm in midpoint

diameter and >87% <40 cm in midpoint diameter. Length dis-

tribution of logs also did not differ between years in mixed-

conifer forest (K-S test, P = 0.722). Over 42% of logs were <5 m

Table 1.

Selected parameters for log populations in northern Arizona mixed-conifer and ponderosa pine forests in 2004 and 2009. Shown are mean and median

values and ranges (in parentheses below means and medians) for each year, as well as the number of plots on which the parameter increased or de-

creased between years (the number of plots on which the parameter did not change = total number of plots − [number increasing + number decreas-

ing], with n = 53 and 60 total plots in mixed-conifer and ponderosa pine forest, respectively).

2004 2009 Number of plots showing

Parameter

Mean Median Mean Median Increase Decrease P1

Mixed-conifer forest (sample size = 638 logs in 2004 and 818 logs in 2009)

133.7 122.2 171.5 166.7

Log density (logs·ha–1)

(11.1 to 311.1) (11.1 to 388.9)

41 7 <0.001

66.6 59.5 78.9 79.5

Log volume (m3·ha–1)

(0.0 to 194.7) (2.4 to 230.8)

40 8 <0.001

895.1 792.1 1178.3 1075.5

Total log length (m)

(0.0 to 2583.1) (70.0 to 3026.4)

43 6 <0.001

247.9 205.4 308.3 314.5

Area covered (m2·ha–1)

(0.0 - 743.8) (14.4 to 733.5)

44 7 <0.001

Ponderosa pine forest (sample size = 224 logs in 2004 and 270 logs in 2009)

41.4 33.3 49.1 33.3

Log density (logs·ha–1)

(0.0 to 222.2) (0.0 to 222.2)

27 6 <0.001

16.9 6.4 17.6 6.0

Log volume (m3·ha–1)

(0.0 to 116.3) (0.0 to 116.3)

24 4 <0.001

241.9 141.7 287.8 174.4

Total log length (m)

(0.0 to 1265.4) (0.0 to 1748.7)

26 6 <0.001

66.3 35.3 73.0 37.1

Area covered (m2·ha–1)

(0.0 to 409.9) (0.0 to 409.9)

29 8 <0.001

P-values from the asymptotic uniformly most powerful nonrandomized (ANU) test described by Coakley and Heise (1996: p. 1224).

J. L. GANEY ET AL.

in length in both years, and over 75% were <10 m in length.

Species composition of log populations did not vary across

years in mixed-conifer forest (P > 0.10). In both years, several

species were well represented, including white fir (17.6% of

logs in 2004 and 23.1% in 2009), ponderosa pine (34.7% in

2004, 31.7% in 2009), Douglas-fir (23.5% in 2004, 21.4% in

2009), and Gambel oak (11.7% in 2004, 11.8% in 2009).

Ponderosa Pine Forest

We sampled 224 logs in ponderosa pine forest in 2004. Of

these, 93.3% were relocated in 2009, 1.3% had decayed to the

point where they no longer functioned as a log, and 5.4%

burned.

We sampled 270 logs in 2009, including 61 logs recruited

after 2004. Over 98% of newly recruited logs were classified as

natural in origin, versus 80% of logs existing in 2004.

Logs were present on 81.7% of ponderosa pine plots in 2004

and 90% in 2009. Median estimates varied little between years

for log density and volume, whereas mean log density increased

by 18.6% and mean log volume by 4.1% (Table 1). Median

total log length increased by 23.1%, and median area covered

by logs increased by 5.9% between years in ponderosa pine

forest. As in mixed-conifer forest, spatial variability was pro-

nounced in all of these parameters (Table 1).

Distribution of log decay classes did not differ significantly

across years in ponderosa pine forest. Log populations in this

forest type were dominated by logs in decay class 3, which

comprised >58% of logs in both years.

Diameter distribution of log populations did not differ sig-

nificantly between years in ponderosa pine forest (K-S test; P =

0.099). Log populations were heavily dominated by logs in the

smallest size classes in both years, with >77% of all logs < 30

cm in mean diameter, and 88% <40 cm in mean diameter.

Length distribution of logs also did not differ between years in

ponderosa pine forest (K-S test, P = 1.000). Over 50% of logs

were <5 m in length in both years, and over 85% were <10 m in

length.

Species composition of log populations also did not differ

between 2004 and 2009 in ponderosa pine forest (P > 0.25). In

both years, log populations were heavily dominated by ponder-

osa pine, which comprised >91% of all logs in both years.

Discussion

Our results document rapid increases in log numbers and re-

lated parameters in mixed-conifer forest. Changes were less

pronounced in ponderosa pine forest, but most log parameters

still increased significantly over a five-year period in this forest

type. In both forest types, most newly-recruited logs were clas-

sified as natural in origin rather than cut. Thus, most newly-

recruited logs represented structures left when live or dead trees

broke and fell. In addition, no logs were lost to fuelwood har-

vest in ponderosa pine forest, and only 1.6% of existing logs

were unaccounted for and possibly harvested in mixed-conifer

forest. Thus, most of the observed change in both forest types

was attributable to the balance between creation of logs through

natural tree mortality and loss of logs to decay or fire, rather

than to active forest management or fuelwood harvest.

In both forest types, log populations changed little in terms

of diameter or length distribution and species composition, and

decay-class distribution did not change between years in pon-

derosa pine forest. In contrast, decay-class distribution shifted

somewhat in mixed-conifer forest, with greater proportions of

logs in decay class 1 and lower proportions in decay class 3.

This is consistent with a pulse of new logs into the system,

many of which (46.8%) were in decay class 1 when sampled in

2009, as well as with greater decay of logs in decay class 3;

100% of logs lost to decay between 2004 and 2009 in mixed-

conifer forest were in decay class 3 in 2004. Changes in decay

status can affect fire behavior, with flammability and probabil-

ity of ignition generally increasing as logs progress from sound

to rotten (Brown et al., 2003). Thus, this large pulse of new

logs has implications for fire behavior not only in terms of fuel

loads (see below), but also with respect to fuel type.

The greater increase in log parameters in mixed-conifer for-

est is consistent with patterns of climate-mediated tree mortality

documented in this area. Observed mortality from 1997 to 2007

was far greater in mixed-conifer than in ponderosa pine forest

(Ganey & Vojta, 2011), and is creating a large pulse in log

creation in mixed-conifer forest. Stephens and Ruth (2005) also

noted that fuels accumulate more rapidly in productive mixed-

conifer forests than in ponderosa pine forests, even though

those pine forests typically have missed more fire cycles. This

general process has been exacerbated by recent climatic condi-

tions, with the result that logs are accumulating rapidly in

mixed-conifer forest (i.e., changes ranging from approximately

30 to >50% in various parameters over a 5-yr period; Table 1).

The implications of observed changes in log populations are

not entirely clear at this time. Several studies have documented

positive associations between down wood and various species

of small mammals in southwestern mixed-conifer (Ward 2001)

and ponderosa pine forests (Goodwin & Hungerford, 1979;

Block et al., 2005, 2011; Converse et al., 2006). Thus, the ob-

served increase in down wood may result in improved habitat

quality for some small mammals, at least in the short term.

The changes documented here represent only the initial

changes resulting from a drought-mediated pulse in tree mortal-

ity (Ganey & Vojta, 2011), however, because many of the dead

trees are still standing. As these trees fall, we anticipate greater

increases in log abundance and related parameters in both forest

types (Ganey & Vojta, 2010; Hoffman et al., 2011; Stevens-

Rumann et al., 2012). At some point, this may result in reduced

habitat quality for small mammals. For example, Manning and

Edge (2004) documented curvilinear associations between sur-

vival and amount of down wood for two species of small

mammals in Oregon, with survival reaching a maximum at

intermediate levels of down wood and declining with further

increases of down wood. They speculated that this reflected

tradeoffs between amount of down wood and food supply. At

low levels of down wood cover, increases in woody cover re-

sulted in improved nesting habitat and hiding cover, but herba-

ceous food resources likely declined as woody cover increased

beyond an optimal level. Ward (2001) also reported possible

nonlinear associations between Mexican woodrats (Neotoma

mexicana) and down wood in a New Mexico mixed-conifer

forest, but it was unclear if a similar mechanism was involved.

The observed increases in log populations also reflect

changes in surface fuel loads that affect fire behavior. Brown et

al. (2003) provided recommendations for optimal ranges of

CWD in warm dry coniferous forests, based on factors such as

resistance of fires to control, fire duration, soil heating, wildlife

values, and historical ranges. Recent studies by Hoffman et al.

(2011) and Stevens-Rumann et al. (2012) documented increases

J. L. GANEY ET AL.

in surface fuel loads in ponderosa pine forests following dis-

turbance by bark beetles and wildfire, respectively. Loadings of

1000-hr fuels exceeded recommended ranges for dry coniferous

forests in 20% of plots sampled by Hoffman et al. (2011) five

years after a bark beetle outbreak, and they expected other plots

to exceed those ranges as remaining snags fall. Similarly, areas

suffering high mortality from wildfire exceeded recommended

levels for CWD by up to 28% by 10 yrs postfire (Stevens-Ru-

mann et al., 2012). In contrast, Passovoy and Fulé (2006) did

not observe levels of CWD exceeding recommended levels in a

27-yr chronosequence of postfire ponderosa pine forests.

Fuel loads in our sample plots also will continue to increase

in the short term as dead trees fall (Ganey & Vojta, 2010;

Hoffman et al., 2011; Stevens-Rumann et al., 2012). Increasing

loads of surface fuels may pose challenges for fuels managers

in this region, particularly because tree densities in many pon-

derosa pine and mixed-conifer stands fall significantly outside

of the natural range of variability for these forest types (Cov-

ington & Moore, 1994; Fulé et al., 2009). These high tree den-

sities can interact with surface fuel loads to create high fire

hazard even when fuel loads are within normal ranges (Brewer,

2008).

Drought-mediated tree mortality is simultaneously reducing

the tree densities and canopy fuels that interact with surface

fuel loads, however (Passovoy & Fulé, 2006; Hoffman et al.,

2011; Stevens-Rumann et al., 2012). Ultimately, fire risk in

these stands will represent the interplay between these factors

(surface fuel loads and canopy fuels), as well as other aspects of

forest structure such as fuel ladders that permit fire to reach the

forest canopy. The high spatial variability observed in both

surface fuels (this study) and tree mortality (Ganey & Vojta,

2011) suggests that the outcome of this interplay also will ex-

hibit high spatial variability across the landscape.

Conclusion

Climate change has been implicated in recent large-scale tree

mortality events throughout the world (Allen et al., 2010), and

studies in the southwestern US have documented increases in

CWD levels due to climate-related disturbances such as bark

beetle outbreaks and wildfire (Hoffman et al., 2011; Stevens-

Rumann et al., 2012). This study extends those findings by

documenting rapid climate-driven increases in log populations

across the general landscape, including areas not subject to bark

beetle outbreaks and wildfire. These findings suggest that

managers should plan for increased fuel loads where climate

models predict increasing warmth and aridity.

Acknowledgements

We thank J. Jenness, G. Martinez, M. Stoddard, B. Stroh-

meyer, R. White, and especially A. and J. Iníguez for their as-

sistance in establishing plots, and D. and N. Ganey for assis-

tance with sampling plots. For assistance with initial plot selec-

tion, we thank J. Ellenwood, B. Higgins, K. Menasco, C. Nel-

son, G. Sheppard (Kaibab National Forest), and C. Beyerhelm,

A. Brown, H. Green, T. Randall-Parker, C. Taylor, and M.

Whitney (Coconino National Forest). L. S. Baggett provided

general advice on statistical analyses, and L. S. Baggett and A.

Casas conducted the ANU tests described in the text. Com-

ments by J. Iníguez, C. H. Sieg, and an anonymous reviewer

improved earlier versions of this paper.

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